Incidence of Venous Thromboembolism in Adults Receiving Extracorporeal Membrane Oxygenation: A Systematic Review.

Extracorporeal membrane oxygenation (ECMO) has become an increasingly used tool for cardiorespiratory support. Thrombosis is a well-recognized complication of ECMO, yet the burden of disease remains unclear. We undertook a systematic review to investigate the incidence of venous thromboembolism (VTE) during ECMO or soon after decannulation, in patients screened for VTE. We retrieved all studies that evaluated VTE incidence in ECMO patients in EMBASE, MEDLINE, Web of Science, and Cochrane Library from inception to April 2, 2021. Studies reporting incidence of VTE diagnosed on systematic screening tests during ECMO or within 7 days of decannulation in adult patients were included. A total of 18 studies were included in the systematic review. These studies screened a total of 1095 ECMO patients. Most studies screened for cannula-associated deep vein thrombosis (CaDVT) after decannulation. The overall incidence of DVT was 52.8% (95% CI, 49.8-55.8%). Incidence of DVT was 53.5% (95% CI, 50.0-57.0%) for venovenous ECMO vs. 34.0% (95% CI, 26.5-42.2%) for venoarterial ECMO. No studies screened for pulmonary embolism. Our systematic review found a very high incidence of DVT among patients treated with ECMO. Routine screening for DVT after decannulation for all ECMO patients may be warranted.

Artificial Intelligence for Diagnosis of Acute Coronary Syndromes: A Meta-analysis of Machine Learning Approaches.

BACKGROUND: Machine learning (ML) encompasses a wide variety of methods by which artificial intelligence learns to perform tasks when exposed to data. Although detection of myocardial infarction has been facilitated with introduction of troponins, the diagnosis of acute coronary syndromes (ACS) without myocardial damage (without elevation of serum troponin) remains subjective, and its accuracy remains highly dependent on clinical skills of the health care professionals. Application of a ML algorithm may expedite management of ACS for either early discharge or early initiation of ACS management. We aim to summarize the published studies of ML for diagnosis of ACS. METHODS: We searched electronic databases, including PubMed, Embase, and Web of Science from inception up to January 13, 2019, for studies that evaluated ML algorithms for the diagnosis of ACS in patients presenting with chest pain. We then used random-effects bivariate meta-analysis models to summarize the studies. RESULTS: We retained 9 studies that evaluated ML in a total of 6292 patients. The prevalence of ACS in the evaluated cohorts ranged from relatively rare (7%) to common (57%). The pooled sensitivity and specificity were 0.95 and 0.90, respectively. The positive predictive values ranged from 0.64 to 1.0, and the negative predictive values ranged from 0.91 to 1.0. The positive and negative likelihood ratios ranged from 1.6 to 33.0 and 0.01 to 0.13, respectively. CONCLUSIONS: The excellent sensitivity, negative likelihood ratio, and negative predictive values suggest that ML may be useful as an initial triage tool for ruling out ACS.