Is Frailty Associated with Adverse Outcomes After Orthopaedic Surgery?
Bibliographic record
Abstract
Background: There is increasing evidence supporting the association between frailty and adverse outcomes after surgery. There is, however, no consensus on how frailty should be assessed and used to inform treatment. In this review, we aimed to synthesize the current literature on the use of frailty as a predictor of adverse outcomes following orthopaedic surgery by (1) identifying the frailty instruments used and (2) evaluating the strength of the association between frailty and adverse outcomes after orthopaedic surgery. Methods: A systematic review was performed using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. PubMed, Scopus, and the Cochrane Central Register of Controlled Trials were searched to identify articles that reported on outcomes after orthopaedic surgery within frail populations. Only studies that defined frail patients using a frailty instrument were included. The methodological quality of studies was assessed using the Newcastle-Ottawa Scale (NOS). Study demographic information, frailty instrument information (e.g., number of items, domains included), and clinical outcome measures (including mortality, readmissions, and length of stay) were collected and reported. Results: The initial search yielded 630 articles. Of these, 177 articles underwent full-text review; 82 articles were ultimately included and analyzed. The modified frailty index (mFI) was the most commonly used frailty instrument (38% of the studies used the mFI-11 [11-item mFI], and 24% of the studies used the mFI-5 [5-item mFI]), although a large variety of instruments were used (24 different instruments identified). Total joint arthroplasty (22%), hip fracture management (17%), and adult spinal deformity management (15%) were the most frequently studied procedures. Complications (71%) and mortality (51%) were the most frequently reported outcomes; 17% of studies reported on a functional outcome. Conclusions: There is no consensus on the best approach to defining frailty among orthopaedic surgery patients, although instruments based on the accumulation-of-deficits model (such as the mFI) were the most common. Frailty was highly associated with adverse outcomes, but the majority of the studies were retrospective and did not identify frailty prospectively in a prediction model. Although many outcomes were described (complications and mortality being the most common), there was a considerable amount of heterogeneity in measurement strategy and subsequent strength of association. Future investigations evaluating the association between frailty and orthopaedic surgical outcomes should focus on prospective study designs, long-term outcomes, and assessments of patient-reported outcomes and/or functional recovery scores. Clinical Relevance: Preoperatively identifying high-risk orthopaedic surgery patients through frailty instruments has the potential to improve patient outcomes. Frailty screenings can create opportunities for targeted intervention efforts and guide patient-provider decision-making.
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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 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".