Kiratipaisarl Wuttipat, Surawattanasakul Vithawat, Sirikul Wachiranun, Phinyo Phichayut
Department of Community Medicine, Chiang Mai University Faculty of Medicine, Chiang Mai, Thailand.
Department of Community Medicine, Chiang Mai University Faculty of Medicine, Chiang Mai, Thailand
BMJ Health Care Inform. 2025 Jan 30;32(1):e101180. doi: 10.1136/bmjhci-2024-101180.
Low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) levels are paramount in atherosclerotic cardiovascular disease risk management. However, 94.4% of Thai young adult are unaware of their condition. A diagnostic prediction model may assist in screening and alleviating underdiagnosis.
Development and internal validation of diagnostic prediction models on elevated LDL-C (≥160 mg/dL) and non-HDL-C (≥160 mg/dL).
Retrospective, single-centre, tertiary-care hospital annual health examination data from 29 March 2018 to 30 August 2023 was analysed. Two models with 11 predictors from anthropometry and bioimpedance are fitted with multivariable binary logistic regression predicting elevated LDL-C and non-HDL-C. Predictor selection used the backward stepwise elimination. Four performance metrics were quantified: discrimination using area under the receiver-operating characteristic curve (AuROC); calibration by calibration plot; utility by decision curve analysis and instability by performance instability plots. Internal validation was carried out using 500 repetitions of bootstrap-resampling.
Dataset included 2222 LDL-C and 5149 non-HDL-C investigations, 303 were classed as elevated LDL-C (13.6%) and 1013 as elevated non-HDL-C cases (19.7%). Two predictors, gender and metabolic age, were identified in the LDL-C model with AuROC 0.639 (95% CI 0.617 to 0.661), poor calibration, and utility in the 7%-25% probability range. Three predictors-gender, diastolic blood pressure and metabolic age-were identified in the non-HDL-C model with AuROC 0.722 (95% CI 0.705 to 0.738), good calibration and utility in 9%-55% probability range.
Overall results demonstrated acceptable discrimination for non-HDL-C model but inadequate performance of LDL-C model for clinical practice. An external validation study should be planned for non-HDL-C model.
低密度脂蛋白胆固醇（LDL-C）和非高密度脂蛋白胆固醇（非HDL-C）水平在动脉粥样硬化性心血管疾病风险管理中至关重要。然而，94.4%的泰国年轻成年人不知道自己的病情。诊断预测模型可能有助于筛查和缓解诊断不足的情况。
开发并内部验证关于LDL-C升高（≥160mg/dL）和非HDL-C升高（≥160mg/dL）的诊断预测模型。
分析了2018年3月29日至2023年8月30日期间一家三级护理中心医院的回顾性单中心年度健康检查数据。使用来自人体测量学和生物阻抗的11个预测变量构建两个模型，通过多变量二元逻辑回归预测LDL-C升高和非HDL-C升高情况。预测变量选择采用向后逐步消除法。量化了四个性能指标：使用受试者工作特征曲线下面积（AuROC）进行判别；通过校准图进行校准；通过决策曲线分析评估实用性；通过性能不稳定图评估不稳定性。使用500次重复的自助重采样进行内部验证。
数据集包括2222例LDL-C检查和5149例非HDL-C检查，303例被归类为LDL-C升高（13.6%），1013例为非HDL-C升高病例（19.7%）。在LDL-C模型中确定了两个预测变量，即性别和代谢年龄，AuROC为0.639（95%CI 0.617至0.661），校准效果差，在7%-25%概率范围内具有实用性。在非HDL-C模型中确定了三个预测变量，即性别、舒张压和代谢年龄，AuROC为0.722（95%CI 0.705至0.738），校准良好，在9%-55%概率范围内具有实用性。
总体结果表明非HDL-C模型的判别能力尚可，但LDL-C模型在临床实践中的性能不足。应计划对非HDL-C模型进行外部验证研究。
