Abstract：
OBJECTIVE: To construct a predictive model for Psoriatic Arthritis (PsA) based on clinical and ultrasonic characteristics in patients with plaque psoriasis (PsP).
METHODS: Demographic, clinical, and ultrasound data were collected from patients with PsP and PsA between May 2019 and December 2022.
RESULTS: A total of 212 patients with PsP and 123 with PsA in the training cohort, whereas the validation cohort comprised 91 patients with PsP and 49 with PsA. The multivariate logistic regression identified nail psoriasis (odds ratio [OR] 1.88, 95% CI: 1.07-3.29), synovitis (OR 18.23, 95% CI: 4.04-82.33), enthesitis (OR 3.71, 95% CI: 1.05-13.14), and bone erosion (OR 11.39, 95% CI: 3.05-42.63) as effective predictors for PsA. The area under the curve was 0.750 (95% CI, 0.691-0.806) and 0.804 (95% CI, 0.723-0.886) for the training and validation cohorts, respectively. The Hosmer-Lemeshow goodness-of-fit test showed good consistency for both the training cohort (p = 0.970) and the validation cohort (p = 0.967). Calibration curves also indicated good calibration for both cohorts. The DCA revealed that the predictive model had good clinical utility.
CONCLUSIONS: We have developed a quantitative, intuitive, and convenient predictive model based on nail psoriasis, synovitis, enthesitis, and bone erosion to assess the risk of PsA in patients with plaque psoriasis.
METHODS: Demographic, clinical, and ultrasound data were collected from patients with PsP and PsA between May 2019 and December 2022.
RESULTS: A total of 212 patients with PsP and 123 with PsA in the training cohort, whereas the validation cohort comprised 91 patients with PsP and 49 with PsA. The multivariate logistic regression identified nail psoriasis (odds ratio [OR] 1.88, 95% CI: 1.07-3.29), synovitis (OR 18.23, 95% CI: 4.04-82.33), enthesitis (OR 3.71, 95% CI: 1.05-13.14), and bone erosion (OR 11.39, 95% CI: 3.05-42.63) as effective predictors for PsA. The area under the curve was 0.750 (95% CI, 0.691-0.806) and 0.804 (95% CI, 0.723-0.886) for the training and validation cohorts, respectively. The Hosmer-Lemeshow goodness-of-fit test showed good consistency for both the training cohort (p = 0.970) and the validation cohort (p = 0.967). Calibration curves also indicated good calibration for both cohorts. The DCA revealed that the predictive model had good clinical utility.
CONCLUSIONS: We have developed a quantitative, intuitive, and convenient predictive model based on nail psoriasis, synovitis, enthesitis, and bone erosion to assess the risk of PsA in patients with plaque psoriasis.
摘要：
目的：根据斑块型银屑病（PsP）患者的临床和超声特征，构建PsA的预测模型。
方法：人口统计学，临床,并收集了2019年5月至2022年12月期间PsP和PsA患者的超声数据.
结果：训练组中共有212名PsP患者和123名PsA患者，而验证队列包括91例PsP患者和49例PsA患者。多变量逻辑回归确定了指甲牛皮癣（比值比[OR]1.88，95％CI：1.07-3.29），滑膜炎（OR18.23，95%CI：4.04-82.33），附着点炎（OR3.71，95%CI：1.05-13.14），骨侵蚀(OR11.39，95%CI：3.05-42.63)是PsA的有效预测因子。训练和验证队列的曲线下面积为0.750（95％CI，0.691-0.806）和0.804（95％CI，0.723-0.886），分别。Hosmer-Lemeshow拟合优度测试在训练队列（p=0.970）和验证队列（p=0.967）中均显示出良好的一致性。校准曲线还表明两个队列的校准良好。DCA显示该预测模型具有良好的临床实用性。
结论：我们已经开发了一种定量的，直观，基于指甲牛皮癣的便捷预测模型，滑膜炎,附着性炎,和骨侵蚀评估斑块状银屑病患者PsA的风险。
方法：人口统计学，临床,并收集了2019年5月至2022年12月期间PsP和PsA患者的超声数据.
结果：训练组中共有212名PsP患者和123名PsA患者，而验证队列包括91例PsP患者和49例PsA患者。多变量逻辑回归确定了指甲牛皮癣（比值比[OR]1.88，95％CI：1.07-3.29），滑膜炎（OR18.23，95%CI：4.04-82.33），附着点炎（OR3.71，95%CI：1.05-13.14），骨侵蚀(OR11.39，95%CI：3.05-42.63)是PsA的有效预测因子。训练和验证队列的曲线下面积为0.750（95％CI，0.691-0.806）和0.804（95％CI，0.723-0.886），分别。Hosmer-Lemeshow拟合优度测试在训练队列（p=0.970）和验证队列（p=0.967）中均显示出良好的一致性。校准曲线还表明两个队列的校准良好。DCA显示该预测模型具有良好的临床实用性。
结论：我们已经开发了一种定量的，直观，基于指甲牛皮癣的便捷预测模型，滑膜炎,附着性炎,和骨侵蚀评估斑块状银屑病患者PsA的风险。
