Patent Application
20220137048
The disclosure is directed to methods and systems for surgical prognosis prediction based on the detection of biomarkers, measurement of inflammation, and diagnosis of joint diseases or disorders such as arthritic diseases, infection, and gout.
Total joint arthroplasty (TJA) is the surgical treatment for patients with arthritis of the knee or hip who experience severe pain and activity limitations and for whom other treatments have been unsuccessful. More than 700,000 primary total knee arthroplasties (TKAs) are performed annually in the US, and estimates of TKA are projected to increase to 673% by 2030. Advances in the last 20 years have allowed TKA to become a reliable and cost-effective procedure for patients with low risks for complications. However, TKA is still a resource-intensive procedure that can incur significant costs for patients who encounter peri- and/or post-operative complications, such as wound complications, readmissions, systemic or local infections, prosthetic failure, and periprosthetic joint infection (PJI). Prosthetic failure and PJI often require revision surgery. Revision surgery is a more complex surgery than a primary arthroplasty, and complication, morbidity, and mortality rates are significantly higher in patients that undergo revision surgeries. Identifying clinical and joint-specific profiles that stratify patients based on risks for peri- and post-surgical complications can allow implementation of appropriate risk-based pre-operative treatments and interventions to reduce these complication rates.
Currently, tools to stratify patients based on the risk of the likelihood of post-operative complications have been developed, but they are in their infancy. These surgical risk prediction tools rely predominantly on clinical measures such as patient medical history, co-morbidities, demographics, and patient-reported outcome measures (PROMs). The predicted capabilities of the risk calculators specific for total joint arthroplasties (TJA)s are mediocre, with area under the curve (AUC) ranging from 0.70 to 0.84, and none of them incorporated joint-specific laboratory measures into its calculation algorithm.
The Synovasure® Relative Inflammatory Status Classification (RISC) Panel proposed herein utilizes a comprehensive panel of tests for joint-specific biomarkers to assess and stratify risks for post-surgical complications based on the differential diagnosis for arthritis. The biomarker test results are processed through a decision algorithm coupled to an electronic interface that identifies and differentiates between six arthritic conditions: isolated idopathic osteoarthritis (OA), inflammatory OA, rheumatoid arthritis (RA), Calcium Pyrophosphate Dihydrate (CPPD) Crystal Deposition Disease (Pseudogout), Monosodium urate (MSU) crystals (Gout), and septic arthritis (SA). The electronic algorithm also connects the biomarker results and arthritic diagnosis to a relative inflammatory score of 0-IV, with 0 being none to mild inflammation that is predictive of low post-surgical complication risks to IV being highest in inflammation, complications, and risks for morbidity and mortality. In combination with patient clinical measures, the relative inflammatory score will serve as a predictive factor into the prognostic classification machine learning (ML) algorithm that determines post-operative complication risks from TJA.
There is a significant unmet medical need for an accurate predictive tool that can calculate a patient's risk for peri- and post-surgical complications for TJA. The use of joint-specific clinical biomarker results in combination with patient medical record data will provide a comprehensive assessment of the risk factors to accurately predict the risks prior to surgery. Results from this risk prediction tool can be used to guide medical treatment pathways specific for the patient. However, the diagnostics and predictive tools proposed herein are complicated and should be completed with the aid of a machine implemented tool. Put another way, the battery of tests, analysis of the tests, determination of outcome of such test, results presentation and treatment plan(s) would not be performed by a clinician unaided by a machine implemented tool.
The present inventors have developed a machine implemented panel using biomarkers, compositions, algorithms, and machine implemented methods to aid clinicians in clarifying the differential diagnosis of osteoarthritis, rheumatoid arthritis, crystalline arthritis, and infectious arthritis in synovial fluid of patients experiencing joint pain and/or inflammation, and ensure the possibility of alternative or additional diagnoses are evaluated, particularly in cases where the clinical presentation may not be clear. Biomarkers, compositions, algorithms, and machine implemented methods of the present patent disclosure enable a valid and complete diagnosis of arthritic type along with inflammation level that, along with medical history, is predictive of peri- and post-surgical complications.
Accurate and complete diagnosis and risk prediction provide the best foundation for informing treatment decision and treatment success. Biomarkers, compositions, algorithms, and methods of the present patent disclosure provide a valid and complete differential diagnosis of the most common sources of unspecified joint pain and/or inflammation, e.g. whether due to OA, RA, CPPD, MSU, SA, or a combination of two or more of these disorders, thereby giving clinicians the information necessary for selection of the pharmacological, surgical and other interventions that are most appropriate and helpful to treating the specific disease that inflicts the patient. Thus, an objective of the present application is to identify biomarkers and arthritic diagnosis that corresponds to a relative joint inflammation level that, when used in combination with patient clinical measures, can predict surgical or treatment successes and/or complications.
Furthermore, the present inventors contemplate that results from the machine implemented panel based upon a pre-operative patient-specific joint health result (such as a joint specific biomarker) indicative of the level of inflammation in the joint can be used as objective input to a broader patient risk stratification tool so as to better predict post-operative outcome, and thereby, reduce unnecessary health spending and provide the clinician (and patient) with a more pro-active, personalized pre- and post-surgical treatment plan.
This summary is intended to provide an overview of subject matter of the present patent application. It is not intended to provide an exclusive or exhaustive explanation of the invention or its full scope or all its features. The detailed description is included to provide further information about the present patent application.
The present inventors have recognized that a valid differential diagnosis of arthritis due to joint pain and/or joint inflammation can be performed by analyzing a sample from the patient. Furthermore, the differentiated arthritic diagnosis can be linked to a joint-specific inflammation level that, in combination with patient medical history and demographics, is predictive of post-surgical prognosis. Post-surgical prognosis can include all criteria defined as complications or adverse events by the. Knee Society[1], which includes but not limited to recovery time, wound healing, infection, re-admission, loss of mobility, implant loosening or dissociation, reoperation or revision, and death. (1. Healy, W. L., et al., Complications of total knee arthroplasty: standardized list and definitions of the Knee Society. Clinical orthopaedics and related research, 2013. 471(1): p. 215-220.) The analysed sample can be a joint specific biological material (e.g., a cartilage of the joint, a synovial fluid of the joint, or the like) or other biological fluid such as blood, urine, or cerebrospinal fluid (CSF).
The presence of and/or the levels of one or a combination of biomarkers in the sample can be used to determine the arthritic type, inflammation level, and the risk factor for post-surgical complications. The methods, algorithms, systems, and compositions disclosed herein are useful in diagnosing and stratifying patients based on risks for post-surgical complications in the treatment of arthropathy. To further illustrate the compositions, combinations, methods, algorithms, and systems disclosed herein, a non-limiting list of Working Examples of the invention provided here:
Working Example 1 can include the diagnosis of primary, idiopathic osteoarthritis, which is indicative of none to low level of joint inflammation and, in the absence of any other pre-existing patient medical history risk factors, can be predictive of very low risk for post-surgical complications. Case scenario can include the following: patient presents to clinician for consultation regarding a knee replacement surgery. The physician provides the medical assessment, reviews the patient medical history and demographic information, and finds the patient to be a suitable surgical candidate. The physician orders the Synovasure® RISC™ Panel to evaluate joint-specific risk factors. The RISC Panel Results Report show synovial fluid COMP concentration of above 1500 ng/mL and the COMP/IL-8 result of above 4.3 ng/pg. All other panel components (RF, anti-CCP, crystals, WBC, and % PMN) are not present or not elevated to the level of the clinical decision limit that is indicative of an inflammatory type of arthritis. The test results along with details of the patient's medical history are processed through an algorithm coupled to an electronic interface that provides the comprehensive diagnosis of primary, idiopathic osteoarthritis, which corresponds to a RISC Class level of 0, suggesting low to no inflammation present in the affected joint. Based on the joint-specific inflammatory profile and patient risk level results from the Synovasure® RISC Panel algorithm (which can be the primary factor considered) and patient medical history, the physician determines that the patient remains a good surgical candidate and recommends patient for pre-, peri-, and post-operative treatment pathway designed for low-risk patients. This treatment pathway can include selecting for lower-cost options such as ambulatory surgery center (ASC), non-robotic assisted surgery, standard implant prosthesis, and an elimination or reduced preoperative prophylactic administration of antibiotics.
Working Example 2 can include the diagnosis of mildly inflamed, non-differentiated arthritis, and in the absence of any other pre-existing patient medical history risk factors is predictive of low level of risk for post-surgical complications. Case scenario can include the patient medical assessment, medical history, demographics, and the following synovial fluid RISC Panel test results: COMP levels of above 1500 ng/mL with COMP/IL-8 result of below 4.3 ng/pg. RF, anti-CCP, crystals, WBC, and % PMN are not present or not elevated to the level of the clinical decision limit. The test results and selected items from the patient medical history and demographics are processed through an algorithm coupled to an electronic interface that provides the comprehensive diagnosis of mildly inflamed, non-differentiated arthritis, which corresponds to a RISC Class level of I, suggesting low level of inflammation present in the affected joint. Based on the joint-specific inflammatory profile, and patient medical history and patient risk level results from the Synovasure® RISC Panel algorithm (which can be primary factor considered), the physician determines that the patient remains a good surgical candidate and recommends patient for pre-, peri-, and post-operative treatment pathway designed for low-risk patients. However, to further reduce the risk of post-surgical complications, the physician prescribes a 4-month preoperative diet and activity plan that includes smoking cessation to further reduce inflammation. The surgical treatment pathway can include selecting for lower-cost options such as ambulatory surgery center, non-robotic assisted surgery, standard implant prosthesis, and reduced preoperative prophylactic administration of antibiotics.
