CN105466927B - Method for identifying, correcting and alarming abnormal reaction curve of turbidimetry - Google Patents
Method for identifying, correcting and alarming abnormal reaction curve of turbidimetry Download PDFInfo
- Publication number
- CN105466927B CN105466927B CN201410313267.6A CN201410313267A CN105466927B CN 105466927 B CN105466927 B CN 105466927B CN 201410313267 A CN201410313267 A CN 201410313267A CN 105466927 B CN105466927 B CN 105466927B
- Authority
- CN
- China
- Prior art keywords
- order difference
- point
- points
- abnormal
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000006243 chemical reaction Methods 0.000 title claims abstract description 133
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 119
- 238000000034 method Methods 0.000 title claims abstract description 68
- 238000004879 turbidimetry Methods 0.000 title claims abstract description 9
- 238000001514 detection method Methods 0.000 claims abstract description 32
- 238000002835 absorbance Methods 0.000 claims description 65
- 238000005070 sampling Methods 0.000 claims description 37
- 238000012937 correction Methods 0.000 claims description 30
- 210000004369 blood Anatomy 0.000 claims description 27
- 239000008280 blood Substances 0.000 claims description 27
- 238000004364 calculation method Methods 0.000 claims description 15
- 238000012360 testing method Methods 0.000 claims description 9
- 210000000601 blood cell Anatomy 0.000 claims description 6
- 239000000427 antigen Substances 0.000 claims description 5
- 102000036639 antigens Human genes 0.000 claims description 5
- 108091007433 antigens Proteins 0.000 claims description 5
- 239000003153 chemical reaction reagent Substances 0.000 claims description 5
- 230000005856 abnormality Effects 0.000 abstract description 7
- 108010074051 C-Reactive Protein Proteins 0.000 description 40
- 102100032752 C-reactive protein Human genes 0.000 description 40
- 230000009286 beneficial effect Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 238000002331 protein detection Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 210000002966 serum Anatomy 0.000 description 3
- 230000002547 anomalous effect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000003018 immunoassay Methods 0.000 description 2
- 230000001594 aberrant effect Effects 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000001154 acute effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000000994 depressogenic effect Effects 0.000 description 1
- 239000004816 latex Substances 0.000 description 1
- 229920000126 latex Polymers 0.000 description 1
- 230000031700 light absorption Effects 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Images
Landscapes
- Automatic Analysis And Handling Materials Therefor (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
The invention provides a method for identifying, alarming and correcting an abnormal reaction curve of a turbidimetry, wherein the identification method comprises the following steps: obtaining an original reaction curve; and identifying whether an abnormal point exists on the original reaction curve. According to the embodiment of the invention, the abnormal point identification and alarm are creatively carried out on the turbidimetric reaction curve, so that data which is possibly subjected to abnormal reaction is prevented from being used as a detection result without being processed, and the detection accuracy is improved; moreover, the identified abnormal points are corrected, so that a reaction curve with obvious abnormality can be well compensated, and the detection accuracy is greatly improved.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a method for identifying, correcting and alarming an abnormal reaction curve of a turbidimetry.
Background
C-reactive protein (CRP) is one of acute phase-reactive proteins, and the concentration of the C-reactive protein is usually examined clinically.
Currently, measurement of the concentration of C-reactive protein is mainly performed in the blood serum, and japanese patent No.11575/1983 discloses an immunoassay method, which utilizes the characteristic that an antigen or an antibody on an insoluble substance and a corresponding antibody or antigen on a sample are combined into an agglutinate, irradiates the agglutinate with a light wave having a wavelength of 600nm to 2400nm, and finally obtains parameters of the antigen by analyzing the degree of absorption (absorbance) or scattering (scattering) of the light. Due to the advantages of the use of this method, the mainstream of the immunoassay method is gradually changed, which is called latex immunoturbidimetry, and the measurement object used in this method is serum. In practice, there are many times when simultaneous measurement of blood normal and C-reactive proteins is required. However, since anticoagulated whole blood is routinely measured and serum is measured for C-reactive protein, two tubes of blood need to be drawn simultaneously, which increases the burden on the patient. Also, for some special cases, such as children's examination, it is difficult to draw a large amount of blood, and thus a method capable of measuring whole blood C-reactive protein is required.
Because the blood cells contained in the whole blood may affect the scattering or absorption of light, in order to remove the interference of the blood cells on the measurement of the C-reactive protein, a reagent needs to be added to dissolve the blood cells to remove the interference, and meanwhile, the reagent also needs to be added to make the antigen antibody react when the C-reactive protein is measured; air bubbles may be generated during the process of transporting the reagent to the sample reaction cup, and when the air bubbles appear in the sample reaction cup, the instrument can detect a plurality of interference signals, which affects the detection accuracy of the C-reactive protein.
Unfortunately, the prior art does not consider identifying the abnormal response curve of the whole blood C-reactive protein for measuring the whole blood C-reactive protein and then adopting corresponding treatment. There is an urgent need for a method for identifying an abnormal reaction curve of a turbidimetric method including an abnormal reaction curve of a whole blood C-reactive protein.
Disclosure of Invention
The invention aims to provide a method for identifying, correcting and alarming an abnormal reaction curve of a turbidimetric method, which is beneficial to improving the detection accuracy.
In order to solve the technical problem, the invention provides a method for identifying an abnormal reaction curve of a turbidimetric method, which comprises the following steps:
step S21, acquiring an original reaction curve;
and step S22, identifying whether an abnormal point exists on the original reaction curve.
