• Singap Med J · May 2014

    Clinical characteristics and mortality risk prediction in critically ill children in Malaysian Borneo.

    • Indra Ganesan, Terrence Thomas, Fon En Ng, and Thian Lian Soo.
    • Nephrology Service, KK Women's and Children's Hospital, 100 Bukit Timah Road, Singapore 229899. Indra.Ganesan@kkh.com.sg.
    • Singap Med J. 2014 May 1; 55 (5): 261-5.

    IntroductionMortality risk prediction scores are important for benchmarking quality of care in paediatric intensive care units (PICUs). We aimed to benchmark PICU outcomes at our hospital against the Pediatric Index of Mortality 2 (PIM2) mortality risk prediction score, and evaluate differences in diagnosis on admission and outcomes between Malaysian and immigrant children.MethodsWe prospectively collected demographic and clinical data on paediatric medical patients admitted to the PICU of Sabah Women's and Children's Hospital in Kota Kinabalu, Sabah, Malaysia. The PIM2 risk score for mortality was tabulated.ResultsOf the 131 patients who met the inclusion criteria, data was available for 115 patients. The mean age of the patients was 2.6 ± 3.8 years, with 79% of the cohort aged less than five years. Patients were mainly of Kadazan (38%) and Bajau (30%) descent, and 26% of patients were non-citizens. Leading diagnoses on admission were respiratory (37%), neurological (18%) and infectious (17%) disorders. Out of the 29 patients who died, 23 (79%) were Malaysians and the main mortality diagnostic categories were respiratory disorder (22%), septicaemia (22%), haemato-oncological disease (17%) and neurological disorder (13%). Calculated standardised mortality ratios (SMRs) were not significantly > 1 for any patient category for variables such as age and admission diagnosis. However, infants less than two years old with comorbidities were significantly worse (SMR 2.61, 95% confidence interval 1.02-6.66).ConclusionThe patient profile at our centre was similar to that reported from other PICUs in Asia. The PIM2 score is a useful mortality risk prediction model for our population.

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