Patent Application
20240027476
The present invention relates to a blood coagulation reaction analysis method.
A blood coagulation test is a test in which a predetermined reagent is added to a blood specimen of a patient and a blood coagulation time or the like is measured in order to diagnose a blood coagulation ability of the patient. Typical examples of the blood coagulation time include a prothrombin time (PT), an activated partial thromboplastin time (APTT), and a thrombin time. An abnormality in the blood coagulation ability causes a prolongation of coagulation time. Examples of causes of a prolongation of coagulation time include an effect of a coagulation inhibitor drug, a reduction in a component involved in coagulation, a congenital deficiency of a blood coagulation factor, and an acquired appearance of an autoantibody which inhibits a coagulation reaction.
When a prolongation of a coagulation time such as APTT is observed in a blood coagulation test, generally, a cross-mixing test is further performed in order to determine which of a coagulation factor inhibitor (anti-coagulation factor), a lupus anticoagulant (LA), and a coagulation factor deficiency such as hemophilia is causing the prolongation of APTT. In the cross-mixing test, with respect to normal plasma, plasma being tested, and mixed plasma containing the plasma being tested and the normal plasma at various capacity ratios, an APTT (immediate reaction) immediately after preparation and an APTT (delayed reaction) after incubating for two hours at 37° C. are measured. A measured value of the cross-mixing test is graphically shown with an APTT (seconds) as an axis of ordinate and a capacity ratio between the plasma being tested and the normal plasma as an axis of abscissa. The created graphs of the immediate reaction and the delayed reaction respectively exhibit a pattern which is “convex downward”, “straight”, or “convex upward” in accordance with an APTT prolongation factor. The APTT prolongation factor is determined based on the patterns of the immediate reaction and the delayed reaction.
In a blood coagulation test, a coagulation reaction curve can be obtained by measuring, over time, a blood coagulation reaction amount after adding a reagent to the blood specimen. The coagulation reaction curve has different shapes in accordance with a type of abnormality of a blood coagulation system (Non Patent Literature 1). Therefore, methods of determining an abnormality of the blood coagulation system based on the coagulation reaction curve are disclosed. For example, Patent Literature 1 to 3 and Non Patent Literature 2 to 4 describe methods of assessing a presence or absence of an abnormality of a coagulation factor in a patient based on parameters related to a primary differential curve and a quadratic differential curve of a coagulation reaction curve with respect to blood of the patient such as a maximum coagulation rate, a maximum coagulation acceleration, a maximum coagulation deceleration, and times required to reach the maximum coagulation rate, the maximum coagulation acceleration, and the maximum coagulation deceleration. Patent Literature 4 describes a method of determining a degree of severity of hemophilia based on an average rate of change in a coagulation rate during a period in which a coagulation reaction of a patient reaches a maximum coagulation rate or a maximum coagulation acceleration. Patent Literature 5 and 6 describe methods of calculating a peak width at a predetermined height of a coagulation reaction rate curve and determining a presence or absence of an abnormality of a coagulation factor, a concentration of the coagulation factor, a coagulation time prolongation factor, or the like using information based on the peak width.
The present invention relates to a blood coagulation reaction analysis method which enables data for measuring a coagulation time of a blood specimen and data useful for estimating a coagulation abnormality factor in the blood specimen to be acquired.
Specifically, the present invention provides the following.
[1] A blood coagulation reaction analysis method, comprising:
SDI of objective parameter with respect to the subject specimen=(α−β)÷γ, wherein
The method according to the present invention enables data for measuring a coagulation time of a blood specimen and data useful for estimating a coagulation abnormality factor in the blood specimen to be acquired. According to the present invention, not only can a presence or absence of a coagulation abnormality factor in a blood specimen be estimated but a coagulation abnormality factor in an abnormal specimen can be estimated without having to perform a time-consuming cross-mixing test as was conventional. In addition, according to the present invention, an apparently normal specimen of which a coagulation time is not prolonged but which actually has a coagulation abnormality factor can be detected by a simple procedure.
In a blood coagulation test, a predetermined reagent is added to a blood specimen, a subsequent blood coagulation reaction is measured, and a blood coagulation time is measured from the coagulation reaction. In the present specification described below, a blood specimen may be simply referred to as a specimen. In the measurement of the blood coagulation reaction, general means such as optical means which measures a scattered light quantity, transmittance, absorbance, or the like, mechanical means which measures a viscosity of blood plasma, or the like is used. A blood coagulation reaction is generally represented by a coagulation reaction curve indicating a change in an amount of a coagulation reaction over time. Although dependent on measuring means, the coagulation reaction curve of a normal specimen without a coagulation abnormality factor basically exhibits a sigmoidal shape. For example, the coagulation reaction curve of a normal specimen based on a scattered light quantity usually rises abruptly as coagulation progresses at a time point where a certain amount of time has elapsed from the addition of a reagent and, subsequently, reaches a plateau as the coagulation reaction nears its end. On the other hand, the coagulation reaction curve of an abnormal specimen with a coagulation abnormality factor exhibits various shapes dependent on a cause of the abnormality such as a delayed rise time or a gradual rise of the curve.
In the measurement of a blood coagulation time of a specimen, data can be collected until the end of the coagulation reaction or, in other words, until the coagulation reaction curve reaches a plateau, and a coagulation time can be calculated based on the data. For example, if a reaction amount from a start of a reaction to an end of the reaction is taken as 100%, a time until the reaction amount reaches 50% can be calculated as a coagulation time. Alternatively, the coagulation time can be calculated based on a rate of change of the coagulation reaction curve such as a change over time of an integrated value of a coagulation reaction at a peak or during a small time slot of a coagulation reaction rate (refer to JP-A-6-249855). With the latter method, since the coagulation time can be calculated before the end of the coagulation reaction, the coagulation time can be measured in a shorter period of time. With specimens having a coagulation abnormality factor, in many cases, the coagulation time is prolonged as compared to normal specimens. The prolongation of the coagulation time is an indicator of a presence or absence of a coagulation abnormality factor. On the other hand, a type of the coagulation abnormality factor (a prolongation factor of coagulation time) cannot be estimated from the coagulation time.
Conventionally, determination of a prolongation factor of coagulation time (a type of a coagulation abnormality factor) is mainly performed by a cross-mixing test. Therefore, with conventional methods, a prolongation factor of a coagulation time cannot be determined unless a cross-mixing test is performed separately from a coagulation time measurement. Furthermore, a cross-mixing test requires measurements of an immediate reaction and a delayed reaction after a two-hour incubation with respect to a mixed specimen of a subject specimen and a normal specimen and, therefore, takes time and effort.
In addition, with conventional methods, a cross-mixing test is normally only applied to specimens of which a prolongation of a coagulation time is observed in a coagulation reaction measurement and, meanwhile, a specimen of which a coagulation time is within a normal range is conventionally considered a normal specimen. However, a study conducted by the present inventors has revealed that a specimen which actually has a coagulation abnormality factor may exhibit a coagulation time within a normal range due to the specimen being highly responsive to a reagent for coagulation measurement or the like (refer to
In the present invention, during a coagulation reaction measurement, both data for calculating a coagulation time and data for estimating a coagulation abnormality factor are acquired. Therefore, the present invention enables both a coagulation time of a specimen and data for estimating a coagulation abnormality factor of the specimen to be acquired in one measurement. In addition, the present invention enables a coagulation abnormality factor to be estimated or data for estimating the coagulation abnormality factor to be acquired during a coagulation reaction measurement without having to perform a time-consuming cross-mixing test. Furthermore, according to the present invention, a specimen which does not exhibit a prolongation of a coagulation time but which actually has a coagulation abnormality factor can be detected by a simple procedure.
The present invention provides a blood coagulation reaction analysis method. In the blood coagulation reaction analysis method according to the present invention (hereinafter, also referred to as a method according to the present invention), a blood coagulation reaction of a blood specimen under test (hereinafter, also referred to as a subject specimen) is measured and, based on time-series data of a coagulation reaction obtained by the measurement, data for calculating a blood coagulation time of the subject specimen (first data) and data for estimating a blood coagulation abnormality factor of the subject specimen (second data) are acquired.
Examples of the blood coagulation time which can be calculated in accordance with the present invention include a prothrombin time (PT), an activated partial thromboplastin time (APTT), and a coagulation time in fibrinogen (Fbg) concentration measurement. In the present specification presented below, the method according to the present invention will be described by mainly taking an activated partial thromboplastin time (APTT) as an example of a coagulation time. Any person skilled in the art can modify the method according to the present invention to other coagulation times (for example, a prothrombin time (PT)).
Hereinafter, the method according to the present invention will be described with reference to a basic flow of an embodiment of the method according to the present invention shown in
In the method according to the present invention, blood plasma of a subject being tested is preferably used as a subject specimen. An anticoagulant normally used in a coagulation test can be added to the specimen. For example, blood plasma is obtained by collecting blood using a blood collection tube with sodium citrate and subsequently subjecting the collected blood to centrifugal separation.
