Case fatality rate and its determinants among admitted stroke patients in public referral hospitals, Northwest, Ethiopia: A prospective cohort study.

According to the global burden of disease 5.5 million deaths were attributable to stroke. The stroke mortality rate is estimated to be seven times higher in low-income countries compared to high-income countries. The main aim of the study was to assess the 28 days case fatality rate and its determinants among admitted stroke patients in public referral hospitals, in Northwest Ethiopia. A hospital-based prospective cohort study was conducted from December 2020 to June 2021. The study population was 554 stroke patients. Based on Akakian Information Criteria, the Gompertz model was fitted to predict the hazard of death. The study included admitted stroke patients who were treated in the general medical ward and neurology ward. The mean age of the participants was 61 ± 12.85 years and 53.25% of the patients were female. The 28-days case fatality rate of stroke was 27.08%. The results from Gompertz parametric baseline hazard distribution revealed that female sex adjusted hazard rate (AHR = 0.27, 95% CI:0.18-0.42), absence of a family history of chronic disease (AHR = 0.50, 95%CI:0.29-0.87), good GCS score (AHR = 0.21, 95% CI:0.09-0.50) and the absence of complication during hospital admission (AHR = 0.16, 95% CI:0.08-0.29) were factors which decrease hazard of 28 days case fatality rate. While, hemorrhagic stroke sub-type (AHR = 1.38, 95% CI:1.04-3.19), time from symptom onset to hospital arrival (AHR = 1.49, 95% CI:1.57-2 .71), time from confirmation of the diagnosis to initiation of treatment (AHR = 1.03, 95% CI:1.01-1.04), a respiratory rate greater than 20 (AHR = 7.21, 95% CI:3.48-14.9), and increase in NIHSS score (AHR = 1.16, 95% CI:1.10-1.23) were factors increasing hazard of 28 days case fatality rate of stroke. At 28-days follow-up, more than one-fourth of the patients have died. The establishment of separate stroke centers and a network of local and regional stroke centers with expertise in early stroke evaluation and management may address challenges.

Introduction

In 2020, stroke was turned into the second leading cause of death after ischemic heart disease [1]. WHO estimated that globally 15 million people suffer from stroke each year [2]. Global death due to ischemic stroke is lower than due to hemorrhagic stroke [1]. Stroke mortality is estimated to be seven times higher in low-income countries compared to high-income countries [2]. Sub- Saharan Africa (SSA) is undergoing an epidemiologic transition where the increasing morbidity and mortality of non-communicable disease is competing with the already surging burden of infectious disease [3].

A systematic analysis for the global burden of disease reported, globally the age-standardized rate of deaths due to stroke decreased by 36·2% from 1990 to 2016, these death rates also declined for all but southern sub-Saharan Africa have no significant change in death rate [1].

The best method to control the stroke burden and meet the global goal of a 2% reduction each year is by primary prevention via early detection of stroke risk factors. A study showed that 90.5% of the global burden of stroke was attributable to modifiable factors [4]. Unlike in developing countries, stroke mortality is decreasing in the developed world [5]. The decreased percentage of stroke hospitalization and mortality in developed countries likely reflects the advancements in acute stroke care [6].

On the contrary, the evidence shows an increase in trend and mortality of stroke in SSA, good-quality data on mortality of stroke patients in developing countries specifically Ethiopia are still deficient. So, this study aimed to assess the 28-days case fatality rate and determinants of case fatality rate among admitted stroke patients in public referral hospitals, in Northwest Ethiopia, from December 2020 to June 2021.

Methods

Study design, and settings

A prospective cohort study design was conducted in three public referral hospitals located in Northwest Ethiopia, including the University of Gondar teaching hospital located in Gondar, Tibebe Gion comprehensive specialized hospital, and Felege Hiwot referral hospital which are located in Bahr Dar. All these hospitals are the most equipped in northwest Ethiopia specifically in having CT scans as an imaging modality. The study period was 7 months from December 2020 to June 2021.

Study population

The source population was all stroke patients admitted to the public referral hospitals in Northwest Ethiopia. The study population was all stroke patients diagnosed and confirmed during the study period in the three referral hospitals. Five hundred fifty-four adult stroke patients were included.

Inclusion and exclusion criteria

All patients (age ≥ 18 years) diagnosed with stroke and admitted to the medical or neurology ward of the three public hospitals during the study period were included. The exclusion criteria were specified to exclude admitted patients who died before confirmation of diagnosis, after further evaluation, if the initial assessment or diagnosis of stroke was later changed to another case (ruled out stroke) and patients who were being treated by other health institutions, referred after treatment or due to complications.

Study variables

Dependent variable

Twenty-eight days case fatality rate of admitted stroke patients.

Independent variables

Socio-demographic and behavioral characteristics (age, sex, residence, marital status, occupation, educational level, alcohol use, smoking habit, diet habit, and activity level). Patient characteristics (clinical presentation, presence of co-morbidities and complications, time from onset of symptom to hospital arrival, time to CT scan and time start of treatment from confirmation of diagnosis, type of stroke, length of hospital stay, GCS score, mRS score NIHSS score, respiratory rate, temperature, pulse rate blood pressure, and ICH score) were the independent variables of the study.

