0
Original Research: Diffuse Lung Disease |

Predicting Survival Across Chronic Interstitial Lung DiseasePredicting Survival in Interstitial Lung Disease: The ILD-GAP Model FREE TO VIEW

Christopher J. Ryerson, MD; Eric Vittinghoff, PhD; Brett Ley, MD; Joyce S. Lee, MD; Joshua J. Mooney, MD; Kirk D. Jones, MD; Brett M. Elicker, MD; Paul J. Wolters, MD; Laura L. Koth, MD; Talmadge E. King, Jr, MD, FCCP; Harold R. Collard, MD, FCCP
Author and Funding Information

From the Department of Medicine (Dr Ryerson), University of British Columbia, Vancouver, BC, Canada; the Department of Biostatistics (Dr Vittinghoff), the Department of Medicine (Drs Ley, Lee, Wolters, Koth, King, and Collard), the Department of Radiology (Dr Jones), and the Department of Pathology (Dr Elicker), University of California, San Francisco, San Francisco, CA; and the Department of Medicine (Dr Mooney), Stanford University, Stanford, CA.

Correspondence to: Christopher J. Ryerson, MD, St. Paul’s Hospital, 1081 Burrard St, Ward 8B, Vancouver, BC, V6Z 1Y6, Canada; e-mail: chris.ryerson@hli.ubc.ca


For editorial comment see page 672

Funding/Support: Funding was provided by the Nina Ireland Lung Disease Program.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians. See online for more details.


Chest. 2014;145(4):723-728. doi:10.1378/chest.13-1474
Text Size: A A A
Published online

Background:  Risk prediction is challenging in chronic interstitial lung disease (ILD) because of heterogeneity in disease-specific and patient-specific variables. Our objective was to determine whether mortality is accurately predicted in patients with chronic ILD using the GAP model, a clinical prediction model based on sex, age, and lung physiology, that was previously validated in patients with idiopathic pulmonary fibrosis.

Methods:  Patients with idiopathic pulmonary fibrosis (n = 307), chronic hypersensitivity pneumonitis (n = 206), connective tissue disease-associated ILD (n = 281), idiopathic nonspecific interstitial pneumonia (n = 45), or unclassifiable ILD (n = 173) were selected from an ongoing database (N = 1,012). Performance of the previously validated GAP model was compared with novel prediction models in each ILD subtype and the combined cohort. Patients with follow-up pulmonary function data were used for longitudinal model validation.

Results:  The GAP model had good performance in all ILD subtypes (c-index, 74.6 in the combined cohort), which was maintained at all stages of disease severity and during follow-up evaluation. The GAP model had similar performance compared with alternative prediction models. A modified ILD-GAP Index was developed for application across all ILD subtypes to provide disease-specific survival estimates using a single risk prediction model. This was done by adding a disease subtype variable that accounted for better adjusted survival in connective tissue disease-associated ILD, chronic hypersensitivity pneumonitis, and idiopathic nonspecific interstitial pneumonia.

Conclusion:  The GAP model accurately predicts risk of death in chronic ILD. The ILD-GAP model accurately predicts mortality in major chronic ILD subtypes and at all stages of disease.

Figures in this Article

Risk prediction in patients with chronic interstitial lung disease (ILD) is challenging because of heterogeneity in disease-specific and patient-specific variables. Idiopathic pulmonary fibrosis (IPF) is associated with a significantly worse median survival time than other forms of ILD, such as chronic hypersensitivity pneumonitis (HP), connective tissue disease-associated ILD (CTD-ILD), and idiopathic nonspecific interstitial pneumonia (NSIP).13 Patient characteristics such as age, sex, and pulmonary physiology are associated with survival in IPF.4 No systematic approach to risk prediction in non-IPF chronic ILD has been conducted to date, and prognostication remains challenging for clinicians.

We recently derived and validated the GAP risk prediction model (which includes the GAP Score, Index, and Staging System) for patients with IPF.4 The GAP model integrates patient-specific variables into a simple and clinically useful tool for clinicians. In this study, we extended this analysis to test the performance of the GAP model across other chronic ILDs (chronic HP, CTD-ILD, idiopathic NSIP, and unclassifiable ILD) and to derive a modified GAP model that optimizes mortality estimation by integrating patient-specific and disease-specific variables.

