Number of readmissions and determinants among patients with heart failure at referral hospitals in the Amhara region, Northwest Ethiopia; 2023.

doi:10.21203/rs.3.rs-4105631/v1

Aim: This study aimed to determine the number of readmissions among patients with heart failure and identify the determinants of heart failure among patients at referral hospitals in the Amhara region, North western Ethiopia, in 2023.

Study design: A cross-sectional study design was employed using zero-inflated negative binomial models.

Methods: Data were collected from patients with heart failure at referral hospitals in the Amhara region from September 2022 to February 2023. The data were collected from 663 patients with heart failure using chart reviews and interviewer-administered questions.

Results: In this study, 237 (35.7%) patients with heart failure were readmitted at least once. The study showed that for every unit increase in the respiratory rate, the number of readmissions among patients with heart failure increased by 1.5% (IRR=.0.015; 95% CI=0.0004, 0.031), 0.044. In addition, as the duration of medication intake increased by one unit, the number of readmissions increased by 1.1% (IRR. =.0.011: 95% CI: 0.016, 0.051), 0.0001, while keeping all the other model variables constant. Similarly, compared with those of patients with good social support, the number of readmissions of patients with poor social support decreased by 59.4% (IRR=-1.595: 95% CI=-0.02, -0.005), 0.041. On the other hand, when thebody mass index increased by one unit, the odds of being in the always-zero group increased by 11.5% (IRR = 0.115: 95% CI: 0.035, 0.196), 0.004. Moreover, when the pulse rate increased by one unit, the odds of being in the always zero group decreased by 1.27% (IRR = -0.013: 95% CI: -0.025, -0.008), 0.036.

Implications for Clinical Practice: Determining the factors that lead to heart failure (HF) readmission is essential for developing effective interventions to lower the number of readmissions and hospital stays that may negatively impact patients’ and their families' overall socioeconomic status. Additionally, this study provides a baseline for the progress of nursing research and education as well as input for policy makers, all of which will help to improve nursing practice.

Patient or Public Contribution: Improves patients overall quality of care

Patients with heart failure

Readmissions

Zero-inflation negative binomial regression

Ethiopia

Despite the fact that HF is a major problem, the number of readmissions among patients with HF is not well known.
HF was the cause of approximately half of all non-communicable patient deaths.
Over 20% of patients with HF thought to be readmitted within 30 days

What this study adds

Determining the number of readmissions among patients with HF is essential for developing effective interventions and decreasing the number of readmissions and hospital stays that may negatively affect patients’ and their families' overall socioeconomic status. Additionally, this study provides a baseline for the progress of nursing research and education as well as input for policy makers, all of which will help to improve nursing practice.

Approximately 38 million of the 56 million reported fatalities in the world in 2012 were caused by noncommunicable diseases. Approximately half of these deaths were caused by heart failure (HF)[1]. Like in developed countries, in the present study, HF is anticipated to cause more deaths than all other causes in the majority of emerging countries by the year 2020 ^{[2, 3]}. Obesity, hypertension, and diabetes, which are common risk factors, account for a large proportion of these associations [4]. In addition, studies suggest that the severity of the infection may also have a role in the frequency of readmission[5]. HF is a frequent cause of hospital stays and readmissions and is associated with a higher risk of mortality and unforeseen readmissions[1]. Over 20% of patients with HF are thought to be readmitted within 30 days ^[6]. High readmission rates can have a major impact on HF patients' quality of life[1]. Especially in low- and middle-income countries, including Ethiopia, HFs develop earlier and account for a greater percentage of readmissions than in high-income countries[3, 7]. Therefore, this study aimed to determine the number of readmissions per HF patient and identify potential risk factors.

Study Area and Period

The study included 663 patients with heart failure in medical wards of randomly selected referral hospitals in the Amhara region. Interviewer-administered questionnaires and chart reviews were used from October to February 2023. The Amhara region has 81 hospitals, 858 clinics, and 3560 health posts[8, 9].

