Intraoperative Periprosthetic Fractures in Total Hip Arthroplasty: A 1.6-Million-Patient Analysis of Complications, Costs, and the Challenges in AI-Based Prediction
Abstract
:1. Introduction
Research Questions
2. Methods
2.1. Dataset Acquisition and Inclusion Criteria
2.2. Patient Identification and Exclusions
2.3. Statistical Analyses and Propensity Score Matching
2.4. Outcome Measures and Procedure Identification
2.5. Intraoperative Periprosthetic Fracture Prediction Models
2.6. Ethical Aspects
3. Results
3.1. Analysis of Comorbidities in Patients with and Without Intraoperative Periprosthetic Fractures
3.2. Propensity-Score-Matched Analysis
3.3. Comparison of Length of Stay and Total Charges Between Patients with and Without Intraoperative Periprosthetic Fracture in Propensity-Score-Matched Cohorts
3.4. Odds Ratios of Complications in Patients with Intraoperative Periprosthetic Fractures Compared to Those Without Fractures
3.5. Intraoperative Periprosthetic Fracture Prediction Models
4. Discussion
- Main Findings
- Clinical Implications of Periprosthetic Fractures and Dislocations
- Economic Burden and Healthcare Resource Utilization
4.1. Challenges in Predicting Periprosthetic Fractures
4.2. Limitations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AUC | Area under the curve |
HCUP | Healthcare Cost and Utilization Project |
IBD | Inflammatory bowel disease |
DNN | Deep neural network |
ICD-10 | International Classification of Diseases, 10th Revision |
LOS | Length of stay |
NIS | Nationwide Inpatient Sample |
SPSS | Statistical Package for the Social Sciences |
THA | Total hip arthroplasty |
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Parameter | No Intraoperative Periprosthetic Fracture | Intraoperative Periprosthetic Fracture | Significance |
---|---|---|---|
Average age (y) | 65.5 | 66.8 | p < 0.0001 |
Female (%) | 55.3% | 65.3% | p < 0.0001 |
Primary expected payer—Medicare (%) | 54.6% | 60.9% | p < 0.001 |
Primary expected payer—Medicaid (%) | 5.0% | 7.7% | p < 0.001 p < 0.001 |
Primary expected payer—private, including HMO (%) | 37.2% | 28.2% | |
Primary expected payer—self-pay (%) | 0.7% | 1.0% | |
Primary expected payer—no charge (%) | 0.1% | 0.1% | |
Primary expected payer—other (%) | 2.4% | 2.1% | |
Location/teaching status of hospital (STRATA)—Rural (%) | 7.6% | 10.7% | |
Location/teaching status of hospital (STRATA)—Urban nonteaching (%) | 24.8% | 22.7% | p < 0.001 |
Location/teaching status of hospital (STRATA)—Urban teaching (%) | 67.6% | 66.6% |
Parameter | No Intraoperative Periprosthetic Fracture | Intraoperative Periprosthetic Fracture | Significance |
---|---|---|---|
Hypertension | 52.1% | 54.1% | p < 0.001 |
Dyslipidemia | 42.3% | 42.7% | p = 0.401 |
Obstructive Sleep Apnea | 10.1% | 9.3% | p = 0.005 |
Chronic Anemia | 5.7% | 7.9% | p < 0.001 |
Alcohol Abuse | 1.5% | 2.5% | p < 0.001 |
Osteoporosis | 4.6% | 8.8% | p < 0.001 |
Type 2 Diabetes Mellitus | 14.9% | 17.0% | p < 0.001 |
Chronic Kidney Disease | 6.4% | 8.9% | p < 0.001 |
Congestive Heart Failure | 1.2% | 2.1% | p < 0.001 |
Chronic Lung Disease | 6.6% | 10.2% | p < 0.001 |
History of Myocardial Infarction | 3.4% | 4.4% | p < 0.001 |
History of Cerebrovascular Accident | 3.8% | 5.5% | p < 0.001 |
Dementia | 0.5% | 1.5% | p < 0.001 |
Neoplasms | 1.4% | 1.5% | p = 0.123 |
Neoplasms of Lymphoid and Hematopoietic Tissue | 0.6% | 0.5% | p = 0.152 |
Peripheral Vascular Disease | 1.7% | 2.0% | p = 0.010 |
BMI < 30 | 76.5% | 75.9% | p < 0.001 |
Obesity | 11.2% | 10.3% | p < 0.001 |
