Webster Dan E, Haberman Rebecca H, Perez-Chada Lourdes M, Tummalacherla Meghasyam, Tediarjo Aryton, Yadav Vijay, Neto Elias Chaibub, MacDuffie Woody, DePhillips Michael, Sieg Eric, Catron Sydney, Grant Carly, Francis Wynona, Nguyen Marina, Yussuff Muibat, Castillo Rochelle L, Yan Di, Neimann Andrea L, Reddy Soumya M, Ogdie Alexis, Kolivras Athanassios, Kellen Michael R, Mangravite Lara M, Sieberts Solveig K, Omberg Larsson, Merola Joseph F, Scher Jose U
D.E. Webster, PhD, M. Tummalacherla, MSE, A. Tediarjo, BS, V. Yadav, MS, E. Chaibub Neto, PhD, W. MacDuffie, MS, M.R. Kellen, PhD, L.M. Mangravite, PhD, S.K. Sieberts, PhD, L. Omberg, PhD, Sage Bionetworks, Seattle, Washington, USA.
R.H. Haberman, MD, MSCI, S. Catron, BS, R.L. Castillo, MD, MSCI, S.M. Reddy, MD, J.U. Scher, MD, Department of Medicine, Division of Rheumatology, New York University Grossman School of Medicine and NYU Psoriatic Arthritis Center, NYU Langone Health, New York, New York, USA.
J Rheumatol. 2024 Aug 1;51(8):781-789. doi: 10.3899/jrheum.2024-0074.
Psoriatic disease remains underdiagnosed and undertreated. We developed and validated a suite of novel, sensor-based smartphone assessments (Psorcast app) that can be self-administered to measure cutaneous and musculoskeletal signs and symptoms of psoriatic disease.
Participants with psoriasis (PsO) or psoriatic arthritis (PsA) and healthy controls were recruited between June 5, 2019, and November 10, 2021, at 2 academic medical centers. Concordance and accuracy of digital measures and image-based machine learning models were compared to their analogous clinical measures from trained rheumatologists and dermatologists.
Of 104 study participants, 51 (49%) were female and 53 (51%) were male, with a mean age of 42.3 years (SD 12.6). Seventy-nine (76%) participants had PsA, 16 (15.4%) had PsO, and 9 (8.7%) were healthy controls. Digital patient assessment of percent body surface area (BSA) affected with PsO demonstrated very strong concordance (Lin concordance correlation coefficient [CCC] 0.94 [95% CI 0.91-0.96]) with physician-assessed BSA. The in-clinic and remote target plaque physician global assessments showed fair-to-moderate concordance (CCC 0.72 [0.59-0.85]; CCC 0.72 [0.62-0.82]; CCC 0.60 [0.48-0.72]). Machine learning models of hand photos taken by patients accurately identified clinically diagnosed nail PsO with an accuracy of 0.76. The Digital Jar Open assessment categorized physician-assessed upper extremity involvement, considering joint tenderness or enthesitis (AUROC 0.68 [0.47-0.85]).
The Psorcast digital assessments achieved significant clinical validity, although they require further validation in larger cohorts before use in evidence-based medicine or clinical trial settings. The smartphone software and analysis pipelines from the Psorcast suite are open source and freely available.
银屑病性疾病仍未得到充分诊断和治疗。我们开发并验证了一套基于传感器的新型智能手机评估工具（Psorcast应用程序），该工具可由患者自行操作，用于测量银屑病性疾病的皮肤和肌肉骨骼体征及症状。
2019年6月5日至2021年11月10日期间，在两家学术医疗中心招募了银屑病（PsO）或银屑病关节炎（PsA）患者以及健康对照者。将数字测量和基于图像的机器学习模型的一致性和准确性与其来自训练有素的风湿病学家和皮肤科医生的类似临床测量结果进行比较。
104名研究参与者中，51名（49%）为女性，53名（51%）为男性，平均年龄42.3岁（标准差12.6）。79名（76%）参与者患有PsA，16名（15.4%）患有PsO，9名（8.7%）为健康对照者。数字患者对PsO累及的体表面积（BSA）百分比的评估与医生评估的BSA显示出非常强的一致性（林一致性相关系数[CCC]0.94[95%CI 0.91 - 0.96]）。门诊和远程目标斑块医生整体评估显示出中等程度的一致性（CCC 0.72[0.59 - 0.85]；CCC 0.72[0.62 - 0.82]；CCC 0.60[0.48 - 0.72]）。患者拍摄的手部照片的机器学习模型能够准确识别临床诊断的指甲PsO，准确率为0.76。数字开瓶评估对医生评估的上肢受累情况进行了分类，考虑了关节压痛或附着点炎（曲线下面积[AUC]0.68[0.47 - 0.85]）。
尽管Psorcast数字评估在用于循证医学或临床试验环境之前需要在更大的队列中进一步验证，但已取得了显著的临床有效性。Psorcast套件的智能手机软件和分析管道是开源且免费可用的。