Operational definition

Event: death recorded among admitted stroke patients during 28 days of follow-up (in the hospital or after hospital discharge in the period reported by caregiver).

Censored:(i) if the patient is alive until the 28th day of admission, (ii) if the patient is dead before the diagnosis is confirmed, (iii) if the cause of death is not a stroke, and (iv) if the patient is referred to other health institutes.

Time to event: Time from hospital admission until the death of the patient is confirmed either during admission or after discharge within the 28 days of follow-up.

Twenty-eight days case fatality rate: was calculated using the number of deaths due to stroke during the 28 days follow-up period as the numerator and the total number of stroke patients during the 28-day follow-up period as the denominator.

Data collection procedure

Data collection was carried out by one internal medicine resident (R-3) for each hospital with training on the contents of the data collection tool. Data collectors collected all relevant data from patients’ charts and interviewed the patients/caregivers using a prepared data extraction form and questionnaire. History used for the study was taken from the patient and/or relatives in the language they understood (in Amharic). Important data on the investigations done, medication, duration of treatment, hospital arrival date was collected by chart review, Controversial data like laboratory interpretation, drug therapy problems, and unclear data on patients’ charts were discussed with physicians working in the medical/ neurology ward of the Hospitals during the study period. Data on the event (mortality) was collected as the event happens if it is in the hospital or if the event happens after discharge it was collected by close telephone follow up every week until the 28th day from admission. Using interview baseline socio-demographic information, clinical presentation, past medical illness, duration of symptom, time from symptom onset to hospital arrival, from hospital arrival to CT-scan, time from hospital arrival to treatment initiation was collected by interview while, findings of imaging’s like chest X-rays, and brain imaging encountered complications and management approaches of stroke patients, death during admission was recorded from the death report. Finally, the death of the patient after discharge was recorded using close telephone follow-up.

Data quality assurance

To maintain data quality, the data collection instrument which consisted of the interview questionnaire and the data extraction format was assessed by a neurologist for clarity and comprehensiveness of its contents. Pre-testing was done on 5% (28) of the study participants before the start of the actual study at Debre Tabor comprehensive specialized hospital. All the necessary modifications and adjustments were done before implementing in the main study. The principal investigator close supervised the data collection process. To ensure its completeness and consistency, the maximum effort was done at the level of data entry, analysis, interpretation, and presentation to maintain the quality of the data.

Data processing and analysis

The collected data were coded, checked, and entered by using Epi-info version 7. It was cleaned and edited by simple frequencies and cross-tabulation before analysis. Then, the data was exported to STATA version 16 software for analysis. Descriptive statistics and numerical summary measures were presented using frequencies distribution tables.

Candidate variables were selected at p < 0.25 on binary cox regression for multivariable cox regression. On multivariable Cox regression predictors with a probability value, less than 0.05 was considered statistically significant. A global test was fitted and showed the model was inadequate. The best fit model was selected using Akakian Information Criteria (AIC) and the Log-likelihood ratio test. The lowest Akakian Information Criteria and the highest Loglikelihood ratio value indicate the best fit model, among those Gompertz model was selected. Finally, the Gompertz model was fitted to predict the hazard of death. The 28 days case fatality rate of different variables was compared using the cox-proportional hazards regression model and the Log-Rank test.

Ethical approval and consent of the participant

The study was performed under the ethical standards of the Helsinki declaration, Ethical clearance was obtained from the Ethical Review Committee of the College of Health Science, Debre Tabor University with the reference number of CHS/3238/2013 E.C. Copy of ethical clearance was submitted to medical directors of the three hospitals. Matrons of the medical/neurology wards of the hospitals were informed. Informed written consent was obtained from each selected patient/ when the patients are not able to give informed written consent was obtained from a close relative or caregiver. Neither the case records nor the data extracted were used for any other purpose. The confidentiality and privacy of patients were assured throughout by removing identifiers from data collection tools using different codes.

Results

Sociodemographic, and behavioral characteristics of participants

A total of 554 participants were being treated in the medical and neurology ward. The majority 295(53.25%) of the participants were females. The mean age of the participants was 61 ± 12.85 years. The mean age of patients who died during 28 days of follow-up was 60 ±13.3 years and those who were alive were 61.29 ± 9.6 years. Ischemic stroke was the most prevalent sub-type in both sexes (male 29.60% and female 30.68%) (Table 1). The average time from symptom onset to hospital arrival was 40 hours ranging from 1-to 336 hours. while the mean duration from hospital arrival to CT scan was 2.48 ±2.28 hours. In addition, the time from the confirmation of the diagnosis to the start of treatment was 4.38 hours (SD = 9.45). The mean time from confirmation of the diagnosis to the start of treatment among patients who died was 6.36 hours and 3.64 hours for patients who were alive (Table 2).

Table 1

Distribution of sociodemographic and behavioral factors in male and female stroke patients in public referral hospitals, Northwest Ethiopia, 2021.

