Machine Learning, Non-Alcoholic Fatty Liver Disease
Conditions
Brief summary
The purpose of this study was to use machine learning to explore a more precise classification of NAFLD subgroups towards informing individualized therapy.
Detailed description
Clinical characteristics of NAFLD are heterogenous, but current classification for diagnosis is simply based on pathological examination. The conventional pathological classification is insufficient to reflect the complexity and heterogeneity of NAFLD and can not predict the prognosis. Towards precision treatment, a more refined metabolic classification of NAFLD phenotypes is highly demanded for a personalized diagnosis, aiming to identify patients at elevated risk of cardiovascular disease or cirrhosis. This kind of refined classification can provide a more precise diagnosis and enable more individualized preventive interventions and early treatments. In a cross-sectional cohort, unsupervised machine learning was used to cluster patients with biopsy-proved NAFLD from Drum Tower Hospital Affiliated to Nanjing University Medical School based on clinical variables. Verification of the clustering was performed in a longitudinal cohort.
Interventions
DIAGNOSTIC_TEST10-year ASCVD risk estimation
High CVD risk was defined as a history of CVD or a 10-year ASCVD risk ≥10%. The 10-year ASCVD risk estimation was carried out according to 2016 Chinese guidelines for the management of dyslipidemia in adults.
Sponsors
Yan Bi
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
Yes
Inclusion criteria
* biopsy-proved NALD cohort: 1. age 18 to 75 years 2. receiving liver biopsy at the time of metabolic surgery 3. relatively complete clinical information, including physical examination, biochemical and haematological assessments * longitudinal cohort 1. age 18 to 75 years 2. receiving abdominal imaging examinations, 3. relatively complete clinical information, including physical examination, biochemical and haematological assessments (4）follow-up time at least more than 12 months
Exclusion criteria
* (1)consumed excessive alcohol (≥140 g/week for males or ≥ 70 g/week for females) • * (2) with history of other liver diseases including chronic hepatitis, biliary obstructive diseases or autoimmune hepatitis
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| histological cirrhosis checklist | up to 5 years | cirrhosis was defined as widespread disruption of normal liver structure by the formation of pseudolobules or Scheuer stage 4 fibrosis in pathological findings. |
| hepatocellular carcinoma | up to 5 years | the diagnosis of hepatocellular carcinoma was based on well-established diagnostic imaging criteria and/or histology. |
| Ischemic heart disease | up to 5 years | Objective Findings of Coronary Stenosis (≥ 50%) in at least 2 coronary artery territories (ie, left anterior descending, ramus intermedius, left circumflex, right coronary artery) involving the vain vessel, a major branch, or a bypass graft |
| Documented heart disease checklist | up to 5 years | Documented Myocardial Infarction or Percutaneous Coronary Intervention or Coronary Artery Bypass Grafting |
Countries
China
Outcome results
None listed