Patient-level and system-level determinants of stroke fatality across 16 large hospitals in Ghana and Nigeria: a prospective cohort study

Akpa, Onoja; Ovbiagele, Bruce; Akpalu, Albert; Wahab, Kolawole; Obiako, Reginald; Komolafe, Morenikeji; Owolabi, Lukman; Ogbole, Godwin; Fakunle, Adekunle; Okekunle, Akinkunmi Paul; Asowata, Osahon Jeffery; Calys‐Tagoe, Benedict; Uvere, Ezinne; Sanni, Taofeek Adedayo; Olowookere, Samuel Anu; Ibinaiye, Philip Oluleke; Akinyemi, Joshua; Arulogun, Oyedunni; Jenkins, Carolyn; Lackland, Daniel T.; Tiwari, Hemant K.; Isah, Suleiman Yahaya; Abubakar, Sani; Adebayo, Oladimeji; Adebayo, Philip; Akpalu, Josephine; Onyeonoro, Ugochukwu Uchenna; Ogunmodede, James Ayodele; Akisanya, Cynthia; Mensah, Yaw; Oyinloye, Olalekan Ibukun; Appiah, Lambert; Agunloye, Atinuke M; Osaigbovo, Godwin; Adeoye, Abiodun M.; Adeleye, Osimhiarherhuo; Laryea, Ruth; Olunuga, Taiwo; Oguike, Wisdom; Ogunronbi, Mayowa; Adeniyi, W. O.; Olugbo, Obiabo Y; Bello, Abiodun; Ogunjimi, Luqman; Diala, Samuel; Dambatta, Hamisu A; Singh, Arti; Adamu, Sheila; Obese, Vida; Adusei, Nathaniel; Owusu, Dorcas; Ampofo, Michael; Tagge, Raelle; Fawale, Bimbo; Yaria, Joseph; Akinyemi, Rufus; Owolabi, Mayowa

doi:10.1016/s2214-109x(23)00038-4

Cited by 17 publications

(5 citation statements)

References 28 publications

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“…Therefore, we cannot exclude the possibility of selection bias affecting our results. Although our study only covered one hospital, we could observe meaningful outcomes from this pilot which is crucial since there have not been any publications that have evaluated the usefulness of this kind of technology in LMICs, where stroke care has been dismal [ 8 , 9 ].…”

Section: Discussionmentioning

confidence: 99%

“…Thrombolysis and thrombectomy are not optimally utilized across the world, particularly worse in LMIC settings, that witness increasing incidence and prevalence of stroke, alongside dearth of radiology and neurology expertise [ 4 – 7 ]. Though multiple strategies have been implemented to overcome these barriers, including teleradiology and tele-stroke platforms, recent evidence continues to show incomplete access to and utilization of these platforms and interventions [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence-based automated CT brain interpretation to accelerate treatment for acute stroke in rural India: An interrupted time series study

Chiramal,

Johnson,

Webster

et al. 2024

PLOS Glob Public Health

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In resource-limited settings, timely treatment of acute stroke is dependent upon accurate diagnosis that draws on non-contrast computed tomography (NCCT) scans of the head. Artificial Intelligence (AI) based devices may be able to assist non-specialist physicians in NCCT interpretation, thereby enabling faster interventions for acute stroke patients in these settings. We evaluated the impact of an AI device by comparing the time to intervention (TTI) from NCCT imaging to significant intervention before (baseline) and after the deployment of AI, in patients diagnosed with stroke (ischemic or hemorrhagic) through a retrospective interrupted time series analysis at a rural hospital managed by non-specialists in India. Significant intervention was defined as thrombolysis or antiplatelet therapy in ischemic strokes, and mannitol for hemorrhagic strokes or mass effect. We also evaluated the diagnostic accuracy of the software using the teleradiologists’ reports as ground truth. Impact analysis in a total of 174 stroke patients (72 in baseline and 102 after deployment) demonstrated a significant reduction of median TTI from 80 minutes (IQR: 56·8–139·5) during baseline to 58·50 (IQR: 30·3–118.2) minutes after AI deployment (Wilcoxon rank sum test—location shift: -21·0, 95% CI: -38·0, -7·0). Diagnostic accuracy evaluation in a total of 864 NCCT scans demonstrated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) in detecting intracranial hemorrhage to be 0·89 (95% CI: 0·83–0·93), 0·99 (0·98–1·00), 0·96 (0·91–0·98) and 0·97 (0·96–0·98) respectively, and for infarct these were 0·84 (0·79–0·89), 0·81 (0·77–0·84), 0·58 (0·52–0·63), and 0·94 (0·92–0·96), respectively. AI-based NCCT interpretation supported faster abnormality detection with high accuracy, resulting in persons with acute stroke receiving significantly earlier treatment. Our results suggest that AI-based NCCT interpretation can potentially improve uptake of lifesaving interventions for acute stroke in resource-limited settings.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-based automated CT brain interpretation to accelerate treatment for acute stroke in rural India: An interrupted time series study

Chiramal,

Johnson,

Webster

et al. 2024

PLOS Glob Public Health

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“…Stroke severity is a known predictor of early mortality in SSA. 4,24 The fact that admission leukocytosis was a predictor of mortality indicates that some patients with stroke are presenting with established infections, possibly due to aspiration pneumonia. Not receiving any form of VTE prophylaxis (either medical or physical) is associated with increased risk of developing deep venous thrombosis and pulmonary embolism, an often-fatal complication after stroke.…”

Section: Discussionmentioning

confidence: 99%

Stroke characteristics and outcomes in urban Tanzania: Data from the Prospective Lake Zone Stroke Registry

