Explainable machine learning using echocardiography to improve risk prediction in patients with chronic coronary syndrome.

Aims: The European Society of Cardiology guidelines recommend risk stratification with limited clinical parameters such as left ventricular (LV) function in patients with chronic coronary syndrome (CCS). Machine learning (ML) methods enable an analysis of complex datasets including transthoracic echocardiography (TTE) studies. We aimed to evaluate the accuracy of ML using clinical and TTE data to predict all-cause 5-year mortality in patients with CCS and to compare its performance with traditional risk stratification scores. Methods and results: Data of consecutive patients with CCS were retrospectively collected if they attended the outpatient clinic of Amsterdam UMC location AMC between 2015 and 2017 and had a TTE assessment of the LV function. An eXtreme Gradient Boosting (XGBoost) model was trained to predict all-cause 5-year mortality. The performance of this ML model was evaluated using data from the Amsterdam UMC location VUmc and compared with the reference standard of traditional risk scores. A total of 1253 patients (775 training set and 478 testing set) were included, of which 176 patients (105 training set and 71 testing set) died during the 5-year follow-up period. The ML model demonstrated a superior performance [area under the receiver operating characteristic curve (AUC) 0.79] compared with traditional risk stratification tools (AUC 0.62-0.76) and showed good external performance. The most important TTE risk predictors included in the ML model were LV dysfunction and significant tricuspid regurgitation. Conclusion: This study demonstrates that an explainable ML model using TTE and clinical data can accurately identify high-risk CCS patients, with a prognostic value superior to traditional risk scores.

The impact of valvular heart disease in patients with chronic coronary syndrome.

Background: The European Society of Cardiology 2019 Guidelines on chronic coronary syndrome (CCS) recommend echocardiographic measurement of the left ventricular function for risk stratification in all patients with CCS. Whereas CCS and valvular heart disease (VHD) share common pathophysiological pathways and risk factors, data on the impact of VHD in CCS patients are scarce. Methods: Clinical data including treatment and mortality of patients diagnosed with CCS who underwent comprehensive transthoracic echocardiography (TTE) in two tertiary centers were collected. The outcome was all-cause mortality. Data were analyzed with Kaplan-Meier curves and Cox proportional hazard analysis adjusting for significant covariables and time-dependent treatment. Results: Between 2014 and 2021 a total of 1,984 patients with CCS (59% men) with a median age of 65 years (interquartile range [IQR] 57-73) underwent comprehensive TTE. Severe VHD was present in 44 patients and moderate VHD in 325 patients. A total of 654 patients (33%) were treated with revascularization, 39 patients (2%) received valve repair or replacement and 299 patients (15%) died during the median follow-up time of 3.5 years (IQR 1.7-5.6). Moderate or severe VHD (hazard ratio = 1.33; 95% CI 1.02-1.72) was significantly associated with mortality risk, independent of LV function and other covariables, as compared to no/mild VHD. Conclusions: VHD has a significant impact on mortality in patients with CCS additional to LV dysfunction, which emphasizes the need for a comprehensive echocardiographic assessment in these patients.

Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease.

PURPOSE OF REVIEW: Artificial intelligence (AI) applications in (interventional) cardiology continue to emerge. This review summarizes the current state and future perspectives of AI for automated imaging analysis in invasive coronary angiography (ICA). RECENT FINDINGS: Recently, 12 studies on AI for automated imaging analysis In ICA have been published. In these studies, machine learning (ML) models have been developed for frame selection, segmentation, lesion assessment, and functional assessment of coronary flow. These ML models have been developed on monocenter datasets (in range 31-14,509 patients) and showed moderate to good performance. However, only three ML models were externally validated. Given the current pace of AI developments for the analysis of ICA, less-invasive, objective, and automated diagnosis of CAD can be expected in the near future. Further research on this technology in the catheterization laboratory may assist and improve treatment allocation, risk stratification, and cath lab logistics by integrating ICA analysis with other clinical characteristics.

ICON 2020-International Scientific Tendinopathy Symposium Consensus: A Systematic Review of Outcome Measures Reported in Clinical Trials of Achilles Tendinopathy.

BACKGROUND: Nine core domains for tendinopathy have been identified. For Achilles tendinopathy there is large variation in outcome measures used, and how these fit into the core domains has not been investigated. OBJECTIVE: To identify all available outcome measures outcome measures used to assess the clinical phenotype of Achilles tendinopathy in prospective studies and to map the outcomes measures into predefined health-related core domains. DESIGN: Systematic review. DATA SOURCES: Embase, MEDLINE (Ovid), Web of Science, CINAHL, The Cochrane Library, SPORTDiscus and Google Scholar. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Clinical diagnosis of Achilles tendinopathy, sample size ≥ ten participants, age ≥ 16 years, and the study design was a randomized or non-randomized clinical trial, observational cohort, single-arm intervention, or case series. RESULTS: 9376 studies were initially screened and 307 studies were finally included, totaling 13,248 participants. There were 233 (177 core domain) different outcome measures identified across all domains. For each core domain outcome measures were identified, with a range between 8 and 35 unique outcome measures utilized for each domain. The proportion of studies that included outcomes for predefined core domains ranged from 4% for the psychological factors domain to 72% for the disability domain. CONCLUSION: 233 unique outcome measures for Achilles tendinopathy were identified. Most frequently, outcome measures were used within the disability domain. Outcome measures assessing psychological factors were scarcely used. The next step in developing a core outcome set for Achilles tendinopathy is to engage patients, clinicians and researchers to reach consensus on key outcomes measures. PROSPERO REGISTRATION: CRD42020156763.