In a measurement of a blood coagulation reaction, a reagent for coagulation time measurement is added to the subject specimen to start a blood coagulation reaction. A coagulation reaction of a mixed liquid containing the reagent and the subject specimen can be measured. The reagent for coagulation time measurement to be used can be optionally selected according to the purpose of measurement. Reagents for measuring various coagulation times are commercially available (for example, APTT Reagent Coagpia APTT-N manufactured by SEKISUI MEDICAL CO., LTD.). In the measurement of a coagulation reaction, general means such as optical means which measures a scattered light quantity, transmittance, absorbance, or the like, mechanical means which measures a viscosity of blood plasma, or the like may be used. In the present specification presented below, the method according to the present invention will be described using a coagulation reaction measurement based on a scattered light quantity as an example.
While a reaction start time point of a coagulation reaction can be typically defined as a time point where a reagent is mixed with a specimen and the coagulation reaction is started, other timings may be defined as the reaction start time point. A period of time during which the measurement of the coagulation reaction is continued can be, for example, several ten seconds to around seven minutes from the time point at which the reagent is mixed with the specimen. While the measurement time may be an optionally-set fixed value, the measurement time may conclude at a time point where an end of the coagulation reaction of each specimen is detected. During the measurement time, a measurement (photometry in a case where detection is performed optically) of a progress of the coagulation reaction can be repetitively performed at predetermined intervals. For example, measurements may be performed at 0.1-second intervals. A temperature of the mixed liquid during the measurement corresponds to normal conditions such as 30° C. or higher and 40° C. or lower and, preferably, 35° C. or higher and 39° C. or lower. In addition, various conditions of measurement can be appropriately set in accordance with the subject specimen, the reagent, the measurement means, and the like.
A series of operations in the coagulation reaction measurement described above can be performed using an automated analyzer. An example of the automated analyzer is the Automated Coagulation Analyzer CP3000 (manufactured by SEKISUI MEDICAL CO., LTD.). Alternatively, a part of the operations may be performed manually. For example, the subject specimen can be prepared by hand and subsequent operations can be performed by an automated analyzer.
Due to the coagulation reaction measurement described above, measured data D(i) (a photometric value of a scattered light quantity) is sequentially acquired. In this case, “i” represents the number of measurement points or a time from start of the coagulation reaction (also simply referred to as time). For example, when the measurement (photometry) interval is 0.1 seconds, “i” is represented as time=0.1×number of measurement points.
Next, a reaction R(i) is acquired from the measured data D(i) (step 1). Since the measured data D(i) contains noise during photometry and fluctuations which appear immediately after start of photometry and which are unrelated to the reaction, the measured value is preferably subjected to smoothing by known methods. In addition, when a coagulation reaction is photometrically measured based on a scattered light quantity, zero point adjustment processing of subtracting a scattered light quantity derived from the mixed specimen liquid prior to the reaction is preferably performed. Any of various known methods related to denoising can be used in the smoothing of measured data. For example, examples of smoothing include filtering and processing in which a difference value or a derivative value is obtained by an operation such as an intra-section average gradient to be described later and subsequently integrating the difference value or the derivative value. In zero point adjustment, for example, smoothed measured data may be adjusted so as to assume a value of 0 at a measurement start time point. Furthermore, initial fluctuation removal may be performed with respect to the measured data D(i). The initial fluctuation removal may be performed such that all values from the measurement start time point until an initial fluctuation removal time determined in advance become zero. Preferably, the measured data D(i) is subjected to smoothing or zero point adjustment to acquire reaction R(i). More preferably, the measured data D(i) is subjected to smoothing and zero point adjustment to acquire the reaction R(i). Alternatively, after the measured data D(i) is subjected to smoothing and zero point adjustment, the measured data D(i) is further converted into a relative value to acquire the reaction R(i). For example, the reaction R(i) may be acquired by converting D(i) so that D(i) at the measurement start time point is 0 and D(i) at the coagulation reaction end point is a predetermined value such as 100. The reaction R(i) constitutes a coagulation reaction curve.
A first derivative V(i) thereof is acquired from the obtained reaction R(i) (step 2). Differential processing of obtaining V(i) from R(i) can be performed by any method such as calculating an intra-section average gradient value. In the calculation of an intra-section average gradient value, a certain number of measurement points before and after each measurement point i such as 2K+1-number of measurement points from i−K to i+K can be used, where K denotes any integer. For example, when K is 2, five measurement points of i−2, i−1, i, i+1, and i+2 can be used. An average gradient value refers to a gradient value when linearly approximating the plurality of measurement points. Conventional methods such as a least-squares method can be used as a method to calculate linear approximation. An average gradient value of the measurement points can be considered a first derivative at the measurement point i. Furthermore, a relative value of the first derivative of R(i) may be acquired as V(i). For example, V(i) may be acquired by converting a first derivative value of R(i) so that a value at the measurement start time point is 0 and a maximum value is a predetermined value such as 100. The first derivative V(i) constitutes a curve representing a rate or a rate of change of a coagulation reaction.
From R(i) or V(i) obtained as described above, data (first data) for calculating a blood coagulation time of a subject specimen and data (second data) for estimating a blood coagulation abnormality factor of the subject specimen are acquired. In the method according to the present invention, acquisition of R(i) and V(i) with respect to a subject specimen may be performed in parallel with a measurement of a coagulation reaction of the subject specimen or performed after the measurement of a coagulation reaction ends. In the method according to the present invention, the measurement of the coagulation reaction of the subject specimen is preferably performed until an end of the coagulation reaction. The end of the coagulation reaction can be determined in accordance with any criterion such as a time point where R(i) reaches a plateau or a time point where, after reaching a peak, V(i) decreases to 0 or a constant value (for example, S % or less of a maximum peak).
As described above, each of D(i), R(i), and V(i) in the present specification may be a function of the number of measurement points or a function of time. The first data and the second data to be described later may also be data based on the number of measurement points or data based on time. In the present specification described below, R(i) and V(i) may be respectively abbreviated as simply R and V.
In an embodiment, R and V with respect to a subject specimen are sequentially acquired while performing a coagulation reaction measurement of the subject specimen. At an appropriate time during the coagulation reaction measurement, the first data is acquired from R or V and further, when necessary, a coagulation time of the subject specimen is acquired from the first data. Subsequently, preferably after continuously acquiring R and V until the coagulation reaction ends, the second data is acquired and further, when necessary, a coagulation abnormality factor of the subject specimen is estimated from the second data.
In another embodiment, R with respect to a subject specimen is sequentially acquired while performing a coagulation reaction measurement of the subject specimen. At an appropriate time during the coagulation reaction measurement, the first data is acquired from the R and further, when necessary, a coagulation time of the subject specimen is acquired from the first data. Subsequently, preferably after continuously acquiring R until the coagulation reaction ends, V is acquired from the R, the second data is acquired and further, when necessary, a coagulation abnormality factor of the subject specimen is estimated from the second data.
In another embodiment, R with respect to a subject specimen is preferably sequentially acquired while performing a coagulation reaction measurement of the subject specimen until the end of the coagulation reaction. Subsequently, V, first data, and second data are acquired. Furthermore, when necessary, a coagulation time of the subject specimen is calculated from the first data and a coagulation abnormality factor of the subject specimen is estimated from the second data.
In another embodiment, preferably, after continuing a coagulation reaction measurement of a subject specimen until the end of the coagulation reaction, R and V are acquired and first data and second data are acquired. Furthermore, when necessary, a coagulation time of the subject specimen is calculated from the first data and a coagulation abnormality factor of the subject specimen is estimated from the second data.
In each of the embodiments described above, the coagulation reaction measurement may be ended at a timing preceding the end of the coagulation reaction as long as measured values necessary for acquiring the first data and the second data have already been obtained.
In the method according to the present invention, acquisition of first data and calculation of a coagulation time of a subject specimen using the first data (step 3) can be performed in accordance with any method. Examples of a method of calculating a coagulation time include: a method of calculating, as a coagulation time, a time point where R(i) reaches N % (where N is any value, the same applies hereinafter) of R(E) representing a reaction R at a coagulation reaction end point E; a method of calculating, as a coagulation time, a time point where V(i) reaches a maximum value Vmax or N % thereof; a method of calculating a coagulation time based on a change over time of an integrated value of R(i) in a small time slot (refer to JP-A-6-249855 and Japanese Patent Application No. 2019-237427); a method of calculating a coagulation time based on a weighted average time of V(i) (refer to Japanese Patent Application No. 2020-039344); and a method of calculating, as a coagulation time, using a time point where by taking a time point where V(i) reaches a predetermined value after first reaching the maximum value Vmax as an origin of calculation Te, a time point where R(i) reaches N. of R(Te) (refer to Japanese Patent Application No. 2020-068877).
