Treatment outcome of acute coronary syndrome and associated factors among patients admitted to public hospitals in Harari Regional State, Eastern Ethiopia: a retrospective cross-sectional study

Abstract

Background

Acute coronary syndrome (ACS) is the leading cause of morbidity and mortality among individuals with cardiovascular disease, accounting for half of all global cardiovascular-related deaths. No prior research has examined ACS treatment outcomes and associated factors in the study area. This study aimed to evaluate the risk factors and treatment outcome of ACS patients admitted to public hospitals in Harari Regional State, Eastern Ethiopia.

Methods

A retrospective hospital-based cross-sectional study was conducted among 308 ACS patients. Patient records from admissions between 1 November 2018 and 31 October 2023 were reviewed, with data collected between 10 January and 10 February 2024 using a structured checklist adapted from previous research. Statistical analysis was performed using SPSS V.25.0, with bivariable and multivariable logistic regression identifying significant associations at a p value <0.05.

Results

The mean patient age was 56.4±16 years, with males comprising 77.3% of participants. Half (51.6%) resided in rural areas, and only 16.2% presented within 12 hours of symptom onset. Overall, 81 patients (26.3%) experienced a poor treatment outcome for ACS, including 39 (12.7%) in-hospital deaths, 24 (7.8%) referrals to higher-level facilities and 18 (5.8%) who left against medical advice. Factors significantly associated with poor outcome included hospital presentation more than 72 hours after symptom onset (AOR 2.734 (95% CI 1.006 to 7.435)), left ventricular ejection fraction (LVEF) <30% (AOR 5.32 (95% CI 1.09 to 26.06)) and the presence of ischaemic features on echocardiography (AOR 3.35 (95% CI 1.44 to 7.80)).

Conclusion

Poor treatment outcome was independently predicted by the presence of ischaemia features on the echocardiography, LVEF (<30%) and hospital presentation 72 hours after the onset of symptoms. To improve ACS treatment outcomes, it is crucial to promote early hospital presentation through community education, standardise diagnostic procedures, integrate rapid ECG and biomarker analysis, and enhance prehospital emergency medical services.

STRENGTHS AND LIMITATIONS OF THIS STUDY.

• This study used medical records from two public hospitals, providing a regional overview.

• The retrospective design limited access to some important variables.

• Key factors such as body mass index, dyslipidaemia, alcohol use and khat chewing were not recorded.

• The cross-sectional nature prevents causal inference.

• The study population was limited to public hospitals in one region, which may affect generalisability.

Introduction

Acute coronary syndrome (ACS) is responsible for 50% of all cardiovascular disease (CVD)-related deaths globally, making it a leading cause of morbidity and mortality in individuals with CVD.¹ A prospective study was carried out in Africa and the Sub-Saharan countries, involving 111 (5.1%) individuals with ACS, of which 44% had non-ST-segment elevation myocardial infarction (NSTEMI)/unstable angina (UA) and 56% had ST-segment elevation myocardial infarction (STEMI). According to the study, the in-hospital death rate in that context was between 6% and 10%.² A study conducted on 425 patients from Sub-Saharan Africa found that 13.5% were diagnosed with ACS, of whom 71.5% had STEMI and 28.5% had NSTEMI, with a reported in-hospital mortality rate of 10%.³

A retrospective cohort study conducted at the University of Gondar Comprehensive and Specialised Hospital from January 2018 to February 2023 reported an in-hospital mortality rate of 16.6% among patients with ACS who received optimal medical therapy and 30.8% among those who did not.⁴ Similarly, a retrospective analysis at Ayder Comprehensive Specialised Hospital found an in-hospital mortality rate ranging from 14.4% to 29.4% among ACS patients.⁵ In addition, a systematic review and meta-analysis of ACS outcomes across Ethiopia reported an overall in-hospital mortality rate of 14.8%, highlighting the persistent high burden of ACS in the country.⁶

Inadequate or delayed use of evidence-based medical and non-medical therapies may contribute to poor treatment outcomes in ACS patients, potentially resulting in complications and mortality.⁷ The increasing use of proven therapies such as aspirin, beta blockers (BBs) and thrombolytics may be contributing to a reduction in in-hospital case fatalities.⁸ Considering the high cost of routine care and treatment for ACS, prevention should be a top priority in resource-limited settings like Ethiopia, rather than focusing solely on treatment after the condition occurs. Additionally, no previous research has been conducted in the study area on treatment outcomes and associated factors among individuals with ACS.

