Sigma metric analysis of quality indicators across the testing process as an effective tool for the evaluation of laboratory performance.

Background: Laboratories across the world are successfully using quality indicators (QIs) to monitor their performance. We aimed to analyze the effectiveness of using the peer group comparison and statistical tools such as sigma metrics for periodic evaluation of QIs and identify potential errors in the preanalytical, analytical, and postanalytical phases. Methods: We evaluated the monthly QIs for 1 year. A total of 11 QIs were evaluated across the three phases of the total testing process, using percentage variance, and sigma metric analysis. Results: Our study observed that based on sigma metric analysis, the performance was good for all the QIs except for the number of samples with the inappropriate specimen hemolyzed samples, clotted samples, and turnaround time (Sigma value < 3). The percentage variance of QIs in all the phases was plotted in a Pareto chart, which helped us in identifying turnaround time and internal quality control performance are the key areas that contribute to almost 80% of the errors among all the QIs. Conclusion: Laboratory performance evaluation using QIs and sigma metric analysis helped us in identifying and prioritizing the corrective actions in the key areas of the total testing process.

Comparative Assessment of ADA, IDRS, and FINDRISC in Predicting Prediabetes and Diabetes Mellitus in South Indian Population.

Introduction Diabetes risk-screening tools are validated and implemented across various countries. There is a need for improvement in these risk scores with suitable modifications so as to make them more sensitive, specific, and suitable to the local population. Objectives The aim of this study was to evaluate and compare the diagnostic accuracy and clinical utility of the Indian diabetes risk score (IDRS), the American diabetic association (ADA) risk score, and the Finnish Diabetes Risk Score in healthy subjects of South Indian origin in predicting the risk of diabetes and to correlate these risk scores with the blood glucose and hemoglobin A1c (HbA1c) levels in the study population. Materials and Methods A total of 160 subjects attending the master health checkup/outpatient department of a tertiary care hospital were included in the study. Each subject was asked to fill a questionnaire. Details obtained using the questionnaire were assessed as per the three diabetic risk scores. Fasting blood sugar/random blood sugar and HbA1c were estimated. Statistical Analysis Used Data analysis was done using SPSS 22/23. Pearson correlation was used to compare continuous variables, with p < 0.05 considered statistically significant. Sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and Mitchell's clinical utility indices were calculated for each risk tool. Results We found the prevalence of diabetes to be 11.9%. ADA risk score was the only risk score that showed a statistically significant difference ( p -value = 0.05) between the low- and high-risk subjects. Conclusions ADA or IDRS risk scores can be used for screening diabetes in the South Indian population. We suggest that inclusion of the history of gestational diabetes and hypertension in the IDRS risk score might improve its sensitivity as a screening tool in our local population.