Working Example 3 can include the diagnosis of rheumatoid arthritis, which is indicative of moderate level of joint inflammation and, in the absence of any other pre-existing patient medical history risk factors, is predictive of medium risk for post-surgical complications. Case scenario can include the patient medical assessment, medical history, demographics, and the following synovial fluid RISC Panel test results: COMP levels of below 1500 ng/mL OR COMP levels of above 1500 ng/mL with COMP/IL-8 result of below 4.3 ng/pg, positive for anti-CCP AND RF levels of above 10 IU/mL. Crystals, WBC, and % PMN are not present or not elevated to the level of the clinical decision limit. The test results along with selected information from the patient's medical history are processed through an algorithm coupled to an electronic interface that provides the comprehensive diagnosis of rheumatoid arthritis, which corresponds to a RISC Class level of II, suggesting medium level of inflammation present in the affected joint. Based on the joint-specific inflammatory profile, patient medical history and patient risk level results from the Synovasure® RISC Panel algorithm (which can be the primary factor considered), the physician recommends patient for pre-, peri-, and post-operative treatment pathway designed for medium-risk patients. The pre-operative treatment plan can include patient optimization to reduce surgical risks, such as anti-inflammatory pharmacological treatment, weight loss and rest to reduce joint stress, smoking cessation, and education and engagement through interactive digital applications such as Zimmer Biomet's MyMobility® Platform. Once patient optimization is achieved, the peri-operative and post-operative treatment pathway can include selecting for lower-cost options such as procedures performed in an outpatient setting at an ambulatory surgery center, non-robotic assisted surgery, standard implant prosthesis, reduced preoperative prophylactic administration of antibiotics, and standard post-operative care. If patient optimization is not achieved, then the peri-operative pathway may be escalated to using robotic-assisted surgery such as the Zimmer Biomet ROSA® system, personalized fitted prosthesis such as the Zimmer Biomet PersonaIQ® smart knee implant, and prophylactic antibiotic administration. Post-operative care pathway can include post-operative antibiotic administration, physical therapy, and tracking of motion data from the PersonaIQ® smart knee implant.
Working Example 4 can include the diagnosis of Gout, which is indicative of moderate to high level of joint inflammation and, in the absence of any other pre-existing patient medical history risk factors, is predictive of medium to high risk for post-surgical complications. Case scenario can include the following example: A 53-year-old male presents in office with a swollen right knee. He is complaining about excruciating pain upon activity. Also has a history of self-diagnosed bouts of anterior tibialis pain that has been previously managed with a walking boot. Patient confirms that he occasionally, approximately 3 times a week, consumes alcohol in the form of beer and wine. Radiographs were taken of the knee, there appears to be adequate joint space and there is no obvious appearance of sclerosis or osteophyte formation. To manage the acute symptoms a dose of corticosteroid was administered via injection into the capsule. Prior to administration of the corticosteroid approximately 15 cc of joint fluid was aspirated from the affected knee and sent for analysis using the RISC panel. The specimen was first reviewed for integrity and the absorbance at 280 nm was within the upper and lower bounds. Additionally, the RBC count was below 180,000 cells/microliter. The biomarker analysis revealed a COMP level of 3450 ng/ml and a COMP to IL-8 result of 1.5 ng/pg. Further analysis showed a total nucleated cell count of 2800 cells/microliter with a differential of 65% neutrophils and 5% mononuclear cells. Rheumatoid factor in synovial fluid was 4 IU/ml and Anti-CCP levels were negative. Crystal analysis revealed the presence of Monosodium Urate crystals both intra- and extracellular. Using the RISC algorithm, the results above were negative for the COMP to IL-8 result, RF, and white blood cell count threshold. However, the sample was positive for the presence of MSU crystals, which is indicative of Gout. The patient was consulted to change diet to reduce red meat intake and keep alcohol consumption to a minimum. Additionally, a prescription for febuxostat (Uloric) was also provided to the patient. If lifestyle changes and pharmacological intervention fails and the disease has progressed to the stage where surgical intervention is needed, the results of the RISC panel and patient history can be evaluated using an algorithm coupled with an electronic interface to assess the risk of post-surgical complications. For this patient, based on the patient history and the joint-specific inflammatory level corresponding to the biomarkers present, the physician determines that the patient exhibits medium to high level of risk for post-surgical complications and recommends patient for pre-, peri-, and post-operative treatment pathway designed for medium-high risk patients. The surgical treatment pathway could include increased dosing of pharmacologic agents to address gout, robotic-assisted surgery such as the Zimmer Biomet ROSA® system, personalized fitted prosthesis such as the Zimmer Biomet PersonaIQ® smart knee implant, MyMobility® pre- and post-operative patient education and engagement, and prophylactic antibiotic administration. Post-operative care pathway can include post-operative antibiotic administration, physical therapy, continuation of the current febuxostsat prescription, dietary restrictions, and tracking of motion data from the PersonalQ® smart knee implant.
Working Example 5 can include the diagnosis of Pseudogout or CPPD, which is indicative of medium-high level of joint inflammation and, in the absence of any other pre-existing patient medical history risk factors, is predictive of medium-high risk for post-surgical complications. Case scenario can include the patient medical assessment, medical history, demographics, and the following synovial fluid RISC Panel test results: COMP levels of below 1500 ng/mL OR COMP levels of above 1500 ng/mL with COMP/IL-8 result of below 4.3 ng/pg, positive for CPPD crystals. Anti-CCP and RF may or may not present or elevated to the level of the clinical decision limit. WBC and % PMN may or may not be above the clinical decision limit. The RISC Panel Results Report and the patient medical history are processed through an algorithm using an electronic interface, and this process confirms the diagnosis of Pseudogout or CPPD. The physician then recommends appropriate pharmacological interventions to reduce inflammation, pain, and occurrence of acute attacks. If surgery is warranted, based on the joint-specific inflammatory profile and patient risk level, physician recommends patient for pre-, peri, and post-operative treatment pathway designed for medium-high risk patients that is similar to Working Example 4 above.
Working Example 6 can include the diagnosis of septic arthritis, which is predictive of high level of risk for post-surgical complications and morbidity. Case scenario can include the patient medical assessment, medical history, demographics, and/or other presenting symptoms such as acute joint swelling, pain, erythema, warmth, and joint immobility, and the following synovial fluid RISC Panel test results: COMP levels of below 1500 ng/mL OR COMP levels of above 1500 ng/mL with COMP/IL-8 result of below 4.3 ng/pg, WBC count of >3000 cells/μL and/or % PMN >70, positive for NSA Panel (positive for Alpha Defensin and Lactate level of ≥70 mg/dL) AND/OR positive for one or more Synovasure® MID tests AND/OR positive for culture. Anti-CCP, RF, and Crystals may or may not be present or elevated to the level of the clinical decision limit. The RISC Panel test results and patient medical history is processed using a decision algorithm through an electronic interface, and based on the results of the panel and review of the patient medical history, the physician confirms the diagnosis of septic arthritis. The physician orders immediate appropriate antibiotic therapy as well as evacuation of any present purulent material from the affected joint. Physician determines that the patient is at high risk for post-surgical complications and delays surgery until the infection is cleared. If surgery proceeds after the infection is managed, based on the joint-specific inflammatory profile, patient history and demographics, and patient risk level, physician recommends patient for pre-, peri, and post-operative treatment pathway designed for high risk patients. This treatment pathway can include choosing to perform robotic-assisted surgery in the hospital instead of at an ASC and use of prophylactic antibiotics, antibacterial-loaded cement, antimicrobial coated implants and personalized fitted smart knee implant. Post-surgical care can include post-operative antibiotics, extended hospital stay, extended physical therapy, and proactive monitoring with MyMobility® and PersonaIQ® applications.
Working Example 7 can include the diagnosis of non-septic, non-differentiated inflamed arthritis, which, in the absence of or in combination of other pre-existing risk factors, is predictive of medium to high level of risk for post-surgical complications. Case scenario can include the patient medical assessment, medical history, demographics, and the following synovial fluid RISC Panel test results: COMP levels of below 1500 ng/mL OR COMP levels of above 1500 ng/mL with COMP/IL-8 result of below 4.3 ng/pg, WBC count of >2000 cells/μL and/or % PMN >70, negative for NSA Panel (positive for Alpha Defensin and Lactate level of ≥70 mg/dL), negative for all Synovasure® MID tests, negative for culture. Anti-CCP, RF, and Crystals are not present or not elevated to the level of the clinical decision limit. Based on the joint-specific inflammatory profile from the RISC algorithm interfaced to an electronic system and patient medical history, physician recommends patient for pre-, peri-, and post-operative treatment pathway designed for medium- or high-risk patients.
Working Example 8 can include the diagnosis of osteoarthritis with underlying non-active CPPD, which, in the absence of any other pre-existing conditions, is predictive of low level of risk for post-surgical complications. Case scenario can include the patient medical assessment, medical history, demographics, and the following synovial fluid RISC Panel test results: COMP levels of above 1500 ng/mL with COMP/IL-8 result of above 4.3 ng/pg, positive for CPPD crystals. RF, anti-CCP, MSU crystals, WBC, and % PMN are not present or not elevated to the level of the clinical decision limit. Based on the RISC Panel test results, the physician may determine that the patient exhibits very low joint-specific inflammation and is a good surgical candidate that exhibits low risks for post-surgical complications. Based on the joint-specific inflammatory profile and patient risk level, physician recommends patient for pre-, peri-, and post-operative treatment pathway designed for low-risk patients.
