A Bayesian decision support sequential model for severity of illness predictors and intensive care admissions in pneumonia

• Published in: BMC medical informatics and decision making Vol. 19; no. 1; p. 284
• Main Authors: Baez, Amado Alejandro, Cochon, Laila, Nicolas, Jose Maria
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 30-12-2019; BioMed Central; BMC
• Subjects: Analysis of Variance; Bacterial infections; Bayes Theorem; Bayesian analysis; Biomarkers; Biomarkers - blood; Clinical decision making; Community-Acquired Infections - classification; Conditional probability; Confidence intervals; Critical Care; Decision making; Decision Support Techniques; Diagnosis; Diagnostic systems; Female; Health aspects; Health informatics; Hospitalization - statistics & numerical data; Humans; Illnesses; Infections; Intensive care; Lactates; Lactic acid; Lactic Acid - blood; Likelihood ratio; Male; Medical research; Models, Statistical; Morbidity; Mortality; Nomograms; Patients; Pneumonia; Pneumonia - blood; Pneumonia - classification; Predictions; Probability; Procalcitonin; Procalcitonin - blood; Prognosis; Ratios; Sensitivity analysis; Sensitivity and Specificity; Severity of Illness Index; Statistical analysis; Statistical significance; Subgroups; Variance analysis; United States > US
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-019-1015-5

Community-acquired pneumonia (CAP) is one of the leading causes of morbidity and mortality in the USA. Our objective was to assess the predictive value on critical illness and disposition of a sequential Bayesian Model that integrates Lactate and procalcitonin (PCT) for pneumonia.
Sensitivity and specificity of lactate and PCT attained from pooled meta-analysis data. Likelihood ratios calculated and inserted in Bayesian/ Fagan nomogram to calculate posttest probabilities. Bayesian Diagnostic Gains (BDG) were analyzed comparing pre and post-test probability. To assess the value of integrating both PCT and Lactate in Severity of Illness Prediction we built a model that combined CURB65 with PCT as the Pre-Test markers and later integrated the Lactate Likelihood Ratio Values to generate a combined CURB 65 + Procalcitonin + Lactate Sequential value.
The BDG model integrated a CUBR65 Scores combined with Procalcitonin (LR+ and LR-) for Pre-Test Probability Intermediate and High with Lactate Positive Likelihood Ratios. This generated for the PCT LR+ Post-test Probability (POSITIVE TEST) Posterior probability: 93% (95% CI [91,96%]) and Post Test Probability (NEGATIVE TEST) of: 17% (95% CI [15-20%]) for the Intermediate subgroup and 97% for the high risk sub-group POSITIVE TEST: Post-Test probability:97% (95% CI [95,98%]) NEGATIVE TEST: Post-test probability: 33% (95% CI [31,36%]) . ANOVA analysis for CURB 65 (alone) vs CURB 65 and PCT (LR+) vs CURB 65 and PCT (LR+) and Lactate showed a statistically significant difference (P value = 0.013).
The sequential combination of CURB 65 plus PCT with Lactate yielded statistically significant results, demonstrating a greater predictive value for severity of illness thus ICU level care.