Study Patients

The study cohort included patients enrolled in the University of California, San Francisco (UCSF) ILD database between January 2001 and August 2012. Patients were included if they had chronic ILD of at least 3-months duration and pulmonary function tests (PFTs) available within 6 months of their initial clinic visit date. Chronic ILD was defined as IPF, idiopathic NSIP, CTD-ILD, chronic HP (all according to established criteria57), and unclassifiable ILD (defined as patients without a specific ILD diagnosis following multidisciplinary review of clinical, radiologic, and pathologic data).8 A subcohort of patients with follow-up pulmonary function data were identified for longitudinal model validation. The UCSF Committee on Human Research approved this project, and all patients provided written informed consent (approval #10-01592).

Statistical Analysis

All data analysis was performed using STATA 12.0 (StataCorp LP). The primary outcome was all-cause mortality, verified using the United States Death Registry Index. Lung transplantation was treated as a competing risk. Derivation and validation of the original GAP model has been described previously.4 This same methodology was used in the present study to test the performance of the GAP model and screen potential alternative prediction models for the chronic ILD cohort. Briefly, candidate models with four to seven predictors were screened using 20 repetitions of 10-fold cross-validation of Harrell’s c-index, a measure of discrimination.9 The following candidate predictor variables were considered: age, sex, diagnosis made by surgical lung biopsy, smoking status and number of pack-years, and baseline pulmonary physiology (FVC, total lung capacity, and diffusion capacity of lung for carbon monoxide [Dlco]).1012 We used the approach of Wolbers et al13 to estimate the c-index in the presence of competing risks.14 This analysis was repeated for each individual ILD, except for idiopathic NSIP, which was combined with CTD-ILD because of similar characteristics and small sample size.

Using this approach, a modified GAP score was developed that included a variable to adjust for differences in survival between chronic ILD subtypes, making it possible to calculate 1-, 2-, and 3-year risks using a simple modification of the original GAP Score formulas. In addition, the original GAP Score was previously simplified to the point-score GAP Index.4 A modified ILD-GAP Index was developed using similar methodology. Further methodologic details are provided in e-Appendix 1.

Patient Characteristics

A total of 1,208 patients were identified with chronic ILD, representing 71% of the entire parent cohort enrolled during the study period. The final study population included 1,012 patients (196 patients did not have baseline PFTs available) (Table 1). An additional 1,117 follow-up PFTs were available from 655 of the included patients for longitudinal validation. The final study population included 307 patients with IPF, 281 with CTD-ILD, 206 with chronic HP, 173 with unclassifiable ILD, and 45 with idiopathic NSIP. Most patients were included in previous cohort studies.2,4,8,15 Approximately 40% of patients with HP had an identified antigen exposure.2 Patients with IPF were older, more likely to be men, and had a greater number of pack-years compared with the patients with non-IPF chronic ILD (P < .0005 for all comparisons). There were no significant differences in FVC or Dlco comparing patients with and without a diagnosis of IPF (P = .53 and P = .18, respectively).

Table Graphic Jump Location
Table 1 —Baseline Patient Characteristics

Values are reported as mean (SD) unless otherwise noted. CTD-ILD = connective tissue disease-associated interstitial lung disease; Dlco = diffusion capacity of lung for carbon monoxide; HP = hypersensitivity pneumonitis; ILD = interstitial lung disease; IPF = idiopathic pulmonary fibrosis; NSIP = nonspecific interstitial pneumonia; TLC = total lung capacity.

a 

Data on oxygen use were unavailable for some patients: IPF (n = 6), idiopathic NSIP/CTD-ILD (n = 14), chronic HP (n = 5), unclassifiable ILD (n = 17).

b 

Baseline TLC was not recorded for some patients: IPF (n = 24), idiopathic NSIP/CTD-ILD (n = 28), chronic HP (n = 24), unclassifiable ILD (n = 14).

c 

Baseline Dlco was not recorded for some patients: IPF (n = 14), idiopathic NSIP/CTD-ILD (n = 19), chronic HP (n = 10), unclassifiable ILD (n = 7).

