American journal of physical medicine & rehabilitation · 2024
Risk Factor Prediction and Categorization for Glenohumeral Osteoarthritis: A Classification and Regression Tree Analysis.
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McNamara-Pittler Elyse N, Prakash Ravi, Atem Folefac D, Pathak Rashmi, Liu Wenting, Khazzam Michael, Jain Nitin B
Аннотация
OBJECTIVE: This study aimed to apply classification and regression tree analysis to determine factors associated with glenohumeral osteoarthritis and establish specific cutoff points for risk factors based on this methodology.
DESIGN: The cross-sectional study included 3383 participants with shoulder pain. Cases were selected for glenohumeral osteoarthritis. Patients with other shoulder pathologies were included as controls. Thirty-three potential risk factors were assessed. The classification and regression tree analysis was used to determine the highest-ranked risk factors associated with glenohumeral osteoarthritis. Multivariable logistic regression analysis was then performed using the cutoff points obtained from the classification and regression tree analysis.
RESULTS: The classification and regression tree analysis showed that age and body mass index were the two most significant risk factors for glenohumeral osteoarthritis. Multivariable logistic regression revealed that age categories ≥31 to < 58 yrs (odds ratio = 8.92), ≥58 to < 64 yrs (odds ratio = 20.20), and ≥64 yrs (odds ratio = 42.20), and body mass index categories ≥25-30 kg/m 2 (odds ratio = 1.47) and ≥30 kg/m 2 (odds ratio = 1.71) had higher odds of developing glenohumeral osteoarthritis compared with age <31 yrs and body mass index <25 kg/m 2 .
CONCLUSIONS: This was the first study to use classification and regression tree analysis to evaluate significant risk factors for glenohumeral osteoarthritis and establish cutoff points for increased risk. The findings present age categories that are distinct from the arbitrary age groups used in previous studies.
DESIGN: The cross-sectional study included 3383 participants with shoulder pain. Cases were selected for glenohumeral osteoarthritis. Patients with other shoulder pathologies were included as controls. Thirty-three potential risk factors were assessed. The classification and regression tree analysis was used to determine the highest-ranked risk factors associated with glenohumeral osteoarthritis. Multivariable logistic regression analysis was then performed using the cutoff points obtained from the classification and regression tree analysis.
RESULTS: The classification and regression tree analysis showed that age and body mass index were the two most significant risk factors for glenohumeral osteoarthritis. Multivariable logistic regression revealed that age categories ≥31 to < 58 yrs (odds ratio = 8.92), ≥58 to < 64 yrs (odds ratio = 20.20), and ≥64 yrs (odds ratio = 42.20), and body mass index categories ≥25-30 kg/m 2 (odds ratio = 1.47) and ≥30 kg/m 2 (odds ratio = 1.71) had higher odds of developing glenohumeral osteoarthritis compared with age <31 yrs and body mass index <25 kg/m 2 .
CONCLUSIONS: This was the first study to use classification and regression tree analysis to evaluate significant risk factors for glenohumeral osteoarthritis and establish cutoff points for increased risk. The findings present age categories that are distinct from the arbitrary age groups used in previous studies.