Background: The Distance-Oxygen-Gender-Age-Physiology (DO-GAP) index has been shown to improve prognostication in idiopathic pulmonary fibrosis (IPF) compared to the Gender-Age-Physiology (GAP) score. We sought to externally validate the DO-GAP index compared to the GAP index for baseline risk assessment in patients with IPF. Additionally, we evaluated the utility of serial change in the DO-GAP index in predicting survival. Methods: We performed an analysis of patients with IPF from the Pulmonary Fibrosis Foundation-Patient Registry (PFF-PR). Discrimination and calibration of the two models were assessed to predict transplant-free survival and IPF-related mortality. Joint longitudinal time-to-event modelling was utilised to individualise survival prediction based on DO-GAP index trajectory. Results: There were 516 patients with IPF from the PFF-PR with available demographics, pulmonary function tests, 6-min walk test data and outcomes included in this analysis. The DO-GAP index (C-statistic: 0.73) demonstrated improved discrimination in discerning transplant-free survival compared to the GAP index (C-statistic: 0.67). DO-GAP index calibration was adequate, and the model retained predictive accuracy to identify IPF-related mortality (C-statistic: 0.74). The DO-GAP index was similarly accurate in the subset of patients taking antifibrotic medications. Serial change in the DO-GAP index improved model discrimination (cross-validated area under the curve: 0.83) enabling the personalised prediction of disease trajectory in individual patients. Conclusion: The DO-GAP index is a simple, validated, multidimensional score that accurately predicts transplant-free survival in patients with IPF and can be adapted longitudinally in individual patients. The DO-GAP may also find use in studies of IPF to risk stratify patients and possibly as a clinical trial end-point.

Background: Gastro-oesophageal reflux disease (GORD) is associated with idiopathic pulmonary fibrosis (IPF) in observational studies. It is not known if this association arises because GORD causes IPF or because IPF causes GORD, or because of confounding by factors, such as smoking, associated with both GORD and IPF. We used bidirectional Mendelian randomisation (MR), where genetic variants are used as instrumental variables to address issues of confounding and reverse causation, to examine how, if at all, GORD and IPF are causally related. Methods: A bidirectional two-sample MR was performed to estimate the causal effect of GORD on IPF risk and of IPF on GORD risk, using genetic data from the largest GORD (78 707 cases and 288 734 controls) and IPF (4125 cases and 20 464 controls) genome-wide association meta-analyses currently available. Results: GORD increased the risk of IPF, with an OR of 1.6 (95% CI 1.04-2.49; p=0.032). There was no evidence of a causal effect of IPF on the risk of GORD, with an OR of 0.999 (95% CI 0.997-1.000; p=0.245). Conclusions: We found that GORD increases the risk of IPF, but found no evidence that IPF increases the risk of GORD. GORD should be considered in future studies of IPF risk and interest in it as a potential therapeutic target should be renewed. The mechanisms underlying the effect of GORD on IPF should also be investigated.

Background and objective: Adherence to antifibrotic medications has been evaluated in a few studies using annual proportion of days covered (PDC), a common adherence metric. However, PDC alone cannot identify and distinguish between different patterns of adherence over time, which can be accomplished using group-based trajectory models (GBTM) of monthly PDC. The objective is to assess nintedanib adherence trajectories using GBTM and identify characteristics of patients within each trajectory group. Methods: Individuals with idiopathic pulmonary fibrosis (IPF) who initiated nintedanib during 10/1/2014-12/31/2018 were identified in 100% Medicare claims and enrollment data. The sample consisted of community-dwelling older adults (≥ 66 years) with continuous coverage in Medicare Parts A, B and D for one year before (baseline) and after (follow-up) initiating nintedanib. A series of GBTMs of adherence was estimated to identify the best-fitting specification. Patients were then grouped based on their estimated adherence trajectories. Associations between baseline patient characteristics, including demographics, comorbidities, and health care use, and group membership probabilities were quantified as odds ratios using fractional multinomial logit modeling. Results: Among the 1,798 patients initiating nintedanib, mean age was 75.4 years, 61.1% were male, and 91.1% were non-Hispanic white. The best-fitting GBTM had five adherence trajectory groups: high adherence (43.1%), moderate adherence (11.9%), high-then-poor adherence (10.4%), delayed-poor adherence (13.2%), and early-poor adherence (21.5%). The principal factors associated with higher odds of being in at least one of the poor-adherence groups were older age, female sex, race and ethnicity other than non-Hispanic white, and number of medications during baseline. Conclusions: GBTM identified distinct patterns of nintedanib adherence for the IPF patient cohort. Identifying adherence trajectory groups and understanding the characteristics of their members provide more actionable information to personalize interventions than conventional metrics of medication adherence.

