Skip to main content
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2018 Oct 1.
Published in final edited form as: Respir Med. 2017 Sep 12;131:229–235. doi: 10.1016/j.rmed.2017.08.025

Possible UIP Pattern on High-Resolution Computed Tomography is Associated with Better Survival than Definite UIP in IPF Patients

Margaret L Salisbury 1, Leslie B Tolle 2, Meng Xia 3, Susan Murray 3, Nabihah Tayob 4, Anoop M Nambiar 5, Shelley L Schmidt 6, Amir Lagstein 1, Jeffery L Myers 1, Barry H Gross 1, Ella A Kazerooni 1, Baskaran Sundaram 7, Aamer R Chughtai 1, Fernando J Martinez 8, Kevin R Flaherty 1
PMCID: PMC5679475  NIHMSID: NIHMS905862  PMID: 28947036

Abstract

Background

Idiopathic pulmonary fibrosis (IPF) is a progressive fibrosing lung disease of unknown etiology. Inter-society consensus guidelines on IPF diagnosis and management outline radiologic patterns including definite usual interstitial pneumonia (UIP), possible UIP, and inconsistent with UIP. We evaluate these diagnostic categories as prognostic markers among patients with IPF.

Methods

Included subjects had biopsy-proven UIP, a multidisciplinary team diagnosis of IPF, and a baseline high-resolution computed tomography (HRCT). Thoracic radiologists assigned the radiologic pattern and documented the presence and extent of specific radiologic findings. The outcome of interest was lung transplant-free survival.

Results

IPF patients with a possible UIP pattern on HRCT had significantly longer Kaplan-Meier event-free survival compared to those with definite UIP pattern (5.21 and 3.57 years, respectively, p=0.002). In a multivariable Cox proportional hazards model adjusted for baseline age, gender, %-predicted FVC, and %-predicted DLCO via the GAP Stage, extent of fibrosis (via the traction bronchiectasis score) and ever-smoker status, possible UIP pattern on HRCT (versus definite UIP) was associated with reduced hazard of death or lung transplant (HR=0.42, CI 95% 0.23–0.78, p=0.006).

Conclusions

Radiologic diagnosis categories outlined by inter-society consensus guidelines is a widely-reported and potentially useful prognostic marker in IPF patients, with possible UIP pattern on HRCT associated with a favorable prognosis compared to definite UIP pattern, after adjusting for relevant covariates.

Keywords: Pulmonary fibrosis, prognosis, high resolution computed tomography, usual interstitial pneumonia

INTRODUCTION

Idiopathic pulmonary fibrosis (IPF) is a diffuse parenchymal lung disease of unknown cause characterized by progressive loss of lung function and a poor prognosis. Median survival is 2–3 years from the time of diagnosis1. IPF is defined histopathologically by the usual interstitial pneumonia (UIP) pattern, after exclusion of connective tissue disease, drug toxicity, and occupational/environmental exposure. Current guidelines outline clinical, high-resolution computed tomography (HRCT), and histopathologic criteria consistent with a diagnosis of IPF1. Diagnostic guidelines categorize HRCT findings as “definite UIP” pattern, “possible UIP” pattern, or “inconsistent with UIP” pattern. Definite UIP HRCT pattern in the correct clinical setting obviates the need for a lung biopsy to diagnose IPF1. These radiologic patterns are further described in the “High Resolution Computed Tomography Evaluation” section under Methods.

Although IPF is invariably a progressive disease associated with a poor outcome, the individual disease course is heterogeneous. Older patients and those with greater physiologic impairment at baseline have a higher mortality risk2. More extensive fibrosis on HRCT is also associated with increased risk of death2–5. Knowledge of the HRCT pattern may add relevant prognostic information. Among patients with idiopathic interstitial pneumonias (including some with IPF), those with a UIP pattern on HRCT have shorter survival compared to those with alternative HRCT patterns6,7. Evaluation of variably-defined HRCT patterns as predictors of survival in IPF cohorts have not replicated these findings4,8–11. We sought to assess the prognostic value of the HRCT patterns defined in the 2011 ATS/ERS/JRS/ALAT guideline1 on IPF diagnosis, as well as individual “inconsistent with UIP” HRCT features, in patients with biopsy-proven UIP and a clinical diagnosis of IPF.

