Maureen L Sampson1, Verena Gounden2, Hendrik E van Deventer3, Alan T Remaley4. 1. Department of Laboratory Medicine, National Institutes of Health Clinical Center, Building 10, Room 2C-407, 9000 Rockville Pike, Bethesda, MD 20892, USA. Electronic address: msampson@cc.nih.gov. 2. Department of Laboratory Medicine, National Institutes of Health Clinical Center, Building 10, Room 2C-407, 9000 Rockville Pike, Bethesda, MD 20892, USA. Electronic address: verena.gounden@nhls.ac.za. 3. Department of Laboratory Medicine, National Institutes of Health Clinical Center, Building 10, Room 2C-407, 9000 Rockville Pike, Bethesda, MD 20892, USA. Electronic address: manuel.vandeventer@gmail.com. 4. Department of Laboratory Medicine, National Institutes of Health Clinical Center, Building 10, Room 2C-407, 9000 Rockville Pike, Bethesda, MD 20892, USA. Electronic address: aremaley1@nhlbi.nih.gov.
Abstract
OBJECTIVE: The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. METHOD: Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. RESULTS: The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. CONCLUSION: A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.
OBJECTIVE: The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. METHOD: Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. RESULTS: The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. CONCLUSION: A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.
Entities:
Keywords:
Average of normals; Laboratory test errors; Logistic regression; Quality control
Authors: W Greg Miller; Aybala Erek; Tina D Cunningham; Olajumoke Oladipo; Mitchell G Scott; Robert E Johnson Journal: Clin Chem Date: 2010-11-19 Impact factor: 8.327