In Ethiopia, access to prompt, high-quality cardiac care is still scarce, especially in rural and regional hospitals. Echocardiography, cardiac biomarkers and continuous ECG monitoring are examples of diagnostic tools that are frequently unavailable or underused. The diagnosis and treatment of patients are further delayed by the lack of emergency response systems and qualified cardiologists. These restrictions on healthcare highlight the need to assess the magnitude and associated factors of poor treatment outcomes among ACS patients in resource-limited settings, like Harari Regional State. Therefore, this study aimed to assess the treatment outcomes and associated factors among ACS patients admitted to public hospitals in Harari Regional State, Ethiopia.

Methods and materials

Study settings and duration

The study was conducted in public hospitals of Harari Regional State, Eastern Ethiopia, from 10 January to 10 February 2024. Harar is the capital city of Harari Regional State, which is 526 km east of Addis Ababa, the capital city of Ethiopia. According to the Central Statistical Agency population projection by 2022, the region had an estimated population of 276 431.⁹ The region has two public hospitals, Hiwot Fana Comprehensive Specialized Hospital (HFCSH) and Jugal General Hospital (JGH).

Hiwot Fana Hospital has been managed by Haramaya University since 2011, earning its current name, HFCSH. As a referral hospital in Eastern Ethiopia, it comprises both major and minor departments, including the Internal Medicine department, which has 22 beds in its medical wards divided into male and female wings. Additionally, it features a Medical Intensive Care Unit (MICU) with six beds and a Neurology Unit with eight beds, where ACS patients are admitted and managed in the MICU. JGH, the only regional general hospital, was built in 1910 and includes four major departments. Its Internal Medicine department has 18 beds in male and female medical wards, along with a six-bed intensive care unit serving the community.

Study design and populations

A retrospective hospital-based cross-sectional study was conducted to assess the treatment outcome of ACS and associated factors among patients admitted to public hospitals of Harari Regional State in Eastern Ethiopia. ACS patients admitted to selected public hospitals in Harari Region State during the study period were the study population. Those patients who had incomplete medical records, lacked cardiac biomarkers, and ECGs were excluded from this study.

Sample size determination

The sample size was determined using a single population proportion formula of n= (z (α/2))² p (1−p)/d² by considering the following assumption: p=a 24.5% the proportion of in-hospital mortality of ACS from a study done in Ayder Hospital,⁵ z (α/2) = a 95% CI, d=a 5% margin of error and a 10% non-response rate, yielding a sample size of 308.

Sampling techniques and procedures

Initially, the two public hospitals (HFCSH and JGH) were included in this study. Although the patients were admitted over a 5-year period (1 November 2018 to 31 October 2023), data were retrospectively collected from their medical records between 10 January and 10 February 2024. Since all data were abstracted at a single point in time from existing charts, the study design is considered cross-sectional. This approach allows the evaluation of treatment outcomes and associated factors using available hospital records without following patients prospectively.

The card numbers of ACS patients were accessed via the patient registration book of the Health Management Information System. Following that, patient charts were obtained and gathered from the office of records and documentation. Those with confirmed ACS based on standard diagnosis were obtained.

Based on the total number of ACS patients admitted to each public hospital over a 5-year period, the sample size was then distributed proportionately among them. Specifically, HFCSH, with 310 ACS patients admitted within 5 years, had a sample size of 212; JGH, with 140 ACS patients, had a sample size of 96. Finally, the study participants were selected from each hospital using a simple random sampling technique.

