Abstract
Background:
Post-stroke seizures (PSS) are associated with significant morbidity and mortality across the globe. There is a paucity of data on PSS in Africa.
Purpose:
To assess the frequency and factors associated with PSS by stroke types across 15 hospitals in Nigeria and Ghana.
Methods:
We analyzed data on all stroke cases recruited into the Stroke Investigative Research and Educational Network (SIREN). We included adults aged ≥18 years with radiologically confirmed ischemic stroke (IS) or intracerebral hemorrhage (ICH). PSS were defined as acute symptomatic seizures occurring at stroke onset and/or during acute hospitalization up until discharge. We used logistic regression to estimate adjusted odds ratios (aOR) with 95% Confidence Interval.
Results:
Among 3,344 stroke patients, 499 (14.9%) had PSS (95% CI: 13.7 – 16.2%). The mean duration of admission in days for those with PSS vs no PSS was 17.4 ± 28.6 vs 15.9 ± 24.7, p=0.72. There were 294(14.1%) PSS among 2091 ischemic strokes and 159(17.7%) among 897 with ICH, p=0.01. The factors associated with PSS occurrence were age <50 years, aOR of 1.59 (1.08–2.33), National Institute of Health Stroke Score (NIHSS), 1.29 (1.16–1.42) for each 5 units rise and white cell count 1.07 (1.01–1.13) for each 10^3 mm3 rise. Factors associated with PSS in ischemic were NIHSS score, aOR of 1.17 (1.04–1.31) and infarct volume of 10–30 cm3 aOR of 2.17(1.37–3.45). Among ICH, associated factors were alcohol use 5.91 (2.11–16.55) and lobar bleeds 2.22 (1.03–4.82)
Conclusion:
The burden of PSS among this sample of west Africans is substantial and may contribute to poor outcomes of stroke in this region. Further longitudinal studies are required to understand the impact on morbidity and mortality arising from PSS in Africa.
Keywords: Seizures, epilepsy, stroke types, Africa
INTRODUCTION
Stroke is a major cause of symptomatic seizures among older adults.1–3 It is estimated that between 5 and 15% of stroke patients develop seizures within two years of stroke onset.4 The pathogenesis of early post-stroke seizures (PSS) following an ischemic stroke is putatively linked to a lowering of seizure threshold secondary to local ionic shifts, the release of excitotoxic neurotransmitters and the presence of global hypoperfusion with cortical hyperexcitability.2 The mechanisms for post-stroke seizures in intracerebral hemorrhage involve direct stimulatory effects of blood degradation products on neural tissues and extracellular glutamate toxicity.7,8
Sub-Saharan Africa is currently at the epicenter of a stroke epidemic characterized by a younger age of onset and very poor short- and long-term outcomes from mortality and post-stroke morbidity.5–14 There are no reports from large scale multi-center studies on the burden of post-stroke seizures except a few single center studies.15–17 Furthermore, the delineation of factors associated with occurrence of post-stroke seizures according to stroke types within the sub-Saharan African context remains to be elucidated. We therefore present data on the frequency and factors associated with post-stroke seizures by the primary stroke types from the Stroke Investigative Research and Education Networks (SIREN) study. The SIREN study is the largest study on stroke in Africa to date involving 15 sites in northern and southern belts of Nigeria and Ghana.
METHODS
Study Design:
The study protocol has been previously published.18 In brief, stroke cases were consecutively consenting adults aged ≥18 years with clinical stroke presenting within 8 days of current symptom onset or ‘last seen without deficit’. We confirmed all stroke diagnosis using either CT or MRI scan typically within 10 days of symptom onset. Ethical approval was obtained from all study sites and informed consent was obtained from all subjects.18 In unconscious or aphasic patients, consent was obtained from next of kin.
Stroke Phenotyping:
Stroke diagnosis and phenotyping were based on clinical evaluation and brain neuroimaging (CT or MRI), ECG, transthoracic echocardiography, and carotid Doppler ultrasound performed according to standardized protocols (SOP) at each site. Presumed etiological sub-types of ischemic stroke were defined etiologically using the A-S-C-O-D classification into A: Atherosclerosis, S: Small-vessel occlusion, C: Cardiac pathology, O: Other causes and D: dissection 19 and intracerebral hemorrhage was classified etiologically into Structural, Medication-related, Amyloid angiopathy, Systemic/other disease, Hypertension and Undetermined causes (SMASH-U).20
Definition of terms
Post-stroke seizures:
Post stroke seizures (PSS) were defined as acute symptomatic seizures occurring after an acute stroke following ILAE recommendations.21,22 Early PSS was classified as symptomatic seizures occurring within 7 days of stroke onset while late PSS was defined as symptomatic seizures occurring after 7 days of stroke onset. For this study, seizures were diagnosed clinically and were classified as acute symptomatic PSS based on medical history from a witness (often a family member) of focal or generalized seizures at the time of presentation for admission with stroke or by clinically documented seizures during hospitalization for acute stroke. No electroencephalographic studies were performed to confirm diagnosis of seizures and we did not record seizures into focal-onset or generalized onset for the purposes of this report.
