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 Table of Contents  
Year : 2015  |  Volume : 2  |  Issue : 4  |  Page : 165-169

In-hospital stroke mortality and its predictors within one month of ictus: Result from a tertiary hospital in Ilorin, middle belt Nigeria

Department of Medicine, Neurology Unit, University of Ilorin Teaching Hospital, Ilorin, Nigeria

Date of Submission27-May-2015
Date of Acceptance22-Oct-2015
Date of Web Publication22-Dec-2015

Correspondence Address:
Emmanuel Olatunde Sanya
Department of Medicine, Neurology Unit, University of Ilorin Teaching Hospital, Ilorin
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2384-5147.172439

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Aims: Identification of predisposing factor(s) for acute stroke death is of utmost importance to clinicians as this will assist in instituting specific therapy and management. This study was set out to determine factor(s) that predict mortality within 30 days of stroke ictus among adult Nigerians. Materials and Methods: A prospective hospital-based study. It involved consecutive adult stroke patients (≥18 years) admitted into the medical wards of the University of Ilorin Teaching Hospital Ilorin, North central Nigeria, between June 2011 and May 2013. Results: The mean age of 302 patients studied was 60.47 ± 13.60 years with more male (53.4%) and no significant difference in mean age between stroke survivors (60.8 ± 13.4 years) and the deceased (59.6 ± 14.0 years). The random blood sugar in survivors and case fatality were 9.4 ± 5.7 mMol/l and 9.3 ± 4.7 mMol/l, respectively. Mean cholesterol level was 6.1 ± 1.3 mMol and 7.1 ± 2.4 mMol in stroke survivors and deceased, respectively. Serum creatinine was higher among case fatality (107.1 ± 49.7 μmol/l) than survivors (101.7 ± 67.2 μmol/l). Brain Computer Tomography was available for 166 (55%) patients with pathologic stroke sub-types of cerebral infarction −71.1%, intracerebral hemorrhage −25.5%, and subarachnoid hemorrhage −3.0%. The median (interquartile range) National Institutes of Health Stroke Scale (NIHSS) score for all was 12 (8-15), survivors 11 (7-14), and 16 (10-23.5) for the deceased (P < 0.01). Sixty-four patients died within the study period giving a case fatality rate of 21.2%. Risk factors for stroke deaths were low Glasgow coma score (<8), high NIHSS score, and presence of complication. The independent predictors of 30 days mortality were admitting stroke severity using NIHSS score (≥15) (P < 0.001) and the presence of at least a complication (P < 0.001). Conclusion: More than one-fifth of these stroke patients died within the first 30 days of the ictus. Prevention, early recognition, and prompt treatment of complications of stroke could help reduce mortality.

Keywords: Acute-mortality, in-hospital, predictors, stroke

How to cite this article:
Sanya EO, Wahab K W, Bello A H, Alaofin W A, Ademiluyi B A. In-hospital stroke mortality and its predictors within one month of ictus: Result from a tertiary hospital in Ilorin, middle belt Nigeria. Sub-Saharan Afr J Med 2015;2:165-9

How to cite this URL:
Sanya EO, Wahab K W, Bello A H, Alaofin W A, Ademiluyi B A. In-hospital stroke mortality and its predictors within one month of ictus: Result from a tertiary hospital in Ilorin, middle belt Nigeria. Sub-Saharan Afr J Med [serial online] 2015 [cited 2023 Sep 30];2:165-9. Available from: https://www.ssajm.org/text.asp?2015/2/4/165/172439

  Introduction Top

The industrialized countries have witnessed tremendous improvement in the management and treatment of acute stroke. [1],[2] However, same cannot be said of most countries in Africa, especially in the sub-Saharan region. Despite the improvement in the level of care for stroke in the developed countries, stroke still remains the second leading cause of death globally. [3],[4] In several studies from Africa, stroke is the leading cause of in-hospital death as well as the most common cause of neurological admission. [5],[6] Reported in-hospital acute mortality for ischemic stroke is currently put at 10% in the developed countries, while higher values have been reported for hemorrhagic strokes. [7],[8] Available data from the developing countries have also shown a much higher value up to 23% and 68.8% for ischemic and hemorrhagic strokes, respectively. [9],[10]

Acute stroke period is generally taken as the time interval from first 21-30 days after the stroke ictus. [11] This period is potentially fatal and it is when most stroke deaths occur. Identification of potential risk factors that are associated with early stroke death is of utmost important to the clinicians. This will help institute specific therapies to patients at risk of dying and equally prevent complication. Although several studies have tried to describe factors that predict stroke outcome, it is yet unclear whether the supposed decline in stroke case fatality in developed countries are due to the improvement in acute stroke care or the preventive measure put in place in the community. [12]

In Nigeria, like the other sub-Sahara African countries, there is limited information on outcome of acute stroke care and factors that influence it, especially in the middle belt of Nigeria. Therefore, the study was carried out to determine the factors that predict in-hospital acute stroke mortality among cohorts managed in the medical wards of the University of Ilorin Teaching Hospital (UITH), middle belt, Nigeria.

