Patent Application
20230250479
The present invention relates to an in vitro or ex vivo method for determining the risk of incidence of a healthcare-associated infection, comprising a step of measuring the expression of CD177 in a biological sample from said patient.
The development of healthcare-associated infections is a significant complication associated with medical care, in particular in medical care facilities such as hospitals (where reference is more specifically made to nosocomial infections). It has been demonstrated that nosocomial infections in intensive care units, which occur in 20 to 40% of patients, were associated with increased morbidity and mortality, a longer requirement for supplemental care for organ failure(s), a longer stay in hospital, higher healthcare costs, and greater use of antibiotics, contributing to antimicrobial resistance. The incidence of healthcare-associated infections has become particularly exacerbated in recent years, due to the increase in multi-drug resistant pathogens. The World Health Organization (WHO) estimates the number of nosocomial infections in European hospitals to be around 5 million, leading to approximately 50 000 deaths and an annual excess cost of 13 to 24 billion Euros. Numerous factors influence the incidence and development of healthcare-associated infections, such as the patient's general state of health, but also factors associated with the patient's treatment (e.g. the administration of antibiotics and/or the use of invasive medical devices), factors associated with the hospital environment (e.g. the ratio of the number of nurses to the number of patients), and the varying use of aseptic techniques by hospital personnel. Recommendations have been published, and the establishment of infection control programs has been encouraged, in particular by the U.S. Department of Health and Human Services, the European Center for Disease Prevention and Control, the WHO and national agencies, for which the prevention and reduction of healthcare-associated infections have become a major priority. It has been shown that programs for controlling healthcare-associated infections have proven particularly effective in reducing serious infections. However, it has been estimated that a maximum of 65 to 70% of cases of blood and urinary tract infections, associated with catheterization, and 55% of cases of pneumonia, associated with mechanical ventilation, and of infections at surgical sites, could be prevented. Moreover, compliance with and application of procedures according to recommendations can be complicated in some hospitals, particularly in low- or middle-income countries. The early identification of patients at risk of developing a healthcare-associated infection would be a key step in preventing these infections and in treating these patients. According to certain models, a biomarker which would reduce the identification time of healthcare-associated infections in a high-risk population would make it possible to reduce mortality in these patients, with a good benefit-cost ratio. However, there is currently no clinical in vitro diagnostic test which makes it possible to identify patients with a high risk of contracting a healthcare-associated infection.
The CD177 gene encodes the membrane glycoprotein of CD177 neutrophils, and was discovered in 1971 in a case of neonatal neutropenia. A study has demonstrated an increase in the expression level of CD177 at the mRNA and protein level in circulating neutrophils from patients who have suffered septic shock, without however any association with commonly used immunosuppression markers such as the expression of HLA-DR at the surface of monocytes, the percentage of regulatory T cells or the number of CD4+ T cells (Demaret et al. (2016), Immunology Letters 178:122-130). In another study it was shown that the mRNA expression level of CD177 made it possible to distinguish patients who had suffered septic shock from healthy volunteers and, in combination with other biomarkers, to classify the septic patients into two categories (Schaack et al. (2018), PLoS One 13(6):e0198555). It has also been shown that the expression level of mRNA encoding the CD177 protein, among other biomarkers, made it possible to distinguish patients with community-acquired pneumonia from patients without same (Scicluna et al. (2015), Am J Respir Crit Care Med 192(7):826-35). Moreover, in patients who had undergone surgery, it was recently shown that certain genes, including CD177, were over-expressed in the non-surviving group (Martinez-Paz et al. (2020), J. Clin. Med. 9:1276).
It has now been discovered, entirely surprisingly, that measuring the expression of the CD177 gene made it possible to determine the risk of incidence of a healthcare-associated infection in a patient. This has never been shown or suggested in the literature. Patients having a high risk of contracting a care-associated infection could advantageously benefit from customized treatment.
