Zhang Li-Xiang, Cao Jiao-Yu, Zhou Xiao-Juan
Department of Cardiology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China Hefei 230001, Anhui, China.
Am J Cardiovasc Dis. 2024 Feb 20;14(1):1-8. doi: 10.62347/WRYH1932. eCollection 2024.
This study aimed to create a predictive model for hyperuricemia (HUA) in patients diagnosed with hypertension and evaluate its predictive accuracy.
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. The training group underwent univariate and multivariate logistic regression analyses to identify risk factors for HUA. Additionally, an R software-generated nomogram model estimated HUA risk in hypertensive patients. The validation group assessed the nomogram model's discriminatory power and calibration using receiver operating characteristic curve analysis and the Hosmer-Lemeshow goodness-of-fit test.
The study found a 29.39% prevalence of HUA among the 228 participants. Logistic regression analyses identified age, body mass index, and concomitant coronary heart disease as independent HUA risk factors (odds ratio [OR] > 1 and P < 0.05). Conversely, high-density lipoprotein cholesterol emerged as an independent protective factor against HUA in hypertensive patients (OR < 1 and P < 0.05). Using these factors, a nomogram model was constructed to assess HUA risk, with an AUC of 0.873 (95% confidence interval [CI]: 0.818-0.928) in the training group and 0.841 (95% CI: 0.735-0.946) in the validation group, indicating a strong discriminatory ability. The Hosmer-Lemeshow goodness-of-fit test showed no significant deviation between predicted and actual HUA frequency in both groups (χ = 5.980, 9.780, P = 0.649, 0.281), supporting the nomogram's reliability.
The developed nomogram model, utilizing independent risk factors for HUA in hypertensive patients, exhibits strong discrimination and calibration. It holds promise as a valuable tool for cardiovascular professionals in clinical decision-making.
本研究旨在建立诊断为高血压患者的高尿酸血症（HUA）预测模型，并评估其预测准确性。
采用回顾性队列设计，本研究调查了2018年1月至2021年6月期间从中国安徽省一家三级甲等医院心内科选取的228例原发性高血压患者的HUA发病率及临床资料。患者按7:3的比例随机分为训练组（168例）和验证组（60例）。训练组进行单因素和多因素逻辑回归分析以确定HUA的危险因素。此外，一个由R软件生成的列线图模型估计高血压患者的HUA风险。验证组使用受试者工作特征曲线分析和Hosmer-Lemeshow拟合优度检验评估列线图模型的辨别力和校准度。
研究发现228名参与者中HUA患病率为29.39%。逻辑回归分析确定年龄、体重指数和合并冠心病为独立的HUA危险因素（比值比[OR]>1且P<0.05）。相反，高密度脂蛋白胆固醇是高血压患者预防HUA的独立保护因素（OR<1且P<0.05）。利用这些因素构建了一个列线图模型来评估HUA风险，训练组的曲线下面积（AUC）为0.873（95%置信区间[CI]：0.818 - 0.928），验证组为0.841（95%CI：0.735 - 0.946），表明具有较强的辨别能力。Hosmer-Lemeshow拟合优度检验显示两组预测的和实际的HUA频率之间无显著偏差（χ² = 5.980, 9.780，P = 0.649, 0.281），支持列线图的可靠性。
所建立的列线图模型利用高血压患者HUA的独立危险因素，具有较强的辨别力和校准度。它有望成为心血管专业人员临床决策的有价值工具。
