Readmissions with Heart Failure: Study Results

Congestive heart failure (CHF) is one of the major reasons for hospital readmissions. The investigation of the information gathered during the first phase of the research is conducted in the form of multivariate logistic regression analysis and the Chi-Square independence test. They are used to find correlations between patient factors, interventions, and patients’ readmission rates. The studies such as the ones by Vedel and Khanassov (2015) and Carthon, Lasater, Sloane, and Kutney-Lee (2015) are taken as representative datasets of patients with CHF-related readmissions. The studies describe findings that are based on national and state data since the actual information about readmissions in South Florida is highly limited.

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Results

Descriptive Data

Searches of the discussed databases identified 35 potentially valuable articles since the design of the current research implied a narrow set of suitable traits. All analyzed studies included patients who were readmitted 30 days after their first admission to the facility. According to Lemor, Setareh-Shenas, Alvarado, Gowda, and Gowda (2017), the central portion of all readmissions for patients with CHF is a reoccurring HF-related event. The demographics of the patients whose data is utilized in the study reveal some major selection characteristics.

First of all, the studies documented a total of 500 patients returning to the hospital due to CHF 30 days after discharge. Second, the mean age of all participants was 80 years old, with more than 70% of all patients being 75 and older. Third, females and males were represented in the group in similar proportions, with almost 55% being women. As can be seen in Table 1, among the patients who returned to the hospital in 30 days, more than 30% of them were readmitted only ten days after discharge.

Table 1. Descriptive Data.

Data Point Number Percent
Total number of patients 2200 100%
Number of patients readmitted 500 22.7% (for all further calculations it is considered a 100%)
Females 270 54%
Males 230 46%
Age: 18-34 2 0.4%
Age: 35-54 11 2.2%
Age: 55-74 125 25%
Age: More than 75 362 72.4%
Readmitted less than 10 days after discharge 156 31.2%
Readmitted in 11-20 days after discharge 165 33%
Readmitted in 21-30 days after discharge 179 35.8%

Data Analysis

In the 35 discussed articles, 25 interventions were identified as implemented in the facilities. Among them, various nonpharmacological and pharmacological strategies could be presented. These included predischarge education for patients about medication management, lifestyle change, and CHF control. Many of the interactions incorporated video and paper-based materials such as brochures, short educational films, and presentations that were watched with a special discharge nurse. Moreover, the majority of programs incorporated a discussion with a nurse about the discharge plan for the patient which specified medical treatment and future follow-up meetings with a specialist.

The collected data showed that three levels of intensity could be outlined regarding patient preparedness. Low-intensity plans contained one or two predischarge meetings that did not involve significant patient-provider collaboration. Moderate-intensity programs included a follow-up on the phone and in the clinic. Finally, high-intensity initiatives added a structured home visit of a specialized nurse to the phone- and clinic-based talks.

The regression analysis revealed a correlation between the intensity of programs and people’s rate of readmission. High-intensity programs showed to reduce the number of readmissions and lower the risk of returning to the hospital. Moderate-intensity initiatives were sufficient for some patients, but only if the hospital was using them for an extended period with each patient. Low-intensity and all varieties of short interventions were not effective in preventing readmissions.

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Moreover, such categories as home-visiting and telephone support were the most effective ways to reduce readmissions, while clinic-based plans did not prove to be beneficial for the majority of patients, especially if they were not complemented by other methods.

Limitations

The limitations of this study should be acknowledged to further the research in this sphere. First of all, the present calculations considered only a small portion of the population since the number of patients was restricted to those readmitted in South Florida in 2015-2017. This small sample lowers the applicability of the results to other communities. Therefore, a larger number of people should be analyzed in the future to confirm the findings.

Furthermore, the use of the retrospective cohort study model also weakened the reliability of the research. In contrast to prospective studies, it is challenging for a scholar to conduct retrospective research without the danger of implementing a personal bias in the analysis and interpretation of the outcomes. Some systematic reviews can help eliminate this risk and provide the scholarly world with sound data such as the one provided by Feltner et al. (2014) who focus on transitional care as well.

Finally, the small number of studies also exposed the inability to gather information about all potential interventions and their relation to the hospital’s effectiveness as a healthcare providing service. The length of the discussed interventions was the only data indicator that was measured while others may include the working environment, staffing ratios, nurses’ qualifications, the quality of interventions, and others. Overall, the study’s results showed some correlations while failing to acknowledge others.

References

Carthon, J. M. B., Lasater, K. B., Sloane, D. M., & Kutney-Lee, A. (2015). The quality of hospital work environments and missed nursing care is linked to heart failure readmissions: A cross-sectional study of US hospitals. BMJ Quality and Safety, 24(4), 255-263.

Feltner, C., Jones, C. D., Cené, C. W., Zheng, Z. J., Sueta, C. A., Coker-Schwimmer, E. J.,… & Jonas, D. E. (2014). Transitional care interventions to prevent readmissions for persons with heart failure: a systematic review and meta-analysis. Annals of Internal Medicine, 160(11), 774-784.

Lemor, A., Setareh-Shenas, S., Alvarado, A., Gowda, R. N., & Gowda, R. M. (2017). Etiologies for 30-day readmission in patients admitted with congestive heart failure that were discharged to a nursing home or facility. European Heart Journal, 38(Supplement 1), 1108-1109.

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Vedel, I., & Khanassov, V. (2015). Transitional care for patients with congestive heart failure: A systematic review and meta-analysis. The Annals of Family Medicine, 13(6), 562-571.

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StudyCorgi. (2021, July 9). Readmissions with Heart Failure: Study Results. Retrieved from https://studycorgi.com/readmissions-with-heart-failure-study-results/

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"Readmissions with Heart Failure: Study Results." StudyCorgi, 9 July 2021, studycorgi.com/readmissions-with-heart-failure-study-results/.

1. StudyCorgi. "Readmissions with Heart Failure: Study Results." July 9, 2021. https://studycorgi.com/readmissions-with-heart-failure-study-results/.


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StudyCorgi. "Readmissions with Heart Failure: Study Results." July 9, 2021. https://studycorgi.com/readmissions-with-heart-failure-study-results/.

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StudyCorgi. 2021. "Readmissions with Heart Failure: Study Results." July 9, 2021. https://studycorgi.com/readmissions-with-heart-failure-study-results/.

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StudyCorgi. (2021) 'Readmissions with Heart Failure: Study Results'. 9 July.

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