November 7, 2019

How to identify lab tests of low effectiveness?

Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests

A new approach to understand the size of useless lab tests is to apply: "regularized logistic regression, regress and round, naive Bayes, neural network multilayer perceptrons, decision tree, random forest, AdaBoost, and XGBoost". This means that machine learning has its own space in laboratory medicine. In the article, they show high level of prediction for useless tests.
The best performing machine learning models predicted normal results with an AUROC of 0.90 or greater for 12 stand-alone laboratory tests (eg, sodium AUROC, 0.92 [95%CI, 0.91-0.93]; sensitivity, 98%; specificity, 35%; PPV, 66%; NPV, 93%; lactate dehydrogenase AUROC, 0.93 [95%CI, 0.93-0.94]; sensitivity, 96%; specificity, 65%; PPV, 71%; NPV, 95%; and troponin I AUROC, 0.92 [95%CI, 0.91- 0.93]; sensitivity, 88%; specificity, 79%; PPV, 67%; NPV, 93%) and 10 common laboratory test components (eg, hemoglobin AUROC, 0.94 [95%CI, 0.92-0.95]; sensitivity, 99%; specificity, 17%; PPV, 90%; NPV, 81%; creatinine AUROC, 0.96 [95%CI, 0.96-0.97]; sensitivity, 93%; specificity, 83%; PPV, 79%; NPV, 94%; and urea nitrogen AUROC, 0.95 [95%CI, 0.94, 0.96]; sensitivity, 87%; specificity, 89%; PPV, 77%; NPV 94%).
 This approach goes further than this book: