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American Journal of  Medical 
Science and Innovation (AJMSI) 

Survival Rate Analysis of  Heart Failure Patients at Arbaminch 
General Hospital, Southern Ethiopia, 2022

Sebisibe Kusse Kumaso1*, Markos Abiso Erango2, Belay Belete Anjullo2

Volume 2 Issue 1, Year 2023
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Article Information ABSTRACT

Received: January 07, 2023

Accepted: February 02, 2023

Published: February 08, 2023

Chronic heart failure happens when the heart’s muscle is incapable of  circulate sufficient 
blood to satisfy the requirements of  the body for both oxygen and blood. In other sense, 
the heart is overwhelmed by its load. The goal of  this study is to pinpoint the risk variables 
accountable for the variability in congestive heart failure patients’ survival times from 
January 2017 to December 2021 at ArbaMinch General Hospital in South Ethiopia. Utilizing 
cox proportional hazard models, the data was examined. A retrospective investigation 
was conducted. Data was gathered from the cards of  199 congestive heart failure patients 
who were being followed up on using a straightforward random sampling approach. We 
discovered that 21.6 percent of  the 199 patients with congestive heart failure investigated 
died, while the remaining 78.4% were censoring. Patients’ mean baseline left ventricular 
ejection fraction was 43.18 percent (with a standard deviation of  13.928 percent), implying 
that each pulse expels 43.18 percent of  the blood in the left ventricle. Patients with heart 
failure who tested positive for tuberculosis had a substantially increased chance of  death. 
Left ventricular ejection fraction, tuberculosis, diabetes militias, etiology of  heart failure, 
type of  congestive heart failure, smoking status, and chorionic kidney disease were originate 
to be significant risk factors for death in patients with congestive heart failure. As a result, 
physicians are encouraged to pay greater attention to heart failure patients.

Keywords
Congestive Heart Failure, 
Cox Proportional Model, 
Survival Rate

1 Health Monitoring and Evaluation department, Alle Special Woreda, Health Office Ethiopia
2 Department of  Statistics, Arba Minch University, Arba Minch, Ethiopia
* Corresponding author’s e-mail: sebisibek@gmail.com

INTRODUCTION
Heart failure (HF) is a chronic, neurological disorder which 
occurs when the heart muscle is incapable of  circulates 
enough circulation to fulfill the body’s oxygenated blood 
necessities. In simply, the heart has difficulty keeping up 
with its responsibility. It’s one of  the most prevalent reasons 
people end up in the hospital. Long in-patient stays, tall in 
as well as post-discharge death and illness and whether or 
not the left ventricular ejection fraction is reduced are all 
major determinants to consider”(Carson  et al., 2015).
It’s a major health issue all around the world, with high 
rates of  re-hospitalization and death. After a year, the 
global re-hospitalization rate in patients with HF is over 
50%(2). As a result, 33 million people globally, or 26.4 
percent of  the adult population, suffer from heart failure. 
Adults in the industrialized world make up 65.73 percent 
of  the population, while those in developing countries 
make up 34.27 percent. It is expected that by the end of  
the year, there would have been a 60% growth since 2000 
(Barbey   et al., 2010).
Heart failure has long been recognized as a major 
contribution to the burden of  cardiovascular disease 
in Sub-Saharan Africa. Early twentieth-century case 
reports and case series offered critical information about 
heart failure in the region, identifying viral, dietary, and 
idiopathic causes as the most common (Ibrahim  et al., 
1991). The range of  causes of  heart failure has expanded 
as a result of  increased urbanization, changes in lifestyle 
habits, and population aging, resulting in a considerable 
burden of  both communicable and non-communicable 
etiologies. The wide range of  etiologies that exist, as well 

