In-hospital survival and predictors of mortality among stroke patients at a tertiary hospital in Ghana: A retrospective cohort study

Sulemana Baba Abdulai; Julius Kwabena Karikari; Penias Tembo; Theogene Habumugisha; William Tembo; Alhaji Ibrahim Cobbinah; Yeetey Enuameh

doi:10.1371/journal.pone.0340447

. 2026 Jan 7;21(1):e0340447. doi: 10.1371/journal.pone.0340447

In-hospital survival and predictors of mortality among stroke patients at a tertiary hospital in Ghana: A retrospective cohort study

Sulemana Baba Abdulai ^1,^*, Julius Kwabena Karikari ^1,², Penias Tembo ^3,⁴, Theogene Habumugisha ⁵, William Tembo ⁶, Alhaji Ibrahim Cobbinah ¹, Yeetey Enuameh ¹

Editor: Mickael Essouma⁷

PMCID: PMC12795378 PMID: 41499481

Abstract

Introduction

Stroke is a leading cause of death and disability worldwide. In Ghana, national estimates show a prevalence of 7.9% and an incidence rate of 1.2%, placing a significant burden on the health system. This study aimed to estimate in-hospital survival rates and identify predictors of mortality among stroke patients admitted to Tamale Teaching Hospital (TTH).

Methods

A retrospective electronic medical records review was conducted using data from the Lightwave Health Information Management System (LHIMS) and patients’ registry from January 1 2021 to December 31 2023. Kaplan-Meier survival curve was used to determine the survival rate of stroke patients (mean follow-up: 67 days). Cox proportional hazard regression determined the association between risk factors and survival time. Crude and adjusted hazard ratios with 95% confidence intervals were presented. A p-value of 0.05 was considered statistically significant.

Results

A total of 998 stroke patients were included, of which 39.4% died. The overall survival rate was 21% at the end of follow-up (182 days). The survival probability for female stroke patients was lower compared to males. Female sex (AHR = 1.33, 95% CI: 1.07–1.65), Dagomba ethnicity (AHR = 1.31, 95% CI: 1.05–1.62), pneumonia (AHR = 1.59, 95% CI: 1.27–1.97), diabetes mellitus (AHR = 0.62, 95% CI: 0.47–0.82), systolic blood pressure ≥ 130 mmHg (AHR = 1.35, 95% CI: 1.06–1.73), and temperature ≥ 37.5°C (AHR = 1.79, 95% CI: 1.37–2.33) were predictors of mortality.

Conclusion

Female stroke patients experienced higher mortality and lower survival compared to males. The identified mortality predictors underscore the importance of focused interventions to enhance survival outcomes at TTH. Given the retrospective design and possible unmeasured confounders, the lower mortality risk among diabetic patients should be interpreted with caution, and prospective studies are warranted to confirm these associations.

Introduction

Stroke is an acute neurological dysfunction of the brain, spinal cord, or retina caused by focal ischemia (infarction) or haemorrhage, including cases due to cerebral venous thrombosis [1]. It is a multifaceted disease influenced by risk factors such as hypertension, diabetes, and heart failure [2]. Globally, stroke remains a leading cause of death and disability, responsible for significant health burdens, including an annual loss of approximately 1,484 disability-adjusted life years (DALYs) per 100,000 people [3]. Survivors often endure long-term physical, cognitive, and emotional challenges, imposing substantial demands on families, social, and healthcare systems [4].

In sub-Saharan Africa (SSA), the burden of stroke is rapidly increasing due to an epidemiological shift towards non-communicable diseases (NCDs) [5]. Stroke incidence and mortality rates in SSA are alarmingly high, with incidence reaching up to 1,460 cases per 100,000 people, and a three-year fatality rate exceeding 80% [6]. Challenges such as inadequate health infrastructure, limited neurologists, and low awareness of stroke symptoms exacerbate poor outcomes in the region [7]. In Ghana, stroke ranks among the top five causes of death, with concerning trends in rising admissions and a one-month hospital fatality rate of up to 43% [8,9]. Over the past three decades, the country has witnessed a consistent rise in stroke incidence, admissions, and mortality. For instance, stroke-related admissions in Kumasi increased from 5.32 per 1,000 in 1983 to 13.85 per 1,000 in 2010, with one-month fatality rates reaching 41% in the Central Region [9]. Key modifiable risk factors, including high salt intake, physical inactivity, and limited consumption of green leafy vegetables, are prevalent in Ghana [10]. Additionally, younger populations under 40 years are increasingly affected, posing significant socio-economic challenges [11].

The Tamale Teaching Hospital (TTH) in Northern Ghana, a major referral centre, reports high stroke-related mortality, with stroke ranking as the third leading cause of death in its Accident and Emergency Department [9]. Factors such as delayed presentation, socio-cultural barriers to healthcare, and resource constraints contribute to poor stroke outcomes [9]. While stroke-related mortality has been extensively studied, limited data exist on survival and its determinants, particularly in the Ghana context [7,12].

Despite significant advancements in stroke care, limited region-specific data on stroke survival and its determinants impede the development of targeted interventions in Ghana. Current research has predominantly focused on stroke mortality, with few studies on survival outcomes and their predictors [12–14]. This knowledge gap hinders the development of evidence-based stroke management strategies in LMICs, including the establishment of dedicated stroke units, the availability of diagnostic tools such as MRI machines, and the training of healthcare professionals essential for improving stroke care [15–17]. This study aims to address these gaps by investigating the in-hospital survival rate and predictors of mortality among stroke patients admitted to the Tamale Teaching Hospital, Ghana. The availability of evidence on the determinants of survival among stroke patients will help to inform healthcare planning, enhance resource allocation, and support the development of data-driven public health policies in Ghana. Furthermore, the study aligns with the WHO’s Agenda 2030 for Sustainable Development, particularly its goal of reducing mortality from major NCDs by one-third by 2030.

Materials and methods

Study design

This study employed a retrospective cohort design using routinely collected hospital data. Stroke patients admitted to Tamale Teaching Hospital between 2021 and 2023 were followed from admission until discharge or in-hospital death. The retrospective cohort design was appropriate for identifying predictors of in-hospital mortality among stroke patients using existing clinical records.

Patient identification

We conducted a retrospective review of electronic medical records using the Lightwave Health Information Management System (LHIMS) to identify all patients admitted with a diagnosis of stroke at the Tamale Teaching Hospital (TTH) between January 1, 2021, and December 31, 2023. Medical records were accessed for research purposes between April 15 and June 15, 2024. The study included patients aged 18 years and above who were diagnosed with either ischemic or hemorrhagic stroke during the study period. No a priori sample size or power calculation was performed because the study included all eligible stroke cases within the 3 years. Patients were excluded if their medical records were incomplete or missing, or if the type of stroke was not specified as either ischemic or hemorrhagic (Fig 1). The authors had no access to information that could identify individual participants at any point during or after data collection. All data used for the study were fully anonymised prior to analysis to ensure the privacy and confidentiality of patients. The study adhered to the principles of the Declaration of Helsinki for research involving human participants. Ethical approval for the study was obtained from the Committee on Human Research, Publications and Ethics at the Kwame Nkrumah University of Science and Technology (KNUST), under approval number CHRPE/AP/237/24. Given the retrospective nature of the study, the requirement for informed consent was waived.

