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. 2024 Oct 8;14:23473. doi: 10.1038/s41598-024-71257-x

Incidence and predictors of unplanned 30-day hospital readmissions among heart failure patients in Ethiopia: a 5-year retrospective cohort study

Birhanu Ayenew 1, Prem Kumar 2,✉, Adem Hussein 2
PMCID: PMC11461501  PMID: 39379406

Abstract

The burden of heart failure increases over time and is a leading cause of unplanned readmissions worldwide. In addition, its impact has doubled in countries with limited health resources, including Ethiopia. Identifying and preventing the possible contributing factors is crucial to reducing unplanned hospital readmissions and improving clinical outcomes. The study aimed to assess the incidence and predictors of 30-day unplanned readmission among heart failure patients at selected South Wollo general hospitals in 2022. A hospital-based retrospective cohort study design was employed from January 1, 2016, to December 30, 2020. The data was collected from 572 randomly selected medical records using data extraction checklists. Data were entered in Epi-Data version 4.6 and analyzed with Stata version 17. The Kaplan–Meier and log-rank tests were used to estimate and compare the survival failure time. A Cox proportional hazard analysis was used to identify the predictors of readmission. The statistical significance level was declared at a p-value < 0.05 with an adjusted odds ratio and a 95% confidence interval. A total of 151 (26.40%) heart failure patients were readmitted within 30 days of discharge. Among the study participants, 302 (52.8%) were male, and 370 (64.7%) were rural residents. The mean age was 45.8 ± 14.1 SD years. In the multivariate Cox proportional hazards analysis being an age (> 65 years) (AHR: 3.172, 95% CI:.21, 4.55, P = 0.001), rural in residency (AHR: 2.47, 95%CI: 1.44, 4.24, P = 0.001), Asthma or Chronic Obstructive Pulmonary Disease (AHR: 1.62, 95% CI: 1.11, 2.35, P = 0.012), HIV/AIDS (AHR: 1.84, 95%CI: 1.24, 2.75, P = 0.003), Haemoglobin level 8–10.9 g/dL (AHR: 6.20, 95% CI: 3.74, 10.28, P = 0.001), and Mean platelet volume > 9.1 fl (AHR: 2.08, 95% CI: 1.27, 3.40, P = 0.004) were identified as independent predictors of unplanned hospital readmission. The incidence of unplanned hospital readmission was relatively high among heart failure patients. Elderly patients, rural residency, comorbidity, a higher mean platelet volume, and a low hemoglobin level were independent predictors of readmission. Working on these factors will help reduce the hazards of unplanned hospital readmission.

Keywords: Ethiopia, Hazard, Heart Failure, Readmission, South Wollo, Survival

Subject terms: Cardiology, Health care

Introduction

Unplanned hospital readmissions within 30 days of discharge from an index admission are widely accepted benchmarks of healthcare quality, and they are a global challenge that makes healthcare quality questionable. At the same time, heart failure (HF) is the leading cause of hospital admission and readmission1–3. HF is one of the biggest risks to human wellness and development, and growing more prevalent4. It is a growing problem in low- and middle-income countries, particularly in Sub-Saharan Africa (34%) with high morbidity (42.7%), and mortality in young and middle-aged people, which has a negative impact on socioeconomic and national development5–8. Ethiopia shares the burden of HF. In addition, the country is complicated by a shortage of hospital beds, infrastructure, and intensive care units9,10. It also has the lowest health workforce density in Africa, five times below the World Health Organization (WHO) minimum threshold of 4.45 per 1000 population to meet the Sustainable Development Goals (SDG) health targets11. These challenge the country to achieve Sustainable Development Goal 3.4, which is targeted to reduce non-communicable diseases by a third in 2030. At the same time, unplanned hospital readmissions upset the accessibility and quality of care12. In Ethiopia, New York Heart Association (NYHA) Class III and IV HF patients have limited knowledge of their disease, poor medication adherence, and risky lifestyles, leading to unplanned hospitalizations. The goal of treatment for heart failure patients includes reducing symptoms and disease progression, prolonging survival, and improving quality of life13–15. Risk factors such as smoking, a sedentary lifestyle, salt intake, and alcohol consumption can lead to unplanned hospitalizations, which can be uncomfortable for patients, family members, and healthcare professionals16–18. Unplanned hospital readmission among heart failure patients can be avoided through individualized care, improved clinical management, patient and family collaboration, and appropriate discharge planning19,20. However, HF patients are more vulnerable than others in developed countries but are not yet well characterized in sub-Saharan Africa, particularly in this study area. The previous study21 focused solely on pharmacological factors and lacked data on clinical aspects such as laboratory findings and preexisting comorbidities in heart failure patients. This study fills those gaps by incorporating these additional factors, providing a more comprehensive understanding of heart failure patients in Ethiopia. Therefore, examining the incidence and predictors of unplanned hospital readmission among heart failure patients is crucial for health policymakers. This will support quality improvement efforts by guiding the development of preventive guidelines and treatment protocols aimed at reducing readmission rates and their associated consequences.