Background: Interstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence-assisted lung auscultation and ultrasound (LUS) could constitute an alternative to conventional, subjective, operator-related methods for the accurate and earlier diagnosis of these diseases. This protocol describes the standardised collection of digitally-acquired lung sounds and LUS images of adult outpatients with IPF, NSIP or COPD and a deep learning diagnostic and severity-stratification approach. Methods: A total of 120 consecutive patients (≥ 18 years) meeting international criteria for IPF, NSIP or COPD and 40 age-matched controls will be recruited in a Swiss pulmonology outpatient clinic, starting from August 2022. At inclusion, demographic and clinical data will be collected. Lung auscultation will be recorded with a digital stethoscope at 10 thoracic sites in each patient and LUS images using a standard point-of-care device will be acquired at the same sites. A deep learning algorithm (DeepBreath) using convolutional neural networks, long short-term memory models, and transformer architectures will be trained on these audio recordings and LUS images to derive an automated diagnostic tool. The primary outcome is the diagnosis of ILD versus control subjects or COPD. Secondary outcomes are the clinical, functional and radiological characteristics of IPF, NSIP and COPD diagnosis. Quality of life will be measured with dedicated questionnaires. Based on previous work to distinguish normal and pathological lung sounds, we estimate to achieve convergence with an area under the receiver operating characteristic curve of > 80% using 40 patients in each category, yielding a sample size calculation of 80 ILD (40 IPF, 40 NSIP), 40 COPD, and 40 controls. Discussion: This approach has a broad potential to better guide care management by exploring the synergistic value of several point-of-care-tests for the automated detection and differential diagnosis of ILD and COPD and to estimate severity. Trial registration Registration: August 8, 2022.

No abstract available.

Objectives: To investigate the course of interstitial lung disease (ILD) and the effects of nintedanib in patients with limited cutaneous systemic sclerosis (lcSSc). Methods: In the SENSCIS trial, patients with SSc-ILD were randomised to receive nintedanib or placebo. Patients who completed the SENSCIS trial were eligible to enter SENSCIS-ON, in which all patients received open-label nintedanib. Results: Among 277 patients with lcSSc treated in the SENSCIS trial, the rate (SE) of decline in FVC (mL/year) over 52 weeks was -74.5 (19.2) in the placebo group and -49.1 (19.8) in the nintedanib group (difference: 25.3 [95% CI -28.9, 79.6]). Among 249 patients with data at week 52, mean (SE) changes in FVC at week 52 were -86.4 (21.1) mL in the placebo group and -39.1 (22.2) mL in the nintedanib group. Among 183 patients with lcSSc who participated in SENSCIS-ON and had data at week 52, mean (SE) changes in FVC from baseline to week 52 of SENSCIS-ON were -41.5 (24.0) mL in patients who took placebo in the SENSCIS trial and initiated nintedanib in SENSCIS-ON and -45.1 (19.1) mL in patients who took nintedanib in the SENSCIS trial and continued it in SENSCIS-ON. Conclusion: Patients with lcSSc may develop progressive fibrosing ILD. By targeting pulmonary fibrosis, nintedanib slows decline in lung function in patients with lcSSc and ILD.

Objective: To assess adverse events in relation to baseline body mass index (BMI) and the risk of malnutrition in patients with systemic sclerosis-associated interstitial lung disease (SSc-ILD) treated with nintedanib. Methods: Among patients with SSc-ILD randomized to receive nintedanib or placebo in the SENSCIS trial, we assessed adverse events in subgroups by baseline BMI ≤20 and >20 kg/m2 , and the risk of malnutrition using a modified version of the Malnutrition Universal Screening Tool (MUST), over 52 weeks. Results: The adverse event profile of nintedanib was similar between subgroups with baseline BMI ≤20 kg/m2 (n=61) and >20 kg/m2 (n=515). In these subgroups, respectively, adverse events led to treatment discontinuation in 16.7% and 15.9% of the nintedanib group and 13.5% and 8.0% of the placebo group. Based on the modified MUST, the proportions of patients who had a low risk of malnutrition at baseline and at their last assessment were 74.0% in the nintedanib group and 78.1% in the placebo group, while the proportions who were classified as at low risk at baseline but at high risk by their last assessment were 4.5% in the nintedanib group and 1.0% in the placebo group. Conclusion: In the SENSCIS trial, most patients with SSc-ILD remained at low risk of malnutrition over 52 weeks, but the proportion at high risk was higher in patients treated with nintedanib than placebo. Management of disease manifestations and adverse events that may be associated with weight loss is important to reduce the risk of malnutrition in patients with SSc-ILD.