VARIABLES		MALE N (%)	FEMALE N (%)	TOTAL = 554 N (%)
AGE CATEGORY	20–29	6 (1.08)	3 (0.54)	9 (1.62)
	30–39	5 (0.90)	15 (2.70)	20 (3.61)
	40–49	15 (2.70)	45 (8.12)	60 (10.83)
	50–59	55 (9.93)	70 (12.63)	125 (22.56)
	60–69	85 (15.34)	99 (17.87)	184 (33.21)
	70–79	60 (10.83)	60 (10.83)	120 (21.66)
	80–89	30 (5.41)	6 (1.08)	36 (6.5)
RESIDENCE	Urban	100 (18.05)	75 (13.54)	175 (31.59)
	Rural	159 (28.7)	220 (39.7)	379 (68.41)
MARITAL STATUS	Unmarried	15 (2.70)	0 (0)	15 (2.70)
	Married	229 (41.33)	240(43.32)	469 (84.65)
	Divorced	15(2.70)	20 (3.61)	35 (6.32)
	Widowed	0 (0)	35 (6.32)	35 (6.32)
OCCUPATION	Farmer	160 (28.90)	5 (0.90)	165 (29.78)
	Merchant	35 (6.32)	10 (1.80)	45 (8.12)
	Retired	30 (5.41)	5 (0.90)	35 (6.32)
	Government employee	34 (6.13)	0 (0)	34 (6.13)
	Housewife	0 (0)	275(49.64)	275 (49.64)
EDUCATIONAL STATUS	No formal education	140 (25.27)	235(42.42)	375 (67.69)
	Able to read and write	55 (9.92)		85 (15.34)
	Primary	15 (2.70)	30 (5.41)	35 (6.32)
	Secondary	14 (2.53)	20 (3.61)	19 (3.43)
	Diploma	25 (4.51)	5 (0.90)	25 (4.51)
	Degree and above	10 (1.80)	0 (0)	15 (2.70)
			5 (0.90)
ALCOHOL DRINKING	Non-drinker	120 (21.66)	185(33.39)	305 (55.05)
	Past drinker	100 (18.05)	90 (16.24)	190 (34.30)
	Current drinker	39 (7.04)	20 (3.61)	59 (10.65)
SMOKING HABIT	Non-smoker	240 (43.32)	290(52.34)	530 (95.67)
	Past smoker	19 (3.43)	5 (0.90)	24 (4.33)
FRUIT AND VEGETABLE EATING	< 2 times per week	244 (44.04)	285(51.44)	529 (95.48)
	3–4 times per week	15 (2.70)	10 (1.80)	25 (4.51)
PHYSICAL ACTIVITY LEVEL	Extremely inactive	10 (1.80)	10 (1.80)	20 (3.60)
	Sedentary	35 (6.32)	60 (10.83)	95 (17.15)
	Moderately active	165 (29.78)	210(37.90)	375 (67.69)
	Vigorously active	5 (0.90)	10 (1.80)	15 (2.70)
	Extremely active	44 (7.94)	5 (0.90)	49 (8.84)
TYPE OF STROKE	Ischemic stroke	164 (29.60)	170(30.68)	334 (60.29)
	Hemorrhagic stroke	95 (17.15)	125(22.56)	220 (39.71)

Table 2

Sociodemographic, behavioral, and baseline characteristics of stroke patients who were dead and alive after 28 days of follow up in public referral hospitals, Northwest Ethiopia.

Variable		Dead at 28 days N (%)	Alive at 28 days N (%)
Sex	Male	80 (14.44)	179 (32.31)
	Female	70 (12.63)	225 (40.61)
Age	20–29	0	6 (1.1)
	30–39	5 (0.9)	15 (2.7)
	40–49	20 (3.6)	40 (7.2)
	50–59	30 (5.4)	95 (17.15)
	60–69	69 (12.45)	115 (20.75)
	70–79	15 (2.7)	105 (15.9)
	80–89	11 (2.0)	28 (5.0)
Residence	Urban	46 (8.3)	129 (23.3)
	Rural	104 (18.77)	275 (49.64)
Marital status	Unmarried	0	15 (2.7)
	Married	140 (25.27)	329 (59.38)
	Divorced	5 (0.9)	30 (5.4)
	Widowed	10 (1.8)	25 (4.5)
Occupation	Farmer	45 (8.13)	120 (21.6)
	Merchant	15 (2.7)	30 (5.4)
	Retired	15 (2.7)	20 (3.6)
	Government employee	10 (1.8)	24 (4.33)
	Housewife	65 (11.7)	210 (37.9)
Educational status	No formal education	105(18.9)	270 (48.7)
	Able to read and write	30 (5.4)	55 (9.9)
	Primary	5(0.9)	30 (5.4)
	Secondary	0	19 (3.4)
	Diploma	10 (1.8)	15 (2.7)
	Degree and above	0	15 (2.7)
Alcohol drinking	Non-drinker	70 (12.6)	235 (42.4)
	Past drinker	65 (11.7)	125 (22.5)
	Current drinker	15 (2.7)	44 (7.9)
Smoking habit	Non-smoker	140 (25.3)	390 (70.4)
	Past smoker	10 (1.8)	14 (2.5)
Fruit and vegetable eating	< 2 times per week	145(26.17)	384 (69.3)
	3–4 times per week	5 (0.9)	20 (3.6)
Physical activity level	Extremely inactive	0	20 (3.6)
	Sedentary	20 (3.6)	75 (13.5)
	Moderately active	105 (18.9)	270 (48.7)
	Vigorously active	5 (0.9)	10 (1.8)
	Extremely active	20 (3.6)	29 (5.2)
Type of stroke	Ischemic stroke	60 (10.83)	274 (49.46)
	Hemorrhagic stroke	90 (16.24)	130 (23.46)
Mean symptom onset to hospital arrival (hrs)		45.90	24.2
Time from admission to CT scan (hrs)		1.9	2.7
Time from diagnosis to start of treatment (hrs)		6.36	3.64
Mean Systolic blood pressure at admission		150	148
Mean Diastolic blood pressure at admission		91.53	88.30
Admission Mean Temperature		36.50	36.60
Admission Mean Pulse rate		87.46	82.44
Admission mean Glasgow coma scale score		7.6	12.18
Admission mean modified ranking scale score		4.73	4.17
Admission mean National Institute of Health Stroke Scale score		22.4	14.78

