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    老年良性前列腺增生病人护理依赖风险预测模型构建及验证

    Construction and validation of a nursing dependency risk prediction model for elderly patients with benign prostatic hyperplasia

      摘要
    • 摘要:
      目的： 基于社会生态学理论筛选老年良性前列腺增生（benign prostatic hyperplasia，BPH）病人护理依赖的危险因素，构建并验证护理依赖风险预测模型。
      方法： 采用便利抽样法，选择183例老年BPH病人为研究对象。采用一般资料问卷、国际前列腺症状评分（IPSS）、日常生活活动能力量表（ADL）、健康素养管理量表（HeLMS）、FRAIL量表及护理依赖等评定量表收集病人相关资料。采用二元logistic回归分析确定独立危险因素；采用ROC曲线评价模型区分度，Hosmer–Lemeshow检验评价模型拟合度，绘制列线图并进行验证。
      结果： 183例老年BPH病人中，护理依赖发生率为68.85%。年龄、跌倒史、FRAIL、IPSS、ADL、HeLMS均是护理依赖发生的影响因素，Logit回归方程：Logit(P) = 5.011 + 1.401 × 年龄 + 1.490 × 跌倒 + 0.917 × IPSS + 1.002 × FRAIL – 0.187 × ADL评分–0.064 × HeLMS得分。ROC曲线分析显示，模型曲线下面积（AUC）为0.889（95%CI：0.835 ~ 0.942），灵敏度为80.2%，特异度为84.2%，提示模型具有良好的区分度（0.8 ≤ AUC ＜ 0.9）。当截断值为0.705时，模型的约登指数为0.644，此时模型的预测效能最佳，即能够较好地识别出护理依赖阳性与阴性病人；Hosmer–Lemeshow检验结果为χ2 = 8.28，P = 0.41，提示模型拟合度良好。基于独立危险因素绘制的列线图，可通过赋值快速计算病人护理依赖风险概率，操作简便。
      结论： 本研究构建的老年BPH病人护理依赖风险预测模型具有良好的区分度与校准度，列线图可实现护理依赖风险的快速评估，为识别高危人群提供量化工具，有效预防护理依赖发生率，对减轻家庭及医疗负担有重要意义。

       

      Abstract:
      Objective To screen risk factors for nursing dependency in elderly patients with benign prostatic hyperplasia (BPH) based on social ecology theory, and to construct and validate a risk prediction model for nursing dependency.
      Methods A convenient sampling method was used to select 183 elderly BPH patients as the study subjects. General demographic questionnaires, international prostate symptom score (IPSS), activities of daily living (ADL) scale, health literacy management scale (HeLMS), FRAIL scale, and nursing dependency assessment scale were used to collect patient-related data. Binary logistic regression analysis was used to determine the independent risk factors; the ROC curve was used to evaluate the model's discrimination, and the Hosmer-Lemeshow test was used to evaluate the model's fit, followed by the construction and validation of a nomogram.
      Results Among the 183 elderly BPH patients, the incidence of nursing dependency was 68.85%. Age, history of falls, FRAIL, IPSS, ADL, and HeLMS were all influencing factors for the occurrence of nursing dependency. The logistic regression equation was: Logit(P) = 5.011 + 1.401 × age + 1.490 × falls + 0.917 × IPSS + 1.002 × FRAIL – 0.187 × ADL score –0.064 × HeLMS score. ROC curve analysis showed that the area under the curve (AUC) of the model was 0.889 (95%CI: 0.835–0.942), with a sensitivity of 80.2% and a specificity of 84.2%, indicating good discrimination (0.8 ≤ AUC ＜ 0.9) of the model. When the cutoff value was set at 0.705, the Youden's index of the model was 0.644, indicating optimal predictive performance of the model, which could better identify patients with positive and negative nursing dependency; the Hosmer-Lemeshow test result was χ2 = 8.28, P = 0.41, indicating good fit of the model. The nomogram based on independent risk factors allowed for rapid calculation of the probability of nursing dependency risk through assignment, making it easy to operate.
      Conclusions The risk prediction model for nursing dependency in elderly BPH patients constructed in this study has good discrimination and calibration, and the nomogram can achieve rapid assessment of nursing dependency risk, providing a quantitative tool for identifying high-risk populations, effectively preventing the occurrence of nursing dependency, and significantly reducing family and medical burden.

       

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