Vascular risk factors of stroke:
We collected basic demographic and lifestyle data including, socio-economic status, cardiovascular risk profile, dietary patterns, routine physical activity, stress, depression, cigarette smoking, and alcohol use using a validated INTERSTROKE instrument.23 We have reported these definitions in our previous publications.9,24
Statistical Analysis
We compared demographic and vascular risk factor data among stroke cases who reported with post-stroke seizures versus those without post-stroke seizures using Student’s T-test for parametrically distributed continuous data and Chi-squared tests for categorical data. We assessed factors associated with post-stroke seizures among stroke cases and by stroke types (ischemic and hemorrhagic strokes) using a multivariable logistic regression model. Covariates which were included in the multivariate logistic models were selected if they achieved a p-value of <0.10 in bivariate analyses. Sensitivity analyses for factors associated with early PSS and late PSS were also performed. All statistical tests of hypotheses were two-sided. Statistical analyses were performed with Stata MP version 14.
RESULTS
Characteristics of participants with post-stroke seizures:
We enrolled 3,344 patients meeting study criteria of an acute stroke. The frequency of post stroke seizures was 499 (14.9%; 95% CI: 13.7 – 16.2%). Among those with PSS, 382 (76.6%) had early PSS and 117 (23.4%) with late PSS. The mean age of those with post-stroke seizures of 58.3 ± 15.3 years was significantly lower than 60.2 ± 14.1 years for those without seizures, p=0.006. The characteristics of those with PSS are compared with those without PSS in Table 1. Those with PSS were less likely to earn monthly income > $100 (49.8% vs 54.9%), to be hypertensive (92.6% vs 96.3%), to have cardiac disease (8.7% vs 12.4%) and to consume fish regularly (88.5% vs 93.1). However, the white cell count and National Institute of Health Stroke Score were significantly higher among those with post-stroke seizures than those without. Also 22.2% with post-stroke seizure had a lesion volume of >30cm3 compared with 14.9% among those without seizures. The mean duration of admission days for those with PSS vs no PSS was 17.4 ± 28.6 vs 15.9 ± 24.7, p=0.72. (Table 1)
Table 1.
Comparison of demographic and clinical characteristics of stroke cases with Post-stroke seizures versus those with no Post-stroke seizures
| Variable | All Stroke type | Ischemic Stroke | Intracerebral hemorrhage | ||||||
|---|---|---|---|---|---|---|---|---|---|
| No Post-stroke seizures N=2845 | Post-stroke seizures n=499 | P-value | No Post-stroke seizures N=1797 | Post-stroke seizures n=294 | P-value | No Post-stroke seizures N=738 | Post-stroke seizures n=159 | P-value | |
| Country, Ghana, n (%) | 974 (34.2) | 161 (32.3) | 0.391 | 582 (32.4) | 86 (29.3) | 0.285 | 371 (50.3) | 72 (45.3) | 0.254 |
| Gender, Male, n (%) | 1578 (55.5) | 292 (58.5) | 0.208 | 939 (52.3) | 159 (54.1) | 0.567 | 463 (62.7) | 109 (68.6) | 0.166 |
| Age, mean ± SD | 60.2±14.1 | 58.3±15.3 | 0.006 | 62.4±13.8 | 61.3±15.0 | 0.195 | 54.7±13.2 | 51.7±13.7 | 0.010 |
| <30 | 37 (1.3) | 18 (3.6) | 0.001 | 20 (1.1) | 14 (4.8) | <0.001 | 10 (1.4) | 4 (2.5) | 0.017 |
| 30–49 | 584 (20.6) | 117 (23.5) | 285 (15.9) | 39 (13.3) | 241 (32.7) | 71 (44.7) | |||
