Shapiro J, Getz B, Cohen S B, Jenudi Y, Underberger D, Dreyfuss M, Ber T I, Steinberg-Koch S, Ben-Tov A, Shoenfeld Y, Shovman O
Maccabi Healthcare Services, Israel.
Predicta Med Analytics Ltd, Israel.
J Transl Autoimmun. 2023 Aug 2;7:100207. doi: 10.1016/j.jtauto.2023.100207. eCollection 2023 Dec.
Psoriatic arthritis (PsA), an immune-mediated chronic inflammatory skin and joint disease, affects approximately 0.27% of the adult population, and 20% of patients with psoriasis. Up to 10% of psoriasis patients are estimated for having undiagnosed PsA. Early diagnosis and treatment can prevent irreversible joint damage, disability and deformity. Questionnaires for screening to identify undiagnosed PsA patients require patient and physician involvement.
To evaluate a proprietary machine learning tool (PredictAI™) developed for identification of undiagnosed PsA patients 1-4 years prior to the first time that they were suspected of having PsA (reference event).
This retrospective study analyzed data of the adult population from Maccabi Healthcare Service between 2008 and 2020. We created 2 cohorts: The general adult population ("GP Cohort") including patients with and without psoriasis and the Psoriasis cohort ("PsO Cohort") including psoriasis patients only. Each cohort was divided into two non-overlapping train and test sets. The PredictAI™ model was trained and evaluated with 3 years of data predating the reference event by at least one year. Receiver operating characteristic (ROC) analysis was used to investigate the performance of the model, built using gradient boosted trees, at different specificity levels.
Overall, 2096 patients met the criteria for PsA. Undiagnosed PsA patients in the PsO cohort were identified with a specificity of 90% one and four years before the reference event, with a sensitivity of 51% and 38%, and a PPV of 36.1% and 29.6%, respectively. In the GP cohort and with a specificity of 99% and for the same time windows, the model achieved a sensitivity of 43% and 32% and a PPV of 10.6% and 8.1%, respectively.
The presented machine learning tool may aid in the early identification of undiagnosed PsA patients, and thereby promote earlier intervention and improve patient outcomes.
银屑病关节炎（PsA）是一种免疫介导的慢性炎症性皮肤和关节疾病，影响约0.27%的成年人口以及20%的银屑病患者。据估计，高达10%的银屑病患者患有未确诊的PsA。早期诊断和治疗可预防不可逆转的关节损伤、残疾和畸形。用于筛查未确诊PsA患者的问卷需要患者和医生的参与。
评估一种专有的机器学习工具（PredictAI™），该工具用于识别未确诊的PsA患者，这些患者在首次怀疑患有PsA（参考事件）前1至4年。
这项回顾性研究分析了2008年至2020年期间来自马卡比医疗服务机构的成年人口数据。我们创建了2个队列：包括有和没有银屑病患者的一般成年人口队列（“GP队列”）以及仅包括银屑病患者的银屑病队列（“PsO队列”）。每个队列被分为两个不重叠的训练集和测试集。PredictAI™模型使用参考事件前至少一年的3年数据进行训练和评估。采用受试者操作特征（ROC）分析来研究使用梯度提升树构建的模型在不同特异性水平下的性能。
总体而言，2096名患者符合PsA标准。在参考事件前1年和4年，PsO队列中未确诊的PsA患者被识别出的特异性为90%，敏感性分别为51%和38%，阳性预测值分别为36.1%和29.6%。在GP队列中，在相同时间窗口且特异性为99%时，模型的敏感性分别为43%和32%，阳性预测值分别为10.6%和8.1%。
所展示的机器学习工具可能有助于早期识别未确诊的PsA患者，从而促进更早的干预并改善患者预后。
