Abstract 4346591: Cardiovascular Diagnoses Following Smartwatch Irregular Pulse Notifications: Insights from the Apple Heart Study
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) is a leading cause of cardiovascular morbidity and mortality, and early detection is important for stroke prevention. The Apple Heart Study (AHS) demonstrated that wearable devices capable of irregular pulse notifications (IPN) can aid in identifying AF. The rates of subsequent diagnoses of cardiovascular disease (CVD) remain unknown. Hypothesis: We aimed to evaluate how smartwatch-detected IPNs are associated with key cardiovascular diagnoses. We hypothesized that, among AHS participants, receiving IPNs would be associated with increased odds of subsequently reporting major cardiovascular diagnoses compared to participants without IPNs. Methods: Out of 419,297 enrolled AHS participants, 288,533 (69%) individuals who completed the end-of-study survey (EOS) were included in this analysis. The self-reported diagnoses consisted of heart failure (HF), stroke, transient ischemic attack (TIA), myocardial infarction (MI), and pulmonary embolism (PE), considered individually and as a composite (MACE). Logistic regression was used to estimate odds ratios (ORs) for each diagnosis among IPN versus non-IPN participants, adjusted for sex, age, and CHA2DS2-VASc components. Results: The EOS survey was completed by 908 notified participants (42% among the IPN cohort) and 287,625 non-notified participants (69% among the non-IPN cohort). IPN recipients were older, more often male, and had a higher comorbidity burden and CVD risk factors than the total cohort (Table 1). The IPN cohort reported higher rates of HF, MI, stroke, TIA, and PE compared to those who did not receive an IPN (Table 2). The fully adjusted logistic regression analysis showed increased odds of each diagnosis among the IPN group (Figure 1). Conclusions: Participants with an IPN had higher odds of subsequently reporting major adverse cardiovascular diagnoses. These associations may reflect pre-existing conditions, underlying CVD risk, and comorbidities among participants who received an IPN, which has a high positive predictive value (PPV) for detecting AF. Given the elevated odds and high PPV, IPNs may warrant clinical consideration of evaluation for CVD or comorbidities, and future studies should assess the diagnostic and management pathways following IPNs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".