Wherein, the step S22 specifically includes:
step S221, calculating a first order difference of at least one point on the original reaction curve, wherein the first order difference is a difference value obtained by subtracting the absorbance corresponding to the point from the absorbance corresponding to a sampling time point after the point;
in step S222, if the calculated first order difference of the point is smaller than the first threshold, the point is identified as an abnormal point.
Wherein, still include the step:
selecting at least part of points from a first-order difference curve formed by the calculated first-order difference, and calculating the second-order difference of the points respectively;
identifying at least one of the selected fetch points as an outlier if the second order difference is greater than the first threshold.
Wherein, the calculation method for respectively calculating the second order difference of at least selecting partial points from the first order difference curve comprises the following steps: continuously selecting a part of points to calculate the first-order difference average value as the first-order difference value from the first point on the first-order difference curve according to the sampling time sequence, then continuously selecting the same number of points from the unselected points to calculate the first-order difference average value as the second first-order difference value, subtracting the first-order difference value from the second first-order difference value to be the second-order difference of the first point, and calculating the Nth point in sequence1First order difference value, Nth1Subtracting the Nth from the first order difference value1-1 the first order difference value is Nth1A second order difference of 1 point correspondence.
Wherein, still include the step:
selecting at least part of points from a second order difference curve formed by the calculated second order difference, and respectively calculating the third order difference;
identifying at least one of the selected fetch points as an outlier if the third order difference is less than the first threshold.
Wherein, the calculation method for respectively calculating the third order difference of at least selecting partial points from the second order difference curve comprises the following steps: continuously selecting a part of points to calculate a second order difference average value as a first second order difference value from a first point on the second order difference curve according to the sampling time sequence, continuously selecting the same number of points from the unselected points to calculate the second order difference average value as a second order difference value, subtracting the first second order difference value from the second order difference value to obtain a third order difference of the first point, and sequentially calculating the Nth order difference value2Second order differential value, Nth2Second order difference value minus Nth2-1 second order differential value is Nth2-a third order difference of 1 point correspondence.
The first threshold value is determined according to a first-order difference statistic value corresponding to an absorbance curve of a background test sample of the used detection device.
Wherein the first threshold value is 0.
Wherein, the step S22 specifically includes:
step S22a, acquiring a corresponding normal reaction curve according to the detection value obtained from the original reaction curve;
step S22b, acquiring an absorbance threshold value of each point on the normal reaction curve;
step S22c, determining whether the absorbance of each point on the original reaction curve exceeds the absorbance corresponding to the same sampling time point on the normal reaction curve, and if so, identifying the point on the original reaction curve as an abnormal point.
The invention also provides an alarm method of the turbidimetry abnormal reaction curve, which comprises the following steps:
step S41, recognizing abnormal points on the original reaction curve according to the method;
step S42, calculating the proportion of the identified abnormal points to all the points;
and step S43, if the proportion is larger than or equal to the second threshold value, giving an alarm prompt.
Wherein the second threshold is obtained according to the following:
randomly selecting M normal samples, and calculating the average value of the proportion of the abnormal reaction points in all the normal samples to obtain a first average value;
randomly selecting M samples with abnormal reaction, and calculating the average value of the proportion of abnormal reaction points in all the samples to obtain a second average value;
and calculating the average value of the first average value and the second average value to obtain the second threshold psi.
The invention also provides a correction method of the turbidimetric method abnormal reaction curve, which comprises the following steps:
step S51, recognizing abnormal points on the original reaction curve according to the method;
step S52, calculating an abnormal first-order difference correction value of the abnormal point according to the remaining first-order difference normal points after the abnormal point identification and the corresponding sampling time points thereof, and the sampling time points corresponding to the first-order difference abnormal points;
and step S53, correcting the absorbance of the corresponding abnormal point on the original reaction curve by the abnormal first-order difference correction value.
Wherein the abnormal first order difference correction value is obtained by calculating a function consisting of the normal first order difference.
Wherein the function is: and the abnormal first-order difference correction value is equal to the sum of a second coefficient and a sampling time point corresponding to the abnormal first-order difference correction value multiplied by the first coefficient.
And the first coefficient and the second coefficient are obtained by performing least square fitting on the remaining first-order difference normal point array after the abnormal point identification and the sampling time point array corresponding to the first-order difference normal point array.
Wherein, the step S53 specifically includes:
step S531, selecting any point on the original reaction curve graph as a reference point;
step S532, the correction value of the abnormal first-order difference and the normal first-order difference are combined to be a first-order difference of all the points, and then the following operations are performed:
the corrected absorbance from the first point after the reference point to all the points thereafter is: adding the corrected absorbance of the previous point of the current point and the first-order difference corrected value of the previous point to be used as the absorbance of the point;
starting from the first point before the reference point to all points before the reference point, the corrected absorbance is as follows: and subtracting the first-order difference correction value of the current point from the correction absorbance of the next point of the current point to obtain the absorbance of the point.
The embodiment of the invention has the following beneficial effects: firstly, the embodiment of the invention innovatively identifies the abnormal points of the turbidimetric reaction curve, avoids taking data which is possibly subjected to abnormal reaction as a detection result without processing, and is beneficial to improving the detection accuracy; furthermore, an alarm can be given in time according to the identification result to prompt a detector, so that data which is possibly subjected to abnormal reaction can be prevented from being used as a detection result without processing, and the detection accuracy is improved; furthermore, the identified abnormal points are corrected, so that a reaction curve with obvious abnormality can be well compensated, and the detection accuracy is greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic representation of the normal response curve of whole blood C-reactive protein.
FIG. 2 is a schematic flow chart of a method for identifying an abnormal turbidimetric response curve according to an embodiment of the present invention.