| 50–69 | 1433 (50.4) | 232 (46.7) | 894 (49.8) | 143 (48.6) | 383 (51.9) | 67 (42.1) | |||
| >=70 | 787 (27.7) | 130 (26.2) | 596 (33.2) | 98 (33.3) | 104 (14.1) | 17 (10.7) | |||
| Domicile | |||||||||
| Rural, n (%) | 272 (9.6) | 40 (8.1) | 0.508 | 176 (9.8) | 23 (7.8) | 0.288 | 63 (8.6) | 14 (8.9) | 0.832 |
| Semi-urban, n (%) | 828 (29.2) | 152 (30.6) | 516 (28.8) | 96 (32.7) | 208 (28.3) | 41 (26.0) | |||
| Urban, n (%) | 1735 (61.2) | 305 (61.4) | 1101 (61.4) | 175 (59.5) | 463 (63.1) | 103 (65.2) | |||
| Monthly Income >$100, n (%) | 1551 (54.9) | 247 (49.8) | 0.036 | 989 (55.5) | 150 (51.0) | 0.153 | 418 (56.8) | 81 (51.9) | 0.266 |
| Education, (some) n (%) | 2310 (81.4) | 405 (81.2) | 0.890 | 1417 (79.1) | 226 (76.9) | 0.382 | 640 (86.8) | 138 (86.8) | 0.988 |
| Hypertension, n (%) | 2739 (96.3) | 462 (92.6) | <0.001 | 1721 (95.8) | 264 (89.8) | <0.001 | 725 (98.4) | 155 (97.5) | 0.443 |
| Dyslipidemia, n (%) | 2402 (84.4) | 402 (81.1) | 0.059 | 1570 (87.4) | 253 (86.4) | 0.628 | 588 (79.7) | 117 (73.6) | 0.089 |
| Diabetes, n (%) | 1068 (37.6) | 205 (41.2) | 0.126 | 740 (41.2) | 133 (45.2) | 0.191 | 209 (28.4) | 46 (28.9) | 0.885 |
| Cardiac Disease, n (%) | 352 (12.4) | 43 (8.7) | 0.017 | 266 (14.8) | 32 (10.9) | 0.076 | 54 (7.3) | 9 (5.7) | 0.456 |
| HDL-Cholesterol, mg/dl, mean ± SD | 47.7±19.3 | 48.7±20.3 | 0.316 | 46.4±18.4 | 45.4±17.9 | 0.457 | 52.1±20.5 | 55.3±22.9 | 0.100 |
| HDL-Cholesterol ≤ 18.54 mg/dl, n (%) | 78 (2.7) | 12 (2.4) | 0.668 | 48 (2.7) | 10 (3.4) | 0.480 | 17 (2.3) | 2 (1.3) | 0.406 |
| LDL-Cholesterol, mg/dl, mean ± SD | 121.8±51.1 | 116.1±53.3 | 0.039 | 121.6±50.8 | 116.1±53.5 | 0.131 | 127.8±51.5 | 120.9±53.9 | 0.160 |
| LDL-Cholesterol ≥ 61.2 mg/dl, n (%) | 2203 (90.6) | 338 (86.2) | 0.007 | 1393 (90.4) | 202 (87.1) | 0.116 | 626 (93.3) | 119 (88.8) | 0.071 |
| LDL/HDL ratio, mean ± SD | 3.0±1.9 | 2.7±1.7 | 0.034 | 3.0±1.9 | 2.9±1.8 | 0.433 | 2.8±2.0 | 2.5±1.5 | 0.089 |
| LDL/HDL ratio >2.96, n (%) | 925 (38.4) | 130 (33.4) | 0.063 | 607 (39.7) | 88 (38.3) | 0.683 | 235 (35.4) | 36 (26.9) | 0.057 |
| LDL/HDL ratio by thirds: | |||||||||
| ≤ 2.00, n (%) | 760 (31.5) | 143 (36.8) | 0.079 | 456 (29.8) | 79 (34.4) | 0.351 | 226 (34.0) | 53 (39.6) | 0.159 |
| 2.01 – 2.96, n (%) | 726 (30.1) | 116 (29.8) | 466 (30.5) | 63 (27.4) | 203 (30.6) | 45 (33.6) | |||
| ≥ 2.97, n (%) | 926 (38.4) | 130 (33.4) | 608 (39.7) | 88 (38.3) | 235 (35.4) | 36 (26.9) | |||
| Total Cholesterol, mmol/l, mean ± SD | 192.0±57.7 | 186.3±58.4 | 0.065 | 191.3±57.9 | 183.3±58.9 | 0.051 | 200.0±57.5 | 196.3±57.5 | 0.496 |
| Total Cholesterol ≥ 93.6 mg/dl, n (%) | 2415 (98.0) | 383 (96.0) | 0.014 | 1536 (98.1) | 224 (95.7) | 0.022 | 666 (98.2) | 133 (97.1) | 0.376 |
| Triglyceride, mg/dl, mean ± SD | 126.2±83.9 | 120.9±71.6 | 0.231 | 130.1±85.5 | 122.3±67.2 | 0.185 | 122.4±84.2 | 122.2±81.2 | 0.978 |
| Triglyceride ≥ 30.6 mg/dl, n (%) | 2447 (99.5) | 395 (99.5) | 0.950 | 1559 (99.7) | 234 (100.0) | 0.439 | 668 (99.0) | 134 (99.3) | 0.751 |
| Waist-to-hip Ratio, mean ± SD | 0.9±0.1 | 0.9±0.1 | 0.217 | 0.9±0.1 | 0.9±0.1 | 0.105 | 0.9±0.1 | 0.9±0.1 | 0.473 |
| Waist-to-hip Ratio raised, n (%) | 2207 (83.0) | 387 (82.7) | 0.883 | 1420 (84.5) | 236 (86.1) | 0.493 | 552 (79.9) | 120 (79.0) | 0.795 |