Working Example 9 use of the invention to determine appropriateness of patient for surgical intervention. Case scenario can include: a 5′8″, 463 lbs., 45-year-old male patient presents with a red swollen right knee with a valgus deformity. The patient was having difficulty ambulating and specifically climbing stairs. At the time of presentation, the knee was swollen to approximately twice the size of the contralateral joint and was red and warm to the touch. Radiographs revealed bone on bone on the medial compartment of the right knee with significant osteophyte formation on the posterior medial portion of the tibia. An aspiration of the joint was performed, prior to injection with a cortical steroid. The aspirate was turbid and had a slight brownish appearance. The specimen was sent to CD Laboratories for analysis using the RISC panel. The RISC panel results show the following: A280 and RBC levels indicate the sample was not diluted or contaminated with peripheral blood. COMP level of 2952 ng/μL, COMP/IL-8 result of 2.7 (below the clinical cut-off of 4.3), WBC count of 30000 cells/ILL with differential of 86% neutrophils, RF and anti-CCP at below detection limit, Alpha Defensin positive, L-Lactate positive, MID positive for Candida, and culture positive for both Candida and Staphlacoccus Epidermis. These results combined with an algorithm were interrogated using an electronic interface with the ability to output a risk factor for complications. These results indicate the patient would be a RISC category IV patient and would not be a good candidate for a surgical intervention with a total joint due to an active infection. The patient was then scheduled for an open washout of thin affected knee and prescribed a systemic course of antibiotics to address the organisms encountered. Additionally, the patient was recommended to have a nutritional assessment and begin modification of his lifestyle or seek gastric bypass surgery to reduce weight prior to becoming a surgical candidate.
Further areas of applicability will become apparent from the description provided herein. The description and specific aspects of the invention in this overview are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure.
In the figures, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. The figures illustrate generally, by way of example, but not by way of limitation, various aspects discussed in the present document. The figures described herein are for illustrative purposes only of selected examples and not all possible examples or implementations, and these drawings are not intended to limit the scope of the present disclosure.
The following abbreviations shall be used throughout the figures, specification and occasionally in the claims: OA=osteoarthritis; AD=Alpha Defensin; gout=monosodium urate crystals; CPP=calcium pyrophosphate dihydrate crystals; ANTI-CCP=Anti-cyclic citrullinated peptide; COMP=Cartilage Oligomeric Matrix Protein; CPPD=Calcium Pyrophosphate Deposition; IL-8=Interleukin-8; MID=Microbial ID; MSU=Monosodium Urate; NSA=Native Septic Arthritis; PJI=Periprosthetic Joint Infection; RA=rheumatoid arthritis; RF=Rheumatoid Factor; RBC count=Red Blood Cell count; WBC count=White Blood Cell count C/O=cut-off level of a biomarker to discriminate OA from one or more of the other disease states to discriminate a particular stage of OA form another stage of OA.
Osteoarthritis (OA) is prevalent and results in a significant socio-economic burden. Osteoarthritis is a progressive degenerative disease characterized by progressive destruction and loss of articular cartilage, changes to underlying bone and formation of new bone leading to pain and limitation or ultimately loss of function. Osteoarthritis is a whole joint disease affecting the subchondral bone, synovium, meniscus, ligaments, and periarticular muscles and nerves, in addition to the cartilage. Common signs and symptoms of knee OA include inflammation, swelling, deformity, tenderness, crepitus (joint cracking or popping), and pain. Osteoarthritis occurs in stages. Once the disease has progressed to more severe stages the only recourse is to remove the damaged joint and replace it with an artificial joint. Diagnosis of OA in earlier stages of the disease would enable treatment, e.g., with hyaluronic acid, autologous protein solution, platelet-rich plasma, stem cells, or disease modifying drugs, before further or irreparable damage is done to the joint.
Knee OA is primarily diagnosed based on clinical signs and symptoms as discussed previously. Synovial fluid analysis is infrequently used to rule out other conditions in a differential diagnosis. The high prevalence of OA makes it an obvious leading hypothesis for causes of unspecified knee pain, particularly when paired with an atypical clinical presentation of an alternative hypothesis and/or x-ray evidence of joint space narrowing and osteophytes. Under these circumstances, a patient may be misdiagnosed as having OA when they do not actually have the disease. Alternatively, a patient may be diagnosed with and treated for primary OA when, in fact, the OA is either secondary to another type of arthritis or OA is the primary disease but another type of arthritis is also affecting the joint and should be treated. Inflammatory arthritis, rheumatoid arthritis, crystalline arthritis (presence of monosodium urate crystals and/or calcium pyrophosphate dihydrate crystals), injury/trauma, and/or septic arthritis (joint infection) can contribute to an inaccurate or incomplete diagnosis of knee pain/inflammation as being due to OA.
Rheumatoid arthritis (RA) is the most common inflammatory arthropathy. Published data indicates a prevalence of secondary OA in 71% of patients with rheumatoid arthritis (RA). Notably, to conclusively determine that an OA diagnosis is secondary to RA, the RA diagnosis must have been previously confirmed by presence of anti-cyclic citrullinated peptide (anti-CCP) and/or rheumatoid factor (RF). Secondary OA has been diagnosed predominantly (68.6%) in patients that are seropositive for anti-CPP. The prevalence of seronegative (anti-CCP and RF) RA at initial presentation is as high as 50%. In cases of seronegative RA or early RA prior to seropositive (anti-CCP and RF) test results, it is possible that OA secondary to undiagnosed RA could be misdiagnosed and treated as primary OA, particularly when the symptoms and clinical presentation are atypical of the symmetric, inflammatory, peripheral polyarthritis generally observed in RA patients. One study found that one fifth of the patients diagnosed with RA had been misdiagnosed, and nearly two thirds of these misdiagnosed patients had OA. These misdiagnosed OA patients had been treated with disease modifying antirheumatic drugs, which has substantial clinical health and economic implications.
Gout is a crystal-induced arthritis caused by deposition of the monosodium uric acid (MSU) crystal related to long standing hyperuricemia. It is a common inflammatory arthritis affecting around 5% of the middle-aged and elderly population worldwide. Published data indicates a possible link between gout and OA pathogenesis. Acute attacks of gout at individual joints has been associated with the presence of clinically assessed OA, and the knee joint was identified as a joint where a highly significant association was observed. It is unknown whether OA causes a predisposition to localized deposition of monosodium urate (MSU) crystals or if acute attacks of gout and increased inflammatory mediators in the synovial tissues trigger the pathogenesis of OA. In addition to the known association between MSU crystal deposition and osteoarthritis, where both conditions exist and must be treated, the ability to differentiate between an acute attack of gout and an inflammatory episode of osteoarthritis is necessary to inform treatment decisions. The inflammatory properties which propagate an inflammatory episode of OA may be indicative of an underlying inflammatory arthritis that has not yet been diagnosed.
Calcium pyrophosphate dihydrate (CPP) crystal deposition disease (CPPD) is the most common cause of articular cartilage chondrocalcinosis (CC). The classification of CPPD includes asymptomatic CPPD, OA with CPPD (formerly known as pseudo-OA), acute CPP crystal arthritis (formerly known as pseudogout), and chronic CPP crystal inflammatory arthritis (formerly known as pseudo-RA). CPPD has been reported to be the fourth most prevalent rheumatic condition after OA, rheumatoid arthritis, and gout. There is a clear association between OA and CPPD, with studies demonstrating calcium crystals in the synovial fluid of 30-60% of unselected OA patients. Unfortunately, the causal relationship between OA and CPPD, the impact of calcium crystal deposition on OA disease progression and treatment effects, and the role of calcium crystals in the synovial inflammation often observed in OA joints remain unanswered. It has been noted that grade of synovial fluid effusion is increased in patients with CPPD with OA versus OA alone, indicating a more inflammatory state for joints affected by both conditions. This difference in the inflammatory status may very well impact the natural progression rate of the disease and effectiveness of an OA treatment, and so the identification of calcium pyrophosphate crystals in an OA joint is important when making treatment decisions.
The incidence of septic arthritis (SA), also known as infectious arthritis, varies from 2 to 10 per 100,000 in the general population to 30-70 per 100,000 in patients with rheumatoid arthritis and patients with joint prostheses. Clinical signs of SA include joint pain, swelling, warmth, and restricted movement. Concomitant septic arthritis in osteoarthritis, rheumatoid arthritis, and crystalline arthritis cases is uncommon but is not rare. A history of rheumatoid arthritis and previous intraarticular corticosteroid injections are both risk factors for septic arthritis. Furthermore, an examination of synovial fluid aspirates found concomitant infection in 5% of samples with crystalline arthritis. Early diagnosis of septic arthritis, as well as prompt and effective treatment (antibiotics), is essential to avoid irreversible joint destruction or even death. The emergent nature of native septic arthritis gives rise to medical guidelines recommending arthrocentesis with synovial fluid analysis in all patients who have a joint effusion or signs suggestive of inflammation within the joint, without a known cause.
The biomarkers, compositions, algorithms, and methods disclosed herein provide a valid differential diagnosis, including osteoarthritis, inflammatory arthritis, rheumatoid arthritis, crystalline arthritis (gout and CPPD), and native septic arthritis, which correlates with a relative joint inflammation level that, when combined with patient clinical measures, predicts the risks for complications following total joint replacement surgery.
In describing and claiming the invention, the following terminology will be used in accordance with the definitions set forth below. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention. Specific and preferred values listed below for radicals, substituents, and ranges are for illustration only; they do not exclude other defined values or other values within defined ranges for the radicals and substituents.
As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. By way of example, “an element” means one element or more than one element. Similarly, references to “the method” includes one or more methods, and/or steps of the type described herein and/or which will become apparent to those persons skilled in the art upon reading this disclosure and so forth. For the recitation of numeric ranges herein, each intervening number there between with the same degree of precision is explicitly contemplated. For example, for the range 6-9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and for the range 6.0-7.0, the numbers 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9 and 7.0 are explicitly contemplated.
As used herein, the term “about” means acceptable variations within 20%, of the stated value, such as within 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2% or 1% of the stated value.
The term “sample” is a biological sample from a patient. This can include whole blood, blood plasma, serum, urine, saliva, synovial fluid, synovial tissue, cartilage, muscle, tendon, ligament, and/or other bodily fluid or tissue. The term “joint specific biological material” is a material withdrawn specifically from an inflamed joint of a patient (e.g., a cartilage of the joint, a synovial fluid, or one of the other materials listed above in sample).
