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Iregbu K, Dramowski A, Milton R, et al. Global health systems' data science approach for precision diagnosis of sepsis in early life. Lancet Infect Dis. 2021;doi: 10.1016/S1473-3099(21)00645-9.
Iregbu, K., Dramowski, A., Milton, R., Nsutebu, E., Howie, S. R. C., Chakraborty, M., Lavoie, P. M., Costelloe, C. E., & Ghazal, P. (2021). Global health systems' data science approach for precision diagnosis of sepsis in early life. The Lancet. Infectious diseases, . https://doi.org/10.1016/S1473-3099(21)00645-9
Iregbu, Kenneth, et al. "Global health systems' data science approach for precision diagnosis of sepsis in early life." The Lancet. Infectious diseases vol. (2021). doi: https://doi.org/10.1016/S1473-3099(21)00645-9
Iregbu K, Dramowski A, Milton R, Nsutebu E, Howie SRC, Chakraborty M, Lavoie PM, Costelloe CE, Ghazal P. Global health systems' data science approach for precision diagnosis of sepsis in early life. Lancet Infect Dis. 2021 Dec 13; doi: 10.1016/S1473-3099(21)00645-9. Epub 2021 Dec 13. PMID: 34914924.
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Lancet Infect Dis. 2021 Dec 13; doi: 10.1016/S1473-3099(21)00645-9. Epub 2021 Dec 13.

Global health systems' data science approach for precision diagnosis of sepsis in early life.

The Lancet. Infectious diseases

Kenneth Iregbu, Angela Dramowski, Rebecca Milton, Emmanuel Nsutebu, Stephen R C Howie, Mallinath Chakraborty, Pascal M Lavoie, Ceire E Costelloe, Peter Ghazal

Affiliations

  1. Department of Medical Microbiology, National Hospital Abuja, Nigeria.
  2. Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
  3. Centre for Trials Research, Cardiff University, Cardiff, UK.
  4. Infectious Diseases Division, Sheikh Shakhbout Medical City, Abu Dhabi, United Arab Emirates.
  5. Department of Paediatrics, Child and Youth Health, University of Auckland, Auckland, New Zealand.
  6. Regional Neonatal Intensive Care Unit, University Hospital of Wales, Cardiff, UK.
  7. Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
  8. Global Digital Health Unit, School of Public Health, Imperial College London, London, UK.
  9. Systems Immunity Research Institute, School of Medicine, Cardiff University, Cardiff, UK. Electronic address: [email protected].

PMID: 34914924 DOI: 10.1016/S1473-3099(21)00645-9

Abstract

Neonates and children in low-income and middle-income countries (LMICs) contribute to the highest number of sepsis-associated deaths globally. Interventions to prevent sepsis mortality are hampered by a lack of comprehensive epidemiological data and pathophysiological understanding of biological pathways. In this review, we discuss the challenges faced by LMICs in diagnosing sepsis in these age groups. We highlight a role for multi-omics and health care data to improve diagnostic accuracy of clinical algorithms, arguing that health-care systems urgently need precision medicine to avoid the pitfalls of missed diagnoses, misdiagnoses, and overdiagnoses, and associated antimicrobial resistance. We discuss ethical, regulatory, and systemic barriers related to the collection and use of big data in LMICs. Technologies such as cloud computing, artificial intelligence, and medical tricorders might help, but they require collaboration with local communities. Co-partnering (joint equal development of technology between producer and end-users) could facilitate integration of these technologies as part of future care-delivery systems, offering a chance to transform the global management and prevention of sepsis for neonates and children.

Copyright © 2021 Elsevier Ltd. All rights reserved.

Conflict of interest statement

Declaration of interests We declare no competing interests.

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