Methods and materials

Study design, period, and area

A hospital-based retrospective follow-up study was conducted in 3 general hospitals of the South Wollo Zone from January 1, 2016, to December 31, 2020. All heart failure patients hospitalized during the reflective period and whose age is ≥ 18 years old were included, and those who had incomplete baseline data, and patients discharged with death were excluded.

Sample size determination and sampling procedure

The maximum sample size of this study, obtained from predictors for Cox proportional hazard (PH) regression, calculated using the software STATA version 17.0 assuming a 95% confidence interval, significance level (5%), power 80%, withdrawal effect (15%) and 0.1 overall probability of an event and adjusted hazard ratio (AOR: 1.29) on being a commodity of diabetes mellitus taken from22 giving a total sample size of 626.

Data collection tools, procedures, and quality control

The data was collected using data extraction tools adapted from the literature23,24. The list of patients hospitalized with an index admission of HF was retrieved from the Health Management Information System (HMIS) registration using the medical registration numbers. Three trained BSc nurses were involved in data collection, and supervision was done. The tool was pretested for its completeness and existence of relevant variables through a preliminary chart review of 10% of the sample size at Kemisse General Hospital.

Operational definition

Event: The occurrence of readmission within 30 days after hospital discharge from an index admission.

Follow-up time: From the time of discharge until an event occurs.

Censored: Patients who did not readmit within 30 days during the follow-up period.

Survival status: The status of heart failure patients at the end of the follow-up period (readmission or censored).

Time to readmission: The Time interval from discharge from the hospital till readmission happens.

Incomplete patient data on chart: refer to charts with no discharge date, date of readmission, and laboratory results of complete blood count (CBC) at discharge during the index admission.

Ethical considerations

Ethical clearance was obtained from the Department of Adult Health Nursing and the College of Medicine and Health Sciences at Wollo University with the reference number CMHS/03/14. Formal permission was obtained from the hospitals. This study was carried out under the Helsinki Declaration's ethical principles.

Statistical analysis

Data were checked for completeness and consistency before data entry. Epi-data manager version 4.6 was used for data entry and Stata version 17 for analysis. Exploratory data analysis was done to check the level of missing values, the presence of influential outliers, and normality. Then, the data were described using relative frequency and percent through cross-tabulations of tables and figures. The incidence of readmission was calculated per person-day observation. The lifetime table would be computed to estimate the cumulative probabilities of readmission at different time intervals. The Kaplan-Meir curves were used to estimate the survival status, and the log-rank test was used to compare the association between the outcome variable and the predictor variable. The bi-variable analysis was computed to identify the association between the dependent and each independent variable. Variables with a p-value ≤ 0.25 at the bivariable level were included in multivariable analysis to identify independent predictors of readmission. Multicollinearity was checked using the VIF command. The fitness of the Cox hazard model for the data was checked using Cox-Snell test residuals. Additionally, proportional hazard assumptions were checked using both global tests, and the variable with a p-value greater than 0.05 was considered to fulfill the assumptions. Multivariable Cox-hazard proportional analysis was computed to estimate the size of the effect of predictor variables on the outcome variables with a 95% confidence interval. Then, a p-value ≤ 0.05 was considered statistically significant.

