单侧原发性醛固酮增多症患者高血压消退的术前预测指标:nomogram模型的建立。

IF 2.1 3区 医学 Q2 SURGERY
Lin Yang, Lei Yan, Laiyuan Qiu, Yi Sun, Gangli Gu
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引用次数: 0

摘要

背景:原发性醛固酮增多症(PA)是高血压的主要手术治疗原因,肾上腺切除术是单侧PA (UPA)的最终治疗方法。然而,一些患者术后仍有持续的高血压。本研究旨在确定术前影响手术结果的因素,并建立术后高血压缓解的预测模型。方法:回顾分析山东大学齐鲁医院2011-2022年206例单侧肾上腺切除术治疗UPA患者的病历。作为训练队列,对2013 - 2022年166例患者的数据进行单因素和多因素logistic回归分析,探讨术前临床和生化数据与术后血压正常化的关系。2011年至2012年的其余40例患者被用作验证队列。通过多变量logistic回归分析,利用显著变量构建了nomogram预测模型。采用受试者工作特征(ROC)曲线、决策曲线分析(DCA)和校准曲线评估模型的有效性,并与先前的预测模型进行Delong检验比较。结果:在166例患者的培训队列中,78例(46.9%)患者在没有药物治疗的情况下实现了术后血压正常,88例(53.1%)患者需要持续的降压治疗。多因素分析发现,年龄、降压药物数量、术前最大收缩压(SBP)、左心室射血分数(LVEF)、血清肌酐(Cr)水平和低钾血症史是术后血压正常化的独立预测因素。校正曲线显示预测结果与实际结果非常吻合,DCA表明基于该模型的临床干预在各种风险阈值下都是有益的。与以前的模型比较表明,我们的模型在亚洲人群中的表现优于醛固onoma分辨率评分(ARS),与Morisaki评分相当。结论:一个包含年龄、降压药数量、术前最大收缩压、LVEF、血清Cr水平和低钾血症史等变量的预测模型可以有效预测UPA患者单侧肾上腺切除术后的治疗结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Preoperative predictive indicators for resolution of hypertension in patients with unilateral primary aldosteronism: development of a nomogram model.

Background: Primary aldosteronism (PA) is the leading surgically treatable cause of hypertension, with adrenalectomy as the definitive treatment for unilateral PA (UPA). However, some patients have persistent hypertension after surgery. This study aims to identify preoperative factors affecting surgical outcomes and develop a predictive model for postoperative hypertension resolution.

Methods: We reviewed and analyzed the medical records of 206 patients who underwent unilateral adrenalectomy for UPA at Qilu Hospital of Shandong University (2011-2022). As a training cohort, the data of the 166 patients from 2013 to 2022 was analyzed using univariate and multivariate logistic regression to explore the relationship between preoperative clinical and biochemical data and postoperative BP normalization. The remaining 40 patients from 2011 to 2012 were used as a validation cohort. A predictive model of the nomogram was constructed utilizing significant variables through multivariate logistic regression analysis. The model's effectiveness was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves and compared with previous prediction models using the Delong test.

Results: In the training cohort of 166 patients, 78 (46.9%) achieved postoperative normotension without medication, while 88 (53.1%) required ongoing antihypertensive treatment. Multifactorial analysis identified age, number of antihypertensive medications, preoperative maximum systolic blood pressure (SBP), left ventricular ejection fraction (LVEF), serum creatinine (Cr) levels, and a history of hypokalemia as independent predictors of postoperative BP normalization. Calibration curves showed excellent agreement between predicted and actual outcomes, and DCA indicated that clinical interventions based on this model are beneficial at various risk thresholds. Comparison with previous models showed our model outperformed the Aldosteronoma Resolution Score (ARS) in the Asian population and was comparable to the Morisaki score.

Conclusion: A predictive model developed with variables including age, number of anti-hypertensive medications, preoperative maximum SBP, LVEF, serum Cr levels, and history of hypokalemia effectively predicts therapeutic outcomes following unilateral adrenalectomy for UPA patients.

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来源期刊
CiteScore
3.30
自引率
8.70%
发文量
342
审稿时长
4-8 weeks
期刊介绍: Langenbeck''s Archives of Surgery aims to publish the best results in the field of clinical surgery and basic surgical research. The main focus is on providing the highest level of clinical research and clinically relevant basic research. The journal, published exclusively in English, will provide an international discussion forum for the controlled results of clinical surgery. The majority of published contributions will be original articles reporting on clinical data from general and visceral surgery, while endocrine surgery will also be covered. Papers on basic surgical principles from the fields of traumatology, vascular and thoracic surgery are also welcome. Evidence-based medicine is an important criterion for the acceptance of papers.
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