【摘要】 目的 通过早期识别老年轻度认知障碍（mild cognitive impairment,MCI）的影响因素，根据影响因素构建预测模型并分析其应用价值。方法 选取2023年1月—2024年1月医院收治的300例老年患者作为调查对象，依据其是否发生MCI分为MCI组（n=114）和非MCI组（n=186），采用单因素分析和多因素Logistic回归分析早期老年患者发生MCI的影响因素，据此构建预测模型并分析应用价值。结果 多因素Logistic回归分析显示，年龄（OR=1.820,95%CI:1.559～2.125）、BMI(OR=2.219,95%CI:1.835～2.657)、受教育水平（OR=0.011,95%CI:0.001～0.101）、吸烟史（OR=1.935,95%CI:1.713～2.185）、饮酒史（OR=1.804,95%CI:1.570～2.073）、平均每天睡眠时长（OR=0.034,95%CI:0.009～0.123）、合并糖尿病（OR=1.908,95%CI:1.621～2.245）、合并抑郁症（OR=2.435,95%CI:2.021～2.934）、合并高血脂（OR=2.303,95%CI:1.945～2.725）、Barthel指数（OR=0.014,95%CI:0.003～0.075）是早期老年患者发生MCI的影响因素（P<0.05）。列线图内部验证一致性指数为0.727，校准曲线与理想曲线拟合良好，ROC曲线下面积（AUC=0.783,95%CI:0.677～0.889），敏感度为81.25%，特异性为67.50%。结论 本研究基于年龄、BMI、受教育水平、吸烟史、饮酒史、平均每天睡眠时长、合并糖尿病、合并抑郁症、合并高血脂，以及Barthel指数构建的预测模型对老年患者发生MCI风险具有较好的预测价值，可为早期识别和干预MCI提供有力依据。更多还原

【Abstract】 Objective To construct a prediction model based on the influencing factors of mild cognitive impairment(MCI) in the elderly through early identification of these factors,and to analyze its application value.Methods A total of 300 elderly patients admitted to the hospital from January 2023 to January 2024 were selected as the research subj ects.According to whether they developed MCI,they were divided into an MCI group(n=114)and a non-MCI group(n=186).Univariate analysis and multivariate Logistic regression analysis were used to identify the influencing factors of MCI in elderly patients.Based on these,a prediction model was constructed and its application value was analyzed.Results Multivariate Logistic regression analysis showed that age(OR=1.820),(95%CI:1.559-2.125), BMI(OR=2.219),(95%CI:1.835-2.657),educational level(OR=0.011),(95%CI:0.001-0.101),smoking history(OR=1.935),95%CI:1.713-2.185),drinking history(OR=1.804,95%CI:1.570-2.073),average daily sleep duration(OR=0.034),95%CI:0.009-0.123),comorbid diabetes(OR=1.908),95%CI:1.621-2.245),comorbid depression(OR=2.435,95%CI):2.021-2.934)),comorbid hyperlipidemia(OR=2.303,95%CI:1.945-2.725),and Barthel index(OR=0.014,95%CI:0.003-0.075) were influencing factors for MCI in elderly patients(P<0.05).The consistency index of internal validation of the nomogram was 0.727.The calibration curve fitted well with the ideal curve.The area under the ROC curve(AUC=0.783),95%CI:0.677-0.889),the sensitivity was 81.25%),and the specificity was 67.50%.Conclusion The prediction model constructed in this study based on age,BMI,educational level,smoking history,drinking history,average daily sleep duration,comorbid diabetes,comorbid depression,comorbid hyperlipidemia,and Barthel index has a good predictive value for the influencing factors of MCI in elderly patients,and can provide a strong basis for early identification and intervention of MCI.更多还原