南非高 HIV 流行基层医疗中 HFpEF 风险预测评分开发
基于南非一项横断面研究,纳入 1508 名 ≥40 岁成人,其中 1008 名为接受 ART ≥1 年的 HIV 感染者,500 名为无 HIV 者。利用人口学、临床因素、BNP 和心电图异常构建 HFpEF 风险预测模型,并以列线图评分呈现。最终列线图模型经内部验证 AUC 为 0.77,校准良好。
要点
- 该列线图评分纳入年龄、性别、HIV 状态、吸烟、高血压、BMI 和 BNP。
- 评分可用于基层识别 HFpEF 高危个体并转诊。
核心结论
- 基于常规临床信息和床旁 BNP 开发的列线图评分可支持在高 HIV 流行基层医疗中筛查 HFpEF 高危个体并转诊。
研究设计与样本
- 研究来自南非 Prevalence and Phenotype of HIV-HFpEF 横断面观察性研究。
- 共纳入 1508 名 ≥40 岁成人,其中 1008 名为接受 ART ≥1 年的 HIV 感染者,500 名为无 HIV 者。
主要发现
- 研究人群年龄中位数为 48 岁,77% 为女性,42% 肥胖,38% 有高血压。
- 共 118 名参与者符合 HFpEF 标准,占总人群 7.8%;HIV 感染者中 85 名(8.4%),无 HIV 者中 33 名(6.6%)。
- 两个 logistic 回归模型表现优于随机森林分类器。
- 用于列线图的模型包含年龄、性别、HIV 状态、吸烟、高血压、BMI 和 BNP。
关键数值与推荐
- 列线图模型经 bootstrap 内部验证,过拟合校正后 AUC 为 0.77,校准良好。
临床怎么用
- 该评分工具可利用常规临床信息和床旁 BNP 在基层医疗中识别 HFpEF 高危个体,并转诊进行诊断和管理。
以上解读依据论文摘要(全文未开放获取或许可不允许翻译转载)。
原文
Background Antiretroviral therapy (ART)-treated people with HIV (PWH) have increased risk of heart failure with preserved ejection fraction (HFpEF). The diagnosis of HFpEF relies on cardiac imaging and expertise and is challenging in primary care settings. Further, existing risk prediction tools do not account for HIV-related risk. We developed a practical HFpEF risk prediction tool using data from the Prevalence and Phenotype of HIV-HFpEF in South Africa, a cross-sectional observational study that enrolled 1508 adults aged ≥40 years (1008 PWH on ART ≥1 year and 500 people without HIV (PWoH)) at a primary care clinic in South Africa. Methods We used four logistic regression models for HFpEF risk, including demographics, clinical factors, B-type natriuretic peptide (BNP) and ECG abnormalities. We compared the models’ area under the receiver operating characteristic curve (AUC) with a random forest classifier, translated the final model into a nomogram-based risk score and evaluated its discrimination and calibration through bootstrap internal validation. Results The median age was 48 years, 77% female, 42% obese, 38% had hypertension and 7.8% met HFpEF criteria (118 participants; 85 (8.4%) among PWH and 33 (6.6%) among PWoH). Two logistic regression models outperformed the random forest classifier. The model that was used for the nomogram included age, sex, HIV status, smoking, hypertension, body mass index and BNP and showed good discrimination (overfitting-corrected AUC 0.77) with good calibration. Conclusions The nomogram-based score, developed using routine clinical information and point-of-care BNP, is practical, accessible and may support screening and identification of individuals at high HFpEF risk for targeted referral for diagnosis and care in a sub-Saharan African primary care setting with a high HIV prevalence.
引用
- Open Heart:Development of a heart failure with preserved ejection fraction risk prediction score in primary care settings with a high HIV prevalence
供执业医师工作参考,不替代临床判断;数值与分级以指南原文为准。