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深度学习辅助地塞米松预处理对胃肠道肿瘤患者手术效果的影响

Effect of dexamethasone pretreatment using deep learning on the surgical effect of patients with gastrointestinal tumors.

作者信息

Lu Kun, Li Qiang, Pu Chun, Lei Xue, Fu Qiang

机构信息

Department of Anesthesiology, The Third People's Hospital of Chengdu, Southwest Jiao Tong University, Chengdu, China.

出版信息

PLoS One. 2024 Jul 17;19(7):e0304359. doi: 10.1371/journal.pone.0304359. eCollection 2024.

Abstract

To explore the application efficacy and significance of deep learning in anesthesia management for gastrointestinal tumors (GITs) surgery, 80 elderly patients with GITs who underwent surgical intervention at our institution between January and September 2021 were enrolled. According to the preoperative anesthesia management methodology, patients were rolled into a control (Ctrl) group (using 10 mg dexamethasone 1-2 hours before surgery) and an experimental (Exp) group (using a deep learning-based anesthesia monitoring system on the basis of the Ctrl group), with 40 cases in each group. A comprehensive comparative analysis was performed between the two cohorts, encompassing postoperative cognitive evaluations, Montreal Cognitive Assessment (MoCA) scores, gastrointestinal functionality, serum biomarkers (including interleukin (IL)-6, C-reactive protein (CRP), and cortisol levels), length of hospitalization, incidence of complications, and other pertinent metrics. The findings demonstrated that anesthesia monitoring facilitated by deep learning algorithms effectively assessed the anesthesia state of patients. Compared to the Ctrl group, patients in the Exp group showed significant differences in cognitive assessments (word recall, number connection, number coding) (P<0.05). Additionally, the Exp group exhibited a notably increased MoCA score (25.3±2.4), significantly shorter time to first flatus postoperatively (35.8±13.7 hours), markedly reduced postoperative pain scores, significantly shortened time to tolerate a liquid diet postoperatively (19.6±5.2 hours), accelerated recovery of serum-related indicators, and a significantly decreased mean length of hospital stay (11.4±3.2 days) compared to the Ctrl group. In summary, administering dexamethasone under the anesthesia management of GITs surgery based on gradient boosting decision tree (GBDT) and pharmacokinetics pharmacodynamics (PKPD) models can promote patient recovery, reduce the incidence of postoperative cognitive impairment (POCD), and improve patient prognosis.

摘要

为探讨深度学习在胃肠道肿瘤(GITs)手术麻醉管理中的应用效果及意义,选取2021年1月至9月在我院接受手术干预的80例老年GITs患者。根据术前麻醉管理方法,将患者分为对照组(Ctrl组,术前1 - 2小时使用10 mg地塞米松)和实验组(Exp组,在Ctrl组基础上使用基于深度学习的麻醉监测系统),每组40例。对两组患者进行了全面的对比分析,包括术后认知评估、蒙特利尔认知评估(MoCA)评分、胃肠功能、血清生物标志物(包括白细胞介素(IL)-6、C反应蛋白(CRP)和皮质醇水平)、住院时间、并发症发生率及其他相关指标。结果表明,深度学习算法辅助的麻醉监测能够有效评估患者的麻醉状态。与Ctrl组相比,Exp组患者在认知评估(词语回忆、数字连接、数字编码)方面存在显著差异(P<0.05)。此外,Exp组的MoCA评分显著升高(25.3±2.4),术后首次排气时间明显缩短(35.8±13.7小时),术后疼痛评分显著降低,术后耐受流食时间明显缩短(19.6±5.2小时),血清相关指标恢复加快,平均住院时间显著缩短(11.4±3.2天)。综上所述,在基于梯度提升决策树(GBDT)和药代动力学药效学(PKPD)模型的GITs手术麻醉管理下使用地塞米松,可促进患者康复,降低术后认知功能障碍(POCD)的发生率,改善患者预后。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/530c/11253962/e0be378a6d38/pone.0304359.g001.jpg

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