CN110135731B - Laboratory risk target quality control chart drawing method - Google Patents
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Abstract
The invention relates to a laboratory risk target quality control technology, and discloses a laboratory risk target quality control chart drawing method, which solves the problems that a quality control scheme in the prior art needs complicated rules and cannot intuitively evaluate risks in quality control, and a part of risk evaluation schemes proposed by research are complex and are not beneficial to popularization and use. The method comprises the following steps: calculating a long-term standard difference value representing the long-term stability of the laboratory, calculating error distribution parameters of the laboratory, measuring a quality control product when performing quality control each time to obtain a quality control result, calculating the error distribution parameters of a detection system according to Bayesian theorem, calculating the possibility of different errors, calculating the probability of exceeding the allowable total error when different errors exist, calculating the total risk of the quality control, taking the quality control times or days as an abscissa, taking the total risk size of the quality control and the quality control result as an ordinate, drawing a quality control graph, and setting a risk control line in the quality control graph.
Description
Technical Field
The invention relates to a laboratory risk target quality control technology, in particular to a laboratory risk target quality control chart drawing method.
Background
At present, a more common quality control scheme is a multi-rule scheme provided by Westgard, the quality control scheme needs to combine a plurality of rules, the use process is complex, and the risk in quality control cannot be clearly indicated. While the international EP23 document proposes that the quality control scheme should shift the emphasis of quality control to risk from the risk perspective.
Pavin et al propose a set of risk quality control schemes, but the scheme has a complex algorithm and is not clear enough in risk display, and meanwhile, the risk definition relates to many uncertain factors, so that the application and popularization in actual work are difficult at present.
Therefore, at least the following problems exist for the quality control scheme in the prior art: the method needs complicated rules, cannot visually evaluate the risk in quality control, and is not beneficial to popularization and application due to the fact that a risk evaluation scheme provided by partial research is complex.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: a method for drawing a laboratory risk target quality control chart is provided, and the problems that a quality control scheme in the prior art needs complicated rules and cannot visually evaluate risks in quality control, and a part of risk evaluation schemes proposed by research are complex and are not beneficial to popularization and use are solved.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the method for drawing the laboratory risk target quality control chart comprises the following steps:
s1. long-term standard deviation values were calculated which reflect the long-term stability of the laboratory:
wherein, TEa is the allowable total error of the detection project, bias is the laboratory deviation obtained by the indoor evaluation; sigma l Is the laboratory long-term sigma value;
s2, calculating error distribution parameters of the laboratory:
wherein u is 0 Mean, sd, representing the laboratory error distribution 0 Expressing error distribution standard deviation, mean and sd are the mean value and standard deviation of quality control obtained by quality control data of a period of time;
5363 the quality control is performed each time by s3. to obtain a quality control result x, and the number of times of repeatedly measuring the quality control is recorded as n r And calculating the average number of quality control results:
s4. calculates the error distribution parameters of the detection system according to Bayesian theorem:
s5. calculates the likelihood of different errors:
where se represents the magnitude of the systematic error;
s6. calculates the probability of exceeding the allowable total error in the presence of different errors, i.e. the risk value of an unreliable sample occurring at this time:
s7. calculates the total risk of this quality control:
s8. draws a quality control diagram by using the quality control times or days as an abscissa and using the total quality control risk RM and the total quality control result x as an ordinate, and sets a risk control line in the quality control diagram.
As a further optimization, in step s1, the sigma is l The calculating method comprises the following steps:
sigma l ＝sigma s -1.65；
wherein, sigma s Is the short-term sigma value of the laboratory,
in the scheme, the long-term sigma value of the laboratory is estimated through the short-term sigma value of the laboratory, so that the method is convenient and quick.
