Clinical Application Effect of Cluster Management in Noninvasive Ventilator Nursing Care of Patients with Severe Heart Failure

Author:

Luo Huanli1,Zhu Guangyu2ORCID

Affiliation:

1. Department of Nursing, Henan Vocational College of Nursing, Anyang City, 455000 Henan Province, China

2. Four Departments of Cardiovascular Disease Center, Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai City, 519000 Guangdong Province, China

Abstract

Objective. To elucidate the clinical application effect of cluster management in noninvasive ventilator nursing care of patients with severe heart failure (HF). Methods. A total of 116 severe patients with HF who received treatment in the our hospital between October 2018 and December 2019 were included, including 50 cases (control group) treated with routine nursing and 66 cases (research group) treated with cluster management. The treatment-related indexes (mechanical ventilation time and hospitalization expenses), symptom resolution (dyspnea, insomnia, nausea, and upper abdominal pain), systolic/diastolic blood pressure (SBP/DBP), heart rate (HR), and prognosis (mortality and disability rate) were observed and compared between the two groups. Results. Statistically shorter time of mechanical ventilation and symptom (dyspnea, insomnia, nausea, and upper abdominal pain) resolution were found in the research group compared with the control group. In addition, the research group showed significantly lower hospitalization expenses, SBP, DBP, and HR than the control group. Moreover, lower mortality and disability rates were determined in the research group, yet with no statistical significance between the two cohorts. Conclusion. The above results indicate the remarkable clinical application effect of cluster management in noninvasive ventilator nursing of severe HF, which can enhance the treatment efficacy, blood pressure and HR of patients, and facilitate their recovery.

Publisher

Hindawi Limited

Subject

Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine

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