MATERIALS AND METHODS

University of Michigan patients with UIP on a surgical lung biopsy (included biopsies were dated January 2001 to February 2013), a multidisciplinary team diagnosis of IPF, and a baseline HRCT, were retrospectively identified via the electronic medical record. Documentation of multidisciplinary case review was available for all subjects. Patient characteristics, forced vital capacity (FVC) and diffusing capacity of the lung for carbon monoxide (DLCO) were documented at the time of the clinical evaluation. Transplant-free survival time is time from the HRCT to death or lung transplantation. Vital status (including date of death or lung transplantation) was confirmed via the electronic medical record and Social Security Death Master File, censored on November 17, 2014 (3 months prior to the registry search to account for reporting lag). This study was conducted in accordance with the amended Declaration of Helsinki (http://www.wma.net/en/30publications/10policies/b3/). The Institutional Review Board at the University of Michigan (HUM #00073617) approved this protocol, and need of written informed consent was waived by the IRB. Some of these results were previously presented as an abstract12.

High Resolution Computed Tomography Evaluation

HRCT data was collected with 1.0- or 1.5-mm-thick overlapping sections using a high-spatial-frequency reconstruction algorithm taken during a single breath hold on various CT scanners. One of four experienced thoracic radiologists (A.R.C., B.H.G., E.A.K., and B.S.) independently evaluated each HRCT prior to the scheduled multidisciplinary ILD conference. The presence of honeycombing, septal thickening, traction bronchiectasis, ground-glass opacities, mosaic attenuation, and air trapping was documented categorically, as previously defined13. The overall extent of each individual abnormality was documented as “Major” (one of the 2–3 most prominent features on the scan) or “Minor” (the feature is present but not one of the 2–3 most prominent). The craniocaudal distribution of the primary abnormality was documented as upper lung, lower lung, or diffuse, and axial distribution as central, peripheral, or diffuse. Finally, the radiologist rated the overall HRCT pattern as “definite UIP”, “possible UIP”, and “inconsistent with UIP” as outlined in the 2011 consensus guidelines on IPF1. Specifically, “definite UIP” is assigned when honeycombing is present in addition to subpleural, basal predominant reticular abnormality, with or without traction bronchiectasis, and without inconsistent features. “Possible UIP” pattern is defined by a subpleural, basal predominant reticular abnormality without inconsistent features, but radiologic honeycombing is absent. The “Inconsistent with UIP” pattern is assigned when any of the following HRCT features are present: upper lung, mid-lung, or peribronchovascular distribution of abnormality; extensive ground glass abnormality; profuse micronodules; discrete non-honeycomb cysts; diffuse mosaic attenuation/air-trapping; or consolidation. Figure 1 shows examples of definite, possible, and inconsistent with UIP HRCT patterns seen in four patients with IPF included in this cohort, paired with the individual’s histopathologic findings. The overall extent of fibrosis was measured by the traction bronchiectasis score, obtained by adding the number of lobes in which traction bronchiectasis is present (maximum of 5).

Figure 1. Representative examples of high-resolution computed tomography patterns with corresponding histopathology images.

Figure 1

Panel 1A shows HRCT image (top) and histopathology (bottom) in the same patient with a clinical diagnosis of IPF, and a definite UIP pattern on HRCT. The HRCT shows an axial/subpleural distribution of abnormality marked by septal thickening, mixed ground glass and reticular densities (white box), and several areas of subpleural honeycomb cysts (white arrows), consistent with a definite UIP pattern. The surgical lung biopsy shows patchy fibrosis with architectural distortion in the form of tissue-destructive scarring and only a mild infiltrate of chronic inflammatory cells.

