{"title":"基于临床和3D-EIT参数的CTEPH诊断图模型。","authors":"Jian Xu, Yuhan Wang, Ying Gong, Lu Wang, Yuanlin Song, Xu Wu","doi":"10.1186/s12931-025-03206-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Chronic thromboembolic pulmonary hypertension (CTEPH) is easily misdiagnosed. Three-dimensional (3D) electrical impedance tomography (EIT) can monitor the whole-lung perfusion at the bedside. In this study, three-dimensional electrical impedance tomography (3D-EIT) features in patients with suspected chronic thromboembolic pulmonary hypertension (CTEPH) was investigated, and nomogram models based on clinical and 3D-EIT parameters were constructed to identify CTEPH.</p><p><strong>Methods: </strong>Patients with pulmonary hypertension (PH) due to left heart disease and chronic hypoxia were excluded. The enrolled patients were divided into CTEPH and Non-CTEPH groups by confirmatory tests. Then, history and laboratory results were collected and 3D-EIT examination was performed. Out of 70 enrolled patients, 50 cases were used as the training set to construct the nomogram model. Obtained nomogram diagnostic model was calibrated and then evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and clinical impact curves (CIC).</p><p><strong>Results: </strong>Through a comprehensive univariate analysis, Wald test, Akaike information criterion (AIC), and Bayesian information criterion (BIC), the nomogram model for CTEPH diagnosis based on 50 patients was constructed using venous thromboembolism (VTE) history, D-dimer, maximum of corresponding regional ventilation/perfusion ratio (V/Qmax), range between the maximum and minimum values of regional perfusion (P-Range) and the percentage of ventilation/perfusion match area (VQMatch). The C-index of the nomogram model in the training set was 0.926 (95% CI: 0.859-0.993). In the training set and test set, the nomogram model had a larger area under the curve (AUC) than models containing only VTE history, VTE history + D-dimer and EIT parameters. Both DCA and CIC analyses indicate that this model can provide significant clinical benefits.</p><p><strong>Conclusions: </strong>A nomogram model combining clinical and 3D-EIT parameters facilitated the diagnosis of CTEPH.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":49131,"journal":{"name":"Respiratory Research","volume":"26 1","pages":"137"},"PeriodicalIF":5.8000,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11993967/pdf/","citationCount":"0","resultStr":"{\"title\":\"A nomogram model based on clinical and 3D-EIT parameters for CTEPH diagnosis.\",\"authors\":\"Jian Xu, Yuhan Wang, Ying Gong, Lu Wang, Yuanlin Song, Xu Wu\",\"doi\":\"10.1186/s12931-025-03206-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Chronic thromboembolic pulmonary hypertension (CTEPH) is easily misdiagnosed. Three-dimensional (3D) electrical impedance tomography (EIT) can monitor the whole-lung perfusion at the bedside. In this study, three-dimensional electrical impedance tomography (3D-EIT) features in patients with suspected chronic thromboembolic pulmonary hypertension (CTEPH) was investigated, and nomogram models based on clinical and 3D-EIT parameters were constructed to identify CTEPH.</p><p><strong>Methods: </strong>Patients with pulmonary hypertension (PH) due to left heart disease and chronic hypoxia were excluded. The enrolled patients were divided into CTEPH and Non-CTEPH groups by confirmatory tests. Then, history and laboratory results were collected and 3D-EIT examination was performed. Out of 70 enrolled patients, 50 cases were used as the training set to construct the nomogram model. Obtained nomogram diagnostic model was calibrated and then evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and clinical impact curves (CIC).</p><p><strong>Results: </strong>Through a comprehensive univariate analysis, Wald test, Akaike information criterion (AIC), and Bayesian information criterion (BIC), the nomogram model for CTEPH diagnosis based on 50 patients was constructed using venous thromboembolism (VTE) history, D-dimer, maximum of corresponding regional ventilation/perfusion ratio (V/Qmax), range between the maximum and minimum values of regional perfusion (P-Range) and the percentage of ventilation/perfusion match area (VQMatch). The C-index of the nomogram model in the training set was 0.926 (95% CI: 0.859-0.993). In the training set and test set, the nomogram model had a larger area under the curve (AUC) than models containing only VTE history, VTE history + D-dimer and EIT parameters. 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引用次数: 0
