Assessment of Variation in Ambulatory Cardiac Monitoring Among Commercially Insured Patients
Bibliographic record
Abstract
OBJECTIVES: Ambulatory cardiac monitors (ACMs) enable heart rhythm monitoring for various durations, including Holter monitors (0-48 hours), long-term continuous monitors (LTCMs; 3-14 days), and external ambulatory event monitors (AEMs; up to 30 days). These devices detect intermittent or asymptomatic arrhythmias that might go unnoticed with a standard electrocardiogram. Previous research has explored variations in ACM use among Medicare beneficiaries. This study assessed the incidence of clinical and economic outcomes among commercially insured patients who had never had an arrhythmia diagnosis and received their first ACM. STUDY DESIGN: Retrospective cohort study using a large commercial claims database focused on patients without prior arrhythmia diagnoses who received their first ACM between 2016 and 2023. METHODS: Outcomes included new arrhythmia diagnoses, repeat ACM testing, cardiovascular (CV) events, and health care resource use and costs. Results were stratified by major ACM manufacturers using National Provider Identifiers. To minimize confounding, inverse probability of treatment weighting was used to balance covariates, and adjusted regression models were used to evaluate outcomes during follow-up. RESULTS: Of 428,707 patients meeting inclusion criteria, 36% used LTCMs, 36% used Holter monitors, and 27% used external AEMs. Adjusted analyses showed that a certain LTCM brand was associated with higher odds of a new arrhythmia diagnosis, fewer retests (except vs AEMs), lower odds of CV events, and less follow-up health care resource use and costs than other ACM types and manufacturers. CONCLUSIONS: Clinical and economic outcomes can vary by ACM type among commercially insured patients. A specific LTCM manufacturer demonstrated superior performance, with greater diagnoses of arrhythmia, fewer repeat tests, and fewer CV events compared with other ACM types and manufacturers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".