探索不同方法对原发性醛固酮增多症的筛查价值
Diagnostic Efficiency of Different Screening Indexes for Primary Aldosteronism
作者:李元美, 王雯, 李千瑞, 陈涛, 莫丹, 张婷婷, 周方励, 任艳, 田浩明
Author:LI Yuan-mei, WANG Wen, LI Qian-rui, CHEN Tao, MO Dan, ZHANG Ting-ting, ZHOU Fang-li, REN Yan, TIAN Hao-ming
收稿日期:2019-11-07 年卷(期)页码:2020,51(3):278-286
期刊名称:四川大学学报(医学版)
Journal Name:JOURNAL OF SICHUAN UNIVERSITY (MEDICAL SCIENCE EDITION)
关键字:原发性醛固酮增多症, 筛查, 诊断, 预测模型
Key words:Primary aldosteronism, Screening, Diagnosis, Predicting model
基金项目:
中文摘要
目的 探讨不同方法对原发性醛固酮增多症(PA)的筛查价值。 方法 回顾性分析2009−2018年华西医院确诊的499例PA和479例原发性高血压患者的临床资料。通过绘制受试者工作特征(ROC)曲线,比较不同筛查方法的诊断效能。 结果 立位血浆醛固酮(PAC)与血浆肾素活性(PRA)之比(ARR)的ROC曲线下面积(AUC)大于立位PAC与血管紧张素Ⅱ(AT-Ⅱ)之比(AA2R)、立位PRA、立位PAC、卧位ARR和最低血钾(P<0.05);在ROC曲线中,多因素logistic回归建立的模型(低血钾水平、立位PRA和立位PAC)用于诊断PA的AUC大于单一指标立位ARR(96.3% vs. 94.6%,P<0.05);决策树模型(分数最高的前五个变量依次为立位ARR、卧位ARR、立位PRA、卧位PRA、最低血钾水平)与单一指标立位ARR的AUC差异无统计学意义(94.1% vs. 94.6%,P>0.05)。在测试集中,logistic回归模型和决策树模型的AUC差异无统计学意义(96.3% vs. 94.1%,P>0.05)。Logistic回归模型的校准曲线更接近45°线,logistic回归模型的预测概率与实际发生概率的一致性比决策树模型更好。 结论 立位ARR仍为最优的单个筛查指标;不建议AA2R(放射免疫法)作为PA筛查指标;立位PAC、立位PRA和最低血钾组合的logistic回归模型诊断效能优于立位ARR。
英文摘要
ObjectiveTo investigate the the feasibility and diagnostic efficiencyvalue of different screening indexesmethods for screening primary aldosteronism (PA).MethodsThe clinical data of 499 patients with PA and 479 patients with essential hypertension diagnosed from Jan. 2009 to Dec. 2018 were retrospectively analyzed. The diagnostic performance of different screening indexs was compared by plotting receiver operating characteristic curves (ROC).ResultsThe area under the ROC curve (AUC) of the plasma aldosterone concentration (PAC) to plasma renin activity (PRA) ratio (ARR) was greater than that of the ratio of the upright PAC to the angiotensin Ⅱ (AT-Ⅱ) (AA2R), upright PRA, upright PAC, supine ARR, and lowest blood potassium (P<0 .05). theAUCof logistic regression model was greater than that of upright ARR (96.3% vs. 94.6%,P<0 .05). there was no significant difference inAUCbetween decision tree model and upright ARR (94.1% vs. 94.6%,P>0.05). In the test set, the AUC difference between the logistic regression model and the decision tree model was not statistically significant (96.3% vs. 94.1%,P> 0.05). The calibration curve of the logistic regression model is closer to the 45 ° line, and the consistency between the prediction probability and the actual probability of the logistic regression model is better than that of the decision tree model.ConclusionFor the screening of PA, upright ARR seems to be the best single screening index, while AA2R (radioimmunoassay) is not recommended. The diagnostic efficacy of logistic regression model including upright PAC, PRA and lowest blood potassium is better than that of single upright ARR.
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