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Evidence-based approach for the generation of a multivariate logistic regression model that predicts instrument
Stephan L Cleveland1, Carol A Carman1, Niti Vyas1
1Department of Clinical Laboratory Sciences, School of Health Professions, University of Texas Medical Branch, Galveston, TX, US.
Objective:
Identification of instrument failure (IF) represents a point to improve the quality of services provided by medical laboratories. Here, a logistic regression model was created to define the relationship between instrument downtime and laboratory quality management systems.
Methods:
Interval-level quality control (QC) and categorical quality assurance data from 3 identical chemistry analyzers was utilized to generate a logistic regression model able to predict IF. A case-control approach and the forward stepwise likelihood-ratio method was used to develop the logistic regression model. The model was tested using a case-control dataset and again using the complete sample.
Results:
A total of 650 downtime events were identified. A total of 22,880 QC data points, 187 calibrations, 24 proficiency testing events, and 107 maintenance records were analyzed. The regression model was able to correctly predict 59.2% of no instrument downtime events and 69.2% of instrument downtime events using the case-control data. Using the entire data set, the sensitivity of the model was 69.2% and the specificity was 58.2%.
Conclusion:
A logistic regression model can predict instrument downtime nearly 70% of the time. This study acts as a proof of concept using a limited data set collected by the chemistry laboratory.
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