  Materials And Methods Top

During the 3 years study period, all consecutive stroke patients were encouraged to participate in the study. A total of 302 patients (≥18 years) with first-ever stroke were admitted into the medical wards. Stroke was defined according to WHO criteria. [1] Informed consent was obtained from patients or from a proxy where the patient was aphasic or had impaired level of consciousness. Exclusion criteria included previous stroke and stroke-like syndrome (identified by neuroimaging) and those with transient ischemic attack (TIA) whose neurologic deficit resolved within 24 h. Ethical clearance for study was obtained from the Institution's Research Ethics Committee.

Detailed clinical information on risk factors, neurological and physical examination, as well as baseline clinical investigations, was carried out at presentation. The National Institutes of Health Stroke Scale (NIHSS) was used to assess stroke severity (score <15 represents mild and moderate strokes and ≥15 severe stroke). [13] Stroke disability was assessed using the modified Rankin Scale (mRS). Patients' consciousness level was assessed using the Glasgow coma score (GCS).

Routine blood analyses that included random plasma glucose, lipid profile, serum biochemical profile, complete blood count and erythrocyte sedimentation rate, and 12-lead electrocardiogram were done. Hypertension was defined as a previous diagnosis of hypertension or blood pressure readings consistently ≥140/90 mmHg. Diabetes was defined as a previous diagnosis or fasting plasma glucose ≥7 mMol/L. Hyperlipidemia was defined as total cholesterol >6.2 mMol/L, triglycerides >2.24 mMol/L, or low-density lipoprotein cholesterol >4.11 mMol/L, according to Adult Treatment Panel-III (ATP-III) guidelines of the National Cholesterol Education Program (NCEP). [14]

Brain computed tomography (CT) scan was requested for the patients at presentation as part of our protocol. Statistical analysis was conducted using SPSS version 16 for windows, (SPSS Inc., Chicago, IL, USA). Frequency tables were generated for the collected variables. Means and standard deviations (SDs) were determined when the variables have normal distribution and median and interquartile range (IQR) for values that were not normally distributed (e.g. NIHSS). Univariate and multivariate binary logistic regression were used to determine factors that predicted 30 days mortality.

  Results Top

The mean age of the 302 patients was 60.47 ± 13.60 years (SD), while that of stroke survivors was 60.8 ± 13.4 years (SD) and 59.6 ± 14.0 years for the deceased. Breakdown of demographic data is as shown in [Table 1]. There were more males than females (54.3% vs. 45.7%).
Table 1: Demographic characteristics of patients

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[Table 2] shows the clinical characteristics of these patients with first-ever stroke. Hypertension was the most prevalent risk factor (72.2%), followed by hypercholesterolemia (32.5%), diabetes mellitus (19.2%), current heavy alcoholic beverage intake (17.4%), current cigarette smoking habit (14.6%), and previous TIA (7.0%). The mean values of laboratory parameters in survivors versus deceased are random blood sugar −9.4 ± 5.7 mMol/l versus 9.3 ± 4.7 mMol/l (P > 0.05); serum creatinine − 101.7 ± 67.2 μmol/L versus 107.1 ± 49.7 μmol/L (P < 0.05); total cholesterol 6.1 ± 1.3 mMol/l versus 7.1, ±2.3 mMol/l (P < 0.05); and HDL − 0.98 ± 0.7 mMol/L versus 0.95 ± 0.3 mMol/L (P > 0.05). Only 166 (55%) patients had brain CT scan due to financial constraints. The pathologic stroke sub-types were cerebral infarction (CI) −71.1%, intracerebral hemorrhage (ICH) −25.9%, and subarachnoid hemorrhage (SAH) −3%. The median (IQR) NIHSS score for all strokes was 12 (8-15), survivors 11 (7-14), and the deceased 16 (10-23.5) with P < 0.01.
Table 2: Clinical and laboratory parameters of stroke

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Within the 30 days postictus, 64 patients died giving an in-hospital case fatality rate of 21.2%. Based on pathologic stroke type, the case fatality rates were 19.1%, 33.3%, and 40.0% for CI, ICH, and SAH, respectively. Factors associated with 30 days mortality were admitting GCS < 8, stroke severity using NIHSS score >20, and the presence of at least one complication [Table 3]. On multivariate binary logistic regression, admitting NIHSS score (OR 13.773; CI 5.002-37.922; P ≤ 0.001) and presence of complications (OR 5.002; CI 2.307-10.843; P ≤ 0.001) were independent predictors of 30 days mortality [Table 4].
Table 3: Clinical factors associated with 30 days stroke mortality

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Table 4: Result of multivariate logistic regression analysis for independent predictors of 30 days mortality

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  Discussion Top

The main findings of this prospective hospital-based study are that a total of 302 stroke patients were managed within the 3 years study period, out of which 64 patients died within the first 30 days of their first-ever stroke giving in-hospital case fatality rate of 24.2%. The two independent predictors of deaths were high NIHSS score and the presence of at least one complication.

