Avui, dijous 7 de maig, el Dipòsit Digital no estarà operatiu per tasques d'actualització. Disculpeu les molèsties.
Hoy, jueves 7 de mayo, el Dipòsit Digital no estará operativo debido a tareas de actualización. Disculpen las molestias.
Today, Thursday, May 7th, the Digital Repository will be unavailable due to a system update.

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 i 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, núm. 284. ISSN 1472-6947. [consulta: 8 de maig de 2026]. Disponible a: https://hdl.handle.net/2445/164267

Exportar metadades

JSON - METS

Compartir registre