Thus, a subject of the present invention is an in vitro or ex vivo method for determining the risk of incidence of a healthcare-associated infection, comprising a step of measuring the expression of CD177 in a biological sample from said patient.
In the context of the present invention:
Preferably, in the method as described previously:
In the case of a patient in a septic state (already infected with a first infection), the method according to the invention makes it possible to determine the risk of incidence of a secondary infection. “Patient in a septic state” (or patient suffering from sepsis) means a patient having at least one life-threatening organ failure caused by an inappropriate host response to infection. “Septic shock” means a sub-type of sepsis in which hypotension persists despite sufficient vascular filling.
Preferably, the method according to the invention as described previously in all the embodiments thereof makes it possible to determine the risk of incidence of a healthcare-associated infection in a patient:
Preferably, the method as described previously, in all the embodiments thereof, additionally comprises a step of measuring, in the biological sample from the patient, the expression:
As examples of genes encoding molecules involved in the innate immune system, mention may be made of the following genes: GNLY, S100A9, C3AR1, ADGRE3, CX3CR1, IFIH1, OAS2, OAS3.
As an example of a gene encoding molecules of the cell cycle, mention may be made of the gene CCNB1IP1.
As examples of genes encoding cytokines, mention may be made of the following genes: IL15, IL2, MCP1(CCL2), CXCL10.
As examples of genes encoding anti-inflammatory cytokines, mention may be made of the following genes: IL10, IL1RN.
As examples of genes encoding pro-inflammatory cytokine receptors, located on chromosome 2 in the the region 2q11-2q12, mention may be made of the following genes: IL18R1, IL1R2, URI_ and IL18RAP.
As examples of genes encoding pro-inflammatory cytokines, mention may be made of the following genes: IFNG, IL1B, IL17A, IL18, IL6, TNF.
As examples of genes encoding molecules involved in cytoskeleton formation, mention may be made of the following genes: ARL14EP, GSN.
As examples of genes encoding molecules involved in gene expression and/or transcription, mention may be made of the following genes: CIITA, DYRK2, GATA3, MDC1, NFKB1, RORC, STAT4, TBX21, TDRD9.
As an example of a gene encoding growth factors, mention may be made of the gene CSF2.
As examples of genes encoding molecules of the metabolism, mention may be made of the following genes: ALOX5, BPGM, TRAP1.
As examples of genes encoding molecules involved in the adaptive immune system, mention may be made of the following genes: CD40LG, CD3D, BTLA, CD274, CTLA4, ICOS, PDCD1, TNFSF4, CD74, FCGR1A, LILRB2, TAP2.
As examples of genes encoding molecules involved in signal transduction, mention may be made of the following genes: FLT1, HAVCR2, IL7R, ZAP70.
As an example of a gene encoding molecules involved in modulation of the acute phase, mention may be made of the gene HP.
Table 1 Chromosomal location of the CD177 gene and other genes for which the expression can be measured in combination with that of CD177
Table 2 Classification of biomarkers which can be used in combination with CD177, by families and according to their immune function. The site of expression (blood cell type) and the location are also indicated.
Also preferably, the method as described previously, in all the embodiments thereof, comprises, in addition to the step of measuring the expression of CD177, a step of measuring, in the biological sample from the patient, the expression:
More preferably still, the method as described previously, in all the embodiments thereof, comprises, in addition to the step of measuring the expression of CD177, a step of measuring, in the biological sample from the patient, the expression of at least one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty one, twenty two, twenty three, twenty four, twenty five, twenty six gene(s) selected from the following genes: GNLY, S100A9, C3AR1, ADGRE3, CX3CR1, OAS2, CCNB1IP1, IL10, IL1RN, IL1R2, IFNG, TNF, ARL14EP, CIITA, GATA3, MDC1, TDRD9, BPGM, CD3D, CD274, CTLA4, CD74, TAP2, IL7R, ZAP70, HP.