Study setting

The study was centred at the Tamale Teaching Hospital (TTH) in Tamale, Northern region. The hospital is a referral centre for all other hospitals within the five regions, including the Northeast, Upper East, Savanna, and Upper West [9]. The hospital serves an approximate population of over 4 million people [18]. It is currently Ghana’s third-largest tertiary specialised healthcare facility [19]. In addition to providing medical care to patients, the hospital acts as a teaching facility for the University for Development Studies and many nursing colleges in the region [9]. At the time of the study, stroke care was provided within the general medical wards, as the hospital did not have a dedicated stroke unit. Stroke patients were managed by a multidisciplinary team comprising specialist physicians (including an endocrinologist and a gastroenterologist), medical officers, nurses, pharmacists, and physiotherapists. As a high-volume referral facility, TTH manages significant numbers of stroke patients; for example, 105 ischemic stroke admissions were recorded between January and October 2021 [9].

Study variables

The event of interest was in-hospital death. Survival time was defined as the number of days from admission to death, with discharge alive treated as a censoring event. No post-discharge follow-up data were available. Predictors were grouped into demographic/baseline characteristics (age, sex, educational level, occupation, ethnicity, family history of stroke, smoking status, alcohol intake, body temperature, systolic and diastolic blood pressure, heart rate, respiratory rate, and Glasgow Coma Score) and clinical characteristics (family medical history, hypertension, diabetes mellitus, kidney disease, pneumonia, stroke type, atrial fibrillation, right and left upper limb weakness, seizures, aphasia, dysarthria, and dysphagia) (Table 1). Dates of admission and discharge or death were collected to compute time to in-hospital death or censoring.

Table 1. Demographic/baseline and clinical characteristics of stroke patients at TTH.

Characteristics	Frequency (N = 998)	Percentage (%)
Age
< 45	178	17.84
45-65	441	44.19
≥ 66	379	37.98
Sex
Male	546	54.71
Female	452	45.29
Occupation
Unemployed	281	28.16
Employed	616	61.72
Retired	101	10.12
Educational status
No education	705	70.64
Basic	40	4.01
SHS	105	10.52
Tertiary	148	14.83
Ethnicity
Dagombas	521	52.20
Gonjas	35	3.51
Others¹	442	44.29
Family history of stroke
No	966	96.79
Yes	32	3.21
Smoking
No	937	93.89
Yes	61	6.11
Alcohol intake
No	882	88.38
Yes	116	11.62
Temperature (°C)
< 37.5	859	86.07
≥ 37.5	139	13.93
Systolic BP (mmHg)
< 130	311	31.16
≥ 130	687	68.84
Diastolic BP (mmHg)
< 80	312	31.26
≥ 80	686	68.74
Heart rate (bpm)
≤ 100	371	37.17
> 100	627	62.83
Respiratory rate (breaths/min)
≤ 20	548	54.91
> 20	450	45.09
GCS level
< 9	274	72.55
≥ 9	724	27.45
Hypertension
No	214	21.44
Yes	784	78.56
Diabetes
No	787	78.86
Yes	211	21.14
Kidney disease
No	975	97.70
Yes	23	2.30
Pneumonia
No	635	63.63
Yes	363	36.37
Stroke type
Ischemic	516	51.70
Haemorrhagic	482	48.30
Atrial fibrillation
No	995	99.70
Yes	3	0.30
Right upper limb weakness
No	672	67.33
Yes	326	32.67
Left upper limb weakness
No	710	71.14
Yes	288	28.86
Right lower limb weakness
No	641	64.23
Yes	357	35.77
Left lower limb weakness
No	674	67.54
Yes	324	32.46
Seizures
No	869	87.07
Yes	129	12.93
Aphasia
No	653	65.43
Yes	345	34.57
Dysarthria
No	848	84.97
Yes	150	15.03
Dysphagia
No	849	85.07
Yes	149	14.93
Urea (mmol/L)
Median (IQR)	5.9 (4.82)
Creatinine(µmolL)
Median (IQR)	82.6 (60.30)
Bilirubin (µmolL)
Median (IQR)	11.5 (11.99)
Random blood sugar (mmol/L)
Median (IQR)	7.7(3.20)

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1 = Wala, Kusasi, Frafra;

¹ basic (1–9 years of formal education, primary and junior high school), SHS (Senior High School, 10–12 years of formal education), and Tertiary (≥13 years, post-secondary education).

Data sources and tools

Data for this study were extracted from the Lightwave Health Information Management System (LHIMS) and patient registers at the Tamale Teaching Hospital (TTH) for the years 2021–2023.

A structured checklist, developed in accordance with the RECORD (Reporting of studies Conducted using Observational Routinely collected Data) guidelines for studies using routinely collected health data, was used to extract information from LHIMS and the physical registers. The checklist ensured systematic and standardised retrieval of key variables, including sociodemographic characteristics, stroke type, comorbidities, and treatment outcomes for all patients admitted to the medical ward with a confirmed stroke diagnosis.

Data analysis

Data extracted from LHIMS were entered into Excel, cleaned, and subsequently exported to STATA V.17 for analysis, an appropriate and robust platform for time-to-event analyses [20]. Univariable descriptive statistics, including frequencies and percentages, were presented in tables, with means and standard deviations for normally distributed continuous variables, and medians with interquartile ranges for skewed variables. All continuous variables were assessed for normality using the Shapiro–Wilk test, and their distributions were visualised using histograms. Kaplan–Meier survival curves were used to estimate time to in-hospital death, with discharge treated as a censoring event, and to describe overall survival probabilities. A log-rank test was performed to assess the difference between sex (male and female).

A stepwise Cox proportional hazards regression model was applied to identify variables associated with in-hospital mortality. Hazard ratios were used to measure the strength of these associations. The Schoenfeld residual test was conducted to verify the proportional hazards assumption, with variables meeting a p-value > 0.05 considered to satisfy the assumption. A bivariable Cox proportional hazards model was fitted for all predictors and variables with a p-value of less than 0.25 in order not to leave out important variables that may influence the outcome, and these were selected for inclusion in the stepwise multivariable Cox regression model. Multicollinearity among the predictors included in the multivariable model was assessed using the Variance Inflation Factor (VIF), and predictors with a VIF < 5 were included in the final model. [Mean VIF = 1.64, Max VIF = 2.63, Min VIF = 1.05], indicating no significant multicollinearity among the predictors. Statistical significance was set at p-value < 0.05 and a 95% confidence interval (CI).

Patient and Public Involvement

Patients and members of the public were not involved in the design, conduct, reporting, or dissemination of this study, as it relied entirely on retrospectively collected hospital records.