As a further optimization, in step s3, for multi-concentration level quality control, the method for obtaining the quality control result x includes:
if the difference between the maximum value and the minimum value in the short-term sigma values calculated by the multi-concentration level quality control is within a certain threshold range (namely, is relatively close), the sd and mean values corresponding to the short-term sigma value minimum level quality control are taken as calculation bases and respectively recorded as sd 0 And mean 0 The sd and mean values corresponding to other horizontal quality controls are respectively recorded as sd j And mean j By the formula
Calculating an equivalent result x e X is to e As a quality control result of the repeated measurement.
In the scheme, for multi-concentration level quality control, if short-term sigma values are relatively close, sd and mean values corresponding to the level quality control with the minimum short-term sigma value are used as calculation bases, equivalent results of other levels are calculated through a formula and directly used as quality control results of repeated measurement, and therefore quality control efficiency can be improved.
As a further optimization, in step s3, for multi-concentration level quality control, the method for obtaining the quality control result x includes:
and if the difference between the maximum value and the minimum value in the short-term sigma values calculated by the quality control of the multiple concentration levels is not within a certain threshold range, independently calculating the quality control result of each level.
In the scheme, for multi-concentration-level quality control, if the short-term sigma value difference is large, errors caused by an equivalent calculation mode are large, and results are inaccurate, so that the accuracy of different concentration levels is ensured by independently calculating quality control results of all levels.
The invention has the beneficial effects that: through the method, a quality control graph which is simple and easy to understand and covers the risk indexes can be drawn; the risk size is visually displayed in the quality control diagram, so that a user can see the risk size at a glance; through the arrangement of the risk control line, the risk is larger when the risk control line is exceeded, and the risk can be accepted when the risk control line is lower than the control line, so that complicated quality control rules are omitted; in addition, after the size of the risk control line is determined, the corresponding analysis batch length can be calculated by determining the number of abnormal samples of each analysis, so that the scientificity of quality control is improved.
Drawings
FIG. 1 is an example of a risk objective quality control chart in the present invention;
fig. 2 is an example of a risk target quality control diagram of the bi-level quality control in the embodiment.
Detailed Description
The invention aims to provide a method for drawing a quality control chart of a laboratory risk target, and solves the problems that a quality control scheme in the prior art needs complicated rules and cannot visually evaluate risks in quality control, and a part of risk evaluation schemes proposed by research are complicated and are not beneficial to popularization and use.
In particular implementation, the method for drawing the laboratory risk target quality control chart comprises the following steps:
1. setting a Risk control line size RM cl Setting the number N of samples acceptable per analysis batch exceeding the allowable total error uc ；
2. The length of the analysis batch is calculated,
3. calculating the quality control data of 22 days (or one month), and obtaining the mean value mean and the standard deviation sd of the quality control to obtain the distribution parameters of the errors generated by the system;
4. the laboratory can obtain the bias of the laboratory through the laboratory evaluation;
5. according to different methods, obtaining the allowable total error Tea of the detection item;
6. the short-term sigma value of the laboratory is calculated,
7. calculating the long-term sigma value of the laboratory l ＝sigma s -1.65；
8. Estimating the long-term stability degree of the laboratory, calculating the sd value of the long-term standard deviation,
9. estimating the distribution parameter of the laboratory's own errors, u 0 ＝bias+mean，
Wherein u is 0 Mean, sd, representing the laboratory error distribution 0 Represents the standard deviation thereof;
10. measuring the quality control product each time quality control is performed to obtain a quality control result x, and repeatedly measuring the number of the quality control product n r Quality control result average
11. Calculating parameters of distribution conditions possibly existing in the mean number of error distribution of the detection system under the condition that the quality control measurement result appears according to Bayesian theorem:
12. calculating the probability of different errors
Where se represents the magnitude of the systematic error;
13. calculating the risk value of the unreliable sample when the probability of exceeding the allowable total error exists in the presence of different errors
14. Calculating the total risk of the quality control measurement system
15. Drawing a quality control diagram by taking the quality control times (or days) as an abscissa and the system risk size RM and the quality control result x as an ordinate, and setting a risk control line in the quality control diagram, wherein the quality control diagram is shown in FIG. 1; in the figure, the risk control line RM cl ＝1％
The quality control result exceeding the control line indicates that the risk exceeds the acceptable risk, and the quality control result below the control line indicates that the risk of the system can be accepted, so that the risk assessment result can be visually displayed.