SBP = Systolic Blood Pressure, DBP = Diastolic Blood Pressure, PR = Pulse Rate, mRS = Modified Ranking Scale, NIHSS = National Institute of Health Stroke Scale, GCS = Glasgow Coma Scale, T° = Temperature

28-days follow up outcome and determinants of case fatality rate

Overall, the 28-days case fatality rate was 27.08% (150), the majority of deaths were recorded among hemorrhagic stroke patients 60% (90) and the remaining 40% (60) were ischemic stroke respectively. During the 28 days follow up period, one hundred twenty stroke patients (80%) were dead during in-hospital treatment while the remaining 20% (30) were dead after they were discharged. At 28-days follow-up case fatality rate was slightly higher among male stroke patients 53.3% (80).

The results from Gompertz parametric baseline hazard distribution revealed that the 28-days case fatality rate of female patients was lower by 73% (AHR = 0.27, 95% CI:0.18–0.42) as compared to male patients. Similarly, the risk of death among patients with no family history of chronic disease was lower by 50% (AHR = 0.50, 95% CI:0.29-.87) compared to their counterparts. The estimated hazard of death among stroke patients with good GCS score was lower by 79% as compared to stroke patients with poor GCS score (AHR = 0.21, 95% CI:0.9–0.50). Moreover, stroke patients with no complications were 84% less likely to die than stroke patients with complications (AHR = 0.16, 95% CI:0.08–0.29).

Compared to ischemic stroke the hazard of fatality was higher by 38% in hemorrhagic stroke patients (AHR = 1.38, 95% CI:1.04–3.19). A one-hour delay from symptom onset to hospital arrival leads to a 49% increased risk of death (AHR = 1.49, 95% CI:1.57–2 .71). As well, one hour delay in time from confirmation of the diagnosis to treatment the hazard of death was higher by 3% (AHR = 1.03, 95% CI: 1.01–1.04). 28 days case fatality rate of admitted stroke patients with a respiratory rate greater than twenty was 7.26 times higher than patients with less than or equal to twenty respiratory rates (AHR = 7.26, 95% CI: 2.80–18.8). Besides, the hazard of death stroke among patients with one score increase in NIHSS score resulted in a 16% higher risk of death compared to one NIHSS score lower patients (AHR = 1.16, 95% CI:1.10–1.23) (Table 3). Survival probability curves derived from Log-rank Kaplan Meier of 28 days case fatality rate with different factors were shown (Figs 1–3).

Table 3

Determinants of 28 days case fatality rate among admitted stroke patients in public referral hospitals in Northwest Ethiopia, 2020/21.

Variables	Stroke status		Crude hazard rate (95% CI)	Adjusted hazard rate (95% CI)
	Dead	Alive
Sex
Male	80	179	1	1
Female	70	225	0.71(0.51–0.97)	0. 27 (0.18–0.42)
Age
≤ 60	90	191	1	1
> 60	60	213	1.61 (1.38–1.84)	1.37 (0.84–2.22)
Education level
Primary and below	140	355	1	1
Secondar and above	10	49	0.55(0.32–0.94)	1.10 (0.50–2.45)
Alcohol usage
None drinker	70	235	1	1
Drinker	80	169	1.46(1.06–2.02)	1.31 (0.67–1.48)
Physical activity
≥ moderately active	20	95	1	1
≤ sedentarily active	130	309	1.81 (1.13–2.90)	1.37 (0.68–2.74)
Family history of chronic disease
Yes	36	25	1	1
No	114	379	0.30 (0.21–0.44)	0.50 (0.29–0.87)
Presence of comorbidity
No	65	219	1	1
Yes	85	185	1.54 (1.11–2.12)	1.58 (0.45–2.16)
Presence of complication
Yes	130	95	1	1
No	20	309	0.07(.04–0.11)	0.16 (0.08–0.29)
Type of stroke
Ischemic	60	274	1	1
Haemorrhagic	90	130	2.60 (1.88–3.61)	1.38 (1.04–3.19)
Time to hospital arrival	150	404	1.99 (1.37–2.93)	1.49 (1.57–2 .71)
Time to treatment	150	404	1.02 (1.01–1.03)	1.03 (1.01–1.04)
Admission Glasgow coma scale score
Poor	90	30	1	1
Moderate	50	165	0.19 (0.13–0.27)	1.02(0.55–1.90)
Good	10	209	0.03 (0.01–0.06)	0.21(0.09–0.50)
Diastolic blood pressure	150	404	1.01(1.003–1.02)	1.00 (0.99–1.01)
Respiratory rate
≤ 20	130	394	1	1
> 20	20	10	4.18 (2.61–6.70)	7.21(3.48–14.9)
National Institute of Health Stroke Scale score	150	404	1.2 (1.17–1.24)	1.16 (1.10–1.23)

Fig 1

Survival probability curves derived from Log rank Kaplan Meier of fatality after 28 day follow up and sex.

