Pivneva Irina, Balp Maria-Magdalena, Geissbühler Yvonne, Severin Thomas, Smeets Serge, Signorovitch James, Royer Jimmy, Liang Yawen, Cornwall Tom, Pan Jutong, Danyliv Andrii, McKenna Sarah Jane, Marsland Alexander M, Soong Weily
Analysis Group, Inc., 1190 Avenue des Canadiens-de-Montréal, Tour Deloitte, Suite 1500, Montréal, QC, H3B 0G7, Canada.
Novartis Pharma AG, Basel, Switzerland.
Dermatol Ther (Heidelb). 2022 Dec;12(12):2747-2763. doi: 10.1007/s13555-022-00827-6. Epub 2022 Oct 27.
The time required to reach clinical remission varies in patients with chronic urticaria (CU). The objective of this study is to develop a predictive model using a machine learning methodology to predict time to clinical remission for patients with CU.
Adults with ≥ 2 ICD-9/10 relevant CU diagnosis codes/CU-related treatment > 6 weeks apart were identified in the Optum deidentified electronic health record dataset (January 2007 to June 2019). Clinical remission was defined as ≥ 12 months without CU diagnosis/CU-related treatment. A random survival forest was used to predict time from diagnosis to clinical remission for each patient based on clinical and demographic features available at diagnosis. Model performance was assessed using concordance, which indicates the degree of agreement between observed and predicted time to remission. To characterize clinically relevant groups, features were summarized among cohorts that were defined based on quartiles of predicted time to remission.
Among 112,443 patients, 73.5% reached clinical remission, with a median of 336 days from diagnosis. From 1876 initial features, 176 were retained in the final model, which predicted a median of 318 days to remission. The model showed good performance with a concordance of 0.62. Patients with predicted longer time to remission tended to be older with delayed CU diagnosis, and have more comorbidities, more laboratory tests, higher body mass index, and polypharmacy during the 12-month period before the first CU diagnosis.
Applying machine learning to real-world data enabled accurate prediction of time to clinical remission and identified multiple relevant demographic and clinical variables with predictive value. Ongoing work aims to further validate and integrate these findings into clinical applications for CU management.
慢性荨麻疹（CU）患者达到临床缓解所需的时间各不相同。本研究的目的是使用机器学习方法开发一种预测模型，以预测CU患者的临床缓解时间。
在Optum去识别化电子健康记录数据集（2007年1月至2019年6月）中，识别出具有≥2个ICD-9/10相关CU诊断代码/与CU相关的治疗间隔>6周的成年人。临床缓解定义为≥12个月无CU诊断/与CU相关的治疗。使用随机生存森林根据诊断时可用的临床和人口统计学特征预测每位患者从诊断到临床缓解的时间。使用一致性评估模型性能，一致性表示观察到的和预测的缓解时间之间的一致程度。为了描述临床相关组，在根据预测缓解时间的四分位数定义的队列中总结特征。
在112443名患者中，73.5%达到临床缓解，从诊断到缓解的中位时间为336天。从1876个初始特征中，最终模型保留了176个，该模型预测缓解的中位时间为318天。该模型表现良好，一致性为0.62。预测缓解时间较长的患者往往年龄较大，CU诊断延迟，并且在首次CU诊断前的12个月期间有更多的合并症、更多的实验室检查、更高的体重指数和多种药物治疗。
将机器学习应用于真实世界数据能够准确预测临床缓解时间，并识别出多个具有预测价值的相关人口统计学和临床变量。正在进行的工作旨在进一步验证这些发现并将其整合到CU管理的临床应用中。
