Remote Heart Rhythm Monitoring by Photoplethysmography-Based Smartphone Technology After Cardiac Surgery: Prospective Observational Study
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is the most common arrhythmia after cardiac surgery, yet the precise incidence and significance of arrhythmias after discharge home need to be better defined. Photoplethysmography (PPG)-based smartphone apps are promising tools to enable early detection and follow-up of arrhythmias. OBJECTIVE: By using a PPG-based smartphone app, we aimed to gain more insight into the prevalence of AF and other rhythm-related complications upon discharge home after cardiac surgery and evaluate the implementation of this app into routine clinical care. METHODS: In this prospective, single-center trial, patients recovering from cardiac surgery were asked to register their heart rhythm 3 times daily using a Food and Drug Administration-approved PPG-based app, for either 30 or 60 days after discharge home. Patients with permanent AF or a permanent pacemaker were excluded. RESULTS: We included 24 patients (mean age 60.2 years, SD 12 years; 15/23, 65% male) who underwent coronary artery bypass grafting and/or valve surgery. During hospitalization, 39% (9/23) experienced postoperative AF. After discharge, the PPG app reported AF or atrial flutter in 5 patients. While the app notified flutter in 1 patient, this was a false positive, as electrocardiogram revealed a 2nd-degree, 2:1 atrioventricular block necessitating a permanent pacemaker. AF was confirmed in 4 patients (4/23, 17%) and interestingly, was associated with an underlying postoperative complication in 2 participants (pneumonia n=1, pericardial tamponade n=1). A significant increase in the proportion of measurements indicating sinus rhythm was observed when comparing the first to the second month of follow-up (P<.001). In the second month of follow-up, compliance was significantly lower with 2.2 (SD 0.7) measurements per day versus 3.0 (SD 0.8) measurements per day in the first month (P=.002). The majority of participants (17/23, 74%), as well as the surveyed primary care physicians, experienced positive value by using the app as they felt more involved in the postoperative rehabilitation. CONCLUSIONS: Implementation of smartphone-based PPG technology enables detection of AF and other rhythm-related complications after cardiac surgery. An association between AF detection and an underlying complication was found in 2 patients. Therefore, smartphone-based PPG technology may supplement rehabilitation after cardiac surgery by acting as a sentinel for underlying complications, rhythm-related or otherwise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".