冠心病患者经皮冠脉介入术后便秘风险列线图预测模型的构建及验证

doi:10.20047/j.issn1673-7210.2022.26.03

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摘要目的构建冠心病患者经皮冠脉介入术后便秘风险列线图预测模型，并进行内部验证。方法回顾性收集2019年1月至2020年12月在空军军医大学第二附属医院心内科接受经皮冠脉介入术治疗的398例冠心病患者临床资料，根据是否发生便秘将其分为排便正常组（262例）和便秘组（136例）。比较两组临床资料的差异，采用logistic回归模型筛选患者便秘的影响因素。采用R 3.4.3软件构建冠心病患者经皮冠脉介入术后便秘风险列线图预测模型，采用Bootstrap法和Hosmer-Lemeshow拟合优度检验对模型进行内部验证，受试者操作特征（ROC）曲线分析该模型的诊断价值。结果便秘组年龄＞60岁、Killip分级Ⅲ～Ⅳ级、术后服用阿片类药物、支架植入数量＞3个、焦虑/抑郁、糖尿病、吸烟史占比均高于排便正常组，规律运动占比低于排便正常组，差异有统计学意义（P ＜ 0.05）。logistic回归分析显示，年龄＞60岁、Killip分级为Ⅲ～Ⅳ级、术后服用阿片类药物、焦虑/抑郁、糖尿病是冠心病患者经皮冠脉介入术后便秘的独立危险因素（OR = 2.945、4.859、3.624、3.082、2.770，P ＜ 0.05），规律运动是保护因素（OR = 0.447，P ＜ 0.05）。列线图预测模型内部验证结果显示，观察曲线与可接受曲线、理想曲线基本吻合（P ＞ 0.05）。列线图预测模型的ROC曲线下面积为0.788（95%CI：0.742～0.833，P ＜ 0.05）。结论冠心病患者经皮冠脉介入术后便秘风险列线图预测模型具有良好的校准度和区分度，有助于医护人员对患者便秘风险的个体化识别。

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	问芳芳1 杜海维2 郭晓岚1 席小立1 胡健强1 程苗苗1▲

关键词 ：冠心病, 经皮冠脉介入术, 便秘, 列线图, 预测

Abstract：Objective To construct a nomogram prediction model for the risk of constipation in patients with coronary heart disease after percutaneous coronary intervention, and to conduct internal verification. Methods The clinical data of 398 patients with coronary heart disease who underwent percutaneous coronary intervention in the Department of Cardiology, the Second Affiliated Hospital of Air Force Military Medical University from January 2019 to December 2020 were collected retrospectively. The patients were divided into normal defecation group (262 cases) and constipation group (136 cases) according to whether constipation occurred. The differences of clinical data between the two groups were compared, and the influencing factors of constipation were screened by logistic regression model. R 3.4.3 software was used to construct a nomogram prediction model for the risk of constipation in patients with coronary heart disease after percutaneous coronary intervention, and the Bootstrap method and Hosmer-Lemeshow goodness-of-fit test were used to verify the model internally. Receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of the model. Results In the constipation group, the proportions of age > 60 years, Killip grade Ⅲ-Ⅳ, opioid use after surgery, number of stents implanted > 3, anxiety/depression, diabetes mellitus, and smoking history were higher than those in the normal defecation group, and the proportion of regular exercise was lower than that in the normal defecation group, and the differences were statistically significant (P ＜ 0.05). Logistic regression analysis showed that age > 60 years old, Killip grade Ⅲ-Ⅳ, opioid use after surgery, anxiety/depression, and diabetes mellitus were independent risk factors for constipation after percutaneous coronary intervention (OR = 2.945, 4.859, 3.624, 3.082, 2.770, P ＜ 0.05), regular exercise was a protective factor (OR = 0.447, P ＜ 0.05). The internal validation results of the nomogram prediction model showed that the observed curve was basically consistent with the acceptable curve and the ideal curve (P ＞ 0.05). The area under ROC curve of the nomogram prediction model was 0.788 (95%CI: 0.742-0.833, P < 0.05). Conclusion The nomogram prediction model for the risk of constipation in patients with coronary heart disease after percutaneous coronary intervention has a good degree of calibration and discrimination, which is helpful for medical staff to individualize the risk of constipation in patients.

Key words： Coronary heart disease Percutaneous coronary intervention Constipation Nomograms Prediction

基金资助:国家自然科学基金青年科学基金项目（82000 350）。

通讯作者: ▲通讯作者

引用本文:

问芳芳1 杜海维2 郭晓岚1 席小立1 胡健强1 程苗苗1▲. 冠心病患者经皮冠脉介入术后便秘风险列线图预测模型的构建及验证[J]. 中国医药导报, 2022, 19(26): 15-18,33.
WEN Fangfang1 DU Haiwei2 GUO Xiaolan1 XI Xiaoli1 HU Jianqiang1 CHENG Miaomiao1▲. Construction and validation of a nomogram prediction model for the risk of constipation in patients with coronary heart disease after percutaneous coronary intervention. 中国医药导报, 2022, 19(26): 15-18,33.

