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Assessing Length of Stay and Risk Factors Among Unplanned Pediatric Intensive Care Unit Admissions Following Surgery:
Abdullah Almutairi1,2, Tarek R Hazwani1,2,3, Ahmed Haroun M Mahmoud4,5,6
1Pediatric Critical Care, King Abdulaziz Medical City, Ministry of National Guard - Health Affairs, Riyadh, SAU.
Abstract:
Introduction Unplanned pediatric intensive care unit (PICU) admissions following surgery are associated with increased monitoring demands, resource utilization, and potential complications. While the Modified Pediatric Risk Prediction Score (PRPS) has been proposed to identify children at risk for ICU admission, its ability to predict postoperative pediatric (PICU) length of stay (LOS) remains uncertain. Methods This retrospective observational study included pediatric patients (>37 weeks gestational age to 14 years) with unplanned postoperative PICU admissions at King Abdullah Specialized Children's Hospital, Riyadh, from January 2019 to December 2023. Patients with cardiac surgery or pre-planned ICU beds were excluded. Demographic, perioperative, and clinical variables were analyzed. PRPS categories (low <10, intermediate 10-18) were compared using t-tests and chi-square tests to identify predictors of prolonged LOS (>12 days). Results A total of 102 patients were analyzed; 34 (33.3%) were infants (less than one year), and 58 (56.9%) were male. Neurosurgery 35 (34.3%), pediatric surgery 31 (30.4%), and otolaryngology (ENT) 26 (25.5%) were the most common specialties. No patients were classified as high-risk by PRPS. Mean LOS was 3.30 ± 6.56 days. PRPS category was not significantly associated with LOS (p = 0.44). Age (1-14 years), higher American Society of Anesthesiologists (ASA) status (≥ 3), and need for organ support were significantly associated with longer LOS (p < 0.05). Conclusion The Modified PRPS did not predict PICU length of stay among unplanned postoperative admissions, suggesting limited utility for estimating resource use. Age, ASA class, and organ support requirements were the main determinants of prolonged LOS. Incorporating intra- and postoperative factors into perioperative risk models may enhance prediction accuracy and optimize PICU bed allocation.