Thirty-day unplanned readmission rates and risk factors in spine surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Despite the rise in the incidence of spinal disorders due to the global aging population, advances in surgical techniques and perioperative care have led to an increase in the number of spinal surgeries.However, postoperative complications remain difficult to prevent completely, and the rate of unplanned readmissions within 30 days varies greatly and increases the burden on medical care.This study aims to integrate existing research data through meta-analysis to systematically assess the incidence and risk factors of unplanned readmission within 30 days in patients undergoing spinal surgery, providing evidence-based support for clinical decision-making. METHODS: For this systematic review and meta-analysis, we systematically searched PubMed, Web of Science, Embase, the Cochrane Library, CNKI, the Wanfang Medical Database, the VIP Database, and the CBM Database from January 1, 2015, to July 4, 2025, for cohort studies reporting 30-day unplanned readmission rates and associated risk factors in patients undergoing spinal surgery.The pooled incidence rates of 30-day unplanned readmission were calculated using the DerSimonian-Laird (DL) random-effects model. For risk factors, odds ratio (OR) and 95% confidence intervals (95% CIs) from multivariate analyses were extracted and pooled using the DL model, followed by Hartung-Knapp-Sidik-Jonkman (HKSJ) adjustment.The quality of evidence was assessed using the Newcastle-Ottawa Scale(NOS).This study is registered with PROSPERO(registration number:CRD420251045270). RESULTS: 19 studies (n = 367,027) reported a pooled readmission rate of 4.50%(95%CI:3.80-5.30, P < 0.05). Surgical site infection(SSI) was the leading cause of readmission, and that the mean time to postoperative readmission was 17.3 ± 5.6 days. After applying the HKSJ correction, multivariate analysis identified that ASA classification ≥ 3(OR = 1.70,95%CI:1.16-2.48), length of stay > 5d(OR = 2.17,95%CI:1.62-2.91),BMI ≥ 30(OR = 1.23,95%CI:1.06-1.44),hypertension(OR = 1.20,95%CI:1.08-1.33), tumor(OR = 2.33, 95%CI:1.25-4.35), multi-level fusion(OR = 1.27,95%CI:1.09-1.47), diabetes mellitus(OR = 1.28, 95%CI:1.07-1.52), chronic pulmonary disease(OR = 1.44,95%CI:1.11-1.88), peripheral vascular disease(OR = 1.25,95%CI:1.02-1.53), and age ≥ 60 years(OR = 1.44,95%CI:1.11-1.88) were significant risk factors for unplanned 30-day readmission in patients undergoing spinal surgery. CONCLUSION: The 30-day unplanned readmission rate of spinal surgery patients remains elevated, and we recommend implementing stratified management strategies, including:preoperative assessment and risk screening, intraoperative optimization of surgical techniques, and focused postoperative monitoring in high-risk patients to mitigate readmission risk and improve patient outcomes.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.052 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".