Fig. 3A-3D are schematic diagrams of the calculation of the second and third order differences to further identify abnormal response curves according to the first embodiment of the present invention.
FIG. 4 is a flow chart of the method for alarming an abnormal reaction curve of a turbidimetric method according to an embodiment of the present invention.
FIG. 5 is a schematic flow chart of a method for correcting an abnormal reaction curve of a three-turbidimetric method according to an embodiment of the present invention.
Fig. 6A to 6G are schematic diagrams of respective reaction curves applied to example one by respectively adopting the identification method of embodiment one and the correction method of embodiment three of the present invention.
Fig. 7A to 7G are schematic diagrams of respective reaction curves applied to example two by respectively adopting the identification method of the first embodiment of the present invention and the correction method of the third embodiment of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described below with reference to the accompanying drawings.
The identification, alarm and correction method of the embodiment of the present invention is applicable to all turbidimetric reaction curves, and for simplicity of description, the reaction curve of the whole blood C-reactive protein will be described as an example, but the method is not limited to the reaction curve of the whole blood C-reactive protein.
An embodiment of the present invention provides a method for identifying an abnormal reaction curve of a turbidimetry, as shown in fig. 2, which specifically includes:
step S21, acquiring an original reaction curve;
in step S22, it is identified whether or not an abnormal point exists on the original reaction curve.
When the prior art is used for measuring the whole blood C reactive protein, bubbles are easy to appear in a sample reaction cup, so that a plurality of interference signals are generated and detected by an instrument, and if the reaction curve obtained by measuring the whole blood C reactive protein is not identified, whether the abnormality exists is judged, so that the detection accuracy of the C reactive protein is greatly influenced.
FIG. 1 shows a normal reaction curve of whole blood C-reactive protein, in which the horizontal axis represents sampling time and the vertical axis represents absorbance. A sampling time point corresponds to an absorbance, and the ith time point TiThe absorbance of the detection was recorded as AiI =1, 2, 3, …, N. N is a natural number. Ith sampling time point TiCorresponding absorbance AiA point on the reaction curve, denoted P, is determined jointlyiI.e. PiBy sampling time TiAs abscissa, corresponding to absorbance AiIs the ordinate. This embodiment is to identify PiWhether it is an outlier.
Because the prior art does not have a technical scheme for identifying the reaction curve, the identification method of the embodiment is used for identifying the reaction curve of the obtained whole blood C reactive protein, if the abnormal point is judged to exist, the reaction curve is indicated to be abnormal, the result cannot be used as a formal detection result, and the detection accuracy can be improved.
As an example of the identification method of the abnormal point, step S22 specifically includes:
step S22a, acquiring a corresponding normal reaction curve according to the detection value obtained from the original reaction curve;
step S22b, acquiring an absorbance threshold value of each point on the normal reaction curve;
step S22c, determining whether the absorbance of each point on the original reaction curve exceeds the absorbance corresponding to the same sampling time point on the normal reaction curve, and if so, identifying the point on the original reaction curve as an abnormal point.
Taking a whole blood C-reactive protein reaction curve as an example, CRP value is obtained from the original reaction curve, and a corresponding normal reaction curve is found in a standard library, wherein the normal reaction curve is equivalent to an ideal template reference of the current sample, no interference occurs, and the absorbance corresponding to each point on the normal reaction curve is equivalent to a threshold value. And comparing the absorbance of each point on the original reaction curve with the absorbance of the corresponding point on the normal reaction curve, and if the absorbance deviation between a certain point on the original reaction curve and the absorbance of the corresponding sampling time point on the normal reaction curve is larger, indicating that the point is obviously abnormal, and identifying the point as an abnormal point. In this way, whether abnormal points exist on the original reaction curve of the whole blood C reactive protein can be easily identified, and the detection accuracy is improved.
As another way to identify the outlier, and as shown in fig. 1, step S22 specifically includes:
step S221, calculating a first order difference of at least one point on the original reaction curve, wherein the first order difference is a difference value obtained by subtracting the absorbance corresponding to the point from the absorbance corresponding to a sampling time point after the point;
in step S222, if the calculated first order difference of the point is smaller than the first threshold, the point is identified as an abnormal point.
As described above, the sampling time point subsequent to the ith sampling time point, i.e., the (i + 1) th time point Ti+1Corresponding to the absorbance Ai+1Another point on the original reaction curve, denoted P, is determined jointlyi+1，Pi+1Point by sample time Ti+1As abscissa, corresponding to absorbance Ai+1Is the ordinate.
According to the above method, the ith sampling time point T on the original reaction curve is identifiediCorresponding PiIf the point is an abnormal point, P needs to be calculated firstiFirst order difference d of pointsi：
di= Ai+1- Ai，i=1，2，…，N-1
From the above, PiFirst order difference d of pointsiIs formed by Pi+1Corresponding absorbance Ai+1Minus PiCorresponding absorbance AiAnd obtaining the product. Specifically, the first order differences of the points are respectively: d1，d2，…，dN-1。
To obtain PiFirst order difference d of pointsiThen, the first order difference d is further dividediCompared to a first threshold. The first threshold is set as lambda, and the value of the lambda is related to the precision of the whole blood C reactive protein detection device, and is exactly determined by a first-order difference statistic value corresponding to an absorbance curve of a background test sample of the detection device. If the detection devices are different, the values of the first threshold λ are different.
If d isi<λ, indicates PiIf the point is abnormal, P is addediThe points are identified as outliers.