Data collection tools and methods

Data were collected using a structured and pretested checklist, adapted from previously published literature with necessary modifications to fit the local context.^{4 5 10 11} The checklist was divided into three sections: sociodemographic factors (four items), clinical factors (23 items) and treatment-related and outcome factors of ACS (four items). The adapted checklist is provided in online supplemental appendix A. Because the checklist items were primarily factual and extracted from medical records, internal consistency testing (eg, Cronbach’s alpha) was not applicable. However, the checklist underwent expert review and pretesting to ensure clarity, relevance and content validity. Trained data collectors, consisting of four medical interns, gathered the data through a medical record review, while two internal medicine residents supervised the process.

Variables

The outcome variable for this study was the treatment outcomes of ACS. Independent variables include sociodemographic factors (age, sex and residency), clinical factors (smoking, obesity, hypertension (HTN), diabetes, dyslipidaemia, previous myocardial infarction (MI), family history of coronary artery disease, clinical and laboratory factors (time of presentation, systolic blood pressure (SBP) (mm Hg), diastolic blood pressure (mm Hg), Killip class, type of ACS (STEMI, NSTEMI, UA), echo left ventricular ejection fraction (LVEF), lipids panels, serum creatinine (Cr), treatment related factors (medications given, antiplatelet (Aspirin (ASA), Clopidogrel), statins, BBs, angiotensin converting enzyme inhibitor (ACEI), anticoagulants, Calcium Channel Blocker (CCB) and nitrates).

It should be noted that some potentially important factors, including body mass index (BMI), obesity, alcohol use and khat chewing, were not included as independent variables due to inconsistent or missing documentation in medical records.

Operational definitions

Treatment outcome

Patients with ACS are classified as having either a good treatment outcome (when they were discharged with fewer complaints and physical findings than when they were admitted) or a poor treatment outcome (in-hospital mortality, referral to a higher-level facility due to critical conditions or leaving against medical advice in deteriorating condition).¹²

ST-segment elevation myocardial infarction

If there is a new ST elevation at the J point in two contiguous leads with cut points of ≥1 mm in all leads except leads V2–V3, or if the ECG shows a new left bundle branch block with biochemical evidence of myocyte necrosis.¹³

Non-ST-segment elevation myocardial infarction

Necrosis was indicated by at least one increased cardiac biochemical marker on admission or subsequent ECGs, but no new STEMI was seen.¹³

Cardiac biomarkers

High-sensitivity cardiac troponin I (hs-cTnI) was the biomarker used for the diagnosis of ACS in both study hospitals. Measurements were performed using standard hospital laboratory assays following the manufacturer’s protocols. MI was diagnosed when hs-cTnI levels exceeded the 99th percentile upper reference limit, in the presence of clinical evidence of ischaemia and/or ischaemic ECG changes.¹⁴

Previous MI

The patient has had at least one documented previous MI before admission.¹⁴

Hypertension

A history of antihypertensive treatment, a prior diagnosis of HTN or a blood pressure reading of greater than 140/90 mm Hg on two measurements.¹⁵

Dyslipidaemia or hyperlipidaemia

Defined as one or more of the following, based on fasting serum lipid measurements: total cholesterol ≥5.17 mmol/L (≥200 mg/dL), low-density lipoprotein (LDL) cholesterol ≥2.59 mmol/L (≥100 mg/dL), high-density lipoprotein (HDL) cholesterol <1.04 mmol/L (<40 mg/dL) for males or <1.29 mmol/L (<50 mg/dL) for females, or serum triglyceride level ≥1.70 mmol/L (≥150 mg/dL), or a history of hyperlipidaemia or the use of lipid-lowering medication.¹⁶ Serum lipids were measured using enzymatic colourimetric assays with automated analysers.

