Trends and Predictors of In-Hospital Mortality among Heart Failure Patients in a Cameroonian Tertiary Care Center, 2021-2024
—Mortality Predictors in Acute Heart Failure in Cameroon ()
1. Introduction
1.1. Background
Cardiovascular diseases have been reported as the second most significant cause of non-communicable disease (NCD) burden in Sub-Saharan Africa, contributing 22.9 million disability-adjusted life years (DALYs), or 15.1% of the total NCD burden [1]. Heart failure has emerged as a dominant form of cardiovascular disease in Africa [2].
HF is a clinical syndrome resulting from various underlying causes, rather than a distinct disease on its own. The reported incidence of heart failure (HF) in Europe and the U.S. ranges from 1 to 9 cases per 1,000 person-years, varying by population and diagnostic criteria [3]. In Latin America and the Caribbean, HF prevalence is estimated at 1% [4]. Limited population-based data exist for Sub-Saharan Africa, with recent studies from Cameroon and other SSA countries providing insights into HF outcomes but lacking detailed in-hospital mortality predictors specific to resource-constrained tertiary settings [5] [6]. In hospital prevalence studies, HF is reported to be responsible for 9.4% - 42.5% of all medical admissions and 25.6% - 30.0% of admissions into the cardiac units [7]. Overall mortality from heart failure (HF) is reported at 16.5%, with the highest rates observed in Africa (34%) and India (23%), moderate rates in Southeast Asia (15%), and the lowest in China (7%), South America (9%), and the Middle East (9%) [8].
In sub-Saharan Africa (SSA), HF leads to high death rates, with 29% to 58% of patients dying within a year and 8% to 26% dying during hospital stays [9]. A review in 2020 suggested that this high mortality is driven by several factors, including genetic traits in SSA populations that increase risks of high blood pressure and heart muscle thickening, as well as a higher prevalence of HF with reduced ejection fraction (HFrEF), a type associated with greater mortality [9]. Patients in SSA also face additional health challenges like heart valve issues, kidney disease, anemia, lung problems, infections, and severe HF symptoms at diagnosis [9]. Limited access to proven treatments, such as guideline-directed medical therapy (GDMT) and heart devices like Implantable Cardioverter-Defibrillators (ICDs), further worsens outcomes [5] [6] [9].
1.2. Study Rationale and Objectives
The advent of new treatment regimens like Sodium-Glucose Co-transporter-2 (SGLT2) inhibitors which offer a survival advantage [10], and adjustments in models of care for HF post Covid-19 [11] suggest that mortality rates from HF may differ significantly from older reports.
We therefore sought to determine the intra-hospital mortality rate from HF in Cameroon and explore the factors associated with it. This study, involving patients from a diverse population with restricted access to advanced cardiac care, would provide a critical lens on HF outcomes in resource-constrained environments and inform equitable healthcare strategies. This study aims to quantify in-hospital mortality rates and identify specific clinical and therapeutic predictors in a Cameroonian tertiary center, building on prior SSA registries to inform targeted interventions in resource-limited settings.
2. Methodology
2.1. Study Design, Period and Setting
This was a retrospective cohort study of patients admitted with heart failure (HF) from January 2021 to December 2024. The study was conducted at a Laquintinie Hospital Douala, a second level hospital in Cameroon serving a socioeconomically diverse population with limited access to advanced cardiac care. Data were extracted from medical records and patient charts in the cardiac unit.
2.2. Participants
Eligible participants were patients with a clinical diagnosis of HF admitted during the study period. Patients with incomplete data on outcome or not hospitalised for an acute episode of heart failure as primary diagnoses were excluded. Participants were followed from admission until death or discharge.
2.3. Sampling and Sample Size
The sample size was calculated to detect predictors of in-hospital mortality, assuming a 34% mortality rate from regional heart failure studies in sub-Saharan Africa [8]. To ensure model stability and prevent overfitting, we aimed for an events-per-variable (EPV) ratio of 10 based on recommendations for logistic regression, as outlined in statistical literature [12]. Targeting 10 predictors, a minimum of 100 mortality events was required, necessitating at least 340 patients (100/0.34).
2.4. Variables
The primary outcome was in-hospital mortality, defined as death during hospitalization. HF diagnosis was confirmed using ESC 2021 criteria, incorporating clinical symptoms, and/or echocardiography or biomarker data where available, to minimize misclassification [13]. Covariates assessed included age, sex, comorbidities (hypertension, kidney disease, anemia, via laboratory tests), and use of recommended medications including renin-angiotensin-aldosterone system inhibitors, beta-blockers, mineralocorticoid receptor antagonists, or sodium-glucose co-transporter-2 inhibitors.
2.5. Data Sources and Collection
Data were sourced from hospital medical records, echocardiography reports, and laboratory results. Trained personnel used standardized protocols to extract data, with 10% of records cross-checked for accuracy. Data were collected using a structured tool that included: 1) demographic information (such as age, sex, marital status, and place of residence); 2) clinical parameters (including ejection fraction categorized as HFrEF <40%, HFmrEF 40% - 49%, and HFpEF ≥50%, systolic blood pressure, and oxygen saturation); 3) laboratory results (such as haemoglobin levels and blood urea nitrogen [BUN]); 4) prescribed medications (including RAAS inhibitors) before admission, during admission and at discharge; and 5) outcomes (mortality and length of hospital stay). Patients with missing outcome data were excluded from analysis. Potential confounding by socioeconomic factors was addressed by adjusting for relevant covariates such as marital status in the analysis.