More preferably still, the method as described previously, in all the embodiments thereof, comprises, in addition to the step of measuring the expression of CD177, a step of measuring, in the biological sample from the patient, the expression of at least one, two, three, four, five, six, seven, eight, nine, ten, eleven gene(s) selected from the following genes: S100A9, C3AR1, CD177, CX3CR1, IL1R2, IFNG, CIITA, CD3D, CTLA4, CD74, TAP2, HP.
Particularly preferred combinations of one or more other gene(s), the expression of which can be measured in combination with the expression of CD177, are presented in table 3.
Table 3 List of preferred combinations of 1 to 11 other gene(s), the expression of which can be measured in combination with the expression of CD177.
Preferably, in the method as described previously, in all the embodiments thereof, the biological sample is a blood sample, preferably a blood sample or a blood-derived sample (e.g. PBMCs which can be obtained using the Ficoll method, well known to those skilled in the art, or purified monocytes).
The measurement of the expression (or expression level) of a gene consists in quantifying at least one expression product of the gene. For the purposes of the present invention, the expression product of a gene is any biological molecule resulting from the expression of said gene. More particularly, the expression product of the gene may be an RNA transcript. “Transcript” means RNA, and in particular messenger RNA (mRNA) resulting from the transcription of the gene. More specifically, the transcripts are RNAs produced by the transcription of a gene followed by post-transcriptional modifications of the pre-RNA forms. In the context of the present invention, the expression level of one or more RNA transcripts of the same gene can be measured. Thus, preferably, in the method as described previously, in all the embodiments thereof, the expression of the gene(s) (i.e. the expression of CD177, and optionally of one or more other genes of interest, as described previously) is measured at the RNA or mRNA transcript level. In the case of an mRNA transcript, the detection may be performed by a direct method, by any process known to those skilled in the art which makes it possible to determine the presence of said transcript in the sample, or by indirect detection of the transcript after conversion of the latter into DNA, or after amplification of said transcript or after amplification of the DNA obtained after conversion of said transcript into DNA. Numerous methods exist for the detection of nucleic acids (see for example Kricka et al., Clinical Chemistry, 1999, no. 45(4), p. 453-458; Relier G. H. et al., DNA Probes, 2nd Ed., Stockton Press, 1993, sections 5 and 6, p. 173-249). The expression of the genes may particularly be measured by Reverse Transcription-Polymerase Chain Reaction or RT-PCR, preferably by quantitative RT-PCR or RT-qPCR (for example using the FilmArray® technology or the Biomark™ platform from Fluidigm), by sequencing (preferably by high-throughput sequencing) or by hybridization techniques (for example with hybridization microarrays or by techniques of the NanoString® nCounter® type).
The measurement of the expression level of a gene makes it possible in particular to determine the quantity of one or more transcripts present in the biological sample or to give a value derived therefrom. A value derived from the quantity may for example be the absolute concentration, calculated by virtue of a calibration curve obtained from successive dilutions of a solution of amplicons having a given concentration. It may also correspond to the value of the normalized and calibrated quantity, such as the CNRQ (Calibrated Normalized Relative Quantity, (Hellemans et al (2007), Genome biology 8(2):R19), which integrates the values of a reference sample (or of a calibrator) and of one or more housekeeping genes (also referred to as reference genes). By way of example of housekeeping genes, mentioned may be made of the genes DECR1, HPRT1, PPIB, RPLP0, PPIA, GLYR1, RANBP3, 18S, GAPDH and ACTB.
Thus, preferably, in the method as described previously, in all the embodiments thereof, the expression of the gene(s) of interest is normalized in relation to the expression of one or more housekeeping genes (or reference genes), as is known to those skilled in the art; more preferably still, using one or more of the following housekeeping genes: DECR1 (chromosomal location of the gene according to GRCh38/hg38: chr8:90,001,352-90,053,633), HPRT1 (chromosomal location of the gene according to GRCh38/hg38: chrX:134,452,842-134,520,513) and PPIB (chromosomal location of the gene according to GRCh38/hg38: chr15:64,155,812-64,163,205).
