Schiebler, Mark

Abstract Background Despite advances in diagnosis and management, the interstitial lung disease (ILD) lexicon is plagued by ambiguous and inconsistent terminology that complicates communication and impedes knowledge generation. The objective of this Fleischner Society Consensus Statement was to produce standardized terminology for ILD multidisciplinary diagnoses and major phenotypes. Methods Interviews with 10 experts were used to identify ILD clinical diagnoses and major phenotypes. The preferred terms for each entity and potential alternatives were identified, alongside the rationale for the preferred term. Entities with more than one potential term were the subject of an online modified Delphi survey posed to the 29 committee members, aiming to achieve consensus. Committee members rated their agreement with the initially preferred term—5 (strongly agree), 4 (agree), 3 (neutral/unsure), 2 (disagree), and 1 (strongly disagree)—with the option to provide additional comments. Median score ≥4 and interquartile range ≤1 were considered consensus agreement. Terms not reaching agreement were discussed by video conference, followed by an additional survey that incorporated feedback. Results From the 60 initial terms, there were two root terms that required upfront consensus before survey initiation (ILD and interstitial pneumonia) and another eight terms that had no alternative suggested by the committee or in the literature. Agreement was met by 47/50 terms (94%) in Round 1 of the survey. The three terms (6%) that did not reach agreement met agreement in Round 2. Conclusions This document provides standardized recommended terms for ILD multidisciplinary diagnoses and major phenotypes that will facilitate communication among clinicians, researchers, patients, and other stakeholders.

AbstractBackgroundComputed tomography (CT)‐derived measures of tissue quality can add to frailty assessment and improve selection of candidates for heart transplant. We investigated the prognostic value of CT measures of tissue density for predicting hospital length of stay (LOS) and mortality post‐transplant.MethodsAll patients at a quaternary care hospital between 1999 to 2018 with preheart transplant CT scans and available data on transplant outcomes were eligible (n = 189), including a subset within the total cohort with scans 6‐month pretransplant (n = 103). Axial chest CT scans were analysed for liver and muscle density at the 12th thoracic vertebrae and aortic arch landmarks, respectively. Cox and linear regression models examined the risk of death and LOS, respectively, according to median (above or below) pectoral muscle density. Low‐density muscle (LDM) area and liver density were analysed as continuous variables.ResultsOut of 157 patients with readable CT scans (median age 55 years, interquartile range [50–60] 10% women), 31 died on 1‐year follow up. Patients with higher than and at median pectoral muscle density (39.5 Hounsfield Unit [HU]) had better 1‐year survival in the overall cohort (hazard ratio [HR] 0.82, 95% confidence interval [CI] 0.673, 0.989; p = 0.039), with the 6‐month cohort showing a trend (HR 0.79, 95% CI 0.603, 1.023; p = 0.074) towards improved survival. Conversely, every 5‐cm2 increase in pectoral LDM area was associated with 2.4‐day lower LOS (p = 0.045) in the overall cohort, and a 2.6‐day lower LOS in the 6‐month cohort (p = 0.05). Patients with higher ratio of normal‐density muscle to LDM had higher LOS (p < 0.01). Every 5‐HU increase in liver density at a region of interest was associated with 0.24‐day higher post‐transplant LOS in the overall cohort, and a 0.41 higher LOS in the 6‐month cohort (p ≤ 0.05).ConclusionsPatients with higher preheart transplant pectoral muscle density had greater 1‐year survival. Higher pectoral LDM area was associated with decreased LOS post‐transplant and higher liver density was associated with increased LOS. These findings raise possibilities that measures of muscle density as they reflect to quality of muscle may have prognostic implications. Future studies with prospective design are needed to confirm these findings.

Pulmonary hypertension (PH) is highly heterogeneous and despite treatment advances it remains a life‐shortening condition. There have been significant advances in imaging technologies, but despite evidence of their potential clinical utility, practice remains variable, dependent in part on imaging availability and expertise. This statement summarizes current and emerging imaging modalities and their potential role in the diagnosis and assessment of suspected PH. It also includes a review of commonly encountered clinical and radiological scenarios, and imaging and modeling‐based biomarkers. An expert panel was formed including clinicians, radiologists, imaging scientists, and computational modelers. Section editors generated a series of summary statements based on a review of the literature and professional experience and, following consensus review, a diagnostic algorithm and 55 statements were agreed. The diagnostic algorithm and summary statements emphasize the key role and added value of imaging in the diagnosis and assessment of PH and highlight areas requiring further research.

There are physiological consequences of overeating that can lead to increased morbidity and mortality. The purpose of this review article is to acquaint the reader with the current state of the art in the non–cardiac-gated, noncontrast chest computed tomographic (NCCT) imaging biomarkers of the metabolic syndrome and their prognostic significance found in the lower neck and chest. NCCT imaging biomarkers associated with metabolic syndrome in the chest include premature coronary artery calcification, acceleration of large vessel arterial and valvular calcifications associated with atherosclerosis, and pulmonary arterial enlargement from pulmonary hypertension associated with sleep apnea. These easily identified imaging biomarkers have prognostic implications for major adverse cardiac events (MACE). These NCCT chest-imaging biomarkers are likely targets for artificial intelligence algorithms to harvest for longitudinal assessment of their individual and multifactorial contributions to chronic disease, MACE, and mortality. Early recognition and treatment of these common disorders may help improve patient outcomes and quality of life while decreasing medical costs.