Dataset Information

Thoracic skeletal muscle quantification: low muscle mass is related with worse prognosis in idiopathic pulmonary fibrosis patients.

ABSTRACT:

Background

Sarcopenia can contribute to negative outcomes in patients with various lung diseases. However, whether sarcopenia affects prognosis in patients with idiopathic pulmonary fibrosis (IPF) has not been reported. Simple measures of muscle mass, derived from chest computed tomography (CT), are increasingly being used to identify patients with sarcopenia. We hypothesized that skeletal muscle mass could be a predictor of prognosis in IPF patients.

Methods

We retrospectively evaluated 180 patients diagnosed with IPF between January 2010 and December 2015 at a tertiary care hospital in South Korea. We measured thoracic muscle volume by using the cross-sectional area (CSA) of the pectoralis, paraspinal, serratus, and latissimus muscles at the 4th vertebral region (T4_CSA) and the erector spinae muscle (ESM_CSA) at the 12th vertebral region. CT scans at the time of diagnosis were used for analysis and respective CSA were divided by height squared to normalize for stature. Survival times were estimated with the Kaplan-Meier method and compared with the log-rank test. Multivariate Cox proportional hazards models were performed to investigate relationships between clinical parameters and mortality.

Results

Male patients in the lowest quartile of T4_CSA divided by height squared (m²) (T4MI) and in the lowest quartile of ESM_CSA divided by height squared (m²) (T12MI) were more likely to have higher Gender-Age-Physiology Index scores (T4MI, 3.3?±?1.3 vs 4.0?±?1.6, P?=?0.012; T12MI, 3.2?±?1.3 vs 4.1?±?1.6, P?=?0.002). Male patients in the lowest quartile of T4MI exhibited a significantly lower survival rate (P?=?0.035). After multivariate Cox proportional hazards analysis, T4MI was a significant risk factor for all-cause mortality (HR, 0.955; 95% CI, 0.913-0.998; P?=?0.041), whereas T12MI was not (HR, 0.980; 95% CI, 0.856-1.121; P?=?0.766).

Conclusions

Low skeletal mass normalized for stature at the level of 4th vertebrae which can be acquired by quantifying thoracic skeletal muscle on single-slice axial chest CT, may be a strong risk factor for all-cause mortality in patients with IPF.

Trial registration

The research protocol was approved by the Institutional Review Board of Severance Hospital, South Korea (IRB No.4-2018-0454).

SUBMITTER: Moon SW

PROVIDER: S-EPMC6376641 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Publications

Thoracic skeletal muscle quantification: low muscle mass is related with worse prognosis in idiopathic pulmonary fibrosis patients.

Moon Sung Woo SW Choi Ji Soo JS Lee Sang Hoon SH Jung Kyung Soo KS Jung Ji Ye JY Kang Young Ae YA Park Moo Suk MS Kim Young Sam YS Chang Joon J Kim Song Yee SY

Respiratory research 20190215 1

<h4>Background</h4>Sarcopenia can contribute to negative outcomes in patients with various lung diseases. However, whether sarcopenia affects prognosis in patients with idiopathic pulmonary fibrosis (IPF) has not been reported. Simple measures of muscle mass, derived from chest computed tomography (CT), are increasingly being used to identify patients with sarcopenia. We hypothesized that skeletal muscle mass could be a predictor of prognosis in IPF patients.<h4>Methods</h4>We retrospectively ev ...[more]