Fig 3

Survival probability curves derived from Log rank Kaplan Meier of fatality after 28 day follow up and GCS score.

Discussion

Main findings

This is the first study reporting case fatality rate for acute ischaemic and haemorrhagic stroke in Northwest Ethiopia. After twenty-eight days follow up greater than one-fourth of stroke patients have died. The results from Gompertz parametric baseline hazard distribution revealed that female sex, absence of a family history of chronic disease, good GCS score, and the absence of complications during hospital admission were factors that decrease the hazard of 28 days case fatality rate. While, hemorrhagic stroke sub-type, time from symptom onset to hospital arrival, time from confirmation of the diagnosis to initiation of treatment, a respiratory rate greater than 20, and an increase in NIHSS score were factors increasing the hazard of 28 days case fatality rate.

Comparison with literatures

This prospective cohort study was conducted among stroke patients in Northwest Ethiopia to identify the 28 days follow-up case fatality rate and its determinants among admitted stroke patient. On a 28-day follow-up, 27.08% of patients have died which was comparable with a studies from Ethiopia (29.3%) (30.1%), and Uganda (26.8%) (15,19,20). However, the rate was higher compared to studies from Nigeria (21.2%), (17.7%), Cameroon (23.2%), Kenya (18.4%), Brazil (12.5%), and Saudi (9.7%) [5, 7–10]. On contrary, the fatality rate was lower than in a study conducted in Tanzania (33.3%) and Senegal (38%) [9, 11]. The possible variation in mortality rate might be with the milder cases might not seek health care while the most severe case may die before reaching hospitals, set up of the hospital, complications, available resources, and comorbidities.

Consistent with previous studies [5, 10, 12–15] the present study revealed that the 28 days case fatality rate was higher in hemorrhagic stroke patients compared to ischemic stroke patients.

This study found out the 28-days case fatality rate of female stroke patients was lower as compared to male stroke patients which were supported by a study from Nigeria [16]. This finding contradicted a study from Kenya [10]. The possible justification might be variation with behavioral risk factors like alcohol drinking and cigarette smoking [8, 17].

This study evidenced that the risk of death among patients with no family history of chronic disease was lower compared to their counterparts. Moreover, stroke patients without complications were less likely to die than stroke patients with complications. This finding is in line with existing literature [7–9, 12, 18–20].

If stroke patients were one hour delayed from symptom onset to hospital arrival, they were more likely to die. This finding is also in line with a study from the Democratic Republic of Congo [14]. While a study from Saudi reported time to hospital arrival was insignificant [8]. Possibly, time will be great in the prevention of either neurologic or medical complications and the other is in the prevention of ischemic to hemorrhagic transition.

The estimated hazard of death among stroke patients with a good GCS was lower as compared to stroke patients with a poor GCS. Similarly studies witnessed that a Poor GCS is a risk factor for mortality of admitted stroke patients [7, 12, 21, 22]. The explanation might be poor GCS indicates the severity of the brain injury, the presence of neurological complications like increased ICP and herniation which are fatal even if the patient survives it will leave devastating disability.

Respiratory rate was a significant predictor of case fatality rate among admitted stroke patients. Respiratory rates greater than twenty were seven times higher risk of death compared to their counterparts. Different studies supported the finding of the current study [12, 23]. The reason for this association might be tachypneic patients may have complications like aspiration pneumonia, pulmonary embolism, CHF, and other clinical conditions making the treatment difficult and the treatment outcome worrisome.

Besides, the hazard of death among stroke patients with higher NIHSS resulted in a higher risk of death compared to the ones with lower NIHSS scores which was in line with existing literature [5, 7, 12]. Possibly, it was prudent the NIHSS is a well-validated tool for assessing initial stroke severity irrespective of the subtype and has previously been shown to be associated with predicting mortality [24].

Strength and limitations of the study

Important strengths of our study were, that it was a multicentered, prospective cohort study and the enrollment of CT scan confirmed stroke cases which make the fatality rate more accurate and reliable. The study provides a preliminary database on case fatality rate which can inform stroke management strategies and interventions required to decrease the fatality rate associated with stroke. Additionally, no patients were lost to follow-up. Our study included patients who were admitted to the hospital whereby stroke severity extremes might not be included. In addition, we excluded patients who died before having a CT scan case fatality rate might be underestimated.

We followed discharged patients by telephone, not by face-to-face interview. Thus, the detailed data on stroke severity, recovery, and disability could not be collected in this study and the accuracy of this self-reported event may limit the result.

