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Radiological Estimation of Intracranial Blood Volume and Occurrence of Hydrocephalus Determines Stress-Induced Hyperglycemia After Aneurysmal Subarachnoid Hemorrhage

  • Joshua A Santucci
  • Stephen R Ross
  • John C Greenert
  • Faranak Aghaei
  • Lance Ford
  • Kimberly M Hollabaugh
  • Benjamin O Cornwell
  • Dee H Wu
  • Bin Zheng
  • Bradley N Bohnstedt
  • Bappaditya Ray
Original Article
  • 37 Downloads

Abstract

Acute phase after aneurysmal subarachnoid hemorrhage (aSAH) is associated with several metabolic derangements including stress-induced hyperglycemia (SIH). The present study is designed to identify objective radiological determinants for SIH to better understand its contributory role in clinical outcomes after aSAH. A computer-aided detection tool was used to segment admission computed tomography (CT) images of aSAH patients to estimate intracranial blood and cerebrospinal fluid volumes. Modified Graeb score (mGS) was used as a semi-quantitative measure to estimate degree of hydrocephalus. The relationship between glycemic gap (GG) determined SIH, mGS, and estimated intracranial blood and cerebrospinal fluid volumes were evaluated using linear regression. Ninety-four [94/187 (50.3%)] among the study cohort had SIH (defined as GG > 26.7 mg/dl). Patients with SIH had 14.3 ml/1000 ml more intracranial blood volume as compared to those without SIH [39.6 ml (95% confidence interval, CI, 33.6 to 45.5) vs. 25.3 ml (95% CI 20.6 to 29.9), p = 0.0002]. Linear regression analysis of mGS with GG showed each unit increase in mGS resulted in 1.2 mg/dl increase in GG [p = 0.002]. Patients with SIH had higher mGS [median 4.0, interquartile range, IQR 2.0–7.0] as compared to those without SIH [median 2.0, IQR 0.0–6.0], p = 0.002. Patients with third ventricular blood on admission CT scan were more likely to develop SIH [67/118 (56.8%) vs. 27/69 (39.1%), p = 0.023]. Hence, the present study, using unbiased SIH definition and objective CT scan parameters, reports “dose-dependent” radiological features resulting in SIH. Such findings allude to a brain injury-stress response-neuroendocrine axis in etiopathogenesis of SIH.

Keywords

Hemorrhagic stroke Hyperglycemia Hydrocephalus Delayed cerebral ischemia Shunt 

Abbreviations

ANOVA

Analysis of variance

AG

Admission glucose

aSAH

Aneurysmal subarachnoid hemorrhage

AUROC

Area under receiver operator characteristic

BCI

Bicaudate index

CAD

Computer-aided detection

CI

Confidence interval

CSF

Cerebrospinal fluid

CT

Computed tomography

DCI

Delayed cerebral ischemia

EVD

External ventricular drain

GG

Glycemic gap

GUI

Graphic user interface

HbA1c

Glycated hemoglobin

H&H

Hunt and Hess

HU

Hounsfield unit

IQR

Interquartile range

mFS

Modified Fisher score

mGS

Modified Graeb score

NSICU

Neurosciences intensive care unit

ROC

Receiver operating characteristic

SAH

Subarachnoid hemorrhage

SIH

Stress-induced hyperglycemia

TH

Threshold Hounsfield

VPS

Ventriculoperitoneal shunt

WFNS

World Federation of Neurological Surgeons

Notes

Acknowledgments

The authors would like to thank Blair Apple, BS, and Brittany Karfonta, BS, with Clinical Studies Unit in Department of Neurology, Oklahoma University Health Sciences Center for their support in data management of the present study.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Not applicable due to retrospective nature of the study design.

