Predictors of mortality in a paediatric intensive care unit in Kigali, Rwanda

Abstract

The observed mortality rate was in the range reported in other resource-limited settings. The initial attempt to create and implement a risk of mortality tool for this setting determined a score that could identify those patients at higher risk of mortality. In PICUs in resource-limited settings, the gathering of data and use of severity of illness tools could improve care in a number of ways.

Desempenho do Pediatric Risk of Mortality (PRISM) e Pediatric Index of Mortality 2 (PIM2) em unidade de terapia intensiva pediátrica terciária na Amazônia brasileira

“…A população estudada apresentou distribuição por faixa etária semelhante às demais séries 16,17,18,19,20,21 . Em relação ao diagnóstico admissional, foram observadas divergências nos achados em relação a outros estudos 17,22,23,24,25,26 ; nesses, os principais diagnósticos foram doenças respiratórias e neurológicas, enquanto que, neste estudo, as principais doenças foram infecciosas, seguidas de doenças respiratórias e neurológicas.…”

“…Muitos autores relataram que o escore PRISM superestima 3,16,17,18 a mortalidade e que não apresenta boa calibração e discriminação em populações específicas 19,20,21 . No presente estudo, o PRISM apresentou boa capacidade de calibração e discriminação, apesar de ter subestimado a mortalidade, corroborando com outros estudos 19,20,21,27,28,29,30 .…”

Good-quality research: a vital step in improving outcomes in paediatric intensive care units in low- and middle-income countries

“…The article by Nyirasafari et al [10] in this issue of Paediatrics and International Child Health, describing mortality in a paediatric intensive care unit in Kigali, Rwanda and highlighting the challenges faced by staff, adds important information to this research area and discusses how the data could be used to improve outcomes. Collecting data even on basic measures to arrive at a score and determine outcomes in paediatric intensive care units (PICU) may result in better adherence to national guidelines [11,12] and can help in comparing units, adjusting for their individual patient populations.…”

“…Collecting data even on basic measures to arrive at a score and determine outcomes in paediatric intensive care units (PICU) may result in better adherence to national guidelines [11,12] and can help in comparing units, adjusting for their individual patient populations. Severity of illness and risk scores make these comparisons and risk-adjustments more straightforward but the authors, along with others, have found that risk scores developed and validated in high-income settings often need to be changed or modified to be applied in LMIC [10,15]. They created a modified paediatric risk of mortality (PRISM) score which had a range of 0-30 and used only 5 of 17 components of PRISM, with a cut-off at 5 for severity; thus children would have had only one or two components with severely deranged physiology to satisfy the PRISM criteria.…”

“…Training staff as teams can also help bring about positive changes in clinical environments and improve communication [23]. But, despite evidence for improved outcomes with protocols and algorithms for care, the study by Nyirasafari et al highlights that their PICU was unable to adopt them, possibly because the unit was new or because of the patient burden combined with limited staff and resources [10]. It was also notable that there was a large number of children on vaso-active drugs, possibly because of an algorithm incorporating aggressive fluid resuscitation despite the modest number with indications on admission for fluidbolus therapy.…”