Background:In clinical laboratories,reagent depletion can significantly compromise the accuracy of K^(+),Na^(+),and Cl^(-)measurements,posing risks to patient safety.Patient-based real-time quality control(PBRTQC)has ...Background:In clinical laboratories,reagent depletion can significantly compromise the accuracy of K^(+),Na^(+),and Cl^(-)measurements,posing risks to patient safety.Patient-based real-time quality control(PBRTQC)has emerged as a valuable tool for early detection of analytical errors.However,the effectiveness of PBRTQC may be influenced using data processing methods.Among these,truncation,tail-retention,and tail-shrinkage are three commonly used data truncation techniques.Despite their potential,the clinical early-warning performance of PBRTQC with these methods under reagent depletion conditions remains unclear.Therefore,this study aims to evaluate the clinical early-warning efficacy of PBRTQC when integrated with these three data truncation methods(truncation,tail-retention,and tail-shrinkage)for K^(+),Na^(+),and Cl^(-)measurements during reagent depletion scenarios.Methods:A controlled experimental design was used with routine patient test results as the control group and simulated reagent depletion scenarios as experimental groups.PBRTQC was applied to integrated datasets processed by the three trun-cation methods.Alarm timeliness rates for detecting abnormal results were evaluated for K^(+),Na^(+),and Cl^(-).Results:Under internal standard solution depletion,the number of samples required for error detection(NPed)for K^(+)using the truncation,tail-retention,and tail-shrinkage methods was 38,10,and 10,respectively.For Na^(+),the NPed was 55,5,and 10,respectively,while for Cl^(-),it was 0,11 and 16,respectively.During diluent depletion,K^(+)and Na^(+)showed 0 with the truncation method but 10 for both the tail-retention and tail-shrinkage methods;Cl^(-)showed 0 with the truncation method but 11 with the tail-retention method and 16 with the tail-shrinkage method.With reference solution depletion,the NPed for K^(+)using the truncation,tail-retention,tail-shrinkage methods was 42,35,and 39,respectively;the NPed for Na^(+)was 135,25,and 25,respectively;and the NPed for Cl^(-)was 140,31,and 36,respectively.Conclusion:During a shortage of reagents,the effectiveness of early warning of the PBRTQC program was the highest with the tail-retention method,followed by the tail-shrinkage method.The truncation method showed the lowest effectiveness with the risk of missed error detection.Considering data variability,the tail-shrinkage method is recommended as the optimal data processing method.展开更多
Background:Patient-based real-time quality control(PBRTQC)has garnered increasing attention,yet false positive alerts are common in practical applications.In patients undergoing dialysis,serum potassium(K^(+))levels e...Background:Patient-based real-time quality control(PBRTQC)has garnered increasing attention,yet false positive alerts are common in practical applications.In patients undergoing dialysis,serum potassium(K^(+))levels exhibit large fluctuations before and after dialysis,often leading to false positive quality control alerts in routine PBRTQC applications.We aimed to reduce false positive alerts in PBRTQC applications by distinguishing between the test results of dialysis and non-dialysis patients and constructing separate PBRTQC models.Methods:We collected K^(+)test results from 362,077 patients at our center from September 2023 to September 2024.The data were divided into dialysis,physical examination,and non-dialysis groups,with data from September 2023 to February 2024 comprising the training set.We constructed PBRTQC models for dialysis patients(n=3217),those undergoing physical examination(n=7339),and non-dialysis patients(n=153,565)using four statistical methods:moving median,moving average,weighted moving average,and exponentially weighted moving average.We validated the three models using data from the dialysis group(validation set 1)from March to September 2024 and the non-dialysis group(validation set 2)from March to April 2024.By comparing false positive rates,the average number of patient results affected prior to error detection or median number of patient results affected prior to error detection,and the average probability of error detection in the three models,we evaluated whether the pre-classified PBRTQC model can reduce the false positive rate of K^(+).Results:Statistical analysis revealed significant differences among the dialysis,physical examination,and non-dialysis groups(p<0.001).Based on the minimum sum of the false positive rate,false negative rate,and average number of patient results affected prior to error detection,the models for the dialysis and non-dialysis groups used the exponentially weighted moving average;the MM method was used in the physical examination group.Validation set 1 showed false positive rates of 69.257% for the physical examination group,1.143% for the dialysis group,and 35.675%for the non-dialysis group.According to the total allowable error(TEA),the median number of patient results affected prior to error detection in the dialysis group(1/2TEA,positive:307.30,negative:795.20)was higher than that in the physical examination group(1/2TEA,positive:10.57,negative:4.67)and non-dialysis group(1/2TEA,positive:24.57,negative:29.57).The average probability of error detection in the dialysis group(1/2TEA,positive:2.83%,negative:0.67%)was lower than that in the physical examination group(1/2TEA,positive:41.47%,negative:45.11%)and non-dialysis group(1/2TEA,positive:16.00%,negative:18.00%).In validation sets 2 and 3,the false positive rate for the non-dialysis group and physical examination group was 1.906%and 2.83%,respectively.This indicates that pre-classifying dialysis specimens can significantly reduce the occurrence of false positives.Additionally,K^(+)results in the non-dialysis group exhibited notable seasonal variations.Conclusions:Establishing PBRTQC models through pre-classification of dialysis patients can significantly lower the false positive rate of K^(+),enhancing the accuracy of real-time monitoring for laboratory testing systems.展开更多