Diabetes mellitus (DM)

If a patient has been diagnosed with any kind of DM, has previously used oral hypoglycaemic medications or insulin, has fasting blood sugar (FBS) >126 mg/dL, and has a recorded random blood sugar (RBS) >200 mg/dL.¹⁷

Killip classes

According to the degree of their heart failure, patients are categorised into four groups using a clinical condition. Class I: no heart failure symptoms, class II: signs of mild to moderate heart failure (jugular vein distension, S3 (a third heart sound indicating increased ventricular filling pressure), lung rales less than halfway up the posterior lung fields), class III: overt pulmonary oedema and class IV: cardiogenic shock.¹⁴

Data quality control

To ensure high data quality, a structured checklist was adapted for the study. All supervisors and data collectors received 2 days of training from the principal investigator and epidemiologist on how to recruit participants, collect data, use the tool for data collection, and approach to medical records and informed consent. A pretest was conducted on 5% of the sample size at Bisidimo General Hospital to identify potential issues. Feedback from this pretest was used to refine the tool. During data collection, supervisors and the principal investigator reviewed the data daily for accuracy and completeness. Additionally, double data entry was implemented to cross-check information and resolve any discrepancies between clerks.

Data processing and analysis

The collected data were coded and entered into Epi Data V.3.1, then exported to SPSS V.25 for cleaning and analysis. Missing values, outliers and inconsistencies were checked by running frequency distributions and cross-tabulations. Incomplete records with missing key variables such as diagnosis, treatment or outcome information were excluded from the final analysis. Minor inconsistencies (eg, discrepancies between admission and discharge notes) were verified and corrected by cross-checking with the original medical records. Implausible values detected in continuous variables were reviewed for possible data entry errors and corrected when necessary.

All variables were initially considered for bivariable logistic regression analysis. Covariates with a p value less than 0.25 were further analysed through multivariable logistic regression. A relatively higher threshold was used at this stage to avoid excluding variables that might become significant after controlling for potential confounders in the multivariable model. Before running the multivariable model, multicollinearity among independent variables was assessed using the variance inflation factor (VIF) and tolerance values. No variable exceeded the acceptable threshold (VIF >10 or tolerance <0.1), indicating no significant multicollinearity. Both crude and adjusted ORs (AORs) with a 95% CI were calculated to assess the strength of the association between the outcome and independent variables. Variables with a p value of less than 0.05 in the multivariable analysis were considered to have a significant association. The model’s fitness was assessed using the Hosmer-Lemeshow goodness-of-fit test, and the model was deemed adequately fitted with a p value greater than 0.05.

Patient and public involvement

Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.

Results

Sociodemographic characteristics

A total of 308 ACS patients admitted to public hospitals of Harari Regional State, Eastern Ethiopia, were involved in the study, with a 100% response rate. Of these patients, 212 (68.8%) were from HFSUH and 96 (31.2%) were from JGH.

The mean age±SD of the patients was 56.4±16 years (95% CI 54.6 to 58.2). The majority of participants (77.3%) were males. Half (51.6%) of the participants were rural residents (table 1).

Table 1. Sociodemographic characteristics of acute coronary syndrome patients admitted to public hospitals of Harari Regional State, Eastern Ethiopia, 2024.

Variables	Category	Frequency	Percentage
Age in years	<55	141	45.8
	55–64	64	20.8
	≥65	103	33.4
Sex	Male	238	77.3
	Female	70	22.7
Residency	Rural	159	51.6
	Urban	149	48.4
Name of hospital patient admitted	HFCSH	212	68.8
	JGH	96	31.2

HFCSH, Hiwot Fana Comprehensive Specialized Hospital; JGH, Jugal General Hospital.

Clinical characteristics and medical factors

The most common initial clinical presentations among patients were chest pain (190, 61.7%), shortness of breath (149, 48.4%) and easy fatigability (111, 36.0%) (figure 1).

Figure 1. Characteristic symptoms at presentation of acute coronary syndrome patients admitted to public hospitals of Harari Regional State, Eastern Ethiopia, 2024. *Cough, body swelling.

Only 50 (16.2%) patients presented within 12 hours of symptom onset, while 111 (36.0%) presented after 72 hours, with the mean time from symptom onset to presentation being 66.4±46.2 hours (95% CI 61.2 to 71.5).

Less than 10% (20, 6.5%) had a SBP of less than 90 mm Hg, with a mean of 123.4±26.3 mm Hg (95% CI 120.5 to 126.4). Regarding Killip classes, 34.4% of patients were classified as Killip class I. Of the total, 198 (64.3%) were diagnosed with STEMI, 89 (28.9%) with NSTEMI and 21 (6.8%) with UA. In terms of risk factors, 133 (43.2%) patients had HTN, 112 (36.4%) had DM and 208 (67.5%) had a history of smoking (table 2).