Therefore, when the methods of calculating a coagulation time cited above are to be used, examples of the first data acquired in the method according to the present invention include: data related to R(E) representing a reaction R at a coagulation reaction end point E or a time point where R(i) reaches N % of R(E); data related to the maximum value Vmax of V(i) or a time point where V(i) reaches Vmax or N % thereof; data related to a change over time of an integrated value of R(i) in a small time slot; a weighted average time of V(i); and data related to an origin of calculation Te representing a point where V(i) reaches a predetermined value after first reaching Vmax or a time point where R(i) reaches Ns of R(Te). However, methods of calculating a coagulation time in the method according to the present invention and types of the first data used in the calculation methods are not limited to the above.
In the method according to the present invention, a first derivative V is used in acquisition of second data and estimation of a coagulation abnormality factor of a subject specimen using the second data (step 4). As described above, V may be sequentially acquired while performing a coagulation reaction measurement or acquired after the coagulation reaction measurement ends. Preferably, in the method according to the present invention, after performing a coagulation reaction measurement until the end of a coagulation reaction, the second data is acquired using the acquired V and, when necessary, estimation of a coagulation abnormality factor is further performed. Alternatively, as long as V necessary for acquiring the second data has already been obtained, the coagulation reaction measurement may be ended at a timing preceding the end of the coagulation reaction.
In a procedure of acquiring the second data, the number of measurement points or a time i where V assumes a reference value Xk set in advance (in other words, where V(i)=Xk is satisfied) is determined. Alternatively, when V(i)<Xk<V(i+1) or V(i−1)<Xk<V(i), i can be selected as the number of measurement points or a time satisfying V(i)=Xk. Since V usually has a peak shape with a maximum value Vmax as a peak top, at least one i satisfying V(i)=Xk exists in each of time points before and after V reaches Vmax. In the method according to the present invention, i satisfying V(i)=Xk is respectively determined with respect to V before and after reaching Vmax. Furthermore, in the method according to the present invention, Xk is a variable and i corresponding to each Xk is determined. For example, when there are n-number of Xk expressed as (X1, . . . , Xn) (where k represents a series of integers from 1 to n and n represents an integer equal to or larger than 2), a point pk where V(i) assumes Xk before reaching Vmax and a point qk where V(i) assumes Xk after reaching Vmax are respectively determined and n-number of pk (p1, . . . , pn) and n-number of qk (q1, . . . , qn) are obtained. While a point where V assumes Xk after reaching Vmax may be detected in plurality in a case where V has a plurality of peaks, in such a case, a maximum point among the detected points is selected as qk. In a similar manner, when a point where V assumes Xk before reaching Vmax is detected in plurality, a minimum point among the detected points (however, points in initial noise are excluded) is selected as pk. As described earlier, pk and qk may be data based on the number of measurement points or data based on time.
A value of the variable Xk can be optionally set. Preferably, Xk is larger than 0 and smaller than Vmax. Xk can be set based on Vmax. For example, Xk is specified by Vmax×Sk(%), where Sk need only be larger than 0 and smaller than 100 and, preferably, Sk can be set within a range from 0.5 to 99. When setting n-number of Xk(X1, . . . , Xn), n-number of Sk (S1, . . . , Sn) are set (where k and n are as described above, and each Sk is larger than 0 and smaller than 100 and preferably ranges from 0.5 to 99). While the number of the variable Xk to be set is not particularly limited, the number preferably ranges from 5 to 50 and more preferably ranges from 10 to 30 or, in other words, k is an integer preferably ranging from 5 to 50 and more preferably ranging from 10 to 30.
Xk, pk, and qk will now be described with reference to
pk and qk described above can be acquired as second data. Alternatively, the second data may include pk and qk or may not include pk and qk and include a parameter calculated from pk or qk. Examples of the parameter which can be included in the second data include statistics of pk or qk. Examples of the statistics of pk or qk include an average value (Ave), a standard deviation (SD), and a coefficient of variation (CV) of pk (p1, . . . , pn) or qk(q1, . . . , qn). In the present specification, the Ave, the SD, and the CV of the point pk which exists before the point where V reaches Vmax may be respectively referred to as a pre-Ave, a pre-SD, and a pre-CV and, in a similar manner, the Ave, the SD, and the CV of the point qk which exists after the point where V reaches Vmax may be respectively referred to as a post-Ave, a post-SD, and a post-CV. Further examples of the statistics of pk or qk include a ratio between a difference between the pre-Ave and the post-Ave and an average value of all pk and qk ([(post-Ave−pre-Ave)/(average value of pk and qk)], also referred to as a pre-post average difference) and a ratio between the pre-SD and the post-SD ([pre-SD/post-SD], also referred to as a pre-post SD ratio).
Further examples of the parameter which can be included in the second data include a midpoint Mk of pk and qk and a peak width Wk representing a width from pk to qk obtained by the equations provided below, and a coefficient of variation (CV) of the midpoint Mk. In the following equations, k represents a series of integers from 1 to n, where n denotes an integer equal to or larger than 2. Therefore, n-number of Mk (M1, . . . , Mn) and n-number of Wk (W1, . . . , Wn) can be calculated. The CV of the midpoint M1, (M1, . . . , Mn) reflects a degree of distortion of a peak shape of V and is also referred to as a distortion index in the present specification. In other words, since the more distorted (the larger the asymmetry of) the peak shape of V, the larger a change in Mk, the larger the CV of Mk or, in other words, the larger the distortion index.
M
k=(pk+qk)/2
W
k
=q
k
−p
k
Further examples of the parameter which can be included in the second data include a peakedness index which reflects a peakedness of the peak shape of V. For example, the peakedness index is represented by a ratio between sums (or average values) of Wk (W1, . . . , Wn) of an upper part and a lower part of a peak of V obtained by the following equation.
peakedness index=(sum or average value of Wk with respect to lower part of peak of V)/(sum or average value of Wk with respect to upper part of peak of V)
For example, when 20 Xk are set at regular intervals as shown in
In an embodiment, the second data acquired by the method according to the present invention may include only at least pk and qk and, preferably, includes pk and qk and at least one selected from the group consisting of the parameters calculated from pk or qk described above. In an example, the second data includes pk and qk and at least one selected from the group consisting of the pre-Ave, the post-Ave, the pre-SD, the post-SD, the pre-CV, the post-CV, the pre-post average difference, the pre-post SD ratio, Mk, Wk, the distortion index, and the peakedness index. In a preferable example, the second data includes pk, qk, the pre-CV, and the post-CV. In another preferable example, the second data includes pk, qk, the pre-post average difference, and the pre-post SD ratio. In another preferable example, the second data includes pk, qk, Mk, and Wk. In another preferable example, the second data includes pk, qk, the distortion index, and the peakedness index. In a further preferable example, the second data includes pk, qk, the pre-CV, the post-CV, the pre-post average difference, the pre-post SD ratio, the distortion index, and the peakedness index.
In an embodiment, the second data acquired by the method according to the present invention includes at least one selected from the group at least consisting of parameters calculated from pk or qk described above. In an example, the second data includes at least one selected from the group consisting of the pre-Ave, the post-Ave, the pre-SD, the post-SD, the pre-CV, the post-CV, the pre-post average difference, the pre-post SD ratio, Mk, Wk, the distortion index, and the peakedness index. In a preferable example, the second data includes the pre-CV and the post-CV. In another preferable example, the second data includes the pre-post average difference and the pre-post SD ratio. In another preferable example, the second data includes Mk and Wk. In another preferable example, the second data includes the distortion index and the peakedness index. In a further preferable example, the second data includes the pre-CV, the post-CV, the pre-post average difference, the pre-post SD ratio, the distortion index, and the peakedness index.
In addition to pk and qk or a parameter calculated from pk or qk, V itself, the maximum value Vmax of V, or VmaxT which represents a time or the number of measurement points where V=Vmax is satisfied may be further included in the second data.
A shape of a coagulation reaction curve of a specimen tends to differ dependent on blood coagulation characteristics (in other words, a blood coagulation abnormality factor) of the specimen, and the tendency is reflected on pk, qk, and a parameter calculated from pk or qk described above. As shown in an upper part of
As a more detailed example, a parameter of a coagulation factor VIII (FVIII)-deficient specimen will be described with reference to
As described above, pk, qk, and parameters calculated from pk or qk described above which can be included in the second data reflects a shape of V of a specimen and, by extension, blood coagulation characteristics of the specimen. Therefore, a coagulation abnormality factor of the specimen can be estimated based on the second data.
