预测早期癌症患者的最佳手术量

A. Dylenok, V. Rybachkov, V. N. Malashenko, S. Kashin, L. Shubin, A. Vasin
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All patients (n = 266) received different volume of surgery: intraluminal surgery (n = 128), wedge gastric resection (n = 36), classical gastrectomy or subtotal gastric resection (n = 102). According to the volume of intervention, the patients were ratified into several study groups. Statistical analysis involved case records of three groups of patients and was conducted using MedCalc Statistical Soft ware version 20.022 and Statistica 12.5.Results. Ten factors were identified to form a patient model corresponding to each method of surgical treatment. Th e fairness of the division of patients into groups was checked by ROC-analysis in order to determine sensitivity and specificity of the set of criteria for the division. Th e following characteristics of the mathematical model were obtained by means of ROC analysis: concordance coefficient = 88.24%, AUC = 0.893; index J = 0.811; Se = 87.92; Sp = 89.04; +LR = 3.27; -LR = 1.31.Conclusion. 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引用次数: 0

摘要

介绍癌症的发病率仍然很高,尽管I-II期癌症的比例在2019年增加了37.1%。手术治疗仍然是相关的,即使在“早期”形式的癌症(EGC)患者中也是如此。因此,迫切需要开发用于确定此类患者的外科医生容量的可靠方法。目标估计为EGC患者建立稳定预测模型的概率,以便选择合适的手术干预措施。材料和方法。这项研究涉及从“癌症患者数据库中获得的数据,反映了2009年至2019年期间在雅罗斯拉夫尔地区临床肿瘤医院接受治疗的特定手术干预变体患者的统计数据”。所有患者(n=266)接受了不同数量的手术:腔内手术(n=128)、楔形胃切除术(n=36)、经典胃切除术或胃大部切除术(n=102)。根据干预的数量,将患者分为几个研究组。统计分析涉及三组患者的病例记录,并使用MedCalc统计软件版本20.022和Statistica 12.5进行。结果。确定了10个因素,形成了与每种手术治疗方法相对应的患者模型。通过ROC分析检查患者分组的公平性,以确定分组标准的敏感性和特异性。通过ROC分析,数学模型具有以下特征:一致性系数=88.24%,AUC=0.893;指数J=0.811;Se=87.92;Sp=89.04+LR=3.27-LR=1.31。结论:在过去三年中,将这种方法引入临床实践将胃切除术和胃切除率降低了15%。
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Predicting optimal surgeon volume in patients with early gastric cancer
Introduction. The incidence of gastric cancer remains high, despite the increase in the share of stage I–II cancers — 37.1% in 2019. Surgical treatment remains relevant even in patients with “early” forms of gastric cancer (EGC). Therefore, the reliable means for determining the surgeon volume in such patients are to be urgently developed.Aim. To estimate the probability of building a stable predictive model for patients with EGC in order to choose the proper surgical intervention.Materials and methods. Th e research involved the data obtained from “Database of patients with gastric cancer, reflecting statistics of patients with a particular variant of surgical intervention, treated at Yaroslavl Regional Clinical Oncological Hospital during the period from 2009 to 2019”. All patients (n = 266) received different volume of surgery: intraluminal surgery (n = 128), wedge gastric resection (n = 36), classical gastrectomy or subtotal gastric resection (n = 102). According to the volume of intervention, the patients were ratified into several study groups. Statistical analysis involved case records of three groups of patients and was conducted using MedCalc Statistical Soft ware version 20.022 and Statistica 12.5.Results. Ten factors were identified to form a patient model corresponding to each method of surgical treatment. Th e fairness of the division of patients into groups was checked by ROC-analysis in order to determine sensitivity and specificity of the set of criteria for the division. Th e following characteristics of the mathematical model were obtained by means of ROC analysis: concordance coefficient = 88.24%, AUC = 0.893; index J = 0.811; Se = 87.92; Sp = 89.04; +LR = 3.27; -LR = 1.31.Conclusion. Introduction of this approach into clinical practice decreased the rate of gastrectomies and gastric resections by 15% for the last three years.
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