Published in PLOS One on 2026-09-24, this study is presented as an attributed evidence briefing. The excerpts below are the original researchers’ words, reproduced under their Creative Commons Attribution licence. Health Signal has selected the abstract and, where needed, opening background paragraphs; the complete methods, results, limitations and references remain in the linked paper.
Original study title: A cluster analysis of neonates using clinical signs of possible serious bacterial infection at hospital admission in Kenya: A retrospective multicentre cohort study
Original authors: Timothy Tuti, Tabitha Muema, Mike English, on behalf of The Clinical Information Network Group, Jalemba Aluvaala, Khin Thet Wai
Source: PLOS One • DOI 10.1371/journal.pone.0359212
Read the complete primary study ↗ · Creative Commons Attribution licence
From the researchers
Abstract
Neonatal sepsis remains a major cause of mortality in Sub-Saharan Africa (SSA). Despite presenting with considerable clinical heterogeneity, suspected cases are managed uniformly with broad-spectrum antibiotics. Typical data-driven approaches developed in high-resource settings to identify clinically meaningful phenotypes and support management of neonatal sepsis have limited generalisability to many SSA public hospital settings, due to inclusion of variables that are largely unavailable at admission. This study’s objective was to identify sepsis clusters using signs of possible Serious Bacterial Infection (pSBI) readily available at the time of admission, and to assess the clusters performance in predicting mortality.
We conducted unsupervised model-based cluster analysis using Latent Class Analysis based on pSBI data collected at admission. All neonates <28 days old admitted to 21 Kenyan hospitals between January 2022 and December 2024 with ≥1 pSBI sign/symptom at admission were eligible for inclusion. We further explored the external validity of this clustering approach on new patient populations, and assessed the ability of the identified clusters to accurately predict in-hospital mortality compared to the World Health Organization neonatal sepsis severity classification guidelines.
Five clusters of minimal, low, moderate, substantial and critical mortality risk were identified from development dataset with 33094 patients from eight hospitals. The models had an accuracy, positive predictive value and specificity of at least 83.16% (82.72% to 83.62%), 81.02% (80.58% to 81.45%) and 86.91% (86.61% to 87.23%) respectively in predicting cluster membership of 23704 patients in the external validation dataset admitted to thirteen different hospitals. From an internal-external cross-validation approach of the in-hospital mortality risk, the model-based clustering approach had discrimination (AUROC) of 0.867 (0.863 to 0.871) and calibration intercept and slope of −0.004 (−0.031 to 0.023) and 0.996 (0.979 to 1.014) respectively, outperforming the WHO sepsis severity classification whose discrimination was 0.721 (0.715 to 0.727) and calibration intercept and slope being 0.018 (−0.005 to 0.041) and 1.015 (0.986 to 1.043) respectively.
The identified clusters can complement clinicians’ judgement in assessing risk among neonates with sepsis at admission. Future work evaluating the utility of these clusters and potential differences in treatment response across clusters are therefore recommended to help strengthen the case for more targeted, risk-based neonatal sepsis management.
Background
The most vulnerable time for a child’s survival are the first 28 days of life (the neonatal period), with 2.3 million children dying during this period globally, with a child from Sub-Saharan Africa (SSA) being 10 times more likely to die than a child from a high-income country [1]. Sepsis, which is a dysregulated immune response to infection that leads to acute organ dysfunction, is a leading contributor to burden of disease in neonates in SSA, both as primary cause of death and as a frequent contributor [2,3], and carries a high risk of death even when care is provided promptly [4].
Reading this evidence
Use the full paper to check the study design, participant selection, comparator and outcome definitions before drawing a practical conclusion. An association does not by itself show cause and effect. A reported performance measure needs its denominator and uncertainty. A finding from a particular facility, sample or time period should retain that scope when it is discussed elsewhere.
This briefing makes the source accessible within the wider hospital systems desk. It does not assess an individual patient or certify a product. When considering relevance to another health system, examine the original authors’ account of context and limitations, then seek local implementation evidence. The related reporting below provides other studies to read alongside this one; it is a topic connection, rather than a claim that their results are directly comparable.
© 2026 Timothy Tuti, Tabitha Muema, Mike English, on behalf of The Clinical Information Network Group, Jalemba Aluvaala, Khin Thet Wai. Excerpts reproduced under CC BY. Selection, formatting and contextual notes by Health Signal. The researchers are the authors of the source study, not members of Health Signal’s staff.
Explore connected evidence
- Evidence brief: Sepsis neonatorum: Bacterial profile, antimicrobial resistance patterns, among neonates and associated factors, Dilla University Referral Hospital, southern Ethiopia, with special reference to WHO prioritized pathogens
- Evidence brief: Bacteriological profiles, antimicrobial resistance patterns, and predictors of culture-confirmed neonatal Sepsis at Asella Teaching Hospital, Southeast Ethiopia
- Evidence brief: Survival status and predictors of mortality among preterm neonates admitted to the neonatal intensive care unit at public hospitals of Harari region and Dire Dawa administration, eastern Ethiopia: Retrospective cohort study 2025
