Rates, Causes, and Predictive Factors of Hospital Readmissions After Spine Surgery for Lumbar Spinal Stenosis: A Nationwide Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine the rates, causes, and predictive factors of readmissions at different periods following spine surgery, up to 180 days. METHODS: This study utilized data from the 2018 to 2019 Nationwide Readmissions Database and included four postoperative periods: 0 to 7 days, 8 to 30 days, 31 to 90 days, and 91 to 180 days. The causes of readmissions and potential predictive factors were systematically identified. All analyses were performed for each period. RESULTS: For the 180,281 patients (mean age, 65.4 years) included, 2.4% were readmitted between 0 and 7 days, 3.5% between 8 and 30 days, 3.7% between 31 and 90 days, and 4.3% between 91 and 180 days (cumulative rates: 2.4%, 5.9%, 9.3%, and 12.1%, respectively). The causes of readmissions varied across different periods: surgical site-related causes predominated within the first 30 days, whereas nonsurgical site-related causes were more prevalent from 31 to 180 days; other surgical care complication (e.g., infection) was the most prevalent cause between 0 and 7 days (10.7%) and between 8 and 30 days (29.2%), while spondylopathies/spondyloarthropathy (e.g., spinal stenosis) were the leading causes between 31 and 90 days (12.6%) and between 91 and 180 days (17.5%). The predictive factors associated with readmissions also varied across different periods. For example, patients who underwent fusion was associated with a decreased risk of readmissions between 31 and 180 days (e.g., between 91 and 180 days: odds ratio [OR], 0.79; 95% confidence interval [CI], 0.72-0.86; p<0.001), rather than between 0 and 30 days (e.g., between 0 and 7 days: OR, 0.99; 95% CI, 0.90-1.08; p=0.81). CONCLUSION: About 6% of patients with lumbar spinal stenosis who underwent spine surgery were readmitted within 30 days and 12% by 180 days. The causes of readmissions and predictive factors varied by period, providing valuable insights for quality improvement efforts and the burden of readmission reductions.
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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 | 0.000 | 0.002 |
| 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".