Preferably, in the method as described previously, in all the embodiments thereof, the expression of the gene(s) of interest (preferably the normalized expression) in the biological sample from the patient is compared to a reference value or to the expression of the same gene(s) of interest (preferably the normalized expression) in a reference biological sample (this data being used to calculate the CNRQ, as mentioned above). The reference sample may for example be a sample originating from a volunteer (healthy individual), from a patient, or from a mixture of samples from several volunteers (on the one hand) or from several patients (on the other hand). The reference sample can also be a sample taken from a volunteer (or a mixture of samples taken from several volunteers) then treated ex vivo by an immune system-stimulating agent (such as LPS or lipopolysaccharide). The reference sample can also be a mixture of untreated sample(s) and sample(s) treated ex vivo by an immune system-stimulating agent.
Preferably, the method for determining the risk of incidence of a healthcare-associated infection, as described previously, in all the embodiments thereof, also comprises a step of managing the healthcare provisions in order to reduce the risk of incidence of a healthcare-associated infection. A patient identified as having an increased risk of incidence of a healthcare-associated infection may have adapted management of healthcare provisions with the aim of reducing the risk of incidence of a healthcare-associated infection and, for example, in order to reduce the risk of developing sepsis, septic shock or even the risk of death. By way of examples of healthcare provision management, mention may be made of an immunomodulatory treatment adapted to the patient, or else a prophylactic antibiotic treatment, it being possible for the two treatments to be combined, and/or admittance to an ongoing care unit or resuscitation unit in order to reduce the risk of incidence of a healthcare-associated infection, for example to reduce the risk of developing sepsis, septic shock or even the risk of death, in the days following the measurement of the expression of the biomarker(s). Preferably, the immunomodulatory treatment is an immunostimulant treatment if it is determined that the individual is immunosuppressed, or an anti-inflammatory treatment if it is determined that the individual has an inflammatory state. Among the immunostimulant treatments which may be selected, mention may for example be made of the group of the interleukins, in particular IL-7, IL-15 or IL-3, growth factors, in particular GM-CSF, interferons, in particular IFNγ, Toll agonists, antibodies, in particular anti-PD1, anti-PDL1, anti-LAG3, anti-TIM3, anti-IL-10 or anti-CTLA4 antibodies transferrins and apoptosis inhibitor molecules, FLT3L, Thymosin al, adrenergic antagonists. Among the anti-inflammatory treatments, mention may particularly be made of the group of glucocorticoids, cytostatic agents, molecules acting on immunophilins and cytokines, molecules which block the IL-1 receptor and anti-TNF treatments. Examples of prophylactic antibiotic treatments suitable for preventing pneumonia are described in particular in Annales Françaises d'Anesthésie et de Réanimation [French Annals of Anaesthesia and Resuscitation] (30; 2011; 168-190). Conversely, a patient not at risk of incidence of a healthcare-associated infection could be quickly admitted to an outpatient hospital service, for example an infectious diseases unit, rather than remaining in a closely-monitored unit which they do not need.
Another subject of the invention is a kit comprising means for amplifying (e.g. primers) and/or means for detecting (e.g. probes) the expression of CD177 and of one or more other gene(s), as indicated previously, in all the embodiments, and particularly preferably one, two, three, four, five, six, seven, eight, nine, ten or eleven genes selected from the group consisting of: CD74, CIITA, IFNG, IL1R2, C3AR1, TAP2, HP, CX3R1, S100A9, CTLA4 and CD3D); said kit being characterized in that all the amplification and/or detection means of said kit enable the detection and/or amplification of at most 100, 90, 80, 70, 60, 50, 40, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2 biomarkers in total. “Biomarker” (or “marker”) means a biological characteristic which can be objectively measured and which is indicative of normal or pathological biological processes or of a pharmacological response to a therapeutic intervention. This biomarker may in particular be detectable at the mRNA level. More particularly, the biomarker may be an endogenous biomarker or loci (such as a gene or an HERV/Human Endogenous RetroVirus, found in an individual's chromosomal material) or an exogenous biomarker (such as a virus).