Table 2. Clinical and medical factors of acute coronary syndrome patients admitted to public hospitals of Harari Regional State, Eastern Ethiopia, 2024.

Variables	Category	Frequency	Percentage
Time of presentation in hours	<12	50	16.2
	13–72	147	47.8
	>72	111	36
Killip class	I	106	34.4
	II	96	31.2
	III	85	27.6
	IV	21	6.8
Systolic blood pressure	<90	20	6.5
	90–119	119	38.6
	120–139	79	25.6
	140–159	56	18.2
	≥160	34	11
Diastolic blood pressure	<60	31	10.1
	60–69	60	19.5
	70–79	77	25
	80–89	66	21.4
	≥90	74	24
Risk factors	Hypertension	133	43.2
	Diabetes mellitus	112	36.4
	Family history of coronary artery disease	11	3.6
	Previous history of myocardial infarction	9	2.9
	Exertional angina	41	13.3
	Heart failure	39	12.7
	Previous history of stroke or TIA (Transient Ischemic Attack)	13	4.2
	History of smoking	208	67.5

Laboratory and imaging findings

Serum lipid profile was measured in varying proportions of patients: total cholesterol, HDL cholesterol and triglycerides were measured in 79.9%, 68.8% and 77.9% of the patients, respectively. Due to substantial missing data (81.5%), LDL) cholesterol measurements were excluded from the descriptive summary and subsequent analyses to avoid potential bias. Serum Cr levels were normal in 230 (77.7%) participants, while 66 (22.3%) had elevated levels.

Most patients (299, 97.1%) underwent echocardiography, with LVEF documented. The mean LVEF was 45.7±11.4 (95% CI 44.5 to 47.1). The distribution of LVEF was fairly consistent across the ranges, with the highest proportions observed in the 40–49% and 50–59% groups, accounting for 27.3% and 28.9% of patients, respectively. A total of 167 (54.2%) patients showed ischaemic characteristics, including wall motion abnormalities, on echocardiography (table 3).

Table 3. Laboratory and imaging factors of acute coronary syndrome patients admitted to public hospitals of Harari Regional State, Eastern Ethiopia, 2024.

Variables	Category	Frequency	Percentage
Total cholesterol	<200	196	63.6
	≥200	50	16.2
	Not measured	62	20.1
High-density lipoprotein cholesterol	<40	61	19.8
	≥40	151	49
	Not measured	96	31.2
Triglyceride	<150	113	36.7
	≥150	127	41.2
	Not measured	68	22.1
Serum creatinine	Normal	230	77.7
	Elevated	66	22.3
	Not measured	12	3.9
Left ventricular ejection fraction (%)	<30	28	9.1
	30–39	59	19.2
	40–49	84	27.3
	50–59	89	28.9
	≥60	39	12.7
Presence of ischaemic features	Yes	167	54.2
	No	141	45.8

Values are presented as frequencies and percentages. Low-density lipoprotein cholesterol data were excluded from the descriptive analysis due to the high proportion of missing values (81.5%), which may have introduced bias.

Treatment-related factors and in-hospital complications

At presentation and during hospitalisation, 294 (95.5%) patients received a loading dose of aspirin, while 292 (94.8%) continued with a maintenance dose. Similarly, clopidogrel was administered to 289 (93.8%) patients as a loading dose and to 281 (91.2%) as a maintenance dose. Other commonly prescribed medications included heparin (92.9%), BB (83.8%) and ACEIs or angiotensin receptor blockers (ARBs) (84.7%).

The average hospital stay was 8.34±3.67 days (95% CI 7.9 to 8.8), range: 2–22 days. Common in-hospital complications included congestive heart failure, myocardial reinfarction, major arrhythmias, cardiogenic shock and left ventricular thrombosis (table 4).

Table 4. Treatment-related factors and in-hospital complications of acute coronary syndrome patients admitted to public hospitals of Harari Regional State, Eastern Ethiopia, 2024.

