CN115169986A - Quality control system of clinical laboratory - Google Patents
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Abstract
The invention discloses a data quality control system of a clinical laboratory, which comprises a data acquisition module, a data preposition module, a data filtering module, a data preprocessing module and a calculation module, wherein the data preprocessing module comprises: the device comprises a first sub-processing module and a second sub-processing module. According to the scheme, a PBRTQC system is established through an EWMA operation program to realize indoor quality monitoring, meanwhile, a medical technology sharing platform is established to promote data interconnection and intercommunication and result mutual recognition of PBRTQCs of large medical institutions, and reasonable supporting measures are taken to ensure that the platform runs well.
Description
Technical Field
The invention belongs to the technical field of medicine, and particularly relates to a quality control system for a clinical laboratory.
Background
Traditional Indoor Quality Control (IQC) is the core content of a quality control system in a clinical laboratory, and is an important method for evaluating the precision and accuracy of a detection system. But with the ever increasing number of tests and the increasing rise in modern testing models, today's laboratories are not the ones in the traditional concept. For example, in some large medical institutions, multiple devices in the laboratory are used for testing the same test items, or different medical institutions test the same test items, so the quality control mode should be advanced. At present, more and more scholars think that the accuracy and the rapidness cannot be achieved on the analysis error by only using the traditional indoor quality control, and the limitations of the traditional quality control are mainly reflected in the following aspects: 1. the lack of interchangeability in the traditional quality control can lead to the reduction of true positive rate or the increase of false positive rate of error identification on one hand and lead to the correlation of the IQC target value and an instrument instead of reflecting the true level of an object to be detected on the other hand. 2. Since the IQC assumes that the error is persistent and needs to be detected until the next quality control is performed, it is not possible to effectively monitor short-term system drift. 3. The IQC only monitors and analyzes the middle links, and cannot find the change of detection performance in real time, and when the detection result of the quality control product is out of control, samples before the detection time point of the quality control product are often required to be reserved and then detected so as to trace and evaluate the affected samples before the out of control time point. 4. The quality control product has the problems of poor stability, narrow detection range, matrix effect and the like, and the intersystem comparison function is weak. 5. The cost of IQC (quality control and reagents) is not negligible.
In 1965, professor Hoffman and Waid proposed to estimate the stability of the test system by monitoring the test results of patient samples, i.e. monitoring the average of patient data in the "normal range" of the day (AoN) for the concept of quality control, thus laying the foundation of real-time quality control of patient data in testing medicine. However, this method is difficult to acquire and large in calculation amount, and thus is not applied to clinic. In 1974, a variant of the AoN method, the BULL method, appeared under continued research and improvement by international scholars and was widely used for quality control in automatic blood analyzers. Over Time, a class of Patient data-Based derivation algorithms, collectively referred to as Patient-Based Real-Time Quality Control (PBRTQC), emerges afterwards. The quality committee under the international union of clinical chemistry and test medicine analysis indicates in the guidance published in 2020: the PBRTQC is a quality control method for monitoring the analysis performance of a detection process in real time and continuously by using the detection result of a patient clinical specimen based on a statistical and mathematical model, has the advantages of evaluating the comparability of clinical test items, monitoring and analyzing the error (the links of specimen collection, transportation, processing and the like) before analysis, monitoring and analyzing the error (from reagents, instruments and calibration) in analysis, continuously monitoring the change of the authenticity performance of a detection system in the whole time period during the batch number replacement of indoor quality control products, directly analyzing the patient result without consumption, matrix effect, real-time whole-course comparison (longitudinal comparison of one instrument or transverse comparison of a plurality of instruments), low cost and the like, is effective supplement of the IQC, free quality control in a certain sense, and is favorable for laboratories. The PBRTQC includes various operation programs including an averaging of normals (AON), a fill method, a moving Median (MA), an Exponentially Weighted Moving Average (EWMA), and the like. The EWMA method, which was first proposed in 1959 by Roberts, introduces a weight coefficient (λ) and calculates a new MA value for each new test result by assigning weights to the two previous and subsequent test results to achieve the best and fast bias test, representing a more realistic and continuous moving average, with the advantage of finding small variations in inaccuracy or imprecision in the assay process early.