Conclusion

After a 28-days follow-up, greater than one-fourth of the patients have died. Female sex, absence of a family history of chronic disease, good GCS score, and the absence of complications during hospital admission were factors that decrease the hazard of 28 days case fatality rate. While, hemorrhagic stroke sub-type, time from symptom onset to hospital arrival, time from confirmation of the diagnosis to initiation of treatment, a respiratory rate greater than 20, and an increase in NIHSS score were factors increasing the hazard of 28 days case fatality rate. Therefore, preventive measures for individuals with a family history of chronic illness and comorbidities should be planned. Efforts should be made to establish best practices for acute stroke care in our settings. The establishment of separate stroke centers and a network of local and regional stroke centers with expertise in early stroke evaluation and management may address the challenges. Moreover, Prospective community-based stroke incidence and prevalence studies are required to define the true mortality of stroke in Ethiopia. Hence, future studies collecting community-based data, and carrying out a pooled data analysis may detect further determinants of stroke mortality rate.

(DTA)

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10 Mar 2022

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

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Reviewer #1: Partly

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

Reviewer #1: No

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

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Reviewer #1: No

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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: The study by Gashaw Walle Ayehu and colleagues reported on the 28-day mortality rate following a hospital admission for stroke in three referral hospitals in Ethiopia, and analysed a number of risk factors associated with the increased or decreased mortality rate. The reported results are important as there is limited data on stroke outcomes in Ethiopia. The descriptive data provided in the study are valuable, but I have reservations about their choice of statistical methods. Moreover, I find the quality of reported results suboptimal. Some of the reported findings, including a dramatic excess death among ischaemic stroke patients compared to haemorrhagic, which was left without an explanation, is making me hesitant about recommending this manuscript for publication. I have provided an extensive list of comments, which hopefully can help the authors to improve their manuscript. I will leave the final decision with the Editor.

Abstract

Background: a missed word between “was” and “28”,

Results: rewrite this section and organise your findings more efficiently. Perhaps, group the risk factors/determinants into those that increase and those that decrease the 28-day mortality rate. List and properly name the risk factors, so that the readers can understand which sex was the predictor. For example, male sex.

Line 14. Please change the wording. The majority (80%) died in hospital and the rest after a discharge from hospital.

Introduction

Line 2. Please, change the wording. By 2020, stroke has become the second leading cause of death.

Please, check the reference N4, African continent is not responsible for 86% of all stroke deaths. It is an overestimation. I believe the correct wording would be that 86% of admitted stroke patients in Africa, die.

Inclusion and Exclusion criteria

p 12, line 2. Delete admitted patients, keep “All patients diagnosed with stroke and admitted …”

p12, line 3. Change the beginning of the new sentence: We excluded individuals who died before …

The exclusion criteria were specified to exclude people admitted with stroke who died before having a CT scan. These were likely the most severe stroke patients. If excluded from analysis, the mortality rates would be underestimated. Please, report how many patients with suspected stroke died on admission before they could undergo a CT scan. Address this in the limitation section. If the proportion is high (15-20%) among all stroke patients who died in hospital, you might want to run a sensitivity analysis adding these individuals.

p 12, line 13, a comma is missing between education level and alcohol use.

In the method section: please describe in detail how the 28 mortality rate was calculated, what was used in the numerator and denominator. Why 28 mortality rate? Should it be 28-day case fatality?

Definitions, no definition was provided for how the 28-day mortality rate was calculated, and what constituted the numerator and denominator. Therefore, I cannot clearly understand what rate was reported, however I wondered if a more appropriate terminology to use here would be 28-day case fatality. From what I gathered in the paper, the study included all stroke patients and the outcome of interest was death at 28 days from any cause, the rate was calculated as percentage, so it is a case fatality. How did you differentiate between first stroke and readmission for stroke? Did you include only the first hospital admission for stroke into the denominator or any subsequent hospital admission?

Study variables

Please, describe how crude hazard rate and adjusted hazard rate were calculated. How was the adjustment made?

Data collection

p 13, line 27. I was surprised to read that the data collection was carried out by a GP (general practitioner). Is this correct or was it just a translation issue? Do you have neurologists who review patients with stroke? If this information is indeed correct, you might want to say something more general, for example, “the data collection was carried out by a treating doctor and validated by another physician”, or something similar. This will get you around additional criticism. However, I do have my own reservations about the quality of stroke diagnosis in this study if there was no input from a neurologist or trained stroke physician with data collection. I read in a sentence describing the data quality that a neurologist has assessed the clarity and comprehensiveness of data collected, but how about the quality and accuracy of stroke diagnosis? Have they validated or assessed it in any way?

Table 1. Not the most informative and conventional presentation with these three columns, dead at 28 days and alive at 28 days, and then one for the total population. I would suggest to make a new table 1 with just the descriptive statistics separately for men and women admitted with stroke. This will give a more detailed information about your study population. Then, make table 2 with sociodemographic and other characteristics of men and women who died from stroke at 28 days only. Ideally, you would separate ischaemic and haemorrhagic stroke, as these two types have important differences in risk factors, and particularly, management. I would stress out the importance of presenting the results for specific stroke types, especially given the findings of much higher mortality in patients with ischaemic stroke reported in this study.

