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1.陆军特色医学中心消化内科,重庆 400042
2.陆军军医大学第一附属医院急诊医学科,重庆 400038
3.陆军军医大学第一附属医院临床护理学教研室,重庆 400038
李夏薇,通信作者:Email:xianyinxw@tmmu.edu.cn, 研究方向:急危重症护理、临床护理。
孙 溦,Email:sunfamily@tmmu.edu.cn。
收稿:2024-08-07,
修回:2024-10-19,
录用:2024-10-23,
纸质出版:2024-11-28
移动端阅览
李夏薇,李天椒,李伊敬,等.基于病例研究的上消化道出血患者病情变化护理预警模型构建[J].重庆医科大学学报,2024,49(11):1434-1442.
Li Xiawei,Li Tianjiao,Li Yijing,et al.Establishment of a nursing warning model for disease change in patients with upper gastrointestinal bleeding based on case studies[J].Journal of Chongqing Medical University,2024,49(11):1434-1442.
李夏薇,李天椒,李伊敬,等.基于病例研究的上消化道出血患者病情变化护理预警模型构建[J].重庆医科大学学报,2024,49(11):1434-1442. DOI: 10.13406/j.cnki.cyxb.003621.
Li Xiawei,Li Tianjiao,Li Yijing,et al.Establishment of a nursing warning model for disease change in patients with upper gastrointestinal bleeding based on case studies[J].Journal of Chongqing Medical University,2024,49(11):1434-1442. DOI: 10.13406/j.cnki.cyxb.003621.
目的
2
明确上消化道出血患者发生病情变化的危险因素,构建患者入院早期病情变化护理预警模型并进行验证。
方法
2
选取重庆市某三甲医院400例上消化道出血入院患者作为研究对象,采取病例回顾研究方法,以
再出血、转入监护室及死亡作为病情变化的复合指标,根据患者是否出现上述病情变化分为病情变化组(176例)及无病情变化组(224例),采用卡方检验、
t
检验及Logistic回归分析探究病情变化的危险因素,构建上消化道出血患者病情变化护理预警模型,并采用受试者工作曲线(receiver operating characteristic,ROC)评价其预测效能。
结果
2
单因素分析发现,年龄、入院时体温、入院时脉搏、入院时呼吸、性别、非甾体类抗炎药使用、就诊主诉(黑便、呕血)、血红蛋白在病情变化组与无病情变化组组间差异有统计学意义(
P
<
0.05)。二元logistics回归分析发现,年龄(
OR
=1.02,95
%
CI=1.00~1.03)、性别(
OR
=2.27,95%CI
=
1.36~3.77)、非甾体类抗炎药使用(
OR
=2.32,95%CI=1.32~4.09)、入院时体温(
OR
=7.36,95%CI=3.17~17.08)、就诊主诉黑便(
OR
=2.09,95%CI=1.04~4.22)、呕血(
OR
=4.42,95%CI=2.13~9.19)、入院时呼吸(
OR
=0.78,95%CI=0.67~0.91)、血红蛋白(
OR
=0.98,95%CI=0.98~0.99)是上消化道出血患者发生病情变化的独立影响因素,构建病情变化护理预警模型的ROC曲线下面积AUC=0.790,模型校准度Hosmer-Lemeshow检验:
χ
2
=3.702,
P
=0.883,提示拟合度良好。
结论
2
构建的病情变化护理预警模型具有可靠的预测价值,为入院时早期筛查上消化道出血患者潜在病情变化、进行预见性护理提供了参考。
Objective
2
To identify the risk factors for disease change in patients with upper gastrointestinal bleeding,and to establish and validate a nursing warning model for disease change in patients in the early stage after admission.
Methods
2
A total of 400 patients with upper gastrointestinal bleeding who were admitted to a grade A tertiary hospital in Chongqing,China were enrolled as subjects,and a retrospective study was conducted with rebleeding,transfer to the ICU,and death as the composite indicators of disease change. According to the presence or absence of the above changes,the patients were divided into disease change group with 176 patients and non-disease change group with 224 patients. The chi-square test,
t
-test,and logistic regression analysis were used to investigate the risk factors for disease change and establish a nursing warning model for disease change in patient
s with upper gastrointestinal bleeding,and the receiver operating characteristic(ROC) curve was used to assess the predictive efficacy of the model.
Results
2
The univariate analysis showed that there were significant differences between the disease change group and the non-disease change group in age,body temperature on admission,pulse on admission,respiration on admission,sex,use of nonsteroidal anti-inflammatory drugs,chief complaint(tarry stool and hematemesis),and hemoglobin(
P
<
0.05). The binary logistic regression analysis showed that age(odds ratio[
OR
]
=1.02,95%CI=1.00-1.03),sex(
OR
=2.27,95%CI=1.36-3.77),use of nonsteroidal anti-inflammatory drugs(
OR
=2.32,95%CI=1.32-4.09),body temperature on admission(
OR
=7.36,95%CI=3.17-17.08),chief complaint of tarry stool(
OR
=2.09,95%CI=1.04-4.22),chief complaint of hematemesis(
OR
=4.42,95%CI=2.13-9.19),respiration on admission(
OR
=0.78,95%CI=0.67-0.91),and hemoglobin(
OR
=0.98,95%CI=0.98-0.99) were independent influencing factors for disease change in patients with upper gastrointestinal bleeding. The nursing warning model for disease change established had an area under the ROC curve of 0.790,and the Hosmer-Lemeshow test of model calibration showed good degree of fitting(
χ
2
=3.702,
P
=0.883).
Conclusion
2
The nursing warning model for disease change established has a reliable predictive value,which provides a reference for the early screening for potential disease changes in patients with upper gastrointestinal bleeding and the performance of predictive nursing.
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