Article Text

Prediction modelling of inpatient neonatal mortality in high-mortality settings
  1. Jalemba Aluvaala1,2,3,
  2. Gary Collins4,5,
  3. Beth Maina6,
  4. Catherine Mutinda6,
  5. Mary Waiyego6,
  6. James Alexander Berkley3,7,8,
  7. Mike English1,3
  1. 1Health Services Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya
  2. 2Paediatrics and Child Health, University of Nairobi, Nairobi, Kenya
  3. 3Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
  4. 4Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, United Kingdom
  5. 5Oxford University Hospitals, NHS Foundation Trust, Oxford, United Kingdom
  6. 6Pumwani Maternity Hospital, Nairobi, Kenya
  7. 7KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
  8. 8The Childhood Acute Illness & Nutrition (CHAIN) Network, P.O Box 43640 – 00100, Nairobi, Kenya
  1. Correspondence to Dr Jalemba Aluvaala, Health Services Unit, KEMRI-Wellcome Trust Research Programme, P.O Box 43640 – 00100, Nairobi, Kenya; jaluvaala{at}kemri-wellcome.org

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.

  • neonatology
  • mortality
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Footnotes

  • Twitter @GScollins

  • Contributors JA, GC, JAB and ME conceived the study. JA designed the data collection platform and implemented it with BM, MW and CM. JA analysed the data with support from GC, JAB and ME. JA wrote the manuscript with support from GC, JAB and ME. All authors revised the manuscript, approved the final version and agreed to be accountable for the findings.

  • Funding This work was primarily supported by a Wellcome Trust Senior Fellowship (#097170) awarded to ME. Additional support was provided by Health Systems Research Initiative joint grant provided by the Department for International Development, UK, Economic and Social Research Council, Medical Research Council and Wellcome Trust (grant number MR/M015386/1 and a Wellcome Trust core grant awarded to the KEMRI-Wellcome Trust Research Programme (#092654).

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval Ethical approval was provided by the KEMRI Scientific and Ethical Review Committee (SERU 3459)

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available upon reasonable request. The source data are owned by the Kenyan Ministry of Health, County Governments and as the data might be used to deidentify hospitals the study authors are not permitted to share the source data directly. Users who wish to reuse the source data can make a request initially through the KEMRI-Wellcome Trust Research Programme data governance committee. This committee will supply contact information for the KEMRI Scientific and Ethical Review unit, County Governments and individual hospitals as appropriate. The KEMRI-Wellcome Trust Research Programme data governance committee can be contacted on: dgc@kemri-wellcome.org.

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