Panel 1B shows HRCT image (top) and histopathology (bottom) in the same patient a clinical diagnosis of IPF and a possible UIP pattern on HRCT. The HRCT shows abnormalities including septal thickening (white arrow) and mixed ground glass and reticular densities (examples in white boxes) in a peripheral axial and basilar craniocaudal distribution. The prone images demonstrate that the posterior abnormalities persist and are not compressive atelectasis. There is no radiologic honeycombing. The histopathology shows a characteristic pattern of “patchwork” fibrosis, scarring, and easily identifiable fibroblast foci (*).

Panel 1C shows a coronal inspiratory HRCT image (top) and histopathology (bottom) in the same patient with a clinical diagnosis of IPF and an inconsistent with UIP pattern on HRCT due to findings of upper-lung predominant craniocaudal and diffuse axial distribution of abnormality, and mosaic attenuation; areas of upper lobe fibrosis (including honeycomb cysts) are outlined in white boxes. Mosaic attenuation is seen with geographic areas of decreased attenuation (thin white arrow) interspersed with areas of increased attenuation (thick white arrow). The histopathology shows extensive collagen fibrosis with multiple fibroblast foci (*) and microscopic honeycomb change (black arrows).

Panel 1D shows and expiratory HRCT image from the same patient shown in Panel 1C. With expiration, the lung segment outlined in a white box shows evidence of air trapping and remains abnormally black while surrounding areas become whiter. This area of air trapping likely corresponds to the decreased attenuation marked with a thin white arrow in Panel 1C.

Statistical Analysis

Demographic characteristics and summary of HRCT findings are displayed for the entire cohort and by HRCT pattern (definite, possible, or inconsistent with UIP), with means and standard deviations for continuous variables and percentages for categorical variables. Statistically significant differences between HRCT patterns for continuous and categorical variables are assessed using analysis of variance and Pearson’s chi-squared (or Fisher’s exact, when appropriate) methods, respectively. A combined endpoint of time to death or transplant was used in time-to-event analyses. The Kaplan-Meier method illustrates transplant-free-survival (combined endpoint of death or lung transplant) according to HRCT pattern, with statistically significant differences in event rates assessed via the log-rank and restricted means tests14. Univariable and multivariable Cox proportional hazards models15 and pseudo-observation models16 assess clinical and radiologic variables associated with lung-transplant-free survival. Pseudo-observation models do not rely on an assumption of proportional hazards. The DLCO was missing in n=33 patients, and multiple imputation methods (with 5 imputations) were used to handle missing data in univariate and multivariate analyses involving DLCO17, with complete case analysis (unimputed) results included in the e-appendix. For Cox models, index of concordance (c-statistic) gives model discrimination for various models18, with higher values indicating improved prediction. Statistically significant differences/inferences are noted at the P<0.05 level. All analysis was done using SAS version 9.4 (SAS Institute Inc., Cary, NC) and R version 3.1.2 (The R Project, Vienna, Austria).

RESULTS

One-hundred and thirty-three patients met inclusion criteria, of whom 41 (31%) had definite UIP pattern, 77 (58%) had possible UIP, and 15 (11%) had inconsistent with UIP pattern. As shown in Table 1, mean age at the time of the HRCT was 63.6 (SD 8.1) years, 63.2% (n=84) were male, 62.1% (n=82) were ever-smokers, and the mean baseline FVC %predicted was 67.3 (SD 15.8). The mean age and baseline pulmonary function were not significantly different across HRCT pattern categories. The proportion of patients falling into each Gender-Age-Physiology (GAP) index stage19 did not differ across HRCT patterns. Sex distributions differed, with the inconsistent with UIP group having fewer males (p=0.04) compared to the other HRCT patterns, and smoking history differed with a smaller proportion of possible UIP patients having ever smoked compared to other groups (p=0.003).

Table 1.

