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Original Research: Diffuse Lung Disease |

Blood Biomarkers MMP-7 and SP-APrognostic Value of Blood Biomarkers: Predictors of Outcome in Idiopathic Pulmonary Fibrosis FREE TO VIEW

Jin Woo Song, MD; Kyung Hyun Do, MD; Se Jin Jang, MD; Thomas V. Colby, MD; Seungbong Han, PhD; Dong Soon Kim, MD
Author and Funding Information

From the Department of Pulmonary and Critical Care Medicine (Drs Song and Kim), Department of Radiology (Dr Do), Department of Pathology (Dr Jang), and Department of Clinical Epidemiology and Biostatistics (Dr Han), Asan Medical Center, University of Ulsan, College of Medicine, Seoul, South Korea; Department of Laboratory Medicine and Pathology (Dr Colby), Mayo Clinic, Scottsdale, AZ.

Correspondence to: Dong Soon Kim, MD, Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan, College of Medicine, Asanbyungwon-gil, Songpa-gu, Seoul, South Korea; e-mail: dskim@amc.seoul.kr


Funding/Support: This study was supported by grant number 2010-495 from the Asan Institute for Life Sciences, Seoul, South Korea.

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


Chest. 2013;143(5):1422-1429. doi:10.1378/chest.11-2735
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Background:  Because of the variable course of idiopathic pulmonary fibrosis (IPF), it is important to generate an accurate prognosis at the time of diagnosis. The aim of this study was to investigate the prognostic value of blood biomarkers in IPF.

Methods:  The plasma level of the biomarkers, matrix metalloproteinase-7 (MMP-7), Krebs von den Lungen-6 antigen, and surfactant protein (SP)-A and SP-D were retrospectively compared with the clinical course of 118 patients with IPF, 68 of whom had biopsy-proven IPF.

Results:  The median follow-up period was 24 months. Multivariate Cox analysis showed MMP-7 (HR, 1.056; P = .0063) and SP-A (HR, 1.011; P = .0001) were significant predictors of survival along with age, FVC, and extent of honeycombing. The patients with high levels of both MMP-7 (≥ 12.1 ng/mL) and SP-A (≥ 80.3 ng/mL) had shorter survival (1-year survival rate: 59%) and higher frequency (42%) of lung function decline (> 10% reduction in FVC in 6 months) compared with those with high levels of one biomarker (1-year survival rate: 81%; FVC decline: 27%) or low levels of both (1-year survival rate: 83.3%; FVC decline: 9%). Multivariate models demonstrated marginal improvement in the prediction of mortality (concordance index [C-index]: 0.731; P = .061) when MMP-7 and SP-A were included and compared with standard clinical predictors only (C-index: 0.686); however, it became significant with addition of MMP-7, SP-A, and Krebs von den Lungen-6 antigen (C-index: 0.730; P = .037).

Conclusions:  Our retrospective study suggested that at least three biomarkers are necessary to improve predictability of mortality in IPF compared with clinical parameters. Further study in a greater number of patients is warranted.

Idiopathic pulmonary fibrosis (IPF) is characterized by progressive parenchymal fibrosis of unknown etiology.1 The overall prognosis is poor, with a median survival of 3 years2; however, the course of disease in individual patients is highly variable.3 Several factors have been reported as prognostic predictors: age, FVC, diffusing capacity (Dlco), serial changes in lung function, and distance or desaturation during the 6-min walk test (6MWT).49 These physiologic parameters have limitations, however, such as dependency on patient effort and need for follow-up, and failure to predict mortality in a significant number of subjects.10 Peripheral blood biomarkers can overcome these disadvantages and are easy to test. Several biomarkers, such as surfactant proteins (SP),1115 Krebs von den Lungen-6 antigen (KL-6),16,17 CC chemokine ligand-18 (CCL-18),18 and circulating fibrocytes19 have been reported to be useful in predicting outcomes in patients with IPF; however, most of these studies were small and evaluated only one biomarker.

We hypothesized that plasma matrix metalloproteinase-7 (MMP-7) level may be a useful predictor of IPF outcome2023 and a combination of biomarkers including MMP-7 may be a more accurate predictor than any single biomarker alone. To test this hypothesis, we retrospectively analyzed the predictive power of plasma levels of biomarkers (SP-A, SP-D, KL-6, and MMP-7) for clinical outcomes of patients with IPF. Additionally, we compared the predictive power of biomarkers in combination with clinical parameters.