as the healthcare environment characterized by inadequate 
resources, weak national healthcare systems, and a scarcity 
of  national level data on illness patterns, distinguish Sub-
Saharan Africa (Bloomfield   et al., 2013).
It can, however, manifest itself  in the form of  pulmonary 
edema or even cardiogenic shock within 24 hours. 
Dickstein claims that Heart failure was formerly thought 
to be caused by the heart’s inability to pump enough blood 
into the circulation due to ventricular a systolic dysfunction 
(LVEF 40% to 50%). (HF  with depressed ejection fraction 
[HFDEF]). Patients with no diminished left ventricular 
ejection fraction (LVEF) can develop HF if  higher filling 
pressures are required to achieve a normal end-diastolic 
ventricular volume (HFPEF) (Shah et al., 2015).
This disorder is more frequent in women, the elderly, 
and persons with long-standing high blood pressure 
(HBP), and it has a similar prognosis as HFDEF. Right 
and left heart failure are conditions that are characterized 
by systemic or pulmonary congestion, resulting in jugular 
venous gurgitation and pulmonary edema, respectively. 
Many papers have been written addressing various 
elements of  the general population’s burden of  heart 
failure. Cardiac societies, most notably the heart failure 
association arm of  the European Society of  Cardiology 
(ESC) and the American Heart Association, are addressing 
issues associated with this wide-ranging they need to 
publish consensus statements regarding this important 
topic (Pazos et al., 2011). However, it is the last stage of  all 
cardiac illnesses and could represent a significant source of  
morbidity and mortality (Davis et al., 2000). 
Over the years, cardiologists and cardiac associations 

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have presented their own definitions of  heart failure. 
The European Society of  Cardiology (ESC) task force 
published guidelines on the diagnosis of  heart failure, 
which specify the presence of  symptoms and objective 
evidence of  cardiac dysfunction, as well as symptom 
reversibility with appropriate treatment. Heart failure 
was one of  the major causes of  mortality in Ethiopia in 
2013, according to health and health-related data and the 
American Heart Association, approximately a respiratory 
rate of  more than 24 breaths per minute can identify 50% 
of  patients at risk (Organization et al., 2015).
According to the American Heart Association, an 
increase in Respiration Rate might be an indication of  
developing pulmonary edema, or fluid in the lungs, 
which is a common and deadly symptom of  congestive 
heart failure (CHF) (Baddour   et al., 2015).Patients must 
estimate their health and survival time by assessing the 
burden of  congestive heart failure. The investigator’s 
motivation for exploring this topic stems from concerns 
about the survival time of  individuals with congestive 
heart failure. The majority of  research focuses exclusively 
on longitudinal data. However, it is critical to evaluate the 
survival time of  individuals with congestive heart failure 
(Hickey et al., 2018).
Heart failure (HF) is the fastest-growing cardiovascular 
illness in the world, putting enormous demand on 
healthcare systems worldwide (Ziaeian et al., 2016). 
Congestive heart failure has risen to the top of  the list 
of  leading causes of  mortality among individuals with a 
worse quality of  life and a shorter lifespan. In the United 
States alone, 960,000 new cases of  congestive heart 
failure (CHF) were discovered in 2017, and this figure 
is predicted to climb year after year as the population 
ages. The global prevalence is expected to grow by 8 
million persons by 2030(Heidenreich et al., 2013). Low- 
and middle-income nations have a higher rate of  heart 
failure-related mortality than high-income ones. The 
Ministry of  Health states (Seid et al., 2019). According 
to health-related data (2014-2015), heart failure was one 
of  Ethiopia’s major causes of  death in 2013(Kitzman et 
al., 2016).
It is, however, more than just a significant public health 
concern; it will have a massive economic impact when 
a large portion of  the productive age group population 
becomes chronically ill and remains at home, quits their 
jobs, and dies, leaving their families in poverty.
The risk factors for congestive heart failure have grown 
considerably as a result of  a lack of  understanding 
regarding the risk factors and management of  heart 
failure. Previous studies found predictors of  congestive 
heart failure without taking survival time into account 
(Beck et al., 2016).  Furthermore, the study focused on 
survival time and overlooked significant confounders.
To cover the holes, this study examined the risk factors 
for the survival time to death of  congestive heart failure 
patients using conventional cox proportional modeling. 
In general, the motivations for this work address the 
following important research questions:

1. What variables influence the survival time of  congestive 
heart failure patients after they begin treatment?
2. What is the median survival time for patients with 
congestive heart failure at ArbaMinch General Hospital?
The crucial goal of  this study was to recognize risk 
variables associated with survival time to death of  
congestive heart failure patients at Arba Minch General 
Hospital in Southern Ethiopia in 2021.

