Show simple item record

dc.contributor.authorAluvaala, Jalemba
dc.contributor.authorCollins, Gary
dc.contributor.authorMaina, Beth
dc.contributor.authorMutinda, Catherine
dc.contributor.authorWaiyego, Mary
dc.contributor.authorBerkley, James A
dc.contributor.authorEnglish, Mike
dc.identifier.citationAluvaala J, Collins G, Maina B, Mutinda C, Waiyego M, Berkley JA, English M. Prediction modelling of inpatient neonatal mortality in high-mortality settings. Arch Dis Child. 2020 Oct 22;106(5):449–54. doi: 10.1136/archdischild-2020-319217. Epub ahead of print. PMID: 33093041; PMCID: PMC8070601.en_US
dc.description.abstractObjective: Prognostic models aid clinical decision making and evaluation of hospital performance. Existing neonatal prognostic models typically use physiological measures that are often not available, such as pulse oximetry values, in routine practice in low-resource settings. We aimed to develop and validate two novel models to predict all cause in-hospital mortality following neonatal unit admission in a low-resource, high-mortality setting. Study design and setting: We used basic, routine clinical data recorded by duty clinicians at the time of admission to derive (n=5427) and validate (n=1627) two novel models to predict in-hospital mortality. The Neonatal Essential Treatment Score (NETS) included treatments prescribed at the time of admission while the Score for Essential Neonatal Symptoms and Signs (SENSS) used basic clinical signs. Logistic regression was used, and performance was evaluated using discrimination and calibration. Results: At derivation, c-statistic (discrimination) for NETS was 0.92 (95% CI 0.90 to 0.93) and that for SENSS was 0.91 (95% CI 0.89 to 0.93). At external (temporal) validation, NETS had a c-statistic of 0.89 (95% CI 0.86 to 0.92) and SENSS 0.89 (95% CI 0.84 to 0.93). The calibration intercept for NETS was -0.72 (95% CI -0.96 to -0.49) and that for SENSS was -0.33 (95% CI -0.56 to -0.11). Conclusion: Using routine neonatal data in a low-resource setting, we found that it is possible to predict in-hospital mortality using either treatments or signs and symptoms. Further validation of these models may support their use in treatment decisions and for case-mix adjustment to help understand performance variation across hospitals.en_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.subjectMortality; neonatology.en_US
dc.titlePrediction modelling of inpatient neonatal mortality in high-mortality settings.en_US

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States