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

B Jason Brotherton; Mugdha Joshi; George Otieno; Sarah Wandia; Hannah Gitura; Ariel Mueller; Tony Nguyen; Steve Letchford; Elisabeth D Riviello; Evelyn Karanja; Kristina E Rudd

doi:10.3389/fmed.2023.1127672

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

Front Med (Lausanne). 2023 Apr 5:10:1127672. doi: 10.3389/fmed.2023.1127672. eCollection 2023.

Authors

Affiliations

¹ Department of Internal Medicine, AIC Kijabe Hospital, Kijabe, Kenya.
² The Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
³ Department of Medicine, Stanford University, Palo Alto, CA, United States.
⁴ Department of Emergency and Critical Care Medicine, AIC Kijabe Hospital, Kijabe, Kenya.
⁵ Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States.
⁶ Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.

Abstract

Importance: 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.

Objective: 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.

Design setting and participants: 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.

Main outcomes and measures: 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% (n = 207) were male. Fifty-nine percent (n = 199) required mechanical ventilation and 35% (n = 118) received vasopressors upon ICU admission. Overall hospital mortality was 31% (n = 104). 323 patients had all component variables recorded for R-MPM, 261 for MEWS, and 253 for UVA. The AUROC was highest for MEWS (0.76), followed by R-MPM (0.75), qSOFA (0.70), and UVA (0.69) (p < 0.001). Predictive capacity was similar among patients with suspected infection.

Conclusion and relevance: 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.

Keywords: Kenya; critical care; global health; mortality prediction; resource variable; severity of illness.

Grants and funding

K23 GM141463/GM/NIGMS NIH HHS/United States