Early mortality prediction in pediatric patients undergoing cardiac surgery at a pediatric hospital in Lima, Peru
DOI:
https://doi.org/10.58597/rpe.v4i4.134Keywords:
Cardiac Surgery, Child, Mortality, Cardiopulmonary Bypass, Quality ControlAbstract
Objetive: To develop and validate an integrated prediction model for early mortality incorporating preoperative, intraoperative, and postoperative clinical variables in a national referral center in Peru. Methods: We conducted a retrospective analytical cohort study including all patients under 18 years undergoing their first cardiac surgery with cardiopulmonary bypass between 2001 and 2020. Early mortality was defined as death from any cause within 30, 90, or 120 days after surgery. Candidate predictors were selected through literature review and bivariate analysis (p < 0.20). Robust Poisson multivariate models were developed for the preoperative, intraoperative, and postoperative periods after evaluating the adequacy of the models. Model performance was evaluated using the c-statistic, Nagelkerke R², Hosmer–Lemeshow test, and calibration plots. Results: A total of 1,759 patients were analyzed after excluding deaths within the first 48 hours. Mortality at 30, 90, and 120 days was 3.5%, 5.1%, and 5.5%, respectively. Preoperative predictors included low weight, cardiac defect type, higher RACHS-1 category, and pulmonary hypertension. Intraoperative predictors included longer cardiopulmonary bypass duration, longer aortic cross-clamp time, and intraoperative complications. Key postoperative predictors were open chest, low cardiac output syndrome, cardiac arrest, major infection, peritoneal dialysis, and surgical bleeding. The postoperative model showed the best discrimination (c ≈ 0.93), with adequate calibration across all time points. Conclusions: We developed and validated a comprehensive early mortality prediction model for pediatric cardiac surgery with strong discrimination and good calibration. This tool may enhance quality monitoring, facilitate benchmarking across centers, optimize resource allocation, and support clinical and managerial decision-making.
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Copyright (c) 2025 Luis Eduardo Silva-Delgado, Katherine Milagros Silva-Delgado, Eduardo Wilfredo Silva-Rivera
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