Predictors of Hospital Readmission within Six Months in Heart Failure Patients

Mohammed Habib* and Ashraf Shaath

Department of Cardiology, Al-shifa Hospital, Gaza, Palestine

*Corresponding Author:
Mohammed Habib
Department of Cardiology, Al-shifa Hospital, Gaza, Palestine
Tel: 972 599514060

Received: 31 August, 2020, Manuscript No. IPJHCR-20-5995; Editor assigned: 03 September, 2020, PreQC No. IPJHCR-205995 (PQ); Reviewed: 17 September, 2020, QC No. IPJHCR-20-5995; Revised: 20 July, 2022, QI No. IPJHCR-205995, Manuscript No. IPJHCR-20-5995 (R); Published: 17 August, 2022, DOI: 10.36648/ipjhcr.6.4.1

Citation: Habib M, Shaath A (2022) Predictors of Hospital Readmission within Six Months in Heart Failure Patients. J Heart Cardiovasc Res Vol: 6 No: 4.1.

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Background: Heart Failure (HF) has high in-hospital mortality and is associated with high readmission rates. Reasons for and ways to avoid HF readmissions are unclear. Approximately one-third of patients admitted for HF are readmitted within 6 months; however, there are few markers that can identify those at highest risk for readmission. The purpose of this study was to identify clinical and laboratory markers associated with hospital readmission in decompensated HF.

Methods: This is a prospective, observational study enrolled 164 patients with heart failure and reduced Ejection Fraction (EF) <40%, were admitted to cardiology department because of acute decompensation of heart failure. The patients were divided into 2 groups (group 1, those not rehospitalized (single admission); and group 2, those rehospitalized (one or more readmission) during 6 months follow-up.

Results: The 110 patients were single admission group, and 54 patients were rehospitalized during 6 months follow-up, 63% of the patients were male. The mean age of the study population was 65.79 ± 14.12. The risk factors of readmissions were acute coronary syndrome (P=0.015), mitral regurgitation (P=0.002), aortic regurgitation (P=0.014), LBBB (P=0.048), Urosepsis (P=0.008), and low EF (P=0.041). Logistic regression analysis anemia (OR, 1.7; CI, 0.8-3.4), hypertension (OR, 2.5; CI, 1.2-5.4), ejection fraction less than 30% (OR, 1.9; CI, 0.9-3.7), mitral regurgitation (OR, 2.8; CI, 1.4-5.6), hyponatremia (OR, 2.0. CI, 0.5-7.6), and high creatinine (OR, 1.3; CI, 0.6-2.8) were independently increased the risk of rehospitalization at six months.

Conclusion: Rehospitalization rate was 33%. Higher rates of readmission were noted in those with older male, hypertensive patients, low EF, mitral regurgitation, impaired kidney function, anemia, hyponatremia, living at home independently and infections.


Heart failure; Hospital readmission; Patients


Heart Failure (HF) is a prevalent and morbid chronic illness [1]. According to the European society of cardiology and the American heart association, HF affects approximately 15 million Europeans and over 5 million Americans [2].

HF is not only taxing to the patient, but also to the healthcare system. Studies evaluating the economic burden of HF among several countries reveal estimated direct HF costs of 1%-2% of total healthcare expenditures, with approximately two-thirds of costs attributable to hospitalization [3].

Heart Failure (HF) remains a rising global epidemic with an estimated prevalence of >37.7 million individuals globally [4].

According to annual report of palestinian ministry of health 2013 the data related to CHF was 11.9% in west bank and 15.5% in age group 60 years and above (Health Annual Report, 2013), no data were available for Gaza [5].

Heart Failure (HF) is a complex clinical syndrome characterized by the reduced ability of the heart to pump and/or fill with blood, from a physiological point of view, HF can be defined as an inadequate cardiac output to meet metabolic demands or adequate cardiac output secondary to compensatory neurohormonal activation (generally manifesting as increased left ventricular filling pressure) [6].

HF has recently been classified into three subtypes, namely HF with reduced Ejection Fraction (HFrEF), HF with preserved Ejection Fraction (HFpEF) and HF mid-range Ejection Fraction (HFmrEF), according to the ejection fraction, natriuretic peptide levels and the presence of structural heart disease and diastolic dysfunction [7].

In Europe, 60% of the economic cost of HF is related to hospital admissions, this is because HF is associated with high rates of recurrent hospitalizations and frequent clinic visits [8].

