R Dunn1, J Greenhouse2, D James3, D Ohlssen4, P Mesenbrink5. 1. Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA. Electronic address: dunnr@cmu.edu. 2. Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA. Electronic address: joel@stat.cmu.edu. 3. Novartis Pharmaceuticals Corporation, East Hanover, NJ, 07936, USA. Electronic address: davida.james@novartis.com. 4. Novartis Pharmaceuticals Corporation, East Hanover, NJ, 07936, USA. Electronic address: david.ohlssen@novartis.com. 5. Novartis Pharmaceuticals Corporation, East Hanover, NJ, 07936, USA. Electronic address: peter.mesenbrink@novartis.com.
Abstract
OBJECTIVE: This study constructs a risk score for patients' progression to end-stage knee osteoarthritis (OA) within 4 years. DESIGN: The Osteoarthritis Initiative (OAI) was a longitudinal study of the onset and progression of knee OA. Using a recent definition of end-stage knee OA, we implement interval-censored survival forests to select predictors of this endpoint. We fit an interval-censored Cox model for time to end-stage knee OA, using the selected predictors. The risk score is the Cox model's fitted linear combination of the nine selected baseline structural and symptomatic knee OA variables. RESULTS: We fit our models on a training set of 2,701 patients, and we evaluate on an independent test set of 1,436 patients. On the test sample, we observe a concordance index of 0.86 between risk score and time to end-stage, AUC of 0.87 for predicting end-stage within 24, 36, and 48 months, and positive predictive values that increase with the risk score. This risk stratification algorithm could enrich clinical trial patient enrollment. By enrolling test sample patients with scores above a threshold, a trial could have included 91% of test set patients who reach end-stage within 4 years while only enrolling 45% of the test sample. CONCLUSION: Using statistical methods, we construct and validate an interpretable risk score for time to end-stage knee OA. This score can help disease-modifying OA treatment developers to select candidates with the highest risk of fast-progressing knee OA.
OBJECTIVE: This study constructs a risk score for patients' progression to end-stage knee osteoarthritis (OA) within 4 years. DESIGN: The Osteoarthritis Initiative (OAI) was a longitudinal study of the onset and progression of knee OA. Using a recent definition of end-stage knee OA, we implement interval-censored survival forests to select predictors of this endpoint. We fit an interval-censored Cox model for time to end-stage knee OA, using the selected predictors. The risk score is the Cox model's fitted linear combination of the nine selected baseline structural and symptomatic knee OA variables. RESULTS: We fit our models on a training set of 2,701 patients, and we evaluate on an independent test set of 1,436 patients. On the test sample, we observe a concordance index of 0.86 between risk score and time to end-stage, AUC of 0.87 for predicting end-stage within 24, 36, and 48 months, and positive predictive values that increase with the risk score. This risk stratification algorithm could enrich clinical trial patient enrollment. By enrolling test sample patients with scores above a threshold, a trial could have included 91% of test set patients who reach end-stage within 4 years while only enrolling 45% of the test sample. CONCLUSION: Using statistical methods, we construct and validate an interpretable risk score for time to end-stage knee OA. This score can help disease-modifying OA treatment developers to select candidates with the highest risk of fast-progressing knee OA.
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