Ideally, λ is 0. In this embodiment, λ may be set to 0, that is, λ =0, for simplicity of determination. At this time, d is comparediThe magnitude relation with λ is substantially equivalent to the comparison Pi+1Corresponding absorbance Ai+1And PiCorresponding absorbance AiIf A isi+1<Ai(i.e. d)i= Ai+1-Ai<0) Then P will beiThe points are identified as outliers. In general, on the normal reaction curve, the absorbance at the later sampling time point is always greater than the absorbance at the previous sampling time point (i.e., A)i+1>Ai) Therefore, when the absorbance at the next sampling time point appears to be smaller than the absorbance at the previous sampling time point (i.e., A)i+1<Ai) In case (2), it means PiThe point is abnormal, and the reaction curve is also differentA general reaction curve.
Through the simple identification mode, whether abnormal points exist on the original reaction curve can be quickly identified, and the detection accuracy is improved.
As an automatic implementation manner, by using the steps S221 and S222, the first-order difference of each point on the original reaction curve can be calculated one by one and compared with the first threshold respectively, so as to perform anomaly identification on each point on the original reaction curve, which not only enhances the identification accuracy, but also lays a good foundation for further processing (e.g., correction) to be subsequently taken. After the first order difference of each point of the original reaction curve is calculated, a first order difference curve can be obtained.
In some cases, calculating only the first order difference for each point on the original reaction curve is not enough to identify outliers very accurately, and further calculation of the second and third order differences is required.
Let PiSecond order difference of points is dfiThe second order difference df of each point on the first order difference curve can be calculatediIt is also possible to select partial points, and this embodiment takes the partial points as an example, where i =1, 2, …, N1，N1Are integers. N is a radical of1May depend on the detection means. Specifically, the second order differences of the points are respectively: df is a1，df2，…，dfN1。
PiSecond order difference df of pointsiThe calculation method of (1) is as follows: df is ai= d'i+1- d'i，i=1，2，…，N1
Wherein, d'iThe calculation method of (1) is as follows:
it can also be known that the calculation method for respectively calculating the second order difference of at least some points selected from the first order difference curve is as follows: starting from the first point on the first-order difference curve, continuously selecting a part of points according to the sampling time sequence to calculate the first-order difference average value as the first-order difference valueThen, continuously selecting the same number of points from the unselected points to calculate the first-order difference average value as the second first-order difference value, and calculating the Nth point sequentially by subtracting the first-order difference value from the second first-order difference value to obtain the second-order difference of the first point1First order difference value, Nth1Subtracting the Nth from the first order difference value1-1 the first order difference value is Nth1A second order difference of 1 point correspondence.
And forming a second-order difference curve by the calculated second-order difference. To obtain PiSecond order difference df of pointsiThen, the second order difference df is also further appliediCompared to a first threshold λ:
if df is presenti>λ, then indicates calculated d'iAt least one of the points used is an anomaly, i.e. from PiAt least one of the points from the point to all the selected points is an outlier. In this example, it is assumed that d 'will be calculated'iAll points used are identified as outliers.
Similarly, in this embodiment, λ may be set to 0, that is, λ = 0.
Let PiThe third order difference of points is dffiThe third order difference dff of each point on the second order difference curve can be calculatediIt is also possible to select partial points, and this embodiment takes the partial points as an example, where i =1, 2, …, N2，N2Are integers. N is a radical of2And may be dependent on the detection means as well. Specifically, the second order differences of the points are respectively: dff1，dff2，…，dffN2。
PiThird order difference of points dffiThe calculation method of (1) is as follows:
dffi= df'i+1– df'i，i=1，2，…，N2
wherein, df'iThe calculation method of (1) is as follows:
it can also be known that at least some points are selected from the second order difference curve,the calculation method for respectively calculating the third order difference is as follows: continuously selecting a part of points to calculate a second order difference average value as a first second order difference value from a first point on the second order difference curve according to the sampling time sequence, continuously selecting the same number of points from the unselected points to calculate the second order difference average value as a second order difference value, subtracting the first second order difference value from the second order difference value to obtain a third order difference of the first point, and sequentially calculating the Nth order difference value2Second order differential value, Nth2Second order difference value minus Nth2-1 second order differential value is Nth2-a third order difference of 1 point correspondence.
And forming a third-order difference curve by the calculated third-order difference. To obtain PiThird order difference of points dffiThen, the third order difference df is also further appliediCompared to a first threshold λ:
if dffi<λ, indicating calculation of df'iAt least one of the points used is an anomaly, i.e. from PiAt least one of the points from the point to all the selected points is an outlier. In this example, it is assumed that df 'will be calculated'iAll points used are identified as outliers.
Similarly, in this embodiment, λ may be set to 0, that is, λ = 0.
In this embodiment, the first threshold λ compared with the first-order difference, the second-order difference, and the third-order difference may be the same or different.
By further calculating the second-order difference, the abnormal reaction curve which cannot be identified by calculating the first-order difference can be identified; and the abnormal reaction curve which cannot be identified by calculating the first-order difference and the second-order difference can be identified by calculating the third-order difference. This is illustrated below by the diagrams in fig. 3A-3D.
The response curve of FIG. 3A, in which the aberrant reaction region is "depressed," is incremental, i.e.: the absorbance at the latter point is greater than the absorbance at the former point, so there is no abnormality in calculating the first-order difference, and thus the abnormal region of the reaction curve cannot be identified using the first-order difference. If a second order difference is calculated, it can be identified, as shown in FIG. 3B.
As shown in fig. 3C, the reaction is unchanged in the initial period of the reaction curve, that is, the absorbance detection values are consistent, such abnormality cannot be identified by calculating the first-order difference and the second-order difference, if the third-order difference is calculated, the abnormality can be identified, and the identification effect of the third-order difference is shown in fig. 3D.