Data from developed countries show that one-half of HF patients are rehospitalized within 6 months of discharge, and that 70% of these are due to worsening of previously diagnosed HF [9].

Materials and Methods

Study design and clinical setting

This an observational prospective study for 164 patients who admitted (one or more admission) to cardiology department with diagnosis of heart failure patients with the diagnosis of CHF who were hospitalized in cardiology department Al Shifa central hospital in Gaza from 1 May 2019 to 31 October 2019 [10].

Cardiac readmission was defined as any subsequent admission for various causes that are related to the heart failure [11].

Inclusion and exclusion criteria

Inclusion criteria included patients with age >18 years, 2D Echocardiogram showing systolic dysfunction (Left Ventricle Ejection Fraction [LVEF]<40%) within 6 months of admission, having at least one of the following findings on Chest X-Ray (CXR): Pulmonary edema, pulmonary congestion, cardiomegaly, and/or pleural effusion and at least two of the following: Dyspnea, peripheral edema, clinical signs of volume overload, jugular venous distention, left ventricle S3 gallop, heart rate >100 beats per minute exclusion criteria included patients with age <18 years, patients with cancer and live expectancy <1 year [12].

Clinical evaluation

Informed consent was obtained from the subjects and/or parents and the protocol was cleared by the Institutional ethical commit [13]. A standard case report form was used in data collection also clinical data were collected by face to face interview and from patient`s hospital files, baseline clinical and demographic variables, such as age, contact addresses, telephone numbers of patients and their relations, marital status, occupation and history of cardiovascular risk factors, such as hypertension, diabetes mellites, family history, cigarette smoking, etc, were collected [14]. Also collected were the signs and symptoms, clinical diagnoses, comorbidities, medications, result of investigation, date of discharge, and the date of next appointment to cardiology out clinic [15].

Blood pressure was recorded according to a standard guideline, patients were weighed without shoes and in light clothing on a standard beam balance. Heart rate, NYHA classification (New York heart association) etiology of heart failure, presence of Coronary Artery Disease (CAD), Atrial Fibrillation (AF), stroke, Hypertension (HTN) is defined as BP higher than 140/90 or taking antihypertensive medication, Diabetes Mellitus (DM) was determined by fasting blood sugar>126 mg/dl, HbA1c>6.5 or taking anti diabetic medication [16].

History of documented coronary artery disease, valvular heart diseases, history of thyroid disease, surgical cardiac intervention, cardiac devices implantations, chronic kidney disease, and laboratory test (CBC, sodium, potassium, creatinine, urea, uric acid and HbA1clevel. Urine analysis and culture also were obtained [17].

The study variables also included: Living dependently or independently, competence with physicians instruction, also we ask about the resources of medical treatment (health insurance system, self-pay, mixed, or has no resources) and if it is guideline directed medical treatment or not, length of dtay in hospital and check the list of medication on discharge [18].

Time from discharge to visit cardiology out clinic also was considered in the study and was divided to five groups: Within one week, within two weeks, within one month, one to 6 months and no visit within six months [19].

Anemia was defined by the World Health Organization as Hemoglobin (Hb) <12 g/dL in females and <13 g/dL in males [20].

Chronic kidney disease (Renal dysfunction) was defined as eGFR of <60 mL/min/1.73 m2 [21].

The diagnosis of heart failure was done by cardiologist based on clinical history, physical examination, chest radiography and transthoracic Echocardiography [22].

Twelve-lead electrocardiographic tracing was obtained with the use of a cardioline electrocardiograph, and the reports were analyzed by the authors blinded to the clinical history of the patients. M-mode, 2-dimensinal, and doppler echocardiography were performed with the use of Philips HD7 XE ultra sound machine [23].


The patients were followed for a period of 6 months. Information on readmissions was assessed again face to face, by clinical examination and by proceeding Lab, CXR, ECG, Echo Doppler examination, also was assessed through hospital case record at the end of 6 months. [24].

The patients were divided into 2 groups (group 1, those not rehospitalized (single admission); and group 2, those rehospitalized (one or more readmission) [25].