In the method according to the present invention, a coagulation abnormality factor of an abnormal specimen of which a coagulation time has been prolonged (in other words, a prolongation factor of a coagulation time) can be estimated based on the second data. In addition, in the method according to the present invention, even with respect to a specimen in which prolongation of a coagulation time is not observed, a coagulation abnormality factor which potentially exists in the specimen can be estimated based on the second data in a similar manner. Furthermore, in the method according to the present invention, a presence or absence of a coagulation abnormality factor of a specimen can be estimated based on the second data. In the following present specification, a coagulation abnormality factor of an abnormal specimen of which a coagulation time has been prolonged (in other words, a prolongation factor of a coagulation time) and a coagulation abnormality factor which potentially exists in a specimen in which prolongation of a coagulation time is not observed will be collectively referred to as a coagulation abnormality factor (or simply an abnormality factor). In addition, in the following present specification, an estimation of a type of a coagulation abnormality factor and an estimation of a presence or absence of a coagulation abnormality factor will be collectively referred to as an estimation of a coagulation abnormality factor.
Examples of types of a coagulation abnormality factor which can be estimated by the method according to the present invention include a coagulation factor deficiency, lupus anticoagulant (LA)-positive, a coagulation factor inhibitor (inhibitor), and heparin-positive (to be precise, a heparin-containing specimen). Examples of a coagulation factor deficiency include a coagulation factor V (FV) deficiency, a coagulation factor VIII (FVIII) deficiency, a coagulation factor IX (FIX) deficiency, a coagulation factor X (FX) deficiency, a coagulation factor XI (FXI) deficiency, and a coagulation factor XII (FXII) deficiency. Examples of inhibitors include an FVIII inhibitor. Preferably, the type of a coagulation abnormality factor which can be estimated by the method according to the present invention is selected from the group consisting of a coagulation factor deficiency, LA-positive, an inhibitor, and heparin-positive and, more preferably, selected from the group consisting of an FVIII deficiency, an FIX deficiency, LA-positive, an FVIII inhibitor, and heparin-positive.
In an embodiment of the method according to the present invention, an abnormality factor of a subject specimen can be estimated based on relative values of the pre-CV and the post-CV of the specimen with respect to the pre-CV and the post-CV based on a normal specimen group. The pre-CV and the post-CV based on the normal specimen group are, for example, average values of the pre-CV and the post-CV of a plurality of normal specimens and are also referred to as a reference_pre-CV and a reference post-CV in the present specification. Data with respect to the normal specimens may be prepared in advance before measurement of the subject specimen or may be acquired by measuring the normal specimens together with the subject specimen.
In the embodiment, a value obtained by subtracting the reference_pre-CV from the pre-CV of the subject specimen is a relative_pre-CV of the subject specimen, and a value obtained by subtracting the reference_post-CV from the post-CV of the subject specimen is a relative_post-CV of the subject specimen.
In another embodiment, the relative_pre-CV of the subject specimen is represented by (pre-CV of subject specimen/reference_pre-CV)−1, and the relative_post-CV of the subject specimen is represented by (post-CV of subject specimen/reference_post-CV)−1.
An abnormality factor of the subject specimen can be estimated from the relative_pre-CV and the relative_post-CV of the subject specimen. For example, with respect to each of a normal specimen and various abnormal specimen types (for example, an FVIII deficiency, an FIX deficiency, LA-positive, an FVIII inhibitor, and heparin-positive), by determining reference values (or ranges) of the relative_pre-CV and the relative_post-CV in advance and studying either the reference values of any specimen type which the relative_pre-CV and the relative_post-CV of the subject specimen most closely resemble or the reference ranges of any specimen type which includes the relative_pre-CV and the relative_post-CV of the subject specimen, an abnormality factor of the subject specimen can be estimated.
In an embodiment of the method according to the present invention, an abnormality factor of a subject specimen can be estimated based on relative values of the distortion index and the peakedness index of the specimen with respect to the distortion index and the peakedness index based on a normal specimen group. The distortion index and the peakedness index based on the normal specimen group are, for example, average values of the distortion index and the peakedness index of a plurality of normal specimens and are also referred to as a reference_distortion index and a reference_peakedness index in the present specification. Data with respect to the normal specimens may be prepared in advance before measurement of the subject specimen or may be acquired by measuring the normal specimens together with the subject specimen.
In the embodiment, a value obtained by subtracting the reference_distortion index from the distortion index of the subject specimen is a relative_distortion index of the subject specimen, and a value obtained by subtracting the reference_peakedness index from the peakedness index of the subject specimen is a relative_peakedness index of the subject specimen.
In another embodiment, the relative_distortion index of the subject specimen is represented by (distortion index of subject specimen/reference_distortion index)−1, and the relative_peakedness index of the subject specimen is represented by (peakedness index of subject specimen/reference_peakedness index)−1.
An abnormality factor of the subject specimen can be estimated from the relative_distortion index and the relative_peakedness index of the subject specimen. For example, in a similar manner to the relative_pre-CV and the relative_post-CV described above, by studying either the reference values of any specimen type which the relative_distortion index and the relative_peakedness index of the subject specimen most closely resemble or the reference ranges of any specimen type which includes the relative_distortion index and the relative_peakedness index of the subject specimen, an abnormality factor of the subject specimen can be estimated.
In an embodiment of the method according to the present invention, an abnormality factor of a subject specimen can be estimated based on relative values of the pre-post average difference and the pre-post SD ratio of the specimen with respect to the pre-post average difference and the pre-post SD ratio based on a normal specimen group. The pre-post average difference and the pre-post SD ratio based on the normal specimen group are, for example, average values of the pre-post average difference and the pre-post SD ratio of a plurality of normal specimens and are also referred to as a reference_pre-post average difference and a reference_pre-post SD ratio in the present specification. Data with respect to the normal specimens may be prepared in advance before measurement of the subject specimen or may be acquired by measuring the normal specimens together with the subject specimen.
In the embodiment, a value obtained by subtracting the reference_pre-post average difference from the pre-post average difference of the subject specimen is a relative_pre-post average difference of the subject specimen, and a value obtained by subtracting the reference_pre-post SD ratio from the pre-post SD ratio of the subject specimen is a relative_pre-post SD ratio of the subject specimen.
In another embodiment, the relative_pre-post average difference of the subject specimen is represented by (pre-post average difference of subject specimen/reference_pre-post average difference)−1, and the relative_pre-post SD ratio of the subject specimen is represented by (pre-post SD ratio of subject specimen/reference_pre-post SD ratio)−1.
An abnormality factor of the subject specimen can be estimated from the relative_pre-post average difference and the relative_pre-post SD ratio of the subject specimen. For example, in a similar manner to the relative_pre-CV and the relative_post-CV described above, by studying either the reference values of any specimen type which the relative_pre-post average difference and the relative_pre-post SD ratio of the subject specimen most closely resemble or the reference ranges of any specimen type which includes the relative_pre-post average difference and the relative_pre-post SD ratio of the subject specimen, an abnormality factor of the subject specimen can be estimated.
In another embodiment of the method according to the present invention, an abnormality factor of a subject specimen can be estimated based on a standard deviation interval (SDI) of a parameter calculated from pk or qk described above with respect to the specimen. The SDI of an objective parameter with respect to the subject specimen is calculated by the following equation.
SDI of objective parameter with respect to the subject specimen=(α−β)×γ, wherein
In the equation provided above, the reference value (β) and the reference deviation (γ) based on data of a normal specimen group can be determined in advance.
For example, an average value and a standard deviation of the objective parameter calculated from a given normal specimen group can be respectively applied to the reference value (β) and the reference deviation (γ). Alternatively, the reference value (β) and the reference deviation (γ) can be determined by the following procedure: calculate an average value of an objective parameter from an arbitrarily determined first normal specimen group and exclude a specimen having an objective parameter which deviates from the average value (for example, deviates by more than ±3 SD) from the first normal specimen group. Next, the same procedure is repeated using a second normal specimen group made up of remaining specimens. The procedures described above are repeated until, finally, specimens having an objective parameter which deviates from the average value are no longer detected, and an average value and a standard deviation of an objective parameter from a normal specimen group made up of last remaining specimens are adopted as the reference value (β) and the reference deviation (γ).
The SDI represents a bias of the parameter value (α) from the subject specimen with respect to the reference value (β) based on a normal specimen. For example, when the SDI of a parameter α1 in the subject specimen is 3 and the SDI of a parameter α2 is −4, the value of the parameter α1 is higher than the reference value (β) by 3SD and the value of the parameter α2 is lower than the reference value (β) by 4SD in the subject specimen. Converting a parameter value into an SDI enables a difference between a subject specimen and a normal specimen to be assessed at a same scale (based on a relative value with respect to an SD) in all parameters. Furthermore, since obtaining an SDI enables different parameters to be compared with each other, a parameter more suitable for estimating an abnormality factor (a parameter which enables estimation of an abnormality factor to be performed with higher accuracy) can be selected.
