赵振凯, 王睿, 王宥霖, 于翀立, 汤自洁, 晏亮, 董美含, 李承新. 斑块型银屑病骨密度减低的关联因素分析及预测模型构建[J]. 解放军医学院学报, 2022, 43(11): 1128-1132. DOI: 10.3969/j.issn.2095-5227.2022.11.005
引用本文: 赵振凯, 王睿, 王宥霖, 于翀立, 汤自洁, 晏亮, 董美含, 李承新. 斑块型银屑病骨密度减低的关联因素分析及预测模型构建[J]. 解放军医学院学报, 2022, 43(11): 1128-1132. DOI: 10.3969/j.issn.2095-5227.2022.11.005
ZHAO Zhenkai, WANG Rui, WANG Youlin, YU Chongli, TANG Zijie, YAN Liang, DONG Meihan, LI Chengxin. Low bone mineral density in plaque psoriasis: Associated factors and predictive model[J]. ACADEMIC JOURNAL OF CHINESE PLA MEDICAL SCHOOL, 2022, 43(11): 1128-1132. DOI: 10.3969/j.issn.2095-5227.2022.11.005
Citation: ZHAO Zhenkai, WANG Rui, WANG Youlin, YU Chongli, TANG Zijie, YAN Liang, DONG Meihan, LI Chengxin. Low bone mineral density in plaque psoriasis: Associated factors and predictive model[J]. ACADEMIC JOURNAL OF CHINESE PLA MEDICAL SCHOOL, 2022, 43(11): 1128-1132. DOI: 10.3969/j.issn.2095-5227.2022.11.005

斑块型银屑病骨密度减低的关联因素分析及预测模型构建

Low bone mineral density in plaque psoriasis: Associated factors and predictive model

  • 摘要:
      背景  银屑病患者较正常人群发生骨密度减低、骨折等骨病的风险更高,给患者家庭和社会造成巨大的经济负担。因此早期精准识别低骨密度和骨折高危人群,对于银屑病患者而言具有重要意义。
      目的  分析斑块型银屑病骨密度减低的关联因素,并构建斑块型银屑病骨密度减低的风险预测模型。
      方法  选取2021年1月- 2022年3月解放军总医院第一医学中心皮肤科收治的80例斑块型银屑病患者和同时期就诊的年龄、性别相近的60例非银屑病患者为研究对象。记录并分析两组患者的临床资料。基于单因素和多因素logistic回归,分析斑块型银屑病患者发生骨密度减低的影响因素,构建预测模型,最后通过绘制受试者工作特征曲线(ROC)并计算曲线下面积(AUC)来评估模型的预测效能。
      结果  斑块型银屑病组80例,男57例,女23例,平均年龄(46.29 ± 15.78)岁;非银屑病组60例,男41例,女19例,平均年龄(50.82 ± 17.39)岁,两组性别、年龄差异均无统计学意义(P>0.05)。但斑块型银屑病组的腰椎和髋关节骨密度显著低于非银屑病组(-1.27 ± 1.11) g/cm2 vs (-0.72 ± 1.40) g/cm2;(-0.92 ± 0.82) g/cm2 vs (-0.62 ± 0.81) g/cm2P均<0.05。单因素logistic回归分析结果显示,年龄、体质量指数(body mass index,BMI)、银屑病面积与严重程度指数评分(psoriasis area and severity index,PASI)与斑块型银屑病患者低骨密度发生有关(P均<0.05)。多因素logistic回归分析结果显示,年龄(OR=1.040;95% CI:1.004 ~ 1.078)、PASI评分(OR=1.111;95% CI:1.004 ~ 1.229)、BMI(OR=0.808;95% CI:0.697 ~ 0.938)与斑块型银屑病骨密度减低独立关联。根据各变量的回归系数构建预测模型,通过绘制ROC曲线,计算出AUC值为0.772(95% CI:0.667 ~ 0.877),当预测模型的最佳临界值取0.697时,该模型具有预测价值,敏感度为62.7%,特异性为82.8%。
      结论  斑块型银屑病患者腰椎及髋关节骨密度水平较对照人群显著降低。基于logistic回归分析构建的预测模型对于预测斑块型银屑病患者是否发生骨密度减低具有一定的敏感度和较高的特异性,可根据该模型采取有针对性的预防措施,减少骨密度减低、骨质疏松甚至骨折的发生。

     

    Abstract:
      Background   Psoriatic patients have a higher risk of low bone mineral density and fracture than normal people, causing huge economic burden to patients' families and society. Therefore, early and accurate identification of high-risk patients is of great significance for early prevention and treatment.
      Objective   To analyze the associated factors of low bone mineral density in patients with plaque psoriasis, and build a risk prediction model of low bone mineral density for plaque psoriasis.
      Methods   From January 2021 to March 2022, clinical data about 80 plaque psoriasis patients and 60 non-psoriasis patients with similar age and gender admitted to the Dermatology Department of the First Medical Center of Chinese PLA General Hospital were recorded and analyzed. Based on univariate and multivariate logistic regression, the associated factors of low bone mineral density in patients with plaque psoriasis were analyzed, and the prediction model was constructed. Finally, the predictive performance of the model was evaluated by the receiver operating characteristic curve (ROC) and calculating the area under the curve (AUC).
      Results   Of the 80 patients in the plaque psoriasis group, there were 57 males and 23 females, with an average age of 46.29 ± 15.78 years. Of the 60 patients in the non-psoriasis group, there were 41 males and 19 females, with an average age of 50.82 ± 17.39 years. No statistically significant differences between the two groups were found (P > 0.05). However, the BMD of lumbar spine and hip joint in the plaque psoriasis were significantly lower than those in the non-psoriasis group (-1.27 ± 1.11 vs -0.72 ± 1.40, -0.92 ± 0.82 vs -0.62 ± 0.81, P < 0.05, respectively). Univariate logistic regression analysis showed that age, BMI and PASI scores were related to the occurrence of low bone mineral density in patients with plaque psoriasis (P < 0.05). Multivariate logistic regression analysis showed that age (OR: 1.040; 95%CI: 1.004 - 1.078), PASI score (OR: 1.111; 95%CI: 1.004 - 1.229) and BMI (OR: 0.808; 95%CI: 0.697 - 0.938) were independently associated with the decrease of bone mineral density in plaque psoriasis. A prediction model was constructed according to the regression coefficient of each variable, and the ROC curve was drawn, the AUC value was 0.772 (95%CI: 0.667 - 0.877). When the optimal cut-off of the prediction model was 0.697, the sensitivity was 62.7% and specificity was 82.8%.
      Conclusion   The bone mineral density of lumbar spine and hip joint in patients with plaque psoriasis are significantly lower than the other patients. The prediction model based on logistic regression analysis has high sensitivity and specificity for predicting whether plaque psoriasis patients have low bone mineral density. Targeted preventive measures can be taken according to this model to reduce the incidence of low bone mineral density, osteoporosis and even fracture.

     

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