Results

Socio-demographic characteristics, baseline laboratory marker, and pre-existing co-morbidity status of adult heart failure patients

Among the 626 medical records reviewed, 572 met the inclusion criteria, resulting in a completeness rate of 91.37%. From this, 302 (52.8%) were male, and 370 (64.7%) were rural in residency. Regarding age distribution, the median age of the study participants was 45 years, and the mean age was 45.8 ± 14.1 SD years. Concerning baseline laboratory markers of the study participants, from study participants, a Platelet count greater than 150 cells/ L 136 (90.07%) was readmitted, and White blood cell count (103 /µL) from ≥ 1.3 to ≤ 4 is 122 (80.79%) was readmitted. Participants with a mean platelet volume (fl) greater than 9.1 were readmitted 116 (76.82%). Regarding preexisting comorbidities among study participants, hypertension was the most prevalent at 106 (18.53%), followed by COPD/Asthma at 95 (16.61%) (Table 1).

Table 1.

Socio-demographic characteristics, baseline laboratory marker, and pre-existing co-morbidity status of adult heart failure patients.

Variables Category Status Total (%)
readmitted (%) (n = 151) Censored (%) (n = 421)
Age  < 65 103 (68.21) 388(92.16) 491(85.84)
 > 65 48 (31.79) 33(7.84) 81(14.16)
Sex Male 81 (53.64) 221(52.49) 302(52.80)
Female 70 (46.36) 200(47.51) 270(47.20)
Residency Urban 37 (24.50) 165(39.19) 202(35.31)
Rural 114 (75.50) 256(60.81) 370(64.69)
White blood cell (103 /µL)  < 1.3 17 (11.26) 51(12.11) 68(11.89)
 ≥ 1.3—≤ 4 122 (80.79) 329(78.15) 451(78.85)
 > 4 12 (7.95) 41(9.74) 53(9.27)
Platelet count (cells/ L)  ≤ 150 15 (9.93) 162(38.48) 177(30.94)
 > 150 136 (90.07) 259(61.52) 395(69.06)
Mean platelet volume (fl)  < 9.1 35(23.18) 292(69.36) 327(57.17)
 ≥ 9.1 116(76.82) 129(30.64) 245(42.83)
Hgb(g/dL)  > 13 21(13.91) 299 (71.02) 320(55.94)
11–12.9 36(23.84) 59 (14.01) 95(16.61)
8–10.9 94(62.25) 63 (14.95) 157(27.45)
COPD/Asthma No 109(72.19) 368(87.41) 477 (83.39)
Yes 42(27.81) 53(12.59) 95(16.61)
HIV No 114 (75.50) 389(92.40) 503(87.94)
Yes 37(24.50) 32 (7.60) 69(12.06)
HTN No 123(15.23) 343(81.47) 466(81.47)
Yes 28(18.54) 78 (18.53) 106 (18.53)
DM No 130(19.87) 361(85.75) 491 (85.84)
Yes 21(13.91) 60 (14.25) 81 (14.16)
CKD No 145(96.03) 399(94.77) 544 (95.10)
Yes 6 (3.97) 22 (5.23) 28 (4.90)
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The incidence and Kaplan- Meier hazard estimation of unplanned hospital readmission

A total of 572 adult heart failure patients were involved. The median time of readmission was 16 days (95% CI: 14, 17), with a minimum of 3 days and a maximum of 30 days of follow-up time. In this study, 421 patients were censored, and 151 were readmitted within 30 days of discharge, resulting in a cumulative incidence of readmission of 26.40% (95% CI: 23.0–30.2) during the follow-up period. The total follow-up time was 15,093 person-days, with an incidence rate of 10.01 readmissions per 1000 person-day observations (95% CI: 8.79, 13.38) (Fig. 1). The overall Kaplan–Meier estimate showed that the hazard of unplanned hospital readmission of heart failure patients is low during the first three days following discharge after index admission. However, this relatively increases the hazard of unplanned readmission as follow-up time increases. The overall median time of unplanned readmission among heart failure patients was 16 days (95% CI: 14, 17), and the meantime for the study participant was 26.38 days (95% CI: 23.82, 30.63). The hazard of unplanned readmission of heart failure patients at the start of follow-up was 100%. The hazards of unplanned readmission at three days, five days, and ten days were 0.52%, 1.05%, and 5.42%, respectively. During the follow-up time following discharge from the hospital, from the index admission to 30 days later, the hazard curve tends to rise rapidly, implying unplanned reemissions in heart failure patients within this period (Figs. 1,2,3,4,5,6).

