Electrocardiogram-based artificial intelligence for the diagnosis of heart failure:a systematic review and meta-analysis

BACKGROUND The electrocardiogram(ECG)is an inexpensive and easily accessible investigation for the diagnosis of cardiovascular diseases including heart failure(HF).The application of artificial intelligence(AI)has contributed to clinical practice in terms of aiding diagnosis,prognosis,risk stratification and guiding clinical management.The aim of this study is to systematically review and perform a meta-analysis of published studies on the application of AI for HF detection based on the ECG.METHODS We searched Embase,PubMed and Web of Science databases to identify literature using AI for HF detection based on ECG data.The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2(QUADAS-2)criteria.Random-effects models were used for calculating the effect estimates and hierarchical receiver operating characteristic curves were plotted.Subgroup analysis was performed.Heterogeneity and the risk of bias were also assessed.RESULTS A total of 11 studies including 104,737 subjects were included.The area under the curve for HF diagnosis was 0.986,with a corresponding pooled sensitivity of 0.95(95%CI:0.86–0.98),specificity of 0.98(95%CI:0.95–0.99)and diagnostic odds ratio of 831.51(95%CI:127.85–5407.74).In the patient selection domain of QUADAS-2,eight studies were designated as high risk.CONCLUSIONS According to the available evidence,the incorporation of AI can aid the diagnosis of HF.However,there is heterogeneity among machine learning algorithms and improvements are required in terms of quality and study design.

Machine learning algorithm as a prognostic tool for Epstein-Barr virus reactivation after haploidentical hematopoietic stem cell transplantation

Epstein-Barr virus(EBV)reactivation is one of the most important infections after hematopoietic stem cell transplantation(HSCT)using haplo-identical related donors(HID).We aimed to establish a comprehensive model with machine learning,which could predict EBV reactivation after HID HSCT with anti-thymocyte globulin(ATG)for graft-versus-host disease(GVHD)prophylaxis.We enrolled 470 consecutive acute leukemia patients,60%of them(n=282)randomly selected as a training cohort,the remaining 40%(n=188)as a validation cohort.The equation was as follows:Probability(EBV reactivation)=1/1+exp(−Y),where Y=0.0250×(age)–0.3614×(gender)+0.0668×(underlying disease)–0.6297×(disease status before HSCT)–0.0726×(disease risk index)–0.0118×(hematopoietic cell transplantation-specific comorbidity index[HCT-CI]score)+1.2037×(human leukocyte antigen disparity)+0.5347×(EBV serostatus)+0.1605×(conditioning regimen)–0.2270×(donor/recipient gender matched)+0.2304×(donor/recipient relation)–0.0170×(mononuclear cell counts in graft)+0.0395×(CD34+cell count in graft)–2.4510.The threshold of probability was 0.4623,which separated patients into low-and high-risk groups.The 1-year cumulative incidence of EBV reactivation in the low-and high-risk groups was 11.0%versus 24.5%(P<.001),10.7%versus 19.3%(P=.046),and 11.4%versus 31.6%(P=.001),respectively,in total,training and validation cohorts.The model could also predict relapse and survival after HID HSCT.We established a comprehensive model that could predict EBV reactivation in HID HSCT recipients using ATG for GVHD prophylaxis.