scholarly journals Impact of lung function decline on time to hospitalisation events in systemic sclerosis-associated interstitial lung disease (SSc-ILD): a joint model analysis

2022 ◽  
Vol 24 (1) ◽  
Author(s):  
Michael Kreuter ◽  
Francesco Del Galdo ◽  
Corinna Miede ◽  
Dinesh Khanna ◽  
Wim A. Wuyts ◽  
...  
Keyword(s):  
Systemic Sclerosis ◽  
Interstitial Lung Disease ◽  
Lung Function ◽  
Lung Disease ◽  
Fold Increase ◽  
Lung Function Decline ◽  
Link Type ◽  
Related Hospitalisation ◽  
The Relationship ◽  
Increase In Risk

Abstract Background Interstitial lung disease (ILD) is a common organ manifestation in systemic sclerosis (SSc) and is the leading cause of death in patients with SSc. A decline in forced vital capacity (FVC) is an indicator of ILD progression and is associated with mortality in patients with SSc-associated ILD (SSc-ILD). However, the relationship between FVC decline and hospitalisation events in patients with SSc-ILD is largely unknown. The objective of this post hoc analysis was to investigate the relationship between FVC decline and clinically important hospitalisation endpoints. Methods We used data from SENSCIS®, a phase III trial investigating the efficacy and safety of nintedanib in patients with SSc-ILD. Joint models for longitudinal and time-to-event data were used to assess the association between rate of decline in FVC% predicted and hospitalisation-related endpoints (including time to first all-cause hospitalisation or death; time to first SSc-related hospitalisation or death; and time to first admission to an emergency room [ER] or admission to hospital followed by admission to intensive care unit [ICU] or death) during the treatment period, over 52 weeks in patients with SSc-ILD. Results There was a statistically significant association between FVC decline and the risk of all-cause (n = 78) and SSc-related (n = 42) hospitalisations or death (both P < 0.0001). A decrease of 3% in FVC corresponded to a 1.43-fold increase in risk of all-cause hospitalisation or death (95% confidence interval [CI] 1.24, 1.65) and a 1.48-fold increase in risk of SSc-related hospitalisation or death (95% CI 1.23, 1.77). No statistically significant association was observed between FVC decline and admission to ER or to hospital followed by admission to ICU or death (n = 75; P = 0.15). The estimated slope difference for nintedanib versus placebo in the longitudinal sub-model was consistent with the primary analysis in SENSCIS®. Conclusions The association of lung function decline with an increased risk of hospitalisation suggests that slowing FVC decline in patients with SSc-ILD may prevent hospitalisations. Our findings also provide evidence that FVC decline may serve as a surrogate endpoint for clinically relevant hospitalisation-associated endpoints. Trial registration ClinicalTrials.govNCT02597933. Registered on 8 October 2015.

scholarly journals Impact of Lung Function Decline on Time to Hospitalisation Events in Systemic Sclerosis-associated Interstitial Lung Disease (SSc-ILD): a Joint Model Analysis

2021 ◽  
Author(s):  
Michael Kreuter ◽  
Francesco Del Galdo ◽  
Corinna Miede ◽  
Dinesh Khanna ◽  
Wim A. Wuyts ◽  
...  
Keyword(s):  
Systemic Sclerosis ◽  
Interstitial Lung Disease ◽  
Lung Function ◽  
Lung Disease ◽  
Fold Increase ◽  
Phase Iii ◽  
Lung Function Decline ◽  
Related Hospitalisation ◽  
The Relationship ◽  
Increase In Risk

