Diagnosing early-onset neonatal sepsis in low-resource settings: development of a multivariable prediction model

Abstract

Objective To develop a clinical prediction model to diagnose neonatal sepsis in low-resource settings. Design Secondary analysis of data collected by the Neotree digital health system from 01/02/2019 to 31/03/2020. We used multivariable logistic regression with candidate predictors identified from expert opinion and literature review. Missing data were imputed using multivariate imputation and model performance was evaluated in the derivation cohort. Setting A tertiary neonatal unit at Sally Mugabe Central Hospital, Zimbabwe. Patients We included 2628 neonates aged <72 hours, gestation ≥32+0 weeks and birth weight ≥1500 grams. Interventions Participants received standard care as no specific interventions were dictated by the study protocol. Main outcome measures Clinical early-onset neonatal sepsis (within the first 72 hours of life), defined by the treating consultant neonatologist. Results Clinical early-onset sepsis was diagnosed in 297 neonates (11.3%). The optimal model included eight predictors: maternal fever, offensive liquor, prolonged rupture of membranes, neonatal temperature, respiratory rate, activity, chest retractions and grunting. Receiver operating characteristic analysis gave an area under the curve of 0.736 (95% confidence interval 0.701-0.772). For a sensitivity of 95% (92-97%), corresponding specificity was 11% (10-13%), positive predictive value 12% (11-13%), negative predictive value 95% (92-97%), positive likelihood ratio 1.1 (95% CI 1.0-1.1), and negative likelihood ratio 0.4 (95% CI 0.3-0.6). Conclusions Our clinical prediction model achieved high sensitivity with modest specificity, suggesting it may be suited to excluding early-onset sepsis. Future work will validate and refine this model before considering it for clinical use within the Neotree.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This research was supported by the National Institute for Health Research (NIHR) Great Ormond Street Hospital Biomedical Research Centre. Funders of the wider Neotree project, past and present, include the Wellcome Trust Digital Innovation Award, RCPCH, Naughton-Cliffe Mathews, UCL Grand Challenges and Global Engagement Fund, and the Healthcare Infection Society. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Research ethics approval was granted by the University College London Research Ethics Committee (16915/001, 5019/004), Medical Research Council Zimbabwe (MRCZ/A/2570), and Sally Mugabe Central Hospital Ethics Committee (250418/48).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

Data Availability

An open-source, anonymised research database is planned as part of the wider Neotree project. Currently, sharing of deidentified individual participant data will be considered on a case-by-case basis.

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