Early diagnosis of knee osteoarthritis (OA) is important in managing this disease, but such an early diagnostic tool is still lacking in clinical practice. The purpose of this study was to develop diagnostic models for early stage knee OA based on the first 2-year clinical course after the patient’s initial presentation in primary care and to identify whether these course factors had additive discriminative value over baseline factors. We extracted eligible patients’ clinical and radiographic data from the CHECK cohort and formed the first 2-year course factors according to the factors’ changes over the 2 years. Clinical expert consensus-based diagnosis, which was made via evaluating patients’ 5- to 10-year follow-up data, was used as the outcome factor. Four models were developed: model 1, included clinical course factors only; model 2, included clinical and radiographic course factors; model 3, clinical baseline factors + clinical course factors; and model 4, clinical and radiographic baseline factors + clinical and radiographic course factors. All the models were built by a generalized estimating equation with a backward selection method. Area under the receiver operating characteristic curve (AUC) and its 95% confidence interval (CI) were calculated for assessing model discrimination. Delong’s method compared AUCs. Seven hundred sixty-one patients with 1185 symptomatic knees were included in this study. Thirty-seven percent knees were diagnosed as OA at follow-up. Model 1 contained 6 clinical course factors; model 2: 6 clinical and 3 radiographic course factors; model 3: 6 baseline clinical factors combined with 5 clinical course factors; and model 4: 4 clinical and 1 radiographic baseline factors combined with 5 clinical and 3 radiographic course factors. Model discriminations are as follows: model 1, AUC 0.70 (95% CI 0.67–0.74); model 2, 0.74 (95% CI 0.71–0.77); model 3, 0.77 (95% CI 0.74–0.80); and model 4, 0.80 (95% CI 0.77–0.82). AUCs of model 3 and model 4 were slightly but significantly higher than corresponding baseline-factor models (model 3 0.77 vs 0.75, p = 0.031; model 4 0.80 vs 0.76, p = 0.003). Four diagnostic models were developed with “fair” to “good” discriminations. First 2-year course factors had additive discriminative value over baseline factors.

中文翻译：

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基于早期临床病程的早期膝关节骨关节炎诊断：来自 CHECK 队列的数据

膝关节骨关节炎（OA）的早期诊断对于管理这种疾病很重要，但在临床实践中仍然缺乏这种早期诊断工具。本研究的目的是根据患者在初级保健机构初次就诊后的第一个 2 年临床病程开发早期膝关节 OA 的诊断模型，并确定这些病程因素是否比基线因素具有附加的判别价值。我们从 CHECK 队列中提取符合条件的患者的临床和影像学数据，并根据 2 年内因素的变化形成第一个 2 年病程因素。通过评估患者 5 至 10 年的随访数据而做出的基于临床专家共识的诊断被用作结果因素。开发了四种模型：模型 1，仅包括临床病程因素；模型2，包括临床和放射学过程因素；模型3，临床基线因素+临床病程因素；和模型 4，临床和放射学基线因素 + 临床和放射学过程因素。所有模型均采用后向选择方法通过广义估计方程建立。计算受试者工作特征曲线下面积 (AUC) 及其 95% 置信区间 (CI) 以评估模型辨别力。Delong 的方法比较了 AUC。761 名患者有 1185 个有症状的膝盖被纳入这项研究。37% 的膝盖在随访中被诊断为 OA。模型 1 包含 6 个临床过程因素；模型 2：6 个临床因素和 3 个放射学过程因素；模型3：6个基线临床因素结合5个临床病程因素；和模型 4：4 个临床和 1 个放射学基线因素与 5 个临床和 3 个放射学过程因素相结合。模型判别如下：模型 1，AUC 0.70 (95% CI 0.67–0.74)；模型 2，0.74（95% CI 0.71–0.77）；模型 3，0.77（95% CI 0.74–0.80）；和模型 4，0.80（95% CI 0.77–0.82）。模型 3 和模型 4 的 AUC 略微但显着高于相应的基线因子模型（模型 3 0.77 对 0.75，p = 0.031；模型 4 0.80 对 0.76，p = 0.003）。开发了四种诊断模型，区分“一般”到“良好”。前 2 年课程因素对基线因素具有附加的判别价值。和模型 4，0.80（95% CI 0.77–0.82）。模型 3 和模型 4 的 AUC 略微但显着高于相应的基线因子模型（模型 3 0.77 对 0.75，p = 0.031；模型 4 0.80 对 0.76，p = 0.003）。开发了四种诊断模型，区分“一般”到“良好”。前 2 年课程因素对基线因素具有附加的判别价值。和模型 4，0.80（95% CI 0.77–0.82）。模型 3 和模型 4 的 AUC 略微但显着高于相应的基线因子模型（模型 3 0.77 对 0.75，p = 0.031；模型 4 0.80 对 0.76，p = 0.003）。开发了四种诊断模型，区分“一般”到“良好”。前 2 年课程因素对基线因素具有附加的判别价值。