DOI: 10.3390/jcm15218539 ISSN: 2077-0383

Predictors of Emergency Surgery in Acute Ischaemic Colitis: A Systematic Review and Meta-Analysis

Naomi Abara, Abdulazeez Mustapha, Mohamed Elsaigh, Awofaa Gogo-Abite

Background: Acute ischaemic colitis ranges from reversible mucosal injury to transmural necrosis requiring emergency resection. This review aimed to identify factors associated with emergency surgery, quantify comparable unadjusted associations, and assess their robustness. Methods: PubMed, Cochrane Library, Embase, and Ovid MEDLINE were searched to 10 June 2026. Adult cohort studies reporting predictors or outcomes relevant to emergency surgery for ischaemic colitis (IC) were included. Surgery-specific unadjusted odds ratios (ORs) were pooled only when at least three independent, clinically comparable studies were available, using random-effects models with restricted maximum-likelihood estimation; Hartung–Knapp confidence intervals were used for primary inference. Risk of bias was assessed with QUIPS, and certainty with GRADE adapted for prognostic-factor evidence. The protocol was registered in PROSPERO (CRD420261411575). Results: Twenty-six cohort studies were included; five contributed data to at least one of three retained meta-analyses. Conventional REML estimates suggested higher odds of emergency surgery with hypotension (OR 3.53, 95% CI 1.10–11.28; I2 = 75.0%) and right-colonic involvement (OR 4.60, 95% CI 1.47–14.40; I2 = 80.4%), while fever was imprecise (OR 3.42, 95% CI 0.59–19.79; I2 = 86.3%). Hartung–Knapp 95% CIs included one for hypotension (0.30–41.71), fever (0.08–142.15), and right-colonic involvement (0.66–31.86), and no pooled association remained statistically robust. GRADE certainty was very low for all three. Conclusions: Hypotension and right-sided disease remain clinically plausible candidate risk markers, but the evidence is sparse and very uncertain and does not establish independent predictors or operative thresholds. Prospective multicentre studies using standardised definitions are needed to develop and externally validate a surgery-specific prediction model.

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