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dc.contributor.authorGeorge, Elizabeth C.
dc.contributor.authorWalker, A. Sarah
dc.contributor.authorKiguli, Sarah
dc.contributor.authorOlupot-Olupo, Peter
dc.contributor.authorOpoka, Robert O.
dc.contributor.authorEngoru, Charles
dc.contributor.authorAkech, Samuel O.
dc.contributor.authorNyeko, Richard
dc.contributor.authorMtove, George
dc.contributor.authorReyburn, Hugh
dc.contributor.authorBerkley, James A.
dc.contributor.authorMpoya, Ayub
dc.contributor.authorLevin, Michael
dc.contributor.authorCrawley, Jane
dc.contributor.authorGibb, Diana M.
dc.contributor.authorMaitland, Kathryn
dc.contributor.authorBabiker, Abdel G.
dc.date.accessioned2021-07-27T19:21:09Z
dc.date.available2021-07-27T19:21:09Z
dc.date.issued2015
dc.identifier.citationGeorge, E. C., Walker, A. S., Kiguli, S., Olupot-Olupot, P., Opoka, R. O., Engoru, C., ... & Babiker, A. G. (2015). Predicting mortality in sick African children: the FEAST paediatric emergency triage (PET) score. BMC medicine, 13(1), 1-12.en_US
dc.identifier.urihttps://hdl.handle.net/123456789/294
dc.description.abstractBackground: Mortality 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. Methods: Data 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. Results: A 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. Conclusions: Eight 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.en_US
dc.language.isoenen_US
dc.publisherBMC Medicineen_US
dc.subjectAfricaen_US
dc.subjectChildrenen_US
dc.subjectFEAST trialen_US
dc.subjectMortalityen_US
dc.subjectRisk scoreen_US
dc.titlePredicting mortality in sick African children: the FEAST Paediatric Emergency Triage (PET) Scoreen_US
dc.typeArticleen_US


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