2.6. Bias
Missing key severity markers such as NYHA class, natriuretic peptides, and ejection fraction (EF) for 45% of patients introduces potential biases. NYHA class was not routinely documented due to inconsistent practices, risking misclassification bias and underestimating mortality risk (bias toward null). Natriuretic peptides were unavailable due to limited diagnostic resources, causing confounding bias by omitting a key prognostic factor, potentially over- or underestimating covariate effects. Missing EF data for 45% of patients, due to scarce echocardiography access, introduces selection bias, as tested patients may differ (e.g., wealthier or sicker). This may underestimate mortality risk in severe cases (e.g., undiagnosed HFrEF), biasing results toward the null. Mitigation included MICE imputation, covariate adjustment (e.g., age, comorbidities), and ROSE for class imbalance. Residual confounding persists, necessitating future studies with robust diagnostic data.
2.7. Statistical Methods
The study developed a logistic regression model to predict in-hospital mortality using a robust methodology. Normality of variables was assessed with the Shapiro-Wilk test, and descriptive statistics (medians, interquartile ranges, percentages) summarized baseline characteristics and mortality rates due to non-normal distributions. Univariable logistic regression identified factors associated with mortality (p < 0.250 and clinical relevance) for inclusion in the multivariable model. The dataset was pre-processed by renaming columns for clarity and ensuring appropriate data types. Missing data were handled using Multiple Imputation by Chained Equations (MICE), generating five datasets, with the first used for analysis; imputation assumptions were based on observed variable distributions, and sensitivity analyses confirmed robustness. To address moderate class imbalance (17% mortality), the Random Over-Sampling Examples (ROSE) technique was applied to balance discharge and death outcomes, as it improved model sensitivity for detecting mortality events in preliminary analyses. Predictor selection involved univariate screening, multicollinearity assessment (removing variables with Pearson’s |r| > 0.7 or variance inflation factors [VIF] > 5; all included variables had VIF < 5), and stepwise regression (forward/backward selection via AIC). Clinically relevant interaction terms were cautiously included. Adjusted odds-ratio estimates and standard errors in multivariable model were pooled across all imputed datasets, Model performance was assessed via AUC, sensitivity, specificity, and Hosmer-Lemeshow test. Missing data imputation and sensitivity analyses were conducted to ensure robustness. Model performance was evaluated using AUC, Gini coefficient, accuracy, sensitivity, specificity, and the Hosmer-Lemeshow test (p > 0.05 indicating good calibration). Calibration was visualized using calibration plots, and model robustness was tested via bootstrap validation (1000 iterations). Analyses were conducted using SPSS v26 and R v2025.05.0 + 496.
2.8. Ethical Considerations
The study was approved by the Regional Ethics Committee for the Littoral under reference N 2024/CE/CRERSH-LITTORAL and informed consent was waived for retrospective data.
3. Results
3.1. Participants
Figure 1. Flowchart of participant recruitment and study inclusion.
This retrospective cohort study included 757 patients hospitalized for heart failure (HF) at Laquintinie Hospital in Douala, Cameroon, from 2021 to 2024 as shown in Figure 1. Eligible patients were identified from hospital records with a confirmed HF diagnosis, admitted between January 2021 and December 2024. No patients were excluded due to missing outcome data (in-hospital mortality). The cohort was distributed across admission years: 18.0% (n = 136) in 2021, 26.9% (n = 204) in 2022, 24.3% (n = 184) in 2023, and 30.8% (n = 233) in 2024.
3.2. Baseline Characteristics (Sociodemographic and Clinical)
The median age was 63 years (IQR: 51 - 74), with 51.3% female (n = 388) and 48.7% male (n = 369). Most patients resided in Douala (87.5%, n = 643), and 47.6% (n = 360) were married, while 21.5% (n = 163) were single, 23.0% (n = 174) widowed, and 6.6% (n = 50) unreported as shown in Table 1. Professionally, 32.8% (n = 248) were unemployed or retired. Prevalent comorbidities included hypertension (48.6%, n = 368), chronic heart failure (34.2%, n = 259), diabetes mellitus (16.9%, n = 128), and history of lung pathology (8.6%, n = 65) as shown in Table 2. Routine cardiovascular medications included ACE inhibitors (12.9%, n = 98), beta-blockers (11.9%, n = 90), and calcium channel blockers (16.5%, n = 125). At admission, median systolic blood pressure was 137 mmHg (IQR: 114 - 162), with 4.6% (n = 35) having hypotension (SBP <90 mmHg) as shown in Table 3. Diagnostic procedures included echocardiography (54.4%, n = 412) and ECG (46.5%, n = 352). Common HF aetiologies were hypertensive heart disease (28.5%, n = 216), dilated cardiomyopathy (12.9%, n = 98), and Cor pulmonale (9.1%, n = 69) as shown in Table 4. Median ejection fraction was 39% (IQR: 26 - 60) and HF subtypes were distributed as follows: HFpEF (36.9%, n = 152), HFmrEF (10.2%, n = 42), and HFrEF (52.9%, n = 218).