Matuja,

Ngimbwa,

Andrew

et al. 2023

International Journal of Stroke

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Background: Stroke is a second leading cause of death globally, with an estimated one in four adults suffering a stroke in their lifetime. We aimed to describe the clinical characteristics, quality of care, and outcomes in adults with stroke in urban Northwestern Tanzania. Methods: We analyzed de-identified data from a prospective stroke registry from Bugando Medical Centre in Mwanza, the second largest city in Tanzania, between March 2020 and October 2022. This registry included all adults ⩾18 years admitted to our hospital who met the World Health Organization clinical definition of stroke. Information collected included demographics, risk factors, stroke severity using the National Institutes of Health Stroke Scale, brain imaging, indicators for quality of care, discharge modified Rankin Scale, and in-hospital mortality. We examined independent factors associated with mortality using logistic regression. Results: The cohort included 566 adults, of which 52% (294) were female with a mean age of 65 ± 15 years. The majority had a first-ever stroke 88% (498). Premorbid hypertension was present in 86% (488) but only 41% (200) were taking antihypertensive medications before hospital admission; 6% (32) had HIV infection. Ischemic strokes accounted for 66% (371) but only 6% (22) arriving within 4.5 h of symptom onset. In-hospital mortality was 29% (127). Independent factors associated with mortality were severe stroke (adjusted odds ratio (aOR) = 1.81, 95% confidence interval (CI) = 1.47–2.24, p < 0.001), moderate to severe stroke (aOR = 1.49, 95% CI = 1.22–1.84, p < 0.001), moderate stroke (aOR = 1.80, 95% CI = 1.52–2.14, p < 0.001), leukocytosis (aOR = 1.19, 95% CI = 1.03–1.38, p = 0.022), lack of health insurance coverage (aOR = 1.15, 95% CI = 1.02–1.29, p = 0.025), and not receiving any form of venous thromboembolism prophylaxis (aOR = 1.18, 95% CI = 1.02–1.37, p = 0.027). Conclusion: We report a stroke cohort with poor in-hospital outcomes in urban Northwestern Tanzania. Early diagnosis and treatment of hypertension could prevent stroke in this region. More work is needed to raise awareness about stroke symptoms and to ensure that people with stroke receive guidelines-directed therapy.

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“…A total of ten (10) significant risk factors were identified. These were low Glasgow Coma Score <8 [24,28,34,35,48,50,58,61,63,73,95,96,100,105], high (�10) National Institute Health Stroke Scale score [40,57,58,60,78,82,87,92,114], aspiration pneumonia [27,63,73,82,87,88,90,95,100,114], hemorrhagic stroke [29,40,92,95,105], brain edema/ intra-cranial pressure [28,34,88,114], hyperglycemia [35,40,50], untyped stroke (stroke diagnosis not confirmed by neuroimaging) [28,34,95], recurrent stroke [29,…”

Section: Risk Factors For 30-day In-hospital Stroke Case Fatalitymentioning

confidence: 99%

30-day in-hospital stroke case fatality and significant risk factors in sub-Saharan–Africa: A systematic review and meta-analysis

Ackah,

Ameyaw,

Appiah

et al. 2024

PLOS Glob Public Health

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Existing studies investigating 30-day in-hospital stroke case fatality rates in sub-Saharan Africa have produced varying results, underscoring the significance of obtaining precise and reliable estimations for this indicator. Consequently, this study aimed to conduct a systematic review and update of the current scientific evidence regarding 30-day in-hospital stroke case fatality and associated risk factors in sub-Saharan Africa. Medline/PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), APA PsycNet (encompassing PsycINFO and PsychArticle), Google Scholar, and Africa Journal Online (AJOL) were systematically searched to identify potentially relevant articles. Two independent assessors extracted the data from the eligible studies using a pre-tested and standardized excel spreadsheet. Outcomes were 30-day in-hospital stroke case fatality and associated risk factors. Data was pooled using random effects model. Ninety-three (93) studies involving 42,057 participants were included. The overall stroke case fatality rate was 27% [25%-29%]. Subgroup analysis revealed 24% [21%-28%], 25% [21%-28%], 29% [25%-32%] and 31% [20%-43%] stroke case fatality rates in East Africa, Southern Africa, West Africa, and Central Africa respectively. Stroke severity, stroke type, untyped stroke, and post-stroke complications were identified as risk factors. The most prevalent risk factors were low (<8) Glasgow Coma Scale score, high (≥10) National Institute Health Stroke Scale score, aspiration pneumonia, hemorrhagic stroke, brain edema/intra-cranial pressure, hyperglycemia, untyped stroke (stroke diagnosis not confirmed by neuroimaging), recurrent stroke and fever. The findings indicate that one in every four in-hospital people with stroke in sub-Saharan Africa dies within 30 days of admission. Importantly, the identified risk factors are mostly modifiable and preventable, highlighting the need for context-driven health policies, clinical guidelines, and treatments targeting these factors.

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Patient-level and system-level determinants of stroke fatality across 16 large hospitals in Ghana and Nigeria: a prospective cohort study

Cited by 17 publications

References 28 publications

Artificial Intelligence-based automated CT brain interpretation to accelerate treatment for acute stroke in rural India: An interrupted time series study

Artificial Intelligence-based automated CT brain interpretation to accelerate treatment for acute stroke in rural India: An interrupted time series study

Stroke characteristics and outcomes in urban Tanzania: Data from the Prospective Lake Zone Stroke Registry

30-day in-hospital stroke case fatality and significant risk factors in sub-Saharan–Africa: A systematic review and meta-analysis

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