Results

Baseline demographic and clinical characteristics of stroke patients

Most patients were aged between 45 and 65 years (44.19%). More than half of the patients were male (54.71%). Regarding occupation, most were employed (61.72%). Majority had no formal education (70.64%). Ethnically, Dagombas represented the largest group (52.20%). Most patients reported no family history of stroke (96.79%), were non-smokers (93.89%), and did not consume alcohol (88.38%). Clinically, most patients presented with a temperature below 37.5°C (86.07%) and had systolic and diastolic blood pressures of ≥130 mmHg (68.84%) and ≥80 mmHg (68.74%), respectively. A heart rate above 100 bpm was observed in 62.83% of patients, while 54.91% had a respiratory rate of 20 or fewer breaths per minute. The majority of patients had a Glasgow Coma Scale (GCS) score of ≥9 (72.55%). Hypertension was the most common, affecting 78.56% of patients, followed by diabetes (21.14%). Only a small proportion had kidney disease (2.30%). Pneumonia was documented in 36.37% of cases. Ischemic stroke was slightly more common than haemorrhagic stroke, accounting for 51.70% and 48.30% of cases, respectively. Atrial fibrillation was rare, reported in only 0.30% of patients. Regarding motor function and neurological deficits, the most common presentation was right upper limb weakness (32.67%), followed by left upper limb weakness (28.86%). Right lower limb weakness was observed in 35.77% of patients, while left lower limb weakness occurred in 32.46%. Seizures were noted in 12.93% of patients. Aphasia was present in 34.57% of cases, dysarthria in 15.03%, and dysphagia in 14.93% (Table 1). Approximately 60.62% of patients survived their admission, while 39.38% died during hospitalisation (Fig 2).

Survival probability of stroke patients

The overall survival probability was 21.0%, with a mean survival time of 67 days (95% CI: 42–91) (Fig 3). The results also show a decline in survival rates with longer hospital stays. The probability of survival was 90.0% on the 1st day, 71.0% on the 5th day, and 60.0% on the 10th day. By the end of the 50-day observation period, although some patients continued to survive, the survival probability had dropped significantly to 42.5%.

Hemorrhagic stroke patients show shorter survival times than ischemic stroke patients, with a median survival of 31 days for ischemic stroke and 14 days for hemorrhagic stroke (Fig 4).

Throughout the follow-up period, males consistently demonstrate a higher survival probability than females. The survival rate for females declined sharply, indicating a higher mortality rate over the same timeframe (Fig 5). For instance, on the 5th day, the survival probability for females was 68.0%, compared to 73.0% for males, and on the 10th day, it was 64.0% for males and 55.0% whereas for females. The median survival time for females was 13 days (95% CI: 10–31), while for males, it was 17 days.

Sociodemographic and clinical predictors of in-hospital mortality among stroke patients

Patients aged 45–65 years had a significantly higher hazard of death compared to those younger than 45 years (AHR = 1.28; 95% CI: 1.03–1.58). Female patients had a significantly 33% higher risk of mortality compared to males (AHR = 1.33; 95% CI: 1.07–1.65). Educational status was also significantly associated with mortality. Patients with basic education had a 54% lower risk of death compared to those with no education. Among ethnic groups, Dagombas had a 31% increased risk of death compared to other groups (AHR = 1.31; 95% CI: 1.05–1.62). Patients with diabetes had a 39% lower risk of mortality compared to non-diabetics (AHR = 0.62; 95% CI: 0.47–0.82). Similarly, individuals presenting with right lower limb weakness had a 33% lower risk of death than those without the condition (AHR = 0.67; 95% CI: 0.53–0.85). Patients with elevated temperature (≥ 37.5°C) had a 79% higher hazard of death (AHR = 1.79; 95% CI: 1.37–2.33), while those with pneumonia had a 1.59 times higher risk of mortality than patients without pneumonia (AHR = 1.59; 95% CI: 1.27–1.97). Additionally, patients with systolic blood pressure ≥ 130 mmHg exhibited a 32% higher risk of death compared to those with lower systolic pressure (AHR = 1.35, 95% CI: 1.06–1.73). Interestingly, a heart rate above 100 bpm was associated with a significantly lower risk of mortality (AHR = 0.50, 95% CI: 0.40–0.62), as was a respiratory rate above 20 (AHR = 0.63, 95% CI: 0.49–0.79). These associations may reflect differences in stroke subtype or patient management and should be interpreted with caution. Higher oxygen saturation was significantly associated with better survival (AHR = 0.96, 95% CI: 0.95–0.98), while for each mmol/L increase in urea, the risk of mortality increased by 2% (AHR = 1.02; 95% CI: 1.01–1.03 per mmol/L) (Table 2).

Table 2. Sociodemographic and clinical predictors of in-hospital mortality among stroke patients.

Characteristics	Crude HR	95% CI	p-value	Adjusted HR	95% CI	p-value
Age group
< 45	Ref			Ref
45-65	1.25	0.92-1.70	0.154	1.28	1.03-1.58	0.027
≥ 66	1.07	0.78-1.48	0.663
Sex
Male	Ref			Ref
Female	1.34	1.08-1.66	0.008	1.33	1.07-1.65	0.010
Educational status
No education	Ref			Ref
Basic	0.49	0.24-0.98	0.044	0.46	0.23-0.93	0.039
SHS	0.95	0.67-1.36	0.669
Tertiary	0.57	0.40-0.83	0.003
Ethnicity
Others¹	Ref			Ref
Gonjas	0.62	0.29-1.33	0.219
Dagombas	1.34	1.08-1.67	0.008	1.31	1.05-1.62	0.018
Alcohol intake
No	Ref			Ref
Yes	0.75	0.52-1.07	0.113	0.69	0.48-0.99	0.044
Temperature (°C)
< 37.5	Ref			Ref
≥ 37.5	2.26	1.75-2.91	<0.001	1.79	1.37-2.33	<0.001
Systolic BP (mmHg)
< 130	Ref			Ref
≥ 130	1.45	1.14-1.85	0.003	1.35	1.06-1.73	0.017
Diastolic BP (mmHg)
< 80	Ref
≥ 80	1.45	1.13 −1.85	0.003
Heart rate (bpm)
≤ 100	Ref			Ref
> 100	0.40	0.32-0.49	<0.001	0.52	0.42-0.66	<0.001
Respiratory rate (breaths/min)
< 20	Ref			Ref
≥ 20	0.51	0.41-0.64	<0.001	0.60	0.47-0.76	<0.001
GCS level
≥ 9	Ref
< 9	1.23	0.97-1.53	0.094
Diabetes
No	Ref			Ref
Yes	0.73	0.55-0.95	0.021	0.62	0.47-0.82	0.001
Pneumonia
No	Ref			Ref
Yes	1.89	1.53-2.34	<0.001	1.59	1.27-1.97	<0.001
Stroke type
Ischaemic	Ref
Haemorrhagic	1.17	0.95-1.45	0.144
Right upper limb weakness
No	Ref
Yes	0.76	0.60-0.96	0.025
Right lower limb weakness
No	Ref
Yes	0.72	0.57-0.91	0.005	0.67	0.53-0.85	0.001
Dysarthria
No	Ref
Yes	0.69	0.49-0.96	0.030
Urea (mmol/L)
	1.02	1.01-1.03	<0.001	1.02	1.01-1.03	<0.001
Creatinine (µmolL)
	1.00	1.00-1.00	<0.001
Bilirubin (µmolL)
	1.01	0.99-1.02	0.196
Random blood sugar (mmol/L)
	1.01	1.00-1.02	0.022

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HR = Hazard ratio, Ref = reference group, CI =Confidence interval, 1 = Wala, Kusasi, Frafra

Discussion

This study investigated the in-hospital survival rate and predictors of mortality among stroke patients admitted to the Tamale Teaching Hospital, Ghana. The study found that several factors, including female sex, Dagomba ethnicity, elevated temperature, pneumonia, higher systolic blood pressure, and increased urea levels, were associated with a higher risk of death due to stroke. Conversely, basic education, diabetes, right lower limb weakness, elevated heart and respiratory rates, and higher oxygen saturation were linked to improved survival outcomes.