P 17, line 1. In brackets missing 0.87

P 17. Line 4. The authors reported that mortality was 82% higher among ischaemic stroke patients compared to haemorrhagic. This is an odd finding. There is a general agreement in medicine that haemorrhagic stroke is associated with higher mortality than ischaemic. This is observed in all populations despite their ethnic origins. This is probably the first study to report such dramatic excess in ischaemic stroke death. The possible explanation for the increased mortality rate here is some kind of a misclassification. Perhaps, the type of stroke was not well defined in this study, possibly due to a low quality of the brain imaging, or maybe because of the large proportion of individuals with stroke type not specified, which was actually haemorrhagic stroke. Although, usually cases of unspecified stroke are ischaemic strokes. The authors have mentioned that the stroke diagnosis was confirmed by a CT scan. Do they have access to the MRI or angio? Were all hospitals equipped with the same medical devices? Alternative explanation, might be that patients with severe haemorrhagic stroke don’t get admitted to the hospitals included in the study and were transported to a different facility. I really find it difficult to believe a report that describes such a dramatic difference, unless they can offer a convincing explanation. The current manuscript just states the findings on page 19 without any explanation. However, when I checked the results, in table 2 which reports Hazard Ratios, authors have chosen haemorrhagic stroke as a reference category, and the results showed a lower HR for ischaemic stroke 0.38 (0.2- 0.53). so I am puzzled about why they reported 82%.

Page 17, line 6. Instead of “As the Time”, use “If” or “A one hour delay lead to..”

The graphs with Kaplan-Mayer survival curves are not numbered and not referred to in the results section. They are just pinned at the end of the text. Please, name the graphs and refer to them in the text.

In the conclusion paragraph, the opening sentence brings up the chronic and infectious disease, but these have not been assessed as comorbidities in the analysis. I would suggest omit the first sentence. Conclusion should be rewritten to provide the summary of the findings, put the findings in the broader context, as well as discuss the implications for public health, research and policy. Next, the authors have discussed the importance of establishing stroke centres and prospective community-based stroke studies, please, omit or explain how these are relevant to the study findings.

At the end of the paper, authors have provided a list of acronyms and abbreviations, but did not use some of them in the manuscript. Please, revise the list and delete unnecessary, including HIV, AF, HIV and some other. Second, it would be really interesting to know if stroke outcomes, either survival curves or 28-day mortality rate, were different between HIV positive and HIV negative patients. If the HIV status was known, please, add there results to the text.

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Reviewer #1: Yes: Olena Seminog

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19 Apr 2022

Dear respected Chief editor and reviewers I have tried to go through all the comments and suggestions, tried to make our manuscript more clear. specifically we have sent the data as supplementary file and it can be accessed through your data base. thank you indvance

Submitted filename: Response to reviewers.docx

Click here for additional data file.

26 May 2022

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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)

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

**********

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

Reviewer #1: No

**********

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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6. 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: Reviewer’s comments

Terminology. Stroke fatality rate is wrong.

Dear Editor,

The authors have made important changes to the manuscript, making it much stronger. However, they still didn’t get the terminology quite right. 1. They have changed 28 mortality rate to stroke fatality rate, which is a wrong term. In their response letter, they have agreed that the reported rate is case fatality rate. However, they are not using the correct terminology in the manuscript. It is important that the authors change the terminology. Since, fatality rate is the death rate observed in a designated series of individuals affected by a simultaneous event (e.g. victims of a disaster). This term should be avoided. – Dictionary of Epidemiology by M. Porta. Instead, use case fatality or case-fatality rate. Some researchers disagree about “rate”, because from a mathematical point of view, by taking values in the range 0 to 100%, it is not a rate, but risk. However, case-fatality rate remains widely used. Check this recent paper:

Hatem A. Wafa ,Charles D. A. Wolfe,Ajay Bhalla,Yanzhong Wang

Long-term trends in death and dependence after ischaemic strokes: A retrospective cohort study using the South London Stroke Register (SLSR).

2. Next, some of the findings presented are hard to believe. In particular, that ischaemic stroke had a higher hazard rate of 28-day case fatality than haemorrhagic stroke after adjusting (Table 2). I would strongly suggest that the authors check their data and their model again.

In the initial response, they have acknowledged that a mistake occurred with data transferring, which resulted in a wrong conclusion. It is likely that something like this has happened again.

Further comments to authors

Dear Author, thank you for revising the manuscript, which made it look much stronger. However, I am not completely satisfied with the quality of your paper and would like you to work on it again. These are my comments.

3. Abstract and text – Abbreviations - AHR and CHR. Please, when you introduce these words, first write them in full with the abbreviations in brackets. You can then use the abbreviations. In Table 2 headings, use the words not abbreviations.

4. Line 6, page 34. You are making a misleading statement about 86% in-hospital mortality. And using a wrong reference again. Remove reference 2 about stroke and hyperthermia. It doesn’t give information about stroke statistics. Change your statement that Africa accounts for 86% in-hospital death, because it is wrong. I have made this comment before.

5. Line 19, page 34. The sentence needs a reference to support it. The decreased percentage of stroke hospitalization and mortality in developed countries likely reflects the advancements in acute stroke care.