The terms “biomarker” and “marker” can be used interchangeably herein and refer to generally refer to a protein or polypeptide, nucleic acid molecule, clinical indicator, physiological indicator, a blood cell count (red blood cell (RBC), white blood cell (WBC), polymorphonuclear cell count(PMN)), or other evidence of a physical or physiological condition or state of a subject that is associated with a disease and that can be used as a target for analysing samples obtained from subjects. Biomarker can encompass proteins or polypeptides themselves as well as antibodies against same that may be present in a test sample. Proteins or polypeptides used as a marker include any variants and fragments thereof and, immunologically detectable fragments. Proteins or fragments thereof can also occur as part of a complex. Proteins or polypeptides used as biomarkers according to the present disclosure also include such complexes. The terms “biomarker” and “marker” also encompass nucleic acid molecules comprising a nucleotide sequence that codes for a marker protein, and polynucleotides that can hybridize under stringent conditions with a part of such nucleic acid molecules. The terms “biomarker” and “marker” also include “biomarker(s) of osteoarthritis,” “OA biomarker,” and “biomarker of OA” as defined herein.
As used herein the terms “treat,” “treating,” and “treatment,” mean therapeutic or preventative measures such as those described herein. The methods of “treatment” employ administration to a patient of a treatment regimen in order to prevent, cure, delay, reduce the severity of, or ameliorate one or more symptoms of the disease or disorder or recurring disease or disorder. Treatments for osteoarthritis can include, without limitation, one or more of: lifestyle modifications (e.g., weight-loss, exercise to increase muscle strength at the affected joints); physical therapy; analgesics, e.g., aspirin, acetaminophen, opioids; oral or injectable non-steroidal anti-inflammatory drugs (NSAIDs), e.g., indomethacin, ibuprofen, naproxen, ketoprofen, piroxicam or diclofenac, celecoxib, rofecoxib, valdecoxib, corticosteroids, disease-modifying osteoarthritis drugs (DMODs), viscosupplementation e.g., hyaluronic acid or hyaluronan (HA), platelet-rich plasma (PRP), cartilage transplant, and total or partial joint replacement surgery. Treatments for RA can include, without limitation: physical and/or occupational therapy for affected joints; nonsteroidal anti-inflammatory drugs (NSAIDs); corticosteroid medications (oral or injectable) e.g., dexamethasone, betamethasone, prednisone; disease-modifying antirheumatic drugs (DMARDs), e.g., methotrexate, leflunomide, hydroxychloroquine and sulfasalazine; biologic response modifying drugs, e.g., abatacept, adalimumab, anakinra, baricitinib, certolizumab, etanercept, golimumab, infliximab, rituximab, sarilumab, tocilizumab and tofacitinib; and surgery to repair damaged joints, e.g., synovectomy to remove the inflamed lining of the joint (synovium), tendon repair, joint fusion and/or joint replacement. Treatments for monosodium urate crystalline arthritis (gout) can include, without limitation: dietary modifications (gout); oral or injectable NSAIDs; colchicine; corticoids; xanthene oxidase inhibitors (XOIs), including allopurinol and febuxostat; uricosuric agents, e.g., probenecid, fenofibrate, losartan, azapropazone, calcium channel blockers; pegloticase, rasburicase, lesinurad and arthroscopic irrigation. Treatments for calcium pyrophosphate dihydrate disease (CPPD, pseudogout) can include, without limitation; NSAIDs; corticosteroid (oral or injection); colchicine; phosphocitrate; polyphosphate; magnesium carbonate; viscosupplementation (e.g., HA); hydroxychloroquine; methotrexate; biologic response modifying drugs, e.g., as listed above; synovectomy; surgery such as arthroscopic irrigation. Treatments for septic arthritis (native septic arthritis and periprosthetic joint infection) can include, without limitation: analgesics; NSAIDs; antibiotics, antifungals and anti-viral drugs, as appropriate to the nature of the infectious agent. Treatments for trauma/injury can include, without limitation; ice and/or heat; analgesics; NSAIDs; corticosteroids (oral or injection); viscosupplementation (e.g., hyaluronon); PRP; physical therapy; exercise; and surgery.
As used herein the term “comprising,” “having” and “including” and the like are used in reference to compositions, systems, methods, and algorithms, and respective component(s) and feature(s) thereof, that are present in a given aspect, yet open to the inclusion of one more or more unspecified elements. The term “including” is used herein to mean, and is used interchangeably with, the phrase “including but not limited to.”
Detecting Biomarkers
The present application incorporates the disclosures of United States Patent Application Publication No. 2018/0045737AI and PCT Application Publication No. 2021/087116 (Application Serial No. PCT/US2020/057967) by reference in their entirety.
In one aspect biomarkers can be measured in joint specific biological material, e.g., synovial fluid from a reference individual or from a subject experiencing joint pain and/or joint inflammation. In an aspect a biomarker expression profile or biomarker level can be of one or a combination of biomarker polypeptides or proteins (which shall be used herein interchangeably, and the term protein shall include polypeptides). In a preferred aspect, biomarkers can be proteins in a synovial fluid sample from a subject experiencing joint pain and/or inflammation. In one aspect, the biomarkers comprise proteins that are differentially expressed in different disease states. In an aspect an OA biomarker can be differentially expressed in the varying stages of the disease. In one aspect biomarkers can be differentially increased in OA, i.e., the level of an inflammatory biomarker is increased relative to a reference, such as normal individual with OA, or an individual with a particular stage of OA, or an individual with an inflammatory arthropathy such as RA, CA, septic arthritis, or trauma/injury to a joint, or a reference from the subject at an earlier time. In other aspects OA biomarkers can be differentially decreased, i.e., decreased level relative to a reference, such as normal individual without OA, an individual with a particular stage of OA, or an individual with an inflammatory arthropathy such as RA, CA, septic arthritis, or trauma/injury to a joint, or a reference from the subject at an earlier time. In one aspect an expression profile of OA biomarkers can comprise at least one OA biomarker that is differentially increased in OA and at least one OA biomarker that is differentially increased in an inflammatory arthropathy. In one aspect an expression profile of OA biomarkers can comprise at least one OA biomarker that is differentially increased in OA and at least one OA biomarker that is differentially decreased in OA or in a particular stage of OA. These variations in biomarker profile can also be used to evaluate the likelihood of post surgical complications, such as re-admission, infection, wound healing, loss of mobility and death.
Osteoarthritis can be detected, diagnosed (including differential diagnosis), staged, monitored, and/or treated by determining the presence and/or level of one or more OA biomarkers in a subject sample. Assessing or detecting the presence and/or level (e.g. a concentration) of expression of any one or a plurality of biomarkers can be performed by any one or any combination of a variety of techniques that are known in the art. Detection methods that can be employed for detection of biomarkers include, without limitation, optical methods, electrochemical methods (voltammetry and amperometry techniques), atomic force microscopy, and radio frequency methods, e.g., multipolar resonance spectroscopy. In one aspect assessing or detecting an OA biomarker can be performed using a combination of known techniques to provide more accurate detection of the biomarker (e.g., biochip in combination with mass spectrometry, immunoassay in combination with mass spectrometry, 2-D DIGE in combination with mass spectrometry, and any other combination of known techniques for detecting and/or assessing a level of a nucleic acid, a polypeptide or protein). Expression of a biomarker can be assessed in vitro or in vivo in a subject or a reference. Known methods and techniques for isolating DNA, RNA and protein, and performing the methods and techniques disclosed herein can be found and described more detail in standard molecular biology reference publications, such as: Ausubel et al., (2003) CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, John Wiley & Sons, New York, N.Y., CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, John Wiley & Sons, Online ISDN: 1934-3647; Sambrook et al. (1989) MOLECULAR CLONING: A LABORATORY MANUAL, Cold Spring Harbor Press, Cold Spring Harbor, N.Y.; PROTOCOLS USED IN MOLECULAR BIOLOGY (eds. Singh, S. K., and Kumar, D., 2020), Benthan Science ISBN: 9789811439292 (available at researchgate.net).
In an aspect a biomarker can be a protein that can be assessed or detected using several known techniques that can be antibody-based. In one aspect the level of one or more biomarkers can be detected and/or measured by immunoassay. Immunoassay can typically utilize an antibody (or other agent that specifically binds the biomarker or interest) to detect the presence or level of a biomarker in a sample. Antibodies can be produced by methods well known in the art, e.g., by immunizing animals with the biomarkers, such as biomarker proteins. Biomarkers can be isolated from samples based on their binding characteristics. Alternatively, if the amino acid sequence of a polypeptide or protein biomarker is known, the polypeptide or protein can be synthesized and used to generate antibodies by methods well known in the art. Further, antibodies are commercially available for biomarkers from many sources (R&D Systems, RayBiotech, EMD Millipore, et.c.). Suitable immunoassay detection methods for use in the methods and systems disclosed herein include, without limitation, Western blot, sandwich immunoassays including enzyme-linked immunosorbent assay (ELISA) and other enzyme immunoassays, fluorescence-based immunoassays, and chemiluminescence. Other forms of immunoassay include magnetic immunoassay, radioimmunoassay, and real-time immunoquantitative PCR (iqPCR).
In one aspect an ELISA can be used to detect and quantify biomarker protein levels. This method can include preparing the antigen (i.e., biomarker protein of interest), coating the wells of a microtiter plate with the antigen, incubating the antigen with an antibody that recognizes the antigen, washing away the unbound antibody, and detecting the antibody-antigen complex. The antibody can generally be conjugated to an enzyme, such as horseradish peroxidase or alkaline phosphatase, which can generate colorimetric, fluorescent, or chemiluminescent products. In another aspect an ELISA can use two antibodies, one of which is specific to the biomarker protein of interest and the other of which recognizes the first antibody and is coupled to an enzyme for detection. In still other aspects the antibody can be coated on the well and a second antibody conjugated to a detectable compound is added to the well following the addition of the antigen to the biomarker protein of interest.
