Inflammation plays a prominent role in the development of atherosclerosis and other cardiovascular diseases, and anti-inflammatory agents may improve cardiovascular outcomes. For years, colchicine has been used as a safe and well-tolerated agent in diseases such as gout and familial Mediterranean fever. The widely available therapeutic has several anti-inflammatory effects, however, that have proven effective in a broad spectrum of cardiovascular diseases as well. It is considered standard-of-care therapy for pericarditis, and several clinical trials have evaluated its role in postoperative and postablation atrial fibrillation, postpericardiotomy syndrome, coronary artery disease, percutaneous coronary interventions, and cerebrovascular disease. We aim to summarize colchicine’s pharmacodynamics and the mechanism behind its anti-inflammatory effect, outline thus far accumulated evidence on treatment with colchicine in cardiovascular disease, and present ongoing randomized clinical trials. We also emphasize real-world clinical implications that should be considered on the basis of the merits and limitations of completed trials. Altogether, colchicine’s simplicity, low cost, and effectiveness may provide an important addition to other standard cardiovascular therapies. Ongoing studies will address complementary questions pertaining to the use of low-dose colchicine for the treatment of cardiovascular disease.

Background and Purpose: The National Institutes of Health Stroke Scale (NIHSS) underestimates clinical severity in posterior circulation stroke and patients presenting with low NIHSS may be considered ineligible for reperfusion therapies. This study aimed to develop a modified version of the NIHSS, the Posterior NIHSS (POST-NIHSS), to improve NIHSS prognostic accuracy for posterior circulation stroke patients with mild-moderate symptoms. Methods: Clinical data of consecutive posterior circulation stroke patients with mild-moderate symptoms (NIHSS <10), who were conservatively managed, were retrospectively analyzed from the Basilar Artery Treatment and Management registry. Clinical features were assessed within 24 hours of symptom onset; dysphagia was assessed by a speech therapist within 48 hours of symptom onset. Random forest classification algorithm and constrained optimization were used to develop the POST-NIHSS in the derivation cohort. The POST-NIHSS was then validated in a prospective cohort. Poor outcome was defined as modified Rankin Scale score ≥3 at 3 months. Results: We included 202 patients (mean [SD] age 63 [14] years, median NIHSS 3 [interquartile range, 1–5]) in the derivation cohort and 65 patients (mean [SD] age 63 [16] years, median NIHSS 2 [interquartile range, 1–4]) in the validation cohort. In the derivation cohort, age, NIHSS, abnormal cough, dysphagia and gait/truncal ataxia were ranked as the most important predictors of functional outcome. POST-NIHSS was calculated by adding 5 points for abnormal cough, 4 points for dysphagia, and 3 points for gait/truncal ataxia to the baseline NIHSS. In receiver operating characteristic analysis adjusted for age, POST-NIHSS area under receiver operating characteristic curve was 0.80 (95% CI, 0.73–0.87) versus NIHSS area under receiver operating characteristic curve, 0.73 (95% CI, 0.64–0.83), P =0.03. In the validation cohort, POST-NIHSS area under receiver operating characteristic curve was 0.82 (95% CI, 0.69–0.94) versus NIHSS area under receiver operating characteristic curve 0.73 (95% CI, 0.58–0.87), P =0.04. Conclusions: POST-NIHSS showed higher prognostic accuracy than NIHSS and may be useful to identify posterior circulation stroke patients with NIHSS <10 at higher risk of poor outcome.

Background: Factors contributing to cerebral edema in the post-hyperacute period of ischemic stroke (first 24–72 hours) are poorly understood. Blood-brain barrier (BBB) disruption and postischemic hyperperfusion reflect microvascular dysfunction and are associated with hemorrhagic transformation. We investigated the relationships between BBB integrity, cerebral blood flow, and space-occupying cerebral edema in patients who received acute reperfusion therapy. Methods: We performed a pooled analysis of patients treated for anterior circulation large vessel occlusion in the EXTEND-IA TNK and EXTEND-IA TNK part 2 trials who had MRI with dynamic susceptibility contrast-enhanced perfusion-weighted imaging 24 hours after treatment. We investigated the associations between BBB disruption and cerebral blood flow within the infarct with cerebral edema assessed using 2 metrics: first midline shift (MLS) trichotomized as an ordinal scale of negligible (<1 mm), mild (≥1 to <5 mm), or severe (≥5 mm), and second relative hemispheric volume (rHV), defined as the ratio of the 3-dimensional volume of the ischemic hemisphere relative to the contralateral hemisphere. Results: Of 238 patients analyzed, 133 (55.9%) had negligible, 93 (39.1%) mild, and 12 (5.0%) severe MLS at 24 hours. The associated median rHV was 1.01 (IQR, 1.00–1.028), 1.03 (IQR, 1.01–1.077), and 1.15 (IQR, 1.08–1.22), respectively. MLS and rHV were associated with poor functional outcome at 90 days ( P<0 .002). Increased BBB permeability was independently associated with more edema after adjusting for age, occlusion location, reperfusion, parenchymal hematoma, and thrombolytic agent used (MLS cOR, 1.12 [95% CI, 1.03–1.20], P =0.005; rHV β, 0.39 [95% CI, 0.24–0.55], P <0.0001), as was reduced cerebral blood flow (MLS cOR, 0.25 [95% CI, 0.10–0.58], P =0.001; rHV β, −2.95 [95% CI, −4.61 to −11.29], P =0.0006). In subgroup analysis of patients with successful reperfusion (extended Treatment in Cerebral Ischemia 2b-3, n=200), reduced cerebral blood flow remained significantly associated with edema (MLS cOR, 0.37 [95% CI, 0.14–0.98], P =0.045; rHV β, −2.59 [95% CI, −4.32 to −0.86], P =0.004). Conclusions: BBB disruption and persistent hypoperfusion in the infarct after reperfusion treatment is associated with space-occupying cerebral edema. Further studies evaluating microvascular dysfunction during the post-hyperacute period as biomarkers of poststroke edema and potential therapeutic targets are warranted.

Background and Purpose: The ISLES challenge (Ischemic Stroke Lesion Segmentation) enables globally diverse teams to compete to develop advanced tools for stroke lesion analysis with machine learning. Detection of irreversibly damaged tissue on computed tomography perfusion (CTP) is often necessary to determine eligibility for late-time-window thrombectomy. Therefore, the aim of ISLES-2018 was to segment infarcted tissue on CTP based on diffusion-weighted imaging as a reference standard. Methods: The data, from 4 centers, consisted of 103 cases of acute anterior circulation large artery occlusion stroke who underwent diffusion-weighted imaging rapidly after CTP. Diffusion-weighted imaging lesion segmentation was performed manually and acted as a reference standard. The data were separated into 63 cases for training and 40 for testing, upon which quality metrics (dice score coefficient, Hausdorff distance, absolute lesion volume difference, etc) were computed to rank methods based on their overall performance. Results: Twenty-four different teams participated in the challenge. Median time to CTP was 185 minutes (interquartile range, 180–238), the time between CTP and magnetic resonance imaging was 36 minutes (interquartile range, 25–79), and the median infarct lesion size was 15.2 mL (interquartile range, 5.7–45). The best performance for Dice score coefficient and absolute volume difference were 0.51 and 10.1 mL, respectively, from different teams. Based on the ranking criteria, the top team’s algorithm demonstrated for average Dice score coefficient and average absolute volume difference 0.51 and 10.2 mL, respectively, outperforming the conventional threshold-based method (dice score coefficient, 0.3; volume difference, 15.3). Diverse algorithms were used, almost all based on deep learning, with top-ranked approaches making use of the raw perfusion data as well as methods to synthetically generate complementary information to boost prediction performance. Conclusions: Machine learning methods may predict infarcted tissue from CTP with improved accuracy compared with threshold-based methods used in clinical routine. This dataset will remain public and can be used to test improvement in algorithms over time.