Variables	Category		Frequency	Percentage
Treatments given	Aspirin	Loading	294	95.5
		Maintenance	292	94.8
	Clopidogrel	Loading	289	93.8
		Maintenance	281	91.2
	Heparin		286	92.9
	Beta blocker		258	83.8
	Morphine		265	86.0
	Nitrate		55	17.9
	Statin		284	92.2
	ACEI/ARB		261	84.7
	Calcium Channel Blocker (CCB)		32	10.4
	Other CVD medication*		23	7.5
In-hospital complications	Congestive heart failure		96	31.2
	Myocardial reinfarction		21	6.8
	Major arrhythmias		38	12.3
	Stroke		10	3.2
	Major bleeding		7	2.3
	Cardiogenic shock		31	10.1
	Left ventricular thrombosis		26	8.4
Duration of hospitalisation (days)	≤7		133	43.2
	8–14		163	52.9
	≥15		12	3.9

^*Warfarin, digoxin and amiodarone.

ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker; CVD, cardiovascular disease.

Overall, 81 (26.3%) patients experienced a poor treatment outcome for ACS, including 39 (12.66%) in-hospital deaths, 24 (7.8%) referrals to higher-level facilities and 18 (5.84%) patients who left against medical advice.

Factors associated with the treatment outcome of ACS

In the bivariate logistic regression model, six variables were found to be significant with a p value of less than 0.25. These included time to presentation after symptom onset, Killip class, LVEF, presence of ischaemic features on echocardiography, use of BBs and ACEI/ARB, and SBP. However, in the multivariable logistic analysis, three variables were identified as independent determinants of treatment outcome in ACS patients at a p value of less than 0.05: presentation more than 72 hours after symptom onset, LVEF below 30% and the presence of ischaemic features on echocardiography.

In this study, patients who presented to the hospital 72 hours after the onset of symptoms were a statistically significant determinant of poor treatment outcome. The odds of having a poor treatment outcome were 2.7 times higher among patients who presented to the hospital 72 hours after the onset of symptoms than <12 hours of presentation (AOR 2.734 (1.006 to 7.435)). On the other hand, patients who had a LVEF of <30% were five times more likely to develop a poor treatment outcome than normal LVEF (AOR 5.32 (1.09 to 26.06)). The odds of having a poor treatment outcome were also three times higher among individuals who had ischaemic features on echocardiography than their counterparts (AOR 3.35 (1.44 to 7.80)) (table 5).

Table 5. Factors associated with treatment outcome of acute coronary syndrome patients admitted to public hospitals of Harari Regional State, Eastern Ethiopia, 2024.

Variables	Category	Treatment outcome		COR (95% Cl)	AOR (95% Cl)	P value
		Poor n (%)	Good n (%)
Time to presentation in hours	<12	7 (14.0)	43 (86.0)	1	1	1
	13–72	25 (17.0)	122 (83.0)	1.26 (0.51 to 3.12)	0.99 (0.36 to 2.77)	0.992
	>72	49 (44.1)	62 (55.9)	4.86 (2.01 to 11.73)	2.73 (1.01 to 7.44)	0.049*
Left ventricular ejection fraction	<30	18 (64.3)	10 (35.7)	21.60 (5.28 to 88.37)	5.32 (1.09 to 26.06)	0.039*
	30–39	25 (42.4)	34 (57.6)	8.82 (2.44 to 31.93)	1.30 (0.27 to 6.32)	0.748
	40–49	15 (17.9)	69 (82.1)	2.61 (0.71 to 9.61)	0.71 (0.16 to 3.25)	0.658
	50–59	15 (16.9)	74 (83.1)	2.43 (0.66 to 8.94)	1.33 (0.33 to 5.43)	0.688
	≥60	3 (7.7)	36 (92.3)	1	1	1
Presence of ischaemic features	Yes	60 (35.9)	107 (64.1)	3.20 (1.83 to 5.62)	3.35 (1.44 to 7.80)	0.005*
	No	21 (14.9)	120 (85.1)	1	1	1

1: reference.

^*Significantly associated.

