Intraoperative Periprosthetic Fractures in Total Hip Arthroplasty: A 1.6-Million-Patient Analysis of Complications, Costs, and the Challenges in AI-Based Prediction
Bibliographic record
Abstract
Background: Periprosthetic fractures following total hip arthroplasty are serious complications occurring in up to 2.4% of primary cases, contributing to significant morbidity, extended hospital stays, and elevated healthcare costs. Predicting these fractures remains a challenge despite advances in surgical techniques and prosthetic materials. Methods: This study analyzed 1,634,615 cases of primary THA from the NIS database (2016–2019) using propensity score matching to compare outcomes between patients with and without intraoperative periprosthetic fractures. Predictive models, including logistic regression, decision tree, and deep neural network, were evaluated for their ability to predict fracture risk. Results: Patients with periprosthetic fractures exhibited a 14-fold increase in pulmonary embolism risk, a 12-fold increase in infections, and a 5-fold increase in hip dislocations. Fractures extended hospital stays (3.8 vs. 2.5 days) and added approximately USD 32,000 in costs per patient. The predictive models yielded low accuracy (AUC max = 0.605), underscoring the complexity of predicting periprosthetic fractures. Conclusions: Intraoperative periprosthetic fractures in THA significantly elevate complication rates, costs, and length of stay. Despite extensive modeling efforts, accurate prediction remains difficult, highlighting the need to focus on preventive strategies, such as improved surgical techniques and real-time intraoperative monitoring.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".