Key result
A prediction model combining machine learning and logistic regression with 5 predictors achieved an AUC of 0.831 internally and 0.801 externally for identifying carotid plaque in fatty liver disease.
Why the study?
Carotid plaque screening is not prevalent in asymptomatic populations due to cost-effectiveness, leading to undetected carotid plaques particularly among patients with fatty liver disease.
Cross-Sectional (n=5,420,640)
Yes
A combined machine learning and logistic regression model using 5 routine clinical predictors demonstrated good discrimination (AUC >0.80) for identifying asymptomatic carotid plaque in adults with fatty liver disease.
Answers are grounded in this paper and cite their sources.
May aid carotid plaque risk stratification in fatty liver disease; hypothesis-generating and requires prospective validation before clinical use.
BACKGROUND Carotid plaque can progress into stroke, myocardial infarction, etc, which are major global causes of death. Evidence shows a significant increase in carotid plaque incidence among patients with fatty liver disease. However, unlike the high detection rate of fatty liver disease, screening for carotid plaque in the asymptomatic population is not yet prevalent due to cost-effectiveness reasons, resulting in a large number of patients with undetected carotid plaques, especially among those with fatty liver disease. OBJECTIVE This study aimed to combine the advantages of machine learning (ML) and logistic regression to develop a straightforward prediction model among the population with fatty liver disease to identify individuals at risk of carotid plaque. METHODS Our study included 5,420,640 participants with fatty liver from Meinian Health Care Center. We used random forest, elastic net (EN), and extreme gradient boosting ML algorithms to select important features from potential predictors. Features acknowledged by all 3 models were enrolled in logistic regression analysis to develop a carotid plaque prediction model. Model performance was evaluated based on the area under the receiver operating characteristic curve, calibration curve, Brier score, and decision curve analysis both in a randomly split internal validation data set, and an external validation data set comprising 32,682 participants from MJ Health Check-up Center. Risk cutoff points for carotid plaque were determined based on the Youden index, predicted probability distribution, and prevalence rate of the internal validation data set to classify participants into high-, intermediate-, and low-risk groups. This risk classification was further validated in the external validation data set. RESULTS Among the participants, 26.23% (1,421,970/5,420,640) were diagnosed with carotid plaque in the development data set, and 21.64% (7074/32,682) were diagnosed in the external validation data set. A total of 6 features, including age, systolic blood pressure, low-density lipoprotein cholesterol (LDL-C), total cholesterol, fasting blood glucose, and hepatic steatosis index (HSI) were collectively selected by all 3 ML models out of 27 predictors. After eliminating the issue of collinearity between features, the logistic regression model established with the 5 independent predictors reached an area under the curve of 0.831 in the internal validation data set and 0.801 in the external validation data set, and showed good calibration capability graphically. Its predictive performance was comprehensively competitive compared with the single use of either logistic regression or ML algorithms. Optimal predicted probability cutoff points of 25% and 65% were determined for classifying individuals into low-, intermediate-, and high-risk categories for carotid plaque. CONCLUSIONS The combination of ML and logistic regression yielded a practical carotid plaque prediction model, and was of great public health implications in the early identification and risk assessment of carotid plaque among individuals with fatty liver. CLINICALTRIAL
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Deng et al. (2023) conducted a cross-sectional in Fatty liver disease (n=5,420,640). Machine learning and logistic regression prediction model vs. Single use of either logistic regression or ML algorithms was evaluated on Carotid plaque prediction (Area under the curve) (AUC 0.831). A prediction model combining machine learning and logistic regression with 5 predictors achieved an AUC of 0.831 internally and 0.801 externally for identifying carotid plaque in fatty liver disease.
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