Dataset Information

Relationship of CT densitometry to lung physiological parameters and health status in alpha-1 antitrypsin deficiency: initial report of a centralised database of the NIHR rare diseases translational research collaborative.

ABSTRACT: OBJECTIVES:To establish a database network for the study of alpha-1 antitrypsin deficiency (AATD) and compare the results to CT lung density as the most direct measure of emphysema. DESIGN:A central electronic database was established to permit the upload of anonymised patient data from remote sites. Prospectively collected CT data were recorded onto disc, anonymised, analysed at the coordinating centre and compared with the clinical features of the disease. SETTING:Tertiary referral centres with expertise in the management of AATD focused on academic Biomedical Research Units and Wellcome Clinical Research Facilities. PARTICIPANTS:Data were collected from 187 patients over 1?year from eight UK academic sites. This included patient demographics, postbronchodilator physiology, health status and CT. Analysis was undertaken at the coordinating centre in Birmingham. RESULTS:Patient recruitment in the 12 months reached 94% of target (set at 200) covering the whole spectrum of the disease from those with normal lung function to very severe chronic obstructive lung disease. CT scan suitable for analysis was available from 147 (79%) of the patients. CT density, analysed as the threshold for the lowest 15% of lung voxels, showed statistically significant relationships with the objective physiological parameters of lung function as determined by spirometric Global Initiative for Chronic Obstructive Lung Disease (GOLD) severity staging (p<0.001) and carbon monoxide gas transfer (p<0.01). Density also correlated with subjective measures of quality of life (p=0.02). CONCLUSIONS:Establishment of the network for data collection and its transfer was highly successful facilitating future collaboration for the study of this rare disease and its management. CT densitometry correlated well with the objective clinical features of the disease supporting its role as the specific marker of the associated emphysema and its severity. Correlations with subjective measures of health, however, were generally weak indicating other factors play a role.

SUBMITTER: Crossley D

PROVIDER: S-EPMC7328802 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Relationship of CT densitometry to lung physiological parameters and health status in alpha-1 antitrypsin deficiency: initial report of a centralised database of the NIHR rare diseases translational research collaborative.

Crossley Diana D Stockley James J Bolton Charlotte E CE Hopkinson Nicholas S NS Mahadeva Ravi R Steiner Michael M Wilkinson Tom T Hurst John R JR Gooptu Bibek B Stockley Robert A RA

BMJ open 20200630 6

<h4>Objectives</h4>To establish a database network for the study of alpha-1 antitrypsin deficiency (AATD) and compare the results to CT lung density as the most direct measure of emphysema.<h4>Design</h4>A central electronic database was established to permit the upload of anonymised patient data from remote sites. Prospectively collected CT data were recorded onto disc, anonymised, analysed at the coordinating centre and compared with the clinical features of the disease.<h4>Setting</h4>Tertiar ...[more]

PMID: 32606060

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Project description:BackgroundComputed tomography (CT) lung densitometry has been demonstrated to be the most sensitive and specific outcome measure for the assessment of emphysema-modifying therapy, but the optimum densitometric index has yet to be determined and targeted sampling may be more sensitive than whole lung assessment. The EXAcerbations and CT scan as Lung Endpoints (EXACTLE) trial aimed to clarify the optimum approach to the use of CT densitometry data for the assessment of alpha 1-antitrypsin (AAT) augmentation therapy on the progression of emphysema in AAT deficiency (AATD).MethodsPatients with AATD (n = 77) were randomised to weekly infusions of 60 mg/kg human AAT (Prolastin) or placebo over 2 to 2.5 years. Lung volume was included as a covariate in an endpoint analysis and a comparison was made of different CT densitometric indices (15th percentile lung density [PD15], mean lung density [MLD] and voxel index at a threshold of -910 [VI-910] and -950 [VI-950] Hounsfield Units) obtained from whole lung scans at baseline and at 24 to 30 months. Targeted regional sampling was compared with whole lung assessment.ResultsWhole lung analysis of the total change (baseline to last CT scan) compared with placebo indicated a concordant trend that was suggestive of a treatment effect for all densitometric indices (MLD [1.402 g/L, p = 0.204]; VI-910 [-0.611, p = 0.389]; VI-950 [-0.432, p = 0.452]) and that was significant using PD15 (1.472 g/L, p = 0.049). Assessment of the progression of emphysema in the apical, middle and basal regions of the lung by measurement with PD15 showed that this treatment effect was more evident when the basal third was sampled (1.722 g/L, p = 0.040). A comparison between different densitometric indices indicated that the influence of inspiratory variability between scans was greatest for PD15, but when adjustment for lung volume was made this index was the most sensitive measure of emphysema progression.ConclusionPD15 is the most sensitive index of emphysema progression and of treatment modification. Targeted sampling may be more sensitive than whole lung analysis.Trial registrationRegistered in ClinicalTrials.gov as 'Antitrypsin (AAT) to Treat Emphysema in AAT-Deficient Patients'; ClinicalTrials.gov Identifier: NCT00263887.

