Abstract
Background and Objectives: Total knee arthroplasty (TKA) is widely performed to improve mobility and quality of life for symptomatic knee osteoarthritis patients. The accurate prediction of patients' length of hospital stay (LOS) can help clinicians for rehabilitation decision-making and bed assignment planning, which thus makes full use of medical resources.Methods: Clinical characteristics were retrospectively collected from 1,298 patients who received TKA. A total of 36 variables were included to develop predictive models for LOS by multiple machine learning (ML) algorithms. The models were evaluated by the receiver operating characteristic (ROC) curve for predictive performance and decision curve analysis (DCA) for clinical values. A feature selection approach was used to identify optimal predictive factors.Results: The areas under the ROC curve (AUCs) of the nine models ranged from 0.710 to 0.766. All the ML-based models performed better than models using conventional statistical methods in both ROC curves and decision curves. The random forest classifier (RFC) model with 10 variables introduced was identified as the best predictive model. The feature selection indicated the top five predictors: tourniquet time, distal femoral osteotomy thickness, osteoporosis, tibia component size, and post-operative values of Hb within 24 h.Conclusions: By analyzing clinical characteristics, it is feasible to develop ML-based models for the preoperative prediction of LOS for patients who received TKA, and the RFC model performed the best.
Highlights
Total knee arthroplasty (TKA) has been confirmed the most efficient treatment for improving outcomes of knee diseases such as end-stage osteoarthritis [1]
To our knowledge, fast-track arthroplasty has been rarely applied in Chinese hospitals, and few studies have focused on the length of hospital stay (LOS) after TKA in the Chinese population
In the decision curve (Figure 2), all models we developed performed much better than the two extreme lines except adaptive boosting (ADB), which overlapped with the extreme lines in most of the thresholds
Summary
Total knee arthroplasty (TKA) has been confirmed the most efficient treatment for improving outcomes of knee diseases such as end-stage osteoarthritis [1]. The accurate prediction of patients’ LOS can help clinicians for decision-making and bed assignment planning, which makes full use of medical resources and decreases wasted hospital stays. Youssef F et al reported multiple variables were associated with increased hospital LOS following primary TKA including age ≥80 years and Hispanic race and reviewed measures designed to decrease LOS [6]. Lo CK et al reviewed 1,622 primary total knee replacements performed in Hong Kong and the mean length of hospital stay was 6.8 days [9]. Total knee arthroplasty (TKA) is widely performed to improve mobility and quality of life for symptomatic knee osteoarthritis patients. The accurate prediction of patients’ length of hospital stay (LOS) can help clinicians for rehabilitation decision-making and bed assignment planning, which makes full use of medical resources
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