Supplementary material

12975_2018_646_Fig6_ESM.png (394 kb)
Supplementary Figure 1

Segmentation software shows image processing of blood products with intracerebral hemorrhage (a,b), intraventricular hemorrhage (c,d) and subarachnoid hemorrhage (e,f) (PNG 394 kb)

12975_2018_646_MOESM1_ESM.tif (511 kb)
High resolution image (TIF 510 kb)
12975_2018_646_MOESM2_ESM.avi (6 mb)
Supplementary Figure 2 Video shows image analyses software in use (AVI 6125 kb)
12975_2018_646_Fig7_ESM.png (83 kb)
Supplementary Figure 3

Linear correlation noted between modified Graeb score with intracranial blood volume (a) and with intracranial blood+CSF volumes (b). (PNG 83 kb)

12975_2018_646_MOESM3_ESM.tif (118 kb)
High resolution image (TIF 118 kb)
12975_2018_646_Fig8_ESM.png (74 kb)
Supplementary Figure 4

ROC curves shows AUROC for various parameters determining cohort’s mortality (a) and stress-induced hyperglycemia (b). (PNG 73 kb)

12975_2018_646_MOESM4_ESM.tif (109 kb)
High resolution image (TIF 108 kb)

References

  1. 1.
    Kruyt ND, Musters A, Biessels GJ, Devries JH, Coert BA, Vergouwen MD, et al. Beta-cell dysfunction and insulin resistance after subarachnoid haemorrhage. Neuroendocrinology. 2011;93(2):126–32.  https://doi.org/10.1159/000324097.CrossRefPubMedGoogle Scholar
  2. 2.
    Kruyt ND, Biessels GJ, DeVries JH, Luitse MJ, Vermeulen M, Rinkel GJ, et al. Hyperglycemia in aneurysmal subarachnoid hemorrhage: a potentially modifiable risk factor for poor outcome. J Cereb Blood Flow Metab. 2010;30(9):1577–87.  https://doi.org/10.1038/jcbfm.2010.102.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Kruyt ND, Roos YW, Dorhout Mees SM, van den Bergh WM, Algra A, Rinkel GJ, et al. High mean fasting glucose levels independently predict poor outcome and delayed cerebral ischaemia after aneurysmal subarachnoid haemorrhage. J Neurol Neurosurg Psychiatry. 2008;79(12):1382–5.  https://doi.org/10.1136/jnnp.2007.142034.CrossRefPubMedGoogle Scholar
  4. 4.
    Nathan DM, Kuenen J, Borg R, Zheng H, Schoenfeld D, Heine RJ. Translating the A1C assay into estimated average glucose values. Diabetes Care. 2008;31(8):1473–8.  https://doi.org/10.2337/dc08-0545.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Liao WI, Sheu WH, Chang WC, Hsu CW, Chen YL, Tsai SH. An elevated gap between admission and A1C-derived average glucose levels is associated with adverse outcomes in diabetic patients with pyogenic liver abscess. PLoS One. 2013;8(5):e64476.  https://doi.org/10.1371/journal.pone.0064476.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Liao WI, Wang JC, Chang WC, Hsu CW, Chu CM, Tsai SH. Usefulness of glycemic gap to predict ICU mortality in critically ill patients with diabetes. Medicine (Baltimore). 2015;94(36):e1525.  https://doi.org/10.1097/md.0000000000001525.CrossRefGoogle Scholar
  7. 7.
    Ray B, Ludwig A, Yearout LK, Thompson DM, Bohnstedt BN. Stress-induced hyperglycemia after spontaneous subarachnoid hemorrhage and its role in predicting cerebrospinal fluid diversion. World neurosurgery. 2017;100:208–15.  https://doi.org/10.1016/j.wneu.2017.01.008.CrossRefPubMedGoogle Scholar
  8. 8.
    Ko SB, Choi HA, Carpenter AM, Helbok R, Schmidt JM, Badjatia N, et al. Quantitative analysis of hemorrhage volume for predicting delayed cerebral ischemia after subarachnoid hemorrhage. Stroke. 2011;42(3):669–74.  https://doi.org/10.1161/strokeaha.110.600775. CrossRefPubMedGoogle Scholar
  9. 9.
