| 安静,杨蕙,杜晶,等.老年射血分数正常心力衰竭病人合并衰弱的风险列线图的建立[J].安徽医药,2025,29(12):2394-2398. |
| 老年射血分数正常心力衰竭病人合并衰弱的风险列线图的建立 |
| Establishment of a risk nomogram for frailty in elderly patients with heart failure with preserved ejection fraction |
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| DOI：10.3969/j.issn.1009-6469.2025.12.013 |
| 中文关键词: 心力衰竭 射血分数 衰弱 老年人 列线图预测模型 |
| 英文关键词: Heart failure Ejection fraction Weakness Elderly people Line chart prediction model |
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| 中文摘要: |
| 目的分析老年射血分数保留心力衰竭( HFpEF)病人合并衰弱风险的影响因素，并构建列线图模型。方法选取 2020年 2月至 2023年 2月于赤峰学院附属医院诊治的 275例 HFpEF病人作为研究对象，根据有无合并衰弱分为衰弱组( 78例)和非衰弱组( 197例)。采用 logistic回归模型分析影响 HFpEF病人发生衰弱的影响因素，再利用 R软件构建列线图预测模型，使用受试者操作特征曲线( ROC曲线)、校准曲线、 Hosmer-Lemeshow拟合优度检验对已构建的列线图预测模型的预测效能进行评估。结果 HFpEF病人中衰弱组和非衰弱组性别、受教育程度、吸烟史、饮酒史、合并用药类型、血红蛋白、高密度脂蛋白胆固醇( HDL-C)、低密度脂蛋白胆固醇( LDL-C)、收缩压、舒张压、血肌酐、 C反应蛋白( CRP)及脑钠肽比较，差异无统计学意义(P>0.05)衰弱组和非衰弱组年龄、身体质量指数( BMI)基础疾病共病数、心功能分级、左室射血分数( LVEF)［(54.37±6.28)%比( 62.07±7.，11)%］、左心房内径( LAD)［( 43.62±5.62)mm比、(40.79±4.44)mm］、左心室舒张末期内径( LVEDD)［( 54.09±6.29) mm比( 51.87±5.92)mm］和收缩末期内径( LVESD)［( 39.06±4.09)mm比( 37.30±3.92)mm］比较，差异有统计学意义( P<0.05)；多因素 logistic回归分析结果显示，年龄 ≥75岁、 BMI≥22 kg/m2、基础疾病共病数 ≥3个、心功能分级为 Ⅲ+Ⅳ级及 LVEF为影响因素(P<0.05)；以筛选出的这 5项因素构建列线图预测模型， ROC曲线验证列线图预测模型的区分度，曲线下面积为 0.81，95%CI:(0.76，0.86)；校准曲线、 Hosmer-Lemeshow拟合优度检验验证列线图预测模型的拟合效度，校准曲线斜率接近 1，拟合优度检验的 χ2=5.47、P=0.707。结论基于 logistic回归模型筛选出的 5项影响因素构建的列线图模型对 HFpEF病人是否发生衰弱具有较好的预测价值。 |
| 英文摘要: |
| Objective To analyze the influencing factors for frailty in elderly patients with heart failure with preserved ejection frac-tion (HFpEF), and to construct a nomogram model.Methods A total of 275 HFpEF patients diagnosed and treated in The AffiliatedHospital of Chifeng University from February 2020 to February 2023 were collected as the study subjects. They were grouped into afrailty group (n=78) and a non-frailty group (n=197) based on the presence or absence of comorbidities. The Logistic regression modelwas applied to analyze the influencing factors for frailty in HFpEF patients, then a nomogram prediction model was constructed using Rsoftware, and receiver operating characteristic curve (ROC curve), calibration curve and Hosmer-Lemeshow goodness of fit test were ap-plied to evaluate the prediction efficiency of the constructed nomogram prediction model.Results There were no statistically signifi-cant differences in gender, education background, smoking history, alcohol consumption history, type of combined medication, hemoglo-bin (Hb), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), systolic blood pressure (SBP), dia-stolic blood pressure (DBP), serum creatinine, C-reactive protein (CRP), and brain natriuretic peptide between the frailty and non-frail-ty groups of HFpEF patients (P>0.05), while significant differences were observed in age, body mass index (BMI), number of comorbidi-ties with underlying diseases, heart function grading, left ventricular ejection fraction (LVEF) [(54.37±6.28)% vs. (62.07±7.11)%], left atrial diameter (LAD) [(43.62 ±5.62) mm vs. (40.79±4.44) mm], left ventricular end diastolic diameter (LVEDD) [(54.09±6.29) mm vs. (51.87±5.92) mm] and left ventricular end systolic diameter (LVESD) [(39.06±4.09) mm vs. (37.30±3.92) mm] (P<0.05). The results of multivariate logistic regression analysis showed that age≥75 years old, BMI≥22 kg/m2, number of comorbidities with underlying diseas-es≥3, cardiac function grade Ⅲ+Ⅳ, and LVEF were the influencing factors (P<0.05). A nomogram prediction model was constructedbased on the five selected factors, and the ROC curve was applied to verify the discrimination of the model. The area under the curvewas 0.81, 95%CI: (0.76 , 0.86). The calibration curve and Hosmer Lemeshow goodness of fit test were adopted to verify the fitting validi-ty of the model. The slope of the calibration curve was close to 1, and the goodness-of-fit test yielded χ2=5.47, P=0.707. Conclusion The nomogram model constructed based on the five influencing factors screened by the Logistic regression model has good predictivevalue for whether HFpEF patients experience frailty. |
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