文摘Background:In clinical laboratories,reagent depletion can significantly compromise the accuracy of K^(+),Na^(+),and Cl^(-)measurements,posing risks to patient safety.Patient-based real-time quality control(PBRTQC)has emerged as a valuable tool for early detection of analytical errors.However,the effectiveness of PBRTQC may be influenced using data processing methods.Among these,truncation,tail-retention,and tail-shrinkage are three commonly used data truncation techniques.Despite their potential,the clinical early-warning performance of PBRTQC with these methods under reagent depletion conditions remains unclear.Therefore,this study aims to evaluate the clinical early-warning efficacy of PBRTQC when integrated with these three data truncation methods(truncation,tail-retention,and tail-shrinkage)for K^(+),Na^(+),and Cl^(-)measurements during reagent depletion scenarios.Methods:A controlled experimental design was used with routine patient test results as the control group and simulated reagent depletion scenarios as experimental groups.PBRTQC was applied to integrated datasets processed by the three trun-cation methods.Alarm timeliness rates for detecting abnormal results were evaluated for K^(+),Na^(+),and Cl^(-).Results:Under internal standard solution depletion,the number of samples required for error detection(NPed)for K^(+)using the truncation,tail-retention,and tail-shrinkage methods was 38,10,and 10,respectively.For Na^(+),the NPed was 55,5,and 10,respectively,while for Cl^(-),it was 0,11 and 16,respectively.During diluent depletion,K^(+)and Na^(+)showed 0 with the truncation method but 10 for both the tail-retention and tail-shrinkage methods;Cl^(-)showed 0 with the truncation method but 11 with the tail-retention method and 16 with the tail-shrinkage method.With reference solution depletion,the NPed for K^(+)using the truncation,tail-retention,tail-shrinkage methods was 42,35,and 39,respectively;the NPed for Na^(+)was 135,25,and 25,respectively;and the NPed for Cl^(-)was 140,31,and 36,respectively.Conclusion:During a shortage of reagents,the effectiveness of early warning of the PBRTQC program was the highest with the tail-retention method,followed by the tail-shrinkage method.The truncation method showed the lowest effectiveness with the risk of missed error detection.Considering data variability,the tail-shrinkage method is recommended as the optimal data processing method.
文摘Background:Patient-based real-time quality control(PBRTQC)has garnered increasing attention,yet false positive alerts are common in practical applications.In patients undergoing dialysis,serum potassium(K^(+))levels exhibit large fluctuations before and after dialysis,often leading to false positive quality control alerts in routine PBRTQC applications.We aimed to reduce false positive alerts in PBRTQC applications by distinguishing between the test results of dialysis and non-dialysis patients and constructing separate PBRTQC models.Methods:We collected K^(+)test results from 362,077 patients at our center from September 2023 to September 2024.The data were divided into dialysis,physical examination,and non-dialysis groups,with data from September 2023 to February 2024 comprising the training set.We constructed PBRTQC models for dialysis patients(n=3217),those undergoing physical examination(n=7339),and non-dialysis patients(n=153,565)using four statistical methods:moving median,moving average,weighted moving average,and exponentially weighted moving average.We validated the three models using data from the dialysis group(validation set 1)from March to September 2024 and the non-dialysis group(validation set 2)from March to April 2024.By comparing false positive rates,the average number of patient results affected prior to error detection or median number of patient results affected prior to error detection,and the average probability of error detection in the three models,we evaluated whether the pre-classified PBRTQC model can reduce the false positive rate of K^(+).Results:Statistical analysis revealed significant differences among the dialysis,physical examination,and non-dialysis groups(p<0.001).Based on the minimum sum of the false positive rate,false negative rate,and average number of patient results affected prior to error detection,the models for the dialysis and non-dialysis groups used the exponentially weighted moving average;the MM method was used in the physical examination group.Validation set 1 showed false positive rates of 69.257% for the physical examination group,1.143% for the dialysis group,and 35.675%for the non-dialysis group.According to the total allowable error(TEA),the median number of patient results affected prior to error detection in the dialysis group(1/2TEA,positive:307.30,negative:795.20)was higher than that in the physical examination group(1/2TEA,positive:10.57,negative:4.67)and non-dialysis group(1/2TEA,positive:24.57,negative:29.57).The average probability of error detection in the dialysis group(1/2TEA,positive:2.83%,negative:0.67%)was lower than that in the physical examination group(1/2TEA,positive:41.47%,negative:45.11%)and non-dialysis group(1/2TEA,positive:16.00%,negative:18.00%).In validation sets 2 and 3,the false positive rate for the non-dialysis group and physical examination group was 1.906%and 2.83%,respectively.This indicates that pre-classifying dialysis specimens can significantly reduce the occurrence of false positives.Additionally,K^(+)results in the non-dialysis group exhibited notable seasonal variations.Conclusions:Establishing PBRTQC models through pre-classification of dialysis patients can significantly lower the false positive rate of K^(+),enhancing the accuracy of real-time monitoring for laboratory testing systems.