Significance of  the Study
By evaluating patient survival time, the findings of  this 
research will reveal data regarding risk variables for the 
death of  heart failure patients. To perform statistical 
analysis, uncover potential factors linked to the survival 
time of  patients with congestive heart failure, and develop 
a better approach for addressing heart failure difficulties 
faced by patients.
In order to design and promote the good health and long-
term well-being of  congestive heart failure patients, the 
results of  this research are anticipated to provide necessary 
suggestions for relevant stakeholders, governmental 
organizations at various levels, and non-governmental 
organizations that work in the field of  congestive heart 
failure. This will be done by determining the key factors 
encompassing the clients under follow-up.

METHODS AND MATERIALS
Study Area and period
Arba Minch is a town in southern Ethiopia, 505 
kilometers south of  Addis Abeba, located at a height of  
1285 meters above sea level inside the Gamo Zone of  
the Southern Nations, Nationalities, and Peoples Region. 
Because it is the most important town in the region, it 
acts as the Gamo Zone’s capital. Arba Minch and Zuria 
Woreda surround it.
This study was lead at Arba Minch General Hospitals from 
January 2017 to December 2021, and this hospital assists 
as a referral hospital for people who came from numerous 
surrounding areas, as well as providing healthcare to their 
districts and exercise for students from various health 
institutions, including ArbaMinch University.

Study Design 
Because of  occurrences of  exposure had already occurred 
once during the follow-up time in the past on the 
evaluation of  the patient’s cards, information sheets, and 
registration books, a retrospective cohort research design 
was used. Patients were tracked on pulse and respiratory 
rate every three months at Arba Minch General Hospital 
from January 2017 to December 2021, and those aged 18 
or older were eligible.

Source Population and Target Population
The population was derived from totally health registers 
of  patients diagnosed with congestive heart failure in 
Arba Minch General Hospital who attended follow-
up, and the study’s target population includes all 
congestive heart failure patients below continuation at 

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Arba Minch General Hospital Ethiopia from January 
2017 to December 2021, who had at least three pulse 
and respiratory rate measurements after the first report 
of  congestive heart failure. According to the study’s 
insertion measures, all congestive heart failure patients at 
Arba Minch General Hospital who received diagnosis and 
treatment within a predetermined timeframe, and whose 
pulses and respiratory rates were measured at least three 
times, were comprised. Patients, whose medical registers 
were missing information, couldn’t be located, or who 
had a previous judgment of  heart failure and registered 
during the data collection period was excluded.

Sampling Technique and Sample Size Determination
The most fundamental probability sampling technique is 
simple random sampling, in which each independent unit 
of  the population has an equal opportunity (possibility) 
of  being taken into the sample, which is the scientific 
technique of  selecting a representative of  the target 
population to provide the required estimation. As a 
consequence, the sample technique was the standard 
random sampling approach. Using the simple random 
sampling method, we recruited 199 patients from a total 
of  424 congestive heart failure patients under follow-up 
(Cochran et al., 2007).  
Choosing a sample size is one of  the first steps in 
creating a sample survey. The sample size decision is 
crucial since a large sample suggests more precision, 
but a small sample restricts the precision of  estimate of  
demographic characteristics or the usability of  the results. 
As a result, determining the appropriate sample size is 
preferred. Concerns include the research’s objective, 
its compatibility with the available resources, such as 
cost, labor, time, and materials, and the margin of  error 
(Cochran et al., 2007). 
 
Method of  Data Collection
To meet the study’s aims, data were collected from a 
secondary source of  congestive heart failure patients 
under follow-up at Arba Minch general hospital between 
January 2017 and December 2021. The data was taken 
after the patient’s registration diagram and postcards, as 
well as epidemiological, laboratory, and clinical evidence 
from the patients who were being followed up on. 
Following data extraction, the data was entered, edited, 
coded, and organized before being analyzed with R 
software version 4.2.

Study Variables 
Response Variables
The response variables addressed in this study were 
survival outcome variables, and the survival outcome 
variable was the time to death of  the patient under 
follow-up in Arba Minch General Hospital.

Explanatory Variables
The  explanatory variables considered in this study 
where patients age (in year), weight(in kilogram), body 

temperature, left ventricular ejection fraction in percent, 
patient gender, place of  residence, smoking status of  
patients  ,diabetes status of  patients  (present , absent 
), tuberculosis status of  patients  (positive, negative), 
chronic kidney status of  patients  (present ,absent), 
alcohol intake (yes, no), pneumonia status of  patients   
(present , absent),etiology of  heart failure (VHD, HHD, 
IHD, Other),and type of  congestive heart failure  patient 
(Left Ventricular, Right Ventricular, Biventricular) (see 
table 1).