Fig. 1.

Fig. 1

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Kaplan- meier hazard estimation of unplanned readmission in heart failure patients with categories of age at selected South Wollo general Hospitals; Ethiopia, 2022.

Fig. 2.

Fig. 2

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Kaplan- meier hazard estimation of unplanned readmission in heart failure patients with categories of residence at selected South Wollo general Hospitals; Ethiopia, 2022.

Fig. 3.

Fig. 3

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Kaplan- meier hazard estimation of unplanned readmission in heart failure patients with categories of COPD/Asthma at selected South Wollo general Hospitals; Ethiopia, 2022.

Fig. 4.

Fig. 4

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Kaplan- meier hazard estimation of unplanned readmission in heart failure patients with categories of HIV/AIDS at selected South Wollo general Hospitals; Ethiopia, 2022.

Fig. 5.

Fig. 5

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Kaplan- meier hazard estimation of unplanned readmission in heart failure patients with categories of mean platelet volume (MPV) at selected South Wollo general Hospitals; Ethiopia, 2022.

Fig. 6.

Fig. 6

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Kaplan- meier hazard estimation of unplanned readmission in heart failure patients with categories of haemoglobin (Hgb)at selected South Wollo general Hospitals; Ethiopia, 2022.

Predictors of unplanned hospital readmission

A Cox proportional hazard regression model was computed to identify the relationship between the hazard of readmission and independent variables. In the bivariable Cox proportional hazards regression model, sex, age, HIV/AIDS, COPD/asthma, hemoglobin level (Hgb), mean platelet volume (MPV), and platelet level were found to have a p-value of less than 0.25 with unplanned hospital readmission. Those variables having a p-value of < 0.25 in the bivariable analysis were fitted in the multivariable analysis. In the multivariable Cox proportional hazards model, age, HIV/AIDS, COPD/asthma, hemoglobin level (Hgb), and mean platelet volume (MPV) were significant predictors of unplanned hospital readmission with a P-value of < 0.05. The multivariable Cox-proportional Hazards analysis revealed that patients with heart failure who were aged ≥ 65 years had a more than three-fold hazard of unplanned readmission (AHR: 3.172, 95% CI: 2.1, 4.55), compared to heart failure patients in the age group between 18 and 64 years. The hazard of unplanned readmission among heart failure patients was 2.47 times (AHR: 2.47, 95% CI: 1.44, 4.24), higher for rural residents than for urban residents). Patients with baseline hemoglobin levels of 11–12.9 g/dL and hemoglobin levels of 8–10.9 g/dL had more than three and six times the high hazard of unplanned readmission (AHR: 3.67, 95% CI: 2.09, 6.44), and (AHR: 6.20, 95% CI: 3.74, 10.28) than those with baseline hemoglobin levels > 13 g/dL during index admission (Table 2).

Table 2.

Cox proportional hazard regression model of the study of heart failure patients.

Independent variables Category Status cHR (95%CI) aHR (95%CI) P-value
Readmitted Censored
Age  < 64 61 388 1 1
 > 65 90 33 8.22 (5.92, 11.42) 3.17 (2.21, 4.55) 0.001
Residency Urban 16 185 1 1
Rural 135 236 5.32 (3.17, 8.93) 2.47 (1.44, 4.24) 0.001
COPD/Asthma No 109 368 1 1
Yes 42 53 2.24 (1.57, 3.20) 1.62 (1.11, 2.35) 0.012
HIV/AIDS No 114 389 1 1
Yes 37 32 2.78 (1.92, 4.02) 1.84 (1.24, 2.75) 0.003
MPV(fL)  < 9.1 35 292 1 1
 > 9.1 116 129 5.51 (3.77, 8.05) 2.08 (1.27, 3.40) 0.004
Hgb(g/dL)  > 13 21 299 1 1
11–12.9 36 59 6.88 (4.02. 11.80) 3.67 (2.09, 6.44) 0.001
8–10.9 94 63 13.12 (8.11,21.10) 6.20 (3.74, 10.28) 0.001
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Discussion

