Prognostic factors of death in children admitted to pediatric intensive care unit, Cipto Mangunkusumo Hospital, Jakarta, Indonesia

Antonius Hocky Pudjiadi; Meri Yanti; Aland Roland Tumbelaka

doi:10.14238/pi42.6.2002.254-60

Antonius Hocky Pudjiadi Department of Child Health, University of Indonesia Medical School/Dr. Cipto Mangunkusumo Hospital, Jakarta
Meri Yanti Department of Child Health, University of Indonesia Medical School/Dr. Cipto Mangunkusumo Hospital, Jakarta
Aland Roland Tumbelaka Department of Child Health, University of Indonesia Medical School/Dr. Cipto Mangunkusumo Hospital, Jakarta

DOI: https://doi.org/10.14238/pi42.6.2002.254-60

Keywords: pediatric intensive care unit, PICU, probability of death, outcome assessment

Abstract

Background The primary goal of intensive care is to prevent mortality in patients with reversible critical illness, while preserving or improving functional outcome. It follows that the capability to estimate patient's risk of death is extremely important.

Objective To identify the prognostic factors of death, evaluate the probability of death by using Pediatric Index of Mortality (PIM) model, and develop the new model for predicting probability of death in children admitted to PICU in accordance with characteristic of patients in the study unit.

Methods Design Retrospective study. Setting Pediatric intensive care unit of Cipto Mangunkusumo Hospital, Jakarta, Indonesia. Patient Two hundreds and sixty five consecutive admissions, <18 years old, during one year period.

Results Logistic regression of 18 variables identified 6 prognostic factors of death (P<0.05): age, consciousness level, heart rate, platelet count, PaO₂/FiO₂, and use of mechanical ventilation at the first hour in PICU. PIM model predicted 17.9 deaths and this study model predicted 113.2 deaths from 200 subjects (56 died) in this study, with the area under ROC curve was 0.82 for PIM model and 0.83 for this study model.

Conclusion Both PIM model and this study model cannot predict mortality in this study unit accurately. It may due to the different characteristics between sample in this study and sample from which the PIM model was derived, or the lack of sample and variable in this study

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