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Hierarchical analysis of factors associated with hospital readmissions for coronary heart disease: A case–control study
Author(s) -
Albuquerque Nila Larisse Silva,
Araujo Thelma Leite,
Oliveira Lopes Marcos Venicios,
Moreira Thereza Maria Magalhães
Publication year - 2020
Publication title -
journal of clinical nursing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.94
H-Index - 102
eISSN - 1365-2702
pISSN - 0962-1067
DOI - 10.1111/jocn.15244
Subject(s) - acute coronary syndrome , medicine , logistic regression , observational study , emergency medicine , epidemiology , intensive care medicine , myocardial infarction
Aims and objectives To analyse, hierarchically, factors associated with hospital readmissions for acute coronary syndrome. Background Hospital readmissions have risen, especially in patients with multiple comorbidities, which are most often chronic. The leading causes of hospital readmission include acute coronary syndrome, which is costly and often preventable. Determining clinical and nonclinical variables that increase the chances of readmission is important to assess and evaluate patients hospitalised for coronary heart diseases. Design A case–control study whose dependent variable was hospital readmission for acute coronary syndrome. Methods The study included 277 inpatients, of whom 132 were in their first hospitalisation and 145 had already been hospitalised for acute coronary syndrome. The independent variables for this hierarchical model were sociodemographic conditions, life habits, access to health services and physical health measures. Data were obtained by interviews, anthropometric measurements and patient records. Logistic regression analysis was performed using the stepwise technique, with Microsoft Excel and R version 3.2.3. The research was reported via strengthening the reporting of observational studies in epidemiology (STROBE). Results In the final hierarchical logistic model, the following risk factors were associated with readmission for acute coronary syndrome: inadequate drug therapy adherence, stress, history of smoking for 30 years or more, and the lack of use of primary healthcare services. Conclusions Clinical and nonclinical variables are related to hospital readmission for acute coronary syndrome and can increase the chance of readmission by up to six times. Relevance to clinical practice The predictive model can be used to avoid readmission for acute coronary syndrome, and it represents an advance in the prediction of the occurrence of the outcome. This implies the need for a reorientation of the network for postdischarge care in the first hospitalisation for acute coronary syndrome.