Stroke care and outcomes in the Department of Neurology in Parakou, Benin: Retrospective cohort study

Adoukonou, Thierry; Agbétou, Mendinatou; Sowanou, Arlos; Kossi, Oyéné; Fotso, Pervenche; Houéhanou, Corine; Vallat, Jean‐Michel; Houinato, Dismand Stéphan; Preux, Pierre‐Marie; Lacroix, Philippe

doi:10.1016/j.amsu.2020.07.041

Cited by 7 publications

(13 citation statements)

References 15 publications

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“…However, despite the high stroke mortality rates in Africa, little is known about stroke subtypes and prognosis, 11 stroke awareness among health professionals is lacking, 12,13 and there is a scarcity of stroke units and health services. 14,15…”

Section: Introductionmentioning

confidence: 99%

Stroke services in Africa: What is there and what is needed

Roushdy

Aref

Kesraoui

et al. 2022

International Journal of Stroke

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Background: Over the past few years, the incidence and prevalence of stroke has been rising in most African countries and has been reported as one of the leading causes of morbidity and mortality. To study this problem, we need to realize the quality and availability of stroke care services as a priori to improve them. Methods and Results: In this study, we investigated the availability of different stroke-related services in 17 countries from different African regions. An online survey was conducted and fulfilled by stroke specialists and included primary prevention, acute management, diagnostic tools, medications, postdischarge services, and stroke registries. The results showed that although medications for secondary prevention are available, yet many other services are lacking in various countries. Conclusion: This study displays the deficient aspects of stroke services in African countries as a preliminary step toward active corrective procedures for the improvement of stroke-related health services.

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Section: Introductionmentioning

confidence: 99%

Stroke services in Africa: What is there and what is needed

Roushdy

Aref

Kesraoui

et al. 2022

International Journal of Stroke

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“…In summary, our study in a large cohort of stroke patients from the Conakry stroke registry identified a predictive model for favorable stroke outcomes with an AUC similar to predictive models in HIC 12 , 15 , 33 . There were four modifiable variables: early brain CT imaging, fever, sores and mean blood pressure increased the proportion of stroke favorable outcome variability explained by the model beyond admission NIHSS.…”

Section: Discussionmentioning

confidence: 56%

“…Indeed, the combination of admission NIHSS and age was found to be highly predictive of favorable stroke outcome in several studies, outperforming other clinical or imaging-based scores in HIC 12 , 14 . In our population, we found that age failed to predict better outcomes than HIC-based models, where age is consistently associated with outcome prediction 33 – 35 . This specificity in our population is likely because our population is almost fifteen years younger than in the HIC stroke studied populations 28 , 29 , 33 – 35 , with over a quarter of the patients who experience stroke before fifty years old in Guinea 21 .…”

Section: Discussionmentioning

confidence: 72%

“…In our population, we found that age failed to predict better outcomes than HIC-based models, where age is consistently associated with outcome prediction 33 – 35 . This specificity in our population is likely because our population is almost fifteen years younger than in the HIC stroke studied populations 28 , 29 , 33 – 35 , with over a quarter of the patients who experience stroke before fifty years old in Guinea 21 . This age gap between stroke patients in HIC and Guinea and the overrepresentation of stroke in young individuals is not specific to Guinea but reflects the situation described in other African LMICs 6 , 36 .…”

Section: Discussionmentioning

confidence: 72%

“…This suggests that age helps to predict favorable stroke outcome only in populations where stroke age distribution has a median age of approximately seventy, which is not the case in most LMICs 37 , 38 . On the other hand, admission NIHSS, expectedly, proved to be as important in our context than in HIC to predict favorable outcomes and was thus included in the null model of the logistic regression as a nuisance parameter 13 , 15 , 33 .…”

Section: Discussionmentioning

confidence: 95%

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Predictors of stroke favorable functional outcome in Guinea, results from the Conakry stroke registry

Cisse

Ligot

Condé

et al. 2022

Sci Rep

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Low- to middle-income countries (LMICs) now bear most of the stroke burden. In LMICs, stroke epidemiology and health care systems are different from HICs. Therefore, a high-income country (HIC)-based predictive model may not correspond to the LMIC stroke context. Identify the impact of modifiable variables in acute stroke management in Conakry, Guinea as potential predictors of favorable stroke outcome. Data were extracted from the Conakry stroke registry that includes 1018 patients. A logistic regression model was built to predict favorable stroke outcomes, defined as mRS 0–2. Age, admission NIHSS score, mean arterial blood pressure and capillary glycemia were chosen as covariates. Delay to brain CT imaging under 24 h from symptom onset, fever, presence of sores and abnormal lung auscultation were included as factors. NIHSS score on admission, age and ischemic stroke were included in the null model as nuisance parameters to determine the contribution of modifiable variables to predict stroke favorable outcome. Lower admission NIHSS, brain CT imaging within 24 h of symptoms onset and lower mean arterial blood pressure emerged as a significant positive predictors of favorable stroke outcome with respective odd ratios (OR) of 1.35 [1.28–1.43], 2.1 [1.16–3.8] and 1.01 [1.01–1.04]. The presence of fever or sores impacted negatively stroke favorable outcomes with OR of 0.3 [0.1–0.85] and 0.25 [0.14–0.45]. The area under receiver operating characteristic curves (AUC) of the model was 0.86. This model explained 44.5% of the variability of the favorable stroke outcome with 10.2% of the variability explained by the modifiable variables when admission NIHSS, and ischemic stroke were included in the null model as nuisance parameter. In the Conakry stroke registry, using a logistic regression to predict stroke favorable outcome, five variables that led to an AUC of 0.86: admission NIHSS, early brain CT imaging, fever, sores and mean blood pressure. This paves the way for future public health interventions to test whether modulating amendable variables leads to increased favorable stroke outcomes in LMICs.

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