AOR, adjusted OR; COR, crude OR.

The multivariable logistic regression model demonstrated good discrimination for poor treatment outcomes in ACS patients, with an area under the Receiver Operating Characteristic (ROC) curve Area Under the Curve (AUC) of 0.787 (95% CI 0.727 to 0.847), p<0.001. This indicates that the model reliably differentiates patients with poor versus good outcomes. At the optimal cut-off value of predicted probability=0.29, the model achieved a sensitivity of 67.1% and a specificity of 75.8%, further supporting its good discriminatory performance.

Discussion

This study investigated the treatment outcomes of ACS and associated factors for patients who were admitted to public hospitals of Harari Regional State, Eastern Ethiopia. This study demonstrated that the presence of ischaemic features on echocardiography, LVEF <30% and presentation in >72 hours after the onset of symptoms were significant predictors of treatment outcome for ACS. The logistic regression model showed good discriminatory ability (AUC 0.787 (95% CI 0.727 to 0.847), p<0.001). At the optimal predicted probability cut-off of 0.29, the model achieved a sensitivity of 67.1% and specificity of 75.8%, indicating reliable differentiation between patients with poor versus good outcomes.

The patients’ age distribution was 56.41±16.029 years, which is consistent with research done in South Africa (54.6±10.9),¹⁸ India (55.86±13.4)¹⁹ and Ethiopian tertiary care hospitals (mean age of 56 years).²⁰ However, it is lower than that of the Global Registry of Acute Coronary Events (66.3±10 years), which is a multinational registry of ACS.²¹ This could be associated with the socioeconomic status of the country. The majority of our patients are men (238 or 77.3%), which is consistent with the research mentioned above and may be because men are more likely to have certain risk factors.

According to this study, patients significantly delay seeking medical attention (a mean time to hospital presentation was 66.28±46.25 hours). Merely 16.2% of patients attended within 12 hours of the onset of symptoms, whereas 36.0% of patients arrived after 72 hours, and no patient showed up within 2 hours. This is longer than the Kenyan study, which found that 78% of patients came within 12 hours of the onset of symptoms,²² and in the Senegal study, the average time delay before medical care was 14.5 hours.²³ According to Ethiopian research, the average presentation times were 79.3 hours (St Paul Hospital),¹² 95.85±145.68 hours (Ayder Hospital)⁵ and 91.7 hours (Tikur Anbessa Specialized Hospital (TASH)).²⁴ Less awareness of the warning signs of ACS and the advantages of getting to the local hospital as soon as possible may be the cause of the notable delay in seeking medical attention in this study.

In this study, poor treatment outcome was experienced by 81 (26.3%) of the study participants, which is more than one-fourth of the total. In-hospital mortality for all admitted ACS patients was 12.66%; 5.84% left against medical advice, and 7.8% were referred to other institutions. Among these poor outcomes, mortality accounted for nearly half of the cases, followed by referrals and patients leaving against medical advice, highlighting how limitations in advanced cardiac care capacity, as well as financial or social barriers to continuous treatment, contribute substantially to the overall poor outcomes observed. In a research study done 3 years ago at St Peter Hospital in Ethiopia, out of 471 ACS patients, 38.22% had poor treatment outcomes.²⁵ This study result is lower than the 27.4% in-hospital mortality rate at TASH Hospital.²⁴ Nonetheless, it is significantly greater than a St Paul Hospital study (3.80%).¹² The high proportion of patients who were referred to other hospitals would indicate that the study hospitals were unable to adequately manage a significant number of patients. Patient deterioration, low awareness and financial difficulties may be the causes of patients departing against medical advice.

This study’s high rate of poor outcomes might be a reflection of the study area’s insufficient healthcare capacity. Inadequate critical care facilities, a lack of cardiac specialists and limited diagnostic resources hinder timely treatment. Delayed hospital presentation is further worsened by weak prehospital services and low public awareness of ACS symptoms.

