Introduction: Sub-Saharan Africa (SSA) has the highest stroke prevalence along with a case fatality that amounts to 40%. We aimed to assess the effect of a minimal setting stroke unit in SSA Public hospital on stroke mortality and main medical complications. Materials and Methods: The study was set in Conakry, Guinea, Ignace Deen public referral hospital. Clinical characteristics, hospital mortality and main medical stroke complications rates (pneumonia, urinary tract infections, sores and venous thromboembolism) of admitted stroke patients after the installation of a minimal stroke unit equipped with heart rate, blood pressure and blood oxygen saturation monitoring and portable oxygen concentrator (POST) were compared to a similar number of stroke patients admitted before the stroke unit creation (PRE). Results: PRE ( n = 318) and POST ( n = 361) stroke, patients were comparable in term of age (61 ± 14 vs. 60 ± 14.8 years, p = 0.24), sex (56 vs. 50% males, p = 0.09), High blood pressure rate (76.7 vs. 79%, p = 0.44), stroke subtype (ischemic in 72 vs. 78% of cases, p = 0.05) and NIHSS (11 ± 4 vs. 11 ± 4, p = 0.85). Diabetes was more frequent in the PRE group (19 vs. 9%, p < 0.001). Mortality was significantly lower in the POST group (7.2 vs. 22.3%, p < 0.0001) as well as medical complications (4.1 vs. 27.7%, p < 0.001) and lower pneumonia rate (3.3 vs. 14.5%, p < 0.001). Conclusions: Minimally equipped stroke units significantly reduce stroke mortality and main medical complications in SSA.

The performance of prognostic scores of status epilepticus (SE) has been reported in very heterogeneous cohorts. We aimed to provide a summary of the available evidence on their respective performance. PubMed and EMBASE were searched for relevant articles. Studies were reviewed for eligibility for meta‐analysis of the area under the receiver‐operating characteristic curve (AUC) and for meta‐analysis of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) in predicting in‐hospital mortality with scores in which at least two external evaluations had been published. This study was registered with PROSPERO (international prospective register of systematic reviews) (CRD42022325766). Study quality was assessed using Prediction model Risk Of Bias ASsessment Tool (PROBAST). In the meta‐analysis of AUC, 21 studies were pooled for STESS (Status Epilepticus Severity Score), five for EMSE‐EAC (Epidemiology‐based Mortality Score in Status Epilepticus ‐ Etiology, Age, level of Consciousness), five for EMSE‐EACE (EMSE ‐ Etiology, Age, level of Consciousness, EEG), and two for ENDIT (Encephalitis, nonconvulsive status epilepticus, Diazepam resistance, Imaging abnormalities, Tracheal intubation). The pooled AUC of STESS, EMSE‐EAC, EMSE‐EACE, and ENDIT was 0.74 (95% CI: 0.71–0.78), 0.68 (95% CI 0.63–0.72), 0.77 (95% CI: 0.72–0.81), and 0.78 (95% CI: 0.70–0.87), respectively. The pooled sensitivity of STESS‐3, STESS‐4, EMSE‐EACE‐64, and ENDIT‐4 was 0.83 (95% CI: 0.80–0.86), 0.60 (95% CI: 0.55–0.65), 0.76 (95% CI: 0.67–0.83), and 0.70 (95% CI: 0.55–0.82), respectively. Their pooled specificity was 0.50 (95% CI: 0.48–0.52), 0.74 (95% CI: 0.72–0.76), 0.63 (95% CI: 0.59–0.67), and 0.65 (95% CI: 0.61–0.70), respectively. Their pooled PPV was 0.27 (95% CI: 0.24–0.30), 0.35 (95% CI: 0.29–0.41), 0.33 (95% CI: 0.24–0.43), and 0.20 (95% CI: 0.13–0.27). Their pooled NPV was 0.94 (95% CI: 0.93–0.96), 0.90 (95% CI: 0.89–0.92), 0.89 (95% CI: 0.80–0.98), and 0.95 (95% CI: 0.92–0.98). Variations in performance were observed in patients' subgroups, such as critically ill patients and refractory cases. Investigated scores only have acceptable AUC, sensitivity, and specificity for predicting in‐hospital mortality, with the EMSE‐EAC having a lower discriminative power. STESS‐3 has the highest sensitivity, and STESS‐4 the highest specificity, but neither combines acceptable sensitivity and specificity. All these scores had high NPV but very low PPV. Caution should be exercised in their clinical use. Further studies are required to develop more accurate scores.

IntroductionCurrent guidelines suggest that perfusion imaging should only be performed &gt; 6 h after symptom onset. Pathophysiologically, brain perfusion should matter whatever the elapsed time. We aimed to compare relative contribution of recanalization time and stroke core volume in predicting functional outcome in patients treated by endovascular thrombectomy within 6-h of stroke-onset.MethodsConsecutive patients presenting between January 2015 and June 2021 with (i) an acute ischaemic stroke due to an anterior proximal occlusion, (ii) a successful thrombectomy (TICI &gt;2a) within 6-h of symptom-onset and (iii) CT perfusion imaging were included. Core stroke volume was automatically computed using RAPID software. Two linear regression models were built that included in the null hypothesis the pre-treatment NIHSS score and the hypoperfusion volume (Tmax &gt; 6 s) as confounding variables and 24 h post-recanalization NIHSS and 90 days mRS as outcome variables. Time to recanalization was used as covariate in one model and stroke core volume as covariate in the other.ResultsFrom a total of 377 thrombectomies, 94 matched selection criteria. The Model null hypothesis explained 37% of the variability for 24 h post-recanalization NIHSS and 42% of the variability for 90 days MRS. The core volume as covariate increased outcome variability prediction to 57 and 56%, respectively. Time to recanalization as covariate marginally increased outcome variability prediction from 37 and 34% to 40 and 42.6%, respectively.ConclusionCore stroke volume better explains outcome variability in comparison to the time to recanalization in anterior large vessel occlusion stroke with successful thrombectomy done within 6 h of symptoms onset. Still, a large part of outcome variability prediction fails to be explained by the usual predictors.