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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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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\u2009+\u2009Procalcitonin + Lactate Sequential value.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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\u201320%]) 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 (<jats:italic>P<\/jats:italic>value\u2009=\u20090.013).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-019-1015-5","type":"journal-article","created":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T13:02:39Z","timestamp":1577710959000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Bayesian decision support sequential model for severity of illness predictors and intensive care admissions in pneumonia"],"prefix":"10.1186","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8399-9205","authenticated-orcid":false,"given":"Amado Alejandro","family":"Baez","sequence":"first","affiliation":[]},{"given":"Laila","family":"Cochon","sequence":"additional","affiliation":[]},{"given":"Jose Maria","family":"Nicolas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,30]]},"reference":[{"issue":"9","key":"1015_CR1","first-page":"698","volume":"94","author":"A Kaysin","year":"2016","unstructured":"Kaysin A, Viera AJ. 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