Survival Across ILD Category

Median follow-up was 3.0 years (range, 0-14.0 years). Two hundred eighty-one patients died, including 129 with IPF, 55 with CTD-ILD, 33 with chronic HP, 54 with unclassifiable ILD, and 10 with idiopathic NSIP. Forty-four patients had lung transplantation, including 25 with IPF, eight with CTD-ILD, eight with chronic HP, one with unclassifiable ILD, and two with idiopathic NSIP. Unadjusted mortality was similar in idiopathic CTD-ILD, chronic HP, and idiopathic NSIP (cumulative incidence, 11% at 2 years in all three groups), worse in unclassifiable ILD (cumulative incidence, 25% at 2 years), and worst in IPF (cumulative incidence, 28% at 2 years) (Fig 1). Median time to death or transplant was 3.2 years in IPF and was undefined in other ILD subtypes (ie, the majority of patients were alive at the time of censoring).

Figure Jump LinkFigure 1. Unadjusted Kaplan-Meier figure stratified by ILD subtype. CT-ILD = connective tissue disease-associated interstitial lung disease; HP = hypersensitivity pneumonitis; ILD = interstitial lung disease; IPF = idiopathic pulmonary fibrosis; NSIP = nonspecific interstitial pneumonia.Grahic Jump Location
The GAP Model

The original GAP model performed well in all ILD subtypes, with a c-index that ranged from 69.8 in CTD-ILD/NSIP to 79.3 in chronic HP (e-Table 1). Patients with IPF and unclassifiable ILD had higher mortality for a given GAP score compared with other ILD subtypes.

The ILD-GAP Model

A modified GAP model (the ILD-GAP model) was created by adding an ILD subtype variable to the original GAP model that accounted for better adjusted survival in patients with CTD-ILD, chronic HP, and idiopathic NSIP (Fig 2). A diagnosis of CTD-ILD vs idiopathic NSIP did not carry independent prognostic significance when adjusting for the ILD-GAP model. The ILD-GAP Score had good performance in all ILD subtypes, with acceptable calibration across disease severity and no meaningful loss of discrimination (c-index of 74.6 in the combined ILD cohort) compared with potential alternative models that were developed to optimally fit the data from the non-IPF cohort (e-Fig 1, e-Table 1). Satisfactory mortality estimation was maintained in the corresponding ILD-GAP Index across subtypes of chronic ILD (Fig 3). The ILD-GAP Index maintained a c-index > 70.0 in all ILD subtypes (e-Table 2), indicating satisfactory discrimination. Calibration was also acceptable, excluding some uncommon combinations of diagnosis and point score values. Performance of the ILD-GAP Score and ILD-GAP Index was maintained at follow-up evaluation (longitudinal evaluation) with overall c-indices of 74.6 for both models.

Figure Jump LinkFigure 2. The ILD-GAP Index. Points are assigned for each variable to obtain a total point score (range, 0-8). Negative total point scores are reset to 0. DLCO should be scored in the “Cannot perform” category if symptoms or lung function prohibit performance of the DLCO maneuver. The model cannot be used if DLCO is unavailable because it was not ordered or not completed because of nonrespiratory limitations. The original points assigned for the GAP Index are represented by the G, A, and P components of the ILD-GAP Index. DLCO = diffusion capacity of lung for carbon monoxide. See Figure 1 legend for expansion of other abbreviations.Grahic Jump Location
Figure Jump LinkFigure 3. Survival in each ILD subtype stratified by the ILD-GAP Index. See Figure 1 legend for expansion of abbreviations.Grahic Jump Location

Previous risk prediction models in ILD have focused overwhelmingly on IPF,1619 thereby excluding a large number of patients with other forms of chronic ILD.4 We used a diverse cohort of > 1,000 patients to validate the original GAP model in patients with multiple chronic ILD subtypes, extending the clinical usefulness of the original model to substantially more patients. Furthermore, we added an ILD subtype variable to the well-validated IPF-specific GAP model to develop a single risk prediction model, the ILD-GAP model, that accurately predicts mortality in IPF and other chronic ILD subtypes at all stages of disease.