Data collection procedure and tools

Six hundred sixty-three (663) patients with heart failure were included from each selected hospital after providing written consent from the ethical review committee 'REDACTED' and oral consent from each patient. The data were collected using six validated data collection tools. personal and socio-demographic data, clinical characteristics, laboratory results, self-care practices[10], Oslo Social Support Scale (OSSS-3) scores[11] and MARS-10 rating scale scores[12].

Analysis

The variable of interest in this study is count data, which are frequently described as having a no normal distribution. To handle the over dispersion (variance = 2.192 and mean = 0.89) and excessive zeros, as well as the zero-inflated negative binomial, regression was the best fit for the analysis. To evaluate the efficacy of various models based on various probability measures, the Akaike information criterion (AIC) and the Bayesian Schwartz information criterion (BIC) were used. The incidence rate ratio (IRR) and 95% confidence interval were used to determine the relationship between the independent variable and the outcome variable. Variables with p values less than 0.05 at the 95% confidence interval were found to be determinants of the number of readmissions among patients with heart failure.

Sociodemographic and clinical characteristics of the study participants

Among the 663 patients with heart failure, 371 (56.0%) were females and 484 (73.0%) were married; the mean age was 51 + 18.41 years. Approximately 6.81% of the patients had pulmonary tuberculosis (Table 1). The minimum number of readmissions among patients with heart failure was zero, and the maximum was 8. Moreover, the mean systolic and diastolic BP were 112.46 and 68.93 kg/m2, respectively. See Table 2.

Table 1

Sociodemographic and clinical factors of readmission and its determinants among patients with heart failure at referral hospitals in the Amhara region, Northwest Ethiopia; 2023,
			Frequency	Percent
Factors	sex	female	371	56.0
	sex	male	292	44.0
	PTB	Yes	31	6.81
		No	424	93.2
	Marital status	divorced	39	5.9
		married	484	73.0
		single	85	12.8
		widowed	55	8.3
	Educational status	College and above	90	13.6
		No formal education	397	59.9
		Primary school	107	16.1
		Secondary school	69	10.4
	Family history of HF	No	568	85.7
	Family history of HF	Yes	95	14.3
	Stage of HF	Stage 1	5	0.8
		Stage 2	12	1.8
		Stage 3	419	63.2
		Stage 4	227	34.2
	NYHA functional class	Class 2	8	1.2
		Class 3	339	51.1
		Class 4	316	47.7
	Social support	poor	512	77.2
		moderate	132	19.9
		strong	19	2.9
	medication adherence	poor	328	49.5
	medication adherence	poor	335	50.5
	Self-care practice	poor	351	52.9
	Self-care practice	good	312	47.1
	Type of HF medication	BB	218	32.9
		Digitalis	284	42.8
		Diuretics	89	13.4
		ACE/ARB	72	10.9
	Framingham criteria	PE	244	36.8
		Cardiomegaly	207	31.2
		NVD	115	17.3
		PND	21	3.2
		Rales	56	8.4
		S3 gallop	20	3.0
		Total	663	100.0

Table 2

Covariates of the number of readmissions and their determinants among patients with heart failure at referral hospitals in the Amhara region, Northwest Ethiopia; 2023.
		Minimum	Maximum	Mean	Std. Deviation
Dependent Variable	Number of readmission	0	8	0.89	1.497
Covariates	age	15	94	51.25	18.410
	Body mass index	16	33	21.88	3.529
	Systolic BP	10	260	112.72	22.697
	Diastolic BP	24	150	69.14	13.067
	Respiratory rate	10	80	24.87	4.806
	Pulse rate	24	155	96.93	19.854
	Hemoglobin	2.50	36.00	13.4231	3.35706
	Serum sodium	13	214	137.00	12.768
	Serum potassium	0.56	136.00	4.3079	7.12482
	Serum creatinine	0.00	105.00	1.9190	8.06690

Number of readmissions per HF patient

As shown in Table 35.7% of patients with HF experienced at least one readmission, with a sample mean of 0.89 and a variance of 2.19. See Table 3. The general pattern of readmissions per patient is heavily skewed to the right and has an excessive amount of zeros. See Fig. 1.

