Comparison of Paediatric Index of Mortality 3, Paediatric Risk of Mortality III, Paediatric Logistic Organ Dysfunction-2 for Assessing Patient Mortality: A Prospective Observational Study - SV Kishore; Anil Kumar Mohanty (Journal of Clinical and Diagnostic Research, 2022)
Introduction: Numerous scoring systems have been proposed in an effort to increase the prognostic accuracy and predicting outcome. In order to measure the risk of mortality, scores are employed that establish a numerical scale and in this way, they compare estimated mortality in % with the observed mortality. Known as prognostic scores, these can be used to evaluate the quality of medical care and to optimise the employment of resources, aiming at improving the cost-benefit relationship. Since, they compare mortality adjusted by disease severity these scores can also be used for comparisons between clinical trials and for planning technological resources. Aim: To compare the performance of the Paediatric Risk of Mortality III (PRISM III), the Paediatric Index of Mortality 3 (PIM 3) and Paediatric Logistic Organ Dysfunction-2 (PELOD-2) scores in a Paediatric Intensive Care Unit (PICU) in a tertiary care hospital. Materials and Methods: The present study was prospective observational study which included children from one month to 14 years of age admitted to PICU, and who remained in PICU after 24 hours. Within the first hour of admission PIM 3 was assessed. Further at 24 hours of admission, PRISM III and PELOD-2 score were assessed. Performance of different scores were evaluated. Calibration by HosmerLemeshow goodness-of-fit test {χ2(p)} Discrimination was assessed by the ROC curve. Standardised Mortality Rate (SMR) was calculated to predict the mortality. Results: Total 281 children were enrolled in the study, out of which 62 patients died. Neurological illness was the most common cause of death (12, 19.35%) followed by respiratory and haemato-onco cases (10, 16.13%) each. The Area Under the ROC Curve-Receiver Operating Characteristics (AUC-ROC) of PELOD-2, PIM 3 and PRISM III were 0.862, 0.847 and 0.838, respectively. Among the three scores PELOD-2 had poor calibration for the study population (χ2=18.837, p=0.016, d=8). PIM 3 was a better predictor of mortality (with SMR of 1.33) when compared with PRISM III and PELOD-2 (which had SMR of 1.57 and 1.83, respectively). Conclusion: All the three scores had good discrimination, however PELOD-2 had poor calibration for the given study population, with respect to better predictor of mortality all the scores underestimate the mortality. Among these, the better predictor mortality was PIM 3. Since, PIM 3 also had good calibration for the study population and is associated with less variables to monitor there is ease of estimation and hence it is more suitable to score and to assess mortality.