The overall survival probability was high in the initial days of admission but decreased as follow-up time increased. Revealing further that a high number of patients died in the first 10 days after being diagnosed with stroke. This is comparable to a study in North West Ethiopia, with similar observations [4]. Also, the median survival time of 21 days was lower than in a study in Ethiopia that had a median survival time of 41 days [21]. This difference is likely explained by contextual factors such as variation in stroke severity and complication rates, health systems, and variation in sample size [4,21].

Males consistently showed a higher survival probability throughout the follow-up period, indicating a gender disparity in stroke outcomes. This aligns with findings from a study by [22] conducted in South Korea. The steep decline in survival rates among females, indicates a higher mortality rate, evident as early as the 5th-day post-stroke, where the survival probability for females was 68.0% compared to 73.0% for males. This pattern persists, with a significant gap by the 10th day, as survival probabilities drop to 55.0% for females and 64.0% for males. The median survival time further underscores this difference, with females showing a notably shorter median survival time of 13 days compared to 17 days for males. This disparity may be attributed to more severe post-stroke complications or other factors impacting female survival rates over time [23].

The findings also revealed that the odds of mortality were high among females compared to males. This aligns with a study in Ghana at the Korlebu Teaching Hospital [24]. Several factors may contribute to the higher burden of stroke-related mortality and disability among females. Sociocultural gender roles and biological differences influence stroke risk, assessment, treatment, and outcomes. The relationship between general stroke risk factors and female-specific risk variables differs considerably. Additionally, there are differences in how women experience stroke symptoms, respond to treatment, and recover after a stroke compared to men [23].

The study also revealed that stroke patients with no formal education faced a higher risk of mortality compared to those with education. Specifically, having a basic education was associated with approximately a 54% lower risk of death. This finding is consistent with studies conducted in Ghana and China [14,25]. This finding may be attributed to individuals with no education often displaying less healthy lifestyle behaviours and greater clinical risk factors for stroke, a pattern observed in both genders [26].

Also, the study found that individuals of Dagomba ethnicity had a higher risk of mortality following stroke. While direct comparisons are limited, this observation aligns with findings from the United States, where studies have consistently reported higher stroke mortality rates among Black populations [27,28]. A possible explanation is that cultural beliefs within certain ethnic groups may encourage reliance on traditional healing practices, leading to delays in seeking hospital care [29,30]. These disparities include a lack of awareness about stroke symptoms, delays in seeking timely treatment, and limited understanding of risk factors. Differences in attitudes, beliefs, and adherence to medical advice also vary across races and ethnicities.

The study revealed that pneumonia was also a predictor of mortality. The risk of mortality was high among stroke patients with pneumonia complications. This is consistent with a study in Ghana, Nigeria and Ethiopia [14,21,31]. The increased risk of mortality can be linked to several factors. Post-stroke patients frequently encounter swallowing difficulties and limited mobility, both of which can contribute to the development of lung infections like pneumonia [32]. Moreover, strokes can induce an immune response that heightens vulnerability to infections, disrupts the tracheal epithelium, diminishes lung clearance, and hampers the expulsion of secretions, further elevating the likelihood of pneumonia [33].

Interestingly, the study revealed that stroke patients with diabetes had a lower risk of mortality, which contrasts with findings observed in Ghana [14,34]. This finding needs to be further investigated as it is very counterintuitive unless something special is being done in the population or the health facility within which these cases are being picked

Furthermore, we found that patients presenting with right lower limb weakness had a lower risk of death compared to those without such weakness. Although this specific finding has limited direct support in existing literature, a study conducted in Canada reported that outcomes tend to be more favourable in individuals with motor strokes, particularly those presenting with monoparesis, defined as weakness in a single limb [35]. While this does not directly align with our findings, it suggests that isolated motor deficits may be associated with less severe stroke presentations, potentially contributing to improved survival. Overall, patients with motor stroke symptoms often experience fewer complications and shorter hospital stays, which may partly explain the observed association in our study. Also, these patients are more likely to experience symptom resolution by the time of hospital discharge, which further supports the observation that isolated motor deficits may predict better prognoses compared to more extensive motor involvement.

Implications for practice and research

Despite the limitations, the findings of this study provide an important contribution with relevant implications for improving the survival of stroke patients. The identification of both demographic and clinical predictors of stroke mortality has important implications for clinical practice. High-risk patients, such as females, individuals with low or no education, those presenting with aphasia, pneumonia, elevated temperature, or high systolic blood pressure, may benefit from closer monitoring and aggressive management during the acute phase of stroke. The protective associations observed with diabetes suggest potential responsiveness to treatment that warrants further investigation. Early identification and prompt intervention for modifiable risk factors like diabetes could improve survival outcomes, especially during the critical first 10 days post-admission when mortality risk is highest.

Strengths and limitations

This study provides valuable insights into the factors that influence survival among stroke patients in a real-world clinical setting. By using a multivariable Cox regression model, we were able to control for several demographic and clinical variables, helping us identify the key predictors of mortality more accurately. The study also benefits from a relatively large sample size and a three-year follow-up period, which strengthens the reliability of our findings. Importantly, by examining both social and clinical factors, we offer a more holistic picture of the challenges stroke patients face in our context.

That said, our study has a few limitations worth noting. Because it is based on retrospective data, we were limited to the information recorded in patients’ medical files, which were sometimes incomplete or missing key details. We also could not assess stroke severity scores (e.g., NIHSS, mRS) or functional outcomes at admission, which might have further clarified the survival patterns we observed. Although Kaplan–Meier survival analysis was used to compare survival between ischaemic and haemorrhagic stroke patients, this study did not conduct subtype-specific multivariable regression analyses to identify predictors of mortality within each stroke subtype. Also, detailed information on arterial territories in ischaemic stroke and cerebral localization of haemorrhagic stroke was not available. These limitations may have reduced the ability to detect subtype-specific prognostic factors. In addition, we only looked at what happened during the hospital stay, so we might have missed deaths that occurred after discharge.

Conclusion

The study revealed that stroke patients treated at the Tamale Teaching Hospital face a declining probability of survival over time, with the highest risk of death occurring within the first 10 days of admission. To reduce preventable deaths, health system actions are needed including: (1) establishing or strengthening dedicated stroke care pathways or a stroke unit at TTH to ensure rapid assessment and standardized management; (2) prioritizing early monitoring and control of modifiable, high-risk comorbidities such as fever, pneumonia, and elevated systolic blood pressure; (3) implementing targeted screening and public-health outreach for high-risk groups and tailored education for patients and caregivers; and (4) ensuring protocols for infection prevention and early rehabilitation and discharge planning to reduce complications and length of stay. However, given the retrospective design and potential for residual confounding, large and prospective studies are still needed.