In another aspect an antibody array platform, e.g., Luminex (Luminex Corp., Austin, Tex.) can be used to detect and quantify biomarker protein levels using multiplexed assays based on a capture bead system in which microsphere beads are color-coded with dyes. Each color-coded bead set is coated with a specific binding reagent such as an antibody specific to a selected biomarker protein, allowing the capture and detection of specific protein analytes from a very small amount of fluid, e.g. a drop of fluid from plasma, serum, urine, cells lysates or synovial fluid. Depending upon the analyte(s) being screened, at least one or several bead sets may be incubated with a sample to capture the analytes. In one aspect lasers can be used to excite the dyes that identify each microsphere bead and any reporter dye captured during the assay. Exemplary multiplex immunoassay platforms that can be used in the present methods, systems and algorithms include the xMAP platform (Qiagen, Inc.).
In an aspect a biomarker protein level can be assessed using a protein microarray or an antibody microarray. In these methods, the proteins or antibodies are covalently attached to the surface of the microarray or biochip. The biomarker protein of interest can be detected by interaction with an antibody, and the antibody/antigen complexes are generally detected through fluorescent tags on the antibody. An exemplary microarray that can be used in the methods, systems and algorithms disclosed herein includes the Quantibody™ platform (RayBiotech, Inc.).
In another aspect biomarker protein levels can be assessed by immunohistochemistry in which a protein is localized in cells of a tissue section by its interaction with a specific antibody. The antigen/antibody complex may be visualized by a variety of methods. One or two antibodies may be used, as described above for ELISA. The detection antibody may be tagged with a fluorophore, or it may be conjugated to an enzyme that catalyzes the production of a detectable product. The labeled complex is typically visualized under a microscope.
In yet another aspect a biomarker protein level can be measured by Western blotting. Western blotting generally comprises preparing protein samples, using gel electrophoresis to separate the denatured proteins by mass, and probing the blot with antibodies specific to the biomarker protein of interest. Detection can be accomplished using two antibodies, the second of which is conjugated to an enzyme for detection or another reporter molecule. Methods used to detect differences in protein levels include colorimetric detection, chemiluminescent detection, fluorescent detection, and radioactive detection.
In one aspect a biomarker protein profile can be assessed by Two-dimensional difference gel electrophoresis (2D-DIGE). 2D-DIGE is a modified form of 2D electrophoresis (2DE) that allows the comparison of two or three protein samples simultaneously on the same gel. The proteins in each sample can be covalently tagged with different colored fluorescent dyes that are designed to have no effect on the relative migration of proteins during electrophoresis. The proteins in the sample are separated in 2 dimensions using electrophoresis (molecular weight in one dimension; isoelectric point (or net charge) in the second dimension). When illuminated with appropriate wavelengths of light, the color contribution and intensity of individual protein spots indicates which sample (disease group) the protein came from. Protein spots of interest are cut from the gel and the identity of the protein is determined by mass spectrometry.
In one aspect the level of biomarkers can be detected by mass spectrometry (MS). Mass spectrometry is a well-known tool for analyzing chemical compounds that employs a mass spectrometer to detect gas phase ions. Mass spectrometers are well known in the art and include, but are not limited to, time-of-flight, magnetic sector, quadrupole filter, ion trap, ion cyclotron resonance, electrostatic sector analyzer and hybrids of these. The method may be performed in an automated (Villanueva, et al., Nature Protocols (2006) 1(2):880-891) or semi-automated format. This can be accomplished, for example with the mass spectrometer operably linked to a liquid chromatography device (LC-MS/MS or LC-MS) or gas chromatography device (GC-MS or GC-MS/MS).
In certain aspects biomarkers, e.g., MSU and/or CPP crystals, RBCs, WBCs, can be detected using various forms of microscopy, such as light polarizing microscopy and phase contrast microscopy. In one aspect biomarkers comprising whole cells can be detected and quantified by well-known manual counting methods and/or automated counting methods using automated devices.
In certain aspects biomarkers, e.g., microbial growth and identification, can be detected using various forms of aerobic and anaerobic microbial culture techniques.
Determinations
The system 100 can communicate with a network 104 of other electronic devices in addition to the electronic devices 102A and 102B. The system 100 and network 104 can communicate with various of the testing components/techniques previously discussed (sometime referred to herein as other system components). The system 100 can include an example architecture and componentry for a computer-implemented system. The electronic device 102A and/or the electronic device 102B can include a memory 106 to implement various algorithms. However, the system 100 can include a database according to some examples that implements all or portions of the algorithm(s). According to some examples, the electronic device 102A and/or the electronic device 102B can be configured as a client that can run portions or all of the data processing discussed herein. The electronic device 102A and/or the electronic device 102B can be patient, clinician, insurer, laboratory, manufacturer, or healthcare provider electronic devices for monitoring and/or collecting data locally or remotely via the network 104.
The electronic device 102A and/or the electronic device 102B can be associated with and used for multiple data storage functions. Algorithms implemented by the electronic device 102A and/or the electronic device 102B may be performed on circuitry (e.g., a processor, software, firmware, hardwired circuitry, etc.) that is capable of performing various functions. The electronic device 102A and/or the electronic device 102B and/or other system components not specifically shown (e.g., data repository, server, etc.) can be configured to communicate with one another such as via a communication unit and/or can execute functions alone or in conjunction with one another over the network 104. The electronic device 102A can include any number of different portable electronic mobile devices, including, e.g., cellular phones, personal digital assistants (PDA's), laptop computers, portable gaming devices, portable media players, e-book readers, watches, as well as non-portable devices such as desktop computers. The electronic device 102A and/or the electronic device 102B can include one or more input/output devices configured to allow user interaction with one or more programs. Thus, the electronic device 102A has a display 108 showing data (e.g, sample results, diagnosis, treatment recommendation, etc.). In one example, the electronic device 102A and/or the electronic device 102B may be jettisoned in favor of a web browser that accesses/executes and presents a web application for use by the user. In another example, the electronic device 102A and/or the electronic device 102B can execute an application outside of a web browser, e.g. an operating system specific application that accesses/executes and presents a native OS application for use by the user.
Network 104 can include one or more terrestrial and/or satellite networks interconnected to provide a means of communicatively connecting the electronic device 102A and/or the electronic device 102B and other system components. In one example, network 104 can be a private or public local area network (LAN) or Wide Area Network (WANs). Network 104 can include both wired and wireless communications according to one or more standards and/or via one or more transport mediums. In one example, network 104 includes wireless communications according to one of the 802.11 or Bluetooth specification sets, or another standard or proprietary wireless communication protocol. Network 104 can also include communications over a terrestrial cellular network, including, e.g. a GSM (Global System for Mobile Communications), CDMA (Code Division Multiple Access), EDGE (Enhanced Data for Global Evolution) network. Data such as tests and test results can be transmitted over network 104, e.g., from the various of the testing apparatuses discussed previously to the electronic device 102A and/or the electronic device 102B via the communication unit. Data can be formatted in accordance with a variety of different communications protocols. For example, all or a portion of network 104 can be a packet-based, Internet Protocol (IP) network that communicates data in Transmission Control Protocol/Internet Protocol (TCP/IP) packets, over, e.g., Category 5, Ethernet cables
The memory 106 of the electronic device 102A and/or the electronic device 102B (or other system components) can include, e.g., a standard or proprietary electronic database or other data storage and retrieval mechanism. In one example, memory includes one or more databases, such as relational databases, multi-dimensional databases, hierarchical databases, object-oriented databases, or one or more other types of databases. The memory 106 can be implemented in software, hardware, and combinations of both. In one example, memory include proprietary database software stored on one of a variety of storage mediums on a data storage server connected to network 104 and configured to store data such as measured/collected pre-operative sensor data or other information. Storage media included in or employed in cooperation with memory can include, e.g., any volatile, non-volatile, magnetic, optical, or electrical media, such as a random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, or any other digital media.
The electronic device 102A and/or the electronic device 102B can employ the memory 106 to store and retrieve various types of data, including but not limited to tests, test results, data relating to biomarkers, etc. Additionally, the electronic device 102A and/or the electronic device 102B can store and retrieve data or other information from analytics executed on data, sample results data, as well as other information related to patient population modeling and analysis (e.g., risk stratification) as further discussed herein.
The system 100 can implement the technique 200 shown in
Diagnosing Joint Pain and/or Inflammation using the Technique of
As shown in
A subject sample integrity can be assessed to determine whether the subject sample is hemorrhagic, e.g., includes an excessive quantity (concentration) of red blood cells (RBCs) 216 or is diluted by blood. In one aspect a subject sample can be assessed for the quantity or RBCs (RBC count) relative to a reference level. Methods of quantifying RBCs are well known in the art and can include manual counting and automated counting methods. In one aspect a reference joint specific biological material can have less than 1,000,000 RBCs per microliter, i.e., is not hemorrhagic. In an aspect a subject reference joint specific biological material having greater than 1,000,000 RBCs per microliter can be considered hemorrhagic 218. In one aspect a subject sample having an A280 within the range of 0.342 to 1.190 (within normal range, not diluted or contaminated) can be assessed 216 to determine whether the sample has greater than 1,000,000 RBCs per microliter and can be indicated/reported as (hemorrhagic 218). In another aspect a subject sample that has been determined to be contaminated 214 can be assessed to determine whether the sample has greater than 1,000,000 RBCs per microliter (hemorrhagic 218). In still another aspect a subject sample that has been determined to be diluted 212 can be assessed to determine whether the sample has greater than 1,000,000 RBCs per microliter (hemorrhagic 218). In one aspect an RBC count 216 for a subject sample can be determined by reviewing an electronic report automatically generated to indicate the results of the assessment. In one aspect a report can indicate a subject sample is classified as hemorrhagic 218 and provide a cautionary statement that the results of a biomarker assay of the sample should be interpreted with caution due to the hemorrhagic status.
