Interstitial lung disease (ILD) is a common pulmonary manifestation of rheumatoid arthritis. There is lack of clarity around predictors of mortality and disease behaviour over time in these patients.We identified rheumatoid arthritis-related interstitial lung disease (RA-ILD) patients evaluated at National Jewish Health (Denver, CO, USA) from 1995 to 2013 whose baseline high-resolution computed tomography (HRCT) scans showed either a nonspecific interstitial pneumonia (NSIP) or a "definite" or "possible" usual interstitial pneumonia (UIP) pattern. We used univariate, multivariate and longitudinal analytical methods to identify clinical predictors of mortality and to model disease behaviour over time.The cohort included 137 subjects; 108 had UIP on HRCT (RA-UIP) and 29 had NSIP on HRCT (RA-NSIP). Those with RA-UIP had a shorter survival time than those with RA-NSIP (log rank p=0.02). In a model controlling for age, sex, smoking and HRCT pattern, a lower baseline % predicted forced vital capacity (FVC % pred) (HR 1.46; p<0.0001) and a 10% decline in FVC % pred from baseline to any time during follow up (HR 2.57; p<0.0001) were independently associated with an increased risk of death.Data from this study suggest that in RA-ILD, disease progression and survival differ between subgroups defined by HRCT pattern; however, when controlling for potentially influential variables, pulmonary physiology, but not HRCT pattern, independently predicts mortality.

Download full-text PDF

Source
http://dx.doi.org/10.1183/13993003.00357-2015DOI Listing

Publication Analysis

Top Keywords

predictors mortality
12
interstitial lung
12
lung disease
12
disease behaviour
8
interstitial pneumonia
8
interstitial
5
disease
5
mortality rheumatoid
4
rheumatoid arthritis-associated
4
arthritis-associated interstitial
4

Similar Publications

ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalized.

Glob Epidemiol

June 2025

Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz, Brazil.

Unlabelled: COVID-19 is no longer a global health emergency, but it remains challenging to predict its prognosis.

Objective: To develop and validate an instrument to predict COVID-19 progression for critically ill hospitalized patients in a Brazilian population.

Methodology: Observational study with retrospective follow-up.

View Article and Find Full Text PDF

Background: Liver synthetic dysfunction predicts outcomes in cardiac intensive care unit (CICU) patients.

Objectives: The purpose of this study was to evaluate the associations between the severity and extent of admission liver function test (LFT) abnormalities and mortality in a mixed CICU population.

Methods: This historical cohort study included unique CICU patients from 2007 to 2018 with available data for admission LFT values.

View Article and Find Full Text PDF

Background: Chronic kidney disease (CKD) is a prevalent global health issue affecting millions of patients worldwide, impacting quality of life, impeding physical and psychological well-being, causing financial stress, and increasing mortality rates. This study aimed to highlight the prevalence of CKD and its associated risk factors across Saudi Arabia.

Method: This is a cross-sectional study conducted from 2015 to 2022, using data from 42 branches of a major network of diagnostic laboratories in Saudi Arabia, covering the country's 13 administrative areas.

View Article and Find Full Text PDF

Purpose: Catheter ablation (CA) for atrial fibrillation (AF) in heart failure patients with preserved ejection fraction (HFPEF) has shown promising results in reducing mortality and improving heart function. However, previous studies have been limited by a lack of control groups and significant heterogeneity in their methodologies.

Hypothesis: CA for AF in HFPEF patients may not increase the complications and had similarly the rate of freedom from AF vs.

View Article and Find Full Text PDF

Background: Traumatic injuries are a leading cause of morbidity and mortality globally, with a disproportionate impact on populations in low- and middle-income countries (LMICs). The Kampala Trauma Score (KTS) is frequently used for triage in these settings, though its predictive accuracy remains under debate. This study evaluates the effectiveness of machine learning (ML) models in predicting triage decisions and compares their performance to the KTS.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!