Incidence and Risk Factors for Postoperative Delirium in Patients Undergoing Spine Surgery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The present study aims to investigate the incidence and risk factors associated with postoperative delirium in patients undergoing spine surgery. METHODS: PubMed, EMBASE, Cochrane Library, and Science Citation Index were searched up to August 2019 for studies examining postoperative delirium following spine surgery. Incidence and risk factors associated with delirium were extracted. Odds ratios (OR) and 95% confidence intervals (CI) were calculated for outcomes. The Newcastle-Ottawa Scale (NOS) was used for the study quality evaluation. RESULTS: The final analysis includes a total of 40 studies. The pooled analysis reveals that incidence of delirium is 8%, and there are significant differences for developing delirium in age (OR 1.07; 95% CI 1.04-1.09), age more than 65 (OR 4.77; 95% CI 4.37-5.16), age more than 70 (OR 15.87; 95% CI 6.03-41.73), and age more than 80 (OR 1.91; 95% CI 1.78-2.03) years, male (OR 0.81; 95% CI 0.76-0.86), a history of alcohol abuse (OR 2.11; 95% CI 1.67-2.56), anxiety (OR 1.74; 95% CI 1.04-2.44), congestive heart failure (OR 1.4; 95% CI 1.21-1.6), depression (OR 2.5; 95% CI 1.52-3.49), hypertension (OR 1.12; 95% CI 1.04-1.2), kidney disease (OR 1.41; 95% CI 1.16-1.66), neurological disorder (OR 4.66; 95% CI 4.22-5.11), opioid use (OR 1.86; 95% CI 1.18-2.54), psychoses (OR 2.77; 95% CI 2.29-3.25), pulmonary disease (OR 1.81; 95% CI 1.27-2.35), higher mini-mental state examination (OR 0.7; 95% CI 0.5-0.89), preoperative pain (OR 1.88; 95% CI 1.11-2.64), and postoperative urinary tract infection (OR 5.68; 95% CI 2.41-13.39). CONCLUSIONS: A comprehensive understanding of incidence and risk factors of delirium can improve prevention, diagnosis, and management. Risk of postoperative delirium can be reduced based upon identifiable risk factors.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.009 | 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) | 0.002 | 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".