Table 3

Frequency and percentage distributions of readmissions and their determinants among patients with heart failure at referral hospitals in the Amhara region, Northwest Ethiopia; 2023,
Number of readmission per HF patient	Frequency	Percent
0	426	64.3
1	72	10.9
2	70	10.6
3	47	7.1
4	23	3.5
5	14	2.1
6	7	1.1
7	2	0.3
8	2	0.3
Total	663	100.0
Minimum	0 8 .89 2.19 1.88
Maximum
Mean
Variance
Skewness

As shown in Table 4, the respiratory rate (RR) was significantly associated with the number of readmissions per patient. Accordingly, the expected number of readmissions per patient increased by 1.5% for every unit increase in the RR (Coef. =.0.015; 95% CI: 0.00, 0.031), 0.044. In addition, as the duration of medication intake increased by 1 month, the expected number of readmissions per patient decreased by 1.1% (coef.=.-0.01: 95% CI: -0.016, -0.0051), 0.0001, while all the other variables were held constant in the model. The number of readmissions in the always zero group among patients with HF who had poor social support decreased by 59.4% compared to patients with good social support (Coef. =. -1.595: 95% CI: -0.016, -0.005), 0.041. On the other hand, the odds of being in the always zero group decreased by 11.5%, while their body mass index increased by 1 kg/m² (Coef. = 0.115: 95% CI: 0.034, 0.196), 0.004. Moreover, the odds of being in the always zero group decreased by 1.27%, while their pulse rate increased by 1 b/m for Coef. =. -0.013: 95% CI: -0.025, -0.001), 0.036. See Table 4.

