Predicting mortality in sick African children: the FEAST Paediatric Emergency Triage (PET) Score

Abstract

BackgroundMortality in paediatric emergency care units in Africa often occurs within the first 24 h of admission and remains high. Alongside effective triage systems, a practical clinical bedside risk score to identify those at greatest risk could contribute to reducing mortality.MethodsData collected during the Fluid As Expansive Supportive Therapy (FEAST) trial, a multi-centre trial involving 3,170 severely ill African children, were analysed to identify clinical and laboratory prognostic factors for mortality. Multivariable Cox regression was used to build a model in this derivation dataset based on clinical parameters that could be quickly and easily assessed at the bedside. A score developed from the model coefficients was externally validated in two admissions datasets from Kilifi District Hospital, Kenya, and compared to published risk scores using Area Under the Receiver Operating Curve (AUROC) and Hosmer-Lemeshow tests. The Net Reclassification Index (NRI) was used to identify additional laboratory prognostic factors.ResultsA risk score using 8 clinical variables (temperature, heart rate, capillary refill time, conscious level, severe pallor, respiratory distress, lung crepitations, and weak pulse volume) was developed. The score ranged from 0–10 and had an AUROC of 0.82 (95 % CI, 0.77–0.87) in the FEAST trial derivation set. In the independent validation datasets, the score had an AUROC of 0.77 (95 % CI, 0.72–0.82) amongst admissions to a paediatric high dependency ward and 0.86 (95 % CI, 0.82–0.89) amongst general paediatric admissions. This discriminative ability was similar to, or better than other risk scores in the validation datasets. NRI identified lactate, blood urea nitrogen, and pH to be important prognostic laboratory variables that could add information to the clinical score.ConclusionsEight clinical prognostic factors that could be rapidly assessed by healthcare staff for triage were combined to create the FEAST Paediatric Emergency Triage (PET) score and externally validated. The score discriminated those at highest risk of fatal outcome at the point of hospital admission and compared well to other published risk scores. Further laboratory tests were also identified as prognostic factors which could be added if resources were available or as indices of severity for comparison between centres in future research studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-015-0407-3) contains supplementary material, which is available to authorized users.

Prevalence, aetiology, treatment and outcomes of shock in children admitted to Kenyan hospitals

“…However, the diagnoses we report are almost exclusively made on the basis of clinical signs interpreted often by clinicians with limited experience. Typically, measures of blood pressure and oxygen saturation are not available and neither are microbiological investigations of those for inflammatory markers [23]. Furthermore, although we had access to discharge diagnoses, it is possible that shock emerged during the admission but these diagnoses were not recorded.…”

Symptom- and Laboratory-Based Ebola Risk Scores to Differentiate Likely Ebola Infections

“…We used previously established methods ( 13 , 25 ) to develop an easy-to-use Ebola symptom-based risk (ESR) score using the selected model. First, we ran a multivariable logistic regression of the model against EVD.…”

“…Such risk scores are especially useful in resource-limited settings because a range of persons, from community health workers to clinicians, can quickly obtain the necessary patient information ( 9 , 10 ). Symptom-based risk scores have been used to identify patients at higher risk of various diseases (e.g., pulmonary tuberculosis, gastric cancer) or outcomes (e.g., predicting mortality in sick children) in resource-limited settings ( 11 – 13 ). However, symptom-based risk scores are limited by the accuracy of symptom reporting and the predictive power of those symptoms ( 14 , 15 ).…”

“…Additional variables, such as laboratory tests, can improve the accuracy of risk scores, at the cost of less versatility ( 16 , 17 ). One solution has been to develop risk scores with optional additional variables to improve accuracy when those variables can be ascertained ( 13 ). As rapid POC laboratory devices such as the Piccolo Xpress (Abaxis, Inc., Union City, CA, USA) and i-STAT (Abbott Point of Care, Princeton, NJ, USA) analyzers have become more common even in low-income settings ( 18 ), adding optional laboratory tests to risk scores has become feasible.…”

Development of a Pediatric Ebola Predictive Score, Sierra Leone1

“…We created the PEP score by assigning integer scores to variables in the validation dataset on the basis of their regression coefficients in the training dataset model (score = 1 for coefficients <1, score = 2 for coefficients > 1) ( 12 ). We calculated each child’s overall PEP score by adding together the integer scores for the variables present, which resulted in possible PEP scores of 0–10.…”