You can use the following as a citation

Seminog OO, Scarborough P, Wright FL, Rayner M, Goldacre MJ. Determinants of the decline in mortality from acute stroke in England: linked national database study of 795 869 adults. BMJ. 2019 May 22;365:l1778. doi: 10.1136/bmj.l1778.

6. Line 4, page 35 Remove Study aim. Replace with Study design and settings

7. Line 11, page 35. Formatting. Remove a full stop

8. Line 12 Remove source and sample size. Replace with Study population.

9. Line 15. Remove the number in brackets (554)

10. Line 16. You don’t need to say that you used the convenient sampling method.

11. Line 10, page 36. Change the dependent variable to 28-days case-fatality rate

Type of stroke

12. Table 1 type of stroke and Line 6, page 39. Check your data again. There is another error somewhere in your analysis or elsewhere. Around 60% of strokes in your study were classified as haemorrhagic and around 40% as ischaemic. It is very unlikely that you have more haemorrhagic strokes than ischaemic in Ethiopia. Change the sentence “Haemorrhagic stroke was the most prevalent…”, as it is not true. Haemorrhagic stroke is not more common or prevalent in any country. If the results remain the same after you have checked the data and analysis, you must discuss in study limitations why there were more haemorrhagic strokes in your study. Are ischaemic strokes too mild for the admission threshold? Do ischaemic stroke patients go to another hospital for some reason? These are just some possibilities for you to think about.

13. Table 2. Describe in methods how you did the adjustment of hazard rate? What were your confounders? The adjustment made a big difference to HR by stroke type. The findings that HR for ischaemic stroke was higher than for haemorrhagic are hard to believe. Check your model.

14. Line 4, page 42. Change the wording. For example, “We reported 150 deaths from stroke at 28 days among 554 patients hospitalised with stroke. There were 90 deaths were from haemorrhagic and 60 from ischaemic stroke. Next, report CFR for ischaemic and haemorrhagic stroke in men and women separately. Check your data really well. When reporting your findings, do not confuse the proportion of ischaemic and haemorrhagic strokes in admitted patients, and the proportion of these two types in patients who died. The classic scenario would be - more ischaemic strokes on admission (higher %), but more deaths from haemorrhagic stroke at 28 days. So far, your presentation of results is confusing.

15. Line 7, page 42. What is the “follow-up stroke fatality rate”? Change this term, as it is not something recognised in epidemiology. Are you talking about death in the community after patient was discharged?

16. Line 9 to 13, page 42. Remove this paragraph. It doesn’t provide any useful information, as you are only listing that the variables that you’ve put in the model.

17. Table 1. Missing percentages and numbers in numerous places (married female, missing number in the column divorced female, missing % in non-drinker female and missing number in past drinker, etc)

18. Table 2 and Table 3. Authors have changed the Table 1 to present findings for men and women separately. They should do the same for Tables 2 and 3. This is particularly important, since female sex seems to be protective.

Discussion

19. In the first paragraph of discussion, summarise the main findings. Then, discuss these findings and compare with literature.

20. Line 4. Remove the first sentence and rephrase the second sentence. You could say this “This is the first study (check if this is correct) reporting case fatality rate for acute ischaemic and haemorrhagic stroke in Northwest Ethiopia. We found that nearly one third of stroke patients dies within 28 days after hospital admission. Case fatality was higher for haemorrhagic stroke (or not)

21. I would advise the authors to use the following subheadings in the discussion. Main findings. Comparison with literature. Strengths and limitations. Conclusions

22. Line 13, page 45. The authors report that 28 day mortality was higher in ischaemic stroke. Not only this sounds wrong, but also contradict their own findings reported earlier, line 4 and line 19, page 42. You reported a 38% higher hazard rate for haemorrhagic stroke. Check your data.

Conclusions

23. Line 13, page 46. Instead of listing the variables that you’ve used in the analysis, provide the specific answers. For example, don’t say type of stroke, but haemorrhagic stroke, not sex, but male sex, and so on. This whole sentence might work better in the discussion in main findings section. But I will leave it with you to decide.

24. Remove acronyms at the end.

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Reviewer #1: No

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23 Jun 2022

Thank you Dear editor and Reviewer, we have made all the changes and adjustments as requested/commented. in addition we have given justifications for the questions raised in author response form. Thank you !

Submitted filename: 2nd Response to reviewers .docx

Click here for additional data file.

9 Aug 2022

15 Aug 2022

Thank you Dear reviewer and editor, We have made the requested changes and accepted the comments

Submitted filename: 3rd Response to reviewers.docx

Click here for additional data file.

19 Aug 2022

CASE FATALITY RATE AND ITS DETERMINANTS AMONG ADMITTED STROKE PATIENTS IN PUBLIC REFERRAL HOSPITALS, NORTHWEST, ETHIOPIA: A PROSPECTIVE COHORT STUDY Y

PONE-D-22-03279R3

Dear Dr. Ayehu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Colin Johnson, Ph.D.

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Additional Editor Comments (optional):

Reviewers' comments:

26 Aug 2022

PONE-D-22-03279R3

Case fatality rate and its determinants among admitted stroke patients in public referral hospitals, Northwest, Ethiopia: A prospective cohort study

Dear Dr. Ayehu:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

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Academic Editor

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