Chronic Thromboembolic Pulmonary Hypertension: An Update

Ruaro, Barbara; Baratella, Elisa; Caforio, Gaetano; Confalonieri, Paola; Wade, Barbara; Marrocchio, Cristina; Geri, Pietro; Pozzan, Riccardo; Andrisano, Alessia Giovanna; Cova, Maria Assunta; Cortale, Maurizio; Confalonieri, Marco; Salton, Francesco

doi:10.3390/diagnostics12020235

Cited by 22 publications

(44 citation statements)

References 108 publications

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“…Chronic thromboembolic pulmonary hypertension (CTEPH) results from the organization of thrombi and vascular remodeling in the pulmonary circulation that is associated with mean pulmonary artery pressure (mPAP) equal to or exceeding 25 mmHg [ 1 , 2 , 3 , 4 ]. It is generally screened using ventilation/perfusion (V/Q)-lung scintigraphy [ 5 , 6 ] or V/Q-lung single photon emission CT (SPECT) [ 7 , 8 , 9 ], which shows excellent sensitivity in detecting mismatched perfusion defects.…”

Section: Introductionmentioning

confidence: 99%

Correlation between CT Value on Lung Subtraction CT and Radioactive Count on Perfusion Lung Single Photon Emission CT in Chronic Thromboembolic Pulmonary Hypertension

et al. 2022

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Background: Lung subtraction CT (LSCT), the subtraction of noncontrast CT from CT pulmonary angiography (CTPA) without spatial misregistration, is easily applicable by utilizing a software-based deformable image registration technique without additional hardware and permits the evaluation of lung perfusion as iodine accumulation, similar to that observed in perfusion lung single photon emission CT (PL-SPECT). The aim of this study was to use LSCT to newly assess the quantitative correlation between the CT value on LSCT and radioactive count on PL-SPECT as a reference and validate the quantification of lung perfusion by measuring the CT value in chronic thromboembolic pulmonary hypertension (CTEPH). Methods: We prospectively enrolled 47 consecutive patients with CTEPH undergoing both LSCT and PL-SPECT; we used noncontrast CT, CTPA, and LSCT to measure CT values and PL-SPECT to measure radioactive counts in areas representing three different perfusion classes—no perfusion defect, subsegmental perfusion defect, and segmental perfusion defect; we compared CT values on noncontrast CT, CTPA, and LSCT and radioactive counts on PL-SPECT among the three classes, then assessed the correlation between them. Results: Both the CT values and radioactive counts differed significantly among the three classes (p < 0.01 for all) and showed weak correlation (ρ = 0.38) by noncontrast CT, moderate correlation (ρ = 0.61) by CTPA, and strong correlation (ρ = 0.76) by LSCT. Conclusions: The CT value measurement on LSCT is a novel quantitative approach to assess lung perfusion in CTEPH and only correlates strongly with radioactive count measurement on PL-SPECT.

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Section: Introductionmentioning

confidence: 99%

Correlation between CT Value on Lung Subtraction CT and Radioactive Count on Perfusion Lung Single Photon Emission CT in Chronic Thromboembolic Pulmonary Hypertension

et al. 2022

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“…Chronic thromboembolic pulmonary hypertension (CTEPH) is a relatively rare disease characterized by intravascular macroscopic thromboembolic lesions and microscopic pulmonary vascular remodeling. The consequence of these pathological changes is increased pulmonary vascular resistance (PVR), leading to right ventricular failure (RVF) and death if left untreated [ 1 , 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Prognostic Value of Pulmonary Artery Pulsatility Index in Right Ventricle Failure-Related Mortality in Inoperable Chronic Thromboembolic Pulmonary Hypertension

