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. 2021 Apr 21;106(5):449-454.
doi: 10.1136/archdischild-2020-319217.

Prediction modelling of inpatient neonatal mortality in high-mortality settings

Jalemba Aluvaala  1   2   3 , Gary Collins  4   5 , Beth Maina  6 , Catherine Mutinda  6 , Mary Waiyego  6 , James Alexander Berkley  3   7   8 , Mike English  9   3
Affiliations

Prediction modelling of inpatient neonatal mortality in high-mortality settings

Jalemba Aluvaala et al. Arch Dis Child. .

Abstract

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.

Keywords: mortality; neonatology.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Calibration plot for NETS and SENSS in the derivation data sets. NETS, Neonatal Essential Treatment Score; SENSS, Score for Essential Neonatal Symptoms and Signs;RCS, Restricted Cubic Splines;CL, Confidence Limits (95%).
Figure 2
Figure 2
Calibration curves for NETS and SENSS in the external validation data sets. NETS, Neonatal Essential Treatment Score; SENSS, Score for Essential Neonatal Symptoms and Signs.
See this image and copyright information in PMC

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