The GAP and ILD-GAP models could help clinicians more accurately counsel patients with IPF and non-IPF ILDs. This population of patients is substantial, and until now there have been few data to help inform their prognosis. In particular, the ILD-GAP model could help determine the appropriate timing for lung transplantation evaluation in the non-IPF population. For example, patients with an ILD-GAP Index of 0 to 3 have a low expected 1-year mortality and can likely defer referral for lung transplantation evaluation and undergo careful clinical monitoring. Those with an ILD-GAP Index ≥ 4, on the other hand, have a substantially higher mortality risk and should be considered for lung transplantation, if appropriate. Additional research is required to determine whether the ILD-GAP model could help direct the use of other therapies, current and future, that may be more appropriate in patients with better or worse prognoses.

Interestingly, the ILD-GAP model is slightly more accurate in non-IPF ILDs than in IPF. We believe this is because of the more homogeneous natural history of most non-IPF ILDs, compared with the multiple patterns of progression that are observed in IPF.20 An additional possibility is that comorbid conditions (eg, emphysema, lung cancer, and coronary atherosclerosis) are more common in IPF than other ILD subtypes and may impact survival independent of ILD severity. The lower accuracy in the CTD-ILD/idiopathic NSIP subgroup suggests that there may be prognostically important variables for this population that are not captured in the ILD-GAP model, such as extrapulmonary CT disease involvement. Additional variables may further improve prediction in individual ILD subtypes, but evaluation of this possibility was beyond the scope of this study.

Patients with chronic non-IPF ILD are often treated with immunosuppressive and biologic agents that are associated with clinical response but also with unpredictable side effects. These medical therapies may positively or negatively impact mortality in these patients. We do not have sufficient data on medication treatment to determine its impact on the GAP and ILD-GAP models in this cohort. It is likely that most patients with non-IPF ILD in this cohort received some form of immunosuppressive therapy, which may explain why the GAP and ILD-GAP models still perform well in the absence of this variable (ie, the “use of immunosuppressive therapy” variable is largely fixed at yes in non-IPF ILD). Importantly, patients were recruited from a specialized ILD clinic, and, thus, their characteristics, treatments, and outcomes may not be representative of all patients with ILD. Additional studies are required to determine the impact of treatment on model performance and to evaluate the GAP model in nonacademic clinical settings.

The original GAP model also included the GAP Staging System with proposed relevance to both clinical practice and research.4 These stages were created to help direct IPF-specific management decisions, such as timing of medical therapies and lung transplantation, as well as to inform clinical trialists regarding optimal populations for the evaluation of potential therapies. Although a similar staging system could be applied to the ILD-GAP model, we believe the clinical heterogeneity of the non-IPF ILDs limits the usefulness of such a staging system. In addition, very few patients with chronic non-IPF ILD would be placed in the most advanced stage (stage III, ILD-GAP Index 6-8), rendering the GAP stages less relevant in these diseases.

The original GAP model was developed using cross-validation and then externally validated in a distinct cohort of patients with IPF.4 We applied the original GAP model to a new cohort of patients with chronic non-IPF ILD, thus validating the GAP model in this additional group of patients. For the ILD-GAP model, changes to the original GAP model were limited to addition of coefficients for non-IPF diagnosis (ie, CTD-ILD, chronic HP); all externally validated components of the GAP model were carried over without modification. Discrimination of the ILD-GAP model has been assessed using internal cross-validation to protect against overfitting.

In summary, we extend the application of the previously validated GAP model by showing that it accurately estimates mortality in patients with multiple chronic ILD subtypes. This finding has broad clinical usefulness, applying to a large and diverse population of patients with chronic ILD at all stages of disease. Furthermore, we developed the ILD-GAP model that allows use of a single clinical prediction model for accurate mortality estimation across multiple chronic ILD subtypes. Additional research is required to determine if the ILD-GAP Index can be applied to other less common ILD subtypes and whether additional clinical variables or biologic factors (eg, serum biomarkers) could further improve prognostic accuracy.

Author contributions: Dr Ryerson is the guarantor of the paper.

Dr Ryerson: contributed to conceiving the study design, performing the data analysis, and producing the initial draft of the manuscript; participated in data generation, interpretation of the analysis, and final preparation of the manuscript; and read and approved the final manuscript.

Dr Vittinghoff: contributed to performing the data analysis and producing the initial draft of the manuscript; participated in data generation, interpretation of the analysis, and final preparation of the manuscript; and read and approved the final manuscript.

Dr Ley: contributed to performing the data analysis and producing the initial draft of the manuscript; participated in data generation, interpretation of the analysis, and final preparation of the manuscript; and read and approved the final manuscript.