This study aimed to assess the incidence and predictors of 30-day unplanned hospital readmission in heart failure patients at selected South Wollo General Hospitals. The study revealed a 26.40%; 95% CI: (23.0, 30.2) cumulative unplanned hospital readmissions incidence during the follow-up period, which was reasonably close to the results of the USA's (29.7%)25 and Kano’s (24.3%)26. However, this study finding is higher than the studies conducted in Iran (6.6%)27 , Nigeria (20.6%), Mozambique (13.3%), South Africa (16.0%), Australia and New Zealand (22.3%), and Japan (6.56%)26–28 . The discrepancy in the current study may be due to demographic characteristics and follow-up period differences, as the previous studies in Mozambique, Nigeria, South Africa, and Japan were 1-year cohorts and 5-month (Iran) studies respectively.

This study revealed that being older (age > 65) increases the hazard of unplanned hospital readmission as compared to adults in the age group of 18–64. This finding is supported by a study results Japan29. The possible reason might be older patients are at higher risk for physiological deterioration, comorbidity, psychological stress, medication errors, and a sedentary lifestyle due to increased vulnerability30,31.

Indeed, the study reveals that older patients are more susceptible to readmission due to non-heart failure-related conditions, highlighting the need for targeted attention. It was found that, being a rural resident was an independent predictor of unplanned hospital readmission. This finding is supported by a study conducted in Canada32 and Chile33. The possible reason might be that rural heart failure patients are slower to adopt healthy behaviors and have lower health literacy levels than residents of urban communities34. Interventions aimed at expanding disease-related knowledge in patients with heart failure can positively impact re-hospitalization and quality of life.

The study found that patients with heart failure and pre-existing COPD or asthma during index admission had a higher risk of unplanned readmission. This finding is consistent with studies conducted in the US35, Massachusetts36, and India37 which reported that respiratory diseases like COPD or asthma increase the risk of 30-day readmissions, particularly in heart failure patients.

In this study, heart failure patients living with HIV/AIDS had a higher risk of unplanned hospital readmission than patients without. This finding is in line with studies from the United States38, Massachusetts39, and New York City40. This might be because living with HIV/AIDS is directly associated with low immune function, thereby increasing the risk of both bacterial and viral infection and, consequently, poor outcomes41. This finding highlights the importance of timely monitoring and more conservative management for those individuals with these comorbidities. Furthermore, optimal antiretroviral therapy is essential for patients with HIV who develop HF. Effective antiretroviral treatment that results in immunological rebound and viral suppression may partially protect people living with HIV who also have heart failure from the adverse events of heart failure.

This study found that those patients who had lower hemoglobin levels at discharge during index admission had an increased risk of unplanned readmission to the hospital compared to those with a normal hemoglobin level. This result is consistent with the studies conducted by the European Society of Cardiology42, the United Arab Emirates43, India44, and a meta-analysis of 26 studies45. Prolonged anemia can lead to left ventricular hypertrophy (LVH), causing apoptosis and worsening heart failure46. This implies that heart failure patients with lower hemoglobin levels at index admission need special attention to non-pharmacological treatment in addition to pharmacological intervention, particularly hemoglobin-enhancing nutrients, and need close follow-up.

The present study found that an increase in mean platelet volume (MPV) of greater than 9.1 fL during index admission was an independent predictor of an increase in the risk of unplanned hospital readmission. This report is consistent with the studies conducted in Turkey47, Germany48, and Romania49. This could be explained by the fact that MPV is one of the potential biomarkers of platelet activity50. This implies that MPV is a meek marker of platelet function and may represent a risk factor for adverse vascular events.

Limitations of the study

The study was retrospective and prone to data incompleteness. Important variables like personal behavior, such as smoking, alcohol use, and chat chewing, were strong predictors of unplanned hospital readmission in other studies but were not or inadequately recorded and not included in the analysis. HFrEF vs HFpEF, LVEF, and natriuretic peptides were not included due to a lack of documentation. Patients discharged from one hospital during an index admission may be admitted within 30 days from another hospital, but this is not recorded as a readmission due to the retrospective nature of the study and the hospitals not being linked. In these circumstances, the readmission rate might be underestimated.