Corresponding to the method for identifying the abnormal turbidimetric reaction curve in the first embodiment of the present invention, the second embodiment of the present invention provides a method for alarming the abnormal turbidimetric reaction curve, as shown in fig. 4, including:
step S41, identifying outliers on the original response curve according to the method of the first embodiment of the present invention;
step S42, calculating the proportion of the identified abnormal points to all the points;
and step S43, if the proportion is larger than or equal to the second threshold value, giving an alarm prompt.
Specifically, the method for identifying the outlier in step S41 is completely the same as the identification method in the first embodiment of the present invention, and is not described herein again.
Assuming that the total number of the identified abnormal points is A _ num, and the Ratio of the total number of the abnormal points to all the points is Ratio _ A, then:
ratio _ a = a _ num/N, where N is the total number of sampling time points.
According to the identification method in the first embodiment of the present invention, the number of abnormal points identified by calculating only the first order difference is set as a1_ num, the number of abnormal points identified by calculating the second order difference is set as a2_ num, and the number of abnormal points identified by calculating the third order difference is set as A3_ num, so there are three corresponding ways of determining the second threshold ψ:
(1) if Ratio _ A = A1_ num/N is larger than psi, giving an alarm prompt;
(2) if Ratio _ A = (A1 _ num + A2_ num)/N ≧ ψ, an alarm prompt is given;
(3) if Ratio _ A = (A1 _ num + A2_ num + A3_ num)/N ≧ ψ, an alarm prompt is given;
in terms of accuracy, the number of abnormal points identified in the case (3) is the largest (a 1_ num + a2_ num + A3_ num), and whether an alarm is required is determined most accurately based on the number of abnormal points, so that false alarm can be prevented. Of course, in some cases, the anomalous response is particularly marked, and the number of anomalous points identified by the first order difference is already very high, so that the proportion of all the points is sufficient to exceed the second threshold value, reaching the alarm condition, without also losing accuracy.
In the above three determination methods, the value of the second threshold ψ is different. For the value of the second threshold ψ, the present embodiment gives the following setting:
randomly selecting M normal samples, and counting the proportion phi 1 of abnormal reaction points in all the pointsiRandomly selecting M samples with abnormal reaction, and counting the proportion phi 2 of abnormal reaction points in all the samplesiThe value of ψ is as follows:
that is, the average value of the ratios of the abnormal reaction points to all the points in the M normal samples is calculated and set as the first average value ψ 1 (1)M(ii) a (2) In M samples with abnormal reaction, the average value of the proportion of all the abnormal reaction points is set as the second average value psi 2M(ii) a And then calculating the average value of the first average value and the second average value to obtain a second threshold psi.
In practice, M can be freely selected, and as a preferred mode, the value of M in this embodiment is 100, that is, 100 normal samples and 100 samples with abnormal reactions are selected.
Corresponding to the method for identifying the abnormal turbidimetric reaction curve in the embodiment of the present invention, a third embodiment of the present invention provides a method for correcting the abnormal turbidimetric reaction curve, as shown in fig. 5, including:
step S51, identifying outliers on the original turbidimetric reaction curve according to the method of the first embodiment of the present invention;
step S52, calculating an abnormal first-order difference correction value of the abnormal point according to the remaining first-order difference normal points after the abnormal point identification and the corresponding sampling time points thereof, and the sampling time points corresponding to the first-order difference abnormal points;
step S53, correcting the absorbance of the corresponding abnormal point on the original reaction curve by the abnormal first order difference correction value.
Specifically, the identification of the singular point in step S51 is based on:
step S511, calculating a first order difference of at least one point on the original reaction curve, wherein the first order difference is a difference value obtained by subtracting the absorbance corresponding to the point from the absorbance corresponding to a sampling time point after the point;
in step S512, if the calculated first order difference of the point is smaller than the first threshold, the point is identified as an abnormal point.
For the specific identification manner, refer to the related description of the first embodiment of the present invention, and are not described herein again.
And (3) counting the first-order difference normal points remained after the abnormal points are identified through the steps as: dNiThe corresponding time point is denoted as TNiFirst order difference anomaly point count is denoted dAiThe corresponding time point is denoted as TAiThe array after the first order difference correction is recorded as dA _ correctiAnd then:
dA_correcti= f（dN1，dN2，…，dNn，TN1，TN2，…，TNn）
f represents the abnormal first order difference correction value as a functional relation of the normal first order difference, wherein one functional relation of f is as follows:
dA_correcti= k × TAi+ b
the calculation method of k and b is as follows: using least squares pairs (TN)1，TN2，…，TNn) And (dN)1，dN2，…，dNn) And performing least square fitting to obtain. For least squares calculation methods sources can be found in: numerical analysis, 5 th edition, Li Qingyang, WangSuper, easy to make sense, Qinghua university press.
Step S53 specifically includes:
step S531, selecting any point on an original reaction curve graph as a reference point;
step S532, the correction value of the abnormal first-order difference and the normal first-order difference are combined to be a first-order difference of all the points, and then the following operations are performed:
the corrected absorbance from the first point after the reference point to all the points thereafter is: adding the corrected absorbance of the previous point of the current point and the first-order difference corrected value of the previous point to be used as the absorbance of the point;
starting from the first point before the reference point to all points before the reference point, the corrected absorbance is as follows: and subtracting the first-order difference correction value of the current point from the correction absorbance of the next point of the current point to obtain the absorbance of the point.
