Frailty and Postoperative Outcomes in Patients Undergoing Surgery for Degenerative Spine Disease
Bibliographic record
Abstract
Introduction Frailty is defined as a state of decreased reserve and susceptibility to stressors. The relationship between frailty and outcomes after degenerative spine surgery has not been studied. Objectives (1) Determine prevalence of frailty in the degenerative spine population; (2) Describe patient characteristics associated with frailty; (3) Determine the ability of frailty to predict postoperative outcomes. Material and Methods We analyzed 52,671 patients in the National Surgical Quality Improvement Program who underwent degenerative spine surgery. A modified frailty index (mFI) was used to determine the prevalence and severity of frailty as previously described. The association of frailty with postoperative outcomes was determined using multivariate logistic regression. Results Frailty was present in 2,041 patients within the total population (4%), and 8% of patients older than 65 years. Frailty severity increased with increasing age, male sex, African-American race, higher body mass index, recent weight loss, paraplegia or quadriplegia, ASA score, and pre-admission residence in a care facility. Frailty severity was an independent predictor of major complication (OR 1.15 for every 0.10 increase in mFI, 95%CI 1.09–1.22, p < 0.0005), and specifically predicted re-operation for post-surgical infection (OR 1.3, 95%CI 1.16–1.46, p < 0.0005). Prolonged length of stay and discharge to a new facility were also independently predicted by frailty severity ( p < 0.0005). Frailty severity predicted 30-day mortality on unadjusted (OR 2.05, 95%CI 1.69–2.47, p < 0.0005) and adjusted analysis (OR 1.44, 95%CI 1.15–1.81, p < 0.005). Conclusions Frailty is an important predictor of postoperative outcomes following degenerative spine surgery. Preoperative recognition of frailty may be useful for perioperative optimization, risk stratification and patient counseling.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".