Acute exacerbations of idiopathic pulmonary fibrosis: Does clinical stratification or steroid treatment matter?

Acute exacerbation (AE) of idiopathic pulmonary fibrosis (IPF) is defined as a sudden acceleration of the disease with the appearance of pulmonary infiltrates superimposed on the characteristic pattern of IPF that leads to a significant decline in lung function. It has high in-hospital mortality rates, despite medical treatment with systematic steroids. We sought to investigate whether there were in-hospital mortality differences according to clinical stratification (AE, suspected AE, or AE of known cause) and/or treatment with systemic steroids. We reviewed the clinical characteristics and outcomes of patients with IPF admitted to our hospital during the years 2003-2014 due to a worsening of their clinical status. We identified 50 IPF patients, 9 with AE (18%), 12 with suspected exacerbation (24%), and 29 with AE of known cause (58%), mostly respiratory infections. In-hospital mortality was similar in the three groups (33% vs. 17% vs. 34%, respectively). Likewise, we did not find differences between them with respect to the use of systemic steroids (length of treatment duration or total dose). Nevertheless, there was an independent association between in-hospital mortality and high average daily steroid dose. We did not observe significant differences in prognosis or use of systemic steroids according to current diagnostic stratification groups in patients hospitalized because of an exacerbation of IPF

Auscultation of velcro crackles is associated with usual interstitial pneumonia

Auscultation of Velcro crackles has been proposed as a key finding in physical lung examination in patients with interstitial lung diseases (ILDs), especially in idiopathic pulmonary fibrosis (IPF). However, no studies have been carried out to assess the association of Velcro crackles with other clinical variables. We evaluated a cohort of 132 patients, prospectively and consecutively included in our ILD diagnostic program at a tertiary referral center. All patients were auscultated during the physical examination. The patients were divided into 2 groups: "presence" or "nonpresence" of bilateral Velcro crackles. Of all patients assessed, 83 (63%) presented Velcro crackles in the respiratory auscultation. Patients with Velcro crackles usually had more frequently cough and dyspnea at the moment of diagnosis. Forced vital capacity (P = 0.002) and lung diffusion capacity for carbon monoxide (P = 0.04) was lower in these patients. The ILD-GAP index was higher in the group with Velcro crackles (P = 0.01). All patients with usual interstitial pneumonia (UIP) in high-resolution computed tomography and all patients with final IPF diagnosis presented Velcro crackles. In multivariate analysis, the presence of Velcro crackles was independently associated with an UIP pattern. In patients suspected of having ILD, the auscultation of Velcro crackles was associated with UIP, a possibility which must be taken into consideration in early ILD detection in primary care

Impact of a systematic evaluation of connective tissue disease on diagnosis approach in patients with interstitial lung diseases

To date, there is no clear agreement regarding which is the best method to detect a connective tissue disease (CTD) during the initial diagnosis of interstitial lung diseases (ILD). The aim of our study was to explore the impact of a systematic diagnostic strategy to detect CTD-associated ILD (CTD-ILD) in clinical practice, and to clarify the significance of interstitial pneumonia with autoimmune features (IPAF) diagnosis in ILD patients. Consecutive patients evaluated in an ILD Diagnostic Program were divided in 3 groups: IPAF, CTD-ILD, and other ILD forms. Clinical characteristics, exhaustive serologic testing, high resolution computed tomography (HRCT) images, lung biopsy specimens, and follow-up were prospectively collected and analyzed. Among 139 patients with ILD, CTD was present in 21 (15.1%), 24 (17.3%) fulfilled IPAF criteria, and 94 (67.6%) were classified as other ILD forms. Specific systemic autoimmune symptoms such as Raynaud phenomenon (19%), inflammatory arthropathy (66.7%), and skin manifestations (38.1%) were more frequent in CTD-ILD patients than in the other groups (all P< .001). Among autoantibodies, antinuclear antibody was the most frequently found in IPAF (42%), and CTD-ILD (40%) (P= .04). Nonspecific interstitial pneumonia, detected by HRCT scan, was the most frequently seen pattern in patients with IPAF (63.5%), or CTD-ILD (57.1%) (P< .001). In multivariate analysis, a suggestive radiological pattern by HRCT scan (odds ratio [OR] 15.1, 95% confidence interval [CI] 4.7-48.3, P< .001) was the strongest independent predictor of CTD-ILD or IPAF, followed by the presence of clinical features (OR 14.6, 95% CI 4.3-49.5, P< .001), and serological features (OR 12.4, 95% CI 3.5-44.0, P< .001). This systematic diagnostic strategy was useful in discriminating an underlying CTD in patients with ILD. The defined criteria for IPAF are fulfilled by a considerable proportion of patients referred for ILD

