Ofer Sadan1, Chen Feng2, Branislav Vidakovic3, Yajun Mei2, Kathleen Martin4, Owen Samuels4, C L Hall4. 1. Department of Neurology and Neurosurgery, Division of Neurocritical Care, Emory University Hospital, Emory University School of Medicine, 1364 Clifton Rd. NE, Atlanta, GA, 30322, USA. ofer.sadan@gmail.com. 2. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA. 3. H. Milton Stewart School of Industrial & Systems Engineering and Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332-0205, USA. 4. Department of Neurology and Neurosurgery, Division of Neurocritical Care, Emory University Hospital, Emory University School of Medicine, 1364 Clifton Rd. NE, Atlanta, GA, 30322, USA.
Abstract
BACKGROUND: Critically ill aneurysmal subarachnoid hemorrhage (aSAH) patients suffer from systemic complications at a high rate. Hyperglycemia is a common intensive care unit (ICU) complication and has become a focus after aggressive glucose management was associated with improved ICU outcomes. Subsequent research has suggested that glucose variability, not a specific blood glucose range, may be a more appropriate clinical target. Glucose variability is highly correlated to poor outcomes in a wide spectrum of critically ill patients. Here, we investigate the changes between subsequent glucose values termed "inter-measurement difference," as an indicator of glucose variability and its association with outcomes in patients with aSAH. METHODS: All SAH admissions to a single, tertiary referral center between 2002 and 2016 were screened. All aneurysmal cases who had more than 2 glucose measurements were included (n = 2451). We calculated several measures of variability, including simple variance, the average consecutive absolute change, average absolute change by time difference, within subject variance, median absolute deviation, and average or median consecutive absolute percentage change. Predictor variables also included admission Hunt and Hess grade, age, gender, cardiovascular risk factors, and surgical treatment. In-patient mortality was the main outcome measure. RESULTS: In a multiple regression analysis, nearly all forms of glucose variability calculations were found to be correlated with in-patient mortality. The consecutive absolute percentage change, however, was most predictive: OR 5.2 [1.4-19.8, CI 95%] for percentage change and 8.8 [1.8-43.6] for median change, when controlling for the defined predictors. Survival to ICU discharge was associated with lower glucose variability (consecutive absolute percentage change 17% ± 9%) compared with the group that did not survive to discharge (20% ± 15%, p < 0.01). Interestingly, this finding was not significant in patients with pre-admission poorly controlled diabetes as indicated by HbA1c (OR 0.45 [0.04-7.18], by percentage change). The effect is driven mostly by non-diabetic patients or those with well-controlled diabetes. CONCLUSIONS: Reduced glucose variability is highly correlated with in-patient survival and long-term mortality in aSAH patients. This finding was observed in the non-diabetic and well-controlled diabetic patients, suggesting a possible benefit for personalized glucose targets based on baseline HbA1c and minimizing variability. The inter-measure percentage change as an indicator of glucose variability is not only predictive of outcome, but is an easy-to-use tool that could be implemented in future clinical trials.
BACKGROUND:Critically ill aneurysmal subarachnoid hemorrhage (aSAH) patients suffer from systemic complications at a high rate. Hyperglycemia is a common intensive care unit (ICU) complication and has become a focus after aggressive glucose management was associated with improved ICU outcomes. Subsequent research has suggested that glucose variability, not a specific blood glucose range, may be a more appropriate clinical target. Glucose variability is highly correlated to poor outcomes in a wide spectrum of critically illpatients. Here, we investigate the changes between subsequent glucose values termed "inter-measurement difference," as an indicator of glucose variability and its association with outcomes in patients with aSAH. METHODS: All SAH admissions to a single, tertiary referral center between 2002 and 2016 were screened. All aneurysmal cases who had more than 2 glucose measurements were included (n = 2451). We calculated several measures of variability, including simple variance, the average consecutive absolute change, average absolute change by time difference, within subject variance, median absolute deviation, and average or median consecutive absolute percentage change. Predictor variables also included admission Hunt and Hess grade, age, gender, cardiovascular risk factors, and surgical treatment. In-patient mortality was the main outcome measure. RESULTS: In a multiple regression analysis, nearly all forms of glucose variability calculations were found to be correlated with in-patient mortality. The consecutive absolute percentage change, however, was most predictive: OR 5.2 [1.4-19.8, CI 95%] for percentage change and 8.8 [1.8-43.6] for median change, when controlling for the defined predictors. Survival to ICU discharge was associated with lower glucose variability (consecutive absolute percentage change 17% ± 9%) compared with the group that did not survive to discharge (20% ± 15%, p < 0.01). Interestingly, this finding was not significant in patients with pre-admission poorly controlled diabetes as indicated by HbA1c (OR 0.45 [0.04-7.18], by percentage change). The effect is driven mostly by non-diabeticpatients or those with well-controlled diabetes. CONCLUSIONS: Reduced glucose variability is highly correlated with in-patient survival and long-term mortality in aSAH patients. This finding was observed in the non-diabetic and well-controlled diabeticpatients, suggesting a possible benefit for personalized glucose targets based on baseline HbA1c and minimizing variability. The inter-measure percentage change as an indicator of glucose variability is not only predictive of outcome, but is an easy-to-use tool that could be implemented in future clinical trials.
Authors: Masoud Behravesh; Juan Fernandez-Tajes; Angela C Estampador; Tibor V Varga; Ómar S Gunnarsson; Helena Strevens; Simon Timpka; Paul W Franks Journal: Sci Rep Date: 2021-12-13 Impact factor: 4.379