    Jimenez-Roldan L, Alen JF, Gomez PA, Lobato RD, Ramos A, Munarriz PM, et al. Volumetric analysis of subarachnoid hemorrhage: assessment of the reliability of two computerized methods and their comparison with other radiographic scales. J Neurosurg. 2013;118(1):84–93.  https://doi.org/10.3171/2012.8.jns12100.CrossRefPubMedGoogle Scholar
  10. 10.
    Cortnum S, Sorensen P, Jorgensen J. Determining the sensitivity of computed tomography scanning in early detection of subarachnoid hemorrhage. Neurosurgery. 2010;66(5):900–2; discussion 3.  https://doi.org/10.1227/01.Neu.0000367722.66098.21.PubMedCrossRefGoogle Scholar
  11. 11.
    Morgan TC, Dawson J, Spengler D, Lees KR, Aldrich C, Mishra NK et al. The Modified Graeb score. An enhanced tool for intraventricular hemorrhage measurement and prediction of functional outcome. 2013;44(3):635–641. doi: https://doi.org/10.1161/strokeaha.112.670653.
  12. 12.
    van Gijn J, Hijdra A, Wijdicks EF, Vermeulen M, van Crevel H. Acute hydrocephalus after aneurysmal subarachnoid hemorrhage. J Neurosurg. 1985;63(3):355–62.  https://doi.org/10.3171/jns.1985.63.3.0355.CrossRefPubMedGoogle Scholar
  13. 13.
    Rincon F, Gordon E, Starke RM, Buitrago MM, Fernandez A, Schmidt JM, et al. Predictors of long-term shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage. J Neurosurg. 2010;113(4):774–80.  https://doi.org/10.3171/2010.2.JNS09376 CrossRefPubMedGoogle Scholar
  14. 14.
    Bian L, Liu L, Wang C, Hussain M, Yuan Y, Liu G, et al. Hyperglycemia within day 14 of aneurysmal subarachnoid hemorrhage predicts 1-year mortality. Clin Neurol Neurosurg. 2013;115(7):959–64.  https://doi.org/10.1016/j.clineuro.2012.09.026.CrossRefPubMedGoogle Scholar
  15. 15.
    Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920–30.  https://doi.org/10.1161/circulationaha.115.001593.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Poulymenopoulou M, Malamateniou F, Vassilacopoulos G. Machine learning for knowledge Extraction from PHR big data. Stud Health Technol Inform. 2014;202:36–9.PubMedGoogle Scholar
  17. 17.
    Roos YB, de Haan RJ, Beenen LF, Groen RJ, Albrecht KW, Vermeulen M. Complications and outcome in patients with aneurysmal subarachnoid haemorrhage: a prospective hospital based cohort study in the Netherlands. J Neurol Neurosurg Psychiatry. 2000;68(3):337–41.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Hijdra A, Van Gijn J, Stefanko S, Van Dongen KJ, Vermeulen M, Van Crevel H. Delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage: clinicoanatomic correlations. Neurology. 1986;36(3):329–33.CrossRefPubMedGoogle Scholar
  19. 19.
    Vergouwen MD, Vermeulen M, van Gijn J, Rinkel GJ, Wijdicks EF, Muizelaar JP, et al. Definition of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage as an outcome event in clinical trials and observational studies: proposal of a multidisciplinary research group. Stroke. 2010;41(10):2391–5.  https://doi.org/10.1161/strokeaha.110.589275. CrossRefPubMedGoogle Scholar
  20. 20.
    Alberti O, Becker R, Benes L, Wallenfang T, Bertalanffy H. Initial hyperglycemia as an indicator of severity of the ictus in poor-grade patients with spontaneous subarachnoid hemorrhage. Clin Neurol Neurosurg. 2000;102(2):78–83.CrossRefPubMedGoogle Scholar
  21. 21.
    Vergouwen MD, van Geloven N, de Haan RJ, Kruyt ND, Vermeulen M, Roos YB. Increased cortisol levels are associated with delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage. Neurocrit Care. 2010;12(3):342–5.  https://doi.org/10.1007/s12028-010-9331-8.CrossRefPubMedGoogle Scholar
  22. 22.
    Schwartz MW, Porte D, Jr. Diabetes, obesity, and the brain. Science 2005;307(5708):375–379. doi: https://doi.org/10.1126/science.1104344.
  23. 23.