Objectives: To assess CCL18 and OX40L as biomarkers of interstitial lung disease (ILD) and/or progressive fibrosing (PF-) ILD in idiopathic inflammatory myopathies (IIMs). Methods: Patients with IIMs seen in our center from July 2020 to March 2021 were consecutively enrolled. ILD was detected by high-resolution CT. CCL18 and OX40L serum levels were measured by validated ELISA assays in 93 patients and 35 controls. At the 2-year follow-up, PF-ILD was evaluated according to the INBUILD criteria. Results: ILD was diagnosed in 50 (53.7%) patients. CCL18 serum levels were higher in IIMs patients vs. controls (232.9 [IQR 134.7-399.07] vs. 48.4 [29.9-147.5], p < 0.0001), with no difference for OX40L. IIMs-ILD patients exhibited higher levels of CCL18 than those without ILD (306.8 [190.8-520.5] vs. 162 [75.4-255.8], p < 0.0001). High CCL18 serum levels were independently associated with IIMs-ILD diagnosis. At follow-up, 22/50 (44%) patients developed a PF-ILD. Patients who developed PF-ILD had higher CCL18 serum levels than non-progressors (511 [307-958.7] vs. 207.1 [149.3-381.7], p < 0.0001). Multivariate logistic regression analysis revealed CCL18 as the only independent predictor of PF-ILD (OR 1.006 [1.002-1.011], p = 0.005). Conclusions: Although in a relatively small sample, our data suggest that CCL18 is a useful biomarker in IIMs-ILD, particularly in the early identification of patients at risk of developing PF-ILD.

Systemic sclerosis is a rare connective tissue disease; and interstitial lung disease (SSc-ILD) is associated with significant morbidity and mortality. There are no clinical, radiologic features, nor biomarkers that identify the specific time when patients are at risk for progression at which the benefits from treatment outweigh the risks. Our study aimed to identify blood protein biomarkers associated with progression of interstitial lung disease in patients with SSc-ILD using an unbiased, high-throughput approach. We classified SSc-ILD as progressive or stable based on change in forced vital capacity over 12 months or less. We profiled serum proteins by quantitative mass spectrometry and analyzed the association between protein levels and progression of SSc-ILD via logistic regression. The proteins associated with at a p value of < 0.1 were queried in the ingenuity pathway analysis (IPA) software to identify interaction networks, signaling, and metabolic pathways. Through principal component analysis, the relationship between the top 10 principal components and progression was evaluated. Unsupervised hierarchical clustering with heatmapping was done to define unique groups. The cohort consisted of 72 patients, 32 with progressive SSc-ILD and 40 with stable disease with similar baseline characteristics. Of a total of 794 proteins, 29 were associated with disease progression. After adjusting for multiple testing, these associations did not remain significant. IPA identified five upstream regulators that targeted proteins associated with progression, as well as a canonical pathway with a higher signal in the progression group. Principal component analysis showed that the ten components with the highest Eigenvalues represented 41% of the variability of the sample. Unsupervised clustering analysis revealed no significant heterogeneity between the subjects. We identified 29 proteins associated with progressive SSc-ILD. While these associations did not remain significant after accounting for multiple testing, some of these proteins are part of pathways relevant to autoimmunity and fibrogenesis. Limitations included a small sample size and a proportion of immunosuppressant use in the cohort, which could have altered the expression of inflammatory and immunologic proteins. Future directions include a targeted evaluation of these proteins in another SSc-ILD cohort or application of this study design to a treatment naïve population.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease characterized by progressive lung fibrogenesis and histological features of usual interstitial pneumonia. IPF has a poor prognosis and presents a spectrum of disease courses ranging from slow evolving disease to rapid deterioration; thus, a differential diagnosis remains challenging. Several biomarkers have been identified to achieve a differential diagnosis; however, comprehensive reviews are lacking. This review summarizes over 100 biomarkers which can be divided into six categories according to their functions: differentially expressed biomarkers in the IPF compared to healthy controls; biomarkers distinguishing IPF from other types of interstitial lung disease; biomarkers differentiating acute exacerbation of IPF from stable disease; biomarkers predicting disease progression; biomarkers related to disease severity; and biomarkers related to treatment. Specimen used for the diagnosis of IPF included serum, bronchoalveolar lavage fluid, lung tissue, and sputum. IPF-specific biomarkers are of great clinical value for the differential diagnosis of IPF. Currently, the physiological measurements used to evaluate the occurrence of acute exacerbation, disease progression, and disease severity have limitations. Combining physiological measurements with biomarkers may increase the accuracy and sensitivity of diagnosis and disease evaluation of IPF. Most biomarkers described in this review are not routinely used in clinical practice. Future large-scale multicenter studies are required to design and validate suitable biomarker panels that have diagnostic utility for IPF.