An abnormality factor of the subject specimen can be estimated from the SDI of the parameter of the subject specimen. For example, with respect to each of a normal specimen and various abnormal specimen types (for example, an FVIII deficiency, an FIX deficiency, LA-positive, an FVIII inhibitor, and heparin-positive), a standard value (or range) of the SDI of various parameters is to be determined in advance. By studying either the reference value of any specimen type which the SDI of a same parameter of the subject specimen most closely resembles or the reference range of any specimen type which includes the SDI of the same parameter of the subject specimen, an abnormality factor of the subject specimen can be estimated.
In a more detailed example, an abnormality factor of the subject specimen can be estimated using a two-dimensional plot of a parameter or an SDI thereof. In this case, a two-dimensional plot using any two parameters selected from the parameters described above or any two SDIs selected from SDIs of the parameters described above is used. In a preferable example, the parameters used in the two-dimensional plot are any two selected from the group consisting of the pre-CV, the post-CV, the distortion index, the peakedness index, the pre-post average difference, and the pre-post SD ratio. In another preferable example, the parameters used in the two-dimensional plot are any two selected from the group consisting of the relative_pre-CV, the relative_post-CV, the relative_distortion index, the relative_peakedness index, the relative_pre-post average difference, and the relative_pre-post SD ratio. In another preferable example, the parameters used in the two-dimensional plot are any two selected from the group consisting of an SDI of the pre-CV, an SDI of the post-CV, an SDI of the distortion index, an SDI of the peakedness index, an SDI of the pre-post average difference, and an SDI of the pre-post SD ratio.
A procedure of estimating an abnormality factor with a two-dimensional plot will be described using the pre-CV and the post-CV as an example. The pre-CV and the post-CV of a subject specimen are plotted as a single point (pre-CV, post-CV) or (post-CV, pre-CV) on a two-dimensional plane of the pre-CV and the post-CV (refer to
An abnormality factor of the subject specimen can be estimated based on a position of the plot of the relative_pre-CV and the relative_post-CV on the two-dimensional plane. For example, with respect to each of a normal specimen and various abnormal specimen types (for example, an FVIII deficiency, an FIX deficiency, LA-positive, an FVIII inhibitor, and heparin-positive), by determining a standard distribution area of the plot of the relative_pre-CV and the relative_post-CV on the two-dimensional plane in advance and studying a standard distribution area of any specimen type which includes the plot of the subject specimen, an abnormality factor of the subject specimen can be estimated.
When using an SDI, the SDI of the pre-CV and the SDI of the post-CV of the subject specimen are plotted as a single point (SDI of pre-CV, SDI of post-CV) or (SDI of post-CV, SDI of pre-CV) on a two-dimensional plane (refer to
As an example, the following estimation can be performed with reference to Table 1-2,
Similar estimations may be performed with respect to the distortion index and the peakedness index and with respect to the pre-post average difference and the pre-post SD ratio. In other words, procedures similar to those used in the case of the pre-CV and the post-CV described above can be used with respect to a plot by the relative_distortion index and the relative_peakedness index, a plot by the relative_pre-post average difference and the relative_pre-post SD ratio, a plot by the SDI of the distortion index and the SDI of the peakedness index, and a plot by the SDI of the pre-post average difference and the SDI of the pre-post SD ratio. For example, with respect to each of a normal specimen and various abnormal specimen types, by determining a standard distribution area of the plot of the SDIs of the distortion index and the peakedness index or the plot of the SDIs of the pre-post average difference and the pre-post SD ratio on the two-dimensional plane in advance (refer to
When two or more abnormality factors are estimated from a subject specimen in the estimation described above, an estimated abnormality factor of the subject specimen can be narrowed down by performing an estimation with the procedures described above using a different parameter or performing another test (for example, a conventionally known prolongation factor differential test such as a cross-mixing test).
For example, when both an FVIII deficiency and LA-positive are estimated as an abnormality factor of the subject specimen in an estimation using a two-dimensional plot of the SDI of the pre-CV and the SDI of the post-CV as shown in
In another embodiment of the method according to the present invention, a coagulation abnormality factor of a specimen can be estimated in accordance with a coagulation abnormality factor estimation model (machine learning model) constructed by machine learning. As supervised data for machine learning, data of a blood coagulation reaction from a supervised specimen group and data with respect to a presence or absence of a coagulation abnormality or a type of an abnormality factor are used. For example, a machine learning model for estimating a coagulation abnormality factor of a subject specimen is constructed by machine learning which uses a feature amount representing a blood coagulation reaction of each specimen in the supervised specimen group as an explanatory variable and which uses data with respect to a presence or absence of a coagulation abnormality or a type of an abnormality factor of each specimen in the supervised specimen group as a target variable.
As the supervised specimen group, a blood specimen group of which a blood coagulation reaction and a presence or absence of a coagulation abnormality or a type of an abnormality factor are known is used. In an embodiment, the supervised specimen group includes a blood specimen (normal specimen) without a coagulation abnormality and a blood specimen (abnormal specimen) with a coagulation abnormality. In an embodiment, the supervised specimen group includes abnormal specimens respectively having different coagulation abnormality factors (preferably, a coagulation factor deficiency, LA-positive, an inhibitor, heparin-positive, and the like). Preferably, the supervised specimen group includes a normal specimen and abnormal specimens respectively having different coagulation abnormality factors (preferably, a coagulation factor deficiency, LA-positive, an inhibitor, heparin-positive, and the like).
Examples of a feature amount with respect to a blood coagulation reaction used in the explanatory variable include at least one selected from the group consisting of parameters acquired in the second data acquisition step described above.
In a preferable example, the explanatory variable is at least one selected from the group consisting of the pre-CV, the post-CV, the pre-post average difference, the pre-post SD ratio, the distortion index, and the peakedness index and, more preferably, at least one selected from the group consisting of a combination of the pre-CV and the post-CV, a combination of the pre-post average difference and the pre-post SD ratio, and a combination of the distortion index and the peakedness index. Alternatively, all of the pre-CV, the post-CV, the pre-post average difference, the pre-post SD ratio, the distortion index, and the peakedness index may be used as the explanatory variable.
In another preferable example, the explanatory variable is at least one selected from the group consisting of a relative_pre-CV, a relative_post-CV, a relative_pre-post average difference, a relative_pre-post SD ratio, a relative_distortion index, and a relative_peakedness index and, more preferably, at least one selected from the group consisting of a combination of the relative_pre-CV and the relative_post-CV, a combination of the relative_pre-post average difference and the relative_pre-post SD ratio, and a combination of the relative_distortion index and the relative_peakedness index. Alternatively, all of the relative_pre-CV, the relative_post-CV, the relative_pre-post average difference, the relative_pre-post SD ratio, the relative_distortion index, and the relative_peakedness index may be used as the explanatory variable.
In another preferable example, the explanatory variable is at least one selected from the group consisting of an SDI of the pre-CV, an SDI of the post-CV, an SDI of the pre-post average difference, an SDI of the pre-post SD ratio, an SDI of the distortion index, and an SDI of the peakedness index and, more preferably, at least one selected from the group consisting of a combination of the SDI of the pre-CV and the SDI of the post-CV, a combination of the SDI of the pre-post average difference and the SDI of the pre-post SD ratio, and a combination of the SDI of the distortion index and the SDI of the peakedness index. Alternatively, all of the SDI of the pre-CV, the SDI of the post-CV, the SDI of the pre-post average difference, the SDI of the pre-post SD ratio, the SDI of the distortion index, and the SDI of the peakedness index may be used as the explanatory variable.
Together with the above, other parameters such as at least one selected from the group consisting of pk, qk, the pre-Ave, the post-Ave, the pre-SD, the post-SD, Mk, Wk, Vmax, VmaxT, and the like may be added to the explanatory variable.
Examples of data of a presence or absence of a coagulation abnormality or data of a type of an abnormality factor include data representing a presence or absence of a coagulation abnormality or data representing a type of an abnormality factor (for example, a coagulation factor deficiency, LA-positive, an inhibitor, or heparin-positive).
Examples of a machine learning algorithm used to construct the machine learning model include known machine learning algorithms such as a support vector machine (SVM), a neural network (NN), a decision tree, a random forest, and k-nearest neighbors.
Data for verification is input to the constructed model to calculate an estimation result of a coagulation abnormality factor. A model in which the estimation result best matches an actual result such as a model in which the estimation result has a highest accuracy rate with respect to the actual result or a model with a smallest error between the estimation result and the actual result can be selected as an optimal model.
The constructed machine learning model outputs, from a feature amount with respect to a blood coagulation reaction with respect to a subject specimen (in other words, data corresponding to the explanatory variable described earlier), an estimation result of a presence or absence of a coagulation abnormality or a type of an abnormality factor (for example, a coagulation factor deficiency, LA, an inhibitor, or heparin-positive) of the subject specimen.