| Waist-to-hip Ratio by thirds: | |||||||||
| ≤ .90, n (%) | 703 (26.4) | 118 (25.2) | 0.294 | 426 (25.3) | 61 (22.3) | 0.055 | 202 (29.2) | 42 (27.6) | 0.772 |
| .91 – .96, n (%) | 941 (35.4) | 183 (39.1) | 591 (35.2) | 117 (42.7) | 251 (36.3) | 53 (34.9) | |||
| ≥.97+, n (%) | 1017 (38.2) | 167 (35.7) | 664 (39.5) | 96 (35.0) | 238 (34.4) | 57 (37.5) | |||
| WHR**, Lowest vs highest thirds, n (%) | 1017 (59.1) | 167 (58.6) | 0.866 | 664 (60.9) | 96 (61.2) | 0.956 | 238 (54.1) | 57 (57.6) | 0.529 |
| WHR**, 1st vs 2nd+3rd thirds, n (%) | 1958 (73.6) | 350 (74.8) | 0.585 | 1255 (74.7) | 213 (77.7) | 0.274 | 489 (70.8) | 110 (72.4) | 0.693 |
| BMI*** (kg/m2), mean ± SD | 26.8±5.3 | 26.7±5.6 | 0.867 | 26.9±5.4 | 26.5±5.2 | 0.230 | 26.3±4.9 | 27.2±6.3 | 0.066 |
| BMI*** >30kg/m2, n (%) | 491 (21.3) | 87 (21.9) | 0.812 | 332 (22.5) | 55 (23.0) | 0.867 | 103 (17.6) | 22 (18.2) | 0.880 |
| Physical Activity (some activity), n (%) | 2658 (95.1) | 460 (95.0) | 0.957 | 1680 (95.2) | 271 (94.4) | 0.553 | 699 (95.6) | 149 (96.1) | 0.777 |
| Tobacco use in past 12 months, n (%) | 102 (3.7) | 20 (4.1) | 0.620 | 54 (3.1) | 7 (2.5) | 0.581 | 41 (5.6) | 12 (7.6) | 0.331 |
| Tobacco (any use), n (%) | 272 (9.6) | 51 (10.4) | 0.616 | 170 (9.5) | 33 (11.4) | 0.325 | 81 (11.0) | 16 (10.2) | 0.758 |
| Alcohol (current user), n (%) | 478 (16.9) | 91 (18.4) | 0.397 | 262 (14.6) | 34 (11.7) | 0.181 | 182 (24.8) | 49 (31.0) | 0.104 |
| Alcohol (any use), n (%) | 926 (32.7) | 172 (34.8) | 0.350 | 545 (30.5) | 88 (30.2) | 0.943 | 311 (42.3) | 70 (44.3) | 0.646 |
| Alcohol use categories: | |||||||||
| Never Use, n (%) | 1908 (76.5) | 322 (74.9) | 0.129 | 1245 (78.4) | 203 (81.2) | 0.442 | 424 (68.2) | 88 (62.9) | 0.020 |
| Ever Low Use, n (%) | 521 (20.9) | 89 (20.7) | 308 (19.4) | 44 (17.6) | 172 (27.7) | 38 (27.1) | |||
| Ever High Use, n (%) | 66 (2.7) | 19 (4.4) | 35 (2.2) | 3 (1.2) | 26 (4.2) | 14 (10.0) | |||
| Stress, n (%) | 525 (19.9) | 101 (22.7) | 0.178 | 321 (19.3) | 59 (22.4) | 0.234 | 156 (22.8) | 30 (20.8) | 0.606 |
| Depression, n (%) | 203 (7.3) | 40 (8.3) | 0.479 | 130 (7.5) | 25 (8.7) | 0.451 | 60 (8.3) | 11 (7.1) | 0.595 |
| Family history of CVD, n (%) | 1096 (38.5) | 169 (33.9) | 0.048 | 689 (38.3) | 102 (34.7) | 0.232 | 328 (44.4) | 57 (35.9) | 0.047 |
| Adding salt at table, n (%) | 188 (6.8) | 45 (9.5) | 0.035 | 109 (6.3) | 20 (7.3) | 0.529 | 65 (9.0) | 19 (12.4) | 0.193 |
| Adding salt at table categories: | |||||||||
| Never/rarely, n (%) | 1913 (69.4) | 334 (70.8) | 0.029 | 1230 (70.8) | 191 (69.5) | 0.803 | 481 (66.6) | 116 (75.8) | 0.002 |
| Occasionally, n (%) | 655 (23.8) | 93 (19.7) | 399 (23.0) | 64 (23.3) | 176 (24.4) | 18 (11.8) | |||
| Very often, n (%) | 188 (6.8) | 45 (9.5) | 109 (6.3) | 20 (7.3) | 65 (9.0) | 19 (12.4) | |||
| Green vegetable consumption, n (%) | 1940 (73.4) | 329 (74.1) | 0.758 | 1222 (73.6) | 193 (75.4) | 0.547 | 504 (72.3) | 101 (69.2) | 0.445 |
| Whole grains consumption, n (%) | 2225 (83.5) | 375 (84.1) | 0.743 | 1399 (83.8) | 215 (83.3) | 0.859 | 588 (83.8) | 125 (86.2) | 0.462 |
| Legumes consumption, n (%) | 1791 (67.7) | 299 (68.0) | 0.912 | 1117 (67.5) | 170 (66.9) | 0.848 | 477 (68.1) | 99 (69.2) | 0.781 |
| Fruit consumption, n (%) | 2259 (85.3) | 374 (83.9) | 0.447 | 1410 (84.9) | 217 (83.8) | 0.630 | 587 (84.1) | 118 (81.9) | 0.524 |