The following two principles are permitted when multi-level quality control monitoring is utilized;
principle one: when the sigma values of the multi-concentration level quality control calculation are close to each other, the sd and mean values corresponding to the level with the minimum sigma value are taken as the calculation basis and are recorded as sd 0 And mean 0 Other levels of the parameter are denoted as sd j And mean j Calculating the equivalence by using the following formulaResult x e As a result of repeated assays;
principle two: the larger the difference between the quality control sigma values of different concentration levels is, the greater the inaccuracy of the result obtained by combining and calculating is, and the laboratory is recommended to consider whether to check the risk independently according to the self condition so as to ensure the accuracy of different concentration levels.
In the practical process, when the quality control scheme is stably operated for more than one month in a laboratory and the quality control batch number is not changed, all the quality control results (excluding out-of-control data) under control in the real-time accumulation process are calculated to obtain the mean and the standard deviation, and the mean and the standard deviation are used for replacing the calculation results of mean and sd in the step 3. Meanwhile, accumulating all quality control data (including out-of-control data) in real time to calculate standard deviation as long-term sd to replace the calculation of the long-term sd in the steps 7 and 8; the average of the error distribution calculated as the laboratory itself replaces u in step 9 0 And (4) calculating. In practice, when the batch number changes and the mean value changes, the mean value should be accumulated again in time.
Example (b):
in this embodiment, taking the quality control of two horizontal concentrations as an example, the process of drawing the quality control map is as follows:
(1) Accumulating the results of the laboratory albumin assay using two levels of concentration, level 1 parameter mean 1 ＝69.1，sd 1 =0.88, parameter mean after level 2 determination 2 ＝39.6，sd 2 =0.56, total error allowed, TEa =6%, bias =0.
(2) Setting a risk control line RM cl =1%, the number N of samples acceptable per analysis batch exceeding the allowable total error is set uc ＝2。
(3) Calculating the length of the analysis batch,
(4) Calculating the short-term sigma value of the laboratory,
(5) And if the sigma values of the two levels are judged to be relatively close, selecting the parameter of the level 2 for calculation, and recording the result of each measurement of the level 1 as the repeated measurement result of the level 2 through equivalent conversion. For example, day 1 month 1, the level 1 quality control measurement result is 68.6, the level 2 measurement result is 39.6, and the equivalent transformation formula is used
I.e. equivalent converted horizontal 1 calculation value x 1 =39.3; calculated value of level 2 is x 2 ＝39.6
(6) Calculating the long-term sigma value of the laboratory s ＝sigma l +1.65。sigma l ＝sigma s2 -1.65＝4.24--1.65＝2.59
(7) Estimating the long-term stability degree of the laboratory, calculating the sd value of the long-term standard deviation,
(8) Estimating the distribution parameter of the laboratory's own errors, u 0 ＝bias+mean＝0+39.6＝39.6，
(9) Measuring the quality control product every time the quality control is carried out to obtain a quality control result x, and recording the number of times of repetition as n r =2, quality control result average number
Take 1 month and 1 day results as an example
(10) Calculating the error distribution parameters of the detection system at the moment according to Bayesian theorem:
(11) Calculating the possibility of different errors
(12) Calculating the probability of exceeding the allowable total error and the risk value of unreliable sample when different errors exist
(13) Calculate the total risk of the system at this time:
(14) And calculating the quality control data of other days in the same way, drawing a quality control graph by taking the quality control times (or days) as an abscissa and taking RM and x as ordinates, wherein as shown in FIG. 2, a square mark represents a level 2, a circular mark represents the equivalent quality control concentration of the level 1, a histogram represents the risk size, and a horizontal line represents a risk control line.
