Practical Geriatrics ›› 2022, Vol. 36 ›› Issue (12): 1225-1228.doi: 10.3969/j.issn.1003-9198.2022.12.010

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Influence and predictive value of operation duration on abnormal blood glucose in elderly non-diabetic patients receiving surgery

WANG Lin, LIU Ting, WANG Ran, CAO Peng   

  1. Department of Anesthesia and Surgery, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
  • Received:2022-01-15 Online:2022-12-20 Published:2022-11-30

Abstract: Objective To explore the predictive value of operation duration for the abnormal blood glucose in the elderly non-diabetic patients receiving surgery. Methods The data of 307 elderly patients receiving surgery from May to August 2021 in Xuanwu Hospital of Capital Medical University was analyzed. An arterial catheter was established before the operation to measure the blood glucose level during the operation. The arterial blood glucose during operation ranging from 4.2~6.4 mmol/L was defined as normal blood glucose. SPSS 24.0 software was used to analyze the data. Logistic regression analysis was used to analyze the correlation between operation time and abnormal blood glucose in the elderly non-diabetic patients receiving surgery, and the area under the receiver operating characteristic curve (AUC) was used to evaluate the predictive ability. Results A total of 200 elderly non-diabetic patients receiving surgery were enrolled. After adjusting for important confounding factors, the operation duration> 4 h was significantly correlated with intraoperative blood glucose abnormalities (OR=8.983,95% CI: 2.953-27.320,P<0.001). The AUC for predicting abnormal blood glucose during operation was 0.819, with a sensitivity of 0.735, and a specificity of 0.774. Conclusions The operation time has a better predictive value for abnormal blood glucose during surgery in the elderly non-diabetic patients receiving surgery. It is important to improve the awareness and management of intraoperative blood glucose abnormalities.

Key words: duration of surgery, aged, blood glucose management, predictive value

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