In an embodiment, the machine learning model is a model for estimating the presence or absence of a coagulation abnormality and, by inputting a feature amount with respect to a blood coagulation reaction of a subject specimen, a presence or absence of a coagulation abnormality of the subject specimen (for example, whether the subject specimen is normal or has a coagulation abnormality) is estimated.
In another embodiment, the machine learning model is a model for estimating a coagulation abnormality factor and, by inputting a feature amount with respect to a blood coagulation reaction of a subject specimen, a type of a coagulation abnormality factor of the subject specimen (for example, a coagulation factor deficiency, LA-positive, an inhibitor, or heparin-positive) is estimated.
For example, the machine learning model is a model for estimating the presence or absence of a coagulation abnormality and a type of an abnormality factor and, by inputting a feature amount with respect to a blood coagulation reaction of a subject specimen, a presence or absence of a coagulation abnormality of the subject specimen and, when abnormal, a type of the abnormality factor is estimated.
A blood coagulation time measurement method according to the present invention has been described above using a coagulation reaction measurement based on a scattered light quantity as an example. However, a person skilled in the art should be able to apply the method according to the present invention to blood coagulation time measurement methods using other coagulation reaction measurement methods (for example, a blood coagulation reaction measurement method based on transmittance, absorbance, viscosity, or the like) and, therefore, such an application is included in a scope of the present invention. For example, a reaction R(i) obtained from a coagulation reaction curve with an inverse sigmoidal shape based on scattered light quantity has reverse polarity with respect to a coagulation reaction curve based on the scattered light quantity described above. In such a case, it should be apparent to a person skilled in the art that signs of R(i) and V(i) in steps 1 to 4 described above are to be reversed, a minimum value Vmin of V is to be determined instead of the maximum value Vmax, pk and qk are respectively determined as points where V(i) assumes Xk before and after reaching Vmin, and the like.
The blood coagulation reaction analysis method according to the present invention described above can be automatically performed using a computer program. Therefore, an aspect of the present invention is a program for performing the blood coagulation reaction analysis method according to the present invention described above. In addition, the series of steps of the method according to the present invention described above can be automatically performed by an automated analyzer. Therefore, an aspect of the present invention is an apparatus for performing the blood coagulation reaction analysis method according to the present invention described above.
An embodiment of the apparatus according to the present invention will be described below. An embodiment of the apparatus according to the present invention is an automated analyzer 1 such as that shown in
The control unit 10 controls operations of the automated analyzer 1 as a whole. The control unit 10 can be constituted of, for example, a personal computer (PC). The control unit 10 includes a CPU, a memory, a storage, a communication interface (I/F), and the like and performs processing of a command from the operation unit 20, control of operations of the measurement unit 30, storage and data analysis of measured data received from the measurement unit 30, storage of an analysis result, control of output of measured data and/or an analysis result by the output unit 40, and the like. Furthermore, the control unit 10 may be connected to other devices such as an external medium and a host computer. In the control unit 10, a PC which controls operations of the measurement unit 30 may be the same as or may differ from a PC which performs analysis of measured data.
The operation unit 20 acquires input from an operator and transmits obtained input information to the control unit 10. For example, the operation unit 20 includes a user interface (UI) such as a keyboard or a touch panel. The output unit 40 outputs, under control by the control unit 10, measured data of the measurement unit 30, first data and/or second data based on the measured data and, when necessary, an analysis result such as a coagulation time or an estimation result of a coagulation abnormality factor or the like. For example, the output unit 40 includes a display apparatus such as a display.
The measurement unit 30 executes a series of operations for a blood coagulation test and acquires measured data of a coagulation reaction of a sample including a blood specimen. The measurement unit 30 includes various equipment and analysis modules necessary for a blood coagulation test such as a specimen container for storing a blood specimen, a reagent container for storing a test reagent, a reaction container for a reaction between the specimen and the reagent, a probe for dispensing the blood specimen and the reagent to the reaction container, a light source, a detector for detecting scattered light or transmitted light from the reagent in the reaction container, a data processing circuit which sends data from the detector to the control unit 10, and a control circuit which receives an instruction of the control unit 10 and which controls operations of the measurement unit 30.
The control unit 10 performs an analysis of a coagulation reaction of a specimen based on data measured by the measurement unit 30. The present analysis can include acquisition of the coagulation reaction curve R and the first derivative V, acquisition of the first data and the second data, calculation of a coagulation time using the first data, and estimation of a coagulation abnormality factor using the second data described above. Alternatively, the coagulation reaction curve R and the first derivative V may be created by the control unit 10 based on measured data from the measurement unit 30 or may be created by another device such as the measurement unit 30 and sent to the control unit 10. The control unit 10 may store reference values such as the reference_pre-CV and the reference_post-CV used in estimation of a coagulation abnormality factor, a reference value (β) and a reference deviation (γ) of various parameters, and the like or the control unit 10 may import, when performing an analysis, the reference values stored in an external device or on a network.
The estimation of a coagulation abnormality factor in the control unit 10 may be performed based on the machine learning model for coagulation abnormality factor estimation described above. In this case, preferably, the control unit 10 may store the machine learning model for coagulation abnormality factor estimation. The machine learning model may be constructed outside and sent to the control unit 10 to be stored or used or the machine learning model may be constructed and stored or used in the control unit 10.
The present analysis described above can be implemented by a program for performing the method according to the present invention. Therefore, the control unit 10 can include a program for performing the blood coagulation reaction analysis method according to the present invention.
An analysis result obtained by the control unit 10 is sent to the output unit 40 to be output. The output may take any form such as a display on a screen, a transmission to a host computer, a printout, or the like. Output information from the output unit can include waveform data of R or V, a coagulation time, an estimation result of a coagulation abnormality factor, information related to determination criteria of the coagulation time included in the first data, information related to Vmax, VmaxT, pk, qk, or a parameter calculated therefrom included in the second data, and a two-dimensional plot image of the parameter. Types of output information from the output unit can be controlled by the program according to the present invention.
In an embodiment of the apparatus according to the present invention, the measurement unit 30 continues measurement of a subject specimen until an end of a coagulation reaction and data is sequentially sent to the control unit 10. The control unit 10 sequentially continues calculations for acquiring the first data from the coagulation reaction curve R or the first derivative V and calculates a coagulation time in a timely manner. On the other hand, in parallel to the acquisition of the first data, the coagulation reaction curve R or the first derivative V is continuously acquired and R and V until the end of the coagulation reaction are acquired. Next, the control unit 10 acquires the second data and performs calculation for estimating a coagulation abnormality factor. An obtained analysis result is sent to the output unit to be output. For example, R and V are sequentially output until the end of the coagulation reaction in parallel with the measurement, a coagulation time is output midway through the measurement, and an estimation result of a coagulation abnormality factor is output after the end of the measurement.
While the present invention will be described below in greater detail by citing examples, the present invention is not limited by the following examples.
Coagpia APTT-N (manufactured by SEKISUI MEDICAL CO., LTD.) which is a reagent for APTT measurement was used as a reagent for measurement and Coagpia APTT-N Calcium Chloride Solution (manufactured by SEKISUI MEDICAL CO., LTD.) was used as a calcium chloride solution. A coagulation reaction measurement of samples including a specimen was performed using an Automated Coagulation Analyzer CP3000 (manufactured by SEKISUI MEDICAL CO., LTD.). After heating 50 μL of a specimen in a cuvette at 37° C. for 45 seconds, 50 μL of the reagent for measurement at approximately 37° C. was added and, after a lapse of 171 seconds, 50 μL of a 25 mM calcium chloride solution was added to start a coagulation reaction. The reaction was performed at 37° C. In the measurement of the coagulation reaction, the cuvette was irradiated with light with a wavelength of 660 nm from an LED light source, and a scattered light quantity of light scattered 90 degrees to the side was measured at 0.1-second intervals. The measurement time was set to 360 seconds.
After performing smoothing including denoising with respect to photometry data from each specimen, zero point adjustment was performed so that a scattered light quantity at a time point of start of photometry equaled zero to create reaction R(i). A first derivative V(i) was calculated from R(i).
An APTT of each specimen was measured by a percent method. In other words, a time point where R(i) reached a maximum value Rmax within the measurement time was detected as a coagulation reaction end point E, and a time point where R(i) reached 50% of R(E) was calculated and determined as the APTT.