Conclusion

The incidence of unplanned hospital readmission among heart failure patients is relatively high. The hazard of readmission was higher among rural residents, elderly patients aged ≥ 65 years, patients living with HIV/AIDS, COPD/asthma, increased mean platelet volume (MPV), and a decrease in hemoglobin levels during the index admission, which were independent predictors of unplanned hospital readmission among patients with heart failure.

Acknowledgements

We thanks to Boru-Meda general hospital, Akesta general hospital, and Mekane-Selam general hospital for quality unit control, HMIS coordinators, card room officers, and data collectors for their cooperation.

Abbreviations

HF

Heart Failure

HMIS

Health Management Information System

MPV

Mean platelet volume

SDG

Sustainable Development Goal

SSA

Sub-Saharan Africa

Author contributions

Contributions BA designed the study. Both BA and PK wrote the first draft. PK and AH participated in the study implementation while BA performed the statistical analyses. BA, PK, and AH supervised all stages of study implementation. All the authors read and approved the final manuscript.

Data availability

All data generated or analyzed during this study are included in this published article.

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

We followed the Declaration of Helsinki Principles of Ethics for medical research involving human subjects. The Wollo University Ethics Committee approved the study protocol (CMHS/03/14 IRB number). We obtained official approval from the relevant officials at various levels of the hospital through written communication and secured their consent. We preserved the data confidentiality.