3.3. Outcomes
In-hospital mortality was 17.0% (n = 129), with rates of 11.8% (2021), 15.2% (2022), 19.0% (2023), and 20.2% (2024). Among the 628 patients discharged alive, 74.0% (n = 560) had authorized discharges, 6.9% (n = 52) were discharged against medical advice, and 2.1% (n = 16) were transferred. The median hospitalization duration was 8 days (IQR: 5 - 12) as shown in Table 5.
3.4. Univariate Analysis of Significant Predictors
Univariate logistic regression identified predictors significantly associated with in-hospital mortality (p < 0.05). Admission in 2023-2024 (vs. 2021-2022), cerebrovascular accident, chronic liver disease/hepatitis, hypotension (SBP <90 mmHg), elevated blood urea nitrogen (>60 mg/dL), Cor pulmonale, and high-output heart failure increased mortality risk. Conversely, leucocytosis was observed in 22.0% (n = 93/424) of patients (20.4% alive, n = 74/363; 30.6% deceased, n = 19/62), and anemia (Hb <12 mg/dL) in 60.0% (n = 273/455) (58.5% alive, n = 227/388; 68.7% deceased, n = 46/67). Hypertensive heart disease, and hypertensive crisis were linked to lower mortality. Dobutamine use increased mortality, while beta-blockers and RAAS inhibitors reduced it.
3.5. Multivariable Analysis
(a) (b)
Figure 2. Receiver operating characteristic curve and performance metrics of the stepwise model.
(a) (b)
Figure 3. Calibration plots for stepwise and ROSE-Adjusted models.
The logistic regression models were evaluated for predicting in-hospital mortality, with the stepwise model selected as the best due to its superior calibration (Hosmer-Lemeshow p = 0.811) and balanced performance metrics as shown in Figure 2 and Figure 3. The stepwise model achieved an AUC of 0.769, indicating good discriminatory ability, and a Gini coefficient of 0.538. Its accuracy was 0.680, with a sensitivity of 0.783 and specificity of 0.659, reflecting effective identification of both mortality and survival cases. The model outperformed the initial (AUC = 0.767, accuracy = 0.674), refined (AUC = 0.767, accuracy = 0.674), and optimized (AUC = 0.738, accuracy = 0.679) models, which either had lower calibration (optimized: p = 0.068) or similar but less optimal metrics. The stepwise model’s most potent predictors identified as statistically significant (p < 0.05), were: Blood urea Nitrogen (OR 1.01; 95% CI 1.01 - 1.02, p < 0.001); Systolic pressure on admission mmHg (OR 0.99; 95% CI 0.98 - 1.00, p = 0.009); Dobutamine use (OR 2.85; 95% CI 1.30 - 6.25, p = 0.009); Hypertensive heart disease (OR 0.40; 95% CI 0.21 - 0.77, p = 0.006)); Age in years (OR 1.01; 95% CI 1.00 - 1.02, p = 0.069), Year of admission 2023-2024 (OR 1.79; 95% CI 1.15 - 2.80, p = 0.010) were visualized in Figure 4. Other predictors, including age, calcium channel blockers, RAAS inhibitors/ARNi, beta blockers, and ischemic/dilated cardiomyopathy, were included but not significant.
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(HF Etiology 1: Hypertensive heart disease; HF Etiology 2: Ischemic/Dilated cardiomyopathy; Year of admission 2023-2024: Ref-2021-2022).
Figure 4. Forest plot of predictors of in-hospital mortality.
Table 1. Sociodemographic characteristics in patients hospitalised for HF at LHD from 2021-2024.
Sociodemographic variables |
N |
Variable distribution |
Alive n = 628 (%) |
Death n = 129(%) |
Crude OR (95% CI) |
p-value |
Year of admission |
|
|
|
|
|
|
2021 |
757 |
136 (18.0) |
120 (19.1) |
16 (12.4) |
Ref |
|
2022 |
757 |
204 (26.9) |
173 (27.5) |
31 (24.0) |
1.34 (0.70 - 2.57) |
0.370 |
2023 |
757 |
184 (24.3) |
149 (23.7) |
35 (27.1) |
1.76 (0.93 - 3.34) |
0.082 |
2024 |
757 |
233 (30.8) |
186 (29.6) |
47 (36.4) |
1.89 (1.03 - 3.49) |
0.041 |
Median age in years (IQR; Q1 - Q3) |
757 |
63.0 (51 - 74) |
63.0 (50.3 - 73) |
67 (56 - 75.5) |
1.01 (1.00 - 1.03) |
0.031 |
Sexe |
|
|
|
|
|
|
Male |
757 |
369 (48.7) |
312 (49.7) |
57 (44.2) |
Ref |
|
Female |
757 |
388 (51.3) |
316 (50.3) |
72 (55.8) |
1.25 (0.85 - 1.83) |
