Evidence map›Paper›PMID 27876024›Full record

ArticleBMC medicine2016

Evaluation of computer-based computer tomography stratification against outcome models in connective tissue disease-related interstitial lung disease: a patient outcome study.

Joseph Jacob, Brian J Bartholmai, Srinivasan Rajagopalan, Anne Laure Brun, Ryoko Egashira, Ronald Karwoski, Maria Kokosi, Athol U Wells, David M Hansell

Abstract readEvaluation Study
In one paragraph

Article in BMC medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

49 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. A systematic review of the role of quantitative CT in the prognostication and disease monitoring of interstitial lung disease.European respiratory review : an official journal of the European Respiratory Society · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Joseph JacobDepartment of Radiology, Royal Brompton Hospital, Royal Brompton and Harefield NHS Foundation Trust, London, SW3 6NP, UK. akelajacob@gmail.com.
Brian J BartholmaiDivision of Radiology, Mayo Clinic Rochester, Rochester, Minnesota, USA.
Srinivasan RajagopalanDepartment of Physiology and Biomedical Engineering, Mayo Clinic Rochester, Rochester, Minnesota, USA.
Anne Laure BrunDepartment of Radiology, Royal Brompton Hospital, Royal Brompton and Harefield NHS Foundation Trust, London, SW3 6NP, UK.
Ryoko EgashiraDepartment of Radiology, Royal Brompton Hospital, Royal Brompton and Harefield NHS Foundation Trust, London, SW3 6NP, UK.
Ronald KarwoskiDepartment of Physiology and Biomedical Engineering, Mayo Clinic Rochester, Rochester, Minnesota, USA.
Maria KokosiInterstitial Lung Disease Unit, Royal Brompton Hospital, Royal Brompton and Harefield NHS Foundation Trust, London, UK.
Athol U WellsInterstitial Lung Disease Unit, Royal Brompton Hospital, Royal Brompton and Harefield NHS Foundation Trust, London, UK.
David M HansellDepartment of Radiology, Royal Brompton Hospital, Royal Brompton and Harefield NHS Foundation Trust, London, SW3 6NP, UK.

Funding

Versus Arthritis 20719
6 · The paper itself

Abstract

backgroundTo evaluate computer-based computer tomography (CT) analysis (CALIPER) against visual CT scoring and pulmonary function tests (PFTs) when predicting mortality in patients with connective tissue disease-related interstitial lung disease (CTD-ILD). To identify outcome differences between distinct CTD-ILD groups derived following automated stratification of CALIPER variables.

methodsA total of 203 consecutive patients with assorted CTD-ILDs had CT parenchymal patterns evaluated by CALIPER and visual CT scoring: honeycombing, reticular pattern, ground glass opacities, pulmonary vessel volume, emphysema, and traction bronchiectasis. CT scores were evaluated against pulmonary function tests: forced vital capacity, diffusing capacity for carbon monoxide, carbon monoxide transfer coefficient, and composite physiologic index for mortality analysis. Automated stratification of CALIPER-CT variables was evaluated in place of and alongside forced vital capacity and diffusing capacity for carbon monoxide in the ILD gender, age physiology (ILD-GAP) model using receiver operating characteristic curve analysis.

resultsCox regression analyses identified four independent predictors of mortality: patient age (P < 0.0001), smoking history (P = 0.0003), carbon monoxide transfer coefficient (P = 0.003), and pulmonary vessel volume (P < 0.0001). Automated stratification of CALIPER variables identified three morphologically distinct groups which were stronger predictors of mortality than all CT and functional indices. The Stratified-CT model substituted automated stratified groups for functional indices in the ILD-GAP model and maintained model strength (area under curve (AUC) = 0.74, P < 0.0001), ILD-GAP (AUC = 0.72, P < 0.0001). Combining automated stratified groups with the ILD-GAP model (stratified CT-GAP model) strengthened predictions of 1- and 2-year mortality: ILD-GAP (AUC = 0.87 and 0.86, respectively); stratified CT-GAP (AUC = 0.89 and 0.88, respectively).

conclusionsCALIPER-derived pulmonary vessel volume is an independent predictor of mortality across all CTD-ILD patients. Furthermore, automated stratification of CALIPER CT variables represents a novel method of prognostication at least as robust as PFTs in CTD-ILD patients.

Indexed as

AgedCohort StudiesConnective Tissue DiseasesFemaleFollow-Up StudiesHumansLung Diseases, InterstitialMaleMiddle AgedOutcome Assessment, Health CareRespiratory Function TestsRetrospective StudiesTomography, X-Ray ComputedVisual PerceptionComputer tomographyConnective tissue diseaseInterstitial lung diseasePulmonary fibrosisQuantitative CT

Identifiers

PMID27876024
PMCPMC5120564

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.