摘要
背景：慢性血栓栓塞性肺动脉高压（CTEPH）很容易误诊。三维（3D）电阻抗断层扫描（EIT）可以在床边监测全肺灌注。本研究探讨疑似慢性血栓栓塞性肺动脉高压（CTEPH）患者的三维电阻抗断层扫描（3D-EIT）特征，并建立基于临床和3D-EIT参数的nomogram模型来识别CTEPH。方法：排除左心疾病合并慢性缺氧所致肺动脉高压（PH）患者。通过确证试验将入组患者分为CTEPH组和非CTEPH组。然后收集病史和实验室结果，并进行3D-EIT检查。在70例入组患者中，50例作为训练集构建nomogram model。对获得的nomogram诊断模型进行校正，然后使用受试者工作特征（ROC）曲线、决策曲线分析（DCA）和临床影响曲线（CIC）进行评估。结果：通过综合单因素分析、Wald检验、Akaike信息准则（AIC）和Bayesian信息准则（BIC），基于50例患者的静脉血栓栓塞（VTE）病史、d -二聚体、相应区域最大通气/灌注比（V/Qmax）、区域最大灌注值与最小灌注值之差（P-Range）和通气/灌注匹配面积百分比（VQMatch），构建CTEPH诊断的nomogram模型。训练集的nomogram model的C-index为0.926 （95% CI: 0.859 ~ 0.993）。在训练集和测试集中，nomogram模型比只包含VTE历史、VTE历史+ d -二聚体和EIT参数的模型具有更大的curve under area （AUC）。DCA和CIC分析均表明该模型可提供显著的临床效益。结论：结合临床和3D-EIT参数的nomogram model有助于CTEPH的诊断。临床试验号：不适用。
A nomogram model based on clinical and 3D-EIT parameters for CTEPH diagnosis.
Background: Chronic thromboembolic pulmonary hypertension (CTEPH) is easily misdiagnosed. Three-dimensional (3D) electrical impedance tomography (EIT) can monitor the whole-lung perfusion at the bedside. In this study, three-dimensional electrical impedance tomography (3D-EIT) features in patients with suspected chronic thromboembolic pulmonary hypertension (CTEPH) was investigated, and nomogram models based on clinical and 3D-EIT parameters were constructed to identify CTEPH.
Methods: Patients with pulmonary hypertension (PH) due to left heart disease and chronic hypoxia were excluded. The enrolled patients were divided into CTEPH and Non-CTEPH groups by confirmatory tests. Then, history and laboratory results were collected and 3D-EIT examination was performed. Out of 70 enrolled patients, 50 cases were used as the training set to construct the nomogram model. Obtained nomogram diagnostic model was calibrated and then evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and clinical impact curves (CIC).
Results: Through a comprehensive univariate analysis, Wald test, Akaike information criterion (AIC), and Bayesian information criterion (BIC), the nomogram model for CTEPH diagnosis based on 50 patients was constructed using venous thromboembolism (VTE) history, D-dimer, maximum of corresponding regional ventilation/perfusion ratio (V/Qmax), range between the maximum and minimum values of regional perfusion (P-Range) and the percentage of ventilation/perfusion match area (VQMatch). The C-index of the nomogram model in the training set was 0.926 (95% CI: 0.859-0.993). In the training set and test set, the nomogram model had a larger area under the curve (AUC) than models containing only VTE history, VTE history + D-dimer and EIT parameters. Both DCA and CIC analyses indicate that this model can provide significant clinical benefits.
Conclusions: A nomogram model combining clinical and 3D-EIT parameters facilitated the diagnosis of CTEPH.
期刊介绍：
Respiratory Research publishes high-quality clinical and basic research, review and commentary articles on all aspects of respiratory medicine and related diseases.
As the leading fully open access journal in the field, Respiratory Research provides an essential resource for pulmonologists, allergists, immunologists and other physicians, researchers, healthcare workers and medical students with worldwide dissemination of articles resulting in high visibility and generating international discussion.
Topics of specific interest include asthma, chronic obstructive pulmonary disease, cystic fibrosis, genetics, infectious diseases, interstitial lung diseases, lung development, lung tumors, occupational and environmental factors, pulmonary circulation, pulmonary pharmacology and therapeutics, respiratory immunology, respiratory physiology, and sleep-related respiratory problems.
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