For example, the corrected first order difference array is recorded as: d _ correct _ Totali(the array comprises the array dA _ correct corrected by the abnormal first-order differenceiAnd also includes normal first order difference array), taking any one point of the original response curve graph as a reference point, and the reference point can be positioned on the original response curve or positioned outside the original response curve, and the position outside the original response curve is equivalent to that the point is translated from the original response curve along the coordinate axis, and the position corresponds to a sampling time point. Let the selected reference point be AiCalculating the value of each point of the original response curve, and recording the corrected value of the original response curve as an array A _ correctjThen there is
If i>j，A_correctj= A_correctj+1– d_correct_Totalj；
If i<j，A_correcti= A_correctj-1+ d_correct_Totalj-1。
The method for correcting the abnormal response curve of the whole blood C-reactive protein according to the third embodiment of the present invention is illustrated by two examples.
Example one:
the whole blood C reactive protein detection equipment is a BC-5390 blood cell analyzer produced by Shenzhen Meyer biomedical electronics Limited, and carries out a first test of C reactive protein detection on a blood sample of a patient, wherein the reaction curve is shown in FIG. 6A, and the corresponding C reactive protein is as follows: 4.4 mg/L.
According to the identification method of the embodiment of the invention, the reaction curve is subjected to anomaly identification, wherein first-order difference anomaly point identification is shown in fig. 6C, second-order difference anomaly point identification is shown in fig. 6D, third-order difference anomaly point identification is shown in fig. 6E, and the anomaly point Ratio A identified by the first-order difference, the second-order difference and the third-order difference is 48.18%.
In the present apparatus, the second threshold ψ is set to 10% based on the statistical result. In this embodiment, the abnormal point ratio exceeds the threshold value, and an alarm prompt is given.
Fig. 6F shows a corrected first-order difference curve obtained by the correction method according to the embodiment of the present invention, fig. 6G shows a corrected original reaction curve, and the corrected CRP result is: 5.92 mg/L.
The sample was tested again, the response curve is shown in fig. 6B, and the results corresponding to C-reactive protein are: 5.86 mg/L. The corrected CRP results obtained with the first test curve are close to the true values.
Closer to the second test result.
Example two:
the whole blood C reactive protein detection equipment is a BC-5390 blood cell analyzer produced by Shenzhen Meyer biomedical electronics Limited, and carries out a first test of C reactive protein detection on a blood sample of a patient, wherein the reaction curve is shown in FIG. 7A, and the corresponding C reactive protein is as follows: 3.5 mg/L.
According to the identification method of the embodiment of the invention, the reaction curve is subjected to anomaly identification, wherein first-order difference anomaly point identification is shown in fig. 7C, second-order difference anomaly point identification is shown in fig. 7D, third-order difference anomaly point identification is shown in fig. 7E, and the anomaly point Ratio A identified by the first-order difference, the second-order difference and the third-order difference is 59.12%.
In the present apparatus, the second threshold ψ is set to 10% based on the statistical result. In this embodiment, the abnormal point ratio exceeds the threshold value, and an alarm prompt is given.
Fig. 7F shows a corrected first-order difference curve obtained by the correction method according to the embodiment of the present invention, fig. 7G shows a corrected original reaction curve, and the corrected CRP result is: 43.02 mg/L.
The sample was subjected to a single reproducibility test, the reaction curve is shown in fig. 7B, and the results corresponding to C-reactive protein are: 45.9 mg/L. The corrected CRP results obtained with the first test curve are close to the true values. .
The two examples fully prove that the method for identifying and correcting the abnormal reaction curve of the whole blood C-reactive protein, provided by the embodiment of the invention, can bring remarkable effects when applied to the actual detection of the whole blood C-reactive protein.
The embodiment of the invention has the following beneficial effects: firstly, the embodiment of the invention innovatively identifies the abnormal points of the turbidimetric reaction curve, avoids taking data which is possibly subjected to abnormal reaction as a detection result without processing, and is beneficial to improving the detection accuracy; furthermore, an alarm can be given in time according to the identification result to prompt a detector, so that data which is possibly subjected to abnormal reaction can be prevented from being used as a detection result without processing, and the detection accuracy is improved; furthermore, the identified abnormal points are corrected, so that a reaction curve with obvious abnormality can be well compensated, and the detection accuracy is greatly improved.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (12)
1. A method for identifying an abnormal reaction curve of a turbidimetry method is applied to equipment for detecting a whole blood sample by using the turbidimetry method, and comprises the following steps:
conveying the reagent to a sample reaction cup to dissolve blood cells of the sample, enabling the reagent to react with antigen or antibody in the sample, and then detecting the sample by using a turbidimetric method;
step S21, acquiring an original reaction curve;
step S22, calculating a first order difference of at least one point on the original reaction curve, wherein the first order difference is the difference value of the absorbance corresponding to the point after the point and the absorbance corresponding to the point; if the calculated first-order difference of the point is smaller than a first threshold value, identifying the point as an abnormal point;
step S23, at least selecting partial points from the first order difference curve formed by the calculated first order difference, and respectively calculating the second order difference; identifying at least one of the selected fetch points as an outlier if the second order difference is greater than the first threshold.
2. The method of claim 1, wherein at least some points are selected from the first order difference curve, and the second order difference is calculated by: continuously selecting a part of points to calculate the first-order difference average value as the first-order difference value from the first point on the first-order difference curve according to the sampling time sequence, then continuously selecting the same number of points from the unselected points to calculate the first-order difference average value as the second first-order difference value, subtracting the first-order difference value from the second first-order difference value to be the second-order difference of the first point, and calculating the Nth point in sequence1First order difference value, Nth1Subtracting the Nth from the first order difference value1-1 the first order difference value is Nth1A second order difference of 1 point correspondence.