0.256 |
Age group |
|
|
|
|
|
|
Less than 18 years |
757 |
3 (0.4) |
3 (0.5) |
- |
- |
- |
18 - 24 years |
757 |
16 (2.1) |
14 (2.2) |
2 (1.6) |
0.61 (0.14 - 2.75) |
0.520 |
25 - 34 years |
757 |
40 (5.3) |
36 (5.7) |
4 (3.1) |
0.48 (0.17 - 1.38) |
0.170 |
35 - 44 years |
757 |
68 (9.0) |
58 (9.2) |
10 (7.8) |
0.74 (0.36 - 1.51) |
0.405 |
45 - 60 years |
757 |
202 (26.7) |
170 (27.1) |
32 (24.8) |
0.81 (0.52 - 1.26) |
0.347 |
More than 60 years |
757 |
428 (56.5) |
347 (55.3) |
81 (62.8) |
Ref |
- |
Profession |
|
|
|
|
|
|
Unemployed or retired |
757 |
248 (32.8) |
202 (32.2) |
46 (35.7) |
1.17 (0.79 - 1.74) |
0.442 |
Marital status |
|
|
|
|
|
|
Single |
757 |
163 (21.5) |
138 (22.0) |
25 (19.4) |
Ref |
|
Married |
757 |
360 (47.6) |
7.8) |
60 (46.5) |
1.10 (0.66 - 1.84) |
0.703 |
Cohabitation |
757 |
10 (1.3) |
10 (1.6) |
- |
- |
- |
Widow (er) |
757 |
174 (23.0) |
136 (21.7) |
38 (29.5) |
1.54 (0.88 - 2.69) |
0.128 |
Unreported |
757 |
50 (6.6) |
44 (7.0) |
6 (4.7) |
0.75 (0.29 - 1.95) |
0.559 |
Residence |
|
|
|
|
|
|
In Douala |
757 |
643 (87.5) |
533 (87.7) |
110 (86.6) |
0.91 (0.52 - 1.60) |
0.745 |
CORa = Crude Odds Ratio.
Table 2. Past medical history (Comorbidities and routine medications) of patients hospitalised for HF at LHD from 2021-2024.
Past medical history |
N |
Variable distribution |
Alive n = 628 (%) |
Death n = 129 (%) |
COR (95% CI)a |
p-value |
Comorbidities |
|
|
|
|
|
|
Hypertension |
757 |
368 (48.6) |
309 (49.2) |
59 (45.7) |
0.87 (0.59 - 1.27) |
0.473 |
Diabetes mellitus |
757 |
128 (16.9) |
109 (17.4) |
19 (14.7) |
0.82 (0.48 - 1.39) |
0.469 |
Chronic heart failure |
757 |
259 (34.2) |
211 (33.6) |
48 (37.2) |
1.17 (0.79 - 1.74) |
0.431 |
Coronary artery disease |
757 |
7 (0.9) |
6 (1.0) |
1 (0.8) |
0.81 (0.9 - 6.78) |
0.846 |
Valvular heart disease |
757 |
6 (0.8) |
3 (0.5) |
3 (2.3) |
4.96 (0.99 - 25.86) |
0.051 |
Renal disease |
757 |
25 (3.3) |
22 (3.5) |
3 (2.3) |
0.66 (0.19 - 2.22) |
0.498 |
Dyslipidaemia |
757 |
10 (1.3) |
7 (1.1) |
3 (2.3) |
2.11 (0.54 - 8.28) |
0.283 |
Human Immunodeficiency virus |
757 |
28 (3.7) |
24 (3.8) |
4 (3.1) |
0.81 (0.27 - 2.36) |
0.693 |
History of lung pathologyb |
757 |
65 (8.6) |
49 (7.8) |
16 (12.4) |
1.67 (0.92 - 3.05) |
0.092 |
Atrial fibrillation |
757 |
44 (5.8) |
40 (6.4) |
4 (3.1) |
0.47 (0.16 - 1.34) |
0.158 |
History of TB |
757 |
36 (4.6) |
26 (4.1) |
9 (7.0) |
1.74 (0.79 - 3.79) |
0.167 |
Cerebrovascular accident |
757 |
7 (0.9) |
3 (0.5) |
4 (3.1) |
6.67 (1.47 - 30.15) |
0.014 |
Chronic liver disease/Hepatitis |
757 |
13 (1.7) |
8 (1.3) |
5 (3.9) |
3.13 (1.01 - 9.71) |
0.049 |
Gout |
757 |
7 (0.9) |
7 (1.1) |
- |
- |
- |
Cancers/dysplasia |
757 |
11 (1.5) |
8 (1.3) |
3 (2.3) |
1.84 (0.48 - 7.05) |
0.370 |
Routine CV medications |
|
|
|
|
|
|
SGLT2 inhibitors |
757 |
9 (1.2) |
6 (1.0) |
3 (2.3) |
2.47 (0.61 - 10.0) |
0.206 |
Methyldopa |
757 |
1 (0.1) |
1 (0.2) |
- |
- |
- |
MRA |
757 |
21 (2.8) |
16 (2.5) |
5 (3.9) |
1.54 (0.55 - 4.29) |
0.406 |
Calcium channel blockers |
757 |
125 (16.5) |
105 (16.7) |
20 (15.5) |
0.91 (0.54 - 1.54) |
0.735 |
Indapamide |
757 |
28 (3.7) |
25 (4.0) |
3 (2.3) |
0.57 (0.17 - 1.93) |
0.370 |
Diuretics |
757 |
115 (15.2) |
90 (14.3) |
25 (19.4) |
1.44 (0.88 - 2.35) |
0.147 |
ACEI |
757 |
98 (12.9) |
77 (12.3) |
21 (16.3) |
1.39 (0.823 - 2.35) |
0.217 |
ARAII |
757 |
29 (3.8) |
25 (4.0) |
4 (3.1) |
0.77 (0.26 - 2.26) |
0.636 |
Beta blockers |
757 |
90 (11.9) |
74 (11.8) |
16 (12.4) |
1.06 (0.59 - 1.89) |
0.843 |
No routine CV medication |
757 |
496 (65.5) |
412 (65.6) |
84 (65.1) |
0.98 (0.66 - 1.46) |
0.915 |
ACEI = Angiotensin Converting Enzyme Inhibitors, ARAII = Angiotensin II receptor antagonists, COPD = Chronic Obstructive Pulmonary Disease, CV = Cardiovascular, CVA = Cerebrovascular accident, MRA = Mineralocorticoid Receptor Antagonist, SGLT2 = Sodium-glucose cotransporter 2, TB = Tuberculosis. bHistory of lung pathology (Asthma, Pulmonary Embolism, COPD, TB).