A maximum value Vmax of V(i) and a time VmaxT where V(i)=Vmax is satisfied were determined for each specimen. Defining Xk as Vmax×S % (where S increases in 20 stages at 5% intervals from 3% to 98%), 20 pk(p1, . . . , p20) satisfying V(i)=Xk before VmaxT and 20 qk(q1, . . . , q20) satisfying V(i)=Xk after VmaxT were detected (refer to
Pre-Ave: average value of pk(p1, . . . ,p20)
Post-Ave: average value of qkk(q1, . . . ,q20)
All-Ave: average value of pk(p1, . . . ,p20) and qkk (q1, . . . ,q20)
Pre-SD: standard deviation of pk(p1, . . . ,p20)
Post-SD: standard deviation of qkk(q1, . . . ,q20)
Pre-CV: coefficient of variation of pk(p1, . . . ,p20)(%)
Post-CV: coefficient of variation of qkk(q1, . . . ,q20)(%)
M
k: (pk+qk)/2 (where k is any integer from 1 to 20)
W
k
: q
k
−p
k (where k is any integer from 1 to 20)
Pre-post average difference: (post-Ave−pre-Ave)/all-Ave
Pre-post SD ratio: post-SD/pre-SD
Distortion index: coefficient of variation of Mk (M1, . . . ,M20)(%)
Peakedness index: (sum of W1 to W10))/(sum of W11 to W20)
Relative_pre-CV=(pre-CV of subject specimen/reference_pre-CV)−1
Relative_post-CV=(post-CV of subject specimen/reference_post-CV)−1
Next, a standard deviation interval (SDI) of the pre-CV and the post-CV of each specimen was obtained using the following equations.
SDI=(α−β)÷γ, wherein
Table 1-1 shows the APTT, the pre-CV, the post-CV, the relative_pre-CV, and the relative_post-CV of each specimen type. In Table 1-1, the relative_pre-CV is denoted as *pre-CV and the relative_post-CV is denoted as *post-CV. The APTT in Table 1-1 is in bold when exceeding an upper limit (39 seconds) of a normal range. In addition, the pre-CV, the post-CV, the relative_pre-CV, and the relative_post-CV in Table 1-1 is in italics when a value of a specimen is lower than a minimum value of PNP, in bold when a value of a specimen is higher than a maximum value of PNP, and without emphasis when a value of a specimen is within a value range from PNP.
Table 1-2 shows the APTT, the pre-CV, the post-CV, the SDI of the pre-CV, and the SDI of the post-CV of each specimen type. In Table 1-2, the SDI of the pre-CV is denoted as *pre-CV and the SDI of the post-CV is denoted as *post-CV. Character styles of the APTT, the pre-CV, and the post-CV in Table 1-2 have a same meaning as in Table 1-1. In addition, the *pre-CV and the *post-CV in Table 1-2 are represented by white letters against a dark gray background when the SDI is lower than −3, represented by bold black letters against a pale gray background when the SDI is higher than 3, and represented by black letters against a white background when the SDI is within ±3.
54.5
3.2
29.5
−0.32
1.39
54.1
7.5
17.1
0.60
0.39
53.6
8.8
16.8
0.86
0.36
64.7
10.9
16.0
1.31
0.30
51.5
3.3
28.2
−0.30
1.28
68.0
10.5
15.1
1.22
0.22
97.7
2.8
19.3
−0.40
0.57
47.2
4.3
14.8
−0.10
0.20
54.8
3.2
28.0
−0.32
1.27
100.2
2.5
18.2
−0.48
0.47
144.0
6.3
30.3
0.33
1.45
139.7
5.5
33.4
0.17
1.70
129.1
33.0
1.67
156.0
5.3
25.8
0.12
1.09
133.6
39.0
2.16
132.5
36.6
1.96
45.5
7.2
13.1
0.52
0.06
51.9
7.0
14.0
0.48
0.14
59.7
9.8
15.5
1.08
0.25
70.3
11.4
16.6
1.41
0.34
73.1
11.8
16.7
1.50
0.35
81.3
13.7
18.5
1.90
0.50
89.7
13.8
19.8
1.93
0.60
120.0
35.2
1.85
4.2
−0.11
4.2
10.2
−0.11
−0.17
40.9
5.5
9.8
0.16
−0.20
45.6
6.5
10.0
0.37
−0.19
53.4
7.6
10.4
0.62
−0.16
54.5
8.2
10.5
0.73
−0.15
58.2
8.4
10.8
0.78
−0.13
64.0
9.5
1.01
81.3
12.3
14.6
1.60
0.18
11.2
−0.09
4.1
10.1
−0.18
43.5
4.1
9.1
−0.26
54.3
8.1
−0.34
64.7
7.7
−0.38
76.9
7.1
−0.42
97.0
6.6
−0.47
104.3
5.5
6.6
0.17
−0.46
126.3
5.5
6.3
0.17
−0.49
146.2
6.0
6.1
0.27
−0.50
54.5
3.2
29.5
−4.2
28.9
54.1
7.5
17.1
7.8
8.0
53.6
8.8
16.8
11.3
7.5
64.7
10.9
16.0
17.1
6.2
51.5
3.3
28.2
−3.9
26.6
68.0
10.5
15.1
15.9
4.6
97.7
2.8
19.3
−5.2
11.8
47.2
4.3
14.8
4.1
54.8
3.2
28.0
−4.2
26.3
100.2
2.5
18.2
−6.2
9.8
144.0
6.3
30.3
4.3
30.1
139.7
5.5
33.4
35.3
129.1
33.0
34.6
156.0
5.3
25.8
22.6
133.6
39.0
44.7
132.5
36.6
40.7
45.5
7.2
13.1
6.8
51.9
7.0
14.0
6.2
59.7
9.8
15.5
14.1
5.2
70.3
11.4
16.6
18.4
7.1
73.1
11.8
16.7
19.6
7.3
81.3
13.7
18.5
24.8
10.3
89.7
13.8
19.8
25.2
12.5
120.0
35.2
38.3
4.2
4.2
10.2
−3.6
40.9
5.5
9.8
−4.2
45.6
6.5
10.0
4.8
−4.0
53.4
7.6
10.4
8.1
−3.2
54.5
8.2
10.5
9.5
−3.1
58.2
8.4
10.8
10.2
−2.6
64.0
9.5
13.1
81.3
12.3
14.6
20.9
3.8
11.2
4.1
10.1
−3.7
43.5
4.1
9.1
−5.4
54.3
8.1
−7.1
64.7
7.7
−7.8
76.9
7.1
−8.7
97.0
6.6
−9.7
104.3
5.5
6.6
−9.6
126.3
5.5
6.3
−10.1
146.2
6.0
6.1
3.6
−10.4
Relative_distortion index=(distortion index of subject specimen/reference_distortion index)−1
Relative_peakedness index=(peakedness index of subject specimen/reference_peakedness index)−1
Next, the SDI of the distortion index and the peakedness index of each specimen was obtained by the same procedure as in 1.6.1).
Table 2-1 shows the APTT, the distortion index, the peakedness index, the relative_distortion index, and the relative_peakedness index of each specimen type. In Table 2-1, the relative_distortion index is denoted as *distortion index and the relative_peakedness index is denoted as *peakedness index. Table 2-2 shows the APTT, the distortion index, the peakedness index, the SDI of the distortion index, and the SDI of the peakedness index of each specimen type. In Table 2-2, the SDI of the distortion index is denoted as *distortion index and the SDI of the peakedness index is denoted as *peakedness index. Character styles in Table 2-1 and Table 2-2 respectively have a same meaning as in Tables 1-1 and 1-2.