The findings of this study showed that patients who presented after 72 hours from the onset of symptoms had 2.73 times higher odds of poor treatment outcomes of ACS than <12 hours of presentation. This finding is comparable with a study conducted in Pakistan²⁶ and Ethiopia.²⁴ This could be because delayed presentation is frequently linked to more myocardial damage, which can result in problems including arrhythmias and heart failure. A delayed presentation is associated with worse outcomes, such as increased death and heart failure rates, indicating that delaying treatment may make the situation worse.²⁷

This study revealed that the odds of having poor treatment outcomes of ACS were 5.32 times higher among patients with LVEF <30% than among those having normal LVEF. This is similar to a study conducted in Turkey¹⁶ and Ayder Hospital, Ethiopia.⁵ LVEF, which measures the left ventricle’s functional ability to pump or the proportion of blood pumped with each contraction, may indicate that circulatory failure is the result of left ventricular failure.²⁸ In ACS, a lower LVEF is linked to worse treatment results because it signifies compromised heart function, raising the risk of heart failure, death and other problems.²⁹

The poor treatment outcome for patients with ACS has been linked to the existence of wall motion abnormalities and other ischaemia signs on echocardiography. Also, the results of this study showed that patients who had ischaemic features on echocardiography (wall motion abnormalities) were 3.35 times more likely to have a poor treatment outcome of ACS patients than their counterparts. This may be explained by that abnormal wall motion in those patients may be linked to poor systemic perfusion, decreased pumping activity and complications following MI. Ischaemic changes, such as wall motion abnormalities, are indicative of underlying myocardial ischaemia and can signal a higher risk of adverse outcomes.³⁰

Although the predictors identified in this study (delayed hospital presentation, reduced left ventricular function and ischaemic features on echocardiography) are well recognised globally, this study adds context-specific evidence from a low-resource setting in Eastern Ethiopia. The value of this research lies not in discovering new predictors but in quantifying and validating their impact within the Ethiopian healthcare system, where access to timely cardiac diagnostics and interventions remains limited. By documenting the magnitude of delays, treatment outcomes and health system gaps, this study provides an important regional baseline for designing context-appropriate interventions, strengthening emergency cardiac care pathways and guiding public health policy. It also establishes a foundation for future prospective and interventional studies aimed at improving outcomes for ACS patients in similar resource-limited settings.

Limitations

Due to the retrospective nature of our study, we were only able to access secondary sources, such as medical records, and were unable to locate all pertinent information. Key patient variables, including BMI, obesity, dyslipidaemia, alcohol consumption or khat chewing, were often unrecorded; their absence limited our ability to assess their influence on treatment outcomes, and these were therefore not included in the analysis. Additionally, laboratory data such as LDL cholesterol were largely missing, resulting in their exclusion to minimise bias. External factors, including hospital capacity, healthcare resources and regional variations in care, may have affected patient management and outcomes. Finally, the findings may have limited generalisability as the study was conducted in selected hospitals within a specific region of Ethiopia.

Conclusions

Overall, this study found that among patients admitted with an ACS diagnosis, the presence of ischaemia characteristics on echocardiogram, LVEF (<30%) and hospital presentation 72 hours after symptom start were independent predictors of poor treatment outcome. Improving prehospital emergency medical services, raising public awareness of ACS symptoms, incorporating immediate ECG and biomarker analysis, standardising procedures for quick diagnosis and triage in emergency departments, and encouraging timely hospital presentation through community education are all essential. Additionally, tailored treatment strategies should be developed for high-risk patients with low LVEF and ischaemic features, including aggressive medical management and timely revascularisation. Comprehensive postdischarge cardiac rehabilitation programmes, including patient education and follow-up, are also important.

While this study confirms globally established predictors of poor ACS outcomes, its strength lies in generating locally relevant evidence for Eastern Ethiopia. It highlights how well-known risk factors translate into high mortality and referral rates in under-resourced hospitals. Therefore, beyond clinical implications, the findings offer practical guidance for policymakers and healthcare administrators seeking to improve emergency cardiac care systems in similar low-income regions. This study also serves as a baseline for future longitudinal and interventional research aimed at evaluating strategies that can reduce delays, enhance cardiac function preservation and improve survival outcomes in ACS patients.

Data availability statement

Data are available upon reasonable request.