Table 1: Congestive heart failure patient data from 
January 2017 to December 2020 at ArbaMinch 
General Hospital in South Ethiopia was analyzed using 
categorical variable coding.
No Variables Description Categories and 

codes
1 Gender of  CHF 

patients
Female (0)
Male (1)

2 Residence of  CHF 
patients

Rural (0)
Urban (1)

3 Presence of  DM on 
CHF patients

Absent (0)
Present (1)

4 Presence of  TB on 
CHF patients

Negative (0)
Positive (1)

5 Presence of  Smoking 
on CHF patients

No (0)
Yes (1)

6 Presence of  pneumonia 
on CHF patients

Absent (0)
Present (1)

7 Alcohol intake in CHF 
patients

No (0)
Yes (1)

8 Presence of   CKD on 
CHF patients

Absent (0)
Present (1)

9 Types of  CHF  in 
patients

LV (0)
BV (1)
RV (2)

10 Etiology of  CHF in 
patients

VHD (0)
HTN (1)
IHD  (2)
Other (3)

Operational Definition
Heart failure (HF): is also known as congestive heart 
failure, which is a condition that develops when your 
heart does not pump enough blood for your body’s needs.
Time to event data: Time from the start of  the treatment 
to the death of  CHF patients

Data Analysis
In R software version 4.2, the cox proportional model 
for time to event data was utilized to evaluate data from 
congestive heart failure patients.

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Survival Analysis
Time to event data is often the subject of  survival 
analysis. It includes procedures for positive-valued 
random variables such as time to death, time to onset (or 
relapse) of  a disease, length of  stay in a hospital, strike 
duration, and so on. We require a clear-cut time origin or 
a time scale (e.g. actual time (days, weeks, months, years) 
and a characterization of  the event of  interest in order to 
build a survival time random variable.
Survival time random variables are always non-negative, 
that is, if  we denote the survival time by, T and then T 
≥ 0  can be discrete or continuous (defined on (0, ∞)). 
We require statistical approaches that employ data from 
all subjects, whether we monitor their survival times or 
only time until censoring. The probability distribution of  
a survival random variable can be described in a variety 
of  ways. To model survival data, non-parametric, semi-
parametric, and parametric models are available. The 
Cox proportional hazard model is a popular choice for 
modeling the time-to-event data.

Kaplan-Meier Survival Function Estimation
The Kaplan Meier estimator is a standard non-parametric 
survival function estimator that is used to calculate 
survival probabilities. It considers any point in time as 
a succession of  steps defined by observed survival and 
censored times, incorporating information from all 
available observations, both censored and uncensored 

(Cochran et al., 2007).  The estimator is just the sample 
proportion of  observations with event times greater than 
when there is no censoring. When censored times are 
added, the technique becomes a little more difficult, but 
still manageable.

Where, (di)=the number of  CHF patients who experience 
the event (death) at time (ti) 
(ni) = the number of  patients who have not yet experienced 
the event (death) or the total number of  individuals at risk 
before time(ti).

RESULTS AND DISCUSSION
Descriptive Analysis
The current study seeks to determine risk variables that 
are related with survival time to death in congestive heart 
failure patients at ArbaMinch General Hospital. A Cox 
proportional model was supposed to predict survival time. 
The findings of  the models are all evaluated as meaningful 
in different ways. The most recent comparative R program 
version 4.2 was used to examine the data.
Women made up 50.3% of  the 199 congestive heart 
failure patients who underwent therapy, while males made 
up the remaining 49.7%. According to our findings, 21.6 
percent of  the patients died, with the remaining 78.4 
percent censored.