Supporting information

S1 File. Data used for the analysis.

(XLSX)

pone.0340447.s001.xlsx^{(211.5KB, xlsx)}

Acknowledgments

We acknowledge the Senior Health Information Officer at TTH, Mr. Abdul–Hafiz Zakari, for his generous assistance and cooperation during the research.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

References

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PLoS One. doi: 10.1371/journal.pone.0340447.r001

Decision Letter 0

Mehdi Sharafi

4 Sep 2025

PONE-D-25-39162

PREDICTORS OF SURVIVAL OF STROKE PATIENTS IN THE TAMALE TEACHING HOSPITAL IN GHANA: A 3-YEAR RETROSPECTIVE REVIEW

PLOS ONE

Dear Dr. Abdulai,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This study is very standard and has appropriate structure and content. However, there are a few minor methodological flaws in the introduction, methods, and discussion that have been commented on in the article.

Reviewer #2: Overall Evaluation

This study investigates predictors of survival among stroke patients admitted to Tamale Teaching Hospital (Ghana). The topic is highly relevant, as survival data on stroke patients in sub-Saharan Africa are scarce. The manuscript is valuable and based on a relatively large dataset, but several methodological clarifications, improvements in tables, and more cautious interpretation of results are needed before publication.

Abstract

- The follow-up period (mean survival: 67 days) is not mentioned.

- A protective effect of diabetes is reported as an unexpected finding, but it is presented without sufficient explanation or cautious interpretation.

- Suggestion: The follow-up duration and key study limitations (e.g., retrospective design) should be stated. Unexpected findings should be reported with greater caution and accompanied by interpretation.

Methods

- The definition of the outcome is unclear. The text indicates that “survival status” was determined at discharge (alive or dead), but the Kaplan–Meier results report a mean survival of 67 days with follow-up up to 50 days. This creates confusion as to whether the authors considered in-hospital survival only or included post-discharge follow-up.

- Suggestion: Authors should explicitly clarify how the outcome was defined (discharge status only or extended follow-up). Without this, it is difficult to compare results with other studies and to interpret the Kaplan–Meier curves.

- Handling of missing data is not described.

- No justification for sample size or study power is provided.

- Suggestion:

• Clarify the approach to handling missing data.

• Acknowledge limitations due to the absence of stroke severity measures (e.g., NIHSS, mRS).

Results and Tables

- The results are generally clear, but several issues require attention:

• Some associations (e.g., protective effect of diabetes, HR >100 bpm) need further clarification.

• The Kaplan–Meier survival curve should be more fully described (e.g., stratified by stroke type).

• Table 1: For the variable educational level, it should be clarified how many years of formal education each category represents (e.g., Basic, SHS, Tertiary). Abbreviations (such as SHS) should also be explained in the table footnotes or text for the benefit of international readers.

Conclusion

- The current conclusion largely repeats the results.

- Suggestion: The conclusion should instead emphasize policy and clinical implications, such as the need for stroke units, closer monitoring of high-risk patients, and targeted screening for risk factors.

References

- The reference list combines local and international sources but includes some outdated ones. In particular, Hatano 1976, which refers to the old WHO definition of stroke, is outdated.

- Suggestion: This reference should be replaced or supplemented with more recent and comprehensive sources, such as the AHA/ASA 2013 statement or more recent systematic reviews on stroke definition.

**********

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Reviewer #1: No

Reviewer #2: Yes: Hassan Karami

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Attachment

Submitted filename: PONE-D-25-39162.pdf

pone.0340447.s002.pdf^{(929KB, pdf)}

Attachment

Submitted filename: Reviewer Report.docx

pone.0340447.s003.docx^{(27KB, docx)}

PLoS One. 2026 Jan 7;21(1):e0340447. doi: 10.1371/journal.pone.0340447.r002

Author response to Decision Letter 1

6 Oct 2025

Reviewers Comments Author Responses

Reviewer #1

The title needs to be rewritten We revised the title to clarify the outcome and study design. Suggested title: “In-hospital survival and predictors of mortality among stroke patients at a Tertiary Hospital in Ghana: a 3-year retrospective cohort study.”

Summarise the abstract introduction We appreciate the reviewer’s suggestion to make the abstract more concise. In response, we have summarised the Introduction section of the abstract to focus on the essential background and study objective. The revised text now reads:

“Stroke is a leading cause of death and disability globally and imposes a growing burden on Ghana’s healthcare system. This study aimed to estimate in-hospital survival rates and identify predictors of mortality among stroke patients admitted to a tertiary hospital in Ghana over a 3-year period.”

Your literature review is weak. Please cite examples of similar studies. We appreciate the reviewer’s feedback. In response, we have strengthened the literature review by including additional studies on stroke survival and its determinants. Specifically, we have now cited:

• Sarfo FS, et al. Long-Term Outcomes of Stroke in a Ghanaian Outpatient Clinic. J Stroke Cerebrovasc Dis. 2018.

• Sarfo FS, et al. Key determinants of long-term post-stroke mortality in Ghana.(line 91)

Provide logical reasoning for all comparisons (e.g., why your median survival differs from other studies). We rewrote the comparison paragraph to state explicitly why median survival may differ across studies: This difference is likely explained by contextual factors such as variation in stroke severity and complication rates, health systems, and variation in sample size (lines 271-273)

Reviewer #2

1. The follow-up period (mean survival: 67 days) is not mentioned. Thank you for pointing this out. We have revised the Methods section of the abstract to include the mean follow-up duration (Line 35). It now reads: “Kaplan–Meier survival analysis was used to estimate the survival rate of stroke patients (mean follow-up: 67 days).”

2. A protective effect of diabetes is reported as an unexpected finding, but it is presented without sufficient explanation or cautious interpretation. We appreciate this important comment. In the revised abstract, we have reported the association between diabetes mellitus and mortality more cautiously, noting that the apparent lower risk may be due to differences in case management or residual confounding (lines 50 – 53). The Results section now states: “Diabetes mellitus showed an apparently lower risk of mortality (AHR = 0.62, 95% CI: 0.47–0.82); this finding should be interpreted cautiously as it may reflect differences in case management or residual confounding.” (lines 252-254)

The definition of the outcome is unclear. The text indicates that “survival status” was determined at discharge (alive or dead), but the Kaplan–Meier results report a mean survival of 67 days with follow-up up to 50 days. This creates confusion as to whether the authors considered in-hospital survival only or included post-discharge follow-up. Suggestion: Authors should explicitly clarify how the outcome was defined (discharge status only or extended follow-up). Without this, it is difficult to compare results with other studies and to interpret the Kaplan–Meier curves. Thank you for this important observation. We have revised the Materials and Methods section to explicitly state that survival status was determined at hospital discharge only; no post-discharge follow-up was included. The time-to-event variable used for Kaplan–Meier analysis was calculated as the number of days from admission to discharge or in-hospital death. This clarification has been added to the “Study Variables” (lines 129-132) and “Data Analysis” (lines 151-152) subsections.

3. The follow-up duration and key study limitations (e.g., retrospective design) should be stated. Unexpected findings should be reported with greater caution and accompanied by interpretation. We have incorporated the mean follow-up time in the Methods section, added a sentence on the retrospective design as a limitation, and rephrased the Conclusion to reflect the cautious interpretation of unexpected findings. The Conclusion now reads: “Given the retrospective design and possible unmeasured confounders, the observed lower mortality risk among diabetic patients should be interpreted with caution, and prospective studies are warranted to confirm these associations.”

