Key result
A combined machine learning and logistic regression model achieved an AUC of 0.831 in internal validation and 0.801 in external validation for predicting carotid plaque in fatty liver disease.
Why the study?
Carotid plaque screening is not prevalent in asymptomatic populations due to cost-effectiveness reasons, leaving many patients with fatty liver disease with undetected carotid plaques.
Can a combined machine learning and logistic regression model accurately predict carotid plaque risk in adults with fatty liver disease?
Cross-Sectional (n=5,420,640)
Yes
Can a combined machine learning and logistic regression model accurately predict carotid plaque risk in adults with fatty liver disease?
A combined machine learning and logistic regression model using 5 clinical predictors can accurately identify asymptomatic adults with fatty liver disease at high risk for carotid plaque.
Answers are grounded in this paper and cite their sources.
May aid risk stratification in fatty liver disease; hypothesis-generating pending prospective validation.
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.
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Deng et al. (2023) conducted a cross-sectional in Fatty liver disease (n=5,420,640). Carotid plaque prediction model vs. Single use of either logistic regression or ML algorithms was evaluated on Area under the receiver operating characteristic curve for carotid plaque prediction (AUC 0.831). A combined machine learning and logistic regression model achieved an AUC of 0.831 in internal validation and 0.801 in external validation for predicting carotid plaque in fatty liver disease.
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