Table 1: Congestive heart failure patient data from January 2017 to December 2020 at ArbaMinch General Hospital 
in South Ethiopia was analyzed using categorical variable coding.
Variables Frequency (%) Survival Status

Event (%) Censored (%)
Gender Female 100 (50.3) 24 (12.1) 76(38.2)

Male 99 (49.7) 19 (9.5) 80(40.2)
Residence Rural 117(58.8) 21 (10.5) 96(48.3)

Urban 82(41.2) 22 (11.1) 60(30.1)
Diabetes  status Absent 87(43.7) 9(4.5) 78(39.2)

Present 112(56.3) 34(17.1) 78(39.2)
Tuberculosis status Negative 81(40.7) 7(3.5) 74(37.2)

Positive 118(59.3) 36(18.1) 82(41.2)
Smoking status Non smoker 125(62.8 ) 24(12.1) 101(50.7)

Smoker 74(37.2) 19(9.6) 55(27.6)
Pneumonia status Absent 112(56.3) 16(8.1) 96(48.2)

Present 87(43.7) 27(13.6) 60(30.1)
Alcohol intake status No 103(51.8) 21(10.6) 82(41.2)

Yes 96(48.2) 22(11.1) 74(37.1)
Chronic Kidney disease Absent 128(64.3) 21(10.6) 107(53.8)

Present 71(35.7) 22(11.1) 49(24.6)
Types of  congestive heart 
failure

Right ventricular 55(27.6) 5(2.5) 50(25.1)
Bi ventricular 64(32.2) 12(6.1) 52(26.1)
Left ventricular 80(40.2) 26(13.1) 54(27.1)

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Etiology of  congestive heart 
failure

Valvular Heart 
Disease

51(25.6) 16(8.0) 35(17.6)

Hypertensive heart 
disease

43(21.6) 7(3.5) 36(18.1)

Ischemic heart disease 55(27.6) 12(6.0) 43(21.6)
Other 50(25.1) 8(4.0) 42(21.1)

Status Censored 156 (78.4)
Event (death) 43 (21.6)

The large percentage of  congestive heart failure patients 
(58.8%) resided in rural regions. As a consequence, 
ischemic heart disease was responsible for 55 (27.1%), 
valvular heart disease was responsible for 51 (25.6%), 
hypertensive heart disease was responsible for 43 (21.6%), 
and various etiologies were responsible for the remaining 
50 (25.1%).
Concerning diabetes as comorbidities, 56.3% of  heart 
failure patients had diabetes, 35.7% had chronic kidney 
disease, 37.2 percent were smokers, 43.7 percent had 
pneumonia, and 59.3% had tuberculosis. Likewise, of  
the total 199 congestive heart failure therapy participants, 
about 24 (12.1%) female responders died as a result of  
treatment, and the remainder was censored. On the other 
side, approximately 19 (9.5%) of  the male responders died, 
while the remainder were censored. Based on the patient’s 
location, 22 (11.1%) and 21(10.5%) of  the 82 urban and 
117 rural patients had an incident occur, respectively.
The overall average starting point age, weight, and 
left ventricular ejection fraction of  patients were 48.6 

years (with a standard deviation of  17.385 years), 54.21 
kilograms (with a standard deviation of  10.93 kilograms), 
and 43.18 percent (with a standard deviation of  13.93 
percent), trying to imply that an average of  43.18 percent 
of  blood in the left ventricle is pushed out with each 
heartbeat (See Table 2). 

Survival Time Analysis
Based on a cohort involving 199 congestive heart failure 
patients, the median life duration was 24 months, with an 
average and standard deviation of  24.8 and 7.3 months, 
respectively.

Kaplan Meier Estimations
The comparison of  survival functions provides a good 
indication of  the groups’ event experiences, and the 
graphs showed the pattern of  one’s survival function 
lying above another, indicating that the cohort defined by 
the upper curve had a greater chance of  surviving than 
the members to participate by the lower curve.

Figure 1: Kaplan-Meier Survival Plots of  predictors with congestive heart failure patients data from January 2017 to 
December 2020, in ArbaMinch General Hospital, South Ethiopia

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Figure 1 shows the estimate of  survival function graphs 
for TB and diabetes millets. Then, for TB patients, it 
shows that those with tuberculosis negative tests had a 
higher chance of  surviving than those with tuberculosis 
positive results. That is, patients who do not have TB 
have a better probability of  survival than those who have 
it (See figure 1). 
Diabetes mellitus is more common in congestive heart 
failure patients, indicating that people without diabetes 
mellitus have a better probability of  survival than patients 
with diabetes mellitus in congestive heart failure. That is, 
TB positive patients and patients with diabetes militias had 
a higher risk of  dying from medication than tuberculosis 
negative patients and patients without diabetes militias, 

respectively (See figure 1)

