Performance of Brazilian Pediatric Risk of Severity Model for Illness (Brprism) Compared to Pediatric Index of Mortality and Pediatric Risk of Mortality 2 - Mangia, C. M. F.; Toledo, M. D.; Rossi, R.; Nakano, E. Y.; Carneluti, A.; Kopelman, B. I.; Carvalho, W. B.; Andrade, M. C. (European Journal of Applied Sciences, 2023)
Introduction: The best prognosis score with which to evaluate high-risk patients upon admission into pediatric intensive care is not well established in resource-limited settings. The objective of study was to formulate a risk-of-illness severity model for pediatric mortality to be applied upon PICU admission in resource-limited settings. Methods: Our study was designed to develop an illness severity index and a prognostic model for critically ill children. A prospective, observational multicenter pilot study, performed between February 1995 and October 1999, evaluated the variables, methodology and statistical techniques for the development of a model. A single-center prospective cohort study, performed between November 1999 and October 2004, collected information from consecutive admissions into the Pediatric Intensive Care Unit (PICU) at a high-complexity university, teaching, and reference hospital in São Paulo, Brazil. Results: In the pilot study, 1,459 patients (a PICU mortality rate of 16%) were included, and in the second study, 1,033 patients (a PICU mortality rate of 13.9% and a hospital mortality rate of 6.9% after PICU discharge) were included. We used multivariable regression to determine two probabilistic models; the first addressed survival and the overall probability of death (hospital plus PICU deaths), and the second was conditional (i.e., PICU death). An illness severity index stratified these probabilities into three risk strata: low-, medium- and high-risk patients. In the final step, the new death probabilities were estimated using a Bayesian adjustment. Conclusions: The model estimates three probabilities (survival, death in the PICU and death in the hospital after PICU discharge) stratified into three risk categories. To the best of our knowledge, this is the first study using a Bayesian adjustment to determine a prognosis and illness severity, and it should enable us to make therapeutic adjustments and provide appropriate counseling for high-risk patients in resource-limited settings.