Abstract 325: Readmission among Hospitalized Patients with Non-Valvular Atrial Fibrillation
Bibliographic record
Abstract
Background: An important goal of healthcare reform is reducing the need for hospital readmissions. This study examined readmission rates, reasons for readmissions, and risk factors associated with readmissions in non-valvular atrial fibrillation (NVAF) patients, which may facilitate identification of potential gaps in care. Methods: Patients with AF hospitalizations in any diagnostic position in 2004-2009 were extracted from a large, national commercial and Medicare supplemental administrative claims database. Patients with valvular or transient causes of AF, under the age of 18 years, pregnant, or dead at discharge were excluded from the study. All patients had at least 30 days follow up from the index hospitalization discharge date. Readmission rate within 30 days of discharge date was calculated. Reasons for readmission were reported by ICD-9 diagnosis codes in the primary position. ICD-9 diagnosis codes were grouped into common acute conditions (e.g., ischemic heart disease, cerebrovascular disease) and reported. Logistic regression analyses were conducted to identify risk factors for readmission, controlling for patients’ demographic and clinical characteristics. Results: A total of 6439 patients met the study criteria. The overall 30-day readmission rate was 18.0%. Readmission rates for patients with AF as primary or secondary diagnosis in index admissions were 11.8% and 20.3%, respectively (p<0.001). Readmissions on average occurred 9.7 (SD 9.0) days from index admission discharge, with a mean readmission length of stay (LOS) of 7.4 (SD 8.0) days. The 4 most common grouped diagnoses for readmissions were AF (ICD-9 code 427.31, 10.2% of all readmissions), ischemic heart disease (IHD; 410.xx - 414.xx, 7.2%), heart failure (HF; 428.xx, 7.1%), and cerebrovascular disease (CVD; 430.xx - 438.xx, 6.0%). Longer LOS in the index admission, higher Charlson comorbidity index, and emergency room admission for the index admission all significantly increased the likelihood of having a readmission (p<0.001 in all cases). Patients discharged to home from index admission, patients with AF as primary diagnosis in index admissions, and patients living in the South region were less likely to be readmitted (p<0.01 in all cases). Conclusions: Almost one fifth of patients with NVAF were readmitted within 30 days of discharge. AF, IHD, HF, and CVD were the most common reasons for readmission. Identification of risk factors for readmission may assist healthcare providers in targeting good clinical practice aimed at improving quality of care and reducing the need for readmissions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".