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Prediction method for occurrence risk of extrahepatic adverse outcome of thin NAFLD patient within 5 years

A prediction method and bad technology, applied in patient-specific data, medical data mining, instruments, etc., can solve problems such as inaccurate distinction and no prediction, and achieve the effect of reducing social and personal burdens

Pending Publication Date: 2022-07-22
长沙市弘源心血管健康研究院
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although recent studies have shown that patients with NAFLD are at increased risk of extrahepatic complications, this discussion has not been stratified by obesity class (lean and non-lean), which may lead to inaccurate distinctions between lean NAFLD and extrahepatic complications. risk factors
At the same time, there is currently no predictive model for patients with lean NAFLD to assess their risk of extrahepatic adverse outcomes

Method used

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  • Prediction method for occurrence risk of extrahepatic adverse outcome of thin NAFLD patient within 5 years
  • Prediction method for occurrence risk of extrahepatic adverse outcome of thin NAFLD patient within 5 years
  • Prediction method for occurrence risk of extrahepatic adverse outcome of thin NAFLD patient within 5 years

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Embodiment 1

[0066] A method for predicting the risk of extrahepatic adverse outcomes (including cardiovascular and cerebrovascular diseases, type 2 diabetes) within 5 years in patients with lean non-alcoholic fatty liver disease, including the following: (1) Collecting patients with lean non-alcoholic fatty liver disease The clinical baseline data when there is no extrahepatic adverse outcome, and follow-up for the occurrence of extrahepatic adverse outcome within 5 years; (2) The collected data set is divided into training set (70%) and test set (30%), LASSO regression was performed on the collected data by "whether there is an extrahepatic adverse outcome" in the training set research results, the random seed number was set to 123, a 10-fold cross-validation model was defined, and the variation coefficient of variation graph was drawn, and lambda was selected according to the size of the variation coefficient. The corresponding model at 1se, the corresponding covariate b coefficient valu...

Embodiment 2

[0096] figure 1 It is a flow chart of the present invention for predicting the risk of extrahepatic adverse outcomes in lean NAFLD patients within 5 years;

[0097] Step S101, obtaining clinical data such as clinical baseline data and outcomes of patients with lean NAFLD;

[0098] Step S102, establishing a nomogram of the probability of extrahepatic outcomes according to the clinical data and calculating a total risk score;

[0099] Step S103, calculating the predicted value of the probability of extrahepatic outcome in the lean NAFLD patient within 5 years according to the total risk score;

[0100] Step S104, outputting a predicted value of the probability of extrahepatic outcome calculated according to the total risk score.

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Abstract

The invention provides a method for predicting the occurrence risk of extrahepatic adverse outcomes of a thin non-alcoholic fatty liver disease (NAFLD) patient within 5 years, and relates to the field of artificial intelligence and medical application, and the method comprises the steps: collecting clinical baseline data of the thin NAFLD patient, carrying out LASSO regression on the data, and carrying out initial multi-factor cox regression model to obtain a prediction model containing risk factors, after the model distinction degree is verified, each risk factor is assigned to draw a column diagram, the column diagram is converted into an application program, and the risk of an extrahepatic adverse outcome of a patient can be obtained by inputting risk factor data of the patient, so that screening of high-risk groups in thin NAFLD patients is facilitated, early intervention is performed, and social and personal burdens are relieved.

Description

technical field [0001] The invention relates to the fields of artificial intelligence and medical applications, in particular to a method for predicting the occurrence risk of extrahepatic adverse outcomes in lean NAFLD patients within 5 years. Background technique [0002] Non-alcoholic fatty liver disease (NAFLD) is a spectrum of liver diseases that includes non-alcoholic fatty liver disease (NAFL), non-alcoholic steatohepatitis (NASH), progressive liver fibrosis, and liver cirrhosis. Although NAFLD is particularly common in obese patients, an increasing number of findings suggest that NAFLD is present in a substantial proportion of lean individuals. Patients with Lean NAFLD are generally asymptomatic and are usually detected incidentally on imaging. Lean NAFLD had relatively lower body weight and waist circumference compared to non-lean NAFLD patients. Lean NAFLD patients have a more complex metabolic profile, which tends to be younger men, individuals with higher hemog...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/70G16H50/30G16H50/20G16H10/60G06K9/62
CPCG16H50/70G16H50/30G16H50/20G16H10/60G06F18/217G06F18/214
Inventor 陆瑶邓佩之袁洪蔡菁菁缪汝佳
Owner 长沙市弘源心血管健康研究院
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