Accuracy of Pediatric Risk of Mortality (PRISM) III Score in Predicting Mortality Outcomes in a Pediatric Intensive Care Unit in Karachi.

Abstract

BackgroundWith the advancements in medicine and increasing access to modern technology, pediatric intensive care units (PICU) are becoming a vital part of any health care setting. PICUs play a key role in saving the life of young patients. Various scales have been designed by researchers to aid in predicting the mortality of a patient admitted in PICU. Pediatric Risk of Mortality (PRISM) and Pediatric Index of Mortality (PIM) are among the most commonly used scales. Calculating the risk of mortality enables the physicians to classify the patients and helps in identifying which patients require more urgent care and resources. MethodsA hospital-based prospective study was carried out at PICU in a tertiary care hospital in Karachi from December 2017 to June 2019. All patients between the age of one month and 12 years were included in our study after informed consent from parents/guardians. A standard questionnaire was used and the PRISM III score was calculated at 24 hours of admission. All necessary investigations were carried out, and all statistical analyses were carried out using SPSS v.23 (IBM, Armonk, NY).ResultsA total of 407 patients were included in our study with the majority being males (54.5%). The mean age was 27±33 months. The mean duration of stay of patients in PICU was 80.15±36.58 hours. The mortality rate in our study was 37.35 % (n=152). The need for mechanical ventilation, use of inotropic drugs, higher temperatures, and low Glasgow Coma Scale scores were associated with poor survival. It was noted that as the PRISM III score increased, the mortality rate also increased. In our study, we found that PRISM III had good predictive power in our population. The area under the curve was 0.903±0.016 (p<0.001, 95% confidence interval: 0.872-0.934).ConclusionsPRISM III score showed excellent accuracy and predictive ability in our population. There was no significant difference in observed and expected mortality rates in our study. In a resource-limited setting, the prediction models highlight the cases where more medical attention is required and also enable the physicians to assess the prognosis of the patient so adequate measures can be taken beforehand.