Morbid Obesity | 12.2% | 13.8% | p < 0.001 |
Parameter | No Intraoperative Periprosthetic Fracture | Intraoperative Periprosthetic Fracture | Significance |
---|---|---|---|
Total Surgeries (Number) | 12,905 | 12,905 | |
Average Age (Years) | 66.8 | 66.8 | p = 0.90 |
Female (%) | 65.4 | 65.3 | p = 0.89 |
Payer—Medicare (%) | 61.3 | 60.9 | p = 0.06 |
Payer—Medicaid (%) | 7.5 | 7.7 | |
Payer—Private (%) | 28.4 | 28.2 | |
Payer—Self-pay (%) | 0.7 | 1.0 | |
Payer—No charge(%) | 0.0 | 0.1 | |
Payer—Other (including Self-pay) (%) | 2.1 | 2.1 | |
Hypertension Diagnosis (%) | 54.2 | 54.1 | p = 0.80 |
Dyslipidemia Diagnosis (%) | 43.1 | 42.7 | p = 0.53 |
Sleep Apnea Diagnosis (%) | 9.5 | 9.3 | p = 0.59 |
Chronic Anemia (%) | 7.3 | 7.9 | p = 0.08 |
Alcohol Abuse (%) | 2.4 | 2.5 | p = 0.55 |
Osteoporosis (%) | 8.9 | 8.8 | p = 0.83 |
Type 2 Diabetes (%) | 17.2 | 17.0 | p = 0.68 |
Renal Disease (%) | 8.8 | 8.9 | p = 0.66 |
Chronic Heart Failure (%) | 2.1 | 2.1 | p = 0.65 |
Chronic Lung Disease (%) | 10.3 | 10.2 | p = 0.68 |
History of MI (%) | 4.2 | 4.4 | p = 0.07 |
Peripheral Vascular Disease (%) | 2.1 | 2.0 | p = 0.51 |
History of CVA (%) | 5.3 | 5.5 | p = 0.07 |
Neoplasms (%) | 1.3 | 1.5 | p = 0.07 |
Neoplasms (Lymphoid/Hematopoietic) (%) | 0.6 | 0.5 | p = 0.09 |
BMI < 30 | 75.8 | 75.9 | p = 0.09 |
Obesity | 10.5 | 10.3 | |
Morbid Obesity | 13.7 | 13.8 |
Parameter | No Intraoperative Periprosthetic Fracture | Intraoperative Periprosthetic Fracture | Significance |
---|---|---|---|
Length of stay mean in days | 2.5 (Std. deviation 1.7) | 3.8 (Std. deviation 6.0) | p < 0.0001 |
Total charges mean in USD | 60,413 (Std. deviation 32,800) | 93,184 (Std. deviation 78,611) | p < 0.0001 |
Metric | Logistic Regression (Periprosthetic Fracture) | Decision Tree (Periprosthetic Fracture) | Deep Neural Network (Periprosthetic Fracture) | Deep Neural Network (Blood Transfusions) |
---|---|---|---|---|
AUC (Test Set) | 0.60 | 0.50 | 0.60 | 0.820 |
Calibration Intercept (Test Set) | −5.46 | −4.84 | −4.86 | −0.50 |
Brier Score (Test Set) | 0.008 | 0.009 | 0.008 | 0.120 |
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Share and Cite
Maman, D.; Steinfeld, Y.; Yonai, Y.; Fournier, L.; Bar, O.; Safir, O.; Berkovich, Y. Intraoperative Periprosthetic Fractures in Total Hip Arthroplasty: A 1.6-Million-Patient Analysis of Complications, Costs, and the Challenges in AI-Based Prediction. J. Clin. Med. 2024, 13, 6862. https://doi.org/10.3390/jcm13226862
Maman D, Steinfeld Y, Yonai Y, Fournier L, Bar O, Safir O, Berkovich Y. Intraoperative Periprosthetic Fractures in Total Hip Arthroplasty: A 1.6-Million-Patient Analysis of Complications, Costs, and the Challenges in AI-Based Prediction. Journal of Clinical Medicine. 2024; 13(22):6862. https://doi.org/10.3390/jcm13226862
Chicago/Turabian StyleMaman, David, Yaniv Steinfeld, Yaniv Yonai, Linor Fournier, Ofek Bar, Oleg Safir, and Yaron Berkovich. 2024. "Intraoperative Periprosthetic Fractures in Total Hip Arthroplasty: A 1.6-Million-Patient Analysis of Complications, Costs, and the Challenges in AI-Based Prediction" Journal of Clinical Medicine 13, no. 22: 6862. https://doi.org/10.3390/jcm13226862
APA StyleMaman, D., Steinfeld, Y., Yonai, Y., Fournier, L., Bar, O., Safir, O., & Berkovich, Y. (2024). Intraoperative Periprosthetic Fractures in Total Hip Arthroplasty: A 1.6-Million-Patient Analysis of Complications, Costs, and the Challenges in AI-Based Prediction. Journal of Clinical Medicine, 13(22), 6862. https://doi.org/10.3390/jcm13226862