|Year : 2021 | Volume
| Issue : 1 | Page : 64-68
Readmission rates of heart failure and their associated risk factors in a tertiary academic medical City in Riyadh, Saudi Arabia
Dalal A Aldihan1, Mohammad A Alghafees1, Rana O Alharbi1, Raghad S Allahidan1, Razan H AlOmar1, Areej F Alenazi1, Ihab F Suliman2
1 College of Medicine, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
2 Department of Cardiac Sciences, National Guard Hospital, Riyadh, Saudi Arabia
|Date of Submission||01-Jun-2020|
|Date of Decision||19-Jul-2020|
|Date of Acceptance||29-Jul-2020|
|Date of Web Publication||09-Dec-2020|
Mohammad A Alghafees
Uzzam, AlManar, Riyadh 14222
Source of Support: None, Conflict of Interest: None
Background: Readmissions remain a persistent challenge in the care process of heart failure (HF). This study aimed to assess the 30 and 90-day readmission rates of HF and the associated risk factors. Materials and Methods: This retrospective cohort study targeted adult patients admitted for HF to King Abdulaziz Medical City between January 2016 and December 2018. The primary outcome variables were 30- and 90-day readmissions. Descriptive statistics were used for the continuous variables, which displayed the mean, standard deviation, and minimum and maximum values. The binary logistic regression technique was used to investigate risk factors for 30- and 90-day readmissions. Results: The 30- and 90-day readmission rates were 39.6% and 37.6%, respectively. Hypertension (P < 0.001), chronic kidney disease (P = 0.03), hypothyroidism (P = 0.04), and pulmonary diseases (P < 0.001) were all deemed as risk factors for readmission within 30 days. Body mass index (P = 0.03), dyslipidemia (P = 0.04), chronic kidney disease (P = 0.03), arrhythmias (P = 0.007), and pulmonary diseases (P < 0.001) were all deemed as significant predictors of readmission within 90 days. Conclusion: The 30- and 90-day readmission rates were 39.6% and 37.6%, respectively. Hypertension, chronic kidney disease, hypothyroidism, and pulmonary diseases were significant predictors for 30-day readmission. Body mass index, dyslipidemia, chronic kidney disease, arrhythmias, and pulmonary diseases were significant predictors for 90-day readmissions. These predictors must be taken into consideration to develop strategies to reduce readmission rates and mitigate a burden on health-care resources and patients' morbidity.
Keywords: Cardiology, epidemiology, heart failure, readmission rates, Saudi Arabia
|How to cite this article:|
Aldihan DA, Alghafees MA, Alharbi RO, Allahidan RS, AlOmar RH, Alenazi AF, Suliman IF. Readmission rates of heart failure and their associated risk factors in a tertiary academic medical City in Riyadh, Saudi Arabia. J Nat Sci Med 2021;4:64-8
|How to cite this URL:|
Aldihan DA, Alghafees MA, Alharbi RO, Allahidan RS, AlOmar RH, Alenazi AF, Suliman IF. Readmission rates of heart failure and their associated risk factors in a tertiary academic medical City in Riyadh, Saudi Arabia. J Nat Sci Med [serial online] 2021 [cited 2021 Jan 22];4:64-8. Available from: https://www.jnsmonline.org/text.asp?2021/4/1/64/306262
| Introduction|| |
Heart failure (HF) is a condition where the heart cannot pump enough blood to meet the body's demands. HF is one of the most common reasons for hospitalization. In the modern world, HF is considered an epidemic affecting 1%–2% of the adult population. Moreover, a study published in 2011 highlighting the epidemiology and risk profile of HF stated that the prevalence of HF patients in the US is 5.8 million and over 23 million worldwide and has high rates of hospitalizations, readmissions, and outpatient visits. The study also highlighted that HF is a substantial financial burden on the healthcare system, with annual costs reaching $39 billion in the United States alone.
Locally, there is a high prevalence rate of HF. According to a study stating the statistics of HF for each country, the estimated number of HF patients in Saudi Arabia is 455,222 with 37,935 new cases annually. Furthermore, a study done in five Saudi Arabian tertiary care hospitals between 2009 and 2010 that aimed to assess the clinical features, management, and outcomes of inpatients admitted with HF found that among 1090 patients, 66.2% of them had acute HF, and 33.8% had chronic HF. The mean age of patients in acute HF was 60.6 ± 15.3 years. 65.2% of these patients were men. 55.3% had de novo HF, 60.7% had diabetes, and 51.5% had coronary artery disease as the primary etiology.