Abstract Background: Interstitial lung disease (ILD) is a common organ manifestation in systemic sclerosis (SSc) and is the leading cause of death in patients with SSc. A decline in forced vital capacity (FVC) is an indicator of ILD progression and is associated with mortality in patients with SSc-associated ILD (SSc-ILD). However, the relationship between FVC decline and hospitalisation events in patients with SSc-ILD is largely unknown. The objective of this post-hoc analysis was to investigate the relationship between FVC decline and clinically important hospitalisation endpoints.Methods: We used data from SENSCIS®, a Phase III trial investigating the efficacy and safety of nintedanib in patients with SSc-ILD. Joint models for longitudinal and time-to-event data were used to assess the association between rate of decline in FVC% predicted and hospitalisation-related endpoints (including time to first all-cause hospitalisation or death; time to first SSc-related hospitalisation or death; and time to first admission to an emergency room [ER] or admission to hospital followed by admission to intensive care unit [ICU] or death) during the treatment period, over 52 weeks in patients with SSc-ILD.Results: There was a statistically significant association between FVC decline and the risk of all-cause (n=78) and SSc-related (n=42) hospitalisations or death (both P<0.0001). A decrease of 3% in FVC corresponded to a 1.43-fold increase in risk of all-cause hospitalisation or death (95% confidence interval [CI] 1.24, 1.65) and a 1.48-fold increase in risk of SSc-related hospitalisation or death (95% CI 1.23, 1.77). No statistically significant association was observed between FVC decline and admission to ER or to hospital followed by admission to ICU or death (n=75; P=0.15). The estimated slope difference for nintedanib versus placebo in the longitudinal sub-model was consistent with the primary analysis in SENSCIS®.Conclusions: The association of lung function decline with an increased risk of hospitalisation suggests that slowing FVC decline in patients with SSc-ILD may prevent hospitalisations. Our findings also provide evidence that FVC decline may serve as a surrogate endpoint for clinically relevant hospitalisation-associated endpoints.Trial registration: Clinialtrials.gov, NCT02597933. Registered 8 October 2015, https://clinicaltrials.gov/ct2/show/study/NCT02597933.

scholarly journals OP0150 MACHINE LEARNING APPROACHES FOR RISK MODELLING IN INTERSTITIAL LUNG DISEASE ASSOCIATED WITH SYSTEMIC SCLEROSIS USING HIGH DIMENSIONAL IMAGE ANALYSIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 90-90
Author(s):  
M. Maciukiewicz ◽  
J. Schniering ◽  
H. Gabrys ◽  
M. Brunner ◽  
C. Blüthgen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Sclerosis ◽  
Interstitial Lung Disease ◽  
Lung Function ◽  
Lung Disease ◽  
High Dimensional ◽  
Lung Function Decline ◽  
Classifier Selection ◽  
Predicted Values

Background:The interstitial lung disease (ILD) associated with connective tissue diseases including systemic sclerosis (SSc) is heterogenous disease characterized by reduced survival of approximately 3 years (1). “Radiomics’’ is a field of research which describes the in-depth analysis of tissues by computational retrieval of high-dimensional quantitative features from medical images (2). Our previous study suggested capacity of radiomics features to differentiate between “high” and “low” risk groups for lung function decline in two independent cohorts (3).Objectives:  •bTo develop robust, machine learning (ML) workflow for “radiomics” data in SSc-ILD to select optimal methods for prediction.  •oTo predict the time to individual lung function decline defined as defined by the time to a relative decline of ≥ 15% in Forced Vital Capacity (FVC)% as previously (3), using workflow.Methods:We investigated two cohorts of SSc-ILD: 90 patients (76.7% female, median age 57.5 years) from the University Hospital Zurich and 66 patients (75.8% female, median age 61.0 years) from Oslo University Hospital’s. Patients were retrospectively selected if (3): a) diagnosed with early/mild SSc according to the Very Early Diagnosis of Systemic Sclerosis (VEDOSS) criteria, b) presence of ILD on HRCT as determined by a senior radiologist. For every subject, we defined 1,355 robust radiomic features from HRCT images. The follow-up period was defined as the time interval between baseline visit and the last available follow-up visit.We have developed a systematic computational workflow to build predictive ML models. To reduce the number of redundant radiomic features, we applied correlation thresholds. We applied distinct methods including 1) Lasso Penalized Regression for feature selection, and 2) Random Forest (RF) for modeling using the R package ‘caret’. To select the optimal ML model, we randomly divided derivation cohort into Training (70%) and Holdout (30%) sets and applied fivefold cross-validation (5kCV) for feature and classifier selection on Training set only.Results:We have investigated various methods to select the optimal set of predictive radiomic features. Since the ML model performance is affected by both, feature, and classifier selection, we assessed these factors first.Results from feature filtering and selection, suggested that the combination of correlation threshold of 0.9 with Lasso regression proved best. As we perform feature selection in 5k CV workflow, features present in at least 2 sets entered model optimization step.During model selection, we selected RF classifier. We detected positive correlation between actual and predicted values with Spearman’s rho = 0.313, p = 0.167 and Spearman’s rho = 0.341, p = 0.015 in Oslo and Holdout sets respectively, as shown on Figure 1. The percentage of variance remained modest for both Holdout (Rsq = 0.104) and Oslo (Rsq = 0.126) datasets.Figure 1.Performance of the best, RF classifier shown as scatterplot between actual and predicted values of individual time to lung decline.Conclusion:In summary, we: (1) developed ML workflow that allowed to select o optimal methodology for modeling (i.e., feature and classifier selection), and (2) provide models that predicted time to individual lung function decline, characterized by significant correlation between predicted and actual values.References:[1]Hansell DM, Goldin JG, King TE, Jr., Lynch DA, Richeldi L, Wells AU. CT staging and monitoring of fibrotic interstitial lung diseases in clinical practice and treatment trials: a position paper from the Fleischner Society. Lancet Respir Med. 2015;3(6):483-96.[2]Lambin, P. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48, 441–446 (2012).[3]Schniering J. et al. Resolving phenotypic and prognostic differences in interstitial lung disease related to systemic sclerosis by computed tomography-based radiomics. https://www.medrxiv.org/content/10.1101/2020.06.09.20124800v1Disclosure of Interests:None declared