Statistical analysis

Data was analyzed by SPSS version 19. Continuous variables were presented as mean ± Standard Deviation (SD) and categorical variables as absolute numbers and percentages [26]. Comparison of demographic and clinical data among the groups was performed using independent t-test for continuous variables and chi-square (χ2) for categorical variables [27]. Pearson's correlation coefficients were calculated to illustrate certain relationships. P values<0.05 were considered significant [28].


Baseline characteristics

One hundred sixty-four patients, comprising 86 men (52.4%) and 78 (47.6%) women, who were admitted with heart failure and followed for 6 months [29]. The readmitted group were significantly more for men 63.0% vs. women 37.0%; P value=0.042 [30]. The main age study population was 65.79 ± 14.118 years and 65.78 ± 13.52 in readmitted group and there was no difference between two group P-value 0.992 [31]. Similarly, no significant difference has been observed between two groups regarding their marital status and smoking [32].

However a significant difference was observed between the two groups in participants who living at home independently 65.9% in all sample and 51.9% in readmitted patients; P value=0.007, and a significant difference regarding the source of medication; in the readmitted patient who had health insurance, self-pay, mixed or did not use the medications 18.5%, 40.7%, 33.3%, 7.4% respectively with P value=0.001 (Table 1).

Variable All no (164) No readmission (110) Readmission N (54) p-value
Age (mean) 65.78 ± 13.52 65.8 ± 14.46 65.78 ± 13.52 0.992
Male 86 0.524 52 0.473 34 0.63 0.042
Female 78 0.476 58 0.527 20 0.37
Marital status
Married 154 0.939 102 0.927 52 0.963 0.301
Single 10 0.061 8 0.073 2 0.037
Living at home independently
Yes 108 0.659 80 0.727 28 0.519 0.007
No 56 0.341 30 0.273 26 0.481
The source of medications
Health insurance 56 0.341 46 0.418 10 0.185 0.001
Self-pay 48 0.293 26 0.236 22 0.407
Mixed 56 0.341 38 0.345 18 0.333
Didn't use medications 4 0.024 0 0 4 0.074
No 116 0.716 80 0.727 36 0.692 0.308
Yes 18 0.111 14 0.127 4 0.077
X smoker 28 0.173 16 0.145 12 0.231

Table 1: Baseline characteristics.

The co-morbidities

Among 78.0% of patients with CHF had hypertension with 66.7% in the readmitted groups, value for significance was 0.013. The other most common morbidities were diabetes and chronic kidney diseases which were present in 62.7%, 59% of all patients respectively. 40.2% had a history of permanent atrial fibrillation, 48.8% with documented CAD, only 15.4% had old CVA, and 13.4%, 13.9% of all patient had hypothyroidism and Chronic Obstructed Pulmonary Disease (COPD) respectively; for all these variables the difference between two groups doesn`t reach a statistical significance level. We noted in our study that presence of devices like, ICD and CRTD were 12.3% from all population had a statistically significance in the readmission (P=0.001) (Table 2).

Variable All
No (164)
No readmission N (110) Readmission N (54) p-value
Chronic kidney disease
Yes 66 40.2% 46 41.8% 20 37.0% 0.339
No 98 59.8% 64 58.2% 34 63.0%
Yes 128 78.0% 92 83.6% 36 66.7% 0.013
No 36 22.0% 18 16.4% 18 33.3%
Diabetes mellitus
Yes 106 64.6% 76 69.1% 30 55.6% 0.64
No 58 35.4% 34 30.9% 24 44.4%
Permanent atrial fibrillation
Yes 66 40.2% 46 41.8% 20 37.0% 0.339
No 98 59.8% 64 58.2% 34 63.0%
Documented CAD
Yes 80 48.8% 50 45.4% 30 55.6% 0.147
No 84 51.2% 60 54.5% 24 44.4%
Device type
No Device 142 86.6% 104 94.5% 38 70.4% 0
PPM 2 1.20% 0 0.00% 2 3.70%
ICD 8 4.90% 0 0.00% 8 14.80%
CRT 12 7.30% 6 5.50% 6 11.10%
Yes 24 15.4% 16 15.1% 8 16.0% 0.528
No 132 84.6% 90 84.9% 42 84.0%
Thyroid disease
Yes 22 13.4% 14 12.7% 8 14.80% 0.442
No 142 86.6% 96 87.3% 46 85.20%
Chronic Obstructed Pulmonary Disease (COPD)
Yes 22 13.9% 14 13.2% 8 15.40% 0.442
No 136 86.1% 92 86.8% 44 84.6%

Table 2: The co-morbidities.