54.5
17.1
4.0
2.25
0.59
54.1
8.9
2.1
0.69
−0.16
53.6
8.4
2.2
0.60
−0.14
64.7
7.4
2.2
0.40
−0.11
51.5
16.2
3.7
2.08
0.45
68.0
6.8
2.2
0.29
−0.11
97.7
11.9
2.0
1.26
−0.22
47.2
7.7
2.0
0.47
−0.21
54.8
16.0
3.8
2.04
100.2
10.2
2.3
0.94
−0.10
144.0
21.3
2.2
3.04
−0.14
139.7
23.0
3.37
129.1
22.8
3.34
156.0
17.9
2.0
2.41
−0.20
133.6
26.9
3.1
4.11
0.25
132.5
25.3
2.8
3.81
0.13
45.7
2.3
0.09
−0.10
5.7
2.1
0.08
−0.17
45.5
6.0
2.1
0.14
−0.16
51.9
7.0
2.0
0.32
−0.20
59.7
7.5
2.1
0.43
−0.17
70.3
8.4
2.0
0.59
−0.18
73.1
8.6
2.0
0.64
−0.19
81.3
9.8
2.1
0.86
−0.18
89.7
11.1
2.0
1.11
−0.20
120.0
24.9
3.73
−0.01
2.4
−0.04
2.3
−0.10
4.4
2.0
−0.17
−0.19
40.9
4.1
2.0
−0.23
−0.19
45.6
4.1
2.1
−0.22
−0.18
53.4
4.2
2.1
−0.20
−0.18
54.5
4.3
2.1
−0.19
−0.17
58.2
4.4
2.0
−0.17
−0.19
64.0
4.7
2.1
−0.11
−0.16
81.3
6.4
2.1
0.22
−0.15
4.6
−0.12
4.1
−0.23
−0.05
43.5
3.4
2.3
−0.35
−0.07
54.3
2.7
2.4
−0.48
−0.06
64.7
2.3
2.4
−0.56
−0.06
76.9
1.9
2.4
−0.64
−0.04
97.0
1.4
−0.74
104.3
1.2
−0.77
126.3
1.1
−0.80
146.2
0.7
−0.86
54.5
17.1
4.0
32.9
24.6
54.1
8.9
2.1
10.1
−6.6
53.6
8.4
2.2
8.8
−5.9
64.7
7.4
2.2
5.8
−4.7
51.5
16.2
3.7
30.4
18.8
68.0
6.8
2.2
4.2
−4.7
97.7
11.9
2.0
18.4
−9.1
47.2
7.7
2.0
6.8
−8.7
54.8
16.0
3.8
29.8
21.5
100.2
10.2
2.3
13.7
−4.2
144.0
21.3
2.2
44.3
−5.7
139.7
23.0
49.2
129.1
22.8
48.7
156.0
17.9
2.0
35.1
−8.4
133.6
26.9
3.1
60.1
10.4
132.5
25.3
2.8
55.6
5.3
45.7
2.3
−4.2
5.7
2.1
−7.0
45.5
6.0
2.1
−6.6
51.9
7.0
2.0
4.7
−8.5
59.7
7.5
2.1
6.3
−7.0
70.3
8.4
2.0
8.7
−7.6
73.1
8.6
2.0
9.3
−8.0
81.3
9.8
2.1
12.6
−7.4
89.7
11.1
2.0
16.3
−8.5
120.0
24.9
54.4
2.4
2.3
−4.3
4.4
2.0
−7.9
40.9
4.1
2.0
−3.3
−7.9
45.6
4.1
2.1
−3.3
−7.6
53.4
4.2
2.1
−7.5
54.5
4.3
2.1
−7.2
58.2
4.4
2.0
−7.7
64.0
4.7
2.1
−6.7
81.3
6.4
2.1
3.3
−6.0
4.6
4.1
2.4
−3.4
43.5
3.4
2.3
−5.1
54.3
2.7
2.4
−7.0
64.7
2.3
2.4
−8.1
76.9
1.9
2.4
−9.4
97.0
1.4
−10.8
104.3
1.2
−11.2
126.3
1.1
−11.7
146.2
0.7
−12.6
These results revealed that the relative_distortion index and the relative_peakedness index or the SDIs of the distortion index and the peakedness index can be used as indices for distinguishing abnormal specimens from normal specimens and that the indices are effective in detecting a specimen which actually has an abnormality factor despite its coagulation time being within a normal range. However, with respect to two specimens (specimen of 0.1 units of Heparin and specimen of FIX with 50% activity), the specimens could not be distinguished from PNP using the indices due to proximity of their plots to PNP.
Relative_pre-post average difference=(pre-post average difference of subject specimen/reference_pre-post average difference)−1
Relative_pre-post SD ratio=(pre-post SD ratio of subject specimen/reference_pre-post SD ratio)−1
Next, the SDI of the pre-post average difference and the pre-post SD ratio of each specimen was obtained by the same procedure as in 1.6.1).
Table 3-1 shows the APTT, the pre-post average difference (InA), the pre-post SD ratio (InB), the relative_pre-post average difference (*InA), and the relative_pre-post SD ratio (*InB) of each specimen type. Table 3-2 shows the APTT, the pre-post average difference (InA), the pre-post SD ratio (InB), the pre-post average difference SDI (*InA), and the pre-post SD ratio SDI (*InB) of each specimen type. Character styles in Table 3-1 and Table 3-2 respectively have a same meaning as in Tables 1-1 and 1-2.
54.5
48.0
15.1
0.50
3.16
54.1
57.3
4.1
0.80
0.13
53.6
56.5
3.4
0.77
64.7
55.1
2.6
0.73
−0.29
51.5
47.7
13.8
0.49
2.82
68.0
52.2
2.5
0.64
−0.32
97.7
65.3
13.5
1.05
2.72
47.2
51.2
5.9
0.60
0.62
54.8
46.1
14.0
0.44
2.86
100.2
44.6
11.6
0.40
2.20
144.0
100.9
14.7
2.16
3.06
139.7
93.4
16.6
1.93
3.59
129.1
93.5
17.6
1.93
3.85
156.0
98.6
14.4
2.09
2.96
133.6
89.4
22.0
1.80
5.07
132.5
91.5
19.6
1.87
4.40
35.9
4.1
0.12
0.14
38.9
4.0
0.22
0.12
45.5
43.7
2.8
0.37
−0.21
51.9
49.2
0.54
59.7
54.4
2.8
0.70
−0.24
70.3
61.3
2.7
0.92
−0.24
73.1
63.8
2.7
1.00
−0.24
81.3
69.3
2.8
1.17
−0.23
89.7
76.4
3.2
1.39
−0.12
120.0
97.4
20.2
2.05
4.58
40.9
2.5
−0.30
45.6
35.2
2.2
0.10
−0.39
53.4
38.0
2.0
0.19
−0.45
54.5
38.9
1.9
0.22
−0.47
58.2
40.8
1.9
0.28
−0.46
64.0
43.7
1.9
0.37
−0.47
81.3
53.7
2.1
0.68
−0.43
29.1
−0.09
27.5
3.2
−0.14
−0.11
43.5
26.0
2.9
−0.19
−0.21
54.3
24.2
2.4
−0.24
−0.34
64.7
23.6
2.1
−0.26
−0.42
76.9
22.4
1.9
−0.30
−0.49
97.0
21.4
1.6
−0.33
−0.56
104.3
21.8
1.5
−0.32
−0.59
126.3
21.2
1.4
−0.34
−0.61
146.2
21.4
1.3
−0.33
−0.65
54.5
48.0
15.1
8.9
49.1
54.1
57.3
4.1
14.1
53.6
56.5
13.6
64.7
55.1
2.6
12.8
−4.4
51.5
47.7
13.8
8.7
43.7
68.0
52.2
2.5
11.2
−5.0
97.7
65.3
13.5
18.5
42.1
47.2
51.2
5.9
10.7
9.6
54.8
46.1
14.0
7.8
44.3
100.2
44.6
11.6
7.0
34.1
144.0
100.9
14.7
38.3
47.5
139.7
93.4
16.6
34.1
55.7
129.1
93.5
17.6
34.1
59.8
156.0
98.6
14.4
36.9
46.0
133.6
89.4
22.0
31.8
78.7
132.5
91.5
19.6
33.0
68.4
35.9
4.1
28.9
4.0
3.9
45.5
43.7
2.8
6.6
−3.3
51.9
49.2
9.6
59.7
54.4
2.8
12.4
−3.7
70.3
61.3
2.7
16.3
−3.8
73.1
63.8
2.7
17.7
−3.8
81.3
69.3
2.8
20.7
−3.6
89.7
76.4
3.2
24.6
−1.8
120.0
97.4
20.2
36.3
71.1
40.9
2.5
−4.6
45.6
35.2
2.2
−6.1
53.4
38.0
2.0
3.4
−6.9
54.5
38.9
1.9
3.9
−7.3
58.2
40.8
1.9
4.9
−7.2
64.0
43.7
1.9
6.5
−7.3
81.3
53.7
2.1
12.1
−6.7
29.1
27.5
3.2
43.5
26.0
2.9
−3.3
−3.3
54.3
24.2
2.4
−4.3
−5.2
64.7
23.6
2.1
−4.6
−6.5
76.9
22.4
1.9
−5.3
−7.5
97.0
21.4
1.6
−5.8
−8.7
104.3
21.8
1.5
−5.6
−9.1
126.3
21.2
1.4
−6.0
−9.5
146.2
21.4
1.3
−5.8
−10.1
The results described above suggest that the SDIs of the pre-CV and the post-CV, the SDIs of the distortion index and the peakedness index, or the SDIs of the pre-post average difference and the pre-post SD ratio can be used as indices for estimating a coagulation abnormality factor of a subject specimen. In a similar manner, the relative_pre-CV and the relative_post-CV, the relative distortion index and the relative_peakedness index, or the relative_pre-post average difference and the relative_pre-post SD ratio can also be used as indices for estimating a coagulation abnormality factor of a subject specimen.
A relationship among the parameter Vmax, VmaxT, and a coagulation abnormality was studied using the same specimen group as in the first example. Average values of VmaxT and Vmax were respectively obtained with respect to the six normal specimens (PNP) and adopted as reference_VmaxT and reference_Vmax and, subsequently, relative_VmaxT and relative_Vmax of each specimen were obtained using the following equations.
Relative_VmaxT=(VmaxT of subject specimen/reference_VmaxT)−1
Relative_Vmax=(Vmax of subject specimen/reference_Vmax)−1
| Number | Date | Country | Kind |
|---|---|---|---|
| 2020-150701 | Sep 2020 | JP | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/JP2021/032949 | 9/8/2021 | WO |
