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护理学报 ›› 2023, Vol. 30 ›› Issue (15): 16-22.doi: 10.16460/j.issn1008-9969.2023.15.016

• 研究生园地 • 上一篇    下一篇

ICU转出患者睡眠障碍风险列线图预测模型的构建与验证

彭仁梅1, 钱荣2a, 李亚军3, 段缓3, 陈莲芳3, 刘玉文2b   

  1. 1.武汉大学人民医院 护理部,湖北 武汉 430060;
    2.蚌埠医学院 a.护理学院;b.卫生管理学院,安徽 蚌埠 233030;
    3.蚌埠医学院第一附属医院 重症医学科,安徽 蚌埠 233030
  • 收稿日期:2022-11-15 出版日期:2023-08-10 发布日期:2023-09-08
  • 通讯作者: 钱荣（1972-）,女,安徽蚌埠人,硕士,副教授,硕士研究生导师。E-mail:727859613@qq.com
  • 作者简介:彭仁梅（1995-）,女,湖北十堰人,硕士,护师。
  • 基金资助:
    安徽省临床重点专科建设项目（卫科教秘[2016]64号）; 安徽高校人文社会科学重点项目（SK2019A0207）; 蚌埠医学院人文社会科学基金重点项目（2021byzd268sk）

Construction and validation of nomogram to predict sleep disorder risk in patients transferred from ICU

PENG Ren-mei1, QIAN Rong2a, LI Ya-jun3, DUAN Huan3, CHEN Lian-fang3, LIU Yu-wen2b   

  1. 1. Renmin Hospital of Wuhan University, Wuhan 430060, China;
    2a. School of Nursing; 2b. School of Health Management, Bengbu Medical College, Bengbu 233030, China;
    3. Dept. of Critical Care Medicine, the First Affiliated Hospital of Bengbu Medical College, Bengbu 233030, China
  • Received:2022-11-15 Online:2023-08-10 Published:2023-09-08

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摘要： 目的 构建并验证ICU转出患者睡眠障碍风险预测模型。方法 便利选取蚌埠市某2所三级甲等医院ICU住院治疗并成功转出的518例患者作为研究对象,分为训练集363例和验证集155例。采用一般资料和临床资料调查表进行因素的收集,在转出ICU后1个月后采用匹兹堡睡眠质量指数量表（Pittsburgh Sleep Quality Index,PSQI）进行随访,根据随访的结果分为非睡眠障碍组和睡眠障碍组。采用Logistic回归确定影响因素,应用R语言软件建立ICU转出患者睡眠障碍风险列线图预测模型;采用C指数和calibration校准曲线来评价模型的预测性能力。结果 纳入了9个预测因子进入模型,即入住ICU总时间、APACHE II评分、Charlson共病指数评分、气管切开、焦虑、抑郁、创伤后应激障碍、ICU内发生感染、转出ICU时侵入性管路数量。内部验证C指数为0.926（95%CI:0.898~0.953）,外部验证C指数为0.930（95%CI:0.889~0.972）,校准图结果显示均良好。结论 ICU转出患者睡眠障风险列线图预测模型较好,可用于筛查ICU转出患者睡眠障碍高风险人群。

关键词: ICU, 重症监护, 睡眠障碍, 列线图, 预测模型

Abstract: Objective To construct and validate a prediction risk model for sleep disorders in patients transferred from ICU. Methods With convenient sampling, 518 patients hospitalized in the ICU of two tertiary grade-A hospitals in Bengbu were selected and divided into training set (n=363) and verification set (n=155). General Information Questionnaire and Clinical Data Questionnaire were used to collect risk factors, and Pittsburgh Sleep Quality Index (PSQI) was used for one-month follow-up after transfer from the ICU. The patients were further divided into non-sleep disorder group and sleep disorder group according to the results of follow-up. Logistic regression was used to determine the influencing factors; R software to establish a nomogram to predict sleep disorder risk in patients transferred from ICU; C-index and calibration curve were used to evaluate the predictive ability of the model. Results Nine predictors were included in the model including total length of stay in ICU, APACHEII score, Charlson comorbidity index score, tracheotomy, anxiety, depression, post-traumatic stress disorder, ICU infection, and the number of invasive tubes when transferred from ICU. The model was internally validated with a C-index of 0.926 (95%CI:0.898~0.953) and externally validated with a C-index of 0.930 (95%CI:0.889~0.972), and the calibration plot showed good results. Conclusion The nomogram to predict sleep disorder risk for patients transferred from ICU can be used to screen the high risk group with sleep disorder.

Key words: ICU, intensive care, sleep disorder, nomogram, predictive model

中图分类号: 

  • R473
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