Handling of missing data is not described. We appreciate this comment. We have now included a statement describing how missing data were handled. Specifically, patients with incomplete or missing key variables were excluded from the analysis, and the number of excluded cases is reported in Figure 1

No justification for sample size or study power is provided.

We thank the reviewer for this observation. We have added a sentence acknowledging that no a priori sample size or power calculation was performed because the study included all eligible stroke cases within the 3-year period (census sampling). This has been added to the “Patient Identification” subsection (lines 113-114).

Suggestion: Acknowledge limitations due to the absence of stroke severity measures (e.g., NIHSS, mRS). We agree with this suggestion. This has been highlighted in the discussion of limitations.

Some associations (e.g., protective effect of diabetes, HR >100 bpm) need further clarification. We thank the reviewer for this observation. We have added text in the Results section noting that the apparent protective effects of diabetes, elevated heart rate (>100 bpm), and higher respiratory rate may reflect differences in case management, stroke subtype, or residual confounding (lines 250-252). We caution readers that these findings should be interpreted carefully and may not indicate true protective effects.

The Kaplan–Meier survival curve should be more fully described (e.g., stratified by stroke type). We appreciate this suggestion. We have added a description of the Kaplan–Meier curves stratified by stroke type (ischemic vs. hemorrhagic) in the Results section. A new figure (Figure 4) has been included showing separate curves for each type with log-rank p-values reported.

Table 1: For the variable educational level, it should be clarified how many years of formal education each category represents (e.g., Basic, SHS, Tertiary). Abbreviations (such as SHS) should also be explained in the table footnotes or text for the benefit of international readers. Thank you for this important point. We have now clarified in Table 1 footnotes what each educational category represents: Basic (1–9 years of formal education, primary and junior high school), SHS (10–12 years of formal education, senior high school), and Tertiary (≥13 years, post-secondary education). All abbreviations including SHS have been explained in the footnotes.

The current conclusion largely repeats the results.

Suggestion: The conclusion should instead emphasize policy and clinical implications, such as the need for stroke units, closer monitoring of high-risk patients, and targeted screening for risk factors. Thank you. We have rewritten the Conclusion to focus on policy and clinical implications rather than restating results. The revised Conclusion emphasizes the need to strengthen acute stroke care at TTH (including establishing a dedicated stroke unit or protocolized acute stroke pathway), enhanced monitoring and early management of high-risk features (fever, pneumonia, elevated systolic BP), targeted screening and community education, and further prospective research to confirm observed associations and investigate ethnic and sex-based disparities (lines 365-374).

The reference list combines local and international sources but includes some outdated ones. In particular, Hatano 1976, which refers to the old WHO definition of stroke, is outdated.

Suggestion: This reference should be replaced or supplemented with more recent and comprehensive sources, such as the AHA/ASA 2013 statement or more recent systematic reviews on stroke definition. We agree and have replaced the Hatano (1976) citation with the AHA/ASA 2013 expert consensus statement, 'An Updated Definition of Stroke for the 21st Century'. (line 60)

Attachment

Submitted filename: Response to Reviewers.docx

pone.0340447.s006.docx^{(33.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0340447.r003

Decision Letter 1

Mickael Essouma

24 Nov 2025

PONE-D-25-39162R1

IN-HOSPITAL SURVIVAL AND PREDICTORS OF MORTALITY AMONG STROKE PATIENTS AT A TERTIARY HOSPITAL IN GHANA: A 3-YEAR RETROSPECTIVE COHORT STUDY

PLOS ONE

Dear Dr. Abdulai,

Please submit your revised manuscript by Jan 08 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Mickael Essouma, M. D.

Academic Editor

PLOS ONE

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Additional Editor Comments:

I have added some comments to this decision letter to further improve the manuscript.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: (No Response)

Reviewer #2: Yes

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6. Review Comments to the Author

Reviewer #1: The conclusion section of the abstract needs to be rewritten. It should be written more concisely and based on the findings, rather than repeating the findings.

The introduction section provides a poor review of the literature and does not clearly explain and present the research gap.

The type of study should be stated more clearly and precisely.

Reviewer #2: (No Response)

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Reviewer #1: No

Reviewer #2: Yes: Dr. Hassan Karami

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PLoS One. 2026 Jan 7;21(1):e0340447. doi: 10.1371/journal.pone.0340447.r004

Author response to Decision Letter 2

12 Dec 2025

Reviewers Comments Author Responses

I suggest removing “3-year” in the manuscript’s title and perhaps replacing it with “50-day” because it gives the impression that the duration of follow-up was 3 years; meanwhile, this was likely a short-term cohort (50 days of follow-up) with patients being followed up only from the day of hospital admission for stroke to the day of discharge. The 3-year period is rather the study period

Thank you for the suggestion regarding the use of “3-year” in the title. we agree that including a time period may unintentionally imply a 3-year patient follow-up, which does not reflect the study design. Although the study used three years of retrospective hospital records, individual patients were followed only until discharge or death.

Additionally, the follow-up window was not fixed at 50 days; rather, it varied for each patient and ended at discharge or death. The mean survival time was 67 days, and so specifying a “50-day follow-up” in the title would inaccurately describe the study results.

To avoid misinterpretation while ensuring accuracy, we will remove “A 3-Year” from the title entirely.

Abstract. It includes 332 words whereas the max word count of abstracts in PLOS One abstracts articles is 300. Therefore, consider reducing the word count in the abstract section by decreasing for example, the length of the introduction sub-section of the abstract. Review the interpret ation of results of survival predictors

Thank you for the feedback regarding the length of the abstract. We appreciate the reminder about the 300-word limit for PLOS One abstracts. We have revised the abstract and reduced it from 332 words to 300 words, primarily by shortening the introductory section and tightening the presentation of the results. We also reviewed and refined the interpretation of the survival predictors to ensure clarity and accuracy.

I find it incredibly long given that stroke is relatively well known in medicine, even in Africa. Could you then reduce its length to a max of 1.5 pages? This would entail focusing on current knowledge on stroke in sub-Saharan Africa and Ghana, gaps in knowledge on stroke in sub-Saharan Africa and Ghana, and finally, the rationale for conducting this study, the study’s aim, and the overarching goal of the study. Thank you very much for this insightful comment. We appreciate the concern regarding the length of the Introduction section. While we agree that stroke is a well-known condition globally, the burden, patterns, and predictors of survival in sub-Saharan Africa, particularly in Ghana, remain significantly under-documented compared with other regions. For this reason, the Introduction aimed to provide sufficient contextual background on:

1. The epidemiology of stroke in sub-Saharan Africa,

2. Existing knowledge gaps regarding stroke outcomes and survival in Ghana, and

3. The justification for conducting a predictive survival analysis within this setting.

These elements were included to help readers, especially those unfamiliar with the Ghanaian or African context, understand the unique clinical and health-system factors that make stroke survival research in this region essential. However, we appreciate the

Editor’s suggestion on conciseness.