Study Population

The study subjects were 118 patients with IPF (68 of whom had biopsy-proven IPF) in the pulmonary department at Asan Medical Center in Seoul, South Korea, between September 2004 and December 2008 whose blood was collected at the time of diagnosis. Ten long-term survivors (survival > 8 years) in whom blood sampling was performed several years after diagnosis, while they were still clinically stable, were included. All subjects met the diagnostic criteria of the American Thoracic Society (ATS)/European Respiratory Society (ERS) IPF consensus classification.24 Most of these patients were included in our previous reports.25,26 Informed consent was provided by each patient and the study was approved by the Asan Medical Center Institutional Review Board (approval number 2007-0240).

Methods

This was a retrospective study. Clinical and survival data for all patients were obtained from medical records, telephone interviews, and/or the record of National Health Insurance of Korea. Baseline clinical parameters were obtained within 1 month of the diagnosis or of blood sampling.

Lung functions were measured according to ATS recommendations and the results were expressed as percentages of normal predicted values.2729 Changes in lung function were presented as the percentage change from the initial value. The 6MWT was performed according to ATS guidelines with a slight modification: The technician followed the patients with continuous monitoring of oxygen saturation but offered no additional remark or encouragement to the patient.30

High-resolution CT (HRCT) images were obtained by using 1-mm or 1.5-mm collimation at least 10-mm intervals and were reconstructed using a high spatial-frequency algorithm. HRCTs were reviewed by one thoracic radiologist (K. H. D.) who was blinded to clinical information about the patient. The extent of emphysema, ground glass opacity, reticulation, consolidation, and honeycombing (HC) was scored on a 5% interval scale for all lobes.31

Blood Sampling and Enzyme-linked Immunosorbent Assay

Blood was taken using a routine procedure and was immediately centrifuged. Plasma was aliquoted and frozen at −80°C until analysis by enzyme-linked immunosorbent assay (ELISA).

Levels of MMP-7 (R&D Systems, Inc), SP-A and SP-D (Biovender Laboratory Medicine, Inc), and KL-6 (EIDIA Co, Ltd) were measured using commercially available ELISA kits. All assays were performed in duplicate, and the mean values were reported.

Statistical Methods

All values are given as the mean ± SD or median (range) for continuous variables or as percentages for categorical variables. The χ2 or Fisher exact tests were used for categorical data and the unpaired Student t test was used for continuous data. Receiver operating characteristic (ROC) curve analysis was performed to find the best cutoff value for 1-year survival prediction. Survival was evaluated using a Kaplan-Meier approach and the log-rank test. Cox regression analysis was used to identify significant predictors of survival and for multivariate Cox proportional hazards prediction models for mortality in patients with IPF. The discriminatory capability of the models was evaluated using the C statistic with concordance index (C-index), which is similar in concept to the area under the ROC curve, which is a logistic model but is appropriate for censored data. The C-index is the concordance probability assigning a higher risk score to a person with event than to a person without event. To compare the discrimination ability, we resorted to a bootstrap resampling procedure to estimate a SE for the observed C-index difference, which is useful when the theoretical derivation is not known. Additionally, the comparison procedure is robust because it is a distribution-free method. P < .05 was considered statistically significant (two-tailed). All data analyses were performed using R software version 2.10.132 (R Foundation for Statistical Computing) and SAS statistical software version 9.1 (SAS Institute Inc). In particular, the R packages of survival33 and risksetROC34 were used to conduct the survival analysis and the ROC analysis.

Baseline Clinical Features

The mean age of the subjects was 62 years and 81% were male (Table 1). The median follow-up period was 24 months. Nonsurvivors were older with lower albumin levels and lung function, poorer exercise capacity, and higher scores of HC and total extent on HRCT compared with survivors (Table 1).

Table Graphic Jump Location
Table 1 —Baseline Characteristics of the Study Population

Data are presented as mean ± SD unless otherwise indicated. 6MWT = 6-min walk test; Dlco = diffusing capacity for carbon monoxide; GGO = ground glass opacity; HC = honeycombing; HRCT = high resolution CT; P/F ratio = oxygen tension/fraction of oxygen in inspired air; PFT = pulmonary function test; SpO2 = oxygen saturation; TLC = total lung capacity.