Project description:BackgroundThe evaluation of translational health research is important for various reasons such as the research impact assessment, research funding allocation, accountability, and strategic research policy formulation. The purpose of this study was to evaluate the research productivity, strength and diversity of research collaboration networks and impact of research supported by a large biomedical research centre in the United Kingdom (UK).MethodsBibliometric analysis of research publications by translational researchers affiliated with the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) from April 2012 to March 2017.ResultsAnalysis included 2377 translational research publications that were published during the second 5-year funding period of the NIHR Oxford BRC. Author details were available for 99.75% of the publications with DOIs (2359 of 2365 with DOIs), and the number of authors per publication was median 9 (mean = 18.03, SD = 3.63, maximum = 2467 authors). Author lists also contained many consortia, groups, committees, and teams (n = 165 in total), with 1238 additional contributors, where membership was reported. The BRC co-authorship i.e., research collaboration network for these publications involved 20,229 nodes (authors, of which 1606 nodes had Oxford affiliations), and approximately 4.3 million edges (authorship linkages). Articles with a valid DOIs (2365 of 2377, 99.5%) were collectively cited more than 155,000 times and the average Field Citation Ratio was median 6.75 (geometric mean = 7.12) while the average Relative Citation Ratio was median 1.50 (geometric mean = 1.83) for the analysed publications.ConclusionsThe NIHR Oxford BRC generated substantial translational research publications and facilitated a huge collaborative network of translational researchers working in complex structures and consortia, which shows success across the whole of this BRC funding period. Further research involving continued uptake of unique persistent identifiers and the tracking of other research outputs such as clinical innovations and patents would allow a more detailed understanding of large research enterprises such as NIHR BRCs in the UK.

Project description:PurposeTo train and validate a predictive model of mortality for hospitalized COVID-19 patients based on lung densitometry.MethodsTwo-hundred-fifty-one patients with respiratory symptoms underwent CT few days after hospitalization. "Aerated" (AV), "consolidated" (CV) and "intermediate" (IV) lung sub-volumes were quantified by an operator-independent method based on individual HU maximum gradient recognition. AV, CV, IV, CV/AV, IV/AV, and HU of the first peak position were extracted. Relevant clinical parameters were prospectively collected. The population was composed by training (n = 166) and validation (n = 85) consecutive cohorts, and backward multi-variate logistic regression was applied on the training group to build a CT_model. Similarly, models including only clinical parameters (CLIN_model) and both CT/clinical parameters (COMB_model) were developed. Model's performances were assessed by goodness-of-fit (H&L-test), calibration and discrimination. Model's performances were tested in the validation group.ResultsForty-three patients died (25/18 in training/validation). CT_model included AVmax (i.e. maximum AV between lungs), CV and CV/AE, while CLIN_model included random glycemia, C-reactive protein and biological drugs (protective). Goodness-of-fit and discrimination were similar (H&L:0.70 vs 0.80; AUC:0.80 vs 0.80). COMB_model including AVmax, CV, CV/AE, random glycemia, biological drugs and active cancer, outperformed both models (H&L:0.91; AUC:0.89, 95%CI:0.82-0.93). All models showed good calibration (R2:0.77-0.97). Despite several patient's characteristics were different between training and validation cohorts, performances in the validation cohort confirmed good calibration (R2:0-70-0.81) and discrimination for CT_model/COMB_model (AUC:0.72/0.76), while CLIN_model performed worse (AUC:0.64).ConclusionsFew automatically extracted densitometry parameters with clear functional meaning predicted mortality of COVID-19 patients. Combined with clinical features, the resulting predictive model showed higher discrimination/calibration.

Dataset Information

Relationship of CT densitometry to lung physiological parameters and health status in alpha-1 antitrypsin deficiency: initial report of a centralised database of the NIHR rare diseases translational research collaborative.

Publications

Relationship of CT densitometry to lung physiological parameters and health status in alpha-1 antitrypsin deficiency: initial report of a centralised database of the NIHR rare diseases translational research collaborative.

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