Did you use the RECORD guidelines for reporting a retrospective cohort (see the manuscript’s title) or a retrospective chart review (link:http://doi.org/10.3352/jeehp.2013.10.12) study? If the study was a retrospective

cohort, was it a predictive cohort as expected (https://doi.org/10.1186/s12982-018-0080-2)? Consider specifying.

Thank you for raising this important point regarding the reporting framework and study classification. The present study was conducted as a retrospective cohort study, where all patients admitted with a confirmed diagnosis of stroke between 2021 and 2023 were identified and followed until the outcome of discharge or death. Although the data were extracted from existing electronic records (LHIMS), the study design aligns more closely with a retrospective cohort than a chart review because:

1. A defined cohort was established based on clear eligibility criteria (all stroke admissions within the specified period).

2. Patients were followed over time from admission to discharge/death to determine survival outcomes.

3. Predictor variables were assessed in relation to a time-to-event outcome, which is consistent with a predictive survival cohort.

In response to the editor’s suggestion, we have clarified in the Methods section that the study followed the RECORD guidelines (Reporting of studies Conducted using Observational Routinely-collected Data), as recommended for retrospective cohort studies using routinely collected health information. The structured checklist used for data extraction was designed to ensure systematic and standardized retrieval of predictor and outcome variables from LHIMS and the patient registers.

This clarification has now been incorporated into the revised manuscript (lines 156-165).

Could you provide details about the sampling method used and sample size estimation (doi:10.1184/ratoil.22722015019)? Did you conduct the study in agreement with the Declaration of Helsinki?

Thank you for this comment. The study did not employ a sampling procedure because all eligible stroke cases admitted to the Tamale Teaching Hospital during the 3-year study period were included. This approach constitutes a census of all cases, not a sample; therefore, a formal sample size calculation was not applicable. The use of a census ensured complete coverage of all stroke admissions within the defined period, thereby minimizing selection bias and increasing the representativeness of the dataset.

Regarding ethical considerations, the study adhered to the principles of the Declaration of Helsinki for research involving human participants. Ethical approval was obtained from the appropriate institutional review board, and permission was granted by the hospital authorities to access patient records. Since the study involved retrospective review of existing records, no direct patient contact occurred, and all extracted data were anonymized prior to analysis to ensure confidentiality and privacy.

These clarifications have been added to the revised manuscript under the Methods section (lines 107-109, 113-114).

How did you handle missing data (10.1097/EDE.000000000000409)?

We appreciate this comment. We have now included a statement describing how missing data were handled. Specifically, patients with incomplete or missing key variables were excluded from the analysis, and the number of excluded cases is reported in Figure 1

Could you provide more details about the study setting (notably regarding their involvement in the management of stroke in Ghana: specialized unit or non-specialized facility, number of stroke cases managed, credentials of health professionals working there…)?

Thank you for this important comment. We have now expanded the description of the study setting to clarify the hospital’s role in stroke management in Ghana, including the type of facility, availability of specialized care, staffing, and the volume of stroke cases managed.

Tamale Teaching Hospital (TTH) is the main tertiary referral centre for northern Ghana and historically managed stroke patients in general medical wards. In September 2024, the hospital established its first dedicated stroke unit to improve coordinated stroke care. The hospital currently has clinicians with specialized training in neurology and stroke care, including a neurologist and a palliative care nurse specialist who support protocol development. As a high-volume referral facility, TTH manages substantial numbers of stroke patients; for example, 105 ischemic stroke admissions were recorded between January and October 2021 (lines 121-134)

How did you collect data on study predictors and outcomes? What was the duration of follow-up at which the outcome variable survival was recorded?

Thank you for this important comment. We agree that clarity regarding data collection and follow-up duration is essential. However, we would like to highlight that these details were already described in the Study Variables subsection (line 140).

Specifically, the manuscript explains that all predictors and outcomes were extracted from routinely collected patient data (LHIMS and physical patient registers) using a structured checklist. The outcome variable, in-hospital survival status, was defined at discharge as either “alive” or “dead.” As stated, the duration of follow-up was from the date of admission to the date of in-hospital death or discharge, which reflects the full length of each patient’s hospital stay. No post-discharge follow-up was available since the study was based on retrospective review of hospital records.

To further improve clarity in response to the reviewer’s suggestion, we have revised the Methods section to make these details more explicit and easier to locate, while keeping the core content unchanged

Data analysis sub-section: Consider providing a reference supporting appropriateness of STATA for the analyses performed

Thank you for this helpful suggestion. Stata is a widely used and well-validated statistical software for conducting survival analyses, including Kaplan–Meier estimation and Cox proportional hazards regression. In response to the reviewer’s comment, a supporting reference has now been added to the Data Analysis subsection.

For example, Stata has been described as an appropriate and robust platform for time-to-event analysis (e.g., Cleves et al., An Introduction to Survival Analysis Using Stata, Stata Press, 2010). This reference has been included (line 170)

Consider adding a sub-section titled “Patient and public involvement” at the end of this section. Is the first sub-section (see title in line 164) about baseline participant characteristics? Consider specifying.

Patient and Public Involvement:

Because this study was a retrospective review of existing hospital records, patients and the public were not involved in the design, conduct, reporting, or dissemination plans of the research. This is consistent with standard practice for retrospective cohort studies using routinely collected clinical data. In response to the reviewer’s suggestion, we have added a brief subsection titled “Patient and Public Involvement” at the end of the Methods section to explicitly state this (lines 214-217).

Clarification of Subsection on Baseline Characteristics: We appreciate the reviewer’s request for clarification. The first subsection indeed describes the baseline characteristics of the study participants. We have revised the subsection heading and introductory sentence to explicitly reflect that it presents baseline demographic and clinical characteristics at admission (line 192).

Where is each ethnicity group mentioned? If provided in Table 1, this information will help better grasp the fact that the Dagomba people are the lowest-risk group (with the highest survival probability) in the studied population of Ghana

We thank the reviewer for this comment. Ethnicity is reported in Table 1 under baseline participant characteristics, as well as described in the study variables section.

Did the participants report at baseline a family history of major adverse cardiovascular events other than stroke among their first-degree relatives?

We thank the reviewer for this comment. At baseline, participants reported only a family history of stroke among their first-degree relatives. Information on other major adverse cardiovascular events was not collected in this study. They only provided comorbidities, including hypertension, and so on, that they were suffering from by themselves.

What were the frequencies of stroke at baseline? What are the frequencies of haemorrhagic and ischaemic stroke at baseline as well as the arterial territories in ischaemic stroke patients and the cerebral localizations of haemorrhagic stroke?

We thank the reviewer for this comment. The frequencies of stroke subtypes at baseline are reported in lines 173–174 and summarized in Table 1, with ischaemic stroke accounting for 51.7% and haemorrhagic stroke for 48.3% of cases. Data on arterial territories in ischaemic stroke patients and cerebral localizations of haemorrhagic stroke were not collected in this study and therefore cannot be reported

Reviewer #1

• Sarfo FS, et al. Long-Term Outcomes of Stroke in a Ghanaian Outpatient Clinic. J Stroke Cerebrovasc Dis. 2018.