Clinical presentation on admission

A 35.4% of heart failure patients had moderate to severe mitral regurgitation, 51.9% of them significantly had readmission (p=0.002). 7.3% of heart failure patients had moderate to severe aortic valve regurgitation, and 14.8% of patients with aortic valve disease were readmitted and had statistically significance (p=0.014).

7.4% of population sample on presentation had unstable angina pectoris, 2.5% had NSTEMI and 2.5% had STEMI; the value of significance between two groups was were admitted with acute pulmonary edema with no significance differences between the two groups. 12.2% of patients were admitted with cardiogenic shock, 95.1% were admitted due to exacerbation of heart failure. 9.8% of the patients had pneumonia on admission. 3.7%, 22.2% of the readmission groups had URTI and Urosepsis respectively and had statistically significance (P=0.054, P=0.008) (Table 3).

Variable All N (164) No readmission N (110) Readmission
N (54)
Yes 16 9.80% 8 7.30% 8 14.80% 0.108
No 148 90.20% 102 92.70% 46 85.20%
Yes 16 9.80% 14 12.70% 2 3.70% 0.054
No 148 90.20% 96 87.30% 52 96.30%
Yes 20 12.20% 8 7.30% 12 22.20% 0.008
No 144 87.80% 102 92.70% 42 77.80%
Acute Coronary Syndrome (ACS)
STEMI 4 2.50% 4 3.60% 0 0.00% 0.015
NSTEMI 4 2.50% 0 0.00% 4 7.70%
UAP 12 7.40% 8 7.30% 4 7.70%
No ACS 142 87.70% 98 89.00% 44 84.60%
Cardiogenic shock
Yes 20 12.20% 16 14.50% 4 7.40% 0.144
No 144 87.80% 94 85.50% 50 92.60%
Acute pulmonary edema
Yes 92 56.10% 62 56.40% 30 55.60% 0.527
No 72 43.90% 48 43.60% 24 44.40%
Exacerbation of heart failure
Yes 156 95.10% 104 94.50% 52 96.30% 0.476
No 8 4.90% 6 5.50% 2 3.70%
Moderate to severe mitral regurgitation
Yes 58 35.40% 30 27.30% 28 51.90% 0.002
No 106 64.60% 80 72.70% 26 48.10%
Moderate to severe aortic valve regurgitation
Yes 12 7.30% 4 3.60% 8 14.80% 0.014
No 152 92.70% 106 96.40% 46 85.20%
Moderate to severe tricuspid valve regurgitation
Yes 36 22.00% 22 20% 14 25.90% 0.252
No 128 78.00% 88 80.00% 40 74.10%

Table 3: Clinical presentation on admission.

ECG and Echo finding

ECG and Echocardiography finding are 59.8% of study population were in sinus rhythm and 63.0% of the readmitted group were in sinus rhythm with no statistically significance. LBBB was found in 22.2% in the readmission group and 10.9% in the non-readmitted groups, the value of significance p=0.048. In contrast RBBB was present in 11.0% of total population, but with no significance variation in the two groups of readmission and non-readmission. The total population had either severe low ejection fraction 37.8% or moderate low ejection fraction 62.2% and the variation between the readmission and non-readmission was statistically significant; P=0.041 (Table 4).

Variable All N (164) No readmission N (54) Readmission (110) P-Value
Yes 24 14.60% 12 10.90% 12 22.20% 0.048
No 140 85.40% 98 89.10% 42 77.80%
Yes 18 11.00% 8 7.30% 10 18.50% 0.31
No 146 89.00% 102 92.70% 44 81.50%
Sinus rhythm
Yes 98 59.80% 64 58.20% 34 63.00% 0.339
No 66 40.20% 46 41.80% 20 37.00%
Ejection fraction
Severe 62 37.80% 36 32.70% 26 48.10% 0.041
Moderate 102 62.20% 74 67.30% 28 51.90%

Table 4: ECG and ECHO findings.

Laboratory investigations and vital signs

We found that the mean left ventricular Ejection fractions LVEF (± SD) was 31.69 ± 6.978, chi-square test was statically significant p<0.003. The independent t-test difference was significant with the mean level of uric acid, Sodium, RBS, and hemoglobin A1c (Table 5).