| Sugar consumption or otherwise, (%) | 771 (29.6) | 145 (33.3) | 0.122 | 474 (29.0) | 77 (30.7) | 0.580 | 216 (31.2) | 54 (37.8) | 0.125 |
| Meat consumption or otherwise, (%) | 2285 (85.6) | 381 (85.0) | 0.752 | 1409 (84.4) | 214 (82.6) | 0.474 | 603 (85.4) | 128 (87.7) | 0.476 |
| Fish consumption or otherwise, (%) | 2474 (93.1) | 393 (88.5) | 0.001 | 1532 (92.1) | 225 (87.6) | 0.014 | 663 (94.2) | 131 (90.3) | 0.088 |
| Serum sodium | 137.6±9.1 | 138.2±11.6 | 0.278 | 137.5±8.6 | 137.8±13.4 | 0.642 | 137.9±10.3 | 139.2±9.2 | 0.241 |
| White blood cell count, mean ± SD | 16.9 ± 2.9 | 21. 5 ± 3.6 | 0.017 | 17.1 ± 3.0 | 19.3 ± 3.0 | 0.420 | 16.1 ±2.7 | 23.8 ±4.3 | 0.015 |
| NIHSS score, mean ± SD | 12.7 ± 8.6 | 15.2±9.3 | <0.001 | 12.1±8.3 | 14.3±9.2 | <0.001 | 14.3±8.8 | 16.7±9.3 | 0.008 |
| Location of lesions | |||||||||
| Lobar | 149 (20.2) | 57 (35.9) | <0.001 | ||||||
| Non-lobar | 589 (79.8) | 102 (64.2) | |||||||
| Volume of lesions | |||||||||
| ≤10cm3 | 1414 (64.5) | 203 (50.6) | <0.001 | 1109 (74.0) | 158 (62.7) | 298 (43.6) | 44 (29.9) | ||
| 10.1–30cm3 | 452 (20.6) | 109 (27.2) | 209 (14.0) | 53 (21.0) | 0.001 | 240 (35.1) | 56 (38.1) | 0.003 | |
| > 30cm3 | 326 (14.9) | 89 (22.2) | 180 (12.0) | 41 (16.3) | 146 (21.4) | 47 (32.0) | |||
| Duration of admission in days, mean ± SD | 15.9 ± 24.7 | 17.4 ± 28.6 | 0.716 | 15.9 ± 24.9 | 19.1 ± 35.4 | 0.819 | 15.0 ± 22.8 | 14.0 ± 13.3 | 0.529 |
NIHSS – National Institute of Health Stroke Score
Characteristics of participants with post-stroke seizures by stroke type:
There were 294 (14.1%) post-stroke seizures found among 2091 patients with ischemic stroke and 159 (17.7%) among 897 with intracerebral hemorrhage, p=0.01. Data on stroke type information were missing for 356 participants. Among 1,864 patients with Oxfordshire Community Stroke Project (OCSP) classification data, 778 (41.7%) had LACI, 635 (34.1%) had PACI, 268 (14.4%) had TACI and 183 (9.8%) had POCI. Proportions with post-stroke seizures by OCSP classification of ischemic strokes were 18.7% for those with TACI, 16.9% for POCI, 14.1% for LACI and 11.8% among those with PACI (p=0.039), Figure 1A.
Fig. 1.
Frequency distribution of post-stroke seizures by etiology of Intracerebral hemorrhage in West Africa.
Among 1,386 ischemic stroke patients with data on etiology, 54.5% had small vessel occlusion (SVO), 26.3% had large artery atherosclerotic disease (LAA), 16.1% had cardio-embolic stroke (CE) and 3.1% had other causes. In decreasing order, 17.3% with LAA, 14.4% with SVO, 13.4% with CE and 7.0% with other causes, respectively had post-stroke seizures, p=0.23, by chi-squared test for trend (Figure 1B). Among 801 patients with ICH, 90.8% had hypertensive ICH, 4.0% had structural lesions, 2.7% had undetermined causes, 1.5% had cerebral amyloid associated bleeds, and 0.5% each had either medication-related or systemic disease-related bleeds. The frequencies of post-stroke seizure occurrence by etiology of ICH in decreasing order were 50.0% for those with medication-related ICH, 41.7% in amyloid-related bleeds, 18.2% in bleeds of undetermined causes, 17.5% in hypertensive-related bleeds and 9.4% with structural lesions such as aneurysms and AV malformations, p=0.09 (Figure 2). Data on ICH stroke subtype information were missing for 96 participants.
