| dc.description.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 lowresource 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. |
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