Pattnaik, Harsha

The efficacy and safety of extracorporeal membrane oxygenation (ECMO) support during transcatheter aortic valve replacement (TAVR) remains unknown. We conducted a meta-analysis to compare benefit and risk of ECMO in TAVR patients. Bibliographic databases were searched from inception to January 1, 2024. Included studies involved patients ≥18 years old undergoing TAVR and using ECMO emergently or prophylactically. Mortality and procedure success were primary outcomes. Peri- or postoperative complications were the secondary outcomes. We identified 11 observational studies, including 2,275 participants (415 ECMO and 1,860 non-ECMO). The unadjusted mortality risk in ECMO-supported patient was higher than non-ECMO patients (odds ratio [OR] 1.73). The mortality unadjusted risk remained high (OR 3.89) and statistically significant for prophylactic ECMO. Prophylactic ECMO had lower mortality risk compared with emergent ECMO (OR 0.17). Extracorporeal membrane oxygenation-supported patients had lower procedural success rate (OR 0.10). Extracorporeal membrane oxygenation patients undergoing TAVR had significantly increased risk of bleeding (OR 3.32), renal failure (OR 2.38), postoperative myocardial infarction (OR 1.89), and stroke (OR 2.32) compared with non-ECMO patients. Clinical results are not improved by ECMO support in patients with high-risk TAVR. Prophylactic ECMO outperforms emergent. Overall, ECMO support increases mortality and postoperative complications. Transcatheter aortic valve replacement outcomes may improve with prophylactic ECMO in high-risk situations.

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision‐making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension‐related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real‐time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.