Baseline Levels of Plasma Biomarkers

Mean levels of MMP-7, KL-6, SP-A, and SP-D are shown in Table 2. MMP-7 and KL-6 levels were significantly elevated in nonsurvivors compared with survivors (Table 2).

Table Graphic Jump Location
Table 2 —Comparison of Blood Biomarkers Between Survivors and Nonsurvivors in Patients With IPF

Data are presented as mean ± SD unless otherwise indicated. IPF = idiopathic pulmonary fibrosis; KL-6 = Krebs von den Lungen-6 antigen; MMP = matrix metalloproteinase; SP = surfactant protein.

Correlation of Baseline Levels of Plasma Biomarkers and Mortality

On univariate Cox analysis, all biomarkers tested showed significant predictive value for mortality (Table 3). On multivariate analysis, however, MMP-7 (HR, 1.056; P = .0063) and SP-A (HR, 1.011; P = .0001) were the only biomarkers that were significant independent predictors for mortality, as were the clinical parameters of age, FVC % predicted, and extent of HC.

Table Graphic Jump Location
Table 3 —Predicting Value for Mortality in Patients With IPF Assessed by Cox Proportional Hazards Model

See Table 1 and Table 2 legends for expansion of abbreviations.

a 

TLC % predicted, lowest Spo2 during 6MWT, and total extent on HRCT were excluded in multivariate analysis due to close correlation among variables (FVC % predicted-TLC % predicted: r = 0.754, P < .001; Dlco-6MWT, lowest SpO2: r = 0.652, P < .001; HC–total extent: r = 0.586, P < .001).

On ROC curve analysis, all four biomarkers were significant predictors of survival. The most accurate, optimal cutoff levels were 12.1 ng/mL for MMP-7 (sensitivity: 71%, specificity: 54%) and 80.3 ng/mL for SP-A (sensitivity: 75%, specificity: 67.1%). Survival was significantly shorter in patients with higher levels of these biomarkers than in those with lower levels (e-Fig 1A and 1B, e-Table 1). MMP-7 levels > 12.1 ng/mL were associated with risk of death during follow-up of more than twice that of those with < 12.1 ng/ml. SP-A levels > 80.3 ng/mL were not associated with increased mortality risk (e-Table 2).

Combination of Baseline Level of Plasma MMP-7 and SP-A for Prediction of Survival

The combination of MMP-7 and SP-A was a better predictor of mortality than either biomarker alone. The survival time of the patients with high levels of both biomarkers was shorter (median: 16.9 months) than that of patients with high levels of either biomarker (median: 45.3 months). The risk of death for these patients was 3.8 times greater during follow-up compared with the patients with low levels of both biomarkers (e-Table 3). Fifty-nine percent of the patients with high levels of both MMP-7 and SP-A survived 1 year, whereas 83% of the patients with low levels of both biomarkers survived 1 year (e-Table 2).

Performance Characteristics of Different Combinations of Parameters for Prediction of Mortality

To investigate whether the addition of biomarkers would yield a more accurate prognosis than the clinical parameters alone, the C-index of different combinations of clinical parameters and biomarkers were compared. As shown in Table 4, the addition of only one or two biomarkers did not significantly improve the prediction of prognosis compared with clinical parameters alone [age, FVC, diffusing capacity for carbon monoxide (Dlco), and change in FVC during 6 months]. Only addition of three biomarkers (MMP-7, SP-A, and KL-6) significantly improved the accuracy of prediction than clinical parameters only (P = .037); however, the C-index of addition of three biomarkers (0.730) was the same as two biomarkers (MMP7 and SP-A; C-index: 0.731), and further study with a greater number of patients is required.

Table Graphic Jump Location
Table 4 —Performance Characteristics of Different Combination of Parameters for Predicting Mortality in Patients With IPF Using Multivariate Cox Proportional Hazards Models

See Table 1 and Table 2 legends for expansion of abbreviations.

a 

Clinical variables include age, FVC, Dlco, and change in FVC during 6 mo.

