Early Postoperative Mortality and Morbidity Following Elective Spine Surgery in Patients Over 80 Years of Age
Bibliographic record
Abstract
Study Design: Systematic review and meta-analysis. Objective: To systematically evaluate early postoperative morbidity and mortality after elective spine surgery in patients aged 80 years or older, and to critically appraise the adequacy and limitations of the current evidence to inform surgical risk stratification and patient counseling in this high-risk population. Summary of Background Data: The population aged 80 years or older is rapidly increasing, accompanied by rising demand for surgical management of degenerative spinal disease. Reported morbidity and mortality outcomes after elective spine surgery in this group vary widely, limiting accurate risk estimation. Improved evidence synthesis is needed to inform counseling and guide surgical decision-making. Methods: A comprehensive search of MEDLINE, Embase, and CINAHL identified studies published between 1996 and 2024 reporting patients aged 80 years or older undergoing elective spine surgery. Data on survival, complications, and comorbidities were extracted. Methodological quality was assessed with the Newcastle-Ottawa Scale and the Joanna Briggs Institute checklist. Random-effects meta-analyses were performed where feasible. Results: Twelve studies encompassing 89,529 patients met the inclusion criteria. Pooled 30-day survival was high (98%, 95% CI: 94–99), though estimates varied across studies. Complications were frequent: the pooled overall rate was 16.0%, increasing to 21.8% unweighted. Major complications occurred in 5% to 10% of patients and minor complications in 8% to 10%. Studies enrolled relatively healthy older adults (ASA II–III), yet even in this select group, morbidity remained substantial. Reporting was inconsistent, with half of the studies providing major complication data and one-third reporting minor complications, underscoring gaps in the literature. Conclusion: Elective spine surgery in selected octogenarians shows excellent short-term survival but carries clinically meaningful complication rates. Current evidence largely reflects outcomes in the healthiest older adults, suggesting these results represent a best-case scenario. Despite increasing demand, high-quality, standardized data are lacking, limiting our ability to provide evidence-based counseling and prepare health systems for the growing burden of spine disease in an aging population.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".