Blood Pressure Control and Recurrent Stroke After Intracerebral Hemorrhage in 2002 to 2018 Versus 1981 to 1986: Population-Based Study

Background and Purpose: The PROGRESS trial (Perindopril Protection Against Recurrent Stroke Study) conducted in the early 1990s showed that blood pressure (BP) lowering therapy reduced the risks of recurrent stroke by about 50% after spontaneous intracerebral hemorrhage (ICH). However, the ICH subgroup was a minority, and trial cohorts are invariably selective. Therefore, it is unclear whether the impact of BP control on risk of recurrent stroke in ICH observed in PROGRESS would be as great in real-world practice. Methods: We compared BP control (mean BP during study follow-up) and risks of recurrent stroke after first-ever primary ICH in 2 colocated population-based studies before and after the PROGRESS trial (1995–2001) in Oxfordshire: Oxfordshire Community Stroke Project (OCSP; 1981–1986) and OXVASC (Oxford Vascular Study; 2002–2018). Results: Two hundred seventy-seven patients (753 patient-years of follow-up) with first-ever primary ICH were ascertained in OXVASC and OCSP. Baseline systolic BP was comparable between the 2 studies (mean/SD=183.8/36.5 in OXVASC versus 178.1/38.2 in OCSP, P =0.30) but among one hundred thirty-seven 90-day survivors, mean BP during follow-up was substantially lower in OXVASC versus OCSP (135.2/16.4 versus 157.3/17.8, P <0.0001). Risks of recurrent stroke (per 100 patient-years) decreased from 10.3 (95% CI, 4.7–19.5) in OCSP to 3.1 (1.8–4.8) in OXVASC ( P =0.006), predominantly driven by a reduction at younger ages (5-year risk at age <75 years: 35.4% versus 6.9%, P =0.001; hazard ratio, 0.14 [0.04–0.54]). Conclusions: Risks of recurrent stroke after primary ICH have fallen significantly in Oxfordshire over the past 4 decades, coinciding with substantial improvements in BP control during follow-up.

Oxfordshire Community Stroke Project Classification: A proposed automated algorithm

Introduction The Oxfordshire Community Stroke Project (OCSP) proposed a clinical classification for Stroke patients. This classification has proved helpful to predict the risk of neurological complications. However, the OCSP was initially based on findings on the neurological assesment, which can pose difficulties for classifying patients. We aimed to describe the development and the validation step of a computer-based algorithm based on the OCSP classification. Materials and methods A flow-chart was created which was reviewed by five board-certified vascular neurologists from which a computer-based algorithm (COMPACT) was developed. Neurology residents from 12 centers were invited to participate in a randomized trial to assess the effect of using COMPACT. They answered a 20-item questionnaire for classifying the vignettes according to the OCSP classification. Each correct answer has been attributed to 1-point for calculating the final score. Results Six-two participants agreed to participate and answered the questionnaire. Thirty-two were randomly allocated to use our algorithm, and thirty were allocated to adopt a list of symptoms alone. The group who adopted our algorithm had a median score of correct answers of 16.5[14.5, 17]/20 versus 15[13, 16]/20 points, p = 0.014. The use of our algorithm was associated with the overall rate of correct scores (p = 0.03). Discussion Our algorithm seemed a useful tool for any postgraduate year Neurology resident. A computer-based algorithm may save time and improve the accuracy to classify these patients. Conclusion An easy-to-use computer-based algorithm improved the accuracy of the OCSP classification, with the possible benefit of further improvement of the prediction of neurological complications and prognostication.

Análise do perfil dos subtipos clínicos de Doenças Cerebrovasculares Isquêmicas em pacientes admitidos em Unidade Ambulatorial em Salvador – Brasil

Objetivo: Identificar e descrever os subtipos do AVC isquêmico numa população de pacientes atendida em unidade de urgência e emergência. Métodos: Estudo retrospectivo de corte transversal, dos pacientes admitidos consecutivamente na Fundação Baiana de Neurologia de junho de 2017 a dezembro de 2019 com o diagnóstico de AVC isquêmico agudo. Analisamos os subtipos do AVC utilizando a classificação do Oxfordshire Community Stroke Project, os dados demográficos e as comorbidades. Resultados: Dos 1.115 pacientes admitidos com diagnóstico de AVC, 984 (88,25%) deles foram do tipo isquêmico. A média de idade foi de 65.9 ±14 anos e 53,5% eram do sexo feminino. Hipertensão foi o fator de risco mais frequente (73,8 %), entretanto não houve diferença significativa entre os diversos subtipos do AVC. Identificamos 39,70% de PACI (infarto parcial da circulação anterior), 35,10% de TACI (infarto total da circulação anterior), 13,20% de LACI (infarto lacunar) e 12,10% de POCI (infarto da circulação posterior). Encontramos associação estatisticamente significante entre o subtipo PACI e a presença do diabetes mellitus (p = 0,028). Conclusão: O infarto parcial da circulação anterior (PACI) foi o subtipo mais frequente do AVC isquêmico e esteve associado com a presença do diabetes mellitus.

Improving Clinical Detection of Acute Lacunar Stroke

Background and Purpose— We aim to identify factors associated with imaging-confirmed lacunar strokes and improve their rapid clinical identification early after symptom onset using data from the IST-3 (Third International Stroke Trial). Methods— We selected patients likely to have lacunar infarcts as those presenting with: Oxfordshire Community Stroke Project lacunar syndrome; a random sample with National Institutes of Health Stroke Scale (NIHSS) score <7; and recent lacunar infarct identified on imaging by IST-3 central blinded expert panel. An independent reviewer rated brain scans of this sample and classified visible infarcts according to type, size, and location. We investigated factors associated with presence of lacunar infarct on a 24 to 48 hour follow-up scan using multivariable logistic regression and calculated sensitivity and specificity of Oxfordshire Community Stroke Project alone and in combination with NIHSS score <7. Results— We included 568 patients (330 lacunar syndrome; 147 with NIHSS score <7; 91 with lacunar infarct on baseline imaging, numbers exclude overlaps between groups), mean (±SD) age, 73.2 (±13.6) years, 316 (56%) males, and median NIHSS score 5 (IQR, 4–8). On 24 to 48 hour scan, 138 (24%) patients had lacunar infarcts, 176 (31%) other infarct subtypes, 254 (45%) no visible infarct. Higher baseline systolic blood pressure (odds ratio, 1.01 [95% CI, 1.01–1.02]) and preexisting lacunes (odds ratio, 2.29 [95% CI, 1.47–3.57) were associated with recent lacunar infarcts. Sensitivity and specificity of lacunar syndrome was modest (58% and 45%, respectively), but adding NIHSS score <7 increased specificity (99%), positive and negative predictive values (97% and 87%, respectively). Conclusions— In patients presenting within 6 hours of stroke onset, adding NIHSS score <7 to Oxfordshire Community Stroke Project lacunar syndrome classification may increase specificity for identifying lacunar stroke early after stroke onset. Our findings may help selection of patients for clinical trials of lacunar stroke and should be validated externally. Registration— URL: http://www.controlled-trials.com/ ; Unique identifier: ISRCTN25765518.

Automated Detection of Brain Stroke in MRI with Hybrid Fuzzy C-Means Clustering and Random Forest Classifier

Neuroimaging investigation is an essential parameter to detect infarct lesion in stroke patients. Precise detection of brain lesions is an important task related to impaired behavior. In this paper, we aimed to develop an automatic method to segment and classify infarct lesion in diffusion-weighted imaging (DWI) of brain MRI. The method includes hybrid fuzzy [Formula: see text]-means (HFCM) clustering in which the structure of [Formula: see text]-means clustering is modified with rough sets and fuzzy sets to improve the segmentation performance with self-adjusted intensity thresholds. Quantitative evaluation was carried out on 128 MRI slices of brain image collected from ischemic stroke patients at the Department of Radiology, IMS and SUM Hospital, Bhubaneswar. The informative statistical features have been extracted using gray-level co-occurrence matrix (GLCM) and used to classify the types of stroke infarct according to the Oxfordshire Community Stroke Project (OCSP) classification. The parameters such as accuracy, Dice similarity index (DSI) and Jaccard index (JI) were utilized to evaluate the effectiveness of the proposed method in detecting the stroke lesions. The segmentation method achieved the average accuracy, DSI and JI of 96.8%, 95.8% and 92.2%, respectively, in support vector machine (SVM) classifier. The obtained results are higher in terms of random forest (RF) classification. With a high Dice coefficient of 0.958 and other evaluated parameters, the proposed method outperforms earlier published results.