Dr Lee: contributed to data generation, interpretation of the analysis, and final preparation of the manuscript and read and approved the final manuscript.

Dr Mooney: contributed to data generation, interpretation of the analysis, and final preparation of the manuscript and read and approved the final manuscript.

Dr Jones: contributed to data generation, interpretation of the analysis, and final preparation of the manuscript and read and approved the final manuscript.

Dr Elicker: contributed to data generation, interpretation of the analysis, and final preparation of the manuscript and read and approved the final manuscript.

Dr Wolters: contributed to data generation, interpretation of the analysis, and final preparation of the manuscript and read and approved the final manuscript.

Dr Koth: contributed to data generation, interpretation of the analysis, and final preparation of the manuscript and read and approved the final manuscript.

Dr King: contributed to data generation, interpretation of the analysis, and final preparation of the manuscript and read and approved the final manuscript.

Dr Collard: contributed to conceiving the study design, performing the data analysis, and producing the initial draft of the manuscript; participated in data generation, interpretation of the analysis, and final preparation of the manuscript; and read and approved the final manuscript.

Financial/nonfinancial disclosures: The authors have reported to CHEST that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Role of sponsors: The Nina Ireland Lung Disease Program provides research funding support for the UCSF ILD Program and UCSF ILD Database.

Other contributions: We thank Eunice J. Kim, MD, for her contribution to the CTD-ILD dataset, the providers and staff of the UCSF ILD Program and Consortium for their assistance in recruiting patients for this study, and the patients with ILD who, through their generosity and efforts, allow us to conduct clinical research studies such as this in an effort to improve the lives of patients with ILD.

Additional information: The e-Appendix, e-Figure, and e-Tables can be found in the “Supplemental Materials” area of the online article.

CTD-ILD

connective tissue disease-associated interstitial lung disease

Dlco

diffusion capacity of lung for carbon monoxide

HP

hypersensitivity pneumonitis

ILD

interstitial lung disease

IPF

idiopathic pulmonary fibrosis

NSIP

nonspecific interstitial pneumonia

PFT

pulmonary function test

UCSF

University of California, San Francisco

Bjoraker JA, Ryu JH, Edwin MK, et al. Prognostic significance of histopathologic subsets in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 1998;157(1):199-203. [CrossRef]
 
Mooney JJ, Elicker BM, Urbania TH, et al. Radiographic fibrosis score predicts survival in hypersensitivity pneumonitis. Chest. 2013;144(2):586-592. [CrossRef]
 
Navaratnam V, Ali N, Smith CJ, McKeever T, Fogarty A, Hubbard RB. Does the presence of connective tissue disease modify survival in patients with pulmonary fibrosis? Respir Med. 2011;105(12):1925-1930. [CrossRef]
 
Ley B, Ryerson CJ, Vittinghoff E, et al. A multidimensional index and staging system for idiopathic pulmonary fibrosis. Ann Intern Med. 2012;156(10):684-691. [CrossRef]
 
Lacasse Y, Selman M, Costabel U, et al; HP Study Group. Clinical diagnosis of hypersensitivity pneumonitis. Am J Respir Crit Care Med. 2003;168(8):952-958. [CrossRef]
 
Raghu G, Collard HR, Egan JJ, et al; ATS/ERS/JRS/ALAT Committee on Idiopathic Pulmonary Fibrosis. An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management. Am J Respir Crit Care Med. 2011;183(6):788-824. [CrossRef]
 
Travis WD, Hunninghake G, King TE Jr, et al. Idiopathic nonspecific interstitial pneumonia: report of an American Thoracic Society project. Am J Respir Crit Care Med. 2008;177(12):1338-1347. [CrossRef]
 
Ryerson CJ, Urbania TH, Richeldi L, et al. Prevalence and prognosis of unclassifiable interstitial lung disease. Eur Respir J. 2013.42(3):750-757. [CrossRef]
 
Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361-387. [CrossRef]
 
Macintyre N, Crapo RO, Viegi G, et al. Standardisation of the single-breath determination of carbon monoxide uptake in the lung. Eur Respir J. 2005;26(4):720-735. [CrossRef]
 