scholarly journals SAT0286 BIOLOGICAL CORRELATES OF RADIOGRAPHIC FEATURES OF INTERSTITIAL LUNG DISEASE IN SYSTEMIC SCLEROSIS: AN IN DEPTH ANALYSIS OF BRONCHOALVEOLAR PROTEINS OF SCLERODERMA LUNG STUDY I PARTICIPANTS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1087.1-1088
Author(s):  
E. Volkmann ◽  
D. Tashkin ◽  
N. Li ◽  
G. Kim ◽  
J. Goldin ◽  
...  
Keyword(s):  
Systemic Sclerosis ◽  
Growth Factor ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Research Support ◽  
Radiographic Features ◽  
Depth Analysis ◽  
Specific Proteins ◽  
Clinical Measures ◽  
The Relationship

Background:Systemic sclerosis-related interstitial lung disease (SSc-ILD) involves a combination of inflammation, fibrosis and vascular pathology that is typically assessed on CT imaging as a mixture of ground-glass opacification (GGO) and fibrotic changes. We hypothesized that proteins recovered from bronchoalveolar lavage (BAL) could be used to probe the underlying pathobiology associated with GGO and fibrotic changes.Objectives:(1) To assess the relationship between 68 unique BAL proteins measured in participants of Scleroderma Lung Study (SLS) I1and radiographic and physiologic measures of ILD; (2) To identify inter-correlations among specific proteins to enlighten our understanding of how specific biological pathways contribute to SSc-ILD.Methods:Bronchoscopy was performed on 144 of the 158 participants in SLS I with 103 BAL samples available for analysis. BAL was lyophilized, concentrated 10X and used in a multiplex protein analysis for 68 different cytokines, chemokines and other factors. Kendall tau correlations were performed to assess the relationship between individual proteins and baseline measures of pulmonary function and quantitative CT scores for fibrosis, GGO and total ILD. Those proteins found to correlate significantly with at least 2 clinical measures of ILD were entered into a cluster analysis with inter-correlations expressed as a heatmap.Results:Significant correlations were observed between fibrosis scores and several biologic pathways including pro-fibrotic factors (transforming growth factor beta [TGF-β], platelet-derived growth factor [PDGF]), proteins involved in tissue remodeling (Matrix metallopeptidase [MMP]-1,7,8,9; Hepatocyte growth factor [HGF]), and those involved in monocyte/macrophage migration and activation (Monocyte chemoattractant protein [MCP]-1,3; macrophage colony-stimulating factor [MCSF]). These same pathways correlated with the diffusing capacity for carbon monoxide (DLCO). In contrast, GGO scores correlated primarily with immune and inflammatory mediators (interleukin [IL]-5,8,13,15, IL-1 receptor antagonist and interferon gamma) with only limited overlap to proteins that related to fibrosis. Vascular endothelial growth factor (VEGF) levels were lower in patients with more extensive GGO, fibrosis and diffusion impairment, suggesting that vascular changes are a central feature of SSc-ILD. Specific proteins were highly correlated with one another in a pattern suggesting biologically-related networks (Figure) that might provide additional insight regarding disease pathogenesis.Conclusion:Combining a diverse analysis of BAL proteins with the rich dataset available from SSc-ILD patients participating in SLS I, the study findings suggest the involvement of distinct biologic pathways, inter-related networks, and specific biologic signatures associated with unique radiographic features of ILD. The relationship of these factors to other SSc disease features, patient outcomes and as predictors of treatment responses will be studied in future analyses.References:[1]Tashkin DP, et al. NEJM 2006.Figure.Correlation heatmap of BAL proteins associated with at least 2 clinical measures of ILD in SSc patients. Absolute correlations are depicted, and darker colors signify stronger correlations.Disclosure of Interests:Elizabeth Volkmann Grant/research support from: Forbius, Corbus Pharmaceuticals, Consultant of: Boehringer Ingelheim, Forbius, Speakers bureau: Boehringer Ingelheim, Donald Tashkin: None declared, Ning Li: None declared, Grace Kim: None declared, Jonathan Goldin: None declared, Airi Harui: None declared, Michael Roth Grant/research support from: Genentech/Roche

