The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion that relies on the recognition of patterns and the clinical context for detection of specific diseases. In this study, we aimed to explore the accuracy and interrater variability of pulmonologists when interpreting PFTs compared with artificial intelligence (AI)-based software that was developed and validated in more than 1500 historical patient cases.120 pulmonologists from 16 European hospitals evaluated 50 cases with PFT and clinical information, resulting in 6000 independent interpretations. The AI software examined the same data. American Thoracic Society/European Respiratory Society guidelines were used as the gold standard for PFT pattern interpretation. The gold standard for diagnosis was derived from clinical history, PFT and all additional tests.The pattern recognition of PFTs by pulmonologists (senior 73%, junior 27%) matched the guidelines in 74.4±5.9% of the cases (range 56–88%). The interrater variability of κ=0.67 pointed to a common agreement. Pulmonologists made correct diagnoses in 44.6±8.7% of the cases (range 24–62%) with a large interrater variability (κ=0.35). The AI-based software perfectly matched the PFT pattern interpretations (100%) and assigned a correct diagnosis in 82% of all cases (p<0.0001 for both measures).The interpretation of PFTs by pulmonologists leads to marked variations and errors. AI-based software provides more accurate interpretations and may serve as a powerful decision support tool to improve clinical practice.
Background and aims Heart failure with preserved ejection fraction (HFpEF) is a syndrome with a heterogeneous presentation. This study provides an in-;depth description of haemodynamic and metabolic alterations revealed by systematic assessment through cardiopulmonary exercise testing combined with exercise echocardiography (CPETecho) within a dedicated dyspnoea clinic. Methods and results Consecutive patients (n = 297), referred to a dedicated dyspnoea clinic using a standardized workup including CPETecho, with HFpEF diagnosed through a H2FPEF score ≥6 or HFA-PEFF score ≥5, were evaluated. A median of four haemodynamic/metabolic alterations was uncovered per patient: impaired stroke volume reserve (73%), impaired chronotropic reserve (72%), exercise pulmonary hypertension (65%), and impaired diastolic reserve (64%) were the most frequent cardiac alterations. Impaired peripheral oxygen extraction and a ventilatory limitation were present in 40% and 39%, respectively. In 267 patients (90%), 575 further diagnostic examinations were recommended (median of two tests per patient). Cardiac magnetic resonance imaging, coronary or amyloidosis workup, ventilation–perfusion scanning, and pulmonology referral were each recommended in approximately one out of three patients. In 293 patients (99%), 929 cardiovascular drug optimizations were performed (median of 3 modifications per patient). In 110 patients (37%), 132 cardiovascular interventions were performed, with ablation as the most frequent procedure. Conclusion Holistic workup of HFpEF patients within a multidisciplinary, dedicated dyspnoea clinic, including systematic implementation of CPETecho reveals various haemodynamic/metabolic alterations, leading to further diagnostic testing and potential treatment changes in the majority of cases.
Endobronchial lipomas are extremely rare benign tumours of the lung. Their clinical presentation mimics that of obstructive lung diseases such as asthma and chronic obstructive pulmonary disease (COPD), leading to a delay in diagnosis and errors in treatment. Therefore, making precise diagnosis may be challenging. We report a case of a 63-year-old man with paroxysmal attacks of dyspnea, non-productive cough, and wheezing, initially suspect for adult onset asthma, but with a final diagnosis of endobronchial lipoma.
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