Unplanned early hospital readmissions in a vascular surgery population
Bibliographic record
Abstract
Background: Patients who undergo vascular surgery are burdened by high early readmission rates. We examined the frequency and cause of early readmissions after elective and emergent admission to the vascular surgery service at our institution to identify modifiable targets for quality improvement. Methods: Over a 5-year period, all patients admitted and readmitted to the vascular surgery service were identified. Medical records were then individually reviewed to identify baseline characteristics from the index admission and the most responsible diagnosis for readmission within 28 days of discharge. Results: Of a total of 3324 patients, 421 (12.7%) were readmitted to our institution within 28 days of discharge. Forty-seven were found to have more than 1 readmission following their index admission. The readmission rate ranged from 11.8% to 14.1% over the 5-year study period, resulting in an average readmission rate of 12.7%. There were similar rates for men (12.9%) and women (12.3%). Of the readmitted cases, 236 (63.1%) were unplanned readmissions. The most common diagnoses for unplanned readmissions were worsening of peripheral arterial disease status including complications related to peripheral bypass graft (30.9%), surgical site infections (15.3%) and nonsurgical infections (14.8%). Conclusion: To reduce readmission rates effectively, institutions must identify highrisk patients. In our study cohort, the most frequent pathology resulting in readmission was peripheral arterial disease. The most frequent preventable reason for readmission was surgical site infection. Interventions focused on early assessment of clinical status and wounds in addition to avoidance of infectious complications could help reduce readmission rates. Preventive resources can be efficiently targeted by focusing on subgroups at risk for readmission.
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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 | 0.001 | 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) | 0.001 | 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".