Method of predicting serious complication risk degree after gastric cancer operation

Abstract

The present invention is objective, quantitative and precise method of predicting serious complication risk degree after gastric cancer operation. The method includes the following steps: 1. screening out factors affecting the serious complication after gastric cancer operation through overall single factor analysis; 2. determining the prognostic determining factor through two-value multiple non-conditional logic regression analysis; 3. determining the optimal prediction value dividing value through the work characteristic analysis on the testee; and 4. establishing the predicting model with main risk factor as the independent variable and judging the serious complication risk degree after gastric cancer operation by means of the prediction probability. The present invention is one objective and quantitative intelligent risk evaluation system.

Description

1. Technical field [0001] The invention relates to a prediction method used in medicine, in particular to a method for predicting the risk of severe postoperative complications through the preoperative information, operation information, and tumor pathological information of gastric cancer patients. 2. Background technology [0002] In my country, gastric cancer is one of the most common malignant tumors, and its morbidity and mortality rank among the forefront of various malignant tumors. The main treatment methods for gastric cancer are surgery and adjuvant chemotherapy. Radical gastric cancer surgery is currently the only treatment that can achieve the purpose of cure. The standard radical surgery for gastric cancer includes gastrectomy, lymph node dissection, and reconstruction of the digestive tract. The surgical trauma is large and the operation is complicated. Since most gastric cancer patients in my country are elderly, they often have chronic co-morbidities, and mos...

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Problems solved by technology

[0003] In the research field of risk factor determination and risk prediction of severe complications after gastric cancer surgery, there are still many shortcomings in the current research at home and abroad: (1) Some researchers collected few pathogenic factors and participated in fewer cases, which did not meet the One of the core requirements of modern evidence-based medicine, that is, clinical evidence should come from multi-center, large-sample randomized controlled clinical trials (RCT), systematic review (systematic review) and meta-analysis (meta-analysis); (2) some The researchers used a univariate analysis method, but the relationship between severe complications after gastric cancer surgery and many pathogenic factors is very complicated, and the univariate analysis cannot balance the effects of multiple confounding factors in the complex relationship, nor can it form a predictive model; (3) Some researchers use multiple linear regression analysis methods, unable to determine the optimal cut-off value of the prediction probability, unable to accurately verify and evaluate the established model, and have poor practicability and operability; severe postoperative complications and The relationship between various pathogenic factors is actually not a linear relationship. At the same time, a good prediction model must determine the best cut-off value of the prediction probability, and must pass strict verification to prove its high accuracy. Sensitivity, specificity, and requires simple operation and strong practicability; (4) To find out the significant factors from the many factors that affect the serious postoperative complications, in order to achieve the purpose of predicting the prognosis of the disease, it is necessary to analyze the traditional To further expand the prediction method, introduce the knowledge and ideas of the frontier of statistics into the data analysis method

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