Association of clinical prediction scores with hospital mortality in an adult medical and surgical intensive care unit in Kenya.

Kenya critical care global health mortality prediction resource variable severity of illness

Journal

Frontiers in medicine
ISSN: 2296-858X
Titre abrégé: Front Med (Lausanne)
Pays: Switzerland
ID NLM: 101648047

Informations de publication

Date de publication:
2023
Historique:
received: 19 12 2022
accepted: 16 03 2023
medline: 24 4 2023
pubmed: 24 4 2023
entrez: 24 04 2023
Statut: epublish

Résumé

Mortality prediction among critically ill patients in resource limited settings is difficult. Identifying the best mortality prediction tool is important for counseling patients and families, benchmarking quality improvement efforts, and defining severity of illness for clinical research studies. Compare predictive capacity of the Modified Early Warning Score (MEWS), Universal Vital Assessment (UVA), Tropical Intensive Care Score (TropICS), Rwanda Mortality Probability Model (R-MPM), and quick Sequential Organ Failure Assessment (qSOFA) for hospital mortality among adults admitted to a medical-surgical intensive care unit (ICU) in rural Kenya. We performed a pre-planned subgroup analysis among ICU patients with suspected infection. Prospective single-center cohort study at a tertiary care, academic hospital in Kenya. All adults 18 years and older admitted to the ICU January 2018-June 2019 were included. The primary outcome was association of clinical prediction tool score with hospital mortality, as defined by area under the receiver operating characteristic curve (AUROC). Demographic, physiologic, laboratory, therapeutic, and mortality data were collected. 338 patients were included, none were excluded. Median age was 42 years (IQR 33-62) and 61% ( All tools had acceptable predictive capacity for hospital mortality, with variable observed availability of the component data. R-MPM and MEWS had high rates of variable availability as well as good AUROC, suggesting these tools may prove useful in low resource ICUs.

Identifiants

pubmed: 37089585
doi: 10.3389/fmed.2023.1127672
pmc: PMC10113620
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1127672

Subventions

Organisme : NIGMS NIH HHS
ID : K23 GM141463
Pays : United States

Informations de copyright

Copyright © 2023 Brotherton, Joshi, Otieno, Wandia, Gitura, Mueller, Nguyen, Letchford, Riviello, Karanja and Rudd.

Déclaration de conflit d'intérêts

KR reports consulting fees from Janssen Pharmaceuticals. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

B Jason Brotherton (BJ)

Department of Internal Medicine, AIC Kijabe Hospital, Kijabe, Kenya.
The Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, United States.

Mugdha Joshi (M)

Department of Medicine, Stanford University, Palo Alto, CA, United States.

George Otieno (G)

Department of Internal Medicine, AIC Kijabe Hospital, Kijabe, Kenya.

Sarah Wandia (S)

Department of Internal Medicine, AIC Kijabe Hospital, Kijabe, Kenya.

Hannah Gitura (H)

Department of Emergency and Critical Care Medicine, AIC Kijabe Hospital, Kijabe, Kenya.

Ariel Mueller (A)

Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States.

Tony Nguyen (T)

Department of Internal Medicine, AIC Kijabe Hospital, Kijabe, Kenya.

Steve Letchford (S)

Department of Internal Medicine, AIC Kijabe Hospital, Kijabe, Kenya.

Elisabeth D Riviello (ED)

Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.

Evelyn Karanja (E)

Department of Internal Medicine, AIC Kijabe Hospital, Kijabe, Kenya.

Kristina E Rudd (KE)

The Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, United States.

Classifications MeSH