Plasma Biomarkers as Predictors of Disease Progression

The levels of SP-A and MMP-7 were significantly associated with a reduction in FVC during the 6-month follow-up (Table 5). Furthermore, the combination of two biomarkers predicted disease progression better than either single biomarker: 42% of the patients with high levels of both biomarkers had > 10% decline in FVC in 6 months, whereas only 9% of the patients with low levels of both biomarkers showed disease progression in that period (Table 6).

Table Graphic Jump Location
Table 5 —Predictive Value of MMP-7 and SP-A for Disease Progressiona in Patients With IPF: Single Biomarker

See Table 1 and Table 2 legends for expansion of abbreviations.

a 

> 10% FVC decline during 6 mo.

b 

Best cutoff level for disease progression: MMP-7 (sensitivity: 45.3%, specificity: 68.5%), SP-A (sensitivity: 60.9%, specificity: 53.9%).

Table Graphic Jump Location
Table 6 —Predictive Value of MMP-7 and SP-A for Disease Progressiona in Patients With IPF: Combination of Both Biomarkers

See Table 1 and Table 2 legends for expansion of abbreviations.

a 

> 10% FVC decline during 6 mo.

Our study showed that plasma levels of the biomarkers MMP-7 and SP-A were useful predictors of poor disease outcomes, including mortality and disease progression (loss of lung function), in patients with IPF. The combination of two indices yielded a more accurate prediction than either index alone. However, the combination of MMP-7 and SP-A with the clinical parameters was only marginally superior to the clinical characteristics alone; the addition of KL-6 made it statistically significant, suggesting that baseline plasma levels of the biomarkers MMP-7, SP-A, and KL-6 provide substantial additive predictive value for prognosis in patients with IPF.

Reported predictors of survival have included physiologic parameters: the baseline FVC, Dlco, and 6MWT, and serial changes in lung function (ie, FVC, Dlco) over 6- or 12-month intervals.68 However, even serial changes in physiologic parameters failed to predict mortality in a significant number of patients.10 Plasma biomarkers have the advantage of being easy to sample and they are independent of any effort on the patient’s part. Several blood biomarkers, such as SP-A,1113 SP-D,11,12,14,15 KL-6,16,17 CCL-18,18 and circulating fibrocytes19 have been suggested as prognostically useful. However, most of the studies were modest in size and assayed only one or a few markers. Furthermore, some studies were performed before the ATS/ERS consensus classification1 and might have included patients with nonspecific interstitial pneumonia. Therefore, the true value of biomarkers remained unclear and they have not been widely used in clinical practice.

Takahashi et al11 found that high levels of serum SP-A and SP-D were associated with mortality in 52 patients with IPF. Greene et al12 reported that the levels of serum SP-A (log of SP-A, HR: 1.73; P = .031) and SP-D (log of SP-D, HR: 2.04; P = .003) in patients with IPF (n = 142) were significant predictors of mortality after adjusting for smoking history and age. These studies were performed before the current ATS/ERS consensus classification for the diagnosis of IPF.1 Kinder et al13 found that serum SP-A levels (each increase of 48.7 ng/mL, HR: 3.27, P = .003) were associated with an increased risk of 1-year mortality after controlling for known clinical predictors. There was no significant association between SP-D level and mortality, although addition of SP-D to SP-A improved 1-year mortality prediction significantly (area under the curve, 0.89 vs 0.76; P = .03).

The concentration of KL-6, a mucin-like, high-molecular-weight glycoprotein in serum and BAL fluid, was reported to be elevated in various interstitial lung diseases (ILDs); however, it is not specific for ILD and the number of studies is limited. Satoh et al35 reported that patients with higher levels of KL-6 had increased risk of mortality (P = .0004) in 209 patients with ILD. Yokoyama et al17 reported higher median survival (36 months) in patients with a KL-6 level < 1,000 units/mL than in patients with high KL-6 level (18 months) in 27 patients with IPF.17 Thus, KL-6 has not yet been validated as a biomarker for prognosis. Our study showed that as a single biomarker, KL-6 is weakly predictive, but in combination with the established clinical parameters, KL-6 had no additional value.