Log-Rank Test
The log rank test was performed to determine the 
significance of  the observed difference among covariate 
categories using chi-square. The log rank test results 
discovered that there were significant differences in 
the  survival chances of  patients in different categories 
of  Diabetes mellitus (χ2=5.6,p<0.02), Smoking 
(χ2=4.6,p<0.03), Chronickidneydisease (χ2=5.4,p<0.01), 
Tuberculosis χ2=14.3,p<0.0002) and Type of  congestive 
heart failure patients  (χ2=6.3,p<0.004) The remaining 
variables are similarly described in the same manner. (See 
Table 3)

Table 3: Cox Proportional Model Analysis of  Predictors with congestive heart failure patient’s data from January 
2017 to December 2020, in Arba Minch General Hospital, South Ethiopia
Covariates Chi-square Df p-value
Gender 0.7 1 0.4
Place of   residence 2.3 1 0.10
Diabetes status 5.6 1 0.02*
Tuberculosis status 14.3 1 2e-04*
Smoking status 4.6 1 0.03*
Pneumonia status 0.9 1 0.4
Chronic kidney status 5.4 1 0.01*
Type of  congestive heart failure 6.3 2 0.004*
Etiology of  heart failure 2.6 3 0.5
Alcohol intake status 0.6 1 0.4
* Indicates significance of  covariate at 5% level of  significance

DISCUSSION
The Cox proportional hazards model was employed in 
this study for a survival outcome, and variables such 
as diabetes, smoking status, chronic kidney disease, left 
ventricular ejection fraction, type of  congestive heart 
failure, etiology of  heart failure, and tuberculosis are 
significant variables that influence congestive heart failure 
patients.
Congestive Heart Failure Patient who has diabetes 
militias’ disease was positive significant effect on a risk 
death of  congestive heart failure patients. This study is 
in similar with previous studies done by (Barlera et al., 
2013).  That the presence diabetes militias has a positive 

significance effect with quality of  Heart Failure, But other 
study done by (Ahmad et al., 2017). Shows that Diabetes 
Militias has no significance effect with Heart Failure. 
The estimated risk of  death  for a heart failure patient 
with the presence of  chronic kidney disease patients was 
(HR = 4.313, 95% CI: 1.9438  , 9.5724, P = < 0.000325), 
This indicates that the risk of  death  for chronic kidney 
disease patients were 4.313 times higher as compared 
to non- chronic kidney disease patients keeping other 
variables constant. This finding is in line with the previous 
findings (Zeru et al., 2018) which showed chronic kidney 
disease was positive and significantly associated with the 
prevalence of  heart failure (See Table 4)

Table 4: Log-Rank Test Statistics Analysis of  Categorical Predictors in Patients with Congestive Heart Failure from 
January 2017 to December 2020 in ArbaMinch General Hospital South Ethiopia
Parameters  Estimates (SE) HR(95%CI) P-value 
LVEF -0.056 (0.015) 0.945 (0.9178  0.9736) 0.000184 *
Temperature -0.299(0.231) 0.741  (0.4709  1.1665 ) 0.195450
weight -0.026 (0.015) 0.973 (0.9439  1.0040) 0.087931
Age -0.005 (0.0104) 0.994 (0.9747  1.0156) 0.666826
Etiology of  HF (ref=VHD)
Ischemic Heart disease 0.982 (0.554) 2.672 (0.9014  7.9230) 0.076264
Hypertensive heart  disease 0.801 (0.580) 2.229 (0.7153  6.9510) 0.166865
Other 1.336 (0.626) 3.806 (1.1140   13.0079) 0.032991 *