Thus, said kit may for example also comprise means for amplifying and/or detecting one or more housekeeping genes (preferably selected from the list consisting of: DECR1, HPRT1 and PPIB). The kit may also comprise positive control means, making it possible to qualify the quality of RNA extraction, the quality of any amplification and/or hybridization process.
“Primer” or “amplification primer” means a nucleotide fragment which may consist of 5 to 100 nucleotides, preferably 15 to 30 nucleotides, and having hybridization specificity with a target nucleotide sequence under conditions determined for the initiation of enzymatic polymerization, for example in a reaction for the enzymatic amplification of the target nucleotide sequence. Generally use is made of “primer pairs” consisting of two primers. When it is desired to amplify several different biomarkers (e.g. from different genes), several different pairs of primers are preferably used, each preferentially having the ability to hybridize specifically with a different biomarker.
“Probe” or “hybridization probe” means a nucleotide fragment typically consisting of 5 to 100 nucleotides, preferably 15 to 90 nucleotides, even more preferably 15 to 35 nucleotides, having hybridization specificity under conditions determined for forming a hybridization complex with a target nucleotide sequence. The probe also comprises a reporter (such as a fluorophore, an enzyme or any other detection system) which will enable the detection of the target nucleotide sequence. In the present invention, the target nucleotide sequence may be a nucleotide sequence contained in a messenger RNA (mRNA) or a nucleotide sequence contained in a complementary DNA (cDNA) obtained by reverse transcription of said mRNA. When it is desired to target several different biomarkers (e.g. from different genes), several different probes are preferably used, each preferentially having the ability to hybridize specifically with a different biomarker.
“Hybridization” means the process during which, under suitable conditions, two nucleotide fragments, for example a hybridization probe and a target nucleotide fragment, having sufficiently complementary sequences, are able to form a double strand with stable and specific hydrogen bonds. A nucleotide fragment which is “able to hybridize” with a polynucleotide is a fragment which can hybridize with said polynucleotide under hybridization conditions, which can be determined in each case, as is known. The hybridization conditions are determined by stringency, i.e. the strictness of the operating conditions. Hybridization is more specific when it is carried out at higher stringency levels. The stringency is defined particularly on the basis of the base composition of a probe/target duplex, and also by the degree of mismatch between two nucleic acids. The stringency can also be based on the reaction parameters, such as the concentration and type of the ionic species present in the hybridization solution, the nature and the concentration of denaturing agents, and/or the hybridization temperature. The stringency of the conditions under which a hybridization reaction must be carried out will chiefly depend on the hybridization probes used. All this information is well known and the suitable conditions can be determined by those skilled in the art. In general, depending on the length of the hybridization probes used, the temperature for the hybridization reaction is between approximately 20 and 70° C., in particular between 35 and 65° C. in a saline solution at a concentration of approximately 0.5 to 1 M. A step of detecting the hybridization reaction is subsequently carried out.
“Enzymatic amplification reaction” is intended to mean a process which generates multiple copies of a target nucleotide fragment by the action of at least one enzyme. Such amplification reactions are well known to those skilled in the art and mention may particularly be made of the following techniques: PCR (Polymerase Chain Reaction), LCR (Ligase Chain Reaction), RCR (Repair Chain Reaction), 3SR (Self Sustained Sequence Replication) with patent application WO-A-90/06995, NASBA (Nucleic Acid Sequence-Based Amplification), TMA (Transcription Mediated Amplification) with U.S. Pat. No. 5,399,491, and LAMP (Loop mediated isothermal amplification) with U.S. Pat. No. 6,410,278. When the enzymatic amplification reaction is PCR, reference will more particularly be made to RT-PCR (RT for reverse transcription) when the amplification step is preceded by a step of reverse transcription of messenger RNA (mRNA) to give complementary DNA (cDNA), and to qPCR or RT-qPCR when the PCR is quantitative.