PMID: 30767787

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Project description:ObjectivesTo quantify the heterogeneity of fibrosis boundaries in idiopathic pulmonary fibrosis (IPF) using the Gaussian curvature analysis for evaluating disease severity and predicting survival.MethodsWe retrospectively included 104 IPF patients and 52 controls who underwent baseline chest CT scans. Normal lungs below - 500 HU were segmented, and the boundary was three-dimensionally reconstructed using in-house software. Gaussian curvature analysis provided histogram features on the heterogeneity of the fibrosis boundary. We analyzed the correlations between histogram features and the gender-age-physiology (GAP) and CT fibrosis scores. We built a regression model to predict diffusing capacity of carbon monoxide (DLCO) using the histogram features and calculated the modified GAP (mGAP) score by replacing DLCO with the predicted DLCO. The performances of the GAP, CT-GAP, and mGAP scores were compared using 100 repeated random-split sets.ResultsPatients with moderate-to-severe IPF had more numerous Gaussian curvatures at the fibrosis boundary, lower uniformity, and lower 10th to 30th percentiles of Gaussian curvature than controls or patients with mild IPF (all p < 0.0033). The 20th percentile was most significantly correlated with the GAP score (r = - 0.357; p < 0.001) and the CT fibrosis score (r = - 0.343; p = 0.001). More numerous Gaussian curvatures, higher entropy, lower uniformity, and 10th to 30th percentiles (p < 0.001-0.041) were associated with mortality. The mGAP score was comparable to the GAP and CT-GAP scores for survival prediction (mean C-indices, 0.76 vs. 0.79 vs. 0.77, respectively).ConclusionsGaussian curvatures of fibrosis boundaries became more heterogeneous as the disease progressed, and heterogeneity was negatively associated with survival in IPF.Key points• Gaussian curvature of the fibrotic lung boundary was more heterogeneous in patients with moderate-to-severe IPF than those with mild IPF or normal controls. • The 20th percentile of the Gaussian curvature of the fibrosis boundary was linearly correlated with the GAP score and the CT fibrosis score. • A modified GAP score that replaced the diffusing capacity of carbon monoxide with a composite measure using histogram features of the Gaussian curvature of the fibrosis boundary showed a comparable ability to predict survival to both the GAP and the CT-GAP score.

Project description:BackgroundThe course of disease for patients with idiopathic pulmonary fibrosis (IPF) is highly heterogeneous. Prognostic models rely on demographic and clinical characteristics and are not reproducible. Integrating data from genomic analyses may identify novel prognostic models and provide mechanistic insights into IPF.MethodsTotal RNA of peripheral blood mononuclear cells was subjected to microarray profiling in a training (45 IPF individuals) and two independent validation cohorts (21 IPF/10 controls, and 75 IPF individuals, respectively). To identify a gene set predictive of IPF prognosis, we incorporated genomic, clinical, and outcome data from the training cohort. Predictor genes were selected if all the following criteria were met: 1) Present in a gene co-expression module from Weighted Gene Co-expression Network Analysis (WGCNA) that correlated with pulmonary function (p < 0.05); 2) Differentially expressed between observed "good" vs. "poor" prognosis with fold change (FC) >1.5 and false discovery rate (FDR) < 2%; and 3) Predictive of mortality (p < 0.05) in univariate Cox regression analysis. "Survival risk group prediction" was adopted to construct a functional genomic model that used the IPF prognostic predictor gene set to derive a prognostic index (PI) for each patient into either high or low risk for survival outcomes. Prediction accuracy was assessed with a repeated 10-fold cross-validation algorithm and independently assessed in two validation cohorts through multivariate Cox regression survival analysis.ResultsA set of 118 IPF prognostic predictor genes was used to derive the functional genomic model and PI. In the training cohort, high-risk IPF patients predicted by PI had significantly shorter survival compared to those labeled as low-risk patients (log rank p < 0.001). The prediction accuracy was further validated in two independent cohorts (log rank p < 0.001 and 0.002). Functional pathway analysis revealed that the canonical pathways enriched with the IPF prognostic predictor gene set were involved in T-cell biology, including iCOS, T-cell receptor, and CD28 signaling.ConclusionsUsing supervised and unsupervised analyses, we identified a set of IPF prognostic predictor genes and derived a functional genomic model that predicted high and low-risk IPF patients with high accuracy. This genomic model may complement current prognostic tools to deliver more personalized care for IPF patients.

Dataset Information

Thoracic skeletal muscle quantification: low muscle mass is related with worse prognosis in idiopathic pulmonary fibrosis patients.

Background

Methods

Results

Conclusions

Trial registration

Publications

Thoracic skeletal muscle quantification: low muscle mass is related with worse prognosis in idiopathic pulmonary fibrosis patients.

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