Fig. 2.
Frequency distribution of post-stroke seizures by etiology of Ischemic Stroke in West Africa. (1A) by Oxfordshire Community Stroke Project classification; (1B) by ASCO classification.
Factors associated with occurrence of post-stroke seizures:
In Table 2, we show 12 potential factors associated with occurrence of post-stroke seizures overall in bivariate analysis. Upon adjusting for potential confounders, the adjusted odds ratio (95% CI) of three factors which remained independently associated with post-stroke seizures were: age <50 years 1.59 (1.08–2.33), NIHSS score at presentation 1.29 (1.16–1.42) for each 5 units rise and white cell count 1.07 (1.01–1.13) for each 10^3 mm3 rise. Two factors were independently associated with post-stroke seizures among those with ischemic stroke were NIHSS score and lesion volume of >30cm3 (see Table 3). Among patients with ICH, post-stroke seizures were associated with alcohol use 5.91 (2.11–16.55) and lobar bleeds 2.22 (1.03–4.82) (Table 4). Factors associated with early PSS were stroke severity and white blood count (Table S1 in supplementary information). Late-onset PSS was associated with stroke severity as shown in supplementary information Table S2.
Table 2.
Multivariable logistic regression analysis for factors associated Post-stroke seizures
| Unadjusted OR (95% CI) | P-value | Adjusted OR (95% CI) | P-value | |
|---|---|---|---|---|
| Age <50 years | 1.33 (1.07 – 1.66) | 0.009 | 1.46 (0.98 – 2.16 | 0.060 |
| Income <100$ | 1.23 (1.01 – 1.48) | 0.036 | 1.06 (0.75 – 1.51) | 0.735 |
| Hypertension | 0.48 (0.32 – 0.71) | <0.001 | 0.66 (0.28 −1.55) | 0.339 |
| Dyslipidemia | 0.79 (0.62 – 1.01) | 0.059 | 0.82 (0.49 – 1.38) | 0.455 |
| Cardiac disease | 0.67 (0.48 – 0.93) | 0.018 | 0.95 (0.52 – 1.74) | 0.870 |
| Family history of CVD | 0.82 (0.67 – 0.99) | 0.048 | 0.95 (0.67 – 135) | 0.771 |
| Salt (very often) | 1.44 (1.02 – 2.02) | 0.036 | 1.51 (0.90 – 2.52) | 0.118 |
| Fish consumption | 0.57 (0.41 – 0.79) | 0.001 | 0.73 (0.38 – 1.37) | 0.322 |
| Hemorrhagic stroke vs ischemic stroke as referent | 1.32 (1.07 – 1.62) | 0.011 | 1.14 (0.76 – 1.70) | 0.521 |
| NIHSS score as a continuous variable per each 5 units higher | 1.16 (1.09 – 1.24) | <0.001 | 1.29 (1.16 – 1.42) | <0.001 |
| White blood cell count continuous variable | 1.04 (1.01 – 1.08) | 0.022 | 1.06 (1.01 – 1.12) | 0.039 |
| Volume of lesions | ||||
| ≤10cm3 | 1.00 | 1.00 | ||
| 10.1–30cm3 | 1.68 (1.30 – 2.17) | <0.001 | 1.33 (0.87 – 2.04) | 0.192 |
| > 30cm3 | 1.90 (1.44 – 2.51) | <0.001 | 1.03 (0.61 – 1.72) | 0.924 |
| Duration of admission for stroke in days | 1.00 (0.98 – 1.01 | 0.259 | 1.00 (0.99 – 1.01) | 0.309 |
Table 3.
Multivariable logistic regression analysis for factors associated Post-stroke seizures among patients with ischemic strokes
| Unadjusted OR (95% CI) | P-value | Adjusted OR (95% CI) | P-value | |
|---|---|---|---|---|
| Age <50 | 1.07 (0.78 – 1.48) | 0.662 | -- | |
| Hypertension | 0.39 (0.25 – 0.60) | <0.001 | 0.60 (0.26 – 1.38) | 0.230 |
| Cardiac disease | 0.70 (0.48 – 1.04) | 0.078 | 0.84 (0.47– 1.47) | 0.536 |
| Total cholesterol (continuous) | 1.00 (0.99 – 1.00) | 0.051 | 1.00 (0.99 – 1.00) | 0.385 |
| WHR (tertiles) | ||||
| .91 – .96, n (%) | 1.38 (0.99 – 1.93) | 0.057 | 1.59 (0.96 – 2.66) | 0.073 |
| ≥.97+, n (%) | 1.01 (0.72 – 1.42) | 0.956 | 1.24 (0.73 – 2.12) | 0.428 |
| Fish consumption | 0.60 (0.40 – 0.91) | 0.015 | 0.65 (0.33 – 1.25) | 0.198 |
| NIHSS, each 5 units higher | 1.15 (1.06 – 1.25) | 0.001 | 1.21 (1.08 – 1.37) | 0.001 |
| Lesion volume | ||||
| <10.0 cm3 | 1.00 | 1.00 | ||
| 10.1 – 30cm3 | 1.78 (1.26 – 2.51) | 0.001 | 1.90 (1.16 – 3.11) | 0.010 |
| >30 cm3 | 1.60 (1.10 – 2.33) | 0.015 | 1.05 (0.57 – 1.96) | 0.872 |
| Duration of admission for stroke in days | 1.00 (0.99 – 1.01) | 0.082 | 1.00 (0.99 – 1.01) | 0.266 |
Table 4.
