| 急性心肌梗死患者并发细菌感染的特点及干预措施 |
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| 引用本文： | 董凤伟,朱劲舟,汤静. 急性心肌梗死患者并发细菌感染的特点及干预措施[J]. 分子影像学杂志, 2020, 43(1): 179-183. DOI: 10.12122/j.issn.1674-4500.2020.01.38 |
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| 作者姓名： | 董凤伟 朱劲舟 汤静 |
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| 作者单位： | 上海瑞金医院心内科,上海 200025 |
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| 摘 要： | 目的分析急性心肌梗死（AMI）患者并发细菌感染的特点与分布情况分析及应用预见性干预措施的价值。方法将2016年12月~2019年1月间我院收治的160例AMI患者依照患者是否合并细菌感染分为感染组及非感染组;其中感染组90例（男53例,女37例,年龄56.39±8.12岁）,非感染组70例（男41例,女29例,年龄57.01±9.47岁）。将感染组患者依照随机信封法分为观察组45例（男28例,女17例,年龄57.01±9.03岁）及对照组45例（男25例,女20例,年龄55.77±10.83岁）,记录患者年龄、性别、病史等临床资料,对感染组患者感染菌种进行调查;对照组采用常规护理方案干预,观察组采用预见性干预措施干预。结果糖尿病、慢性阻塞性疾病、心功能、经皮冠状动脉介入治疗、预防性应用抗生素、住院时间及年龄不同患者感染发生率的差异有统计学意义（P<0.05）;糖尿病、慢性阻塞性疾病、心功能、经皮冠状动脉介入治疗、预防性应用抗生素、住院时间及年龄是影响细菌感染的独立性影响因素,差异存在统计学意义（P < 0.05）;感染组中革兰氏阴性菌共61例,占比最高（67.78%）;观察组患者血压正常、血中正常、血糖正常率均高于对照组,心绞痛恶化及心肌梗死发生率明显低于对照组,差异有统计学意义（P < 0.05）;治疗后观察组躯体功能、角色功能、情绪功能、社会功能及认知功能评分明显高于对照组（P < 0.05）。结论AMI患者并发细菌感染主要以革兰氏阴性菌为主,且糖尿病、慢性阻塞性疾病、心功能、预防性应用抗生素、住院时间及年龄等因素均是导致患者出现院内感染的独立性影响因素,采用预见性护理模式干预有助于提高AMI合并细菌感染患者治疗疗效。
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| 关 键 词： | 急性心肌梗死 细菌感染 分布 预见性干预 |
| 收稿时间： | 2020-01-05 |
Characteristics and distribution of bacterial infection in patients with AMI and the application of predictive interventions |
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| DONG Fengwei,ZHU Jingzhou,TANG Jing. Characteristics and distribution of bacterial infection in patients with AMI and the application of predictive interventions[J]. Journal of Molecular Imaging, 2020, 43(1): 179-183. DOI: 10.12122/j.issn.1674-4500.2020.01.38 |
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| Authors: | DONG Fengwei ZHU Jingzhou TANG Jing |
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| Affiliation: | Department of Cardiology, Shanghai Ruijin Hospital, Shanghai 200025, China |
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| Abstract: | Objective To analyze the characteristics and distribution of bacterial infections in patients with AMI and the value of using predictive interventions. Methods A total of 160 patients with AMI who were admitted to our hospital from December2016 to January 2019 were included. The patients were divided into the infected group(n=90, 53 males, 37 females, 56.39±8.12 years old) and the non-infected group(n=70, 41 males, 29 females, 57.01±9.47 years old) according to whether the patients had bacterial infection. The patients in the infected group were divided into observation group(n=45, 28 males, 17 females,57.01±9.03 years old) and control group(n=45, 25 males, 20 females, 55.77±10.83 years old) according to the random envelope method. Clinical data such as age, gender, and medical history of the patients were recorded, and the infected strains of the infected group were investigated. The control group was treated with routine nursing intervention, and the observation group was treated with predictive intervention. Results Diabetes, chronic obstructive disease, cardiac function, percutaneous coronary intervention, prophylactic antibiotics, hospitalization days, and age-related infection rates were significantly different(P<0.05). The diabetes, chronic Obstructive disease, cardiac function, percutaneous coronary intervention, prophylactic antibiotics, length of hospital stay and age were independent factors influencing bacterial infection, and the difference was significant(P<0.05). There were 61 cases of negative bacteria, accounting for 67.78%, which was the highest proportion. The normal blood pressure, normal blood, normal blood glucose rate in the observation group were higher than the control group.The incidence of angina pectoris and myocardial infarction were significantly lower than the control group(P < 0.05). The scores of physical function, role function, emotional function, social function and cognitive function of the observation group were significantly higher than those of the control group(P < 0.05). Conclusion The bacterial infections in patients with AMI are mainly Gram-negative bacteria. The factors such as diabetes, chronic obstructive diseases, cardiac function, prophylactic antibiotics, hospitalization days and age are the independent factors that cause patients with nosocomial infection.Interventional care mode intervention can help improve the therapeutic efficacy of patients with AMI complicated with bacterial infection. |
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| Keywords: | acute myocardial infarction bacterial infection distribution predictive intervention |
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