The Association of Poor Preoperative Mental Health and Outcomes After Surgical Correction of Adult Spinal Deformity: A Systematic Review and Meta Analysis
Bibliographic record
Abstract
Background/Objectives: Adult Spinal Deformity (ASD) is a pathologic malalignment of the spine that can lead to significant reductions in quality of life, functional limitations, and increased morbidity. While poor mental health is commonly observed among patients undergoing ASD surgery, its impact on surgical outcomes remains poorly understood. We conducted a systematic review and meta-analysis to examine the association between preoperative mental health and outcomes following surgical correction for ASD. Methods: A comprehensive search of MEDLINE, Embase, Web of Science, and Scopus was performed from inception to April 2025 to identify studies investigating the relationship between preoperative mental health and postoperative health-related quality of life outcomes or complications. Data was pooled using a restricted maximum likelihood (REML) random-effects model. Heterogeneity was assessed using Cochran’s Q statistic, and between-study variance was reported as τ2. Study quality was assessed with the Newcastle–Ottawa Scale, and risk of bias was evaluated using the ROBINS-I tool. Results: Twenty-four studies comprising a total of 248,427 patients met inclusion criteria. In pooled analyses, patients with poor preoperative mental health showed comparable improvements in health-related quality of life measures after surgery (standardized mean difference [SMD] −0.04, 95% CI −0.30 to 0.22; I2 = 91.5%, τ2 = 0.42) and in pain scores (SMD −0.15, 95% CI −0.42 to 0.11; I2 = 71.8%, τ2 = 0.09). However, patients with poor mental health had significantly higher odds of postoperative complications (odds ratio [OR] 1.44, 95% CI 1.23 to 1.67; I2 = 97.4%, τ2 = 0.08). These patients also demonstrated worse preoperative disease severity (SMD –0.94, 95% CI −1.41 to −0.47; I2 = 95.5%, τ2 = 1.64) and worse postoperative disease severity (SMD –0.34, 95% CI −0.44 to −0.25; I2 = 48.9%, τ2 = 0.03). Conclusions: While patients with poor preoperative mental health have a greater disease severity both before and after ASD surgery, they appear to experience comparable benefits from surgical intervention compared to those without. Recognizing and managing mental health may be useful in preoperative management of ASD patients. Further prospective studies to further elucidate these associations are necessary.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".