Disclosure of Invention
It is an object of the present invention to provide a quality control system for clinical laboratories that solves the above-mentioned problems of the prior art.
To achieve the above object, the present invention provides: a clinical laboratory data quality control system comprising:
the data acquisition module is used for acquiring relevant data information of a laboratory;
the data pre-processing module is used for rejecting part of data;
the data filtering module is used for grouping the data and respectively establishing a PBRTQC model based on grouping results;
the data preprocessing module comprises: the first sub-processing module is used for carrying out truncation processing on the grouped data; the second sub-processing module converts the data subjected to the truncation processing into relatively normal distribution through Box-Cox normalization conversion;
the calculation module is used for calculating the average value of the preprocessed data;
and the parameter setting module is used for calculating a control limit based on the preprocessed data and judging whether the control limit is exceeded.
Preferably, calculating the control limit comprises: the observation values are weighted differently by a weighted moving average method, and a moving average value is obtained by the weights.
Preferably, calculating the control limit comprises: calculations were performed using the EWMA method.
Out low＝Mean-4*SD
Error low＝Mean-3*SD’
Warning low＝Mean-2*SD’
Warning high＝Mean+2*SD’
Error high＝Mean+3*SD’
Out high＝Mean+4*SD
SD’＝SD*(λ/(2-λ))1/2
Wherein Mean represents the average value, SD represents the standard deviation, SD 'represents the standard deviation', λ represents the weighting coefficient, out low represents the lower limit of data rejection, out high represents the upper limit of data rejection, error low represents the lower limit of Error, error high represents the upper limit of Error, warning low represents the lower limit of Warning, and Warning high represents the upper limit of Warning.
Preferably, the data acquisition module is connected with a laboratory information management system and used for acquiring data from the laboratory information management system, the data acquisition module is connected with the data filtering module and transmits the acquired data to the data filtering module, and the acquired data are stored in a manner of allowing real-time extraction when being acquired.
Preferably, the collected data comprises an exercise data set and a verification data set, the exercise data set is used for parameter setting and program establishment of the PBRTQC method, and the verification data set is used for performance verification and implementation effect evaluation of the PBRTQC real-time quality control program.
Preferably, the exercise data set is volumetrically distributed with the verification data set.
Preferably, if the control limit is exceeded, an alarm is given, and the set alarm conditions include:
a.1 3s :1 sample measurement value over
Controlling limit, judging as out-of-control;
b.
simultaneously dropping 10 continuous sample measured values on one side of the mean value to prompt a system error;
c.N 2s n consecutive sample measurements simultaneously exceed
Or
Control limit, prompt system error N = NPed.
Preferably, the control system is connected to a medical skill sharing platform system, and the medical skill sharing platform system includes:
the data transmission module is used for accessing medical and technical information systems of hospitals in real time, actively collecting inspection data and uploading the inspection data to the platform repository;
the data sharing module is used for acquiring the past examination data of the patient from the storage library of the platform;
the platform management module is connected with the data transmission module and used for receiving data actively acquired by the data transmission module, and the platform management module is connected with the data sharing module and used for maintaining the data in the storage library and monitoring daily operation and maintenance of the medical technology sharing platform.
The invention has the technical effects that: according to the method, the PBRTQC system is established through an EWMA operation program to realize the indoor quality monitoring of serum biochemical projects and gradually popularize the PBRTQC, meanwhile, the interconnection and intercommunication of PBRTQC data and mutual recognition of results of large medical institutions are promoted through the establishment of a medical technology sharing platform, and reasonable matching measures are taken to ensure that the platform runs well, so that the PBRTQC is promoted to be accepted in domestic clinical laboratories and is widely applied.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
FIG. 1 is a schematic structural diagram in an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a medical technology sharing platform system in an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
As shown in fig. 1, the present embodiment provides a quality control system for clinical laboratories, which is designed based on the Remisol Advance system, and comprises:
the data acquisition module is used for acquiring relevant data information of a laboratory;
the data pre-processing module is connected with the data filtering module and is used for rejecting part of data, wherein the data is data of factors which can not avoid causing large influence on data distribution, and the data comprises capability verification sample data, quality control sample data, research sample data, dialysis sample data, non-numerical results, non-serum sample types and the like; the data pre-processing module.