Table 3. Admission characteristics (Clinical, diagnostic and laboratory findings) of patients hospitalised for HF at LHD from 2021-2024.
Admission characteristics |
N |
Variable distribution |
Alive n = 628 (%) |
Death n = 129(%) |
COR (95% CI)a |
p-value |
Clinical characteristics |
|
|
|
|
|
|
Median SBP (IQR; Q1 - Q3) |
753 |
137 (114 - 162) |
139 (118 - 168) |
121 (103 - 143) |
0.98 (0.98 - 0.99) |
<0.001 |
Hypotension (SBP <90 mmHg) |
753 |
35 (4.6) |
24 (3.8) |
11 (8.6) |
2.07 (1.00 - 4.30) |
0.049 |
Median Heart Rate (IQR; Q1 - Q3) |
746 |
100 (85 - 114) |
100 (86 - 114) |
98 (82 - 113) |
0.99 (0.99 - 1.01) |
0.727 |
Tachycardia |
746 |
363 (48.0) |
307 (49.1) |
56 (43.8) |
0.8 (0.55 - 1.17) |
0.258 |
Median SaO2 % (IQR; Q1 - Q3) |
679 |
94 (89 - 97) |
95 (89 - 97) |
93 (87 - 97) |
0.99 (0.97 - 1.01) |
0.178 |
Hypoxia (SaO2 % <90%) |
679 |
189 (27.8) |
149 (26.6) |
40 (33.6) |
1.40 (0.91 - 2.13) |
0.123 |
Diagnostic investigations |
|
|
|
|
|
|
ECG done |
757 |
352 (46.5) |
306 (48.7) |
46 (35.7) |
- |
- |
Sinus rhythm |
352 |
202 (57.4) |
179 (58.5) |
23 (50.0) |
0.71 (0.38 - 1.32) |
0.279 |
Atrial fibrillation |
352 |
94 (26.7) |
78 (25.5) |
16 (34.8) |
1.56 (0.81 - 3.01) |
0.187 |
Flutter |
352 |
12 (3.4) |
9 (2.9) |
3 (6.5) |
2.30 (0.60 - 8.83) |
0.224 |
LVH |
352 |
90 (25.6) |
82 (26.8) |
8 (17.4) |
0.58 (0.26 - 1.28) |
0.177 |
Ischaemia |
352 |
41 (11.7) |
36 (11.8) |
5 (10.9) |
0.91 (0.34 - 2.45) |
0.848 |
Repolarisation abnormalities |
352 |
67 (19.1) |
60 (19.7) |
7 (15.2) |
0.54 (0.24 - 1.22) |
0.138 |
Echocardiography done |
757 |
412 (54.4) |
363 (57.8) |
49 (38.0) |
- |
- |
HFpEF |
412 |
152 (36.9) |
132 (36.4) |
20 (40.8) |
1.07 (0.58 - 1.99) |
0.826 |
HFmrEF |
412 |
42 (10.2) |
40 (11.0) |
2 (4.1) |
0.35 (0.81 - 1.55) |
0.168 |
HfrEF |
412 |
218 (52.9) |
191 (52.6) |
27 (55.1) |
Ref |
- |
Median EF (IQR; Q1 - Q3) |
412 |
39 (26 - 60) |
39 (26 - 60) |
39 (27 - 60) |
1.01 (0.99 - 1.02) |
0.574 |
Laboratory findings |
|
|
|
|
|
|
Leucocytosis |
424 |
93 (22.0) |
74 (20.4) |
19 (30.6) |
1.71 (0.94 - 3.11) |
0.077 |
Median Haemoglobin level (IQR; Q1 - Q3) |
455 |
11.3 (9.6 - 12.9) |
11.4 (9.8 - 13) |
10.8 (9.2 - 12.7) |
0.93 (0.85 - 1.03) |
0.155 |
Anaemia (Hb <12 mg/dL) |
455 |
273 (60.0) |
227 (58.5) |
46 (68.7) |
1.55 (0.89 - 2.70) |
0.119 |
Thrombocytosis |
410 |
10 (2.4) |
10 (2.9) |
- |
- |
- |
Median BUN in mg/dL (IQR; Q1 - Q3) |
401 |
49 (32.5 - 77.9) |
48 (32.0 - 73.0) |
73.5 (44.0 - 136.5) |
1.01 (1.01 - 1.02) |
<0.001 |
BUN >60 mg/dL |
401 |
149 (37.2) |
116 (34.0) |
33 (55.0) |
2.47 (1.36 - 4.13) |
0.002 |
Median Creatinine in mg/dL (IQR; Q1 - Q3) |
451 |
1.51 (1.1 - 2.7) |
1,5 (1.2 - 2.8) |
1.5 (1.1 - 2.7) |
1.01 (0.94 - 1.09) |
0.804 |
Creatinine >1.5 mg/dL |
451 |
226 (50.1) |
193 (50.1) |
33 (50.0) |
0.99 (0.59 - 1.68) |
0.984 |
Hypernatraemia >145 mEq/L |
307 |
11 (3.6) |
10 (3.7) |
1 (2.6) |
Ref |
|
Hyponatraemia <135 mEq/L |
307 |
130 (42.3) |
110 (40.9) |
20 (52.6) |