scholarly journals Telomere length and genetic variant associations with interstitial lung disease progression and survival

2019 ◽  
Vol 53 (4) ◽  
pp. 1801641 ◽  
Author(s):  
Chad A. Newton ◽  
Justin M. Oldham ◽  
Brett Ley ◽  
Vikram Anand ◽  
Ayodeji Adegunsoye ◽  
...  
Keyword(s):  
Interstitial Lung Disease ◽  
Lung Function ◽  
Lung Disease ◽  
Disease Progression ◽  
Telomere Length ◽  
Hazard Ratio ◽  
Lung Function Decline ◽  
Free Survival ◽  
Genomic Markers ◽  
Observational Cohort

Leukocyte telomere length (LTL), MUC5B rs35705950 and TOLLIP rs5743890 have been associated with idiopathic pulmonary fibrosis (IPF).In this observational cohort study, we assessed the associations between these genomic markers and outcomes of survival and rate of disease progression in patients with interstitial pneumonia with autoimmune features (IPAF, n=250) and connective tissue disease-associated interstitial lung disease (CTD-ILD, n=248). IPF (n=499) was used as a comparator.The LTL of IPAF and CTD-ILD patients (mean age-adjusted log-transformed T/S of −0.05±0.29 and −0.04±0.25, respectively) is longer than that of IPF patients (−0.17±0.32). For IPAF patients, LTL <10th percentile is associated with faster lung function decline compared to LTL ≥10th percentile (−6.43% per year versus −0.86% per year; p<0.0001) and worse transplant-free survival (hazard ratio 2.97, 95% CI 1.70–5.20; p=0.00014). The MUC5B rs35705950 minor allele frequency (MAF) is greater for IPAF patients (23.2, 95% CI 18.8–28.2; p<0.0001) than controls and is associated with worse transplant-free IPAF survival (hazard ratio 1.92, 95% CI 1.18–3.13; p=0.0091). Rheumatoid arthritis (RA)-associated ILD (RA-ILD) has a shorter LTL than non-RA CTD-ILD (−0.14±0.27 versus −0.01±0.23; p=0.00055) and higher MUC5B MAF (34.6, 95% CI 24.4–46.3 versus 14.1, 95% CI 9.8–20.0; p=0.00025). Neither LTL nor MUC5B are associated with transplant-free CTD-ILD survival.LTL and MUC5B MAF have different associations with lung function progression and survival for IPAF and CTD-ILD.

scholarly journals Lung Function and Survival in Systemic Sclerosis Interstitial Lung Disease

2014 ◽  
Vol 41 (11) ◽  
pp. 2326-2328 ◽  
Author(s):  
SAMAR SHADLY AHMED ◽  
SINDHU R. JOHNSON ◽  
CHRISTOPHER MEANEY ◽  
CATHY CHAU ◽  
THEODORE K. MARRAS
Keyword(s):  
Systemic Sclerosis ◽  
Interstitial Lung Disease ◽  
Lung Function ◽  
Lung Disease
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