• Sarfo FS, et al. Key determinants of long-term post-stroke mortality in Ghana.(line 86)

Reviewer #2

1. The follow-up period (mean survival: 67 days) is not mentioned. Thank you for pointing this out. We have revised the Methods section of the abstract to include the mean follow-up duration (Line 32). It now reads: “Kaplan–Meier survival analysis was used to estimate the survival rate of stroke patients (mean follow-up: 67 days).”

2. A protective effect of diabetes is reported as an unexpected finding, but it is presented without sufficient explanation or cautious interpretation. We appreciate this important comment. In the revised abstract, we have reported the association between diabetes mellitus and mortality more cautiously, noting that the apparent lower risk may be due to differences in case management or residual confounding (lines 45 – 48). The Results section now states: “Diabetes mellitus showed an apparently lower risk of mortality (AHR = 0.62, 95% CI: 0.47–0.82); this finding should be interpreted cautiously as it may reflect differences in case management or residual confounding.” (lines 280-282)

The definition of the outcome is unclear. The text indicates that “survival status” was determined at discharge (alive or dead), but the Kaplan–Meier results report a mean survival of 67 days with follow-up up to 50 days. This creates confusion as to whether the authors considered in-hospital survival only or included post-discharge follow-up. Suggestion: Authors should explicitly clarify how the outcome was defined (discharge status only or extended follow-up). Without this, it is difficult to compare results with other studies and to interpret the Kaplan–Meier curves. Thank you for this important observation. We have revised the Materials and Methods section to explicitly state that survival status was determined at hospital discharge only; no post-discharge follow-up was included. The time-to-event variable used for Kaplan–Meier analysis was calculated as the number of days from admission to discharge or in-hospital death. This clarification has been added to the “Study Variables” (lines 141-144) and “Data Analysis” (lines 175-176) subsections.

3. The follow-up duration and key study limitations (e.g., retrospective design) shoul

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PLoS One. doi: 10.1371/journal.pone.0340447.r005

Decision Letter 2

Mickael Essouma

18 Dec 2025

PONE-D-25-39162R2

IN-HOSPITAL SURVIVAL AND PREDICTORS OF MORTALITY AMONG STROKE PATIENTS AT A TERTIARY HOSPITAL IN GHANA: A RETROSPECTIVE COHORT STUDY

PLOS One

Dear Dr. Abdulai,

Please submit your revised manuscript by Feb 01 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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The reviewer has recommended manuscript acceptance for publication. However, there are still some issues that need to be addressed before the manuscript can be finally accepted.

Mickael Essouma, M.D.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: (No Response)

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

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4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: (No Response)

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: (No Response)

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6. Review Comments to the Author

Reviewer #1: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

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PLoS One. 2026 Jan 7;21(1):e0340447. doi: 10.1371/journal.pone.0340447.r006

Author response to Decision Letter 3

20 Dec 2025

First, it is still unclear whether you assessed predictors of survival or predictors of death given the multiple inconsistencies between the manuscript's title and different parts of the manuscript (eg, text of the methods and results sections and titles of tables 4, 5) in this regard. Consider making that information clear from the title of the manuscript to the end of the manuscript. We have revised the manuscript to consistently focus on predictors of in-hospital mortality. The title, methods, results, and Tables 1 and 2 have been updated accordingly

Second, you did not specify the study design in the materials and methods section whilst you clearly specified the design and provided a valid reason why this study was a predictive retrospective cohort. You are therefore urged to copy that information from the response letter and paste it in the first sub-section of the Material and Methods section which would be termed "Study design" We thank the Academic Editor for this important observation. We acknowledge that although the study design was clearly described in our previous response letter, it was not explicitly stated within the Materials and Methods section of the manuscript. We have now addressed this by adding a dedicated subsection titled “Study design” as the first subsection of the Materials and Methods section. This subsection clearly states that the study employed a retrospective cohort design and provides the rationale for its appropriateness. The relevant text has been incorporated into the manuscript (Page 6, Lines 101–106).

Third, in the response letter, you claimed that you could not stratify data by ischaemic and hemorrhagic stroke subtypes. However, you did not mention that information in the limitations statement of the Discussion section. Consider addressing this issue in the manuscript per se. We have clarified this issue in the Discussion section by explicitly stating that although stroke subtypes were reported at baseline, stratified analyses by ischaemic and haemorrhagic stroke were not performed, and detailed localization data were unavailable (Page 26, Lines 390–395).

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PLoS One. doi: 10.1371/journal.pone.0340447.r007

Decision Letter 3

Mickael Essouma

22 Dec 2025

IN-HOSPITAL SURVIVAL AND PREDICTORS OF MORTALITY AMONG STROKE PATIENTS AT A TERTIARY HOSPITAL IN GHANA: A RETROSPECTIVE COHORT STUDY

PONE-D-25-39162R3

Dear Dr. Abdulai,

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Mickael Essouma, M. D.

Academic Editor

PLOS One

Additional Editor Comments (optional):

To avoid disrupting the flow of the results section, consider moving the «Patient and Public Involvement» sub-section, which is currently in the results section (lines 220-223), to the end of the Materials and Methods section.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0340447.r008

Acceptance letter

Mickael Essouma

PONE-D-25-39162R3

PLOS One

Dear Dr. Abdulai,

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. Data used for the analysis.

(XLSX)

pone.0340447.s001.xlsx^{(211.5KB, xlsx)}

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pone.0340447.s002.pdf^{(929KB, pdf)}

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Submitted filename: Reviewer Report.docx

pone.0340447.s003.docx^{(27KB, docx)}

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Submitted filename: Response to Reviewers.docx

pone.0340447.s006.docx^{(33.9KB, docx)}

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Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

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PERMALINK

In-hospital survival and predictors of mortality among stroke patients at a tertiary hospital in Ghana: A retrospective cohort study

Sulemana Baba Abdulai

Julius Kwabena Karikari

Penias Tembo

Theogene Habumugisha

William Tembo

Alhaji Ibrahim Cobbinah

Yeetey Enuameh

Roles

Abstract

Introduction

Methods

Results

Conclusion

Introduction

Materials and methods

Study design

Patient identification

Fig 1. Identification and eligibility criteria used for the study population in the analysis.

Study setting

Study variables

Table 1. Demographic/baseline and clinical characteristics of stroke patients at TTH.

Data sources and tools

Data analysis

Patient and Public Involvement

Results

Baseline demographic and clinical characteristics of stroke patients

Fig 2. Admission outcome of stroke patients at TTH.

Survival probability of stroke patients

Fig 3. Overall Kaplan-Meier survival curve of stroke patients at TTH.

Fig 4. Kaplan-Meier survival analysis by stroke type.

Fig 5. Kaplan-Meier survival analysis by sex.

Sociodemographic and clinical predictors of in-hospital mortality among stroke patients

Table 2. Sociodemographic and clinical predictors of in-hospital mortality among stroke patients.

Discussion

Implications for practice and research

Strengths and limitations

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Mehdi Sharafi

Roles

Author response to Decision Letter 1

Decision Letter 1

Mickael Essouma

Roles

Author response to Decision Letter 2

Decision Letter 2

Mickael Essouma

Roles

Author response to Decision Letter 3

Decision Letter 3

Mickael Essouma

Roles

Acceptance letter

Mickael Essouma

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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