Development of machine learning algorithms to predict achievement of minimal clinically important difference for the KOOS‐PS following total knee arthroplasty

2021 ◽  
Author(s):  
Akhil Katakam ◽  
Aditya V. Karhade ◽  
Austin Collins ◽  
David Shin ◽  
Charles Bragdon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Total Knee Arthroplasty ◽  
Knee Arthroplasty ◽  
Minimal Clinically Important Difference ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Clinically Important Difference ◽  
Total Knee

scholarly journals How much improvement can satisfy patients? Exploring patients’ satisfaction 3 years after total knee arthroplasty

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Xiao Yu Fan ◽  
Jin Hui Ma ◽  
Xinjie Wu ◽  
Xin Xu ◽  
Lijun Shi ◽  
...  
Keyword(s):  
Total Knee Arthroplasty ◽  
Knee Arthroplasty ◽  
Minimal Clinically Important Difference ◽  
Important Change ◽  
Score Change ◽  
Patients Satisfaction ◽  
Clinically Important Difference ◽  
Total Knee ◽  
Hss Score ◽  
Varus Angle

Abstract Background Despite the innovations in total knee arthroplasty (TKA), there is still a subset of patients who do not acquire significant relief or expected satisfaction after primary TKA. However, this subgroup of patients still gains improvements more or less in terms of objective or quantified assessments after the procedure. The purpose of our study is to explore the factors that correlate with patients’ satisfaction and identify minimal clinically important difference (MCID) and minimum important change (MIC) in clinical parameters. Methods We conducted a retrospective study of 161 patients diagnosed with osteoarthritis who underwent unilateral total knee arthroplasty from January 2017 to December 2017. We collected the following parameters: body mass index (BMI), duration of disease, education level, depression state, preoperative flexion contracture angle of knee, HSS scores, 11-point NRS scores, and radiological parameters (preoperative minimal joint space width and varus angle of knee). The satisfaction was graded by self-reported scores in percentage (0–100). Results We revealed that 80.8% of patients were satisfied 3 years overall after primary TKA. HSS score change, NRS-Walking score change, age, and pre-mJSW showed significant difference between satisfied and dissatisfied group. The varus angle change revealed statistical significance according to the levels of satisfaction. Simple linear regression identified the MCID for HSS score to be 5.41 and for the NRS-Walking to be 1.24. The receiver operating characteristics (ROC) curve identified the MIC for HSS score to be 25.5 and for the NRS-Walking score to be 6.5. Conclusions In summary, we identified several factors that correlated with patients’ satisfaction independently after TKA in a long term. In addition, we revealed the minimal clinically important difference (MCID) and minimum important change (MIC) for HSS and NRS score in these patients.

scholarly journals How Much Improvement Can Satisfy Patients? Exploring Patients’ Satisfaction 3 Years After Total Knee Arthroplasty

2021 ◽  
Author(s):  
xiaoyu fan ◽  
jinhui ma ◽  
xinjie wu ◽  
xin xu ◽  
lijun shi ◽  
...  
Keyword(s):  
Total Knee Arthroplasty ◽  
Knee Arthroplasty ◽  
Minimal Clinically Important Difference ◽  
Important Change ◽  
Score Change ◽  
Patients Satisfaction ◽  
Clinically Important Difference ◽  
Total Knee ◽  
Hss Score ◽  
Varus Angle

Abstract Background: Despite the innovations in total knee arthroplasty(TKA), there is still a subset of patients who do not acquire significant relief or expected satisfaction after primary TKA. However, this subgroup of patients still gains improvements more or less in terms of objective or quantified assessments after the procedure. The purpose of our study is to explore the factors that correlate with patients’ satisfaction and identify minimal clinically important difference(MCID) and minimum important change(MIC)in clinical parameters.Methods: We conducted a retrospective study of 161 patients diagnosed with osteoarthritis who underwent unilateral total knee arthroplasty from Jan. 2017-Dec. 2017. We collected the following parameters: body mass index(BMI), duration of disease, education level, depression state, preoperative flexion contracture angle of knee, HSS scores, 11-point NRS scores and radiological parameters(preoperative minimal joint space width and varus angle of knee). The satisfaction was graded by self-reported scores in percentage(0-100). Results: we revealed that 80.8% of patients were satisfied 3 years overall after primary TKA. HSS score change, NRS-Walking score change, age and Pre-mJSW showed significant difference between satisfied and dissatisfied group. The varus angle change revealed statistical significance according to the levels of satisfaction. Simple linear regression identified the MCID for HSS score to be 5.41 and for the NRS-Walking to be 1.24. The receiver operating characteristics (ROC) curve identified the MIC for HSS score to be 25.5 and for the NRS-Walking score to be 6.5. Conclusions: In summary, we identified several factors that correlated with patients’ satisfaction independently after TKA in a long-term. In addition, we revealed the minimal clinically important difference(MCID) and minimum important change(MIC)for HSS and NRS score.

Construction and Comparison of Predictive Models for Length of Stay after Total Knee Arthroplasty: Regression Model and Machine Learning Analysis Based on 1,826 Cases in a Single Singapore Center

2020 ◽  
Author(s):  
Hui Li ◽  
Juyang Jiao ◽  
Shutao Zhang ◽  
Haozheng Tang ◽  
Xinhua Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Total Knee Arthroplasty ◽  
Logistic Regression ◽  
Length Of Stay ◽  
Knee Arthroplasty ◽  
Predictive Models ◽  
Prediction Models ◽  
Univariate Analysis ◽  
Machine Learning Algorithms ◽  
Total Knee

AbstractThe purpose of this study was to develop a predictive model for length of stay (LOS) after total knee arthroplasty (TKA). Between 2013 and 2014, 1,826 patients who underwent TKA from a single Singapore center were enrolled in the study after qualification. Demographics of patients with normal and prolonged LOS were analyzed. The risk variables that could affect LOS were identified by univariate analysis. Predictive models for LOS after TKA by logistic regression or machine learning were constructed and compared. The univariate analysis showed that age, American Society of Anesthesiologist level, diabetes, ischemic heart disease, congestive heart failure, general anesthesia, and operation duration were risk factors that could affect LOS (p < 0.05). Comparing with logistic regression models, the machine learning model with all variables was the best model to predict LOS after TKA, of whose area of operator characteristic curve was 0.738. Machine learning algorithms improved the predictive performance of LOS prediction models for TKA patients.

The oxford knee score minimal clinically important difference for revision total knee arthroplasty

The Knee ◽  
2021 ◽  
Vol 32 ◽  
pp. 211-217
Author(s):  
Yong Zhi Khow ◽  
Ming Han Lincoln Liow ◽  
Graham S. Goh ◽  
Jerry Yongqiang Chen ◽  
Ngai Nung Lo ◽  
...  
Keyword(s):  
Total Knee Arthroplasty ◽  
Knee Arthroplasty ◽  
Minimal Clinically Important Difference ◽  
Oxford Knee Score ◽  
Revision Total Knee Arthroplasty ◽  
Knee Score ◽  
Clinically Important Difference ◽  
Total Knee
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