As can be seen from fig. 2: the risks of 1 month, 23 days and 2 months, 1 day are too high, the prompt risk is unacceptable, the quality control is out of control, and the sample detection can be continued after the treatment.
Claims (4)
1. A laboratory risk target quality control chart drawing method is applied to quality control of laboratory albumin concentration level, and is characterized by comprising the following steps:
s1. long-term standard deviation values were calculated which reflect the long-term stability of the laboratory:
wherein, TEa is the allowable total error of the detection project, bias is the laboratory deviation obtained by the indoor evaluation; sigma l Is a laboratory long-term sigma value;
s2, calculating error distribution parameters of the laboratory:
wherein u is 0 Mean, sd, representing the laboratory error distribution 0 Expressing error distribution standard deviation, mean and sd are the mean value and standard deviation of quality control obtained by quality control data of a period of time;
5363 the quality control is performed each time by s3. to obtain a quality control result x, and the number of times of repeatedly measuring the quality control is recorded as n r And calculating the average number of quality control results:
s4. calculates the error distribution parameters of the detection system according to Bayesian theorem:
s5. calculates the likelihood of different errors:
where se represents the magnitude of the systematic error；
s6. calculates the probability of exceeding the allowable total error in the presence of different errors, i.e. the risk value of an unreliable sample occurring at this time:
s7. calculating the total risk of this quality control:
s8. draws a quality control diagram by using the quality control times or days as an abscissa and the total quality control risk RM and the quality control result x as an ordinate, and sets a risk control line in the quality control diagram.
2. The laboratory risk target quality control chart rendering method of claim 1,
in step s1, the sigma is l The calculation method comprises the following steps:
sigma l ＝sigma s -1.65；
wherein, sigma s For the short-term sigma values of the laboratory,
3. the laboratory risk target quality control chart rendering method of claim 2,
in step s3, for multi-concentration level quality control, the method for obtaining the quality control result x includes:
if the difference between the maximum value and the minimum value in the short-term sigma values calculated by the multi-concentration level quality control is within a certain threshold range, the sd and mean values corresponding to the short-term sigma value minimum level quality control are taken as calculation bases and are respectively recorded as sd 0 And mean 0 The sd and mean values corresponding to other horizontal quality controls are respectively denoted as sd j And mean j By the formula
Calculating an equivalent result x e X is to be e As a quality control result of the repeated measurement.
4. The laboratory risk target quality control chart rendering method of claim 2,
in step s3, for multi-concentration level quality control, the method for obtaining the quality control result x includes:
and if the difference between the maximum value and the minimum value in the short-term sigma values calculated by the quality control of the multiple concentration levels is not within a certain threshold range, independently calculating the quality control result of each level.
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| CN105045220A (en) * | 2015-05-08 | 2015-11-11 | 上海质晟生物科技有限公司 | Quality control method based on Z-score quality control chart for multiple variables |
| CN107545361A (en) * | 2017-08-03 | 2018-01-05 | 广西金域医学检验所有限公司 | Compare System and method between room |
| CN108108863A (en) * | 2016-11-25 | 2018-06-01 | 上海昆涞生物科技有限公司 | Laboratory system allowable range of error appraisal procedure based on Quality Control data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105045220A (en) * | 2015-05-08 | 2015-11-11 | 上海质晟生物科技有限公司 | Quality control method based on Z-score quality control chart for multiple variables |
| CN108108863A (en) * | 2016-11-25 | 2018-06-01 | 上海昆涞生物科技有限公司 | Laboratory system allowable range of error appraisal procedure based on Quality Control data |
| CN107545361A (en) * | 2017-08-03 | 2018-01-05 | 广西金域医学检验所有限公司 | Compare System and method between room |
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