RT Journal Article SR Electronic T1 Prediction modelling of inpatient neonatal mortality in high-mortality settings JF Archives of Disease in Childhood JO Arch Dis Child FD BMJ Publishing Group Ltd and Royal College of Paediatrics and Child Health SP archdischild-2020-319217 DO 10.1136/archdischild-2020-319217 A1 Jalemba Aluvaala A1 Gary Collins A1 Beth Maina A1 Catherine Mutinda A1 Mary Waiyego A1 James Alexander Berkley A1 Mike English YR 2020 UL http://adc.bmj.com/content/early/2020/10/21/archdischild-2020-319217.abstract AB Objective 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.