Miller MR, Hankinson J, Brusasco V, et al; ATS/ERS Task Force. Standardisation of spirometry. Eur Respir J. 2005;26(2):319-338. [CrossRef]
 
Wanger J, Clausen JL, Coates A, et al. Standardisation of the measurement of lung volumes. Eur Respir J. 2005;26(3):511-522. [CrossRef]
 
Wolbers M, Koller MT, Witteman JC, Steyerberg EW. Prognostic models with competing risks: methods and application to coronary risk prediction. Epidemiology. 2009;20(4):555-561. [CrossRef]
 
Schoop R, Beyersmann J, Schumacher M, Binder H. Quantifying the predictive accuracy of time-to-event models in the presence of competing risks. Biom J. 2011;53(1):88-112. [CrossRef]
 
Lee JS, Kim EJ, Lynch KL, et al. Prevalence and clinical significance of circulating autoantibodies in idiopathic pulmonary fibrosis. Respir Med. 2013;107(2):249-255. [CrossRef]
 
Mura M, Porretta MA, Bargagli E, et al. Predicting survival in newly diagnosed idiopathic pulmonary fibrosis: a 3-year prospective study. Eur Respir J. 2012;40(1):101-109. [CrossRef]
 
du Bois RM, Weycker D, Albera C, et al. Ascertainment of individual risk of mortality for patients with idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2011;184(4):459-466. [CrossRef]
 
King TE Jr, Tooze JA, Schwarz MI, Brown KR, Cherniack RM. Predicting survival in idiopathic pulmonary fibrosis: scoring system and survival model. Am J Respir Crit Care Med. 2001;164(7):1171-1181. [CrossRef]
 
Wells AU, Desai SR, Rubens MB, et al. Idiopathic pulmonary fibrosis: a composite physiologic index derived from disease extent observed by computed tomography. Am J Respir Crit Care Med. 2003;167(7):962-969. [CrossRef]
 
Ley B, Collard HR, King TE Jr. Clinical course and prediction of survival in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2011;183(4):431-440. [CrossRef]
 

Figures

Figure Jump LinkFigure 1. Unadjusted Kaplan-Meier figure stratified by ILD subtype. CT-ILD = connective tissue disease-associated interstitial lung disease; HP = hypersensitivity pneumonitis; ILD = interstitial lung disease; IPF = idiopathic pulmonary fibrosis; NSIP = nonspecific interstitial pneumonia.Grahic Jump Location
Figure Jump LinkFigure 2. The ILD-GAP Index. Points are assigned for each variable to obtain a total point score (range, 0-8). Negative total point scores are reset to 0. DLCO should be scored in the “Cannot perform” category if symptoms or lung function prohibit performance of the DLCO maneuver. The model cannot be used if DLCO is unavailable because it was not ordered or not completed because of nonrespiratory limitations. The original points assigned for the GAP Index are represented by the G, A, and P components of the ILD-GAP Index. DLCO = diffusion capacity of lung for carbon monoxide. See Figure 1 legend for expansion of other abbreviations.Grahic Jump Location
Figure Jump LinkFigure 3. Survival in each ILD subtype stratified by the ILD-GAP Index. See Figure 1 legend for expansion of abbreviations.Grahic Jump Location

Tables

Table Graphic Jump Location
Table 1 —Baseline Patient Characteristics

Values are reported as mean (SD) unless otherwise noted. CTD-ILD = connective tissue disease-associated interstitial lung disease; Dlco = diffusion capacity of lung for carbon monoxide; HP = hypersensitivity pneumonitis; ILD = interstitial lung disease; IPF = idiopathic pulmonary fibrosis; NSIP = nonspecific interstitial pneumonia; TLC = total lung capacity.

a 

Data on oxygen use were unavailable for some patients: IPF (n = 6), idiopathic NSIP/CTD-ILD (n = 14), chronic HP (n = 5), unclassifiable ILD (n = 17).

b 

Baseline TLC was not recorded for some patients: IPF (n = 24), idiopathic NSIP/CTD-ILD (n = 28), chronic HP (n = 24), unclassifiable ILD (n = 14).

c 

Baseline Dlco was not recorded for some patients: IPF (n = 14), idiopathic NSIP/CTD-ILD (n = 19), chronic HP (n = 10), unclassifiable ILD (n = 7).