Sławek-Szmyt

Araszkiewicz

Jankiewicz

et al. 2022

JCM

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Chronic thromboembolic pulmonary hypertension (CTEPH) is an ominous disease leading to progressive right ventricular failure (RVF) and death. There is no reliable risk stratification strategy for patients with CTEPH. The pulmonary artery pulsatility index (PAPI) is a novel hemodynamic index that predicts the occurrence RVF. We aimed to investigate prognostic value of PAPI in inoperable CTEPH. Consecutive patients with inoperable CTEPH were enrolled. PAPI was calculated from baseline right heart catheterization data. A prognostic cut-off value was determined, and characteristics of low- and high-PAPI groups were compared. The association between risk assessment and survival was also evaluated. We included 50 patients (mean age 64 ± 12.2 years, 60% female). The number of deaths was 12 (24%), and the mean follow-up time was 52 ± 19.3 months. The established prognostic cut-off value for PAPI was 3.9. The low-PAPI group had significantly higher mean values of mean atrial pressure (14.9 vs. 7.8, p = 0.0001), end-diastolic right ventricular pressure (16.5 vs. 11.2, p = 0.004), and diastolic pulmonary artery pressure (35.8 vs. 27.7, p = 0.0012). The low-PAPI group had lower survival as compared to high-PAPI (log-rank p < 0.0001). PAPI was independently associated with survival and may be applicable for risk stratification in inoperable CTEPH.

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“…The diagnosis of pneumonia and PTE present particular complexity considering the similarity of their symptoms, this is, cough, shortness of breath, and chest pain [ 9 , 10 ]; also, the given signs and symptoms are often subjective and nonspecific. For this reason, the criteria or auxiliary diagnoses specified in the guidelines [ 6 , 11 ] should always be complemented by the treating physician’s determination.…”

Section: Introductionmentioning

confidence: 99%

Pneumonia and Pulmonary Thromboembolism Classification Using Electronic Health Records

Siordia-Millán

Torres-Ramos²,

Salido-Ruiz³

et al. 2022

Diagnostics

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Pneumonia and pulmonary thromboembolism (PTE) are both respiratory diseases; their diagnosis is difficult due to their similarity in symptoms, medical subjectivity, and the large amount of information from different sources necessary for a correct diagnosis. Analysis of such clinical data using computational tools could help medical staff reduce time, increase diagnostic certainty, and improve patient care during hospitalization. In addition, no studies have been found that analyze all clinical information on the Mexican population in the Spanish language. Therefore, this work performs automatic diagnosis of pneumonia and pulmonary thromboembolism using machine-learning tools along with clinical laboratory information (structured data) and clinical text (unstructured data) obtained from electronic health records. A cohort of 173 clinical records was obtained from the Mexican Social Security Institute. The data were preprocessed, transformed, and adjusted to be analyzed using several machine-learning algorithms. For structured data, naïve Bayes, support vector machine, decision trees, AdaBoost, random forest, and multilayer perceptron were used; for unstructured data, a BiLSTM was used. K-fold cross-validation and leave-one-out were used for evaluation of structured data, and hold-out was used for unstructured data; additionally, 1-vs.-1 and 1-vs.-rest approaches were used. Structured data results show that the highest AUC-ROC was achieved by the naïve Bayes algorithm classifying PTE vs. pneumonia (87.0%), PTE vs. control (75.1%), and pneumonia vs. control (85.2%) with the 1-vs.-1 approach; for the 1-vs.-rest approach, the best performance was reported in pneumonia vs. rest (86.3%) and PTE vs. rest (79.7%) using naïve Bayes, and control vs. diseases (79.8%) using decision trees. Regarding unstructured data, the results do not present a good AUC-ROC; however, the best F1-score were scored for control vs. disease (72.7%) in the 1-vs.-rest approach and control vs. pneumonia (63.6%) in the 1-to-1 approach. Additionally, several decision trees were obtained to identify important attributes for automatic diagnosis for structured data, particularly for PTE vs. pneumonia. Based on the experiments, the structured datasets present the highest values. Results suggest using naïve Bayes and structured data to automatically diagnose PTE vs. pneumonia. Moreover, using decision trees allows the observation of some decision criteria that the medical staff could consider for diagnosis.

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Chronic Thromboembolic Pulmonary Hypertension: An Update

Cited by 22 publications

References 108 publications

Correlation between CT Value on Lung Subtraction CT and Radioactive Count on Perfusion Lung Single Photon Emission CT in Chronic Thromboembolic Pulmonary Hypertension

Correlation between CT Value on Lung Subtraction CT and Radioactive Count on Perfusion Lung Single Photon Emission CT in Chronic Thromboembolic Pulmonary Hypertension

Prognostic Value of Pulmonary Artery Pulsatility Index in Right Ventricle Failure-Related Mortality in Inoperable Chronic Thromboembolic Pulmonary Hypertension

Pneumonia and Pulmonary Thromboembolism Classification Using Electronic Health Records

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