Another subject of the invention is the use:
of means for amplifying (e.g. primers) and/or means for detecting (e.g. probes) the expression of CD177 and optionally also the expression of one or more other gene(s), as indicated previously, in all the embodiments (and particularly preferably one, two, three, four, five, six, seven, eight, nine, ten or eleven genes selected from the group consisting of: CD74, CIITA, IFNG, IL1R2, C3AR1, TAP2, HP, CX3R1, S100A9, CTLA4 and CD3D); or
The present invention is illustrated, non-limitingly, by the following examples.
Materials and Methods A prospective, longitudinal, single-center observational clinical study was carried out at Hôpital Edouard Herriot (Lyon, France). The design of this clinical study was published in Rol et al (2017), BMJ Open 7(6): e015734. The clinical study was approved by the Agence Nationale de Sécurité du Médicament et des produits de santé (ANSM) [French Agency for the Safety of Drugs and Health Products] and the Comité de Protection des Personnes Sud-Est II [South-East II Independent Ethics Committee] in December 2015. Amendments to the protocol were made in July 2016 and January 2017. In brief, a total of 377 patients, in a septic state (n=35) or in septic shock (n=72), suffering from severe burns (n=24), from severe injury (n=137) or hospitalized in a resuscitation unit or an intensive care unit following major surgery (n=109), and 175 healthy volunteers, were included between December 2015 and March 2018.
The exclusion criteria related essentially to factors which could have impacted the immune status and could have biased the results (for example: severe neutropenia, corticosteroid treatments, an onco-haematological disease, etc.). Each event leading to a suspected healthcare-associated infection, occurring within the hospital before D30, was reviewed independently by three physicians who were not involved in patient recruitment. Twenty-six percent of the patients developed at least one healthcare-associated infection before D30, or before leaving hospital.
Blood samples were collected in PAXgene® tubes (ref. 762165, PreAnalytiX GmbH Hombrechtikon Switzerland), once for healthy volunteers and several times for a sub-cohort of 242 patients (i.e. 82 patients in a septic state/in septic shock, 83 patients suffering from severe injuries, 61 patients hospitalized in a resuscitation unit or an intensive care unit after major surgery, and 16 patients suffering from severe burns), i.e. 3-4 times in the first week (on days 1 or 2: D1/2, on days 3 or 4: D3/4, and on days 5, 6 or 7: D5/7), then 3 times at later times (around D14, D28 and D60).
The expression level of CD177 was measured in these samples by RT-qPCR. A volume of 100 μl of blood collected in the PAXgene® was directly injected into a FilmArray® pouch optimized for detecting a panel of genes involved in the host response, including CD177, by nested PCR. The steps of extraction of the nucleic acids, reverse transcription and qPCR were carried out sequentially and automatically by the Filmarray® instrument, without external intervention. The cycle thresholds (Ct) determined by the instrument were normalized in relation to the expression of 3 reference genes (DECR1, HPRT1 and PPIB).
Regarding data analysis, associations between the expression of CD177, measured at different times during the first week, and the incidence of a healthcare-associated infection before D30 from inclusion in the study were evaluated. The results were calculated in the form of Hazard Ratios expressed as the inter-quartile distance with the associated 95% confidence interval (HR IQR). Next, univariate logistic regression was implemented in order to predict the risk of incidence of a healthcare-associated infection before D15. The power of the values predicted by logistic regression to distinguish between healthcare-associated infection and lack of healthcare-associated infection was quantified by the area under the curve (AUC) of the ROC curve (Receiver Operating Characteristic), and 95% confidence intervals were estimated.
Next, the association between the expression of CD177 and the incidence of a healthcare-associated infection was evaluated for different time intervals of incidence of the infection (i.e. periods between taking the sample and the 1st incidence of an infection). The different periods considered were: a healthcare-associated infection in the 4 days and in the 7 days following the sample being taken, regardless of when the sample was taken. For each patient who developed a healthcare-associated infection, the sample considered corresponds to the sample taken closest to the incidence of the first episode of healthcare-associated infection.