Footnotes

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Labrosciano, C. et al. Frequency, trends and institutional variation in 30-day all-cause mortality and unplanned readmissions following hospitalisation for heart failure in Australia and New Zealand. Eur. J. Heart Fail.23(1), 31–40 (2021). [DOI] [PubMed] [Google Scholar]
  • 2.van Galen, L. S. et al. Patients’ and providers’ perceptions of the preventability of hospital readmission: a prospective, observational study in four European countries. BMJ Qual. Saf.26(12), 958–969 (2017). [DOI] [PubMed] [Google Scholar]
  • 3.Butt, J. H. et al. Readmission and death in patients admitted with new-onset versus worsening of chronic heart failure: insights from a nationwide cohort. Eur. J. Heart Fail.22(10), 1777–1785 (2020). [DOI] [PubMed] [Google Scholar]
  • 4.Kassa, M. & Grace, J. The global burden and perspectives on non-communicable diseases (NCDs) and the prevention, data availability and systems approach of NCDs in low-resource countries (Public Health in Developing Countries-Challenges and Opportunities, 2019). [Google Scholar]
  • 5.Jagannathan, R., Patel, S. A., Ali, M. K. & Narayan, K. V. Global updates on cardiovascular disease mortality trends and attribution of traditional risk factors. Curr. Diab. Rep.19, 1–12 (2019). [DOI] [PubMed] [Google Scholar]
  • 6.Rosengren, A. et al. Socioeconomic status and risk of cardiovascular disease in 20 low-income, middle-income, and high-income countries: The Prospective Urban Rural Epidemiologic (PURE) study. Lancet Glob. Health7(6), e748–e760 (2019). [DOI] [PubMed] [Google Scholar]
  • 7.Agbor, V. N. et al. Heart failure in sub-Saharan Africa: A contemporaneous systematic review and meta-analysis. Int. J. Cardiol.257, 207–215 (2018). [DOI] [PubMed] [Google Scholar]
  • 8.Okeahialam BN, Agbo HA, Ogbonna C, Chuhwak E, Isiguzoro I, editors. Rarity of Heart Failure in a Traditional African Population. A Rural Community Based Study. International Cardiovascular Forum Journal, (2016).
  • 9.Angaw, D. A., Ali, R., Tadele, A. & Shumet, S. The prevalence of cardiovascular disease in Ethiopia: a systematic review and meta-analysis of institutional and community-based studies. BMC Cardiovasc. Disord.21, 1–9 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Laytin, A. D. et al. Critical care capacity in Addis Ababa, Ethiopia: A citywide survey of public hospitals. J. Critic. Care63, 1–7 (2021). [DOI] [PubMed] [Google Scholar]
  • 11.Ayenew, B. & Pandey, D. Challenges and opportunities to tackle COVID-19 spread in Ethiopia. J. PeerSci.2(2), e1000014 (2020). [Google Scholar]
  • 12.Cerf, M. E. Health worker resourcing to meet universal health coverage in Africa. Int. J. Healthcare Manag.14(3), 789–796 (2021). [Google Scholar]
  • 13.Kosiborod, M. N. et al. Effects of dapagliflozin on symptoms, function, and quality of life in patients with heart failure and reduced ejection fraction: results from the DAPA-HF trial. Circulation141(2), 90–99 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Abebaw, Y., Mohammed, K., Aragaw, A. & Melese, B. Joint modeling of longitudinal pulse rate and time-to-default from treatment of congestive heart failure patients. Research Rep. Clin. Cardiol.12, 41–52 (2021). [Google Scholar]
  • 15.Dessie, G. et al. Effect of a self-care educational intervention to improve self-care adherence among patients with chronic heart failure: a clustered randomized controlled trial in Northwest Ethiopia. BMC Cardiovasc. Disord.21, 1–11 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Gelaw, Y. A. et al. Socio-demographic correlates of unhealthy lifestyle in Ethiopia: A secondary analysis of a national survey. BMC Public Health23(1), 1528 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Andualem, A., Gelaye, H. & Damtie, Y. Adherence to lifestyle modifications and associated factors among adult hypertensive patients attending chronic follow-up units of Dessie Referral Hospital, North East Ethiopia. Integr. Blood Press. Control.2020, 145–156 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Leung, D. Y. et al. The effect of a virtual ward program on emergency services utilization and quality of life in frail elderly patients after discharge: a pilot study. Clin. Inter. Aging10.2147/CIA.S68937 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Mitsutake, S. et al. Associations of hospital discharge services with potentially avoidable readmissions within 30 days among older adults after rehabilitation in acute care hospitals in Tokyo, Japan. Arch. Phys. Med. Rehabil.101(5), 832–840 (2020). [DOI] [PubMed] [Google Scholar]
  • 20.Zhang, Y. et al. Assessing the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission or death. PloS One15(6), e0235064 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Ayenew, B. et al. Heart failure drug classes and 30-day unplanned hospital readmission among patients with heart failure in Ethiopia. J. Pharmaceutical Health Care Sci.9(1), 49 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Echouffo-Tcheugui, J. B., Masoudi, F. A., Bao, H., Spatz, E. S. & Fonarow, G. C. Diabetes mellitus and outcomes of cardiac resynchronization with implantable cardioverter-defibrillator therapy in older patients with heart failure. Circul. Arrhythmia Electrophysiol.10.1161/CIRCEP.116.004132 (2016). [DOI] [PubMed] [Google Scholar]
  • 23.Korda, R. J. et al. Variation in readmission and mortality following hospitalisation with a diagnosis of heart failure: prospective cohort study using linked data. BMC Health Serv. Res.17, 1–18 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Wideqvist, M., Cui, X., Magnusson, C., Schaufelberger, M. & Fu, M. Hospital readmissions of patients with heart failure from real world: timing and associated risk factors. ESC Heart Fail.8(2), 1388–1397 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Kwok, C. S. et al. Early unplanned readmissions after admission to hospital with heart failure. Am J. Cardiol.124(5), 736–745 (2019). [DOI] [PubMed] [Google Scholar]
