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Accurate assessment of prognosis in idiopathic pulmonary fibrosis remains elusive due to significant individual radiological and physiological variability. We hypothesised that short-term radiological changes may be predictive of survival.We explored the use of CALIPER (Computer-Aided Lung Informatics for Pathology Evaluation and Rating), a novel software tool developed by the Biomedical Imaging Resource Laboratory at the Mayo Clinic Rochester (Rochester, MN, USA) for the analysis and quantification of parenchymal lung abnormalities on high-resolution computed tomography. We assessed baseline and follow-up (time-points 1 and 2, respectively) high-resolution computed tomography scans in 55 selected idiopathic pulmonary fibrosis patients and correlated CALIPER-quantified measurements with expert radiologists' assessments and clinical outcomes.Findings of interval change (mean 289 days) in volume of reticular densities (hazard ratio 1.91, p50.006), total volume of interstitial abnormalities (hazard ratio 1.70, p50.003) and per cent total interstitial abnormalities (hazard ratio 1.52, p50.017) as quantified by CALIPER were predictive of survival after a median follow-up of 2.4 years. Radiologist interpretation of short-term global interstitial lung disease progression, but not specific radiological features, was also predictive of mortality.These data demonstrate the feasibility of quantifying interval short-term changes on high-resolution computed tomography and their possible use as independent predictors of survival in idiopathic pulmonary fibrosis. @ERSpublications Short-term quantified CT changes are predictive of survival in IPF

Background: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could predict new-onset AF from the resting 12-lead ECG and that this prediction may help identify those at risk of AF-related stroke. Methods: We used 1.6 M resting 12-lead digital ECG traces from 430 000 patients collected from 1984 to 2019. Deep neural networks were trained to predict new-onset AF (within 1 year) in patients without a history of AF. Performance was evaluated using areas under the receiver operating characteristic curve and precision-recall curve. We performed an incidence-free survival analysis for a period of 30 years following the ECG stratified by model predictions. To simulate real-world deployment, we trained a separate model using all ECGs before 2010 and evaluated model performance on a test set of ECGs from 2010 through 2014 that were linked to our stroke registry. We identified the patients at risk for AF-related stroke among those predicted to be high risk for AF by the model at different prediction thresholds. Results: The area under the receiver operating characteristic curve and area under the precision-recall curve were 0.85 and 0.22, respectively, for predicting new-onset AF within 1 year of an ECG. The hazard ratio for the predicted high- versus low-risk groups over a 30-year span was 7.2 (95% CI, 6.9–7.6). In a simulated deployment scenario, the model predicted new-onset AF at 1 year with a sensitivity of 69% and specificity of 81%. The number needed to screen to find 1 new case of AF was 9. This model predicted patients at high risk for new-onset AF in 62% of all patients who experienced an AF-related stroke within 3 years of the index ECG. Conclusions: Deep learning can predict new-onset AF from the 12-lead ECG in patients with no previous history of AF. This prediction may help identify patients at risk for AF-related strokes.

Purpose High-Resolution chest CT (HRCT) is essential in the characterization of interstitial lung disease (ILD). The HRCT features of some diseases can be diagnostic. Longitudinal monitoring with HRCT can assess progression of ILD; however, subtle changes in the volume and character of abnormalities can be difficult to assess. Accuracy of diagnosis can be dependent on expertise and experience of the radiologist, pathologist or clinician. Quantitative analysis of thoracic HRCT has the potential to determine the extent of disease reproducibly, classify the types of abnormalities and automate the diagnostic process. Materials and Methods Novel software that utilizes histogram signatures to characterize pulmonary parenchyma was used to interrogate chest HRCT data, including retrospective processing of clinical CT scans and research data from the Lung Tissue Research Consortium (LTRC). Additional information including physiologic, pathologic and semi-quantitative radiologist assessment was available to allowcomparison of quantitative results with visual estimates of disease, physiologic parameters and measures of disease outcome. Results Quantitative analysis results were provided in regional volumetric quantities for statistical analysis as well as a graphical representation. Analysis suggests that quantitative HRCT analysis can serve as a biomarker with physiologic, pathologic and prognostic significance. Conclusion It is likely that quantitative analysis of HRCT can be used in clinical practice as a means to aid in identifying probable diagnosis, stratifying prognosis in early disease, and consistently determining progression of disease or response to therapy. Further optimization of quantitative techniques and longitudinal analysis of well-characterized subjects would be helpful to validate these methods.

CALIPER was superior to visual scoring as validated by functional correlations with PFTs. The pulmonary vessel volume, a novel CALIPER CT parameter with no visual scoring equivalent, has the potential to be a CT feature in the assessment of patients with IPF and requires further exploration.