References

Bjoraker JA, Ryu JH, Edwin MK, et al. Prognostic significance of histopathologic subsets in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 1998;157(1):199-203. [CrossRef]
 
Mooney JJ, Elicker BM, Urbania TH, et al. Radiographic fibrosis score predicts survival in hypersensitivity pneumonitis. Chest. 2013;144(2):586-592. [CrossRef]
 
Navaratnam V, Ali N, Smith CJ, McKeever T, Fogarty A, Hubbard RB. Does the presence of connective tissue disease modify survival in patients with pulmonary fibrosis? Respir Med. 2011;105(12):1925-1930. [CrossRef]
 
Ley B, Ryerson CJ, Vittinghoff E, et al. A multidimensional index and staging system for idiopathic pulmonary fibrosis. Ann Intern Med. 2012;156(10):684-691. [CrossRef]
 
Lacasse Y, Selman M, Costabel U, et al; HP Study Group. Clinical diagnosis of hypersensitivity pneumonitis. Am J Respir Crit Care Med. 2003;168(8):952-958. [CrossRef]
 
Raghu G, Collard HR, Egan JJ, et al; ATS/ERS/JRS/ALAT Committee on Idiopathic Pulmonary Fibrosis. An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management. Am J Respir Crit Care Med. 2011;183(6):788-824. [CrossRef]
 
Travis WD, Hunninghake G, King TE Jr, et al. Idiopathic nonspecific interstitial pneumonia: report of an American Thoracic Society project. Am J Respir Crit Care Med. 2008;177(12):1338-1347. [CrossRef]
 
Ryerson CJ, Urbania TH, Richeldi L, et al. Prevalence and prognosis of unclassifiable interstitial lung disease. Eur Respir J. 2013.42(3):750-757. [CrossRef]
 
Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361-387. [CrossRef]
 
Macintyre N, Crapo RO, Viegi G, et al. Standardisation of the single-breath determination of carbon monoxide uptake in the lung. Eur Respir J. 2005;26(4):720-735. [CrossRef]
 
Miller MR, Hankinson J, Brusasco V, et al; ATS/ERS Task Force. Standardisation of spirometry. Eur Respir J. 2005;26(2):319-338. [CrossRef]
 
Wanger J, Clausen JL, Coates A, et al. Standardisation of the measurement of lung volumes. Eur Respir J. 2005;26(3):511-522. [CrossRef]
 
Wolbers M, Koller MT, Witteman JC, Steyerberg EW. Prognostic models with competing risks: methods and application to coronary risk prediction. Epidemiology. 2009;20(4):555-561. [CrossRef]
 
Schoop R, Beyersmann J, Schumacher M, Binder H. Quantifying the predictive accuracy of time-to-event models in the presence of competing risks. Biom J. 2011;53(1):88-112. [CrossRef]
 
Lee JS, Kim EJ, Lynch KL, et al. Prevalence and clinical significance of circulating autoantibodies in idiopathic pulmonary fibrosis. Respir Med. 2013;107(2):249-255. [CrossRef]
 
Mura M, Porretta MA, Bargagli E, et al. Predicting survival in newly diagnosed idiopathic pulmonary fibrosis: a 3-year prospective study. Eur Respir J. 2012;40(1):101-109. [CrossRef]
 
du Bois RM, Weycker D, Albera C, et al. Ascertainment of individual risk of mortality for patients with idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2011;184(4):459-466. [CrossRef]
 
King TE Jr, Tooze JA, Schwarz MI, Brown KR, Cherniack RM. Predicting survival in idiopathic pulmonary fibrosis: scoring system and survival model. Am J Respir Crit Care Med. 2001;164(7):1171-1181. [CrossRef]
 
Wells AU, Desai SR, Rubens MB, et al. Idiopathic pulmonary fibrosis: a composite physiologic index derived from disease extent observed by computed tomography. Am J Respir Crit Care Med. 2003;167(7):962-969. [CrossRef]
 
Ley B, Collard HR, King TE Jr. Clinical course and prediction of survival in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2011;183(4):431-440. [CrossRef]
 
NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).
Supporting Data

Online Supplement

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Find Similar Articles
CHEST Journal Articles
PubMed Articles
  • CHEST Journal
    Print ISSN: 0012-3692
    Online ISSN: 1931-3543