Xiao Guan (Urologist) , Minghao Li (Urologist) , Yingxian Pang (Urologist) , Yao He (Urologist) , Jing Wang (Pathologist) , Xiaowen Xu (Resident Urology) , Kai Cheng (Resident Urology) , Zhi Li (Resident Urology) , Longfei Liu (Urologist)
{"title":"用算法预测接受 phaeochromocytoma 和副神经节瘤手术的血液动力学不稳定性的最新进展。","authors":"Xiao Guan (Urologist) , Minghao Li (Urologist) , Yingxian Pang (Urologist) , Yao He (Urologist) , Jing Wang (Pathologist) , Xiaowen Xu (Resident Urology) , Kai Cheng (Resident Urology) , Zhi Li (Resident Urology) , Longfei Liu (Urologist)","doi":"10.1016/j.beem.2024.101956","DOIUrl":null,"url":null,"abstract":"<div><div>Abdominal pheochromocytomas and paragangliomas (PPGLs) are characterized by the overproduction of catecholamines, which are associated with hemodynamic instability (HDI) during surgery. Therefore, perioperative management to prevent intraoperative HDI is imperative for the surgical treatment of PPGLs. Owing to the rarity and heterogeneous nature of these tumors, pre-surgical prediction of HDI is a clinical dilemma. The reported risk factors for HDI include perioperative preparation, genetic background, tumor conditions, body composition, catecholamine levels, and surgical approach. Additionally, several personalized algorithms or models including these factors have been developed. The first part of this review outlines the prediction models that include clinical features such as tumor size and location, body mass index (BMI), blood glucose level, catecholamine levels, and preoperative management with α-adrenoceptor blockade and crystal/colloid fluid. We then summarize recently reported models that consider additional factors such as genetic background, radiomics, robotic-assisted surgical approach, three-dimensional visualization, and machine-learning models. These findings suggest that a comprehensive model including risk factors is the most likely approach for achieving effective perioperative management.</div></div>","PeriodicalId":8810,"journal":{"name":"Best practice & research. Clinical endocrinology & metabolism","volume":"38 6","pages":"Article 101956"},"PeriodicalIF":6.1000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Recent advances in algorithms predicting hemodynamic instability undergoing surgery for phaeochromocytoma and paraganglioma\",\"authors\":\"Xiao Guan (Urologist) , Minghao Li (Urologist) , Yingxian Pang (Urologist) , Yao He (Urologist) , Jing Wang (Pathologist) , Xiaowen Xu (Resident Urology) , Kai Cheng (Resident Urology) , Zhi Li (Resident Urology) , Longfei Liu (Urologist)\",\"doi\":\"10.1016/j.beem.2024.101956\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Abdominal pheochromocytomas and paragangliomas (PPGLs) are characterized by the overproduction of catecholamines, which are associated with hemodynamic instability (HDI) during surgery. Therefore, perioperative management to prevent intraoperative HDI is imperative for the surgical treatment of PPGLs. Owing to the rarity and heterogeneous nature of these tumors, pre-surgical prediction of HDI is a clinical dilemma. The reported risk factors for HDI include perioperative preparation, genetic background, tumor conditions, body composition, catecholamine levels, and surgical approach. Additionally, several personalized algorithms or models including these factors have been developed. The first part of this review outlines the prediction models that include clinical features such as tumor size and location, body mass index (BMI), blood glucose level, catecholamine levels, and preoperative management with α-adrenoceptor blockade and crystal/colloid fluid. We then summarize recently reported models that consider additional factors such as genetic background, radiomics, robotic-assisted surgical approach, three-dimensional visualization, and machine-learning models. 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Recent advances in algorithms predicting hemodynamic instability undergoing surgery for phaeochromocytoma and paraganglioma
Abdominal pheochromocytomas and paragangliomas (PPGLs) are characterized by the overproduction of catecholamines, which are associated with hemodynamic instability (HDI) during surgery. Therefore, perioperative management to prevent intraoperative HDI is imperative for the surgical treatment of PPGLs. Owing to the rarity and heterogeneous nature of these tumors, pre-surgical prediction of HDI is a clinical dilemma. The reported risk factors for HDI include perioperative preparation, genetic background, tumor conditions, body composition, catecholamine levels, and surgical approach. Additionally, several personalized algorithms or models including these factors have been developed. The first part of this review outlines the prediction models that include clinical features such as tumor size and location, body mass index (BMI), blood glucose level, catecholamine levels, and preoperative management with α-adrenoceptor blockade and crystal/colloid fluid. We then summarize recently reported models that consider additional factors such as genetic background, radiomics, robotic-assisted surgical approach, three-dimensional visualization, and machine-learning models. These findings suggest that a comprehensive model including risk factors is the most likely approach for achieving effective perioperative management.
期刊介绍:
Best Practice & Research Clinical Endocrinology & Metabolism is a serial publication that integrates the latest original research findings into evidence-based review articles. These articles aim to address key clinical issues related to diagnosis, treatment, and patient management.
Each issue adopts a problem-oriented approach, focusing on key questions and clearly outlining what is known while identifying areas for future research. Practical management strategies are described to facilitate application to individual patients. The series targets physicians in practice or training.