For patients who did not develop a healthcare-associated infection (i.e. control patients), a matching method was used to select, for each case, a control patient whose sample was taken on the same day and with close SOFA and Charlson scores. Finally, a single control was selected for each unique case. Univariate logistic regressions were implemented. The power of the values predicted by logistic regression to distinguish between healthcare-associated infection and lack of healthcare-associated infection was quantified by the area under the curve (AUC) of the ROC curve, and 95% confidence intervals were estimated.
Results
An increase in the expression of CD177 at the mRNA level, measured on D3/4 or on D5/7 from inclusion in the cohort, was associated with a greater risk of incidence of a healthcare-associated infection before D30 in the overall patient population (univariate analysis on D3/4: HR IQR=1.64 [1.05-2.55], p=0.0291; on D5/7: HR IQR=2.30 [1.32-4.02], p=0.0034). This association was always significant for the measurement time on D5/7 after adjustment with the SOFA and Charlson scores (multivariate analysis: HR IQR=2.14 [1.20-3.84], p=0.0104).
Moreover, the prediction models showed that the expression of CD177 at the mRNA level, measured on D3/4 or D5.7 from inclusion in the cohort, made it possible to predict the incidence of a healthcare-associated infection before D15 from inclusion in the cohort (table 4).
Table 4 Performance (AUC and 95% confidence intervals, with AUC2.5 and AUC 97.5 limits) of the measurement of the expression of CD177, measured on D3/4 or D5/7 from inclusion in the cohort, in predicting the incidence of a healthcare-associated infection before D15 from inclusion in the cohort in patients in a septic state/in septic shock, suffering from severe injuries, or hospitalized following major surgery.
The prediction models also showed that the expression of CD177 at the mRNA level made it possible to predict the incidence of a healthcare-associated infection in the 4 days or 7 days following the sample being taken (table 5).
Table 5 Performance (AUC and 95% confidence intervals, with AUC2.5 and AUC 97.5 limits) of the measurement of the expression of CD177 in predicting the incidence of a healthcare-associated infection in the 4 days or in the 7 days following the sample being taken.
Thus, the results obtained show that the measurement of the expression of CD177 alone makes it possible to predict the incidence of healthcare-associated infection(s) in the 15 days starting from the immuno-inflammatory attack, in the 4 days following the sample being taken or in the 7 days following the sample being taken.
In this example, the expression level of CD177 and also that of other genes was measured by RT-qPCR. Multivariate logistic regressions (combination of the measurement of the expression of CD177 and of one or more other gene(s)) were then carried out. The measurement of the expression of one or more of these other genes, in addition to the measurement of the expression of CD177, makes it possible to improve the performance (compared to the measurement of the expression of CD177 alone) in predicting the risk of incidence of a healthcare-associated infection, whether before D15 starting from inclusion in the cohort (table 6) or in the 4 days or in the 7 days following the sample being taken (table 7).
Table 6 Performance (AUC and 95% confidence interval, with AUC2.5 and AUC 97.5 limits) of the measurement of the expression of CD177, in combination with one or more other biomarker(s) (multivariate analysis) measured on D3/4 or D5/7 from inclusion in the cohort, in predicting the incidence of a healthcare-associated infection before D15 from inclusion in the cohort in patients in a septic state/in septic shock, suffering from severe injuries, or hospitalized following major surgery.
Table 7 Performance (AUC and 95% confidence interval, with AUC2.5 and AUC 97.5 limits) of the measurement of the expression of CD177 in combination with one or more other biomarker(s) (multivariate analysis) in predicting the incidence of a healthcare-associated infection in the 4 days or in the 7 days following the sample being taken.
| Number | Date | Country | Kind |
|---|---|---|---|
| FR2007134 | Jul 2020 | FR | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/FR2021/051230 | 7/5/2021 | WO |
