ArticleBMC cardiovascular disorders2025
Development of a risk factor nomogram prediction model for patients with acute coronary syndrome complicated by hypertension using LASSO regression analysis.
Article in BMC cardiovascular disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Abstract
backgroundCardiovascular disease (CVD) remains the leading cause of death worldwide, according to global statistics from the WHO and GBD, with the incidence of acute coronary syndromes (ACS) continuing to rise annually. This study aims to develop a nomogram model to predict the risk in ACS patients with hypertension, providing clinicians with a tool for early diagnosis, personalized treatment, and prognostic evaluation.
methodsData were collected from ACS patients at Huangshi Aikang Hospital between 2018 and 2023. Patient characteristics, including age, sex, hypertension history, initial blood test results, and cardiac doppler ultrasonography findings, were recorded. ACS diagnosis followed the 2019 revised Guidelines for the Diagnosis and Treatment of Acute ST-Segment Elevation Myocardial Infarction (STEMI) by the Chinese Society of Cardiology. The 2024 Revised Guidelines for the Diagnosis and Treatment of Non-ST-Segment Elevation Acute Coronary Syndromes from the Chinese Journal of Cardiovascular Diseases were used for NSTEMI and unstable angina (UA) diagnoses. Statistical analyses were performed using SPSS (version 27.0.1) and R software (version 4.3.2), with statistical significance at P < 0.05.
resultsA total of 980 ACS patients were included in the study. Among the three clinical subtypes, 592 patients (60.4%) had UA, which was the most prevalent. The hypertensive group comprised 682 ACS patients (69.59%), with a mean age of 64.93 ± 9.51 years. Significant differences between hypertensive and non-hypertensive groups were found in sex (P = 0.001), age (P < 0.001), clinical subtype (P < 0.001), and several clinical and laboratory parameters, including creatinine (Cr) (P < 0.001), left ventricular ejection fraction (LVEF) (P = 0.049), left ventricular posterior wall thickness (LVPW) (P = 0.003), CK-MB (P = 0.019), AST (P = 0.028), total cholesterol (TC) (P = 0.035), LDL-C (P = 0.007), and APOB (P = 0.005). Using LASSO regression, nine variables were selected for multivariate logistic regression analysis, leading to the construction of the nomogram model. The calibration curve, Hosmer-Lemeshow test, ROC curve, decision curve, and clinical impact curve all demonstrated the model's high quality.
conclusionA high-quality predictive nomogram model for assessing the risk of ACS in patients with hypertension has been developed. This model can assist clinicians in early diagnosis, personalized treatment, and prognostic evaluation.
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