Variable (Mean) No readmission N (110) Readmission N (54) P-Value
GFR 49.82 ± 24.819 51.77 ± 22.321 0.626
Creatinine 1.65 ± 1.077 1.5 ± 0.521 0.418
Urea 73.49 ± 39.147 83.96 ± 44.379 0.126
Na 138.76 ± 5.683 135.59 ± 9.244 0.008
Uric acid 7.17 ± 1.585 7.93 ± 1.888 0.008
K+ 4.51 ± 0.672 4.31 ± 0.765 0.095
Ca++ 8.67 ± 1.345 8.87 ± 0.613 0.888
Hb 10.91 ± 2.060 11.25 ± 2.320 0.338
HbA1c 7.14 ± 1.631 6.34 ± 1.441 0.003
RBS 193.25 ± 92.735 162.85 ± 76.185 0.038
Ejection fraction 31.69 ± 6.978 27.81 ± 8.843 0.003
Systolic BP 123.13 ± 37.312 117.52 ± 27.838 0.329
Diastolic BP 69.78 ± 21.315 73.59 ± 12878 0.228

Table 5: Laboratory investigations and vital signs.

Risk factors associated heart failure

The variables proved to have statistical significance association with occurrence of readmission are included as independent variable in the logistic regression model demonstrate which showed that anemia, high creatinine and urea level, hyponatremia and patients with hypertension, low ejection fraction (below 30%) and patients with mitral valve disease are the major independent risk factors for readmission (Table 6).

Variable P-Value B Odds ratio 95% Confidence interval
Lower Upper
Hb (Anemia) 0.127 0.544 1.723 0.856 3.467
Creatinine<1.2 mg/dl 0.417 0.305 1.357 0.649 2.839
Urea<30 0.169 0.863 2.371 0.693 8.111
Na<135 mg/dl 0.287 0.716 2.045 0.548 7.628
Hypertension 0.015 0.938 2.556 1197 5.456
Ejection fraction>30 0.057 0.646 1.909 0.981 3.715
Moderate to severe mitral regurgitation 0.002 1.055 2.872 1.456 5.663

Table 6: Risk factors associated heart failure.


In this prospective study, we analyzed rehospitalization rate and predictors of rehospitalization in patients with LVEF <40% who were admitted for acut decompensation.

Hospital readmissions remain a continued challenge in the care of the heart failure patient. Although small gains have been made over the past 5 years, still more than 20% of patients are readmitted within 30 days and up to 50% by 6 months. In our study the readmission rates were 33% during the 6 months, Kim conducted a scoping review to include full text articles published between 2002 and 2017; they demonstrated that twelve of 34 studies reported higher heart failure readmission rates for men and six studies reported higher heart failure readmission rates for women.

In our study the readmitted group were significantly more for men 63.0% vs. women 37.0%; P-value=0.042.

Functional status/Activities of Daily Living (ADL), ffrailty, mobilityand disability, are associated with readmissions. Anderson found that individuals with HF who require assistance with ADLs were significantly more likely to be readmitted for heart failure within 60 days similarly we found that re-admission rate increased in patient with impaired ADLs (Living at home independently)

Since most of heart failure patients associated with multiple other comorbidities that place additional medical, logistic, and financial burdens on patients and regardless of causal Heart failure readmission in the relationship between the different type of patient’s source of medications with readmission rates, the fact of the observed association with self-paying the medication completely or partially is of great cause of readmission rates. In our study it was significantly the readmission rate increased in self-paying group of patients.

According to there are also biologically driven neurologiccardiac- inflammatory pathways that physiologically mediate some of the link between socioeconomic stress and worse cardiovascular disease including heart failure.

Therefore, socioeconomic patient factors are crucial components of the HF readmission conundrum.

Also, it was demonstrated in our study that patients not aattending the follow up appointment in regular intervals has increase rate of readmission. Post discharge outpatient followup appointments after a hospitalization for heart failure represent a potentially effective strategy to prevent heart failure readmissions.

It has frequently been incorporated into transitional care model interventions aimed at improving post discharge outcomes, such as readmission and mortality, and is supported by the American heart association and American college of cardiology.

Hypertensive heart disease was the most common (78%) cause of heart failure with 66.7% in the readmitted groups. This is not unusual as hypertension is the most prevalent cardiovascular disease in our population, similar data was reported.

