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慢性阻塞性肺疾病合并症认知障碍风险预测列线图的构建与评估

Construction and evaluation of nomogram for risk prediction of cognitive impairment in chronic obstructive pulmonary disease comorbidity.

作者信息

Luo JiaFeng, Yang Wen, Liu Yang, Ji HongLian, Li XinRan, Bai Jing, Liu TieJun, Chen WeiBin, Xiao Li, Mo GuoXin, Bai JingShan, Liu CongHui, Li WenQiang, Fu AiShuang, Ge YanLei

机构信息

North China University of Science and Technology School of Clinical Medicine, Tangshan, China.

Department of Respiratory Medicine, Tianjin Fifth Central Hospital, Tianjin, China.

出版信息

BMC Psychol. 2025 Mar 19;13(1):273. doi: 10.1186/s40359-025-02516-3.

Abstract

OBJECTIVES

Chronic Obstructive Pulmonary Disease (COPD) remains a serious public health problem globally, and the mortality rate for older COPD patients with cognitive impairment is almost three times that of older patients with cognitive impairment or COPD. The aim of this study was to construct a nomogram prediction model for the risk of comorbid cognitive impairment in COPD patients and to evaluate its clinical application. It helps to detect cognitive impairment in COPD patients at an early stage and give them effective interventions in time, so as to delay the progression of COPD patients and improve their prognosis.

METHODS

In this study, patients with COPD hospitalized at the Affiliated Hospital of North China University of Science and Technology were evaluated for cognitive function using the Montreal Cognitive Assessment(MoCA) scale after stabilization of acute exacerbations. Participants were stratified into two groups: a case group (with cognitive impairment) and a control group (without cognitive impairment), based on predefined MoCA cutoff scores(< 26scores). Based on the basic characteristics of the patients and the laboratory indexes after stabilization of acute exacerbations, we conducted statistical analyses, screened out the risk factors and established the Nomogram Prediction Model by using the R software, and finally, we evaluated the clinical value of the model through the calculation of ROC curves for sensitivity, specificity and kappa value. Finally, the sensitivity, specificity and Kappa value were calculated by ROC curve to evaluate the clinical value of the model.

RESULTS

After statistical analysis, C-reactive protein (CRP) and homocysteine (Hcy) were found to be the risk factors for combined cognitive impairment in COPD patients, and the Nomogram prediction model was constructed by combining CRP and Hcy and plotted the ROC curve, and it was found that its model finally screened the critical value of the total score of 62.55, and the area under the ROC curve of the model was 0.870, and the sensitivity was 84.7%, and the specificity was 80.4%, indicating that it has a high degree of consistency with the actual results, which indicated that the consistency between the prediction results and the actual results was better, and it had a higher clinical application value.

CONCLUSIONS

CRP and Hcy are closely associated with comorbid cognitive impairment in COPD patients after stabilization of acute exacerbations, and increased levels of CRP and Hcy are associated with an increased risk of comorbid cognitive impairment in COPD patients. Combining both CRP and Hcy to create a nomogram model for predicting comorbid cognitive impairment in patients with COPD has good predictive ability.

摘要

目的

慢性阻塞性肺疾病(COPD)仍是全球严重的公共卫生问题,合并认知障碍的老年COPD患者死亡率几乎是合并认知障碍或COPD的老年患者的三倍。本研究旨在构建COPD患者合并认知障碍风险的列线图预测模型并评估其临床应用价值。有助于早期发现COPD患者的认知障碍并及时给予有效干预,从而延缓COPD患者病情进展,改善其预后。

方法

本研究中,华北理工大学附属医院收治的COPD患者在急性加重期病情稳定后,采用蒙特利尔认知评估量表(MoCA)评估认知功能。根据预先设定的MoCA临界值(<26分),将参与者分为两组:病例组(有认知障碍)和对照组(无认知障碍)。根据患者的基本特征及急性加重期病情稳定后的实验室指标进行统计分析,筛选出危险因素,利用R软件建立列线图预测模型,最后通过计算ROC曲线的敏感度、特异度及kappa值评估模型的临床价值。最后通过ROC曲线计算敏感度、特异度及Kappa值来评估模型的临床价值。

结果

经统计分析,发现C反应蛋白(CRP)和同型半胱氨酸(Hcy)是COPD患者合并认知障碍的危险因素,将CRP与Hcy相结合构建列线图预测模型并绘制ROC曲线,发现其模型最终筛选出总分临界值为62.55,模型ROC曲线下面积为0.870,敏感度为84.7%,特异度为80.4%,表明其与实际结果具有高度一致性,即预测结果与实际结果一致性较好,具有较高的临床应用价值。

结论

CRP和Hcy与急性加重期病情稳定后的COPD患者合并认知障碍密切相关,CRP和Hcy水平升高与COPD患者合并认知障碍风险增加有关。联合CRP和Hcy构建COPD患者合并认知障碍的列线图模型具有良好的预测能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1332/11921626/d9076f5f6e9d/40359_2025_2516_Fig1_HTML.jpg

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