Hierarchical analysis of factors associated with hospital readmissions for coronary heart disease: A case-control study

Nurses' healing presence: A panacea for the comfort of acute coronary syndrome patients in CCU, A qualitative study

AIM

The study aimed to explain the acute coronary syndrome (ACS) patients' perception of the nurse's healing presence in their comfort in the critical care unit.

DESIGN

This descriptive qualitative study was conducted from December 2020 to September 2021.

METHODS

Twenty-seven ACS patients were purposively selected from a cardiovascular university Hospital, Iran. Data were collected through semi-structured interviews (45-60 min). Data analysis was performed based on the contractual content analysis method of Graneheim and Lundman.

RESULTS

In the data analysis, the main theme 'nurses' healing presence' includes two categories: 'Nurse-patient communication' with two subcategories and the category 'Compassionate care' with three subcategories.

A nomogram for predicting the readmission within 6 months after treatment in patients with acute coronary syndrome

Purpose

To explore predictors for readmission within 6 months of ACS patients, and to build a prediction model, and generate a nomogram.

Methods

The retrospective cohort study included 498 patients with ACS in the Second Medical Center of the Chinese People’s Liberation Army General Hospital between January 2016 and March 2019. Univariate and multivariate logistic regression with odds ratios (OR) and two-sided 95% confidence interval (CI) analysis were used to investigate predictors for readmission within 6 months. The cohort was randomly divided into training cohort to develop a prediction model, and the validation cohort to validate the model. The receiver operating characteristic curve (ROC) and the calibration curve was used to assess discriminative power and calibration.

Results

Eighty-three ACS patients were readmitted within six months, with a readmission rate of 16.67%. Predictors included ACS type, treatment, hypertension, SUA, length of stay, statins, and adverse events occurred during hospitalization were used to form a six-month readmission prediction model for readmission within 6 months in ACS patients. The area under the curve (AUC) of the model was 0.788 (95%CI: 0.735–0.878) and 0.775 (95%CI: 0.686–0.865) in the training cohort and the validation cohort, respectively. Calibration curves showed the good calibration of the prediction model. Decision-curve analyses and clinical impact curve also demonstrated that it was clinically valuable.

Conclusion

We used seven readily available predictors to develop a prediction model for readmission within six months after treatment in ACS patients, which could be used to identify high-risk patients for ACS readmission.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-022-02873-6.