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[1] 李镒冲，刘世炜，曾新颖，等.1990—2016年中国及省级行政区心血管病疾病负担报告[J].中国循环杂志，2019， 34（8）：729-740.
[2] 胡盛寿，高润霖，刘力生，等.《中国心血管病报告2018》概要[J].中国循环杂志，2019，34（3）：209-220.
[3] 陆蕾，孙艺，刘洪珍，等.模块化膳食干预对急诊PCI患者便秘及血脂水平的影响[J].护理学杂志，2019，34（4）：6-8，13.
[4] 曾清清，李军文，郑太蓉，等.病程日记预防经皮冠状动脉介入治疗患者便秘的作用[J].护理学杂志，2016，31（1）：29-32.
[5] Ishiyama Y，Hoshide S，Mizuno H，et al. Constipation-induced pressor effects as triggers for cardiovascular events [J]. J Clin Hypertens（Greenwich），2019，21（3）：421-425.
[6] Park SY. Nomogram：An analogue tool to deliver digital knowledge [J]. J Thorac Cardiovasc Surg，2018，155（4）：1793.
[7] 汪立.初产妇产后抑郁风险列线图模型的建立[J].护理学杂志，2020，35（13）：30-33.
[8] 韩雅玲.中国经皮冠状动脉介入治疗指南（2016）[J].中华心血管病杂志，2016，44（5）：382-400.
[9] 中华医学会消化病学分会胃肠动力学组，中华医学会外科学分会结直肠肛门外科学组.中国慢性便秘诊治指南（2013年，武汉）[J].中华消化杂志，2013，33（5）：291-297.
[10] 李军祥，陈誩，柯晓.功能性便秘中西医结合诊疗共识意见（2017年）[J].中国中西医结合消化杂志，2018，26（1）：18-26.
[11] Parker G，Hadzi-Pavlovic D. Do Hamilton depression scale items have the capacity to differentiate melancholic and non-melancholic depressive sub-types？[J]. J Affect Disord，2020，274（1）：1022-1027.
[12] Black CJ，Ford AC. Chronic idiopathic constipation in adults：epidemiology，pathophysiology，diagnosis and clinical management [J]. Med J Aust，2018，209（2）：86-91.
[13] Osuafor CN，Enduluri SL，Travers E，et al. Preventing and managing constipation in older inpatients [J]. Int J Health Care Qual Assur，2018，31（5）：415-419.
[14] Sundb?覬ll J，Szépligeti SK，Adelborg K，et al. Constipation and risk of cardiovascular diseases：a Danish population-based matched cohort study [J]. BMJ Open，2020，10（9）：e037080.
[15] Brown CR，Chen Z，Khurshan F，et al. Development of Persistent Opioid Use After Cardiac Surgery [J]. JAMA Cardiol，2020，5（8）：889-896.
[16] Ghoshal A. Fentanyl，Morphine，and Opioid-Induced Constipation in Patients with Cancer-Related Pain [J]. Indian J Palliat Care，2020，26（4）：535-536.
[17] Roeland EJ，Sera CJ，Ma JD. More opioids，more constipation？Evaluation of longitudinal total oral opioid consumption and self-reported constipation in patients with cancer [J]. Support Care Cancer，2020，28（4）：1793-1797.
[18] Salari A，Rouhi Balasi L，Ashouri A，et al. Medication Adherence and its Related Factors in Patients Undergoing Coronary Artery Angioplasty [J]. J Caring Sci，2018，7（4）：213-218.
[19] Bouchoucha M，Fysekidis M，Deutsch D，et al. Biopsychosocial Model and Perceived Constipation Severity According to the Constipation Phenotype [J]. Dig Dis Sci，2021，66（10）：3588-3596.
[20] Kurniawan AH，Suwandi BH，Kholili U. Diabetic Gastroenteropathy：A Complication of Diabetes Mellitus [J]. Acta Med Indones，2019，51（3）：263-271.
[21] Dimidi E，Christodoulides S，Scott SM，et al. Mechanisms of Action of Probiotics and the Gastrointestinal Microbiota on Gut Motility and Constipation [J]. Adv Nutr，2017，8（3）：484-494.
[22] Gao R，Tao Y，Zhou C，et al. Exercise therapy in patients with constipation： a systematic review and meta-analysis of randomized controlled trials [J]. Scand J Gastroenterol，2019，54（2）：169-177.
[23] 郝娟，席娟，陈嘉屿.慢性便秘的危险因素及非手术治疗进展[J].中国临床研究，2020，33（6）：845-848.
[24] 中华医学会老年医学分会，中华老年医学杂志编辑委员会.老年人慢性便秘的评估与处理专家共识[J].中华老年医学杂志，2017，36（4）：371-381.
[25] 周结霞，钱璐，梁丽贞.脑卒中患者便秘因素分析及预见性护理恢复排便功能的效果评价[J].中国医药科学，2020，10（13）：95-98.
[26] 马雪，王强，王渊，等.帕金森病便秘的病理生理机制及治疗进展[J].中国医药导报，2021，18（23）：33-37.
[27] 丛圆圆.循证支持的康复护理在老年脑梗死伴便秘患者中的应用效果[J].中国当代医药，2020，27（30）：223-225.
[28] 陈玉，丁琳，刘菁.消化道肿瘤患者输液港导管相关血流感染列线图模型构建[J].护理学杂志，2021，36（19）：52-55.
[29] 唐倩芸，邢柏.预测PICC导管相关血流感染风险的列线图模型的建立与验证[J].中国医药导报，2020，17（36）：45-48.
[30] 罗时同，徐庆.个体化预测急性结石性胆囊炎合并肝功能异常的列线图模型构建[J].中国临床研究，2022，35（2）：206-209.
[31] 田甜，景慧，荆莉.颈动脉支架植入术后谵妄风险列线图模型构建[J].护理学杂志，2021，36（12）：26-30.