Development and validation of a three-dimensional nomogram prediction model for knee osteoarthritis in middle-aged
Ying Li1, Yabin Guo2,3, Peipei Zhao3
1School of Nursing, Sun Yat-Sen University, Guangzhou, China.
Objectives:
This study aims to identify predictors of knee osteoarthritis (KOA) risk in middle-aged population, construct and validate a nomogram for KOA in this demographic.
Methods:
From June to December 2020, we conducted a cross-sectional survey on 5,527 middle-aged individuals from Changsha and Zhangjiajie cities in Hunan Province, selected using a stratified multi-stage random sampling method. Data collection involved a structured questionnaire encompassing general demographic, physical condition, and lifestyle behaviors dimensions. The dataset was randomly split into a training set (n = 3868) and a validation set (n = 1659) at a 7:3 ratio via computerized randomization. We analyzed the prevalence of self-reported KOA and identified its influencing factors using logistic regression. A nomogram was constructed based on these "three-dimensional" factors. Subsequent validation was conducted, and the nomogram's performance was further evaluated through ROC curves, C-index, Hosmer-Lemeshow test, and calibration curves.
Results:
The self-reported prevalence of KOA in the middle-aged population was 11.4% (632/5527). The risk factor with the greatest impact is: diagnosed with osteoporosis(95% CI 2.269-3.568, OR = 2.845), followed by age between 51 to 60 years (95% CI 2.176-3.151, OR = 2.619), diagnosed with hypertension(95% CI 1.633-2.499, OR = 2.02), diagnosed with diabetes (OR = 1.689), ethnic Han Chinese (OR = 1.673), exercise according to physical condition (OR = 1.643), pay attention to keeping the knee joint warm (OR = 1.535), eating habits are mainly light vegetables (OR = 1.374), male gender (OR = 1.343), drink occasionally in small amounts (OR = 1.286); a higher level of education (OR = 0.477) and frequently or always apply an external or plaster to relieve symptoms after knee discomfort (OR = 0.377; OR = 0.385) are protective factors. The C-index of the training set model was 0.8107 (95% CI: 0.8102-0.8111), with a statistically significant area under the ROC curve (AUC = 0.818), and the calibration curve showed a good fit. The C-index for the validation set was 0.8124 (95% CI: 0.8109-0.8140), with an AUC of 0.812. The Hosmer-Lemeshow test resulted in a P-value of 0.46 (P ≥ 0.05)indicating good calibration of the model.
Conclusion:
The three dimensions nomogram generated in this study was a valid and easy-to-use tool for assessing the risk of KOA in middle-aged population, and helped healthcare professionals to screen the high-risk population.
