Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 5 appraisals
Journal of critical care
Artificial intelligence and computerized decision support in adult intensive care: A systematic review of randomized controlled trials
BACKGROUND: Artificial intelligence (AI) and computerized clinical decision support systems (CDSS) are increasingly applied in intensive care, yet their clinical impact remains uncertain, as most studies focus on model development rather than prospective evaluation. OBJECTIVES: To identify randomized controlled trials (RCTs) evaluating AI-based or CDSS interventions intended to influence real-time decision-making in adult intensive care units (ICUs) and to assess their effects on process and patient-centered outcomes. METHODS: We conducted a systematic review of randomized controlled trials (RCTs). PubMed, Embase, Cochrane CENTRAL, ScienceDirect, and IEEE Xplore were searched from inception to November 2025. Eligible studies evaluated AI-based or CDSS interventions in adult ICU patients. Study quality was assessed using RoB 2 and CONSORT-AI criteria. RESULTS: Ten RCTs were included, enrolling approximately 100,000 adult ICU patients. Five trials evaluated AI-based interventions and five evaluated CDSS. Eight trials showed improvements in at least one process measure, including earlier recognition of deterioration, improved protocol adherence, and physiological stability. However, patient-centered benefits were uncommon. Two trials reported reductions in mortality (a sepsis prediction model and a machine-learning-based early-warning system), while most studies showed no consistent effects on clinical outcomes. Reporting of AI-specific elements-dataset provenance, algorithm versioning, and human-AI interaction-was frequently incomplete. CONCLUSIONS: AI and CDSS interventions in adult ICUs are associated with improvements in process-related outcomes but show limited and inconsistent effects on patient-centered endpoints. These findings highlight a persistent gap between algorithmic innovation and clinical validation and underscore the need for pragmatic randomized trials with improved reporting and integration into clinical workflows.
3 Aug 2026
Read appraisal →BMJ evidence-based medicine
Comparative effects of early physical interventions on preventing intensive care unit-acquired weakness: a systematic review and component network meta-analysis
OBJECTIVE: To compare the effects of early physical interventions on the prevention of intensive care unit-acquired weakness (ICUAW) and the improvement of relevant clinical outcomes in patients with critical illness. METHODS: We systematically searched the Web of Science, PubMed, Embase and the Cochrane Central Register of Controlled Trials from their inception until 20 August 2024, to identify randomised controlled trials (RCTs) enrolling patients ≥18 years old and implementing early physical intervention that commenced at any time point during mechanical ventilation (MV) use or within 7 days after intensive care unit (ICU) admission for review. We synthesised data using a random-effects model and analysed through network meta-analysis (NMA) and component network meta-analysis (CNMA). MAIN OUTCOME MEASURES: Primary outcome is the incidence of ICUAW. Secondary outcomes included Medical Research Council sum score, length of stay in the ICU or hospital, duration of MV and mortality rates in the ICU or hospital. RESULTS: Our analyses included 63 RCTs involving 24 treatments and eight components. The NMA results revealed systematic early mobilisation (SEM) combined with neuromuscular electrical stimulation (NMES), SEM alone and NMES alone may lead to a moderate to large reduction in the incidence of ICUAW (odds ratios [ORs]=0.03, 0.09 and 0.12, 95% confidence intervals [CIs]=0.00 to 0.42, 0.01 to 0.97 and 0.03 to 0.44, respectively) and improved relevant clinical outcomes compared with routine care. The CNMA results further indicated that SEM (OR=0.14, 95% CI=0.02 to 0.83) and NMES (OR=0.22, 95% CI=0.09 to 0.52) effectively mitigated the ICUAW incidence. CONCLUSIONS: SEM and NMES are optimal interventions for preventing ICUAW. Healthcare providers in ICUs should implement early mobilisation with structured protocols and patient assessments or apply NMES to specific muscle groups to prevent ICUAW in critically ill patients and improve relevant clinical outcomes. PROSPERO REGISTRATION NUMBER: CRD42024581173.
23 May 2026
Read appraisal →Critical care explorations
The Effects of Conservative Fluid Management and Active Deresuscitation on Markers of Tissue Perfusion, Kidney Injury, and Vascular Injury in Critically Ill Adults: A Secondary Analysis of the Role of Active Deresuscitation After Resuscitation-2 (RADAR-2) Trial
OBJECTIVES: Test the hypothesis that conservative fluid management with active deresuscitation would not adversely affect tissue perfusion or kidney injury and would be associated with reduced vascular injury compared with usual care. DESIGN: Secondary analysis of the Role of Active Deresuscitation After Resuscitation-2 (RADAR-2) trial. SETTING: ICUs. PATIENTS OR SUBJECTS: Critically ill patients enrolled in the RADAR-2 trial. INTERVENTIONS: Conservative fluid management with active deresuscitation vs. usual care. MEASUREMENTS AND MAIN RESULTS: Measures of tissue hypoperfusion (whole blood lactate), acute kidney injury (AKIRisk score and urinary cystatin-C), and vascular injury (plasma hyaluronan, syndecan-1, and angiopoietin-2) were compared between groups. For each analyte, change from baseline was compared between groups and the median inter-group difference at each timepoint was estimated with bootstrapped CIs. Exploratory logistic regression examined associations between plasma biomarker levels (including N-terminal pro-B-type natriuretic peptide [NT-proBNP]), 28-day mortality, and treatment allocation. Whole blood lactate levels were similar between groups at all timepoints. Using change from baseline comparisons, no statistically detectable between-group differences were observed in AKIRisk scores or urinary cystatin-C levels. Plasma vascular injury biomarkers showed no statistically detectable between-group differences at any timepoint. High baseline hyaluronan (adjusted odds ratio [aOR], 5.75; 95% CI, 1.94-17.02; p = 0.002), syndecan-1 (aOR, 8.82; 95% CI, 2.67-29.15; p < 0.001), and NT-proBNP greater than 2500 pg/mL (aOR, 21.48; 95% CI, 3.57-129.41; p < 0.001) were independently associated with increased 28-day mortality. There was no evidence of differential treatment response based on these biomarker levels. CONCLUSIONS: Conservative fluid management and active deresuscitation were not associated with worsening tissue perfusion or acute kidney injury. A reduction in vascular injury markers was not observed. Given the modest sample size and resultant imprecision, clinically important effects cannot be excluded.
