Method for establishing perioperative period risk prediction model for coronary artery bypass transplantation

A coronary artery and risk prediction technology, applied in the field of biomedicine, can solve the problems of inaccurate prediction of in-hospital mortality, overestimation of mortality, and inaccurate prediction of population mortality

Pending Publication Date: 2021-05-18
FUWAI HOSPITAL CHINESE ACAD OF MEDICAL SCI & PEKING UNION MEDICAL COLLEGE
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Problems solved by technology

At present, the main cardiac surgery risk scoring models at home and abroad for the risk prediction models of CABG for patients with heart failure include EuroSCORE, EuroSCORE II, STS, SinoSCORE, etc. The study found that EuroSCORE, EuroSCORE II and SinoSCORE for the Chinese population are the most effective for CABG in patients with heart failure. The mortality rate cannot be accurately predicted, and both significantly overestimate the mortality rate
The main disadvantages of existing predictive models: (1) The data used to establish these models are relatively old (mostly 10 years ago), and now the advancement of surgical techniques and the improvement of perioperative management have signi...

Method used

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  • Method for establishing perioperative period risk prediction model for coronary artery bypass transplantation
  • Method for establishing perioperative period risk prediction model for coronary artery bypass transplantation
  • Method for establishing perioperative period risk prediction model for coronary artery bypass transplantation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0028] From 2010 to 2019, 3659 domestic patients with complete clinical data who received coronary artery bypass grafting due to heart failure were collected as modeling research objects. In view of the completeness of the patient data and the actual collection results, the sorted patients include: gender, Hyperlipidemia, brain natriuretic peptide, thyroid function, hemoglobin, alanine aminotransferase, hypertension, body mass index, myocardial infarction, diabetes, cardiovascular stent implantation, elevated serum creatinine, cardiac surgery, smoking history, peripheral Arterial disease, cerebrovascular events, preoperative critical state, CCS4 grade, preoperative atrial fibrillation or atrial flutter, NYHA (heart failure) cardiac function III or IV, left ventricular ejection fraction (35%450pg / ml, between 50 and 75 years old, BNP>900pg / ml, over 75 years old, BNP More than 1800pg / ml; thyroid function, whether there is a history of abnormal thyroid; hemoglobin, with 140mmHg or ...

Embodiment 2

[0030]For the modeling group in Example 1, a univariate analysis was performed to analyze the relationship between each of the 25 initial risk factors and perioperative mortality, and the risk with a p value of 5% was screened. Factors, the relevant risk factors screened by the conditions are: gender, hyperlipidemia, brain natriuretic peptide, hemoglobin, elevated alanine aminotransferase (ALT), body mass index, history of myocardial infarction, diabetes, elevated serum creatinine , previous cardiac surgery, cerebrovascular events, medical history of abnormal thyroid function, preoperative critical condition, NYHA cardiac function class III or IV, left ventricular ejection fraction (35%

Embodiment 3

[0032] For the 17 related risk factors in step (2), use logistic regression analysis to judge multi-factor collinearity, remove collinear variables, and remove variables including hyperlipidemia, hemoglobin, body mass index, history of myocardial infarction, diabetes, and cerebrovascular events Seven risk factors, including preoperative critical state, were obtained, including: gender, elevated alanine aminotransferase (ALT), brain natriuretic peptide, history of abnormal thyroid function, previous cardiac surgery, elevated serum creatinine, cardiac The 10 final risk factors (11 independent variable factors), including functional class III or IV, left ventricular ejection fraction, combined valve surgery, and combined aortic surgery, are shown in Table 1 with their weights.

[0033] Table 1 Regression coefficients of risk factors

[0034]

[0035] By including: gender, elevated alanine aminotransferase (ALT), brain natriuretic peptide, history of abnormal thyroid function, ...

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Abstract

The invention discloses a coronary artery bypass transplantation perioperative period risk prediction model establishment method, which comprises the following steps of: collecting and sorting a large number of hospital data of patients in China, combining a single-factor and multi-factor analysis methods, and analyzing by adopting a logistic regression secondary prediction method to obtain a secondary prediction model; the method not only enriches the types of risk factors investigated in the modeling process, but also effectively improves the accuracy and reliability of model prediction, provides an effective evaluation and prediction method for risk prediction of coronary artery bypass transplantation of heart failure patients in China, can effectively reduce the surgical risk. And the method has positive significance on healthy development of the medical health cause in China.

Description

technical field [0001] The invention belongs to the field of biomedicine, in particular to a method for establishing a perioperative risk prediction model of coronary artery bypass grafting. Background technique [0002] Surgical risk prediction for coronary heart disease is a key link to identify high-risk patients, reduce surgical mortality, and improve surgical efficacy. Especially in patients with heart failure, the risk of surgery is significantly increased, and an accurate preoperative risk factor prediction model is needed. At present, the main cardiac surgery risk scoring models at home and abroad for the risk prediction models of CABG for patients with heart failure include EuroSCORE, EuroSCORE II, STS, SinoSCORE, etc. The study found that EuroSCORE, EuroSCORE II and SinoSCORE for the Chinese population are the most effective for CABG in patients with heart failure. The mortality rate cannot be accurately predicted, and the mortality rate is significantly overestim...

Claims

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Application Information

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IPC IPC(8): G16H50/20G16H10/60G16H50/70
CPCG16H50/20G16H10/60G16H50/70
Inventor 侯剑峰林宏远
Owner FUWAI HOSPITAL CHINESE ACAD OF MEDICAL SCI & PEKING UNION MEDICAL COLLEGE
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