Background: The differential diagnosis fibrotic hypersensitivity pneumonitis (HP) versus idiopathic pulmonary fibrosis (IPF) is important but challenging. Recent diagnostic guidelines for HP emphasize including multidisciplinary discussion (MDD) in the diagnostic process, however MDD is not comprehensively available. We aimed to establish the diagnostic accuracy and prognostic validity of a previously proposed HP diagnostic algorithm that foregoes MDD.
Methods: We tested the algorithm in patients with an MDD diagnosis of fibrotic HP or IPF (case control study) and determined diagnostic test performances for diagnostic confidences of ≥ 90% and ≥ 70%. Prognostic validity was established using Cox proportional hazards models.
Results: Thirty-one patients with fibrotic HP and 50 IPF patients were included. The algorithm-derived ≥ 90% confidence level for HP had high specificity (0.94, 95% confidence interval [CI] 0.83-0.99), but low sensitivity (0.35 [95%CI 0.19-0.55], J-index 0.29). Test performance was improved for the ≥ 70% confidence level (J-index 0.64) with a specificity of 0.90 (95%CI 0.78-0.97), and a sensitivity of 0.74 (95%CI 0.55-0.88). MDD fibrotic HP diagnosis was strongly associated with lower risk of death (adjusted hazard ratio [HR] 0.10 [0.01-0.92], p = 0.04), whereas the algorithm-derived ≥ 70% and ≥ 90% confidence diagnoses were not significantly associated with survival (adjusted HR 0.37 [0.07-1.80], p = 0.22, and adjusted HR 0.41 [0.05-3.25], p = 0.39, respectively).
Conclusion: The algorithm-derived ≥ 70% diagnostic confidence had satisfactory test performance for MDD-HP diagnosis, with insufficient sensitivity for ≥ 90% confidence. The lowest risk of death in the MDD-derived HP diagnosis validates the reference standard and suggests that a diagnostic algorithm not including MDD, might not replace the latter.
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http://dx.doi.org/10.1186/s12931-021-01727-7 | DOI Listing |
Nat Commun
January 2025
Bioinformatics and computational systems biology of cancer, Institut Curie, Inserm U900, PSL Research University, Paris, France.
Immunotherapy is improving the survival of patients with metastatic non-small cell lung cancer (NSCLC), yet reliable biomarkers are needed to identify responders prospectively and optimize patient care. In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. We analyze baseline multimodal data from a cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy, including positron emission tomography images, digitized pathological slides, bulk transcriptomic profiles, and clinical information.
View Article and Find Full Text PDFPrenatally diagnosed intracranial hemorrhage in the fetus is associated with a wide range of neonatal disorders, from completely uncomplicated physiological development to severe neurological impairment or death. The incidence is 0.6-1/1,000 births.
View Article and Find Full Text PDFOpen Heart
January 2025
Department of Cardiology, Inserm U1096, Univ Rouen Normandie, CHU Rouen, Rouen, France
Introduction: Conductive disturbances requiring permanent pacemaker (PPM) implantation remain a major concern after transcatheter aortic valve implantation (TAVI).
Aims: To assess the impact of aortic valve calcium score (AVCS) on conductive disturbances requiring PPM after TAVI.
Methods: All patients who underwent TAVI with accessible AVCS from the preprocedural CT scan report were included in this retrospective single-centre study.
Open Heart
January 2025
Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Background: Visual assessment of coronary CT angiography (CCTA) is time-consuming, influenced by reader experience and prone to interobserver variability. This study evaluated a novel algorithm for coronary stenosis quantification (atherosclerosis imaging quantitative CT, AI-QCT).
Methods: The study included 208 patients with suspected coronary artery disease (CAD) undergoing CCTA in Perfusion Imaging and CT Coronary Angiography With Invasive Coronary Angiography-1.
Open Heart
January 2025
Department of Molecular and Clinical Medicine, University of Gothenburg Institute of Medicine, Gothenburg, Sweden.
Purpose: We examined whether end-to-end deep-learning models could detect moderate (≥50%) or severe (≥70%) stenosis in the left anterior descending artery (LAD), right coronary artery (RCA) or left circumflex artery (LCX) in iodine contrast-enhanced ECG-gated coronary CT angiography (CCTA) scans.
Methods: From a database of 6293 CCTA scans, we used pre-existing curved multiplanar reformations (CMR) images of the LAD, RCA and LCX arteries to create end-to-end deep-learning models for the detection of moderate or severe stenoses. We preprocessed the images by exploiting domain knowledge and employed a transfer learning approach using EfficientNet, ResNet, DenseNet and Inception-ResNet, with a class-weighted strategy optimised through cross-validation.
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