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与关节受累相关的变量和预测银屑病关节炎患者关节炎发生的预测规则的建立。意大利 PsoReal 数据库分析。

Variables associated with joint involvement and development of a prediction rule for arthritis in patients with psoriasis. An analysis of the Italian PsoReal database.

机构信息

Centro Studi GISED, Bergamo, Italy; Department of Dermatology, Inselspital University Hospital of Bern, Bern, Switzerland.

Research and Health Foundation (ReS), Bologna, Italy.

出版信息

J Am Acad Dermatol. 2023 Jul;89(1):53-61. doi: 10.1016/j.jaad.2023.02.059. Epub 2023 Mar 23.

Abstract

BACKGROUND

Limited data exist to predict the development of psoriatic arthritis (PsA) in patients with psoriasis (PsO).

OBJECTIVE

To analyze factors associated with incident PsA in patients with PsO, and to develop a predictive algorithm for progression to arthritis using a full set of variables and a restricted one applicable to administrative data.

METHODS

Cohort study within the PsoReal registry in Italy. Multivariable generalized linear models were used to assess factors associated with PsA and to derive a predictive model.

RESULTS

Among 8895 patients, 226 PsA cases were identified (incidence 1.9 per 100 patient-years). Independent predictors in the full model were as follows: female sex, age 40 to 59 years, body mass index ≥ 25, chronic-plaque PsO features, presence of palmoplantar pustulosis, hospitalization for PsO in the last 5 years, and previous use of systemic PsO therapy (area under the receiver operating characteristic curve = 0.74). Female sex, age 40 to 59 years, hospitalization for PsO, and previous use of systemic PsO therapy were independent predictors in the restricted model (area under the receiver operating characteristic curve = 0.72).

LIMITATIONS

Lack of other potential predictors for PsA.

CONCLUSION

Our models could be used by clinicians and health authorities when planning intervention and population surveillance. Future studies should confirm our models using larger datasets and additional variables.

摘要

背景

目前仅有有限的数据可用于预测银屑病(PsO)患者中出现银屑病关节炎(PsA)的情况。

目的

分析与银屑病患者发生 PsA 相关的因素,并利用全套变量和适用于行政数据的简化变量集开发一种用于预测关节炎进展的算法。

方法

在意大利的 PsoReal 登记处进行队列研究。使用多变量广义线性模型来评估与 PsA 相关的因素,并推导出预测模型。

结果

在 8895 例患者中,发现 226 例 PsA 病例(发病率为 1.9/100 患者年)。全模型中的独立预测因素如下:女性、40 至 59 岁、身体质量指数(BMI)≥25、慢性斑块型银屑病特征、存在掌跖脓疱病、过去 5 年内因银屑病住院、以及过去使用过全身银屑病治疗(接受者操作特征曲线下面积[area under the receiver operating characteristic curve,AUROC]为 0.74)。在简化模型中,女性、40 至 59 岁、因银屑病住院以及过去使用过全身银屑病治疗是独立的预测因素(AUROC 为 0.72)。

局限性

缺乏其他可能的 PsA 预测因素。

结论

我们的模型可用于临床医生和卫生当局规划干预措施和人群监测。未来的研究应使用更大的数据集和其他变量来验证我们的模型。

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