Readmissions among heart failure patients after a recent hospital discharge are influenced by multiple potential factors and iidentifying those at high risk of hospital readmission has been a challenging subject. To be consistent with previously published studies of HF rehospitalization, we found infection as the most common cause of precipitation of HF on index admission.

More than 12% of all admitted patient with heart failure had UTI with increased ratio of readmission p value=0.008. Similar data of UTI as comorbidity of heart failure admission was reported by. Rehospitalization for heart failure in the elderly. 10% of all admitted patients with heart failure had URTI with increased ratio of readmission p value=0.054. Similar data reported.

Demonstrated that only half of the patients discharged home following a hospitalization for heart failure had a follow-up appointment scheduled, representing a missed opportunity to provide a recommended care transition intervention. In India in 2020, showed in his study that the anemia was the most common co-morbidity followed by diabetes mellitus and hypertension among HF patients with readmissions. In North- Eastern Tanzania in 2020 Abid M. Sadiq demonstrated that readmission was strongly associated with unemployment, absence of ACEI or ARBs, poor medication adherence, and pleural effusion. We found that simple laboratory test such as hemoglobin and sodium level might act as a predictor for rehospitalization in heart failure.

Anemia is highly prevalent in Heart Failure (HF) patients. Its prevalence among patients with HF estimates can range from 30% to 70% in some studies depending on the cutoff value used to define its presence and, on the population, considered. Lower hemoglobin level at admission is likely related to hemodilution secondary to volume overload, malabsorption due to bowel congestion and the high number of associated chronic diseases. There are several proposed mechanisms by which anemia may worsen HF outcomes, including LV remodeling, increased inflammatory cytokines, activation of neurohumoral systems, and adverse cardiorenal effects. In our study we demonstrated 70.7% of patients at higher risk for readmission odds ratio 1.723 S.S.L. has demonstrated similar effect.

Hyponatremia is a common electrolyte disturbance encountered in 15%-25% of the patients with AHF before and during decongestion and has been associated with impaired diuretic response and adverse events and in our study it is associated with higher rate of readmission odds ratio 2.045, similar finding was seen.

40.2% of the all admitted patient with heart failure had CKD, and we demonstrated that elevated creatinine and urea increasing the risk of readmission and odds ratio was 1.357 and 2.371 respectively demonstrated in 2020 similar findings. One explanation for worse outcomes in CKD could be lack of effective therapeutic options available or possible underutilization of current therapies due to apprehensions surrounding side effects. Moreover, various factors like volume and pressure overload, anemia and uremic toxins may be contributing to disease process in these patients

We only enrolled patients with LVEF<40%. Similar to previous studies, patients with higher EF in our study had lesser risk of rehospitalization at six months of discharge. Our finding of higher risk of readmission in patients with poor left ventricular function EF <30%, odds ratio 1.909. This suggests that patients with very low EF (<30%) should be optimized with better pharmacological treatment, frequent follow-up visits with tailored changes and measures to improve EF like the use of CRT. Presence of moderate to severe mitral regurgitation increased the risk of readmission odds ratio 2.872.

Study limitations

Research is often problematic and challenging in a resourcepoor environment. Our study sample size was relatively small. It was an observational study and thus prone to observer bias. These results could not be generalized to the whole spectrum of HF patients because of potential selection bias. All predictors were not included in the study, so further evaluation with larger study is warranted.


Patients with HFrEF continue to have significantly higher rehospitalization rate. HFrEF rehospitalization within 6 months follow-up occurred in 33% of patients in our cohort. Predictors of worse outcomes after an initial HF hospitalization which increased the rates of readmission were noted in elderly men, living at home independently, medication self-paying group of patients, hypertension, low ejection fraction, mitral regurgitation, URTI, UTI, hyponatremia, hyperuricemia and elevated HbA1c. These predictors can be used to identify patients who require aggressive tailored therapy and follow-up.

It is suggested that all patients hospitalized for HF should be risk-stratified as high or low risk of rehospitalization according to the presence of the number of predictors. Those patients who are at the highest risk for rehospitalization should be given the highest intensity of multi-disciplinary support, education, followup, therapy, and access to resources, while those at lower risk should be followed less frequently in HF clinics. This approach with instructed programs is crucial when resources are sparse and finances limited and may lead to reduced readmission of HF patients.

Further research is needed to identify the targets to reduce the rehospitalization rate and to improve survival among patients with HFrEF.


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