Airway dilation in bronchiolitis obliterans after allogeneic hematopoietic stem cell transplantation

SummaryRationaleBronchiolitis obliterans syndrome (BOS) is a late, non-infectious pulmonary complication following hematopoietic stem cell transplantation (HSCT). There is minimal data published on quantitative radiologic characterization of airway remodeling in these subjects.ObjectivesTo examine quantitative measurements of airway morphology and their correlation with lung function in a cohort of patients who underwent HSCT and developed BOS.MethodsAll adult patients who underwent allogeneic HSCT at the Dana-Farber Cancer Institute/Brigham and Women's Hospital (n = 1854) between January 1st 2000 and June 30th 2010 were screened for the development of BOS. Clinically acquired high resolution CT (HRCT) scans of the chest were collected. For each subjects discrete measures of airway wall area were performed and the square root of wall area of a 10-mm luminal perimeter (Pi10) was calculated.Measurements and main resultsWe identified 88 cases of BOS, and 37 of these patients had available HRCT. On CT scans obtained after BOS diagnosis, the Pi10 decreased (consistent with airway dilation) as compared with pre-BOS values (p < 0.001). After HSCT the Pi10 correlated with FEV1% predicted (r = 0.636, p < 0.0001), and RV/TLC% predicted (r = −0.736, p < 0.0001), even after adjusting for age, sex and total lung capacity (p < 0.0001 for both).ConclusionsOn HRCT scan BOS is characterized by central airway dilation, the degree of which is correlated to decrements in lung function. This is opposite of what has been previously demonstrated in COPD and asthma that quantitative measure of proximal airway wall thickening directly correlate with pulmonary function. Our data suggests that the pathologic process affecting the central airways is different from the pathology observed in the distal airways. Further work is needed to determine if such change can be used as a sensitive and specific tool for the future diagnosis and staging of BOS

An Integrated Clinico-Metabolomic Model Improves Prediction of Death in Sepsis

Sepsis is a common cause of death, but outcomes in individual patients are difficult to predict. Elucidating the molecular processes that differ between sepsis patients who survive and those who die may permit more appropriate treatments to be deployed. We examined the clinical features, and the plasma metabolome and proteome of patients with and without community-acquired sepsis, upon their arrival at hospital emergency departments and 24 hours later. The metabolomes and proteomes of patients at hospital admittance who would die differed markedly from those who would survive. The different profiles of proteins and metabolites clustered into fatty acid transport and β-oxidation, gluconeogenesis and the citric acid cycle. They differed consistently among several sets of patients, and diverged more as death approached. In contrast, the metabolomes and proteomes of surviving patients with mild sepsis did not differ from survivors with severe sepsis or septic shock. An algorithm derived from clinical features together with measurements of seven metabolites predicted patient survival. This algorithm may help to guide the treatment of individual patients with sepsis

Metabolomic derangements are associated with mortality in critically ill adult patients

Objective: To identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults. Rationale: Comprehensive metabolomic profiling of plasma at ICU admission to identify biomarkers associated with mortality has recently become feasi

Modelling airway geometry as stock market data using Bayesian changepoint detection

Numerous lung diseases, such as idiopathic pulmonary fibrosis (IPF), exhibit dilation of the airways. Accurate measurement of dilatation enables assessment of the progression of disease. Unfortunately the combination of image noise and airway bifurcations causes high variability in the profiles of cross-sectional areas, rendering the identification of affected regions very difficult. Here we introduce a noise-robust method for automatically detecting the location of progressive airway dilatation given two profiles of the same airway acquired at different time points. We propose a probabilistic model of abrupt relative variations between profiles and perform inference via Reversible Jump Markov Chain Monte Carlo sampling. We demonstrate the efficacy of the proposed method on two datasets; (i) images of healthy airways with simulated dilatation; (ii) pairs of real images of IPF-affected airways acquired at 1 year intervals. Our model is able to detect the starting location of airway dilatation with an accuracy of 2.5 mm on simulated data. The experiments on the IPF dataset display reasonable agreement with radiologists. We can compute a relative change in airway volume that may be useful for quantifying IPF disease progression.<br/

An integrated clinico-metabolomic model improves prediction of death in sepsis.

Sepsis is a common cause of death, but outcomes in individual patients are difficult to predict. Elucidating the molecular processes that differ between sepsis patients who survive and those who die may permit more appropriate treatments to be deployed. We examined the clinical features and the plasma metabolome and proteome of patients with and without community-acquired sepsis, upon their arrival at hospital emergency departments and 24 hours later. The metabolomes and proteomes of patients at hospital admittance who would ultimately die differed markedly from those of patients who would survive. The different profiles of proteins and metabolites clustered into the following groups: fatty acid transport and β-oxidation, gluconeogenesis, and the citric acid cycle. They differed consistently among several sets of patients, and diverged more as death approached. In contrast, the metabolomes and proteomes of surviving patients with mild sepsis did not differ from survivors with severe sepsis or septic shock. An algorithm derived from clinical features together with measurements of five metabolites predicted patient survival. This algorithm may help to guide the treatment of individual patients with sepsis