HF can experience many readmission cases that could be accounted to a plethora of reasons. For example, a study conducted in Lebanon that recruited 187 patients through the review of medical records between January 1, 2010, and December 31, 2010, to assess the 30-, 60-, 90-day readmission rates stated that the percentages were 15%, 22.2%, and 27.8%, respectively. Of those readmissions, 73.61% were attributed to HF exacerbations. Having diabetes mellitus, coronary artery disease, length of stay at the index admission, and gamma-glutamyl transpeptidase levels were readmission predictors.
We hypothesized that comorbid medical disorders increase the readmission rate of patients with HF. Thus, due to a high prevalence rate of HF, this study aimed to assess the 30-day and 90-day readmission rates of HF and their associated risk factors. Observing the trends of readmission and using them as predictors would not only give a better insight into how to improve HF prognosis, but it would also remove a tremendous financial burden on the health-care system.
| Materials and Methods|| |
This retrospective cohort study targeted all of the adult patients who were admitted to King Abdulaziz Medical City (KAMC) due to HF between January 2016 and December 2018. Patients who had an index HF admission outside of KAMC and are following up in it, or readmitted for reasons not directly related to HF were excluded from the study.
Each patient's complete medical history was obtained from the BestCare system's database. The primary outcome variables were 30- and 90-day readmissions. The variables were grouped according to age, gender, body mass index, length of hospital stay, any comorbidities, and nonroutine discharge.
The extracted data were managed using Microsoft Excel 2010 (Microsoft Ltd., USA) and analyzed using the Statistical Package for the Social Sciences (SPSS), version 20.0 (IBM Corporation, NY, USA). To find the baseline characteristics for the patients, frequencies and percentages were generated for categorical variables, while mean and standard deviation was calculated for quantitative variables. The Binary Logistic Regression technique was used to investigate risk factors associated with 30-day readmission and 90-day readmission. In both cases, 30-day readmission and 90-day readmission were used as the dependent variables, both having two levels – Yes = 1 and No = 0. Variables that were considered as the possible risk factors included age, body mass index, smoking status, gender, length of stay, and any comorbidities. The variables were jointly tested against the dependent variables, and the Wald Statistics (significant value) were used as the basis for decision-making. The significance level was set at 5%.
An ethical approval with the number RC19/291/R was obtained from the Institutional Review Board of King Abdullah International Medical Research Center. Patient confidentiality was ensured, and patient data were collected and used by the research team only. All of the collected data was kept in a secure place in KAMC and accessed by the research team members only. Serial numbers were used instead of medical record numbers. Due to the retrospective nature of the study and the use of anonymized patient data, a requirement for informed consent was waived.
| Results|| |
Among 705 adult patients, 53% (n = 374) were male and 47% (n = 331) were female. 98.3% (n = 693) were routinely discharged. The all-cause mortality rate during the index admission was 12.3% (n = 87). The mean age was 71.59 ± 14.36 years. The mean body mass index was 30.68 ± 10 kg/m2. The mean length of stay was 15.27 ± 32.14 days. 10.6% (n = 75) were smokers. 39.6% (n = 279) were readmitted within 30 days and 37.6% (n = 265) were readmitted within 90 days, as shown in [Table 1].
Among the 279 patients that were readmitted within 30 days following the index discharge, 53.8% (n = 150) were male and 46.2% (n = 129) were female. 97.8% (n = 273) were routinely discharged. The all-cause mortality rate during the readmission was 15.8% (n = 44). The mean age was 72.74 ± 13.57 years. The mean body mass index was 31.09 ± 12.27 kg/m2. The mean length of stay was 14.47 ± 24.17 days. 10.4% (n = 29) were smokers, as shown in [Table 2].
|Table 2: Baseline characteristics of patients who were readmitted during the 30-day period|
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Hypertension (P < 0.001), chronic kidney disease (P = 0.03), hypothyroidism (P = 0.04), and pulmonary diseases (P < 0.001) were all associated with a higher risk of readmission within 30 days, as shown in [Table 3].
|Table 3: Baseline characteristics of patients who were readmitted during the 90-day period|
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Among the 265 that were readmitted within 90 days following the index discharge, 56.2% (n = 149) were male and 43.8% (n = 116) were female. 97.7% (n = 259) were routinely discharged. The all-cause mortality rate during the readmission was 12.1% (n = 32). The mean age was 73.15 ± 13.20 years. The mean body mass index was 30.07 ± 8.86 kg/m2. The mean length of stay was 15.42 ± 44.21 days. 9.1% (n = 24) were smokers, as shown in [Table 4].
