Gait, physical activity and tibiofemoral cartilage damage: a longitudinal machine learning analysis in the Multicenter Osteoarthritis Study

被引:6
|
作者
Costello, Kerry E. [1 ,2 ,3 ]
Felson, David T. [3 ]
Jafarzadeh, S. Reza [3 ]
Guermazi, Ali [4 ]
Roemer, Frank W. [5 ,6 ]
Segal, Neil A. [7 ,8 ]
Lewis, Cora E. [9 ]
Nevitt, Michael C. [10 ]
Lewis, Cara L. [3 ]
Kolachalama, Vijaya B. [11 ,12 ]
Kumar, Deepak [2 ,3 ]
机构
[1] Univ Florida, Mech & Aerosp Engn, Gainesville, FL USA
[2] Boston Univ, Phys Therapy, Boston, MA 02215 USA
[3] Boston Univ, Sch Med, Rheumatol, Boston, MA 02118 USA
[4] VA Boston Healthcare Syst, Radiol, West Roxbury, MA USA
[5] Univ Erlangen Nurnberg, Radiol, Erlangen, Germany
[6] Boston Univ, Sch Med, Radiol, Boston, MA 02118 USA
[7] Univ Kansas, Rehabil Med, Med Ctr, Kansas City, KS USA
[8] Univ Iowa, Epidemiol, Iowa City, IA USA
[9] Univ Alabama Birmingham, Epidemiol, Birmingham, AL USA
[10] Univ Calif San Francisco, Epidemiol & Biostat, San Francisco, CA 94143 USA
[11] Boston Univ, Sch Med, Computat Biomed, Boston, MA 02118 USA
[12] Boston Univ, Comp Sci, Boston, MA 02215 USA
基金
美国国家卫生研究院;
关键词
KNEE OSTEOARTHRITIS; PROGRESSION; RISK; BIOMECHANICS; MODERATE; PEOPLE; ONSET; PAIN; TIME;
D O I
10.1136/bjsports-2022-106142
中图分类号
G8 [体育];
学科分类号
04 ; 0403 ;
摘要
Objective To (1) develop and evaluate a machine learning model incorporating gait and physical activity to predict medial tibiofemoral cartilage worsening over 2 years in individuals without advanced knee osteoarthritis and (2) identify influential predictors in the model and quantify their effect on cartilage worsening. Design An ensemble machine learning model was developed to predict worsened cartilage MRI Osteoarthritis Knee Score at follow-up from gait, physical activity, clinical and demographic data from the Multicenter Osteoarthritis Study. Model performance was evaluated in repeated cross-validations. The top 10 predictors of the outcome across 100 held-out test sets were identified by a variable importance measure. Their effect on the outcome was quantified by g-computation. Results Of 947 legs in the analysis, 14% experienced medial cartilage worsening at follow-up. The median (2.5-97.5th percentile) area under the receiver operating characteristic curve across the 100 held-out test sets was 0.73 (0.65-0.79). Baseline cartilage damage, higher Kellgren-Lawrence grade, greater pain during walking, higher lateral ground reaction force impulse, greater time spent lying and lower vertical ground reaction force unloading rate were associated with greater risk of cartilage worsening. Similar results were found for the subset of knees with baseline cartilage damage. Conclusions A machine learning approach incorporating gait, physical activity and clinical/demographic features showed good performance for predicting cartilage worsening over 2 years. While identifying potential intervention targets from the model is challenging, lateral ground reaction force impulse, time spent lying and vertical ground reaction force unloading rate should be investigated further as potential early intervention targets to reduce medial tibiofemoral cartilage worsening.
引用
收藏
页码:1018 / +
页数:8
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