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    Glycemic Gap Predicts in-Hospital Mortality in Diabetic Patients with Intracerebral Hemorrhage. Zarean Elaheh,Lattanzi Simona,Looha Mehdi Azizmohammad,Napoli Mario Di,Chou Sherry H-Y,Jafarli Alibay,Torbey Michel,Divani Afshin A Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association BACKGROUND AND PURPOSE:The relationship between admission hyperglycemia and intracerebral hemorrhage (ICH) outcome remains controversial. Glycemic gap (GG) is a superior indicator of glucose homeostatic response to physical stress compared to admission glucose levels. We aimed to evaluate the association between GG and in-hospital mortality in ICH. METHODS:We retrospectively identified consecutive patients hospitalized for spontaneous ICH at the 2 healthcare systems in the Twin Cities area, MN, between January 2008 and December 2017. Patients without glycosylated hemoglobin (HbA1c) test or those admitted beyond 24 hours post-ICH were excluded. Demographics, medical history, admission tests, and computed tomography data were recorded. GG was computed using admission glucose level minus HbA1c-derived average glucose. The association between GG and time to in-hospital mortality was evaluated by Cox regression analysis. Receiver operating characteristic (ROC) analysis with the DeLong test was used to evaluate the ability of GG to predict in-hospital death. RESULTS:Among 345 included subjects, 63 (25.7%) died during the hospital stay. Compared with survivors, non-survivors presented with a lower Glasgow coma scale score, larger hematoma volume, and higher white blood cells count, glucose, and GG levels at admission (p<0.001). GG remained an independent predictor of in-hospital mortality after adjusting for known ICH outcome predictors and potential confounders [adjusted hazard ratio: 1.09, 95% confidence interval (CI): 1.02-1.18, p = 0.018]. GG showed a good discriminative power (area under the ROC curve: 0.75, 95% CI: 0.68-0.82) in predicting in-hospital death and performed better than admission glucose levels in diabetic patients (p = 0.030 for DeLong test). CONCLUSIONS:Admission GG is associated with the risk of in-hospital mortality and can potentially represent a useful prognostic biomarker for ICH patients with diabetes. 10.1016/j.jstrokecerebrovasdis.2021.105669
    Stress hyperglycemia is predictive of clinical outcomes in patients with spontaneous intracerebral hemorrhage. BMC neurology BACKGROUND:Stress hyperglycemia is a common condition in patients suffering from critical illness such as spontaneous intracerebral hemorrhage (ICH). Our study aimed to use glucose-to-glycated hemoglobin (HbA1c) ratio to investigate the impact of stress hyperglycemia on clinical outcomes in patients with ICH. METHODS:A sample of eligible 586 patients with spontaneous intracerebral hemorrhage from a multicenter, hospital-based cohort between 2014 and 2016 were recruited in our study. Stress hyperglycemia was evaluated by the index of the glucose-to-HbA1c ratio that was calculated by fasting blood glucose (mmol/L) divided by HbA1c (%). Patients were divided into two groups based on the median of the glucose-to-HbA1c ratio. The main outcomes were poor functional outcomes (modified Rankin Scale score of 3-6) at discharge and 90 days. Multivariable logistic regression and stratified analyses were performed to explore the association of stress hyperglycemia with poor prognosis of ICH. RESULTS:On multivariable analysis, higher glucose-to-HbA1c ratio (≥1.02) was independently correlated with poor functional outcomes at discharge (adjusted OR = 3.52, 95%CI: 1.98-6.23) and 90 days (adjusted OR = 2.27, 95%CI: 1.38-3.73) after adjusting for potential confounding factors. The correlation between glucose-to-HbA1c ratio and worse functional outcomes still retained in patients with or without diabetes mellitus. CONCLUSIONS:Stress hyperglycemia, calculated by glucose-to-HbA1c ratio, was independently correlated with worse functional outcomes at discharge and 90 days in patients with ICH. Moreover, glucose-to-HbA1c ratio, might not only be used as a simple and readily available index to predict clinical outcomes of ICH but also provide meaningful insight into future analysis to investigate the optimal range of glucose levels among ICH patients and develop tailored glucose-lowering strategies. 10.1186/s12883-022-02760-9
    A Prediction Model for Neurological Deterioration in Patients with Acute Spontaneous Intracerebral Hemorrhage. Frontiers in surgery Aim:The aim of this study was to explore factors related to neurological deterioration (ND) after spontaneous intracerebral hemorrhage (sICH) and establish a prediction model based on random forest analysis in evaluating the risk of ND. Methods:The clinical data of 411 patients with acute sICH at the Affiliated Hospital of Jining Medical University and Xuanwu Hospital of Capital Medical University between January 2018 and December 2020 were collected. After adjusting for variables, multivariate logistic regression was performed to investigate the factors related to the ND in patients with acute ICH. Then, based on the related factors in the multivariate logistic regression and four variables that have been identified as contributing to ND in the literature, we established a random forest model. The receiver operating characteristic curve was used to evaluate the prediction performance of this model. Results:The result of multivariate logistic regression analysis indicated that time of onset to the emergency department (ED), baseline hematoma volume, serum sodium, and serum calcium were independently associated with the risk of ND. Simultaneously, the random forest model was developed and included eight predictors: serum calcium, time of onset to ED, serum sodium, baseline hematoma volume, systolic blood pressure change in 24 h, age, intraventricular hemorrhage expansion, and gender. The area under the curve value of the prediction model reached 0.795 in the training set and 0.713 in the testing set, which suggested the good predicting performance of the model. Conclusion:Some factors related to the risk of ND were explored. Additionally, a prediction model for ND of acute sICH patients was developed based on random forest analysis, and the developed model may have a good predictive value through the internal validation. 10.3389/fsurg.2022.886856