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A Bayesian decision support sequential model for severity of illness predictors and intensive care admissions in pneumonia
dc.contributor.author | Báez, Amado Alejandro | |
dc.contributor.author | Cochon, Lila | |
dc.contributor.author | Nicolás, José María | |
dc.date.accessioned | 2020-06-12T22:06:58Z | |
dc.date.available | 2020-06-12T22:06:58Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Báez AA, Nicolás JM, Cochon L. A Bayesian decision support sequential model for severity of illness predictors and intensive care admissions in pneumonia. BMC Medical Informatics and Decision Making (2019) 19:284 Disponible en: https://doi.org/10.1186/s12911-019-1015-5 | en_US |
dc.identifier.uri | https://repositorio.unphu.edu.do/handle/123456789/2639 | |
dc.description.abstract | 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. | en_US |
dc.language.iso | es | en_US |
dc.publisher | BMC Medical Informatics and Decision Making | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | Neumonía | en_US |
dc.subject | Estados Unidos | en_US |
dc.title | A Bayesian decision support sequential model for severity of illness predictors and intensive care admissions in pneumonia | en_US |
dc.type | Article | en_US |