Blood Cultures and Molecular Diagnostics in Intensive Care Units to Diagnose Sepsis: A Bayesian Latent Class Model Analysis

BACKGROUND: Confirmation of sepsis by standard blood cultures (STD) is often inconclusive due to slow growth and low positivity. Molecular diagnostics (MOL) are faster and may have higher positivity, but test performance can be inaccurately estimated if STD methods are used as comparators. Bayesian latent class models (LCMs) can evaluate diagnostic methods when there is no “gold standard.” Intensive care unit studies that have used LCMs to combine and compare STD and MOL method performance and estimate the prevalence of sepsis have not been described. PATIENTS AND METHODS: Results from an ICU sepsis study that used both tests simultaneously were analyzed. Bayesian LCMs combined prior prevalence of sepsis, prior diagnostic characteristics of the two methods, and the study results to estimate the posterior prevalence and diagnostic characteristics. Sensitivity analyses were performed using objective (published studies) and subjective (expert opinion) prior parameters. Positive predictive values (PPVs) of the prevalence of sepsis were estimated for all combinations of test results. RESULTS: The range of posterior estimates was: sepsis prevalence (0.38–0.88), sensitivities (STD: 0.2–0.35, MOL: 0.56–0.86), and specificities (STD: 0.87–0.99, MOL: 0.72–0.95). The PPV (sepsis) of both tests being positive was (0.72–0.99). CONCLUSION: LCMs combined two imperfect methods to estimate prevalence, PPV, and diagnostic characteristics. The posterior estimates (STD sensitivity < MOL and STD specificity > MOL) seem to reflect the clinical experience appropriately. The high PPV when both methods show positive results can be useful for ruling in disease. HOW TO CITE THIS ARTICLE: Sampath S, Baby J, Krishna B, Dendukuri N, Thomas T. Blood Cultures and Molecular Diagnostics in Intensive Care Units to Diagnose Sepsis: A Bayesian Latent Class Model Analysis. Indian J Crit Care Med 2021;25(12):1402–1407.