The data filtering module is used for grouping the data; for example: the grouping can be based on biological variation, specifically, WST403-2012 "clinical biochemical test routine project analysis quality index", american National center for Environmental Prediction (NECP), and a database of biological variation are selected as different sources of project quality control criteria. Selecting a CV therefrom that meets or approximates the criteria to account for the smallest statistical indicator; the medical treatment can be grouped according to outpatient service, ward and physical examination groups; grouping can also be done by age and gender; the grouping can also be done according to instruments or modules; respectively establishing a PBRTQC model based on the grouped data;
the data preprocessing module comprises:
the first sub-processing module is used for intercepting the grouped data, and particularly, for the grouped data with skewed distribution and extreme values, tailing or removing the data outside an intercepting value interval;
and the second sub-processing module converts the data subjected to the truncation processing into relatively normal distribution through Box-Cox normalization conversion.
The calculation module is used for calculating the average value of the preprocessed data; and calculating the mean value by using a Remisol Advance system, specifically, adopting a weighted moving average method, respectively giving different weights to the observed values, obtaining a moving average value according to the different weights, and determining a predicted value based on the final moving average value to reflect the recent change trend of the data.
And the parameter setting module is used for calculating a control limit based on the preprocessed data, and alarming when the control limit is exceeded. In this embodiment, the selected parameters are: target false positive alarm rate (DFAR), specifically:
the control limits are determined based on the Remisol Advance systems manual. Remisol Advance system: the method comprises basic functions of data acquisition, data storage, data extraction and analysis, statistical analysis, visual quality control diagrams, performance verification capability of a test algorithm, real-time operation functions, early warning out-of-control correction and audit recording, system log tracking, a multi-dimensional visualization tool, data normal distribution statistics, a data filter, IQC integration and the like. The specific method of EWMA is as follows:
Out low＝Mean-4*SD
Error low＝Mean-3*SD’
Warning low＝Mean-2*SD’
Warning high＝Mean+2*SD’
Error high＝Mean+3*SD’
Out high＝Mean+4*SD
SD’＝SD*(λ/(2-λ))1/2
wherein Mean represents the average value, SD represents the standard deviation, SD 'represents the standard deviation', λ represents the weighting coefficient, out low represents the data rejection lower limit, out high represents the data rejection upper limit, error low represents the Error lower limit, error high represents the Error upper limit, warning low represents the Warning lower limit, and Warning high represents the Warning upper limit.
In an alternative embodiment, clinical sample data collected from clinical samples were collected from the clinic and in-patient sero-biochemical test at 2021-2023 at 11 months. Wherein, the detection result of the specimen from 10 months at 2021 to 10 months at 2022 is used as an exercise data set for parameter setting and program establishment of the PBRTQC method; and (3) taking the detection result of the specimen at 11 months from 2022 to 2023 as a verification data set, and using the verification data set for performance verification and implementation effect evaluation of the PBRTQC real-time quality control program. The exercise data set is volumetrically distributed in accordance with the validation data set.
Furthermore, the data acquisition module is connected with a laboratory information management system and used for transmitting the data acquired from the laboratory information management system to the data filtering module. In this embodiment, when data is collected, the data is stored in a manner allowing real-time extraction. Care should be taken to cancel the patient name identification information in the data set when transferring the patient data to the data acquisition module. The data includes: basic information such as sample detection date and time (hour, minute and second), medical advice number, sample type, order department, clinical diagnosis information, patient age, sex, hospitalization number, test item name, detection result and unit and the like; the instrument information comprises reagent batch number, bottle number, indoor quality control, instrument alarm information, serum index and other information.
In an optional embodiment, optimizing the PBRTQC model includes: a grid search method similar to an exhaustion method is used for optimizing various PBRTQC algorithms, and the super-parameter combinations such as an alarm upper limit, an alarm lower limit, an alarm upper limit and an alarm lower limit are calculated in a centralized manner on the basis of a target false positive alarm rate (DFAR) in exercise data, so that a PBRTQC model with the best performance is obtained.
In an optional embodiment, the alarm rule of the PBRTQC system is set as follows:
a.1 3S :1 sample measurement value over
And (4) controlling the limit, and judging that the control is out of control.
b.
10 consecutive sample measurements fall on the mean side at the same time, suggesting a systematic error.
c.N 2S : simultaneous exceeding of N consecutive sample measurements
Or
Control limit, prompting for system error (N = NPed).
d. Analyzing the alarm reason: electrode aging, calibrator split charging deviation, reagent batch number replacement, pipeline micro-blockage, problems of not executing calibration or item calibration when replacing a new batch number reagent, and the like.
In an alternative embodiment, the performance of the PBRTQC model is verified by performing a bias detection graph simulation calculation on the verification data set. The method mainly comprises the following aspects of false positive alarm rate (FAR) and the number of samples (NPed) required by error detection, wherein the false positive alarm rate (FAR) is mainly used for evaluating the specificity at present, and the calculation formula is as follows:
FAR = false positive alarm number/total sample number or FAR = 1-specific;
evaluation sensitivity the number of samples required for error detection (NPed) is used, and when evaluating the sensitivity of the model, the data is generally divided (virtual days are constructed) to obtain a plurality of npeds, and the model is completely evaluated by calculating the average NPed (ANPed), the median NPed (MNPed) or the 95% site NPed (95 NPed).
In an optional embodiment, the method further comprises the following step of constructing a BRTQC medical technology sharing platform system: the PBRTQC sharing platform is negotiated and established by a medical inspection center and a cooperative manufacturer, and the PBRTQC sharing platform operates under the supervision of a health quality control center after the PBRTQC sharing platform is established. As shown in fig. 2, the BRTQC medical technology sharing platform includes:
a data transmission module: the system is used for accessing medical and technical information systems of hospitals in real time, actively collects inspection data and uploads the inspection data to the platform storage library, thereby being beneficial to improving the working quality control level of each hospital and effectively preventing the problems of data counterfeiting and incomplete patient data. Specifically, the data transmission module actively collects the data and returns the data to the platform management module, so that the platform management module does not need to intervene additionally, and the pressure of the platform can be relieved in a certain procedure by using the method.
A data sharing module: the doctor can obtain the previous examination data of the patient from the storage library of the platform through the module, on one hand, the problems that the previous examination data of the patient is lost, the diagnosis and treatment result is forgotten to be carried and the like can be solved, on the other hand, the phenomenon that the patient is repeatedly examined due to incomplete entity data can be eliminated, the working efficiency of the doctor is improved, and the burden of the patient is relieved.
The platform management module is connected with the data transmission module and used for receiving data actively collected by the data transmission module, the platform management module is connected with the data sharing module and used for maintaining data in the storage library, and besides, a manager uses the module to perform daily operation maintenance monitoring, such as permission setting of doctors and the like.
The monitoring process comprises the following steps:
1) And (3) quality management system: and performing IQC, instrument maintenance and the like according to the standardized management requirements of an ISO 15189 quality management system, wherein the IQC can detect clinical specimens on the same day after control.
2) Patient data statistics method: and (3) docking the detection result to a Remisol Advance system platform from a Laboratory Information System (LIS), and verifying and dynamically monitoring in real time by adopting an EWMA control chart.
The general calculation formula for the EWMA control chart is: y (r) = λ X (r) + (1- λ) Y (r-1), where X (r) is the value of the running batch r, Y (r) is the monitored value (average), λ is the weighting factor, 0< λ ≦ 1 (λ is usually chosen between the range 0.2-0.5, 0.2 by default).
3) Patient data normal distribution test analysis: and performing Kolmogorov-Smirnov test on the detection result through a patient data normal distribution automatic statistical system of a Remisol Advance system platform to judge whether the range of cutoff values included in the patient population with different biochemical projects is in accordance with normal distribution.