3. The method of claim 2, further comprising the step of:
selecting at least part of points from a second order difference curve formed by the calculated second order difference, and respectively calculating the third order difference; identifying at least one of the selected fetch points as an outlier if the third order difference is less than the first threshold.
4. The method of claim 3, whichCharacterized in that, the calculation method for respectively calculating the third order difference of at least selecting partial points from the second order difference curve is as follows: continuously selecting a part of points to calculate a second order difference average value as a first second order difference value from a first point on the second order difference curve according to the sampling time sequence, continuously selecting the same number of points from the unselected points to calculate the second order difference average value as a second order difference value, subtracting the first second order difference value from the second order difference value to obtain a third order difference of the first point, and sequentially calculating the Nth order difference value2Second order differential value, Nth2Second order difference value minus Nth2-1 second order differential value is Nth2-a third order difference of 1 point correspondence.
5. The method of claim 1, wherein the first threshold is determined according to first order difference statistics corresponding to an absorbance curve of a background test sample of the detection device used.
6. The method of claim 5, wherein the first threshold value is 0.
7. The method according to claim 1, wherein the step S22 specifically includes:
step S22a, acquiring a corresponding normal reaction curve according to the detection value obtained from the original reaction curve;
step S22b, acquiring the absorbance of each point on the normal reaction curve;
step S22c, determining whether the absorbance of each point on the original reaction curve exceeds the absorbance corresponding to the same sampling time point on the normal reaction curve, and if so, identifying the point on the original reaction curve as an abnormal point.
8. A method for alarming an abnormal reaction curve of a turbidimetry comprises the following steps:
step S41, identifying outliers on the original reaction curve according to the method of any of claims 1-7;
step S42, calculating the proportion of the identified abnormal points to all the points;
and step S43, if the proportion is larger than or equal to the second threshold value, giving an alarm prompt.
9. The method of claim 8, wherein the second threshold is obtained according to:
randomly selecting M normal samples, and calculating the average value of the proportion of the abnormal reaction points in all the normal samples to obtain a first average value;
randomly selecting M samples with abnormal reaction, and calculating the average value of the proportion of abnormal reaction points in all the samples to obtain a second average value;
and calculating the average value of the first average value and the second average value to obtain the second threshold psi.
10. A method for correcting a turbidimetric abnormal reaction curve comprises the following steps:
step S51, identifying outliers on the original reaction curve according to the method of claim 1;
step S52, calculating an abnormal first-order difference correction value of the abnormal point according to the remaining first-order difference normal points after the abnormal point identification and the corresponding sampling time points thereof, and the sampling time points corresponding to the first-order difference abnormal points;
and step S53, correcting the absorbance of the corresponding abnormal point on the original reaction curve by the abnormal first-order difference correction value.
11. The method according to claim 10, wherein the abnormal first order difference correction value is obtained by a function calculation consisting of a normal first order difference; wherein,
the normal first-order difference is a first-order difference normal point which is remained after the abnormal point is identified;
the function is: the abnormal first-order difference correction value is equal to the sum of a second coefficient and a sampling time point corresponding to the abnormal first-order difference correction value after the sampling time point is multiplied by the first coefficient; and the first coefficient and the second coefficient are obtained by performing least square fitting on the remaining first-order difference normal point array after the abnormal point identification and the sampling time point array corresponding to the first-order difference normal point array.
12. The method according to claim 11, wherein the step S53 specifically includes:
step S531, selecting any point on an original reaction curve graph as a reference point;
step S532, the correction value of the abnormal first-order difference and the normal first-order difference are combined to be a first-order difference of all the points, and then the following operations are performed:
the corrected absorbance from the first point after the reference point to all the points thereafter is: adding the corrected absorbance of the previous point of the current point and the first-order difference corrected value of the previous point to be used as the absorbance of the point;
starting from the first point before the reference point to all points before the reference point, the corrected absorbance is as follows: and subtracting the first-order difference correction value of the current point from the correction absorbance of the next point of the current point to obtain the absorbance of the point.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410313267.6A CN105466927B (en) | 2014-07-02 | 2014-07-02 | Method for identifying, correcting and alarming abnormal reaction curve of turbidimetry |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410313267.6A CN105466927B (en) | 2014-07-02 | 2014-07-02 | Method for identifying, correcting and alarming abnormal reaction curve of turbidimetry |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105466927A CN105466927A (en) | 2016-04-06 |
| CN105466927B true CN105466927B (en) | 2020-04-10 |
Family
ID=55604858