Multivariable logistic regression analysis for factors associated Post-stroke seizures among patients with intracerebral hemorrhage
| Unadjusted OR (95% CI) | P-value | Adjusted OR (95% CI) | P-value | |
|---|---|---|---|---|
| Age <50 | 1.73 (1.22 – 2.45) | 0.002 | 1.82 (0.93 – 3.55) | 0.080 |
| Dyslipidemia | 0.71 (0.48 – 1.06) | 0.091 | 0.99 (0.41 – 2.36) | 0.979 |
| BMI (continuous) | 1.03 (1.00 – 1.07) | 0.069 | 0.96 (0.88 – 1.03) | 0.266 |
| Alcohol use (Ever High use) | 2.59 (1.30 – 5.17) | 0.007 | 5.79 (1.99 – 16.89) | 0.001 |
| White blood count | 1.07 (1.01 – 1.14) | 0.018 | 1.02 (0.89 – 1.17) | 0.770 |
| NIHSS each 5 units higher | 1.16 (1.04 – 1.30) | 0.009 | 1.19 (0.98 – 1.45) | 0.066 |
| Lobar bleed | 2.21 (1.52 – 3.20) | <0.001 | 2.23 (1.02 – 4.87) | 0.044 |
| Lesion volume | ||||
| <10.0 cm3 | 1.00 | 1.00 | ||
| 10.1 – 30cm3 | 1.58 (1.03 – 2.43) | 0.037 | 1.34 (0.61 – 2.96) | 0.464 |
| >30.0 cm3 | 2.18 (1.38 – 3.44) | 0.001 | 1.70 (0.69 – 4.22) | 0.251 |
| SMASH-U | ||||
| Amyloid angiopathy | 12.08 (2.05 – 71.11) | 0.006 | 7.43 (0.20 – 280.28) | 0.279 |
| Others (referent) | 1.00 | |||
| Duration of admission for stroke in days | 0.99 (0.98 – 1.01) | 0.632 | 1.00 (0.99 – 1.01) | 0.676 |
DISCUSSION
In this large multi-center study across 15 hospitals in Ghana and Nigeria, we found the frequency of post-stroke seizures to be 14.9% (95% CI of 13.7 – 16.2%). The frequency of post-stroke seizures was significantly higher among those with spontaneous intracerebral hemorrhage at 17.7% compared with 14.1% among those with ischemic strokes. The prevalence of post-stroke seizures in the present study was quite high and is comparable with a figure of 17.9% (14.6–21.8%) found in an Indian study.25 Otherwise, most of the previous studies have reported much lower prevalence of post-stroke seizures ranging from 3.0% in Taiwan26, 3.9% to 6.3% from three Italian studies27–29, 4.2% in Denmark30, 4.1% from the US31, 8.9% from an international collaborative study involving tertiary medical centers in Canada, Australia, Israel and Italy32 and one study from Egypt reported 9.3%.15 While differences in study designs and cohort characteristics may underlie these differences in prevalence observed across studies, a key reason could be differences in the time window for defining early post-stroke seizures which has varied between 1–30 days in various studies. In our study, the observation window for identifying PSS was within the period of stroke onset until discharge from hospital or death. The average duration of hospitalization for those with PSS of 17 days was not significantly different from a mean of 16 days for those without PSS. Admittedly, there is potential for the differential duration of hospitalization to influence our ability to identify PSS. For instance, those with mild stroke might have be discharged earlier than those with severe stroke and therefore less likely to have seizures identified. However, we did not find a significant association between duration of hospitalization and risk of PSS.
