Performance of GAP and ILD-GAP models in predicting lung transplant or death in interstitial pneumonia with autoimmune features

OBJECTIVES

To assess the ability of two risk prediction models in interstitial lung disease (ILD) to predict death or lung transplantation in a cohort of patients with interstitial pneumonia with autoimmune features (IPAF).

METHODS

We performed a retrospective cohort study of adults with IPAF at an academic medical center. The primary outcome was a composite of lung transplantation or death. We applied the patient data to the previously described GAP and ILD-GAP models to determine the ability of these models to predict the composite outcome. Model discrimination was assessed using the c-index, and model calibration was determined by comparing the incidence ratios of observed versus expected deaths.

RESULTS

Ninety-four patients with IPAF were included. Mean (standard deviation) age was 58 (13.5) years and the majority were female (62%). The majority met serologic and morphologic criteria for IPAF (94% and 91%, respectively). The GAP model had a c-index of 0.664 (95% confidence interval [CI] 0.547-0.781), while the ILD-GAP model had a c-index of 0.569 (95% CI 0.440-0.697). In those with GAP stage 1 or GAP stage 2 disease, calibration of the GAP model was satisfactory at 2 and 3 years for the cumulative end point of lung transplantation or death.

CONCLUSION

In patients with IPAF, the GAP model performed well as a predictor of lung transplantation or death at 2 years and 3 years from ILD diagnosis in patients with GAP stage 1 and GAP stage 2 disease.

Assessing Predictors of Rheumatoid Arthritis Associated Interstitial Lung Disease using Quantitative Lung Densitometry

OBJECTIVE

To assess predictors of subclinical rheumatoid arthritis-associated interstitial lung disease (RA-ILD) using quantitative lung densitometry (qLD).

METHODS

RA patients underwent multi-detector row computed tomography scanning at baseline and after an average of 39 months. Scans were analyzed with qLD for the percentage of lung parenchyma with high attenuation areas (%HAA: the percentage of voxels of -600 to -250 Hounsfield units). Additionally, a pulmonary radiologist calculated an Expert Read Score (ERS) for RA-ILD features. Generalized linear models were used to identify indicators of baseline %HAA and predictors of %HAA change.

RESULTS

Baseline %HAA was assessed in 193 RA patients and 106 had repeat qLD assessment. %HAA was correlated with ERS (Spearman's rho = 0.261; p< 0.001). Significant indicators of high baseline %HAA (>10% of lung parenchyma with high attenuation) included female sex, higher pack-years of smoking, higher body mass index, and anti-CCP ≥ 200 units, collectively contributing an area under the receiver operator curve (AUROC)=0.88 (95% CI 0.81-0.95). Predictors of %HAA increase, occurring in 49% with repeat qLD, included higher baseline %HAA, presence of MUC5B minor allele, and absence of HLA-DRB1 shared epitope (AUC-ROC = 0.69; 95% CI 0.58-0.79). The association of the MUC5B minor allele with %HAA change was higher among men and those with higher cumulative smoking. Within the group with increased %HAA, anti-CCP level was significantly associated with a greater increase in %HAA.

CONCLUSIONS

%HAA, assessed with qLD, was linked to several known risk factors for RA-ILD and may represent a more quantitative method to identify RA-ILD and track progression than expert radiologist interpretation.

Risk of progression of interstitial pneumonia with autoimmune features to a systemic autoimmune rheumatic disease

OBJECTIVE

The aim of this study was to determine the risk of developing a systemic autoimmune rheumatic disease (ARD) after an initial diagnosis of interstitial pneumonia with autoimmune features (IPAF).

METHODS

We performed a retrospective cohort study of patients with interstitial lung disease (ILD) who were evaluated at Columbia University Irving Medical Center from 2009 to 2017. We divided patients with idiopathic ILD into two groups: those who met IPAF criteria and those who did not meet IPAF criteria at initial ILD diagnosis. We examined the association between IPAF and diagnosis of ARD during the follow-up period using a multivariable-adjusted logistic regression model.

RESULTS

Of the 697 patients with ILD who were screened, 174 met inclusion criteria (50 met IPAF criteria and 124 did not). During a median follow-up period of 5.2 years, 16% (8/50) of subjects with IPAF were diagnosed with an ARD compared with 1.6% (2/124) of subjects without IPAF (P = 0.001). Adjusting for age, sex, smoking status and use of immunosuppressive therapy, the odds of progressing to an ARD were 14 times higher in subjects with IPAF than in those without IPAF (odds ratio 14.18, 95% CI 1.44-138.95, P = 0.02).

CONCLUSION

The presence of IPAF confers an increased risk of developing an ARD. Patients with IPAF should therefore be followed closely for the development of an ARD.

Implementing an Electronic Medical Record-Based Reminder for Cardiovascular Risk Screening in Rheumatoid Arthritis

OBJECTIVE

Although cardiovascular disease (CVD) is the leading cause of death among individuals with rheumatoid arthritis (RA), CVD risks are not being assessed frequently and systematically in RA. We implemented an electronic medical record (EMR)-based reminder in a tertiary care center and assessed the effects of this intervention on CVD risk screening by rheumatologists and primary care providers.

METHODS

The EMR reminder was implemented in December 2013 and included the most recent value and target ranges for body mass index, blood pressure (BP), and lipid profiles. It was displayed for every rheumatology and primary care visit for all patients with the International Classification of Diseases, Ninth Revision code for RA (714.0). Lipid screening rates, as well as changes in BP and obesity rates were compared pre- and postimplementation. Factors associated with lipid screening postimplementation were assessed using multivariate logistic regression.

RESULTS

A total of 138 and 112 RA patients were seen in the outpatient clinics pre- and postimplementation, respectively. The demographic characteristics were similar in the pre- and postimplementation groups. Lipid screening rates were 50% preimplementation and 46% postimplementation (P = 0.58). There were no significant improvements in BP or obesity rates postimplementation. Factors associated with the higher odds of lipid screening included older age and history of diabetes mellitus.

CONCLUSION

Implementing an EMR reminder did not improve CVD risk screening among RA patients. Future research is needed to identify and address barriers to CVD screening, and to educate patients and providers about RA-related risks.