Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: a systematic review and meta-analysis.

BACKGROUND: Cardiovascular disease (CVD) risk prediction is important for guiding the intensity of therapy in CVD prevention. Whilst current risk prediction algorithms use traditional statistical approaches, machine learning (ML) presents an alternative method that may improve risk prediction accuracy. This systematic review and meta-analysis aimed to investigate whether ML algorithms demonstrate greater performance compared with traditional risk scores in CVD risk prognostication. METHODS AND RESULTS: MEDLINE, EMBASE, CENTRAL, and SCOPUS Web of Science Core collections were searched for studies comparing ML models to traditional risk scores for CVD risk prediction between the years 2000 and 2021. We included studies that assessed both ML and traditional risk scores in adult (≥18 year old) primary prevention populations. We assessed the risk of bias using the Prediction Model Risk of Bias Assessment Tool (PROBAST) tool. Only studies that provided a measure of discrimination [i.e. C-statistics with 95% confidence intervals (CIs)] were included in the meta-analysis. A total of 16 studies were included in the review and meta-analysis (3302 515 individuals). All study designs were retrospective cohort studies. Out of 16 studies, 3 externally validated their models, and 11 reported calibration metrics. A total of 11 studies demonstrated a high risk of bias. The summary C-statistics (95% CI) of the top-performing ML models and traditional risk scores were 0.773 (95% CI: 0.740-0.806) and 0.759 (95% CI: 0.726-0.792), respectively. The difference in C-statistic was 0.0139 (95% CI: 0.0139-0.140), P < 0.0001. CONCLUSION: ML models outperformed traditional risk scores in the discrimination of CVD risk prognostication. Integration of ML algorithms into electronic healthcare systems in primary care could improve identification of patients at high risk of subsequent CVD events and hence increase opportunities for CVD prevention. It is uncertain whether they can be implemented in clinical settings. Future implementation research is needed to examine how ML models may be utilized for primary prevention.This review was registered with PROSPERO (CRD42020220811).

Early routine (erCT) versus selective computed tomography (sCT) for acute abdominal pain: A systematic review and meta-analysis of randomised trials.

BACKGROUND: There are ongoing controversies about the routine use of computed tomography (CT) in the evaluation of acute abdominal pain (AAP), our study was designed to evaluate the impacts of early routine use CT (erCT) and selective CT (sCT) on clinical outcomes. METHODS: We conducted a meta-analysis of randomized trials. We included non-quadrant and non-region-specific studies only. The primary outcomes were the number of correct diagnoses at 24 h, mortality, and length of stay (LOS). The secondary outcomes were the number of corrected diagnoses from an initial misdiagnosis, major changes in management, and non-specific abdominal pain (NSAP). RESULTS: 6 Studies from 3 RCTs were included, enrolling 570 patients. erCT showed a higher number of correct diagnoses and corrected diagnoses at 24 h, [risk ratio (RR) 1.13, 95% confidence interval (CI) 1.01-1.26, P = 0.03] and [RR 1.36, 95% CI 1.01-1.85, P = 0.04] respectively, and a lower mortality at 6 months [RR 0.36, 95% CI 0.15-0.87, P = 0.02]. However, no differences were shown in LOS [mean difference (MD) -0.65, 95% CI -2.88 - 1.58, P = 0.57], major changes in management [RR 1.45, 95% CI 0.94-2.22, P = 0.09] and NSAP [RR 0.92, 95% CI 0.57-1.50, P = 0.74]. CONCLUSION: erCT has demonstrated both diagnostic and survival benefits by having more correct diagnoses at 24 h and lower mortality at 6 months. Further study should focus on determining the subpopulation that would most benefit from the potentially differential effects of erCT.