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Early Prediction of Left Ventricular Reverse Remodeling in First-Diagnosed Idiopathic Dilated Cardiomyopathy: A Comparison of Linear Model, Random Forest, and Extreme Gradient Boosting.

ABSTRACT: Introduction: Left ventricular reverse remodeling (LVRR) is associated with decreased cardiovascular mortality and improved cardiac survival and also crucial for therapeutic options. However, there is a lack of an early prediction model of LVRR in first-diagnosed dilated cardiomyopathy. Methods: This single-center study included 104 patients with idiopathic DCM. We defined LVRR as an absolute increase in left ventricular ejection fraction (LVEF) from >10% to a final value >35% and a decrease in left ventricular end-diastolic diameter (LVDd) >10%. Analysis features included demographic characteristics, comorbidities, physical sign, biochemistry data, echocardiography, electrocardiogram, Holter monitoring, and medication. Logistic regression, random forests, and extreme gradient boosting (XGBoost) were, respectively, implemented in a 10-fold cross-validated model to discriminate LVRR and non-LVRR, with receiver operating characteristic (ROC) curves and calibration plot for performance evaluation. Results: LVRR occurred in 47 (45.2%) patients after optimal medical treatment. Cystatin C, right ventricular end-diastolic dimension, high-density lipoprotein cholesterol (HDL-C), left atrial dimension, left ventricular posterior wall dimension, systolic blood pressure, severe mitral regurgitation, eGFR, and NYHA classification were included in XGBoost, which reached higher AU-ROC compared with logistic regression (AU-ROC, 0.8205 vs. 0.5909, p = 0.0119). Ablation analysis revealed that cystatin C, right ventricular end-diastolic dimension, and HDL-C made the largest contributions to the model. Conclusion: Tree-based models like XGBoost were able to early differentiate LVRR and non-LVRR in patients with first-diagnosed DCM before drug therapy, facilitating disease management and invasive therapy selection. A multicenter prospective study is necessary for further validation. Clinical Trial Registration:http://www.chictr.org.cn/usercenter.aspx (ChiCTR2000034128).

SUBMITTER: Xie X

PROVIDER: S-EPMC8371915 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionGastrointestinal stromal tumor (GIST) is the most common gastrointestinal soft tissue tumor. Clinical diagnosis mainly relies on enhanced CT, endoscopy and endoscopic ultrasound (EUS), but the misdiagnosis rate is still high without fine needle aspiration biopsy. We aim to develop a novel diagnostic model by analyzing the preoperative data of the patients.MethodsWe used the data of patients who were initially diagnosed as gastric GIST and underwent partial gastrectomy. The patients were randomly divided into training dataset and test dataset at a ratio of 3 to 1. After pre-experimental screening, max depth = 2, eta = 0.1, gamma = 0.5, and nrounds = 200 were defined as the best parameters, and in this way we developed the initial extreme gradient-boosting (XGBoost) model. Based on the importance of the features in the initial model, we improved the model by excluding the hematological features. In this way we obtained the final XGBoost model and underwent validation using the test dataset.ResultsIn the initial XGBoost model, we found that the hematological indicators (including inflammation and nutritional indicators) examined before the surgery had little effect on the outcome, so we subsequently excluded the hematological indicators. Similarly, we also screened the features from enhanced CT and ultrasound gastroscopy, and finally determined the 6 most important predictors for GIST diagnosis, including the ratio of long and short diameter under CT, the CT value of the tumor, the enhancement of the tumor in arterial period and venous period, existence of liquid area and calcific area inside the tumor under EUS. Round or round-like tumors with a CT value of around 30 (25-37) and delayed enhancement, as well as liquid but not calcific area inside the tumor best indicate the diagnosis of GIST.ConclusionsWe developed a model to further differential diagnose GIST from other tumors in initially clinical diagnosed gastric GIST patients by analyzing the results of clinical examinations that most patients should have completed before surgical resection.

Dataset Information

Early Prediction of Left Ventricular Reverse Remodeling in First-Diagnosed Idiopathic Dilated Cardiomyopathy: A Comparison of Linear Model, Random Forest, and Extreme Gradient Boosting.

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