The technique 200 can be a biomarker expression profile can be used to diagnose joint pain and/or joint inflammation, classify, suggest treatment, predict treatment or surgical prognosis, and be used for input into a patient risk calculator as further discussed herein. A sample such as a joint specific biological material can be extracted from a painful or inflamed joint of a subject. This sample can be assessed by determining a biomarker profile comprising any one or any combination of biomarkers. The RISC panel algorithm can then be utilized to determine the cause of the inflamed joint.
In one aspect a biomarker profile can comprise a first OA biomarker (obtained with a first one or more tests). This first OA biomarker can be, without limitation, COMP at 202, according one example. However, other possible first OA biomarker contemplated include, but are not limited to AD; HNE; COMP; IL-8; OPN; OPG; OC; Leptin; CRTAC1; Tetranectin; FGF2; TIMP1; TIMP2; IL-8; IL-6; CRP; MMP-3; MMP-9; RANTES; PDGF; and NGAL. In one aspect the first OA biomarker evidencing cartilage degradation is differentially increased in OA relative to a reference, e.g. a normal individual.
If the first biomarker (COMP) comes back positive, a second analysis can be performed to calculate a COMP/IL-8 result at 204. However, another result can be calculated such as COMP/X where X is, without limitation, IL-6, CRP, MMP-9, MMP-3, NGAL). If the result of COMP/IL-8 ≥4.3, the sample is considered positive for OA. If the result of COMP/IL-8<4.3, the sample is considered negative for OA and OA is excluded at step 206 in favor or another inflammatory arthropathy such as RA, CA, possible septic arthritis or septic arthritis. If the sample is positive for OA, diagnosis of OA can be confirmed at step 208 such as through medical imaging or other routine technique. A class 0 OA diagnosis can be assigned at 209.
At step 206, further tests are needed to determine the type of inflammatory arthropathy (RA, CA, possible septic arthritis, or septic arthritis) as OA has been ruled out.
Although 4.3 is used as the example ratio above, other ratios using other biomarkers are contemplated. Thus, a biomarker ratio of a first OA biomarker and a second OA biomarker can be compared to a reference ratio of the first OA biomarker and the second OA biomarker. In an aspect a biomarker ratio or a reference result can be based on quantities (e.g., concentrations) that are adjusted to have like units, such that the level of each biomarker is expressed in the result as pg/mL, ng/mL, pg/mL, mg/mL, or mg/dl, or the like such that the units cancel each other. For example, a synovial fluid sample can have a first OA biomarker level of 100 ng/mL and a second OA biomarker level of 100 pg/mL (or 0.01 ng/mL), providing a biomarker result of 100/0.01 or 10,000. In another aspect a biomarker result or a reference ratio can be based on quantities (e.g., concentrations) that are not adjusted to have like units, such that a first OA biomarker can have a level expressed in units that differ from the units of the level of the second OA biomarker. For example, a first OA biomarker can have a level of 1000 ng/mL and the second OA biomarker can have a level of 100 pg/mL providing a biomarker ratio of 1000/100 or 10. In one aspect a biomarker ratio can be the ratio of a first OA biomarker that is differentially increased in OA and a second OA biomarker that is differentially increased in an inflammatory arthropathy, e.g., differentially decreased in OA. In an aspect the first OA biomarker can be selected from COMP, OPN, OPG, OC, Leptin, CRTAC1, Tetranectin, FGF2, TIMP1, and TIMP2, and a second OA biomarker can be selected from IL-8, IL-6, CRP, AD, HNE, MMP-3, MMP-9, NGAL, RANTES or PDGF. In an aspect a biomarker ratio greater than or equal to 2.0 can diagnose OA or discriminate between OA and an inflammatory arthropathy, e.g., 2.1, 2.2., 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9 and any number therebetween. The biomarker ratio can comprise a ratio of COMP:IL-8 and a COMP:IL-8 ratio greater than or equal to 3.0 can diagnose idiopathic OA in a subject.
COMP less than or equal to a reference level of COMP in combination with COMP/IL-Ratio discussed above can exclude OA from a diagnosis. In one aspect a COMP level less than or equal to 1,500 ng/mL in combination with COMP/IL-Ratio discussed above can exclude OA from a diagnosis. In an aspect a level of COMP less than 4,000 ng/mL in combination with COMP/IL-Ratio discussed above can exclude OA from a diagnosis. In another aspect a synovial fluid level of COMP less than 3,500 ng/mL in combination with COMP/IL-Ratio discussed above can exclude OA from a diagnosis. In one aspect a synovial fluid level of COMP less than 3,000 ng/mL in combination with COMP/IL-Ratio discussed above can exclude OA from a diagnosis. In one aspect a synovial fluid level of COMP less than 2,500 ng/mL in combination with COMP/IL-Ratio discussed above can exclude OA from a diagnosis. In one aspect a synovial fluid level of COMP less than 2,000 ng/mL in combination with COMP/IL-Ratio discussed above can exclude OA from a diagnosis. In one aspect a synovial fluid level of COMP less than or equal to 1,500 ng/mL in combination with COMP/IL-Ratio discussed above can exclude OA from a diagnosis. In another aspect a synovial fluid level of COMP less than or equal to 1,000 ng/mL in combination with COMP/LL-Ratio discussed above can exclude OA from a diagnosis.
Returning to the biomarker aspects of the RISC Panel technique 200, in an aspect a quantity of white blood cells (WBC), i.e., a WBC count 220, can be determined for a subject sample if OA is excluded at step 206. In one aspect a WBC count can be a differential WBC count. Methods of quantifying WBCs are well known in the art and can include manual quantification and automated quantification. In an aspect a differential WBC count can include the total number of WBCs per volume (WBC concentration), and the proportion (percentage) of one or more WBC types (e.g., % neutrophils (PMN); % mononuclear cells) relative to the total WBC quantity. In an aspect a subject sample can be compared to a reference e.g., a reference joint specific biological material (e.g., synovial fluid) for a subject that is known to not have a joint infection or inflammatory arthropathy. In an aspect a reference joint specific biological material can have a WBC count 220 with less than or equal to 3,000 WBCs per microliter and/or can have fewer than 70% PMN. In one aspect a subject sample can have a WBC count 220 greater than 3,000 cells per microliter suggesting infection, e.g., native joint sepsis/infection or periprosthetic joint infection, and the subject sample can be further assessed at 222 to determine whether infection is present, e.g., culture of the sample and/or assessing biomarkers AD, L-lactate (NSA Panel), and/or Synovasure® Microbial Identification assays (MID) as described below. In an aspect a subject sample can have a greater than 70% PMN suggesting infection., e.g., native septic joint or periprosthetic joint infection, and the subject sample can be further assessed at 222 to determine whether infection is present, e.g., culture of the sample, assessing biomarkers AD and L-lactate (NSA Panel), and/or MID. In one aspect a WBC count 220 can be determined by reviewing an electronic report for the subject sample analysis. The results of a sample assessment can be provided in the electronic report or the electronic report can be automatically generated to indicate the results of the assessment. In one aspect the RISC panel can include a WBC differential count. In one aspect the report generated by the RISC panel for a subject sample that has greater than 3,000 WBC per microliter or greater than 70% PMN can include a statement that the subject and/or subject sample should receive further assessment to determine whether infection is present, which can include sample culture and/or additional biomarker assessment (e.g., biomarker assessment for AD and L-lactate and/or MID. In one aspect a report for a subject sample that has fewer than 2,000 WBC per microliter can be classified as a non-inflammatory sample 224 (class I inflammatory arthritis) suggesting that infection or an inflammatory arthropathy are unlikely to be present in the subject's joint. In another aspect the electronic report generated by the RISC panel for a subject sample that has greater than 2,000 WBC per microliter but less than 3,000 WBC per microliter can be classified as inflammatory sample class III 226 suggesting the likelihood that an inflammatory arthropathy is present without infection. In another aspect the electronic report generated by the RISC panel for a subject sample that has greater than 2,000 WBC per microliter but less than 3,000 WBC per microliter with confirmed Gout Diagnosis 248 can be classified as inflammatory sample class IIIA 225 suggesting the likelihood that an active inflammation induced by MSU crystalline arthropathy is present without infection. In another aspect the electronic report generated by the RISC panel for a subject sample that has greater than 2,000 WBC per microliter but less than 3,000 WBC per microliter with confirmed CPPD Diagnosis 250 can be classified as inflammatory sample class IIIB 227 suggesting the likelihood that an active inflammation induced by CPP crystalline arthropathy is present without infection.
Thus, the RISC panel can conduct a WBC count 220, which can indicate the subject sample has an inflammatory status 222, 224 or 226 according to one or more aspects disclosed above. The WBC count 220 can indicate the subject sample has an inflammatory status 224 (class I inflammatory arthritis). When the WBC count 220 indicates likely septic arthritis at 222, the sample can be further assessed to make a more accurate determination of septic arthritis using the NSA panel 228, the MID 230 and/or the culture 232.
Thus, with the RISC panel technique 200, the sample can be further assessed for biomarkers AD and L-lactate at NSA Panel 228, MID 230, and/or culture 232 to determine whether the sample was obtained from an infected joint, i.e., the subject has septic arthritis. In an aspect, if the sample that is negative for AD can be confirmed to not have septic arthritis and may be put into class III 226 possible septic arthritis. In another aspect the sample that is positive for AD and that is positive for L-lactate can be confirmed to have septic arthritis at step 234 (Class IV). In an aspect a synovial fluid sample that is positive for AD and that is negative for L-lactate or has L-lactate level less than 70 mg/dL can be considered indeterminate or inconclusive for diagnosis of septic arthritis and may be put into class III 226 possible septic arthritis. In one aspect presence of AD or L-lactate in a synovial fluid sample can be determined by reviewing the electronic report for the subject sample analysis. The results of a sample assessment AD and/or L-lactate can be provided in the electronic report and/or the electronic report can be automatically generated to indicate the results of the assessment.
