Gulliver Wayne P, See Kyoungah, Zhu Baojin, Konicek Bruce W, Harrison Ryan W, McLean Robert R, Kerti Samantha J, Burge Russel T, Leonardi Craig L
St. John's, Newfoundland and Labrador, Memorial University of Newfoundland, Newfoundland, Canada.
Eli Lilly and Company, Indianapolis, IN, USA.
J Psoriasis Psoriatic Arthritis. 2023 Apr;8(2):74-82. doi: 10.1177/24755303231155118. Epub 2023 Mar 21.
Dermatologists would benefit from an easy to use psoriasis severity assessment tool in the clinic.
To develop psoriasis assessment tools to predict PASI and Dermatology Life Quality Index (DLQI) using simple measures typically collected in clinical practice.
Data included 33 605 dermatology visits among plaque psoriasis patients enrolled in the CorEvitas Psoriasis Registry (4/15/15-7/11/20). Performance (adjusted coefficient of determination [R ], root mean square error [RMSE]) in predicting PASI and DLQI was assessed for 16 different linear regression models (specified a priori based on combinations of BSA, Investigator's Global Assessment [IGA], itch, skin pain, patient global assessment, age, sex, BMI, comorbidity index, prior biologic use), and 2 stepwise selection models and 1 elastic net model based on 56 available variables. For each prediction model, concordance (sensitivity, specificity) of predicted PASI75, PASI90 and DLQI 0/1 with observed values was evaluated.
Mean (SD) age, BSA, and PASI were 51 (14) years, 6 (11), and 4 (6), respectively; 46% were women, and 87% were biologic experienced. A model predicting PASI using BSA plus IGA performed best among a priori specified models (R = .72, RMSE = 2.93) and only marginally worse than models including additional variables (R range .64-.74, RMSE range 2.82-3.36). Models including IGA had the best concordance between predicted and observed PASI75 (sensitivity range 83-85%, specificity range 88-91%) and PASI90 (sensitivity range 76-82%, specificity range 94-98%). DLQI prediction was limited.
An assessment tool for psoriasis including BSA and IGA may be an ideal option to predict PASI in a clinic setting.
皮肤科医生在临床中会受益于一种易于使用的银屑病严重程度评估工具。
利用临床实践中通常收集的简单指标开发银屑病评估工具，以预测银屑病面积和严重程度指数（PASI）及皮肤病生活质量指数（DLQI）。
数据包括CorEvitas银屑病登记处登记的斑块状银屑病患者的33605次皮肤科就诊记录（2015年4月15日至2020年7月11日）。针对16种不同的线性回归模型（根据体表面积[BSA]、研究者整体评估[IGA]、瘙痒、皮肤疼痛、患者整体评估、年龄、性别、体重指数[BMI]、合并症指数、既往生物制剂使用情况的组合预先设定）、2种逐步选择模型和基于56个可用变量的1种弹性网络模型，评估预测PASI和DLQI的性能（调整决定系数[R]、均方根误差[RMSE]）。对于每个预测模型，评估预测的PASI75、PASI90和DLQI 0/1与观察值的一致性（敏感性、特异性）。
平均（标准差）年龄、BSA和PASI分别为51（14）岁、6（11）和4（6）；46%为女性，87%有生物制剂使用经历。在预先设定的模型中，使用BSA加IGA预测PASI的模型表现最佳（R = 0.72，RMSE = 2.93），仅略逊于包含其他变量的模型（R范围为0.64 - 0.74，RMSE范围为2.82 - 3.36）。包含IGA的模型在预测和观察的PASI75（敏感性范围83 - 85%，特异性范围88 - 91%）和PASI90（敏感性范围76 - 82%，特异性范围94 - 98%）之间具有最佳一致性。DLQI预测效果有限。
一种包括BSA和IGA的银屑病评估工具可能是在临床环境中预测PASI的理想选择。