Matrix degrading enzymes of the MMP family, which cleave all components of the extracellular matrix and basement membranes,21 also process bioactive mediators and modulate their activity.22 Therefore, MMPs may have a role in the pathogenesis of pulmonary fibrosis. A microarray-based study23 showed MMP7 as the most informative gene in IPF. In support of this hypothesis, MMP-7 knockout mice were protected from bleomycin-induced fibrosis. Rosas et al36 reported that MMP-7 and MMP-1 were elevated in the plasma, serum, BAL fluid, and lung tissue of patients with IPF. However, MMP-7 is not specific for IPF; similar gene expression patterns37 and high levels of MMP-7 in lung tissue and BAL fluid were reported in nonspecific interstitial pneumonia and cryptogenic organizing pneumonia.38,39

The exact mechanism of increased plasma MMP-7 is uncertain; previous results26,37,39 suggest that activated epithelial cells in IPF lungs are the likely source. Rosas et al36 showed that blood MMP-7 level was also significantly higher in patients with early, subclinical lung disease and was correlated with the degree of lung function impairment, suggesting that MMP-7 may be a marker of disease severity. There were, however, no data on the correlation between MMP-7 levels and prognosis. Our study showed that plasma levels of MMP-7 and SP-A were significant independent predictors of both mortality and disease progression. Furthermore, MMP-7 level was correlated with disease severity, showing relationships not only to static lung function but also to the change in FVC over 6 months, the lowest saturation during the 6MWT, and extent of HC and total disease on HRCT (data not shown).

Just prior to submission of our manuscript, Richards et al40 reported that five biomarkers MMP-7, (intercellular adhesion molecule [ICAM]-1, IL-8, vascular cell adhesion molecule [VCAM]-1, and S100A12) among 95 proteins measured by luminex and ELISA were significant predictors of prognosis in 140 patients with IPF. Their study is important because they identified several new possible predictors of prognosis by testing a large number of proteins and the results were confirmed in a separate validation cohort. However, as in the validation cohort, the efficacy of each biomarker varied according to the end points. The major strength of our manuscript is that we tested and compared the efficacy of multiple, known biomarkers both individually and in combination with other biomarkers and clinical parameters to find the best predictors of the course of the disease in routine clinical settings. Furthermore we used C statistics and the best threshold level of each biomarker decided by ROC curve analysis, whereas Richards et al40 dichotomized the plasma level into a low group and a high group using the profile-likelihood method, which tests associations between the time to event and risk factors.

Because we are interested in the predictive value of the biomarkers at the individual level rather than at the population level of relative risk, use of C statistics is more appropriate than association tests.41 du Bois et al42 reported that the combination of age, FVC, change in FVC over 6 months, and hospitalization due to respiratory problems provided a good indicator for future mortality. This important study used also C statistics and showed that the combination of readily ascertainable parameters can predict outcomes more accurately. The predictive efficacy of all parameters (C-index) of du Bois et al42 was higher than in our study, which may be due to larger sample size (n = 1,099) and prospective design. Our study showed only marginal improvement of prediction accuracy by the addition of two biomarkers (MMP7 and SP-A) compared with clinical parameters (P = .061), whereas the improvement by adding three biomarkers (MMP-7, SP-A, and KL-6) was statistically significant (P = .037). However, measurement of three biomarkers is more difficult. Furthermore, the C-index of three biomarkers (0.730) was the same as that of two biomarkers (0.731), which was well demonstrated in the ROC curve analysis (e-Fig 2). Statistical significance of the combination of two biomarkers was only marginal in our study, which might be due to small sample size, and we think that further study with larger number of the subjects will show statistical significance.

The present study had several limitations. This was a retrospective review of patients from a single, tertiary referral center, although it started as a prospective cohort, and the study was restricted to patients who agreed to blood collection. Therefore, the subjects may not be representative of all patients with IPF; however, baseline characteristics and outcome were not different from our whole cohort (data not shown) and other IPF series. The second limitation is that demonstrated prognostic factors (eg, CCL-18)18 were not included; the measurement of CCL-18 level by ELISA requires high standardization of the entire procedure from drawing blood to freezing and thawing. In addition, there was some concern about the reproducibility and internal validity of these methods.43 Finally, the follow-up period of this study was short, although it was sufficient to reveal the prognostic value of such biomarkers. Despite these limitations, our study is the first to attempt to compare the prognostic value of different plasma biomarkers, including MMP-7, and to propose the best biomarkers to predict prognoses in patients with IPF.