1.82 (0.22 - 15.00) |
0.579 |
Hyperkalaemia >5.5 mEq/L |
306 |
34 (11.1) |
29 (10.8) |
5 (13.5) |
1.06 (0.37 - 3.03) |
0.907b |
Hypokalaemia <3.5 mEq/L |
306 |
107 (35.0) |
98 (26.4) |
9 (24.3) |
0.57 (0.25 - 1.28) |
0.171b |
CORa = Crude Odds Ratio; bRef = Normal potassium level.
Table 4. Precipitating factors and aetiologies of HF in patients hospitalised at LHD from 2021-2024.
Aetiologies and precipitating factors |
N |
Variable distribution |
Alive n = 628 (%) |
Death n = 129 (%) |
COR (95% CI)a |
p-value |
Precipitating factors |
|
|
|
|
|
|
Rhythm disorder |
757 |
94 (12.4) |
81 (12.9) |
13 (10.1) |
0.76 (0.41 - 1.41) |
0.378 |
Conduction disorder |
757 |
5 (0.7) |
5 (0.8) |
- |
- |
- |
Angina |
757 |
9 (1.2) |
9 (1.4) |
- |
- |
- |
Myocardial infarction |
757 |
25 (3.3) |
23 (3.7) |
2 (1.6) |
0.41 (0.09 - 1.78) |
0.236 |
Pulmonary embolism/Cor pulmonale |
757 |
16 (2.1) |
10 (1.6) |
6 (4.7) |
3.02 (1.08 - 8.45) |
0.036 |
Hypertensive crisis |
757 |
85 (11.2) |
78 (12.4) |
7 (5.4) |
0.40 (0.18 - 0.90) |
0.026 |
Anemia |
757 |
53 (7.0) |
41 (6.5) |
12 (9.3) |
1.47 (0.75 - 2.88) |
0.263 |
Medication non-adherence |
757 |
84 (11.1) |
72 (11.5) |
12 (9.3) |
0.79 (0.42 - 1.51) |
0.477 |
Infection |
757 |
120 (15.9) |
94 (15.0) |
26 (20.2) |
1.43 (0.88 - 2.32) |
0.143 |
Pneumonia |
757 |
26 (3.4) |
22 (3.5) |
4 (3.1) |
0.88 (0.30 - 2.59) |
0.817 |
Lifestyle non-adherence |
757 |
22 (2.9) |
21 (3.3) |
1 (0.8) |
0.23 (0.03 - 1.69) |
0.148 |
Renal disease |
757 |
42 (5.5) |
33 (5.3) |
9 (7.0) |
1.35 (0.63 - 2.89) |
0.438 |
Abnormal glycemia |
757 |
15 (2.0) |
13 (2.1) |
2 (1.6) |
0.75 (0.17 - 3.34) |
0.701 |
No precipitating factor identified |
757 |
341 (45.0) |
283 (45.1) |
58 (45.0) |
0.99 (0.68 - 1.46) |
0.983 |
Aetiologies |
|
|
|
|
|
|
Ischaemic cardiomyopathy |
757 |
51 (6.7) |
45 (7.2) |
6 (4.7) |
0.63 (0.26 - 1.51) |
0.303 |
Arrhythmic cardiomyopathy |
757 |
74 (9.8) |
60 (9.6) |
14 (10.9) |
1.15 (0.62 - 2.13) |
0.651 |
Dilated cardiomyopathy |
757 |
98 (12.9) |
88 (14.0) |
10 (7.8) |
0.52 (0.26 - 1.02) |
0.058 |
Hypertensive heart disease |
757 |
216 (28.5) |
189 (30.1) |
27 (20.9) |
0.62 (0.39 - 0.97) |
0.037 |
Cor pulmonale |
757 |
69 (9.1) |
51 (8.1) |
18 (14.0) |
1.84 (1.03 - 3.26) |
0.038 |
Congenital heart disease |
757 |
2 (0.3) |
2 (0.3) |
- |
- |
- |
Non-identified causes |
757 |
112 (14.8) |
86 (13.7) |
26 (20.2) |
1.59 (0.98 - 2.59) |
0.061 |
Pericardial disease |
757 |
22 (2.9) |
16 (2.5) |
6 (4.7) |
1.87 (0.72 - 4.86) |
0.202 |
Peripartum cardiomyopathy |
757 |
12 (1.6) |
11 (1.8) |
1 (0.8) |
0.44 (0.06 - 3.42) |
0.432 |
Valvular heart disease |
757 |
56 (7.4) |
48 (7.6) |
8 (6.2) |
0.80 (0.37 - 1.73) |
0.570 |
Rheumatic heart disease |
757 |
2 (0.3) |
2 (0.3) |
- |
- |
- |
Toxic |
757 |
3 (0.4) |
2 (0.3) |
1 (0.8) |
2.45 (0.22 - 27.17) |
0.467 |
High output heart failure (anaemia, sepsis) |
757 |
41 (5.4) |
29 (4.6) |
12 (9.3) |
2.12 (1.05 - 4.27) |
0.036 |
CORa = Crude Odds Ratio.