Table 4

Parameter estimates of the ZINB regression model among patients with heart failure at selected referral hospitals in the Amhara region, 2023
Count model coefficients (negbin with log link)
Number of readmission	IRR.	Std. Err.	z	P > z	[95% Conf.	Interval]
Number of readmission
Sex
Female	-0.0270463	0.1407829	-0.19	0.848	-0.3029757	0.2488832
Male (ref)
Family history of HF
No	-0.0818296	0.156778	-0.52	0.602	-0.3891088	0.2254496
Yes (ref)
Education
College and above	0.5490596	0.2970542	1.85	0.065	-0.0331559	1.131275
No formal education	-0.0885132	0.2134577	-0.41	0.678	-0.5068826	0.3298562
Primary school	-0.4271887	0.2178605	-1.96	0.08	-0.8541875	-0.0001899
Secondary school (ref)
Stage of HF
Stage 2	0.0820675	0.7342817	0.11	0.911	-1.357098	1.521233
Stage 3	-0.1453738	0.481832	-0.3	0.763	-1.089747	0.7989995
Stage 4	-0.2448332	0.4853382	-0.5	0.614	-1.196079	0.7064122
Stage 1(ref)
Social support
poor	0.1706065	0.6632146	0.26	0.797	-1.12927	1.470483
moderate	0.1019159	0.6722723	0.15	0.88	-1.215714	1.419545
Strong (ref)
Self- care practice
poor	0.0402067	0.1232299	0.33	0.744	-0.2013194	0.2817329
Good (ref)
Body mass index	0.0358061	0.0213577	1.68	0.094	-0.0060542	0.0776665
Respiratory rate	0.0154837	0.0076926	2.01	0.044*	0.0004064	0.030561
Pulse rate	-0.0024282	0.0028418	-0.85	0.393	-0.0079981	0.0031417
Duration of medication intake	0.0107112	0.0028843	3.71	0.0001*	0.0163644	0.05058
Hemoglobin	-0.0212857	0.0169621	-1.25	0.21	-0.0545309	0.0119595
Serum potassium	0.0702924	0.0487471	1.44	0.149	-0.0252501	0.165835
Serum creatinine	-0.006113	0.0049025	-1.25	0.212	-0.0157217	0.0034957
_cons	-0.052737	1.042618	-0.05	0.96	-2.09623	1.990756
inflate
Sex
female	0.3563647	0.2868017	1.24	0.214	-0.2057563	0.9184858
Male (ref)
Family history of HF
No	0.4206392	0.3423762	1.23	0.219	-0.2504059	1.091684
Yes (ref)
Educational status
College and above	1.917893	0.6320176	3.03	0.002	0.6791614	3.156625
No formal education	0.3702433	0.501561	0.74	0.46	-0.6127981	1.353285
Primary school	0.0892595	0.5158982	0.17	0.863	-0.9218825	1.100401
Secondary school (ref)
Stage of HF
Stage 1(ref)
Stage 2	1.56772	1.794563	0.87	0.382	-1.94956	5.084999
Stage 3	1.056437	1.56955	0.67	0.501	-2.019825	4.132699
Stage 4	0.9871345	1.581299	0.62	0.532	-2.112154	4.086423
Social support
Poor	-1.594966	0.9131699	-1.75	0.041*	-3.384747	0.1948138
Moderate	-0.7841152	0.9324045	-0.84	0.4	-2.611595	1.043364
Strong (ref)
Self-care practice
Poor	-0.4092432	0.2387236	-1.71	0.086	-0.8771329	0.0586465
Good (ref)
Body mass index	0.1153165	0.0414054	2.79	0.004*	0.0341634	0.1964696
Respiratory rate	0.0036628	0.0224326	0.16	0.87	-0.0403044	-0.04763
Pulse rate	-0.0127085	-0.0060548	-2.1	0.036*	-0.0245757	-0.008412
Duration of medication intake	-0.0064694	0.006211	-1.04	0.298	-0.0186428	0.005704
Hemoglobin	0.0017757	0.0327868	0.05	0.957	-0.0624852	0.0660366
Serum potassium	0.1372274	0.1005604	1.36	0.172	-0.0598674	0.3343223
Serum creatinine	-0.0460511	0.0358494	-1.28	0.199	-0.1163145	0.0242124
_cons	-2.708443	2.264821	-1.2	0.232	-7.147411	1.730524
/lnalpha	-14.78995	1664.181	-0.01	0.993	-3276.525	3246.945
alpha	3.77E-07	0.0006281			0	.
Vuong test of ZINB vs. standard negative binomial: z = 6.41 Pr.>z = 0.0000

In this study, 35.7% of patients with heart failure were readmitted at least once, which is higher than that reported in studies conducted in Iran, Denmark and Iceland (25%)[13], Italy (25.1%)[14] and Ethiopia (26.40%)[15]. However, these findings are lower than those of studies conducted in Japan (28%)[16], North China (28%) and China (39.5%)[1]. This might be related to the differences in the study pairs, Sociodemographic characteristics, setup, utilization of private hospitals and self-care practice awareness. According to the findings of this study, the respiratory rate is an important determinant factor for the number of readmissions among patients with heart failure. Accordingly, the number of readmissions increased by 1.5%, while the respiratory rate increased by one unit (coef.=.0.016; 95% CI = 0.0004,0.031, 0.044). It is widely known that an increase in the respiratory rate induced by HF is a symptom of a deteriorating prognosis caused by unplanned readmission [17]. These findings are consistent with previous studies in the USA[18] and Australia[19]. Furthermore, when their pulse rate increased by one unit, the odds of being in the always zero group decreased by 1.27% (Coef. = -0.013, 95% CI: -0.025, -0.008), 0.036, while the other variables in the model were held constant. These findings are in line with other findings in the USA[18], New York[20], the UK[21], and China[22]. On the other hand, the odds of being in the always zero group decreased by 11.5% for a unit increase in body mass index (Coef. = 0.115: 95% CI: 0.0342, 0.196), 0.004, which is consistent with the findings of previous studies in Japan^[23], [24] and the US[25]. However, these findings contradict those of studies in China [22, 26] and Australia [19]. In addition, compared with that of patients with heart failure who had strong social support, the number of readmissions in the always zero group among patients with heart failure who had poor social support decreased by 59.4% (Coef.=. -1.595: 95% CI:-0.016, -0.005), 0.041 controlling for variables in the model. This might be related to the fact that patients who have poor social support might have poor compliance with their treatment and be less likely to be readmitted regardless of their illness. These findings contradict those of studies conducted in the Netherlands^[27]; Iran, Denmark and Iceland[13]; Switzerland[28]; the US[29]; and China[1]. Additionally, as the duration of medication intake increased by one unit, the number of readmissions increased by 1.1% (Coef. =.0.011: 95% CI: 0.016, 0.05), 0.0001, while all the other variables were held constant in the model. This may be related to the long-term nature of the illness, the number of pills the patient must take, their lack of compliance, and the potential for complications to arise over time.