    Obici S, Zhang BB, Karkanias G, Rossetti L. Hypothalamic insulin signaling is required for inhibition of glucose production. Nat Med. 2002;8(12):1376–82.  https://doi.org/10.1038/nm798.CrossRefPubMedGoogle Scholar
  24. 24.
    Wang YM, Lin YJ, Chuang MJ, Lee TH, Tsai NW, Cheng BC, et al. Predictors and outcomes of shunt-dependent hydrocephalus in patients with aneurysmal sub-arachnoid hemorrhage. BMC Surg. 2012;12:12.  https://doi.org/10.1186/1471-2482-12-12.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Lai L, Morgan MK. Predictors of in-hospital shunt-dependent hydrocephalus following rupture of cerebral aneurysms. J Clin Neurosci. 2013;20(8):1134–8.  https://doi.org/10.1016/j.jocn.2012.09.033. CrossRefPubMedGoogle Scholar
  26. 26.
    Appelboom G, Piazza MA, Hwang BY, Carpenter A, Bruce SS, Mayer S, et al. Severity of intraventricular extension correlates with level of admission glucose after intracerebral hemorrhage. Stroke. 2011;42(7):1883–8.  https://doi.org/10.1161/strokeaha.110.608166.CrossRefPubMedGoogle Scholar
  27. 27.
    Dumont T, Rughani A, Silver J, Tranmer BI. Diabetes mellitus increases risk of vasospasm following aneurysmal subarachnoid hemorrhage independent of glycemic control. Neurocrit Care. 2009;11(2):183–9.  https://doi.org/10.1007/s12028-009-9232-x.CrossRefPubMedGoogle Scholar
  28. 28.
    Ferguson S, Macdonald RL. Predictors of cerebral infarction in patients with aneurysmal subarachnoid hemorrhage. Neurosurgery. 2007;60(4):658–67; discussion 67.  https://doi.org/10.1227/01.Neu.0000255396.23280.31.CrossRefPubMedGoogle Scholar
  29. 29.
    de Rooij NK, Rinkel GJ, Dankbaar JW, Frijns CJ. Delayed cerebral ischemia after subarachnoid hemorrhage: a systematic review of clinical, laboratory, and radiological predictors. Stroke. 2013;44(1):43–54.  https://doi.org/10.1161/strokeaha.112.674291. CrossRefPubMedGoogle Scholar
  30. 30.
    Schlenk F, Vajkoczy P, Sarrafzadeh A. Inpatient hyperglycemia following aneurysmal subarachnoid hemorrhage: relation to cerebral metabolism and outcome. Neurocrit Care. 2009;11(1):56–63.  https://doi.org/10.1007/s12028-009-9222-z.CrossRefPubMedGoogle Scholar
  31. 31.
    Beseoglu K, Steiger HJ. Elevated glycated hemoglobin level and hyperglycemia after aneurysmal subarachnoid hemorrhage. Clin Neurol Neurosurg. 2017;163:128–32.  https://doi.org/10.1016/j.clineuro.2017.10.037.CrossRefPubMedGoogle Scholar
  32. 32.
    Juvela S, Siironen J, Kuhmonen J. Hyperglycemia, excess weight, and history of hypertension as risk factors for poor outcome and cerebral infarction after aneurysmal subarachnoid hemorrhage. J Neurosurg. 2005;102(6):998–1003.  https://doi.org/10.3171/jns.2005.102.6.0998.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Joshua A Santucci
    • 1
  • Stephen R Ross
    • 1
  • John C Greenert
    • 1
  • Faranak Aghaei
    • 2
  • Lance Ford
    • 3
  • Kimberly M Hollabaugh
    • 3
  • Benjamin O Cornwell
    • 4
  • Dee H Wu
    • 4
  • Bin Zheng
    • 2
  • Bradley N Bohnstedt
    • 5
  • Bappaditya Ray
    • 1
  1. 1.Division of Critical Care Neurology, Department of Neurology, College of MedicineThe University of Oklahoma Health Sciences CenterOklahoma CityUSA
  2. 2.Electrical EngineeringUniversity of OklahomaNormanUSA
  3. 3.Epidemiology and Biostatistics, College of Public HealthThe University of Oklahoma Health Sciences CenterOklahoma CityUSA
  4. 4.Radiology, College of MedicineThe University of Oklahoma Health Sciences CenterOklahoma CityUSA
  5. 5.Neurosurgery, College of MedicineThe University of Oklahoma Health Sciences CenterOklahoma CityUSA

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