Frailty Is Associated With Increased Odds of 30-Day Periprosthetic Joint Infection Following Primary Total Hip Arthroplasty
Bibliographic record
Abstract
INTRODUCTION: Periprosthetic joint infection (PJI) following total hip arthroplasty (THA) is associated with notable morbidity and mortality. Previous studies have explored risk scores for predicting complications following THA, such as wound infection and PJI. Despite an aging population with the number of elderly patients requiring THA increasing, few analyses have explored frailty as a risk factor. We analyzed a national database to determine whether frailty as measured by the modified 5-item frailty index (mFI-5) was associated with PJI within 30 days following THA. METHODS: The ACS-NSQIP database was queried from 2015 to 2020 for cases of primary THA with readmission or revision surgery within 30 days due to PJI. The variables used in the mFI-5 were heart failure, chronic obstructive pulmonary disease, hypertension, diabetes, and non-independent functional status. Patients were stratified into one of four frailty groups: robust (mFI-5 = 0), prefrail (mFI-5 = 1), frail (mFI-5 = 2), and severely frail (mFI-5 ≥ 3). Multivariable logistic regression controlling for age, female sex, smoking status, body mass index, and total operative time was performed to evaluate the influence of frailty on PJI. RESULTS: This study included 147,597 patients undergoing primary THA. Of these patients, 352 (0.2%) developed PJI within 30 days. A lower proportion of PJI patients were classified as robust, whereas a higher proportion were categorized as frail. Multivariable logistic regression demonstrated that frail patients were at an increased odds of developing PJI with each additional unit on the mFI-5 scale increasing the odds by 27.4%. CONCLUSION: This study is the first to demonstrate that frailty is an independent predictor of PJI following primary THA. Incorporating frailty screening during surgical candidate selection may assist with identifying high-risk patients.
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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 | 0.005 |
| 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".