A cluster analysis of neonates using clinical signs of possible serious bacterial infection at hospital admission in Kenya: A retrospective multicentre cohort study
4 affiliations
- 1KEMRI-Wellcome Trust Research Program, Nairobi, Kenya
- 2Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- 3Health Systems Collaborative, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- 4Department of Paediatrics and Child Health, University of Nairobi, Kenyatta National Hospital Nairobi, Nairobi, Kenya
Abstract
Background: 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. Methods: 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. Results: 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. Conclusion: 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.
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BibTeX
@article{tuti2026cluster,
title = {{A cluster analysis of neonates using clinical signs of possible serious bacterial infection at hospital admission in Kenya: A retrospective multicentre cohort study}},
author = {Timothy Tuti and Tabitha Muema and Mike English and on behalf of The Clinical Information Network Group and Jalemba Aluvaala},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0359212},
url = {https://doi.org/10.1371/journal.pone.0359212},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A cluster analysis of neonates using clinical signs of possible serious bacterial infection at hospital admission in Kenya: A retrospective multicentre cohort study
AU - Timothy Tuti
AU - Tabitha Muema
AU - Mike English
AU - on behalf of The Clinical Information Network Group
AU - Jalemba Aluvaala
PY - 2026
DA - 2026/09/24
JO - PLOS One
DO - 10.1371/journal.pone.0359212
UR - https://doi.org/10.1371/journal.pone.0359212
AB - Background: 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. Methods: 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. Results: 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. Conclusion: 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.
SN - 1932-6203
ER -