Qiang Liu1, Hongling Chu2, Michael P LaValley3, David J Hunter4, Hua Zhang2, Liyuan Tao2, Siyan Zhan5, Jianhao Lin6, Yuqing Zhang7. 1. Peking University People's Hospital, Arthritis Clinic and Research Center, Beijing, China; Massachusetts General Hospital, Harvard Medical School, Division of Rheumatology, Immunology and Allergy, Boston, MA, USA. 2. Peking University Third Hospital, Research Center of Clinical Epidemiology, Beijing, China. 3. Boston University School of Public Health, Boston, MA, USA. 4. Institute of Bone and Joint Research, The Kolling Institute, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia; Rheumatology Department, Royal North Shore Hospital, Sydney, NSW, Australia. 5. Peking University Third Hospital, Research Center of Clinical Epidemiology, Beijing, China; Peking University Health Science Center, School of Public Health, Beijing, China. 6. Peking University People's Hospital, Arthritis Clinic and Research Center, Beijing, China. 7. Massachusetts General Hospital, Harvard Medical School, Division of Rheumatology, Immunology and Allergy, Boston, MA, USA.
Abstract
Background: Few prognostic prediction models for total knee replacement are available, and the role of radiographic findings in predicting its use remains unclear. We aimed to develop and validate predictive models for total knee replacement and to assess whether adding radiographic findings improves predictive performance. Methods: We identified participants with recent knee pain (in the past 3 months) in the Multicenter Osteoarthritis Study (MOST) and the Osteoarthritis Initiative (OAI). The baseline visits of MOST were initiated in 2003 and of OAI were initiated in 2004. We developed two predictive models for the risk of total knee replacement within 60 months of follow-up by fitting Cox proportional hazard models among participants in MOST. The first model included sociodemographic and anthropometric factors, medical history, and clinical measures (referred to as the clinical model). The second model added radiographic findings into the predictive model (the radiographic model). We evaluated each model's discrimination and calibration performance and assessed the incremental value of radiographic findings using both category-free net reclassification improvement (NRI) and integrated discrimination improvement (IDI). We tuned the models and externally validated them among participants in OAI. Findings: We included 2658 participants from MOST (mean age 62·4 years [SD 8·1], 1646 [61·9%] women) in the training dataset and 4060 participants from OAI (mean age 60·9 years [9·1], 2379 [58·6%] women) in the validation dataset. 290 (10·9%) participants in the training dataset and 174 (4·3%) in the validation dataset had total knee replacement. The retained predictive variables included in the clinical model were age, sex, race, history of knee arthroscopy, frequent knee pain, current use of analgesics, current use of glucosamine, body-mass index, and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score, and the most predictive factors were age, race, and WOMAC pain score. The retained predictive variables in the radiographic model were age, sex, race, frequent knee pain, current use of analgesics, WOMAC pain score, and Kellgren-Lawrence grade, and the most predictive factors were Kellgren-Lawrence grade, race, and age. The C-statistic was 0·79 (95% CI 0·76-0·81) for the clinical model and 0·87 (0·85-0·99) for the radiographic model in the training dataset. The calibration slope was 0·95 (95% CI 0·86-1·05) and 0·96 (0·87-1·04), respectively. Adding radiograph findings significantly improved predictive performance with an NRI of 0·43 (95% CI 0·38-0·50) and IDI of 0·14 (95% CI: 0·10-0·18). Both models, with tuned coefficients, showed a good predictive performance among participants in the validation dataset. Interpretation: The risk of total knee replacement can be predicted based on common risk factors with good discrimination and calibration. Additionally, adding radiographic findings of knee osteoarthritis into the model substantially improves its predictive performance. Funding: National Natural Science Foundation of China, National Key Research and Development Program, and Beijing Municipal Science & Technology Commission.
Background: Few prognostic prediction models for total knee replacement are available, and the role of radiographic findings in predicting its use remains unclear. We aimed to develop and validate predictive models for total knee replacement and to assess whether adding radiographic findings improves predictive performance. Methods: We identified participants with recent knee pain (in the past 3 months) in the Multicenter Osteoarthritis Study (MOST) and the Osteoarthritis Initiative (OAI). The baseline visits of MOST were initiated in 2003 and of OAI were initiated in 2004. We developed two predictive models for the risk of total knee replacement within 60 months of follow-up by fitting Cox proportional hazard models among participants in MOST. The first model included sociodemographic and anthropometric factors, medical history, and clinical measures (referred to as the clinical model). The second model added radiographic findings into the predictive model (the radiographic model). We evaluated each model's discrimination and calibration performance and assessed the incremental value of radiographic findings using both category-free net reclassification improvement (NRI) and integrated discrimination improvement (IDI). We tuned the models and externally validated them among participants in OAI. Findings: We included 2658 participants from MOST (mean age 62·4 years [SD 8·1], 1646 [61·9%] women) in the training dataset and 4060 participants from OAI (mean age 60·9 years [9·1], 2379 [58·6%] women) in the validation dataset. 290 (10·9%) participants in the training dataset and 174 (4·3%) in the validation dataset had total knee replacement. The retained predictive variables included in the clinical model were age, sex, race, history of knee arthroscopy, frequent knee pain, current use of analgesics, current use of glucosamine, body-mass index, and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score, and the most predictive factors were age, race, and WOMAC pain score. The retained predictive variables in the radiographic model were age, sex, race, frequent knee pain, current use of analgesics, WOMAC pain score, and Kellgren-Lawrence grade, and the most predictive factors were Kellgren-Lawrence grade, race, and age. The C-statistic was 0·79 (95% CI 0·76-0·81) for the clinical model and 0·87 (0·85-0·99) for the radiographic model in the training dataset. The calibration slope was 0·95 (95% CI 0·86-1·05) and 0·96 (0·87-1·04), respectively. Adding radiograph findings significantly improved predictive performance with an NRI of 0·43 (95% CI 0·38-0·50) and IDI of 0·14 (95% CI: 0·10-0·18). Both models, with tuned coefficients, showed a good predictive performance among participants in the validation dataset. Interpretation: The risk of total knee replacement can be predicted based on common risk factors with good discrimination and calibration. Additionally, adding radiographic findings of knee osteoarthritis into the model substantially improves its predictive performance. Funding: National Natural Science Foundation of China, National Key Research and Development Program, and Beijing Municipal Science & Technology Commission.
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