Our data showed that blood levels of MMP-7 and SP-A are useful predictors of mortality and disease progression in IPF. The combination of both biomarkers yielded only marginally better prediction than clinical parameters alone, however, with addition of three biomarkers (MMP-7, SP-A and KL-6), the improvement in predictability became statistically significant. Further study with greater numbers of the patients is warranted.

Author contributions: Dr Kim had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Dr Song: contributed to the study design, data analysis, writing of the manuscript, and served as first author.

Dr Do: contributed to data acquisition and analysis, and review and revision of the manuscript.

Dr Jang: contributed to data acquisition and analysis, and review and revision of the manuscript.

Dr Colby: contributed to data acquisition and analysis, and manuscript review.

Dr Han: contributed to the performing and interpretation of all statistical analyses, and review and revision of the manuscript.

Dr Kim: contributed to the study design, data analysis and interpretation, and writing and revision of the 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 sponsor had no role in the design of the study, the collection and analysis of the data, or the preparation of the manuscript.

Other contributions: The authors thank Min Ju Kim, BA, for excellent statistical assistance and EIDIA Co, Ltd, for providing KL-6 kits.

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

6MWT

6-min walk test

ATS

American Thoracic Society

CCL-18

chemokine ligand-18

C-index

concordance index

Dlco

diffusing capacity for carbon monoxide

ERS

European Respiratory Society

HC

honeycombing

HRCT

high-resolution CT

ILD

interstitial lung disease

IPF

idiopathic pulmonary fibrosis

KL-6

Krebs von den Lungen-6 antigen

MMP-7

matrix metalloproteinase-7

ROC

receiver operating characteristic

SP

surfactant protein

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Therneau T, Lumley T. survival: Survival analysis, including penalised likelihood. Comprehensive R Archive Network website. http://CRAN.R-project.org/package=survival. Accessed December 27, 2012.
 
Heagerty PJ, Paramita S. risksetROC: Riskset ROC curve estimation from censored survival data. Comprehensive R Archive Network website. http://CRAN.R-project.org/package=risksetROC. Accessed December 27, 2012.
 
Satoh H, Kurishima K, Ishikawa H, Ohtsuka M. Increased levels of KL-6 and subsequent mortality in patients with interstitial lung diseases. J Intern Med. 2006;260(5):429-434. [CrossRef] [PubMed]
 
Rosas IO, Richards TJ, Konishi K, et al. MMP1 and MMP7 as potential peripheral blood biomarkers in idiopathic pulmonary fibrosis. PLoS Med. 2008;5(4):e93. [CrossRef] [PubMed]
 
Cosgrove GP, Schwarz MI, Geraci MW, Brown KK, Worthen GS. Overexpression of matrix metalloproteinase-7 in pulmonary fibrosis. Chest. 2002;121(suppl 3):25S-26S. [PubMed]
 
Vuorinen K, Myllärniemi M, Lammi L, et al. Elevated matrilysin levels in bronchoalveolar lavage fluid do not distinguish idiopathic pulmonary fibrosis from other interstitial lung diseases. APMIS. 2007;115(8):969-975. [CrossRef] [PubMed]
 
Huh JW, Kim DS, Oh Y-M, et al. Is metalloproteinase-7 specific for idiopathic pulmonary fibrosis?. Chest. 2008;133(5):1101-1106. [CrossRef] [PubMed]
 
Richards TJ, Kaminski N, Baribaud F, et al. Peripheral blood proteins predict mortality in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2012;185(1):67-76. [CrossRef] [PubMed]
 
Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol. 2004;159(9):882-890. [CrossRef] [PubMed]
 
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] [PubMed]
 
Thomeer M, Grutters JC, Wuyts WA, Willems S, Demedts MG. Clinical use of biomarkers of survival in pulmonary fibrosis. Respir Res. 2010;11:89. [CrossRef] [PubMed]
 

Figures

Tables

Table Graphic Jump Location
Table 1 —Baseline Characteristics of the Study Population

Data are presented as mean ± SD unless otherwise indicated. 6MWT = 6-min walk test; Dlco = diffusing capacity for carbon monoxide; GGO = ground glass opacity; HC = honeycombing; HRCT = high resolution CT; P/F ratio = oxygen tension/fraction of oxygen in inspired air; PFT = pulmonary function test; SpO2 = oxygen saturation; TLC = total lung capacity.