The respiratory rate, pulse rate, body mass index, duration of medication intake, and poor social support were determinants of the number of heart failure patients. Therefore, heart failure patients who exhibit the aforementioned conditions should be closely monitored to reduce the risk of readmission.

Data Availability Statement

All related data have been presented within the manuscript. The dataset supporting the conclusions of this article is available from the authors upon request.

Ethics approval and consent to participate

The study was carried out after the patients were approved by the ethical review committee. Each referral hospital approved the study, and each respondent provided verbal consent.

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available but are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Funding

Not applicable

Authors' contributions

MG and BT designed the study and wrote the overall report, HA,TW and GG did the statistical analysis . W.G, TYA, LY and GD drafting, edited and revising the manuscript. All authors approved the manuscript to be published,

Acknowledgement

Not applicable

Lin, T.-K., et al., The impact of sources of perceived social support on readmissions in patients with heart failure. Journal of Psychosomatic Research, 2022. 154: p. 110723.
Celermajer, D.S., et al., Cardiovascular disease in the developing world: prevalences, patterns, and the potential of early disease detection. Journal of the American College of Cardiology, 2012. 60(14): p. 1207-1216.
Wurie, H.R. and F.P. Cappuccio, Cardiovascular disease in low-and middle-income countries: an urgent priority. 2012, Taylor & Francis. p. 543-550.
Gaziano, T.A., et al., Growing epidemic of coronary heart disease in low-and middle-income countries. Current problems in cardiology, 2010. 35(2): p. 72-115.
Epstein, S.E., et al., Insights into the role of infection in atherogenesis and in plaque rupture. Circulation, 2009. 119(24): p. 3133-3141.
Klainin-Yobas, P., et al., Efficacy of psychosocial interventions on psychological outcomes among people with cardiovascular diseases: a systematic review and meta-analysis. Patient Education and Counselling, 2016. 99(4): p. 512-521.
Padilla, C., et al., Periodontal pathogens in atheromatous plaques isolated from patients with chronic periodontitis. Journal of periodontal research, 2006. 41(4): p. 350-353.
Mekonen, E.G., M.H. Gebrie, and S.M. Jemberie, Magnitude and associated factors of medication administration error among nurses working in Amhara Region Referral Hospitals, Northwest Ethiopia. Journal of Drug Assessment, 2020. 9(1): p. 151-158.
Getie, A., A. Wondmieneh, and T. Gebremeskel, Implementation of nursing process among nurses at Woldia Referral hospital, Northern Ethiopia: An institution-based cross-sectional study. 2021.
Baymot, A., D. Gela, and T. Bedada, Adherence to self-care recommendations and associated factors among adult heart failure patients in public hospitals, Addis Ababa, Ethiopia, 2021: cross-sectional study. BMC Cardiovascular Disorders, 2022. 22(1): p. 1-11.
Kocalevent, R.-D., et al., Social support in the general population: standardization of the Oslo social support scale (OSSS-3). BMC psychology, 2018. 6(1): p. 1-8.
Adomako, N.O., et al., Blood Pressure Control, Accessibility, and Adherence to Antihypertensive Medications: Patients Seeking Care in Two Hospitals in the Ashanti Region of Ghana. International journal of hypertension, 2021. 2021.
Shamali, M., et al., The relationship of family functioning and family health with hospital readmission in patients with heart failure: insights from an international cross-sectional study. European journal of cardiovascular nursing, 2023. 22(3): p. 264-272.
