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Meta-Analysis
. 2025 Apr 26;25(1):214.
doi: 10.1186/s12871-025-03093-z.

Incidence and predictors of perioperative mortality in Ethiopia: a systematic review and meta-analysis

Tikuneh Yetneberk  1 , Diriba Teshome  2 , Abebe Tiruneh  2 , Yohannes A Dersesh  3 , Nega Getachew  2 , Moges Gelaw  2 , Meseret Firde  2
Affiliations
Meta-Analysis

Incidence and predictors of perioperative mortality in Ethiopia: a systematic review and meta-analysis

Tikuneh Yetneberk et al. BMC Anesthesiol. .

Abstract

Introduction: The Lancet Commission on Global Surgery highlights perioperative mortality rate (POMR) as a key indicator of a nation's surgical system effectiveness. While POMR is often measured in high-income countries, it is less studied in low- and middle-income countries (LMICs). This study aims to assess the POMR and its predictors in Ethiopia.

Methods: We conducted a thorough literature search across PubMed/MEDLINE, Embase, Web of Science, Scopus, and Google Scholar for studies from Ethiopia between 2019 and 2023 reporting POMR for various surgical procedures. Data were extracted in duplicate from eligible studies. We used random-effects meta-analysis to pool estimates of POMR and its predictors.

Results: The meta-analysis revealed a POMR of 5.36%. Identified predictors of perioperative mortality in Ethiopia included older age, comorbidities, ICU admission, and an ASA physical status classification of III or higher and emergency surgeries.

Conclusion: Ethiopia's perioperative mortality rate is significantly high. Improving surgical care quality and safety, along with expanding access to surgical services, is crucial for bettering surgical outcomes in the country.

Keywords: Anesthesia; Death; Ethiopia; Perioperative mortality; Surgery.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The PRISMA flow diagram of the research identification, screening, and inclusion process
Fig. 2
Fig. 2
Pooled analysis of the prevalence and random effects model of perioperative mortality
Fig. 3
Fig. 3
Analysis of heterogeneity among included studies
Fig. 4
Fig. 4
Pooled analysis of the prevalence and random effects model of perioperative mortality by sample size
Fig. 5
Fig. 5
Sensitivity analysis of the prevalence and random effects model of perioperative mortality
Fig. 6
Fig. 6
The pooled effect of age on the perioperative mortality rate
Fig. 7
Fig. 7
The pooled effect of intensive care unit admission following surgery on the perioperative mortality rate
Fig. 8
Fig. 8
The pooled effect of ASA physical status on the perioperative mortality rate
Fig. 9
Fig. 9
The pooled effect of emergency surgery on the perioperative mortality rate
Fig. 10
Fig. 10
The pooled effect of comorbidity on the perioperative mortality rate
See this image and copyright information in PMC

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