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无创诊断方法和预测模型在检测与间质性肺疾病相关的肺动脉高压中的应用:叙述性综述。

Noninvasive diagnostic modalities and prediction models for detecting pulmonary hypertension associated with interstitial lung disease: a narrative review.

机构信息

National Pulmonary Hypertension Service, Royal Brompton Hospital, London, UK.

National Heart and Lung Institute, Imperial College London, London, UK.

出版信息

Eur Respir Rev. 2024 Oct 9;33(174). doi: 10.1183/16000617.0092-2024. Print 2024 Oct.

DOI:10.1183/16000617.0092-2024
PMID:39384306
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11462299/
Abstract

Pulmonary hypertension (PH) is highly prevalent in patients with interstitial lung disease (ILD) and is associated with increased morbidity and mortality. Widely available noninvasive screening tools are warranted to identify patients at risk for PH, especially severe PH, that could be managed at expert centres. This review summarises current evidence on noninvasive diagnostic modalities and prediction models for the timely detection of PH in patients with ILD. It critically evaluates these approaches and discusses future perspectives in the field. A comprehensive literature search was carried out in PubMed and Scopus, identifying 39 articles that fulfilled inclusion criteria. There is currently no single noninvasive test capable of accurately detecting and diagnosing PH in ILD patients. Estimated right ventricular pressure (RVSP) on Doppler echocardiography remains the single most predictive factor of PH, with other indirect echocardiographic markers increasing its diagnostic accuracy. However, RVSP can be difficult to estimate in patients due to suboptimal views from extensive lung disease. The majority of existing composite scores, including variables obtained from chest computed tomography, pulmonary function tests and cardiopulmonary exercise tests, were derived from retrospective studies, whilst lacking validation in external cohorts. Only two available scores, one based on a stepwise echocardiographic approach and the other on functional parameters, predicted the presence of PH with sufficient accuracy and used a validation cohort. Although several methodological limitations prohibit their generalisability, their use may help physicians to detect PH earlier. Further research on the potential of artificial intelligence may guide a more tailored approach, for timely PH diagnosis.

摘要

肺高血压(PH)在间质性肺疾病(ILD)患者中高度流行,与发病率和死亡率增加有关。需要广泛应用非侵入性筛查工具来识别有 PH 风险的患者,尤其是严重 PH 患者,以便在专家中心进行管理。本综述总结了目前关于 ILD 患者 PH 及时检测的非侵入性诊断方法和预测模型的证据。本文批判性地评估了这些方法,并讨论了该领域的未来展望。在 PubMed 和 Scopus 中进行了全面的文献检索,确定了 39 篇符合纳入标准的文章。目前没有单一的非侵入性测试能够准确检测和诊断 ILD 患者的 PH。多普勒超声心动图估计的右心室压力(RVSP)仍然是 PH 的单一最具预测性因素,其他间接超声心动图标志物可提高其诊断准确性。然而,由于广泛的肺部疾病导致不理想的视野,RVSP 在患者中难以估计。大多数现有的综合评分,包括从胸部计算机断层扫描、肺功能测试和心肺运动测试中获得的变量,都是基于回顾性研究得出的,缺乏在外部队列中的验证。只有两个现有的评分,一个基于逐步超声心动图方法,另一个基于功能参数,具有足够的准确性来预测 PH 的存在,并使用验证队列。尽管存在一些方法学限制,使其无法普遍适用,但它们的使用可能有助于医生更早地发现 PH。关于人工智能的潜在应用的进一步研究可能会指导更有针对性的方法,以便及时进行 PH 诊断。

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本文引用的文献

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Machine Learning for Diagnosis of Pulmonary Hypertension by Echocardiography.机器学习在超声心动图诊断肺动脉高压中的应用。
Mayo Clin Proc. 2024 Feb;99(2):260-270. doi: 10.1016/j.mayocp.2023.05.006.
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Echocardiographic artificial intelligence for pulmonary hypertension classification.超声心动图人工智能用于肺动脉高压分类。
Heart. 2024 Mar 22;110(8):586-593. doi: 10.1136/heartjnl-2023-323320.
3
Unmasking a Silent Threat: Improving Pulmonary Hypertension Screening Methods for Interstitial Lung Disease Patients.揭开沉默的威胁:改善特发性肺纤维化患者肺动脉高压的筛查方法。
Medicina (Kaunas). 2023 Dec 28;60(1):58. doi: 10.3390/medicina60010058.
4
Derivation and validation of a noninvasive prediction tool to identify pulmonary hypertension in patients with IPF: Evolution of the model FORD.特发性肺纤维化患者肺高血压无创预测工具的建立和验证:模型 FORD 的演进。
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Pulmonary hypertension associated with lung disease: new insights into pathomechanisms, diagnosis, and management.肺部疾病相关肺动脉高压:病理机制、诊断和治疗新见解。
Lancet Respir Med. 2023 Sep;11(9):820-835. doi: 10.1016/S2213-2600(23)00259-X. Epub 2023 Aug 14.
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Predictors of Pulmonary Hypertension and Right Ventricular Dysfunction in Patients with Hypersensitivity Pneumonitis.过敏性肺炎患者肺动脉高压和右心室功能障碍的预测因素
Life (Basel). 2023 Jun 8;13(6):1348. doi: 10.3390/life13061348.
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Non-invasive detection of severe PH in lung disease using magnetic resonance imaging.使用磁共振成像对肺部疾病中的重度肺动脉高压进行无创检测。
Front Cardiovasc Med. 2023 Apr 17;10:1016994. doi: 10.3389/fcvm.2023.1016994. eCollection 2023.
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A transparent artificial intelligence framework to assess lung disease in pulmonary hypertension.一种透明的人工智能框架,用于评估肺动脉高压中的肺部疾病。
Sci Rep. 2023 Mar 7;13(1):3812. doi: 10.1038/s41598-023-30503-4.
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