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In patients with acute stroke, four clinical variables were found to be independently associated with the risk of developing fever and, of them, nasogastric tube was the strongest and most significant one.

Background Hyperglycemic non-diabetic stroke patients have a worse prognosis than both normoglycemic and diabetic patients. Aim of this study was to assess whether hyperglycemia is an aggravating factor or just an epiphenomenon of most severe strokes. Methods In this retrospective study, 1219 ischemic or hemorrhagic stroke patients (73.7 ± 13.1 years) were divided into 4 groups: 0 = non-hyperglycemic non-diabetic, 1 = hyperglycemic non-diabetic, 2 = non-hyperglycemic diabetic and 3 = hyperglycemic diabetic. Hyperglycemia was defined as fasting blood glucose ≥ 126 mg/dl (≥ 7 mmol/l) measured the morning after admission, while the diagnosis of diabetes was based on a history of diabetes mellitus or on a glycated hemoglobin ≥ 6.5% (≥ 48 mmol/mol), independently of blood glucose levels. All diabetic patients, except 3, had Type 2 diabetes. The 4 groups were compared according to clinical history, stroke severity indicators, acute phase markers and main short term stroke outcomes (modified Rankin scale ≥ 3, death, cerebral edema, hemorrhagic transformation of ischemic lesions, fever, oxygen administration, pneumonia, sepsis, urinary infection and heart failure). Results Group 1 patients had more severe strokes, with larger cerebral lesions and higher inflammatory markers, compared to the other groups. They also had a high prevalence of atrial fibrillation, prediabetes, previous stroke and previous arterial revascularizations. In this group, the highest frequencies of cerebral edema, hemorrhagic transformation, pneumonia and oxygen administration were obtained. The prevalence of dependency at discharge and in-hospital mortality were equally high in Group 1 and Group 3. However, in multivariate analyses including stroke severity, cerebral lesion diameter, leukocytes and C-reactive protein, Group 1 was only independently associated with hemorrhagic transformation (OR 2.01, 95% CI 0.99–4.07), while Group 3 was independently associated with mortality (OR 2.19, 95% CI 1.32–3.64) and disability (OR 1.70, 95% CI 1.01–2.88). Conclusions Hyperglycemic non-diabetic stroke patients had a worse prognosis than non-hyperglycemic or diabetic patients, but this group was not independently associated with mortality or disability when size, severity and inflammatory component of the stroke were accounted for.

AimsLow-gradient aortic stenosis is a challenging entity that needs accurate preoperative evaluation. For this high-risk patient population, ad hoc predictive scores are not available and profile risk is currently revealed by the EuroSCOREs. Aims of this study are to verify the suitability of the ES II as predictor of mortality in low-gradient aortic stenosis and to analyse the role of surgery as a treatment.MethodsFrom June 2013 to August 2019, 414 patients underwent surgical aortic valve replacement for low-gradient aortic stenosis. Mean age was 75.78 ± 6.77 years and 190 were women. The prognostic value of Logistic EuroSCORE and EuroSCORE II were compared by receiver-operating characteristics (ROC) curve analysis.ResultsIn-hospital, 30-day and 1-year mortality rates were respectively 3.4, 2.9 and 4.8% (14, 12 and 20 patients over 414). In-hospital mortality risk calculated by the Additive EuroSCORE was 7.2 ± 2.7%, by the Logistic EuroSCORE was 9 ± 5.2% and by the ES II was 4.13 ± 2.56%. The prognostic values of the EuroSCORE II and of the EuroSCORE were analysed in a ROC curve analysis for the prediction of in-hospital mortality [area under the curve (AUC): 0.62 vs. 0.58], 30-day mortality (AUC: 0.63 vs. 0.64) and 1-year mortality (AUC: 0.79 vs. 0.65). Both scores did not show significant differences with the only exception of 1-year mortality, for which EuroSCORE II had a better predictive ability than the Logistic EuroSCORE (P < 0.05).ConclusionIn low-gradient aortic stenosis undergoing surgery, the EuroSCORE II is a strong predictor of 1-year mortality.

The presence of imbalanced classes is more and more common in practical applications and it is known to heavily compromise the learning process. In this paper we propose a new method aimed at addressing this issue in binary supervised classification. Re-balancing the class sizes has turned out to be a fruitful strategy to overcome this problem. Our proposal performs re-balancing through matrix sketching. Matrix sketching is a recently developed data compression technique that is characterized by the property of preserving most of the linear information that is present in the data. Such property is guaranteed by the Johnson-Lindenstrauss’ Lemma (1984) and allows to embed an n-dimensional space into a reduced one without distorting, within an $$\epsilon $$ ϵ -size interval, the distances between any pair of points. We propose to use matrix sketching as an alternative to the standard re-balancing strategies that are based on random under-sampling the majority class or random over-sampling the minority one. We assess the properties of our method when combined with linear discriminant analysis (LDA), classification trees (C4.5) and Support Vector Machines (SVM) on simulated and real data. Results show that sketching can represent a sound alternative to the most widely used rebalancing methods.