Canepa, M., et al., Frequency, characteristics and prognostic impact of hospital readmissions in elderly patients with heart failure: a population study from 2013 to 2017 in Liguria, Northern Italy. International Journal of Cardiology, 2022. 363: p. 111-118.
Anleye, B.A., P.K. Kumar, and A.H. Endris, Unplanned readmission to hospital and its predictors in heart failure patients, Ethiopia: a retrospective cohort study. medRxiv, 2022: p. 2022.11. 11.22282211.
Sun, Y., et al., The Effect of Home Care Support Clinics on Hospital Readmission in Heart Failure Patients in Japan. Journal of General Internal Medicine, 2023: p. 1-8.
Barthel, P., et al., Respiratory rate predicts outcome after acute myocardial infarction: a prospective cohort study. European heart journal, 2013. 34(22): p. 1644-1650.
Bennett, M.K., M. Shao, and E.Z. Gorodeski, Home monitoring of heart failure patients at risk for hospital readmission using a novel underthe-mattress piezoelectric sensor: A preliminary single centre experience. Journal of telemedicine and telecare, 2017. 23(1): p. 60-67.
Howie‐Esquivel, J., et al., Rapid 5 lb weight gain is not associated with readmission in patients with heart failure. ESC heart failure, 2019. 6(1): p. 131-137.
Habal, M.V., et al., Association of heart rate at hospital discharge with mortality and hospitalizations in patients with heart failure. Circulation: Heart Failure, 2014. 7(1): p. 12-20.
Vazir, A., et al., Prognostic importance of temporal changes in resting heart rate in heart failure patients: an analysis of the CHARM program. European heart journal, 2015. 36(11): p. 669-675.
Saito, H., et al., Five Prognostic Factors for Readmission in Patients Over 75 Years Old with Worsening Heart Failure. The Showa University Journal of Medical Sciences, 2020. 32(1): p. 33-42.
Umehara, T., et al., Frailty in elderly patients with acute heart failure increases readmission. Heart & Lung, 2023. 57: p. 102-109.
Ito, M., et al., Clinical characteristics and mid-term outcomes of nonelderly obese patients with acute decompensated heart failure in Japan. International Heart Journal, 2018. 59(4): p. 766-771.
Cox, Z.L., et al., Body mass index and all-cause readmissions following acute heart failure hospitalization. International Journal of Obesity, 2020. 44(6): p. 1227-1235.
Yang, M., et al., A novel nomogram to predict all‐cause readmission or death risk in Chinese elderly patients with heart failure. ESC Heart Failure, 2020. 7(3): p. 1015-1024.
Annema, C., M.-L. Luttik, and T. Jaarsma, Reasons for readmission in heart failure: perspectives of patients, caregivers, cardiologists, and heart failure nurses. Heart & Lung, 2009. 38(5): p. 427-434.
Cilla, F., I. Sabione, and P. D’Amelio, Risk Factors for Early Hospital Readmission in Geriatric Patients: A Systematic Review. International Journal of Environmental Research and Public Health, 2023. 20(3): p. 1674.
Sterling, M.R., et al., Social determinants of health and 30-day readmissions among Adults hospitalized for heart failure in the REGARDS study. Circulation: Heart Failure, 2022. 15(1): p. e008409.

No competing interests reported.

Number of readmissions and determinants among patients with heart failure at referral hospitals in the Amhara region, Northwest Ethiopia; 2023.

Status:

Version 1

Abstract

Figures

What is known about this topic?

Background

Methods

Study Area and Period

Data collection procedure and tools

Analysis

Results

Sociodemographic and clinical characteristics of the study participants

Number of readmissions per HF patient

Discussion

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1