Intracerebral hemorrhage, cerebral infarction with hemorrhagic transformation, stroke severity and alcoholism are factors associated with early post-stroke seizures from meta-analytic data.33 Late-onset seizures were associated with cortical involvement and stroke severity.33 In our study, stroke severity was independently associated with post-stroke seizure with each 5 units rise in the NIHSS score corresponding to a 29% higher odd of seizures (95% CI: 16–42%). Acute stroke patients < 50 years old had a 59% higher odds of a post-stroke seizures than those who were older. This higher proclivity for post-stroke seizures occurrence among young west Africans may be explained by the relative preponderance of intracerebral hemorrhage in this age group.9,34 In sensitivity analyses, the odds ratio of post-stroke seizure among those <50 years with ICH was 1.82 (95% CI: 0.94–3.51) compared with 1.12 (0.69–1.82) among those with ischemic stroke. Leukocytosis was also independently associated in a graded manner with post-stroke seizures, with 7% higher odds for each 10^3 rise in white blood cell count at presentation. It is uncertain whether leukocytosis is simply a marker of stroke severity35, is a result of post-convulsive leukocytosis or reflective of infections occurring after PSS such as aspiration pneumonitis. It is tempting to speculate that disruption of the blood brain barrier and invasion of leukocytes into the site of cerebral injury after a stroke may incite abnormal neuronal firing. Indeed, cerebral inflammation is a prime etiological factor for ictogenesis and epileptogenesis via alterations in ionic channel sensitivity, neurotransmitter uptake and glia-associated modulation of the extracellular electrical milieu as elegantly reviewed by Shimada et al.36
There were also differences in risk factor profile for post-stroke seizure by stroke type. We observed among patients with intracerebral hemorrhage that alcohol use and lobar bleeds were significantly associated with post-stroke seizures. However, while binge drinking may predispose to ICH with attendant PSS, alcohol withdrawal seizures after ICH in alcoholic may conversely predispose to PSS. Thus an assessment of serum alcohol levels or specific questions to patients/relatives may help distinguish between these two possible causes of PSS among alcohol users with ICH for clinical management purposes. It is intriguing to note that, unlike the situation in high-income countries, we found a low frequency of medication-related ICH, perhaps as a consequence of low utilization rates of these pharmacological agents for primary or secondary CVD risk reduction in our settings.37,38 Among ischemic strokes however, stroke severity and larger infarct volumes presented a higher risk for occurrence of seizures. This observation aligns with the higher frequency of seizures among those with total anterior circulation infarcts using the OCSP classification observed in our study and found by others.39 Furthermore, those with large artery atherosclerotic disease with its tendency to cause larger territorial infarcts had the highest frequency of seizures among ischemic stroke clinical sub-types. A surprisingly high proportion (≈14%) of ischemic stroke subjects with small vessel occlusive disease had post-stroke seizures. This observation requires further studies because a significant majority of lacunar infarcts occur in the basal ganglia, an area with lower predilection for seizure generation. Further on-going analysis may throw more light on the unique determinants of seizures occurrence in the context of basal ganglionic bleeds and lacunar strokes in the African context.
Implications:
Given that approximately 15% of patients presenting with acute stroke for hospitalization have concomitant seizures, there is the need for a heightened index for suspicion by clinicians for its screening and identification to treatment. International guidelines from the US and Europe do not recommend primary or secondary anti-epileptic drug (AED) treatment for prophylaxis for post-stroke acute symptomatic seizures although in clinical practice clinicians often do so. The use of carbamazepine and phenytoin is rife but more recent studies support the use of levetiracetam, lamotrigine and gabapentine for treatment of post-stroke seizures.40–42 A clearer resolution of the potential role of short or longer-term use of AEDs in the management of post-stroke seizures remains to be resolved. Prospective studies are also urgently required to assess the impact of post-stroke seizures on short- and long-term outcomes of strokes, in particular the subsequent risk of development of post-stroke epilepsy in this region. A recent study from Ghana among 1,101 stroke survivors reported a post-stroke epilepsy prevalence of 11.4% with male sex, cortical infarcts, elevate blood pressure as predictive factors while antihypertensive use was protective against post-stroke epilepsy.43 This finding perhaps highlights the need for prediction models for personalized care for post-stroke seizure management.
Limitations:
This study is limited by non-confirmation of clinically reported seizures with electroencephalographic studies and detailed evaluation of potential causes of seizures in the acute stroke setting. The burden of pre-stroke epilepsy risk among our study population was not ascertained as was documentation of seizures types (focal vs generalized), number of seizures (recurrent or single), and types of seizure treatments offered. None of patients in this series had thrombolytic therapy which has been shown to be associated with PSS at a frequency of 1 in every 15 ischemic stroke patients receiving reperfusion treatment.44 Varying proportions of the study participants did not have data on stroke type or subtypes and had to be excluded from secondary data analysis. Finally, causal associations between seizures and stroke cannot be drawn due to the cross-sectional design of the study.
Conclusion:
The burden of post-stroke seizures among this sample of west Africans is substantial and may contribute to poor outcomes of stroke in this region. Further longitudinal studies are required to understand the impact on morbidity and mortality arising from post-stroke seizures in Africa.
Supplementary Material
HIGHLIGHTS.
There is paucity of data on post stroke seizures (PSS) from multicenter studies in Africa
Across 15 sites in Ghana and Nigeria, the frequency of PSS was 15%
The frequency of PSS in hemorrhagic stroke was 18% vs 14% in ischemic strokes
Age <50 years, severe strokes and elevated leucocyte counts were associated with occurrence of PSS
Acknowledgments
Funding: The SIREN (Stroke Investigative Research and Education Networks) and SIBS Genomics studies were funded by the National Institutes of Health grant U54 HG007479 and R01NS107900 under the H3Africa initiative.
Footnotes
Disclosures: All authors have no conflicts to declare.
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