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Glucose Variability as Measured by Inter-measurement Percentage Change is Predictive of In-patient Mortality in Aneurysmal Subarachnoid Hemorrhage

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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.

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Author notes

  1. Ofer Sadan and Chen Feng have contributed equally to this manuscript.

Authors and Affiliations

  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, Kathleen Martin, Owen Samuels & C. L. Hall

  2. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Chen Feng & Yajun Mei

  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

    Branislav Vidakovic

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  1. Ofer Sadan
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  2. Chen Feng
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Contributions

OS was involved in conception and design, acquisition of data, analysis and interpretation of data, drafting the article, and final approval of the version to be published. CF contributed to conception and design, analysis and interpretation of data, drafting the article, and final approval of the version to be published. BV contributed to conception and design, revising the article, and final approval of the version to be published. YM, OS, and CLH were involved in conception and design, analysis and interpretation of data, revising the article, and final approval of the version to be published. KM contributed to acquisition, analysis, and interpretation of the data; revising the article; and final approval of the version to be published.

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Correspondence to Ofer Sadan.

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This work was performed in adherence to ethical guidelines and was approved by Emory University IRB.

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12028_2019_906_MOESM3_ESM.jpg

Sensitivity analysis. To investigate how sensitive the model coefficients are to different training data sets through a fivefold cross-validation (CV) study. Data were split into fivefold, and each time, we treat onefold as the testing data, train the model on the rest fourfold, and plot the model coefficients. Ideally, the model coefficients should be consistent, meaning the model is stable. For simplicity, the cross-validation results for average consecutive absolute change percentage (ACACP, Panel A) and median consecutive absolute change percentage (MCACP, Panel B) measures. As seen from the plot, the model coefficients are very consistent on each cross-validation fold, indicating the multiple regression models are not sensitive to the training data. At the same time, we can tell the three most influential predictors are the glucose variability, Hunt and Hess grade, and treatment (JPEG 1017 kb)

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Sadan, O., Feng, C., Vidakovic, B. et al. Glucose Variability as Measured by Inter-measurement Percentage Change is Predictive of In-patient Mortality in Aneurysmal Subarachnoid Hemorrhage. Neurocrit Care 33, 458–467 (2020). https://doi.org/10.1007/s12028-019-00906-1

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