Real world evidence on health care resource utilization and economic burden of arrhythmias in patients with COPD
Bibliographic record
Abstract
AIMS: Cardiac arrhythmias are common in chronic obstructive pulmonary disease (COPD) and are associated with poor outcomes. Their impact on healthcare resource utilization (HCRU) and costs is increasingly recognized. The incremental economic burden of arrhythmias in patients with COPD and the impact of early detection using ambulatory electrocardiogram (AECG) monitors remain unknown. This real-world analysis quantified incremental HCRU and costs associated with arrhythmia in COPD, and the impact of early detection through AECG monitoring. METHODS: We conducted a retrospective claims analysis using the Merative MarketScan database (2006-2024) to identify patients with COPD aged ≥ 18 years. We examined three sub-cohort pairs of these patients, with and without arrhythmia, and those who underwent AECG monitoring, comparing demographics, hospitalization rates, all-cause readmissions within 30 days, emergency room visits, and total care costs. Each cohort pair was reweighted using entropy balancing on the following baseline variables: age, sex, region, insurance type, and comorbidities. The HCRU and cost drivers were analyzed over 24 months from the index date, and significance was assessed using Tweedie distributions for costs and zero-inflated binomial distributions for HCRU rates. RESULTS: Healthcare costs and HCRU were significantly higher in the 71,585 patients with COPD and arrhythmia than in the 261,188 matched patients without arrhythmia. In the second comparison, patients with COPD and arrhythmia who were monitored using AECG had a higher utilization rate than their counterparts who did not have arrhythmia. In the third comparison, patients with COPD and arrhythmia who were monitored using AECG had lower utilization than their counterparts who were never monitored. LIMITATIONS: As a retrospective analysis of claims data, this study was limited by the population under investigation and the availability of the measured variables. CONCLUSIONS: Arrhythmias in COPD substantially increase healthcare utilization and costs. Early detection by AECG monitoring may mitigate this burden.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".