|Table 4: Predictors of 30-day readmission in the binary logistic regression equation|
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Body mass index (P = 0.03), dyslipidemia (P = 0.04), chronic kidney disease (P = 0.03), arrhythmias (P = 0.007), and pulmonary diseases (P < 0.001) were all found to be significant predictors of readmission within 90 days, as shown in [Table 5].
|Table 5: Predictors of 90-day readmission in the binary logistic regression equation|
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| Discussion|| |
For 30-day readmission, the observed rate of 39.6% was markedly higher than the ones observed in the global studies: 23.6%, 22%, 21.9%, 21.4%, and 16% in the United States,,,,, 10.5% in China, 7.6% in Spain, 10.9% in Taiwan, 8.82% in Italy, 1.53% in Nigeria, 9.8% in Korea, and 15% in Lebanon.
When it comes to the predictors of 30-day readmissions, the presented study found hypertension, chronic kidney disease, hypothyroidism, and pulmonary diseases to be significant predictors of readmission within 30 days. Deeka et al., (2014)[ 6] found diabetes mellitus, coronary artery disease, and length of stay at the index admission to be significant predictors of readmission within 30 days. These findings contradict the ones stated in the current study. Furthermore, Kanwar et al. found diabetes mellitus, a low hematocrit level at discharge, smoking, and tachypnea to be predictors of 30-day readmission, which also contradicts our study. However, the same study found that chronic kidney diseases to be significant predictors of readmission within 30-da, which is consistent with the presented study's findings.
The 90-day readmission rate was 37.6%. Whereas globally 90-day readmission rate found to be 32% (2007), 33%, 40.2%, and 44% (2017) in the United States,,, 14.4% in Spain 17.6% in Singapore, 18.3% in Japan, 30% in the United Kingdom, 27.8% in Lebanon, and 23.7% in Kuwait.
The significant predictors for 90-day readmission in the presented study were body mass index, dyslipidemia, chronic kidney disease, arrhythmias, and pulmonary diseases. Snyder et al. found that arrhythmias, pulmonary diseases, and metabolic syndromes, such as dyslipidemia and diabetes, to be significant predictors which support the presented study's findings. Furthermore, Gheorghiade et al. (2013) found that coronary artery diseases, pulmonary diseases, chronic kidney disease, and diabetes to be significant risk factors for 90-day readmission, which is partially similar to the presented study. Moreover, Schwarz and Elman found that diabetes, hypertension, and atherosclerotic heart diseases were the significant predictors for 90-day readmission, which contradicts the presented study.
As notified based on previous studies, Biomarkers also affect HF readmission rates. Pacho et al. found that ST2, NT-proBNP, hs-TnI, and CA-125 have value to predict the readmission and mortality rate. ST2 is suggested to be the strongest predictor. Flint et al., however, found that admission, discharge, and the percentage changes of the B-type natriuretic peptide (BNP) level between them has a significant association with the HF rehospitalization. The 30-day readmission rate was 2–3 times more for patients with discharge BNP level ≥1000 ng/L. Nadar and Mujtaba Shaikh agreed with the previous study that those patients have BNP discharge level higher than the admission level are more likely to readmit. As a result, for the HF prognosis, BNP is considered to be the most examined biomarker.
There were some limitations to the study. First, due to the retrospective nature of the study, selection bias may have been introduced. Second, the registered data are incomplete. Many laboratory results of many patients are missing. Thus, we could not collect parameters such as BNP and ST2 levels. Finally, the population size in the presented study is relatively smaller than the ones used in the multicenter, global studies. When it comes to the strengths, first, the study is considered the first of its kind in the region, i.e., Saudi Arabia. Second, the study only included unplanned readmissions. This is important since large centers such as KAMC frequently have planned admissions for HF.
| Conclusion|| |
HF readmissions are a complex dilemma that affects both the healthcare system and the patients. The presented study identified chronic kidney disease, hypothyroidism, and pulmonary diseases as significant predictors for 30-day readmission and body mass index, dyslipidemia, chronic kidney disease, arrhythmias, and pulmonary diseases were significant predictors for 90-day readmissions. These risk factors must be addressed to form strategies that include increased support at discharge, improved communication, and early and close outpatient follow-up for the groups at risk in order to mitigate the burden.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]