Brain Sciences (Nov 2022)

A Glycemia-Based Nomogram for Predicting Outcome in Stroke Patients after Endovascular Treatment

  • Chengfang Liu,
  • Yuqiao Zhang,
  • Xiaohui Li,
  • Yukai Liu,
  • Teng Jiang,
  • Meng Wang,
  • Qiwen Deng,
  • Junshan Zhou

DOI
https://doi.org/10.3390/brainsci12111576
Journal volume & issue
Vol. 12, no. 11
p. 1576

Abstract

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Objective: Higher fasting glucose is thought to be associated with adverse outcome in patients receiving endovascular treatment (EVT), while the effect of glycosylated hemoglobin (HbA1c) on outcome is controversial. We combined fasting blood glucose (FBG) with HbA1c and evaluated their relationship with the three-month functional outcome in patients who underwent EVT. Methods: Data from 739 consecutive ischemic stroke patients who underwent EVT from April 2015 to August 2021 were retrospectively reviewed. HbA1c was used to estimate the chronic glucose level according to the following formula: chronic glucose level (mg/dL) = 28.7 × HbA1c (%) − 6.7. Patients were split into two groups in accordance with the three-month modified Rankin Scale (mRS). Univariate and multivariate analyses were utilized to investigate the association of outcome with blood glucose and to identify other predictors of prognosis. Results: Patients with poor outcome had significantly higher FBG, chronic glycemia, FBG/chronic glycemic ratio, and difference between FBG and chronic glycemia (ΔA-C). FBG, the FBG/chronic glycemic ratio, and ΔA-C remained to be associated with poor outcome after adjustment. We then established a glycemia-based nomogram with a concordance index of 0.841, and it showed favorable clinical utility according to decision curve analysis. Conclusions: Glycemia after EVT was connected with the functional outcome and a nomogram based on glycemia may be used to predict prognosis in stroke patients treated with EVT.

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