4) E, setting quality control parameters of the EWMA and establishing a program: the parameters comprise quality target setting, quality control rule setting, automatic data extraction, parameter setting, intelligent operation, performance verification, optimal PBRTQC method selection, efficiency evaluation, implementation operation and the like. The EWMA estimate for each test result included in the patient population, the cumulative Coefficient of Variation (CV) of the patient data was counted and compared to the precision quality standard; and performing trend correlation analysis by using the EWMA quality control diagram and an external quality control product L-J quality control diagram, confirming whether the reason, the variation trend and the time node of the early warning are consistent or not by field quality record and instrument information verification, and screening out the EWMA quality control parameter with the optimal performance.
5) Implementation of EWMA quality control program: and recording the early warning condition of the EWMA quality control program in the project detection and the corresponding condition of the IQC by using the optimal EWMA quality control parameter. If the early warning happens, the startup time IQC is observed, the quality control is repeated, the reason that the performance of the detection system is possibly influenced is checked through the record of the biochemical analyzer and the field quality record analysis, so that the true/false alarm is judged, and the true alarm is processed correspondingly in time.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A clinical laboratory data quality control system, comprising:
the data acquisition module is used for acquiring relevant data information of a laboratory;
the data pre-processing module is used for rejecting part of data;
the data filtering module is used for grouping the data and respectively establishing a PBRTQC model based on grouping results;
a data pre-processing module, the data pre-processing module comprising: the first sub-processing module is used for carrying out truncation processing on the grouped data; the second sub-processing module converts the data subjected to the truncation processing into relatively normal distribution through Box-Cox normalization conversion;
the calculation module is used for calculating the average value of the preprocessed data;
and the parameter setting module is used for calculating a control limit based on the preprocessed data and judging whether the control limit is exceeded.
2. The clinical laboratory data quality control system according to claim 1, wherein calculating a control limit comprises: the observation values are weighted differently by a weighted moving average method, and a moving average value is obtained by the weights.
3. The clinical laboratory data quality control system according to claim 1, wherein calculating a control limit comprises: calculating by using an EWMA method;
Out low＝Mean-4*SD
Error low＝Mean-3*SD’
Warning low＝Mean-2*SD’
Warning high＝Mean+2*SD’
Error high＝Mean+3*SD’
Out high＝Mean+4*SD
SD’＝SD*(λ/(2-λ))1/2
wherein Mean represents the average value, SD represents the standard deviation, SD 'represents the standard deviation', λ represents the weighting coefficient, out low represents the lower limit of data rejection, out high represents the upper limit of data rejection, error low represents the lower limit of Error, error high represents the upper limit of Error, warning low represents the lower limit of Warning, and Warning high represents the upper limit of Warning.
4. The clinical laboratory data quality control system according to claim 1, wherein said data collection module is connected to a laboratory information management system for collecting data obtained from the laboratory information management system, said data collection module is connected to said data filtering module for transmitting the collected data to the data filtering module, and when collecting data, the data is stored in a manner allowing real-time extraction.
5. The clinical laboratory data quality control system according to claim 4, wherein said collected data comprises an exercise data set for parameter setting and program setup of the PBRTQC method, a validation data set for performance validation and performance effectiveness evaluation of the PBRTQC real-time quality control program.
6. The clinical laboratory data quality control system according to claim 5, wherein said exercise data set is volumetrically distributed with said validation data set.
7. The clinical laboratory data quality control system according to claim 1, wherein if the control limit is exceeded, an alarm is issued, and the set alarm condition includes:
a.1 3s :1 sample measurement value over
A control limit is judged to be out of control;
b.
simultaneously dropping 10 continuous sample measured values on one side of the mean value to prompt a system error;
c.N 2s n consecutive sample measurements simultaneously exceed
Or
Control limit, prompt system error N = NPed.
8. The clinical laboratory data quality control system according to claim 1, wherein said control system is connected to a medical skill sharing platform system, said medical skill sharing platform system comprising:
the data transmission module is used for accessing medical and technical information systems of various hospitals in real time, actively acquiring inspection data and uploading the inspection data to the platform storage library;
the data sharing module is used for acquiring the previous examination data of the patient from the storage library of the platform;
the platform management module is connected with the data transmission module and used for receiving data actively acquired by the data transmission module, and the platform management module is connected with the data sharing module and used for maintaining the data in the storage library and monitoring daily operation and maintenance of the medical technology sharing platform.
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Application publication date: 20221011 |