Effectiveness of Predictive Analytics Algorithms for Early Sepsis Detection in the Intensive Care Unit on Time to Intervention and Mortality : A Systematic Review and Meta-Analysis of Randomized Controlled Trials and Primary Studies

Background: Sepsis remains a leading cause of death in the intensive care unit (ICU), and every hour of delay in antimicrobial therapy is associated with higher mortality. Predictive analytics algorithms — rule-based electronic surveillance and machine-learning (ML) models embedded in the electronic health record (EHR) — promise earlier recognition, but whether they shorten time to intervention and reduce mortality in critically ill patients remains uncertain. Objective: To synthesise evidence from randomised controlled trials (RCTs) and primary studies on the effectiveness of predictive analytics for early sepsis detection in ICU settings on time to intervention, mortality, length of stay (LOS) and diagnostic performance. Methods: The study strictly adhered to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 guidelines. Eligible designs were RCTs, comparative studies, cohorts and case series conducted in ICUs or in mixed hospital settings that included ICU patients; systematic reviews were excluded from synthesis and used for discussion only. Risk of bias was assessed with RoB 2, ROBINS-I and PROBAST. Random-effects meta-analyses (DerSimonian–Laird with modified Hartung–Knapp confidence intervals [CIs] and 95% prediction intervals [PIs]) were performed, and certainty of evidence was rated with GRADE. Results: 26 studies (27 reports) were included: 4 RCTs, 10 comparative cohort/pre–post studies and 12 diagnostic validation cohorts. Across nine studies (30,144 patients), predictive analytics was associated with a non-significant reduction in mortality (odds ratio [OR] 0.71, 95% CI 0.49–1.02; I² = 75%; PI 0.34–1.49), with a consistent direction for the risk ratio (0.66, 0.46–0.94) and the absolute risk difference (-3.8 percentage points, -6.8 to -0.8). The benefit was confined to ML-based/predictive systems (OR 0.60, 0.42–0.84; k = 5) rather than rule-based alerts (OR 0.99, 0.42–2.37; k = 4; P for interaction = 0.089) and disappeared when studies at high/serious risk of bias were excluded (OR 1.01, 0.44–2.32). Time to first antibiotic was shorter by a mean of 1.83 h (95% CI -4.57 to 0.90; k = 5; I² = 88%) and ICU LOS by 1.10 days (-2.92 to 0.72), whereas hospital LOS was unchanged (0.27 days, -3.61 to 4.14). Guideline-concordant care tended to improve (OR 1.43, 0.85–2.41). Pooled discrimination across 15 validation datasets was AUROC 0.864 (0.816–0.901; PI 0.60–0.96), similar in ICU-only (0.854) and mixed settings (0.879), with very high heterogeneity (I² = 99.7%). Conclusions: Predictive analytics for sepsis, particularly ML-based early warning systems, achieve good discrimination and show a consistent signal towards earlier treatment and lower mortality in critically ill patients; however, the certainty of evidence is low to very low because most data derive from non-randomised designs with serious risk of bias and substantial heterogeneity. Adequately powered, pragmatic multicentre RCTs that measure the complete alert-to-action pathway, alert burden and local calibration are required before universal adoption.