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Type CHF (ref=Left Ventricular)
Right ventricular -0.281 (0.619) 0.754 (0.2242    2.5385) 0.648975
Biventricular 1.169 (0.544) 3.221 (1.1091    9.3570) 0.031528 *
Gender (ref=Female)
Male -0.051 (0.398) 0.950 (0.4347    2.0760) 0.897689
Residence (ref=Urban)
Rural 0.046 (0.396) 1.047 (0.4816    2.2779) 0.907037
Alcohol intake(ref=No)
Yes 0.320 (0.357) 1.377 (0.6831    2.7788) 0.370668
Smoking status (ref=No)
Yes 0.899 (0.397) 2.459 (1.1288    5.3570) 0.023522 *
Chronic kidney status (ref=No)
Yes 1.461 (0.4067) 4.313 (1.9438    9.5724) 0.000325 *
Pneumonia status (ref=No)
Yes 0.067 (0.571) 1.069 (0.3491    3.2788) 0.906010
Diabetes millets status (ref=No)
Yes 1.427 (0.445) 4.168 (1.7394    9.9904) 0.001369 *
Tuberculosis (ref=Negative)
Positive 1.905 (0.447) 6.722 (2.7978   16.1526) 2.04e-05 *
*Indicates significant covariates at 5% level of  significance, ref= reference category

CONCLUSION
The purpose of  this study was to recognize the factors 
associated to survival time of  congestive heart failure 
patients admitted to treatment at ArbaMinch General 
Hospital by using cox proportional hazard model. The 
log-rank tests demonstrated that the survival experience 
of  distinct groups of  congestive heart failure patients 
was statistically significant in different categories of  
diabetes mellitus, chronic kidney disease, TB, and type of  
congestive heart failure. However, in the survival analysis, 
the risk factors of  mortality were chronic kidney disease, 
left ventricular ejection fraction, etiology of  heart failure, 
types of  congestive heart failure, smoking status, diabetes 
mellitus, and tuberculosis. When examining overall model 
performance, we decided that the cox proportional model 
was best suited for survival data.

RECOMMENDATIONS
Due to a lack of  understanding about risk factors for 
heart failure, clients were pushed to have a high risk of  
heart failure-related mortality and morbidity. As a result, 
relevant stakeholders should pay closer attention to and 
intervene on known risk factors like chronic kidney 
disease, tuberculosis, diabetes, left ventricular ejection 
fraction, etiology of  heart failure, type of  congestive 
heart failure, and smoking status, which should be 
acknowledged in the community.
Health practitioners are advised to pay special attention to 
congestive heart failure patients who have chronic kidney 
disease, tuberculosis, or diabetes and are at a higher risk of  
mortality in the district; consequently, special care should 
be paid to patients with this co-morbidity. Furthermore, 
based on the results of  the study, individuals who are 

candidates for congestive heart failure should take action 
on early diagnosis and preventive strategies, as well as be 
aware of  the risk factors for congestive heart failure. 
Additionally, the study’s fault is the insufficient use of  
accessible secondary data. As a result, critical information 
such as obesity, family status, and educational background 
were missing from the patients’ records; however, these 
factors were not considered in our study. As a result, 
we advised researchers to incorporate such variables in 
future studies.

Abbreviations 
CHF: Congestive Heart Failure; HF: Heart Failure; 
HHD: Hypertensive Heart Disease; HR: Heart Rate; 
IHD: Ischemic Heart Disease; LVEF Left Ventricular 
Ejection Fraction; PH: Proportional Hazard; VHD: 
Valvular Heart Disease.

Ethical Consideration
The study was carried out with the approval of  Arba 
Minch University’s Statistics Department. In this 
regard, the formal letter of  cooperation referred with 
stat/534/2013 was addressed to the Arba Minch General 
Hospital’s ethical approval committee. The letter was 
then authorized by the ethics committee, who granted 
authority to gather data from recorded patients’ cards. 
There were no ties with specific patients for the sake of  
secrecy, and all data had no personal identity. As a result, 
the Arba Minch General Hospital ethics committee has 
waived the patient’s informed consent.

Consent for Publication 
Not applicable.

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Availability of  Data and Materials
The dataset supporting conclusions of  this article is 
available by contacting the authors.

Competing Interests
The authors declare that they have no competing interests.

Funding
Not Applicable

Authors’ Contributions
Sebisibe Kusse Kumaso planned the study, analyzed the 
data, and wrote the publication; Markos Abiso Erango 
and Belay Belete Anjullo supervised data analysis and 
provided critical feedback on the article. The final 
manuscript was reviewed and approved by all writers.

Acknowledgements
We are grateful to Arba Minch University for providing 
the required facilities. We also like to thank the employees 
at Arba Minch General Hospital for their help in gathering 
all of  the data. 

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