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Given the high burden of health care–associated infections (HAIs) in resource-limited settings, there is a tendency toward overdiagnosis/treatment. This study was designed to create an easy-to-use, dynamic, bedside risk stratification model for classifying children based on their risk of developing HAIs during their pediatric intensive care unit (PICU) stay, to aid judicious resource utilization.A prospective, observational cohort study was conducted in the 12-bed PICU of a large Indian tertiary care hospital between January and October 2011. A total of 412 consecutive admissions, aged 1 month to 12 years with PICU stay greater than 48 hours were enrolled. Independent predictors for HAIs identified using multivariate regression analysis were combined to create a novel scoring system. Performance and calibration of score were assessed using receiver operating characteristic curves and Hosmer-Lemeshow statistic, respectively. Internal validation was done.Age (< 5 years), Pediatric Risk of Mortality III (24 hours) score, presence of indwelling catheters, need for intubation, albumin infusion, immunomodulator, and prior antibiotic use (≥ 4) were independent predictors of HAIs. This model, with area under the ROC curve of 0.87, at a cutoff of 15, had a negative predictive value of 89.9% with overall accuracy of 79.3%. It reduced classification errors from 29.8% to 20.7%. All 7 predictors retained their statistical significance after bootstrapping, confirming the internal validity of the score.The “Pediatric Risk of Nosocomial Sepsis” score can reliably classify children into high- and low-risk groups, based on their risk of developing HAIs in the PICU of a resource-limited setting. In view of its high sensitivity and specificity, diagnostic and therapeutic interventions may be directed away from the low-risk group, ensuring effective utilization of limited resources.