Similarly, a positive test MID 230 can result in a diagnosis (Class IVa at 236) of septic arthritis. A positive culture 232 can result in a diagnosis of (Class IVb at 238) of septic arthritis.
In an aspect of the RISC Panel technique, the sample concurrent with WBC count 220 or after the WBC count 220, can be analyzed for RA and CA at steps 240 and 242. At step 240 a crystal analysis can be performed for CA. At step 242 a test for RA can be performed.
At step 242, a level of anti-CCP can be determined for the sample. In one aspect the level of RFs can be determined for sample. In one aspect the RF level determined for the sample. In one aspect a reference level of anti-CCP can be greater than or equal to 2 U per milliliter can be indicative of RA at 244. Furthermore, if the sample has RF greater than or equal to 10 IU per milliliter this can be indicative of RA at 244. RA diagnosis 244 can be noted and electronically reported and classification Class IIb at 246 can result. A suggest treatment for subject can be issued with the electronic report. A negative result for RA using RF and anti-CCP can result in a diagnosis of inflammatory status 224 (class I inflammatory arthritis).
As shown in
In one aspect the presence of MSU crystals in the sample can confirm the presence of gout at 248 resulting in a Class II categorization at 252. The presence of gout and WBC count of greater than 2000 cells/μL at 220 plus negative for NSA at 228, negative for culture at 232, and negative for MID at 230 can result in a Class IIIa at 225. The presence of CPP crystals in the sample at 250 can confirm a diagnosis of CPP disease in the subject and can result in a Class IIa categorization at 254. The presence of CPPD and WBC count of greater than 2000 cells/μL at 220 plus negative for NSA at 228, negative for culture at 232, and negative for MID at 230 can result in a Class IIb at 227.
In an aspect the presence or absence of crystals (and crystal type) in the sample can be determined and can be electronically reported as results. The electronic report can indicate the results of the assessment, e.g., the absence of crystals, type of crystals present (MSU or CPP), whether the crystals present are intracellular, extracellular, and/or whether there is/is not an active-flare up of CA.
The RISC panel algorithm can classify the inflammatory severity as “none”, “low”, “medium-high” and “high” for further analysis and scoring.
The RISC panel algorithm not only classifies the inflammatory condition but also can evaluate the inflammatory condition of the patient according to category (i.e. class 0, class 1, class II, class IIa, class IIb, class III, class IV, class IVa and class IVb). The classification is be based upon severity of the condition and/or risk to the patient. With the classification increasing with the risk of septic arthritis. Such classification can be used in patient risk assessment and condition evaluation as further discussed subsequently.
Merely by way of example, a class 0 can have no risk weighting and would be a patient of average joint specific risk (e.g., 1.0) or below average joint specific risk with OA suitable for immediate surgery. A class I patient would have average to low joint specific risk (e.g., a weighting of 1.0 to 1.2 as compared to a typical patient). Class 0 and I patients can be recommended for standard of care with minimal added treatment costs. A class II, IIa and IIb patient would be of medium joint specific risk (e.g., a weighting of 1.3 to 1.5 as compared to a typical patient). Surgery would not be recommended and treatment of the CA or RA would be recommended. If surgery is warranted, additional options such as prophylactic antibiotic/anti-inflammatory administration and proactive long-term monitoring may be recommended for Class II, Ha, and IIb patients. A class III, IIIa and IIIB patient would be of medium-high joint specific risk (e.g., a weighting of 1.6 to 1.8 as compared to a typical patient). Further diagnosis for possible septic arthritis would not be recommended and treatment with antibiotics would be recommended. A class III patient should not be subject to surgery until further treatment and diagnosis is completed. A class IV, IVa and IVb patient would be high joint specific risk of a potential poor outcome and/or complication (e.g. a weighting above 1.8 as compared to the typical patient). Treatment for septic arthritis would be recommended. These weighting along with other data (e.g., classification can be input electronically into a patient risk calculator system as further discussed in
The processor 306 can employ the memory 308 to retrieve instructions that are then executed on the processor 306. Thus, the processor 306 can store and retrieve data or other information from analytics such as those already executed by the RISC panel device on joint specific data, as well as data and other information related to patient population modeling.
The memory 308 can include data 310 representative of a plurality of characteristics that are considered in determining the suitability of the patient for surgery including sample results data (e.g., characteristic/category/class based upon input from the RISC panel algorithm. The sample results data can be joint-specific inflammation data as discussed previously.
The processor 306 can determine 312 a likely patient outcome based upon the characteristics including the sample data input that is joint specific regarding inflammation type from the RISC panel algorithm. Further characteristics that can be considered in determining patient outcome likelihood can include any one or combination of: surgery type, anaesthesia level, age, lifestyle, pre-operative activity level, Body-Mass-Index (BMI), patient clinical history (e.g., co-morbidities, diseases, afflictions), clinical measures, patient reported outcome measures (PROM)s, willingness to attend a pre-operative educational class, patient recorded activities (e.g., sensor(s)), willingness to attend post-operative rehab such as physical therapy, surgery duration, patient ability to ambulate independently preoperatively without assist device, joint factors (bone loss, osteophyte size, deformity, soft-tissue envelop, etc.), existence of pre-exiting arthroplasty implants, other characteristics.
A patient may be excluded from surgery purely on the basis of one or more joint specific characteristics determined at the RISC panel level. Thus, the RISC panel assessment can be a primary factor in the treatment the patient receives according to some examples. As discussed, a class III, MIIa, IIIb, or class IV, IVa, and IVb should be excluded from surgery on the basis of CA, possible septic arthritis or septic arthritis determination at the RISC panel level. Furthermore, combinations of characteristics could exclude a patient from surgery as patient outcome would be undesirable for one or more parties to the surgery.
The system 300 can operate with one or more levels having different processing power, processing capabilities, time limits, or the like. Devices described can be wearable devices. However, further devices can include a mobile phone, tablet, computer (e.g., desktop or laptop), cloud-based devices (e.g., a server), which may include access to a database, or the like. The system 300 can utilize large population data sets. Compiled data that is stripped of personally identifiable information may be used with data from other users. Different models may be developed (e.g., for different user populations, for different surgeries, for different user timelines, for different user diagnoses, etc.). Predictive analytics may be used to drive a change in the system 300 dynamically. For example, an output of a model run on the system 300 may be used to change a parameter/characteristic, such as the type of inflammatory arthritis experienced by the patient. The system 300 may change patient outcome based upon such change in characteristic(s).
Machine (e.g., computer system) 500 may include a hardware processor 502 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 504 and a static memory 506, some or all of which may communicate with each other via an interlink (e.g., bus) 508. The machine 500 may further include a display unit 510, an alphanumeric input device 512 (e.g., a keyboard), and a user interface (UI) navigation device 514 (e.g., a mouse). In an example, the display unit 510, input device 512 and UI navigation device 514 may be a touch screen display. The machine 500 may additionally include a storage device (e.g., drive unit) 516, a signal generation device 518 (e.g., a speaker), a network interface device 520, and plurality of sensors 521, such as any of those discussed previously (e.g., an IMU, a global positioning system (GPS) sensor, compass, accelerometer, or other sensor). The machine 500 may include an output controller 528, such as a serial (e.g., Universal Serial Bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate or control one or more peripheral devices (e.g., a printer, card reader, etc.).
The storage device 516 may include a machine readable medium 522 on which is stored one or more sets of data structures or instructions 524 (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein. The instructions 524 may also reside, completely or at least partially, within the main memory 504, within static memory 506, or within the hardware processor 502 during execution thereof by the machine 500. In an example, one or any combination of the hardware processor 502, the main memory 504, the static memory 506, or the storage device 516 may constitute machine readable media.
While the machine readable medium 522 is illustrated as a single medium, the term “machine readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) configured to store the one or more instructions 524. The term “machine readable medium” may include any medium that is capable of storing, encoding, or carrying instructions for execution by the machine 500 and that cause the machine 500 to perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. Non-limiting machine-readable medium examples may include solid-state memories, and optical and magnetic media.
The instructions 524 may further be transmitted or received over a communications network 526 using a transmission medium via the network interface device 520 utilizing any one of a number of transfer protocols (e.g., frame relay, internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Institute of Electrical and Electronics Engineers (IEEE) 802.11 family of standards known as Wi-Fi®, IEEE 802.16 family of standards known as WiMax®), IEEE 802.15.4 family of standards, peer-to-peer (P2P) networks, among others. In an example, the network interface device 520 may include one or more physical jacks (e.g., Ethernet, coaxial, or phone jacks) or one or more antennas to connect to the communications network 526. In an example, the network interface device 520 may include a plurality of antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) techniques. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine 500, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.
A brief reference to various possible examples related to the claims and embodiments is provided. These examples are referenced as aspects and techniques.
In some aspects, the techniques described herein relate to a method of electronically diagnosing a cause of an inflamed and/or painful joint of a patient using a joint specific biological material, the method including: receiving, using an electronic device data regarding tests performed on the joint specific biological material; determining with the electronic device if osteoarthritis (OA) is the cause of the inflamed and/or painful joint based upon one or more of the tests, wherein the diagnosing is based upon a level of cartilage oligomeric matrix protein (COMP) and a ratio of COMP to interleukin-8 (IL-8) in the joint specific biological material; if the one or more of the tests indicate OA is not the cause of the inflamed joint, determining with the electronic device if inflammatory arthritis, crystalline arthritis, rheumatoid arthritis, possible septic arthritis or septic arthritis is the cause of the inflamed joint based upon a further plurality of the tests; and generating with the electronic device a sample results report with result data including diagnosis for use by a clinician.