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410313267.6A Active CN105466927B (en) | 2014-07-02 | 2014-07-02 | Method for identifying, correcting and alarming abnormal reaction curve of turbidimetry |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105466927B (en) |
Families Citing this family (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106340014B (en) * | 2016-08-29 | 2019-11-01 | 上海理工大学 | Binaryzation curve processing method |
| CN108279229B (en) * | 2017-01-05 | 2024-02-27 | 深圳市帝迈生物技术有限公司 | Whole blood CRP detection device |
| CN108287354B (en) * | 2017-01-09 | 2020-09-08 | 北京四维图新科技股份有限公司 | Automatic data error correction method and device and navigation equipment |
| CN107703132B (en) * | 2017-09-30 | 2020-11-24 | 深圳迈瑞生物医疗电子股份有限公司 | Reaction curve abnormity processing method and device, biochemical analyzer and storage medium |
| CN108318436B (en) * | 2018-02-06 | 2021-05-04 | 迈克医疗电子有限公司 | Reaction curve generation method and device and optical detection system |
| CN109239360B (en) * | 2018-09-14 | 2022-04-01 | 深圳开立生物医疗科技股份有限公司 | Reaction curve abnormity detection method and device |
| CN109580549B (en) * | 2018-11-14 | 2022-07-01 | 深圳市理邦精密仪器股份有限公司 | Method and device for calculating and calibrating material content |
| CN110083899B (en) * | 2019-04-17 | 2022-10-21 | 中国西安卫星测控中心 | Recursive diagnosis and self-repairing method for radar measured metadata |
| CN114364983A (en) * | 2019-09-04 | 2022-04-15 | 深圳迈瑞生物医疗电子股份有限公司 | Method, device and computer readable medium for recognizing hook effect in immunoturbidimetry |
| CN111584083A (en) * | 2020-05-25 | 2020-08-25 | 常熟常江生物技术有限公司 | Integrated coagulation index integration algorithm for detecting thrombus elastogram |
| CN112710636B (en) * | 2020-12-09 | 2022-05-24 | 深圳市科曼医疗设备有限公司 | Method and device for detecting concentration of specific protein |
| CN112710627B (en) * | 2020-12-09 | 2022-05-27 | 深圳市科曼医疗设备有限公司 | Detection method and detection device for specific protein concentration |
| CN112836166B (en) * | 2021-01-15 | 2023-12-01 | 北京科技大学 | First-order differential algorithm of monitoring data of equal-sampling experiment based on response peak analysis |
| CN112964673B (en) * | 2021-01-29 | 2024-02-27 | 深圳市科曼医疗设备有限公司 | Method and device for identifying abnormality of specific protein response curve |
| CN114636827B (en) * | 2022-05-11 | 2022-10-25 | 深圳市帝迈生物技术有限公司 | Method, device, equipment and medium for eliminating abnormal points of reaction curve |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1488762A (en) * | 2003-08-21 | 2004-04-14 | 飞 廖 | Method for quantitative determination of biochemical substance by enzyme analysis via predicting background |
| CN102837711A (en) * | 2011-06-21 | 2012-12-26 | 中国铁道科学研究院机车车辆研究所 | Infrared waveform based intelligent identification method for railway bearing |
| CN103091287A (en) * | 2011-10-31 | 2013-05-08 | 深圳迈瑞生物医疗电子股份有限公司 | Self-diagnosis method for measure result of blood analyzer, and device thereof |
| CN103181778A (en) * | 2011-12-31 | 2013-07-03 | 深圳迈瑞生物医疗电子股份有限公司 | Ultrasonic imaging method and system |
| CN103291279A (en) * | 2012-03-02 | 2013-09-11 | 中国石油化工股份有限公司 | Method for optimizing micro abnormal signal of gas logging value |
-
2014
- 2014-07-02 CN CN201410313267.6A patent/CN105466927B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1488762A (en) * | 2003-08-21 | 2004-04-14 | 飞 廖 | Method for quantitative determination of biochemical substance by enzyme analysis via predicting background |
| CN102837711A (en) * | 2011-06-21 | 2012-12-26 | 中国铁道科学研究院机车车辆研究所 | Infrared waveform based intelligent identification method for railway bearing |
| CN103091287A (en) * | 2011-10-31 | 2013-05-08 | 深圳迈瑞生物医疗电子股份有限公司 | Self-diagnosis method for measure result of blood analyzer, and device thereof |
| CN103181778A (en) * | 2011-12-31 | 2013-07-03 | 深圳迈瑞生物医疗电子股份有限公司 | Ultrasonic imaging method and system |
| CN103291279A (en) * | 2012-03-02 | 2013-09-11 | 中国石油化工股份有限公司 | Method for optimizing micro abnormal signal of gas logging value |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105466927A (en) | 2016-04-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105466927B (en) | Method for identifying, correcting and alarming abnormal reaction curve of turbidimetry | |
| CN109239360B (en) | Reaction curve abnormity detection method and device | |
| JP3882720B2 (en) | Odor measuring device | |
| WO2017012168A1 (en) | Optimized biochemical detection method suitable for medical examination | |
| Desharnais et al. | Procedure for the selection and validation of a calibration model II—theoretical basis | |
| US20170363652A1 (en) | Method and apparatus for electronic detection and determination | |
| JP2019086517A (en) | Blood coagulability evaluation method | |
| JP2004347385A (en) | Anomaly detection system and anomaly detecting method | |
| CN102507676A (en) | On-line drift compensation method of electronic nose based on multiple self-organizing neural networks | |
| WO2021132552A1 (en) | Blood coagulation time measurement method | |
| JP2019086518A (en) | Blood coagulability assessment method | |
| AU2020203717A1 (en) | Detecting a transient error in a body fluid sample | |
| EP3394615A1 (en) | Method of detecting presence or absence of a clot in a liquid sample analyzer | |
| CN102866256A (en) | Detection method and detection reagent for hypersensitive C reactive protein | |
| US6787361B1 (en) | Clinical assay calibration adjustment method | |
| KR20230067651A (en) | Sensors for identifying fluid samples and methods for applying quality tests to such sensors | |
| CN109030801B (en) | Automatic biochemical analyzer for clinical samples | |
| CN113484244A (en) | Coagulation analyzer reagent calibration method based on immunoturbidimetry | |
| Seifert et al. | A review on innovative procedures for the analysis of data from gas sensor systems and networks | |
| JP4415731B2 (en) | Odor measuring device | |
| CN115060666B (en) | Calibration method of water quality parameter sensor and water quality parameter measuring method and device | |
| US11982656B2 (en) | Method of control of a spectrometer | |
| CN108957018A (en) | A kind of automatic biochemistry analyzer | |
| Theodorsson | Limit of detection, limit of quantification and limit of blank | |
| CN117538271A (en) | Method and device for detecting the pre-banding phenomenon of a sample to be detected |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |