Chronic periodontitis was independently associated with the presence of LI after adjusting for well-known vascular risk factors for lacunar stroke. Further observational studies are necessary to investigate the pathophysiological mechanisms that can explain this relationship.

Background Periodontitis has been associated with lacunar infarct (LI), a type of cerebral small vessel disease. The objective of this study was to ascertain whether periodontitis is associated with increased circulating levels of systemic inflammation and endothelial dysfunction biomarkers in patients with LI. Methods One hundred twenty patients with LI and 120 healthy controls underwent a full‐mouth periodontal examination. The periodontal inflamed surface area (PISA) was calculated for each participant. Demographic, medical, and neurological information were recorded from all of them. In addition, blood samples were collected in order to investigate differences in terms of interleukin (IL)‐6, IL‐10, pentraxin (PTX) 3, soluble fragment of tumor necrosis factor‐like weak inducer of apoptosis (sTWEAK) and amyloid‐beta (Aβ) peptides (i.e., Aβ1‐40, and Aβ1‐42) measured in serum. Results Periodontitis was independently associated with increased levels of IL‐6 (R2 = 0.656, P < 0.001), PTX3 (R2 = 0.115, P < 0.001), sTWEAK (R2 = 0.527, P < 0.001), and Aβ1‐40 (R2 = 0.467, P < 0.001) in patients with LI. Within patients with poor outcome, PISA positively correlated with IL‐6 (r = 0.738, P < 0.001), PTX3 (r = 0.468, P = 0.008), sTWEAK (r = 0.771, P < 0.001), and Aβ1‐40 (r = 0.745, P < 0.001). Conclusions Our data suggest a link between periodontitis, systemic inflammatory response, and disruption of the vascular endothelial function in patients with LI. Experimental studies are needed to elucidate possible pathways through which periodontitis could lead to this systemic inflammatory state with impairment of the endothelial function in LI. Further longitudinal studies with large samples are warranted to confirm our findings.

We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3-months after admission. The dataset consisted of patients with ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/morbidity according to the following groups: (1) IS + ICH, (2) IS, and (3) ICH. A total of 6022 patients were included: 4922 (mean age 71.9 ± 13.8 years) with IS and 1100 (mean age 73.3 ± 13.1 years) with ICH. NIHSS at 24, 48 h and axillary temperature at admission were the most important variables to consider for evolution of patients at 3-months. IS + ICH group was the most stable for mortality prediction [0.904 ± 0.025 of area under the receiver operating characteristics curve (AUC)]. IS group presented similar results, although variability between experiments was slightly higher (0.909 ± 0.032 of AUC). ICH group was the one in which RF had more problems to make adequate predictions (0.9837 vs. 0.7104 of AUC). There were no major differences between IS and IS + ICH groups according to morbidity prediction (0.738 and 0.755 of AUC) but, after checking normality with a Shapiro Wilk test with the null hypothesis that the data follow a normal distribution, it was rejected with W = 0.93546 (p-value < 2.2e−16). Conditions required for a parametric test do not hold, and we performed a paired Wilcoxon Test assuming the null hypothesis that all the groups have the same performance. The null hypothesis was rejected with a value < 2.2e−16, so there are statistical differences between IS and ICH groups. In conclusion, machine learning algorithms RF can be effectively used in stroke patients for long-term outcome prediction of mortality and morbidity.

Background and purposeThe deleterious effect of hyperthermia on intracerebral hemorrhage (ICH) has been studied. However, the results are not conclusive and new studies are needed to elucidate clinical factors that influence the poor outcome. The aim of this study was to identify the clinical factors (including ICH etiology) that influence the poor outcome associated with hyperthermia and ICH. We also tried to identify potential mechanisms involved in hyperthermia during ICH.MethodsWe conducted a retrospective study enrolling patients with non‐traumatic ICH from a prospective registry. We used logistic regression models to analyze the influence of hyperthermia in relation to different inflammatory and endothelial dysfunction markers, hematoma growth and edema volume in hypertensive and non‐hypertensive patients with ICH.ResultsWe included 887 patients with ICH (433 hypertensive, 50 amyloid, 117 by anticoagulants and 287 with other causes). Patients with hypertensive ICH showed the highest body temperature (37.5 ± 0.8°C) as well as the maximum increase in temperature (0.9 ± 0.1°C) within the first 24 h. Patients with ICH of hypertensive etiologic origin, who presented hyperthermia, showed a 5.3‐fold higher risk of a poor outcome at 3 months. We found a positive relationship (r = 0.717, P < 0.0001) between edema volume and hyperthermia during the first 24 h but only in patients with ICH of hypertensive etiologic origin. This relationship seems to be mediated by inflammatory markers.ConclusionOur data suggest that hyperthermia, together with inflammation and edema, is associated with poor outcome only in ICH of hypertensive etiology.