In some aspects, the techniques described herein relate to a method, wherein generating with the electronic device the sample results report includes a differential diagnosis of arthritis for the patient.
In some aspects, the techniques described herein relate to a method, wherein the determining the inflammatory arthritis, crystalline arthritis, rheumatoid arthritis, or septic arthritis is based upon presence of or absence of monosodium urate (MSU) crystals or calcium pyrophosphate dihydrate (CPPD) crystals in the joint specific biological material, the presence or absence of Immunoglobulin G (IgG) antibodies to citrullinated peptide (Anti-CCP), presence of absence of rheumatoid factor (RF), and by white blood cell (WBC) count and differential in the joint specific biological material.
In some aspects, the techniques described herein relate to a method, wherein the determining one of septic arthritis, inflammatory arthritis, and possible septic arthritis is by WBC count and/or percentage of polymorphonuclear cells (% PMN) in the joint specific biological material.
In some aspects, the techniques described herein relate to a method, wherein a result of WBC >3000 cells/μL and/or % PMN >70 is indicative of septic arthritis or possible septic arthritis.
In some aspects, the techniques described herein relate to a method, wherein the possible septic arthritis is determined by results of WBC >3000 cells/μL and/or % PMN >70, COMP/IL-8 ratio <4.3, negative for native septic arthritis (alpha defensin and lactate), negative for microbial ID, and negative for microbial culture in the joint specific biological material.
In some aspects, the techniques described herein relate to a method, wherein the septic arthritis is determined by results of WBC >3000 cells/μL and/or % PMN >70, COMP/IL-8 ratio <4.3, and at least one of: positive for native septic arthritis (alpha defensin and lactate), positive for microbial ID, or positive for microbial culture in the joint specific biological material.
In some aspects, the techniques described herein relate to a method, wherein the inflammatory arthritis is determined by absence of monosodium urate (MSU) crystals, absence of calcium pyrophosphate dihydrate (CPPD) crystals, absence of Anti-CCP and RF, COMP/IL-8 ratio <4.3 and WBC >2000 cells/μL and/or % PMN >70.
In some aspects, the techniques described herein relate to a method, further including categorizing the diagnosis according to one of a plurality of classes according to at least one of a level or type of inflammation, wherein the plurality of classes correspond to the diagnosis of OA, inflammatory arthritis, crystalline arthritis, rheumatoid arthritis, and septic arthritis.
In some aspects, the techniques described herein relate to a method, wherein the classes include subclassification related to presence of monosodium urate (MSU) crystals, presence of calcium pyrophosphate dihydrate (CPPD) crystals, presence of Anti-CCP, rheumatoid factor, and by one or more of a culture of the joint specific biological material, a microbial ID, or a presence or absence of alpha-defensin (AD) and L-lactate.
In some aspects, the techniques described herein relate to a method, further including electronically communicating the result data as an input to a pre-operative patient risk stratification tool and electronically determining with the patient risk stratification tool a predicted post-surgical patient outcome or risk based upon the result data.
In some aspects, the techniques described herein relate to a method, further including weighting the predicted patient outcome according to the one of several classes.
In some aspects, the techniques described herein relate to a method, wherein the categorizing the diagnosis according to the one of several classes is according to at least one of a type of inflammation or an inflammatory severity in addition to arthritic type.
In some aspects, the techniques described herein relate to an electronically implemented system for diagnosing a cause of an inflamed and/or painful joint of a patient using a joint specific biological material, the system including: processing circuitry; and a memory that includes instructions, the instructions, when executed by the processing circuitry, cause the processing circuitry to: receive data regarding tests performed on the joint specific biological material; determine if osteoarthritis (OA) is the cause of the inflamed and/or painful joint based upon one or more of the tests, wherein the diagnosing is based upon a level of cartilage oligomeric matrix protein (COMP) and a ratio of COMP to interleukin-8 (IL-8) in the joint specific biological material; determine, if the one more of the tests indicate OA is not the cause of the inflamed joint, if inflammatory arthritis, crystalline arthritis, rheumatoid arthritis, possible septic arthritis or septic arthritis is the cause of the inflamed joint based upon a further plurality of the tests; and generate result data including joint specific diagnosis for use by a clinician.
In some aspects, the techniques described herein relate to a system, wherein the determination of the inflammatory arthritis, crystalline arthritis, rheumatoid arthritis, or septic arthritis is based upon presence of or absence of monosodium urate (MSU) crystals or calcium pyrophosphate dihydrate (CPPD) crystals in the joint specific biological material, the presence or absence of anti-cyclic citrullinated peptide, rheumatoid factor, and by a white blood cell (WBC) count and a percentage of polymorphonuclear cells (% PMN) in the joint specific biological material.
In some aspects, the techniques described herein relate to a system, wherein the instructions, when executed by the processing circuitry, cause the processing circuitry to determine one of the septic arthritis, the inflammatory arthritis and the possible septic arthritis is by WBC count or percentage of polymorphonuclear WBCs (% PMN) in the joint specific biological material.
In some aspects, the techniques described herein relate to a system, wherein a result of WBC >3000 cells/μL and/or % PMN >70 is indicative of septic arthritis or possible septic arthritis.
In some aspects, the techniques described herein relate to a system, wherein possible septic arthritis is determined by the result WBC >3000 cells/μL and/or % PMN >70, and COMP/IL-8 ratio <4.3, negative for native septic arthritis, negative for microbial ID, and negative for microbial culture in the joint specific biological material and the septic arthritis is determined by determined by the ratio of WBC >3000 cells/p L and/or % PMN >70, COMP/IL-8 ratio <4.3, and at least one of: positive for native septic arthritis (alpha defensin and lactate), positive for microbial ID, or positive for microbial culture in the joint specific biological material.
In some aspects, the techniques described herein relate to a system, wherein a non-specific inflammatory arthritis is determined by absence of monosodium urate (MSU) crystals, absence of calcium pyrophosphate dihydrate (CPPD) crystals, absence of anti-cyclic citrullinated peptide, absence of RF, COMP/IL-8 ratio <4.3, WBC ≤3000, and % PMN <70.
In some aspects, the techniques described herein relate to a system, wherein the instructions, when executed by the processing circuitry, cause the processing circuitry to categorize the diagnosis according to one of a plurality of classes according to a level of inflammation, wherein the plurality of classes correspond to the diagnosis of OA, inflammatory arthritis, crystalline arthritis, rheumatoid arthritis, and septic arthritis.
In some aspects, the techniques described herein relate to a system, wherein the classes include subclassification related to presence or absence of monosodium urate (MSU) crystals, presence or absence of calcium pyrophosphate dihydrate (CPPD) crystals, presence or absence of anti-cyclic citrullinated peptide, presence of absence of rheumatoid factor, WBC count and differential, and by one or more of a culture of the joint specific biological material, positive identification of causative organism by microbial ID, or a presence or absence of alpha-defensin (AD) and L-lactate.
In some aspects, the techniques described herein relate to a system, further including: a second system including: processing circuitry; and a memory that includes instructions, the instructions, when executed by the processing circuitry, cause the processing circuitry to: communicate with the system to retrieve the result data; and determine, according to a risk stratification tool a predicted patient outcome based upon the result data.
In some aspects, the techniques described herein relate to a system, wherein the instructions, when executed by the processing circuitry, cause the processing circuitry to weight the predicted patient outcome according to the one of several classes.
In some aspects, the techniques described herein relate to a method of electronically assessing a likelihood of an outcome for a patient experiencing an inflamed joint, the method including: determining, with a computing device, if an inflamed joint of the patient is caused by osteoarthritis (OA) inflammatory arthritis, crystalline arthritis, rheumatoid arthritis, possible septic arthritis or septic arthritis based upon tests performed on a joint specific biological material of the patient; generating results data from the determining; categorizing and weighting the results data; and performing a risk analysis on the results data the risk analysis that accounts for one or more of the categorizing and weighting of the results data in accessing the patient outcome.
In some aspects, the techniques described herein relate to a machine-readable medium including instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations to implement elements.
In some aspects, the techniques described herein relate to an apparatus including means to implement elements.
In some aspects, the techniques described herein relate to a system to implement elements.
In some aspects, the techniques described herein relate to the method, system, machine-readable medium, or apparatus including any elements above.
The various aspects and techniques describe can include or use, or can optionally be combined with any portion or combination of any portions of any one or more of the aspects or techniques to include or use, subject matter that can include means for performing any one or more of the functions of various systems, apparatus, method, or a machine-readable medium including instructions that, when performed by a machine, cause the machine to perform any one or more of the functions.
Method examples described herein can be machine or computer-implemented at least in part. Some examples can include a computer-readable medium or machine-readable medium encoded with instructions operable to configure an electronic device to perform methods as described in the above examples, including one or more of the algorithms described in above Examples. An implementation of such methods can include code, such as microcode, assembly language code, a higher-level language code, or the like. Such code can include computer readable instructions for performing various methods. The code may form portions of computer program products. A code can be tangibly stored on one or more volatile, non-transitory, or non-volatile tangible computer-readable media, such as during execution or at other times. Examples of these tangible computer-readable media can include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, random access memories (RAMs), read only memories (ROMs), and the like.
This application is a continuation-in-part of international application PCT/US2020/057967, filed on Oct. 29, 2020, which claims the benefit of U.S. Provisional Patent Application Ser. No. 62/928,114, filed on Oct. 30, 2019, and also claims the benefit of U.S. Provisional Patent Application Ser. No. 63/010,756, filed on Apr. 16, 2020, the benefit of priority of each of which is claimed hereby, and each of which is incorporated by reference herein in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 62928114 | Oct 2019 | US | |
| 63010756 | Apr 2020 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/US2020/057967 | Oct 2020 | US |
| Child | 17573843 | US |