23 Apr 2026
Read appraisal →Medicine
Effectiveness of Chinese herbal medicine mouthwash in preventing ventilator-associated pneumonia in patients with endotracheal intubation: A systematic review and meta-analysis
BACKGROUND: To systematically evaluate the effectiveness and safety of Chinese herbal medicine mouthwash (CHMM) in preventing ventilator-associated pneumonia (VAP) in patients with endotracheal intubation (ETT). METHODS: Computer searches were conducted for relevant randomized controlled trials (RCTs) in the China National Knowledge Infrastructure (CNKI), WANFANG DATA, Chinese Scientific and Technical Journal Database (VIP), Chinese Biomedical Literature Database (SinoMed), PubMed, Embase, Cochrane Library, and CINAHL Database from the establishment to April 5, 2023. The data were analyzed by RevMan 5.3 software. The methodological quality of papers was evaluated by the Cochrane version 5.1.0 and the certainty of evidence was evaluated by the GRADE system. RESULTS: We included 27 RCTs with 2426 patients. Most of the included studies were of poor methodological quality. Meta-analysis showed that CHMM (RR = 0.41, 95% CI: 0.34-0.50, P < .00001, low-certainty evidence) or CHMM + routine oral care (RR = 0.31, 95% CI: 0.18-0.56, P < .0001) could reduce the incidence of VAP compared to non-CHMM. CHMM may be superior to saline (low-certainty evidence) and chlorhexidine (moderate-certainty evidence) in preventing VAP (P < .05). CONCLUSION: Low-certainty evidence shows that CHMM may be more advantageous in reducing the incidence of VAP compared to non-CHMM. However, due to some limitations and the low certainty of evidence, we cannot recommend CHMM for routine clinical use at this time. Large-scale and high-quality RCTs are also needed to definitively evaluate the safety and clinical benefits of CHMM in the future.
12 Apr 2026
Read appraisal →Critical care explorations
Physician Gestalt Compared With Artificial Intelligence Model to Predict Intubation in Critically Ill Patients
IMPORTANCE: Accurate prediction of intubation in critically ill patients could enable interventions that improve patient outcomes. However, the performance of intensive care physicians compared with machine learning (ML) models remains unknown. OBJECTIVES: To investigate intensive care physicians' ability to predict the need for intubation within 24 hours and compare their performance against an established ML model, Vent.io. DESIGN, SETTING, AND PARTICIPANTS: This prospective observational study in two ICUs surveyed intensivists to test their ability to predict the need for intubation of adult patients under their care. Physician predictions of intubation were then compared with predictions from Vent.io. MAIN OUTCOMES AND MEASURES: Primary metrics include prediction sensitivity, specificity, and descriptive statistics for both physicians and ML model. Generalized linear mixed models investigated the fixed effect of the predictor (physician vs. Vent.io) on both sensitivity and specificity while accounting for the random effects from different physicians. Similar modeling was used to investigate the relationship between physician confidence and correctness. RESULTS: Overall, physicians were quite confident in their predictions of intubation with a median score of 8 (on a 0-10 point scale, with 0 being not at all confident and 10 being extremely confident) out of the 302 surveys administered. Sensitivity was 0.190 and 0.714 for physicians and Vent.io, respectively. Specificity was 0.960 and 0.673 for physicians and Vent.io, respectively. Generalized linear mixed modeling showed that physician confidence was associated with greater odds of correctly predicting intubation outcome (odds ratio [OR] 1.49; 95% CI, 1.22-1.84; p < 0.001). Vent.io had significantly greater odds of being correct when patients required intubation compared with physicians (OR 18.68; 95% CI, 1.87-186.31; p = 0.013). However, intensive care physicians outperformed Vent.io at correctly predicting when patients did not require intubation (OR 24.80; 95% CI, 13.22-46.52; p < 0.001). CONCLUSIONS AND RELEVANCE: Although predictive performance compared with human experts is promising, Vent.io needs real-time testing in a randomized clinical trial to determine if its deployment can improve clinical outcomes.
4 Apr 2026
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