Cohort profile: patient characteristics and quality-of-life measurements for newly-referred patients with atrial fibrillation—Keio interhospital Cardiovascular Studies-atrial fibrillation (KiCS-AF)
Bibliographic record
Abstract
Purpose Besides the high rates of morbidity and mortality, atrial fibrillation (AF) is also associated with impairment of quality-of-life (QOL). However, reports covering non-selected AF population within Asian countries remain scarce. The objective of the Keio interhospital Cardiovascular Studies-atrial fibrillation (KiCS-AF) registry is to clarify the baseline and QOL profiles of the AF patients at the time of initial referral to identify areas for improvement and country-specific gaps. Participants The KiCS-AF registry is a multicentre, prospective cohort study designed to specifically recruit AF patients newly referred to the 11 network hospitals within the Kanto area of Japan. The registry completed its enrolment in June 2018. All patients were requested to answer the Atrial Fibrillation Effect on Quality-of-Life (AFEQT) questionnaire both at baseline and 1 year, with planned clinical follow-up for 5 years. The registry also assessed individual treatment strategies including rate and rhythm control, stroke prophylaxis, and their impacts on patient-reported QOL. Findings to date As of December 2016, 2464 AF patients were registered; their mean age was 67.1 years (SD, 11.7), majority (69.7%; n=1717) were men and 49.2% presented with paroxysmal AF. The mean CHA 2 DS 2 -VASc (cardiac failure or dysfunction, hypertension, age ≥75 years, diabetes, stroke including vascular disease, age 65-74 years, and sex category [female]) score was 2.3 (SD, 1.6) and oral anticoagulant therapy was used for 88.6% of patients with CHA 2 DS 2 -VASc scores ≥2. The median AFEQT-overall summary score was 79.1 (IQR, 66.6–89.1). Roughly 50% had significantly impaired QOL (ie, AFEQT <80) at baseline. Currently, 2307 eligible patients (93.6%) have completed the 1-year follow-up, of which 2072 patients (89.8%) answered the second AFEQT questionnaire. Future plans The KiCS-AF allowed for extensive investigation of AF-related QOL in a non-selected population with long-term follow-up using a rigorously validated QOL assessment tool. Almost half of patients had impaired QOL at baseline. Further investigations aimed at providing care and improving patient-reported QOL are required.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".