A Bayesian decision support sequential model for severity of illness predictors and intensive care admissions in pneumonia
Date
2019Author
Báez, Amado Alejandro
Cochon, Lila
Nicolás, José María
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Show full item recordAbstract
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.
Methods: 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.
Results: 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).
Conclusions: 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.
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