Construction and Validation of a Major Depression Risk Predictive Model for Patients with Coronary Heart Disease: Insights from NHANES 2005-2018.

Background: This study aimed to develop and validate a predictive model for major depression risk in adult patients with coronary heart disease (CHD), offering evidence for targeted prevention and intervention.

Methods: Using data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018, 1098 adults with CHD were included. A weighted logistic regression model was applied to construct and validate a nomogram-based prediction tool for major depression in this population.

Construction and verification of the prediction model for risk of sleep disturbance in elderly patients with hypertension: a cross-sectional survey based on NHANES database from 2005 to 2018.

Objective: To construct and verify a risk prediction model for sleep disturbance in elderly patients with hypertension, aiming to offer guidance for sleep management in this demographic.

Methods: A cohort of 6,708 elderly hypertensive patients from the NHANES database, spanning 2005 to 2018, met the inclusion criteria and were selected for this study. Participants were randomly assigned to a development group (n = 4,696) and a verification group (n = 2,012) in a 7:3 ratio.

Development and validation of a prediction model for hyperuricemia risk in hypertensive patients.

Objective: This study aimed to create a predictive model for hyperuricemia (HUA) in patients diagnosed with hypertension and evaluate its predictive accuracy.

Methods: Employing a retrospective cohort design, this study investigated HUA incidence and clinical data among 228 patients with essential hypertension selected from the Department of Cardiology at a tertiary A-level hospital in Anhui Province, China, between January 2018 and June 2021. The patients were divided randomly into a training group (168 cases) and a validation group (60 cases) at a 7:3 ratio.

Development and validation of a nomogram model for predicting the risk of pre-hospital delay in patients with acute myocardial infarction.

Background: Acute myocardial infarction (AMI) is a severe cardiovascular disease caused by the blockage of coronary arteries that leads to ischemic necrosis of the myocardium. Timely medical contact is critical for successful AMI treatment, and delays increase the risk of death for patients. Pre-hospital delay time (PDT) is a significant challenge for reducing treatment times, as identifying high-risk patients with AMI remains difficult.

Establishment and validation of a heart failure risk prediction model for elderly patients after coronary rotational atherectomy based on machine learning.

Objective: To develop and validate a heart failure risk prediction model for elderly patients after coronary rotational atherectomy based on machine learning methods.

Methods: A retrospective cohort study was conducted to select 303 elderly patients with severe coronary calcification as the study subjects. According to the occurrence of postoperative heart failure, the study subjects were divided into the heart failure group ( = 53) and the non-heart failure group ( = 250).

Constructing a prognostic tool for predicting the risk of non-adherence to antiplatelet therapy in discharged patients with coronary heart disease: a retrospective cohort study.

Objective: To investigate the incidence and influencing factors affecting the non-adherence behavior of patients with coronary heart disease (CHD) to antiplatelet therapy after discharge and to construct a personalized predictive tool.

Methods: In this retrospective cohort study, 289 patients with CHD who were admitted to the Department of Cardiology of The First Affiliated Hospital of the University of Science and Technology of China between June 2021 and September 2021 were enrolled. The clinical data of all patients were retrospectively collected from the hospital information system, and patients were followed up for 1 year after discharge to evaluate their adherence level to antiplatelet therapy, analyze their present situation and influencing factors for post-discharge adherence to antiplatelet therapy, and construct a nomogram model to predict the risk of non-adherence.

Study on the risk of coronary heart disease in middle-aged and young people based on machine learning methods: a retrospective cohort study.

Objective: To identify coronary heart disease risk factors in young and middle-aged persons and develop a tailored risk prediction model.

Methods: A retrospective cohort study was used in this research. From January 2017 to January 2020, 553 patients in the Department of Cardiology at a tertiary hospital in Anhui Province were chosen as research subjects.

Effects of acceptance and commitment therapy on self-management skills and psychological resilience of young and middle-aged patients underwent percutaneous transluminal coronary intervention for primary myocardial infarction: a pilot study.

Background: Acceptance and commitment therapy (ACT) is an intervention focusing on altering how patients relate to their thoughts. This study aimed to investigate the effects of ACT on self-management ability and psychological resilience of young and middle-aged patients undergoing percutaneous transluminal coronary intervention (PCI) for primary myocardial infarction (MI).

Methods: This pilot study included 98 young and middle-aged patients who underwent PCI for primary MI using a convenient sampling method.