When our result is viewed in the light of what exists in literatures from other regions in Nigerian and other developing countries, our findings is comparable to the documented case fatality profile by Femi and Mansur [15] and Wahab et al[16] from northwestern Nigeria, reported 1-month mortality of 37%, and 28%, respectively, while from the Southwestern Nigeria, Mustapha et al. [11] documented 1-month mortality of 15.5%. A study done among 246 stroke patients in Malaysia reported case fatality of 20.3%. [17] However, lower mortality rates of between 10.0% and 13.6% have been documented from Europe and America. [18] The most commonly reported predictors of acute stroke mortality in literature include admission GCS, hemorrhagic stroke, old age (>70 years), hyperglycemia, abnormal pupillary size, aspiration pneumonitis, stroke severity, elevated body temperature (>38°C), and female sex. [2],[11],[15],[19]

In this study, the leading complications were raised intracranial pressure, seizures, bed sores, aspiration pneumonitis, and hypostatic pneumonia; similar to what has been previously reported. [12],[17] Equally, reported predictors of acute stroke outcome in previous works included size of infarct or hematoma on neuroimaging, age (>65 years), hyperglycemia, atrial fibrillation, and previous stroke. Level of serum uric acid, white blood cell count, and serum creatinine have also been found to affect stroke survival. [2],[20],[21]

The mean age of first-ever stroke in this report was 60.5 ± 13.6 years (range: 20-89 years) and the peak incidence is between ages of 50 and 70 years. This contrasts the mean age in the Framingham studies in which the mean age was 68.7 ± 13.6 years (range: 69-81 years). [2] Our findings corroborate the view that stroke in sub-Sahara Africans affects majorly younger age group who are the main working class with resultant high morbidity and stroke burden. [22]

Uncontrolled systemic hypertension was the predominant risk factor in this report and this reinforces previous works. [5],[8],[10],[14] Dyslipidemia, especially hypercholesterolemia was the second common risk factor for stroke among our patients. It was found in 32.5% of the patients using the NCEP ATP III criteria. The proportion of patients in this study with dyslipidemia is much lower than what 67.9% reported by Karaye et al. from Northwestern Nigeria. [22] The stricter criteria employed in our study to make the diagnosis compared to the earlier study could be responsible. It is equally plausible that difference in diet among the different communities partly account for the difference. Other modifiable risk factors found in this study included history of TIA, tobacco usage, and hormonal contraceptives. The two none modifiable risks in these patients were family history of stroke and sickle cell disease.

Only 55% of stroke patients had brain CT in this study. The relatively high cost of this investigation amidst prevailing poverty in the community could be partly responsible. This is because out-of-pocket payment of health bills is the rule and not the exception in Nigeria. This contrast what obtains in the developed countries with good social welfare package such a health insurance for majority of the populace. [24] The frequencies of the hemorrhagic strokes among those who had neuroimaging is relatively high and when both ICH and SAH are classified under a single entity of "hemorrhagic stroke," the frequency is 29%, which falls within 29-57% in literature among Africans. However, it is much lower that 16-20% in North America. [25],[26] This suggests a higher burden of uncontrolled hypertension in Africa, because the proportion of hemorrhagic stroke in a population have a linear correlation with the prevalence and severity of uncontrolled hypertension. [24],[25],[26],[27]

The high mortality rate recorded in this study is likely to be due to interplay of several factors. One of such is the dearth of neurosurgical intervention facilities in the study center, like most Nigerian teaching hospitals, which is needed for some patients, especially those with raised intracranial pressure, which often times complicate stroke. Equally important is the late presentation of patients to hospitals. From our observation and anecdotal experience, most Nigerian stroke patients undertake tortuous journey before getting to the teaching hospital. This makes it virtually impossible to give thrombolysis for those with ischemic stroke who would have benefitted. [28] Patients taken to private clinics are referred to the secondary or tertiary hospitals after two or more days when they would have developed complications. To improve stroke survivals, there is the need to educate primary care physicians and nurses and the general populace on stroke care and stroke prevention.

Part of the limitation of this study is its hospital-based nature where there is the likelihood of selection bias in favor of bad cases. The fact that not all patients had brain CT also makes difficult to have conclusive report on the frequency distribution of the pathologic stroke sub-types. Albeit we believe our findings are important and relevant to improve stroke care in the country, attending clinicians need to be more proactive in seeking to prevent complications and to institute specific therapy in patients at risk of dying from stroke complication.

  Conclusion Top

Our results have shown that acute stroke deaths remain high in UITH Ilorin, a second generation Nigerian Teaching Hospital. Admitting stroke severity and presence of at least a complication are independent predictors of 30 days mortality. To reduce stroke mortality, there might be need to pay greater attention to patients with high NIHSS scores (>15) and those coming with complication; while effort should be taken to prevent complications.

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Conflicts of Interest

There are no conflicts of interest.

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  [Table 1], [Table 2], [Table 3], [Table 4]

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