  • 26.Karaye, K. M. et al. Clinical profiles and outcomes of heart failure in five African countries: results from INTER-CHF study. Global Heart10.5334/gh.940 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Naderi, N. et al. Predictors of readmission in hospitalized heart failure patients. J. Cardiovasc. Thoracic Res.14(1), 11 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Aizawa, H., Imai, S. & Fushimi, K. Factors associated with 30-day readmission of patients with heart failure from a Japanese administrative database. BMC Cardiovasc. Disord.15, 1–7 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Mahmoud, A. N. et al. Prevalence, causes, and predictors of 30-day readmissions following hospitalization with acute myocardial infarction complicated by cardiogenic shock: findings from the 2013–2014 National Readmissions Database. J. Am. Heart Assoc.7(6), e008235 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Soto-Perez-de-Celis, E., Li, D., Yuan, Y., Lau, Y. M. & Hurria, A. Functional versus chronological age: geriatric assessments to guide decision making in older patients with cancer. Lancet Oncol.19(6), e305–e316 (2018). [DOI] [PubMed] [Google Scholar]
  • 31.Gomes, M. et al. Physical inactivity among older adults across Europe based on the SHARE database. Age Ageing46(1), 71–77 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Staples, J. A., Thiruchelvam, D. & Redelmeier, D. A. Site of hospital readmission and mortality: A population-based retrospective cohort study. Can. Med. Assoc. Open Access J.2(2), E77–E85 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Verdejo, H. E., Ferreccio, C. & Castro, P. F. Heart failure in rural communities. Heart Fail. Clin.11(4), 515–522 (2015). [DOI] [PubMed] [Google Scholar]
  • 34.Krupp, K. et al. Cardiovascular risk factor knowledge and behaviors among low-income urban women in Mysore India. J. Cardiovasc. Nurs.35(6), 588–598 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Gulea, C., Zakeri, R. & Quint, J. K. Impact of chronic obstructive pulmonary disease on readmission after hospitalization for acute heart failure: A nationally representative US cohort study. Int. J. Cardiol.290, 113–118 (2019). [DOI] [PubMed] [Google Scholar]
  • 36.Kohli, P. et al. The effect of emphysema on readmission and survival among smokers with heart failure. Plos one13(7), e0201376 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Santhakumaran S. Statistical implications of centralised care for estimating neonatal unit mortality rates: Imperial College London, (2017).
  • 38.Brouch, D. T. N., Di Felice, C., Longenecker, C. T. & Al-Kindi, S. G. Human immunodeficiency virus infection and risk of heart failure rehospitalizations. Am. J. Cardiol.124(8), 1232–1238 (2019). [DOI] [PubMed] [Google Scholar]
  • 39.Janjua, S. A. T. V. et al. HIV infection and heart failure outcomes in women. J. Am. College Cardiol.69(1), 107–108 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Pierre-Louis, B. R. S. et al. Clinical factors associated with early readmission among acutely decompensated heart failure patients. Arch. Med. Sci.12(3), 538–545 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Chawla, K. S. R. N. et al. HIV and early hospital readmission: Evaluation of a tertiary medical facility in Lilongwe Malawi. BMC Health Serv. Res.18(1), 1–9 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Nunez, J. C. C. J. et al. Iron deficiency and risk of early readmission following hospitalization for acute heart failure. Eur. J. Heart Fail.18(7), 798–802 (2016). [DOI] [PubMed] [Google Scholar]
  • 43.Al-Tamimi, M.A.-A.G.S., Abd Alhakam, M. E. & Sam, K. G. Factors associated with hospital readmission of heart failure patients. Front. Pharmacol.10.3389/fphar.2021.732760 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Bhosale, K. H. N. R. et al. Rate of Rehospitalization in 60 Days of Discharge and Its Determinants in Patients with Heart Failure with Reduced Ejection Fraction in a Tertiary Care Centre in India. Int. J. Heart Fail.2(2), 131–144 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Kyriakou, M. K. P. Prognosis of the comorbid heart failure and anemia: A systematic review and meta-analysis. Clin. Trials Regulatory Sci. Cardiol.16, 12–21 (2016). [Google Scholar]
  • 46.McCullough, P. A. Anemia of cardiorenal syndrome. Kidney Int. Suppl.11(1), 35–45 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Kaya, H. Y. M., Kurt, R., Beton, O. & Yilmaz, M. B. Mean platelet volume as a predictor of heart failure-related hospitalizations in stable heart failure outpatients with sinus rhythm. Acta Cardiologica Sinica.33(3), 292 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Dahlen, B. S. A. et al. The impact of platelet indices on clinical outcome in heart failure: Results from the MyoVasc study. ESC Heart Fail.8(4), 2991–3001 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Andrei, C. L. C. A. et al. Mean Platelet Volume: A Possible Predictor for Patients with Decompensated Chronic Heart Failure. Int. J. Gen. Med.15, 4131 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Machado, G. P. et al. Comparison of neutrophil-to-lymphocyte ratio and mean platelet volume in the prediction of adverse events after primary percutaneous coronary intervention in patients with ST-elevation myocardial infarction. Atherosclerosis274, 212–217 (2018). [DOI] [PubMed] [Google Scholar]

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