Table Graphic Jump Location
Table 2 —Comparison of Blood Biomarkers Between Survivors and Nonsurvivors in Patients With IPF

Data are presented as mean ± SD unless otherwise indicated. IPF = idiopathic pulmonary fibrosis; KL-6 = Krebs von den Lungen-6 antigen; MMP = matrix metalloproteinase; SP = surfactant protein.

Table Graphic Jump Location
Table 3 —Predicting Value for Mortality in Patients With IPF Assessed by Cox Proportional Hazards Model

See Table 1 and Table 2 legends for expansion of abbreviations.

a 

TLC % predicted, lowest Spo2 during 6MWT, and total extent on HRCT were excluded in multivariate analysis due to close correlation among variables (FVC % predicted-TLC % predicted: r = 0.754, P < .001; Dlco-6MWT, lowest SpO2: r = 0.652, P < .001; HC–total extent: r = 0.586, P < .001).

Table Graphic Jump Location
Table 4 —Performance Characteristics of Different Combination of Parameters for Predicting Mortality in Patients With IPF Using Multivariate Cox Proportional Hazards Models

See Table 1 and Table 2 legends for expansion of abbreviations.

a 

Clinical variables include age, FVC, Dlco, and change in FVC during 6 mo.

Table Graphic Jump Location
Table 5 —Predictive Value of MMP-7 and SP-A for Disease Progressiona in Patients With IPF: Single Biomarker

See Table 1 and Table 2 legends for expansion of abbreviations.

a 

> 10% FVC decline during 6 mo.

b 

Best cutoff level for disease progression: MMP-7 (sensitivity: 45.3%, specificity: 68.5%), SP-A (sensitivity: 60.9%, specificity: 53.9%).

Table Graphic Jump Location
Table 6 —Predictive Value of MMP-7 and SP-A for Disease Progressiona in Patients With IPF: Combination of Both Biomarkers

See Table 1 and Table 2 legends for expansion of abbreviations.

a 

> 10% FVC decline during 6 mo.

References

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R: A language and environment for statistical computing. R Foundation for Statistical Computing website.http://www.R-project.org. Accessed December 27, 2012.
 
Therneau T, Lumley T. survival: Survival analysis, including penalised likelihood. Comprehensive R Archive Network website. http://CRAN.R-project.org/package=survival. Accessed December 27, 2012.
 
Heagerty PJ, Paramita S. risksetROC: Riskset ROC curve estimation from censored survival data. Comprehensive R Archive Network website. http://CRAN.R-project.org/package=risksetROC. Accessed December 27, 2012.
 
Satoh H, Kurishima K, Ishikawa H, Ohtsuka M. Increased levels of KL-6 and subsequent mortality in patients with interstitial lung diseases. J Intern Med. 2006;260(5):429-434. [CrossRef] [PubMed]
 
Rosas IO, Richards TJ, Konishi K, et al. MMP1 and MMP7 as potential peripheral blood biomarkers in idiopathic pulmonary fibrosis. PLoS Med. 2008;5(4):e93. [CrossRef] [PubMed]
 
Cosgrove GP, Schwarz MI, Geraci MW, Brown KK, Worthen GS. Overexpression of matrix metalloproteinase-7 in pulmonary fibrosis. Chest. 2002;121(suppl 3):25S-26S. [PubMed]
 
Vuorinen K, Myllärniemi M, Lammi L, et al. Elevated matrilysin levels in bronchoalveolar lavage fluid do not distinguish idiopathic pulmonary fibrosis from other interstitial lung diseases. APMIS. 2007;115(8):969-975. [CrossRef] [PubMed]
 
Huh JW, Kim DS, Oh Y-M, et al. Is metalloproteinase-7 specific for idiopathic pulmonary fibrosis?. Chest. 2008;133(5):1101-1106. [CrossRef] [PubMed]
 
Richards TJ, Kaminski N, Baribaud F, et al. Peripheral blood proteins predict mortality in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2012;185(1):67-76. [CrossRef] [PubMed]
 
Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol. 2004;159(9):882-890. [CrossRef] [PubMed]
 
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] [PubMed]
 
Thomeer M, Grutters JC, Wuyts WA, Willems S, Demedts MG. Clinical use of biomarkers of survival in pulmonary fibrosis. Respir Res. 2010;11:89. [CrossRef] [PubMed]
 
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