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score

Acute exacerbations of idiopathic pulmonary fibrosis: Does clinical stratification or steroid treatment matter?

Acute exacerbation (AE) of idiopathic pulmonary fibrosis (IPF) is defined as a sudden acceleration of the disease with the appearance of pulmonary infiltrates superimposed on the characteristic pattern of IPF that leads to a significant decline in lung function. It has high in-hospital mortality rates, despite medical treatment with systematic steroids. We sought to investigate whether there were in-hospital mortality differences according to clinical stratification (AE, suspected AE, or AE of known cause) and/or treatment with systemic steroids. We reviewed the clinical characteristics and outcomes of patients with IPF admitted to our hospital during the years 2003-2014 due to a worsening of their clinical status. We identified 50 IPF patients, 9 with AE (18%), 12 with suspected exacerbation (24%), and 29 with AE of known cause (58%), mostly respiratory infections. In-hospital mortality was similar in the three groups (33% vs. 17% vs. 34%, respectively). Likewise, we did not find differences between them with respect to the use of systemic steroids (length of treatment duration or total dose). Nevertheless, there was an independent association between in-hospital mortality and high average daily steroid dose. We did not observe significant differences in prognosis or use of systemic steroids according to current diagnostic stratification groups in patients hospitalized because of an exacerbation of IPF

Auscultation of velcro crackles is associated with usual interstitial pneumonia

Auscultation of Velcro crackles has been proposed as a key finding in physical lung examination in patients with interstitial lung diseases (ILDs), especially in idiopathic pulmonary fibrosis (IPF). However, no studies have been carried out to assess the association of Velcro crackles with other clinical variables. We evaluated a cohort of 132 patients, prospectively and consecutively included in our ILD diagnostic program at a tertiary referral center. All patients were auscultated during the physical examination. The patients were divided into 2 groups: "presence" or "nonpresence" of bilateral Velcro crackles. Of all patients assessed, 83 (63%) presented Velcro crackles in the respiratory auscultation. Patients with Velcro crackles usually had more frequently cough and dyspnea at the moment of diagnosis. Forced vital capacity (P = 0.002) and lung diffusion capacity for carbon monoxide (P = 0.04) was lower in these patients. The ILD-GAP index was higher in the group with Velcro crackles (P = 0.01). All patients with usual interstitial pneumonia (UIP) in high-resolution computed tomography and all patients with final IPF diagnosis presented Velcro crackles. In multivariate analysis, the presence of Velcro crackles was independently associated with an UIP pattern. In patients suspected of having ILD, the auscultation of Velcro crackles was associated with UIP, a possibility which must be taken into consideration in early ILD detection in primary care

A multidisciplinary proposal for a diagnostic algorithm in idiopathic pulmonary fibrosis: the role of transbronchial cryobiopsy

The diagnosis of idiopathic pulmonary fibrosis (IPF) is a complex process that requires the multidisciplinary integration of clinical, radiological, and histological variables. Due to its diagnostic yield, surgical lung biopsy has been the recommended procedure for obtaining samples of lung parenchyma, when required. However, given the morbidity and mortality of this technique, alternative techniques which carry a lower risk have been explored. The most important of these is transbronchial cryobiopsy -transbronchial biopsy with a cryoprobe- which is useful for obtaining lung tissue with less comorbidity. Yield may be lower than surgical biopsy, but it is higher than with transbronchial biopsy with standard forceps. This option has been discussed in the recent clinical guidelines for the diagnosis of IPF, but the authors do not go so far as recommend it. The aim of this article, the result of a multidisciplinary discussion forum, is to review current evidence and make proposals for the use of transbronchial cryobiopsy in the diagnosis of IPF

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score