Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
Novartis Pharmaceuticals Corporation, East Hanover, NJ, 07936, USA.
Osteoarthritis Cartilage. 2020 Aug;28(8):1020-1029. doi: 10.1016/j.joca.2019.12.013. Epub 2020 May 13.
This study constructs a risk score for patients' progression to end-stage knee osteoarthritis (OA) within 4 years.
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.
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.
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.
本研究构建了一个用于预测患者在 4 年内进展为终末期膝骨关节炎（OA）的风险评分。
Osteoarthritis Initiative（OAI）是一项关于膝 OA 发病和进展的纵向研究。我们采用最近的终末期膝 OA 定义，运用区间 censored 生存森林选择该终点的预测因子。我们使用选定的预测因子，拟合了一个区间 censored Cox 模型来预测时间到终末期膝 OA。风险评分是 Cox 模型对 9 个基线结构和症状性膝 OA 变量的拟合线性组合。
我们在 2701 名患者的训练集中拟合了我们的模型，并在 1436 名患者的独立测试集中进行了评估。在测试样本中，我们观察到风险评分与时间到终末期的一致性指数为 0.86，AUC 为 0.87，用于预测 24、36 和 48 个月内的终末期，阳性预测值随风险评分增加而增加。这种风险分层算法可以丰富临床试验的患者入组。通过招募评分高于阈值的测试样本患者，试验可以纳入 91%的在 4 年内达到终末期的测试样本患者，而仅纳入 45%的测试样本。
我们使用统计方法构建并验证了一个用于时间到终末期膝 OA 的可解释风险评分。该评分可以帮助改变 OA 疾病进程的治疗药物开发人员选择进展迅速的膝 OA 风险最高的患者。