Table 5. Management and outcomes of patients hospitalised for HF at LHD from 2021-2024.
Management and outcomes |
N |
Variable distribution |
Alive n = 628 (%) |
Death n = 129(%) |
COR (95% CI)a |
p-value |
Medications during hospitalisation |
|
|
|
|
|
|
Calcium channel blockers |
757 |
218 (28.8) |
198 (31.5) |
20 (15.5) |
0.40 (0.24 - 0.66) |
<0.001 |
Dobutamine |
757 |
39 (5.2) |
24 (3.8) |
15 (11.6) |
3.31 (1.68 - 6.51) |
<0.001 |
Loop diuretics |
757 |
673 (88.9) |
563 (89.6) |
110 (85.3) |
0.67 (0.38 - 1.16) |
0.151 |
Beta blockers |
757 |
299 (39.5) |
260 (41.4) |
39 (30.2) |
0.61 (0.41 - 0.92) |
0.019 |
Mineralocorticoid receptor antagonists |
757 |
174 (23.0) |
150 (23.9) |
24 (18.6) |
0.73 (0.45 - 1.18) |
0.196 |
RAAS inhibitors |
757 |
231 (30.5) |
207 (33.0) |
24 (18.6) |
0.46 (0.29 - 0.75) |
0.002 |
SGLT2 inhibitors |
757 |
72 (9.5) |
62 (9.9) |
10 (7.8) |
0.77 (0.38 - 1.54) |
0.456 |
Medications at discharge |
|
|
|
|
|
|
Calcium channel blockers |
757 |
163 (21.5) |
159 (25.3) |
4 (3.1) |
0.09 (0.03 - 0.26) |
<0.001 |
Loop diuretics |
757 |
490 (64.7) |
449 (71.5) |
41 (31.8) |
0.19 (0.12 - 0.28) |
<0.001 |
Beta blockers |
757 |
337 (44.5) |
321 (51.1) |
16 (12.4) |
0.14 (0.08 - 0.23) |
<0.001 |
Mineralocorticoid receptor antagonists |
757 |
170 (22.5) |
159 (25.3) |
11 (8.5) |
0.27 (0.14 - 0.52) |
<0.001 |
RAAS inhibitors |
757 |
227 (30.0) |
220 (35.0) |
7 (5.4) |
0.11 (0.05 - 0.23) |
<0.001 |
SGLT2 inhibitors |
757 |
83 (11.0) |
75 (11.9) |
8 (6.2) |
0.49 (0.23 - 1.04) |
0.062 |
Median duration of hospitalisation in days (IQR; Q1 - Q3) |
757 |
8 (5 - 12) |
8 (6 - 12) |
5 (3 - 10) |
0.97 (0.94 - 1.00) |
0.062 |
RAAS = Renin-Angiotensin Aldosterone System; SGLT2 = Sodium-glucose cotransporter 2; CORa = Crude Odds Ratio.
4. Discussion
This retrospective cohort study of 757 heart failure (HF) patients at Laquintinie Hospital, Douala (2021-2024), found a 17.0% in-hospital mortality rate. Multivariable logistic regression identified elevated blood urea nitrogen (BUN) (aOR = 1.01, 95% CI: 1.01 - 1.02, p < 0.001), dobutamine use (aOR = 2.85, 95% CI: 1.30 - 6.25, p = 0.009), and admission in 2023-2024 (aOR = 1.79, 95% CI: 1.15 - 2.80, p = 0.010) as independent predictors of increased mortality. Hypertensive heart disease (aOR = 0.40, 95% CI: 0.21 - 0.77, p = 0.006) was associated with lower mortality. Calcium channel blockers (aOR = 0.61, 95% CI: 0.33 - 1.10, p = 0.102), RAAS inhibitors (aOR = 0.65, 95% CI: 0.39 - 1.09, p = 0.100), beta-blockers (aOR = 0.70, 95% CI: 0.43 - 1.11, p = 0.129), age (aOR = 1.01, 95% CI: 1.00 - 1.02, p = 0.069), and ischemic/dilated cardiomyopathy (aOR = 0.82, 95% CI: 0.47 - 1.42, p = 0.475) were not significant in the adjusted model.
