Predicting 30-Day Venous Thromboembolism Following Total Joint Arthroplasty: Adjusting for Trends in Annual Length of Stay

被引:0
作者
Lex, Johnathan R. [1 ,2 ]
Koucheki, Robert [1 ,2 ]
Abbas, Aazad [1 ]
Wolfstadt, Jesse I. [1 ,3 ]
McLawhorn, Alexander S. [4 ]
Ravi, Bheeshma [1 ,5 ]
机构
[1] Univ Toronto, Temerty Fac Med, Dept Surg, Div Orthopaed Surg, Toronto, ON, Canada
[2] Univ Toronto, Inst Biomed Engn, Toronto, ON, Canada
[3] Sinai Hlth, Mt Sinai Hosp, Granovsky Gluskin Div Orthopaed Surg, Toronto, ON, Canada
[4] Hosp Special Surg, Dept Orthoped Surg, New York, NY USA
[5] Sunnybrook Hlth Sci Ctr, Div Orthopaed Surg, Toronto, ON, Canada
关键词
Machine learning; Venous thromboembolism; Outcome prediction; Total hip arthroplasty; Total knee arthroplasty; TOTAL KNEE ARTHROPLASTY; DEEP-VEIN THROMBOSIS; RISK-FACTORS; TOTAL HIP; PROPHYLAXIS; ASSOCIATION;
D O I
10.1016/j.artd.2024.101491
中图分类号
R826.8 [整形外科学]; R782.2 [口腔颌面部整形外科学]; R726.2 [小儿整形外科学]; R62 [整形外科学(修复外科学)];
学科分类号
摘要
Background: Venous thromboembolism (VTE) following total hip arthroplasty and total knee arthroplasty (TKA) is linked to immobility, and preoperative prediction remains difficult. We aimed to evaluate whether annual mean length of stay (LOS) is associated with the incidence of VTE and develop a generalizable machine learning model to preoperatively predict the incidence of symptomatic VTE following total hip and TKA using National Surgical Quality Improvement Program. Methods: Annual incidence of 30-day postoperative VTE, deep vein thrombosis, and pulmonary embolism was calculated over 6 years and tested for trend. Correlation between annual VTE rates and mean LOS was calculated. Predictive models (logistic regression, random forest, and XGBoost) were trained and tested based on year of surgery with different oversampling algorithms used to address data imbalance. Results: A total of 498,314 patients were included, with 0.88% developing a VTE within 30 days. VTE rates decreased from 1.11% in 2014 to 0.76% in 2019 (P < .001). There was a strong correlation between the yearly incidence of VTE, pulmonary embolism, and deep vein thrombosis and mean LOS (r = 0.96, 0.87, and 0.98, respectively). Univariate analysis demonstrated that TKA, inpatient setting, American Society of Anesthesiologists classification, and various patient comorbidities were significantly associated with VTE. The logistic regression model trained on all data with a balanced loss scoring function performed the best (area under the curve = 0.600). Conclusions: This study revealed declining VTE rates strongly correlated to decreasing postoperative LOS and identified patient and surgery-specific factors associated with VTE risk. Development of more accurate machine learning models for VTE prediction may improve risk stratification, prevention, and monitoring for arthroplasty patients. (c) 2024 The Authors. Published by Elsevier Inc. on behalf of The American Association of Hip and Knee Surgeons. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
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页数:9
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