Amb motiu del tancament d'estiu, la validació de documents es reprendrà a partir del 28 d'agost de 2026. Disculpeu les molèsties.
Con motivo del cierre de verano, la validación de documentos se reanudará a partir del 28 de agosto de 2026. Disculpad las molestias
Due to the summer closure, document validation will resume starting August 28, 2026. We apologize for any inconvenience.

Fitxers

694459.pdf (1.68 MB)

Tipus de document

Article

Versió

Versió publicada

Data de publicació

2019-12-30

Llicència de publicació

cc-by (c) Baez, Amado Alejandro et al., 2019
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/164267

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

Títol de la revista

Autors

Director/Tutor

ISSN de la revista

Títol del volum

Recurs relacionat

https://doi.org/10.1186/s12911-019-1015-5

Resum

BACKGROUND: 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.

Matèries

Pneumònia adquirida a la comunitat, Morbiditat, Lactones, Estadística bayesiana

Matèries (anglès)

Community-acquired pneumonia, Morbidity, Lactones, Bayesian statistical decision

Citació

Col·leccions

Articles publicats en revistes (Medicina)

Citació

BAEZ, Amado Alejandro, COCHON, Laila and NICOLÁS ARFELIS, Josep Maria. A Bayesian decision support sequential model for severity of illness predictors and intensive care admissions in pneumonia. BMC Medical Informatics and Decision Making. 2019. Vol. 19, num. 284. ISSN 1472-6947. [consulted: 17 of August of 2026]. Available at: https://hdl.handle.net/2445/164267

Exportar metadades

JSON - METS

Compartir registre