Back

Cardiac Health Assessment using a wearable device before and after TAVI

Eerdekens, R.; Zelis, J. M.; Ter Horst, H.; Crooijmans, C.; van 't Veer, M.; Keulards, D. C.; Kelm, M.; Archer, G.; Kuehne, T.; Brueren, G. R.; Wijnbergen, I.; Johnson, N. P.; Tonino, P.

2023-03-24 cardiovascular medicine
10.1101/2023.03.22.23287604 medRxiv
Show abstract

BackgroundDue to the aging of the population, the prevalence of aortic valve stenosis will increase dramatically in upcoming years. Consequently Transcatheter Aortic Valve Implantation (TAVI) procedures will also expand worldwide. Optimal selection of patients who benefit with improved symptoms and prognosis is key since TAVI is not without risk. Currently we are not able to adequately predict functional outcome after TAVI. Quality of life measurement tools and traditional functional assessment tests do not always agree and can depend on factors unrelated to heart disease. Activity tracking using wearable devices might provide a more comprehensive assessment. ObjectivesIdentify objective parameters from a wearable device (the Philips Health Watch) associated with improvement after TAVI for severe aortic stenosis. Methods and results100 patients undergoing routine TAVI wore a Philips Health Watch for one week before and after the procedure. Watch data were analyzed offline: 97 before and 75 after TAVI. Parameters like the total number of steps and activity time did not change, in contrast to improvements in the six-minute walking test (6MWT) and physical limitation domain of a questionnaire (transformed WHOQOL-BREF). ConclusionsThese findings in an elderly TAVI population show that watch-based parameters like the number of steps do not change after TAVI, unlike traditional 6MWT and QoL assessments that do improve. Basic wearable device parameters might be less appropriate for measurement of treatment effects from TAVI.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.