Patient clusters and cost trajectories in the Swiss Atrial Fibrillation cohort
Bibliographic record
Abstract
OBJECTIVE: Evidence on long-term costs of atrial fibrillation (AF) and associated factors is scarce. As part of the Swiss-AF prospective cohort study, we aimed to characterise AF costs and their development over time, and to assess specific patient clusters and their cost trajectories. METHODS: Swiss-AF enrolled 2415 patients with variable duration of AF between 2014 and 2017. Patient clusters were identified using hierarchical cluster analysis of baseline characteristics. Ongoing yearly follow-ups include health insurance clinical and claims data. An algorithm was developed to adjudicate costs to AF and related complications. RESULTS: A subpopulation of 1024 Swiss-AF patients with available claims data was followed up for a median (IQR) of 3.24 (1.09) years. Average yearly AF-adjudicated costs amounted to SFr5679 (€5163), remaining stable across the observation period. AF-adjudicated costs consisted mainly of inpatient and outpatient AF treatment costs (SFr4078; €3707), followed by costs of bleeding (SFr696; €633) and heart failure (SFr494; €449). Hierarchical analysis identified three patient clusters: cardiovascular (CV; N=253 with claims), isolated-symptomatic (IS; N=586) and severely morbid without cardiovascular disease (SM; N=185). The CV cluster and SM cluster depicted similarly high costs across all cost outcomes; IS patients accrued the lowest costs. CONCLUSION: Our results highlight three well-defined patient clusters with specific costs that could be used for stratification in both clinical and economic studies. Patient characteristics associated with adjudicated costs as well as cost trajectories may enable an early understanding of the magnitude of upcoming AF-related healthcare costs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".