Just 9.5% of patients received newer treatments like SGLT2 inhibitors, a trend consistent with other low- and middle-income countries (LMICs) where usage varies from 10.3% to 36.7%, due to high costs, limited access, and lack of physician familiarity [14]-[16]. These therapies have been proven to reduce mortality and hospitalisations in HF with reduced ejection fraction (HFrEF) [17] but their impact on mortality in African settings is still to be explored.
The 17.0% mortality rate aligns with SSA studies reporting 3.7% - 19% in-hospital HF mortality (2022-2024) [5], but contrasts with lower rates in earlier SSA reports (4.2%, 2007-2010) [18] and European cohorts (2.1% - 3.4%) [19]. A 2024 study from Guadeloupe reported a 14.9% in-hospital mortality rate, noting similar challenges with diagnostic access and medication availability, reinforcing regional trends in poor HF outcomes [20]. The higher mortality in 2023-2024 (36.4% in 2024 vs. 12.4% in 2021), potentially reflect case-mix changes, such as a higher proportion of patients with advanced HF or comorbidities like renal dysfunction, likely contributed to worse outcomes, as seen in elevated BUN levels. Health-system factors, including diagnostic shortages (e.g., limited echocardiography) and delays in care due to post-COVID-19 resource constraints, further exacerbated mortality mirroring trends in SSA where resource constraints exacerbate outcomes [2] [9]. Unlike a Burkina Faso study (2015-2017) where deceased patients were older (65.4 vs. 55.7) [21], age was not significant in our adjusted model, possibly due to the younger cohort (median 63 years) and non-ischemic aetiologies like hypertensive heart disease (28.5%). According to a recent scoping review [5], multiple factors, including electrolyte imbalances, anemia, low blood pressure, renal failure, old age, infections, and medication issues, are associated with heart failure outcomes in sub-Saharan Africa (SSA), but patient heterogeneity complicates comparisons. Our model agrees with The “Get With the Guidelines-Heart Failure” risk score, which is recommended for inpatients [22]. It proposes age, systolic blood pressure, heart rate, serum sodium, blood urea nitrogen, COPD and race as predictive factors. Further research is needed to validate prognostic scores for personalized, risk-based care in SSA heart failure patients in SSA.
Elevated BUN and dobutamine use likely reflect advanced HF and end-organ dysfunction, consistent with global literature [23]. The protective effect of hypertensive heart disease may relate to effective local management or lower severity compared to other aetiologies. The increased mortality in 2023-2024 may stem from higher patient volumes (233 vs. 136 in 2021), delayed presentations, or resource shortages during post-COVID-19 recovery [23]. Leukocytosis indicative of severe infections (20.4% alive, 30.6% deceased, p = 0.077) and anemia (58.5% alive, 68.7% deceased, p = 0.119) associated with hypoxic stress, are usually markers of severity but were not significant in this study, possibly due to limited diagnostic testing or lower severity.
Strengths include the large cohort size and comprehensive multivariable analysis adjusting for key predictors. However, the retrospective design limits validity of causal inferences because of the high risk of misclassification with only few patients doing an echocardiography (54.4%). Although missing laboratory investigations like BUN were handled with robust statistical methods (multiple imputation), missing NYHA class and ejection fraction data limited severity adjustment, potentially underestimating the impact of HF stage on mortality. Survival bias may have influenced retrospective data, and unmeasured confounders (e.g., income, healthcare access) were not fully addressed, consistent with findings from a recent review highlighting socioeconomic barriers to HF care [5].
Implications and Actions Needed
The 17.0% mortality rate and low use of SGLT2 inhibitors highlight the need for health system reforms, including subsidized diagnostics, clinician training on 2022 AHA/ACC/HFSA guidelines, and task-shifting to improve guideline uptake in Cameroon. More studies should be conducted to evaluate the impact of guideline adherence on mortality.
5. Conclusion
In-hospital HF mortality was high, driven by elevated BUN, dobutamine use, and later admission years, potentially reflecting increased patient acuity or resource constraints. Hypertensive heart disease and higher systolic blood pressure were protective. Interventions to enhance diagnostic access (e.g., echocardiography, ECG), and address systemic healthcare barriers are critical. Prospective studies are needed to investigate rising mortality trends and optimize HF management in Cameroon.
Acknowledgements
The authors express their gratitude to the hospital administration for granting permission to conduct this study. Appreciation is also extended to the hospital staff for their cooperation and support throughout the data collection process.
Reporting Checklist
The authors have completed the STROBE reporting checklist.
Data Availability Statement
The data supporting the conclusions of this study are available from the corresponding author (SD) upon reasonable request.
Funding
No external funding was received for this study.
Ethical Approval
Ethical approval was obtained from the Regional Human Health Research Ethics Committee for the Littoral (Reference: 2024/CE/CRERSH-LITTORAL). Informed consent was waived due to the retrospective nature of the study. All procedures adhered to relevant ethical guidelines and regulations for the use of anonymized secondary data.
Author Contributions
Concept and study design: SD and EMM. Data collection: EMM. Data analysis and interpretation: EMM. Manuscript drafting: All authors. Final manuscript approval: All authors Supervision: KF. SD and EMM had full access to all study data and take full responsibility for the integrity and accuracy of the data analysis. All authors have agreed to the submission of the manuscript in its current form.