Patient Characteristics

All Patients (n=133) Definite UIP (n=41) Possible UIP (n=77) Inconsistent with UIP (n=15) P*

Age (yr), Mean (SD) 63.6 (8.1) 64.2 (7.3) 63.1 (8.4) 64.5 (9.3) 0.70

Male, No. (%) 84 (63.2) 28 (68.3) 51 (66.2) 5 (33.3) 0.04

Ever Smoker, No. (%)** 82 (62.1) 33 (80.5) 38 (50.0) 11 (73.3) 0.003

Biopsy-HRCT Interval (Days), Median (IQR) 60 (30–119) 62 (38–116) 57 (21–118) 61 (14–197) 0.01

FVC %-Predicted, Mean (SD) 67.3 (15.8) 66.6 (14.4) 68.3 (17.1) 64.0 (12.4) 0.60

DLCO %-Predicted, Mean (SD)** 48.9 (15.9) 45.6 (14.6) 50.9 (16.9) 48.1 (13.1) 0.32

GAP Stage** 0.39
 1 (Score 0–3) 45 (45.0) 10 (31.3) 29 (51.8) 6 (50.0)
 2 (Score 4–5) 47 (47.0) 19 (59.4) 23 (41.1) 5 (41.7)
 3 (Score 6–8) 8 (8.0) 3 (9.4) 4 (7.1) 1 (8.3)

HRCT Features, No. (%)****
 Honeycombing 46 (34.6) 39 (95.1) 3 (3.9) 4 (26.7) <0.0001
 Septal Thickening*** 125 (94.0) 39 (95.1) 74 (96.1) 12 (80.0) 0.08
 Traction Bronchiectasis (categorical) 110 (82.7) 35 (85.4) 62 (80.5) 13 (86.7) 0.73
 Traction Bronchiectasis Score, Median (IQR) 4 (2–5) 5 (3–5) 3 (2–5) 5 (2–5) 0.04
 Ground Glass 111 (83.5) 31 (75.6) 68 (88.3) 12 (80.0) 0.19
 Mosaic Attenuation 10 (7.5) 2 (4.9) 4 (5.2) 4 (26.7) 0.03
 Air Trapping 4 (3.0) 1 (2.4) 1 (1.3) 2 (13.3) 0.08

HRCT Craniocaudal Distribution, No. (%) <0.001
 Upper 5 (3.8) 1 (2.4) 0 (0) 4 (26.7)
 Lower 99 (74.4) 30 (73.2) 66 (85.7) 3 (20.0)
 Diffuse 23 (17.3) 8 (19.5) 7 (9.1) 8 (53.3)
 None/Missing 6 (4.5) 2 (4.9) 4 (5.2) 0 (0)

HRCT Axial Distribution, No. (%) <0.001
 Central 3 (2.3) 0 (0) 0 (0) 3 (20.0)
 Peripheral 110 (82.7) 38 (92.7) 68 (88.3) 4 (26.7)
 Diffuse 14 (10.5) 1 (2.4) 5 (6.5) 8 (53.3)
 None/Missing 6 (4.5) 2 (4.9) 4 (5.2) 0 (0)
*

P-value is calculated using ANOVA for continuous variables and Pearson’s Chi-squared test (or Fisher’s exact test, when appropriate) for categorical variables.

**

n=1 patient was missing data on ever smoker; n=33 total patients were missing data on DLCO %predicted and GAP stage (n=9 Definite UIP, n=21 Possible UIP, n=3 Inconsistent with UIP)

***

Interlobular septal thickening

****

Shown are the No. (%) of patients with either Major or Minor extent of each HRCT feature, except Traction Bronchiectasis Score. The Traction Bronchiectasis Score is the number of lobes having bronchiectasis (range 0–5), summarized as Median (IQR).

HRCT Features

As expected, almost all patients with definite UIP pattern had honeycombing on HRCT (95.1%, n=39) while very few patients in other groups had this feature (p<0.001). The inconsistent with UIP group had significantly more mosaic attenuation (26.7% of patients, n=4, p=0.03) and air trapping (13.3%, n=2, p=0.08) compared to other groups. Distribution of abnormality also differed across groups, with the inconsistent with UIP group more likely to have diffuse or upper lung craniocaudal and diffuse or central axial distributions (p<0.001).

HRCT Features and Prognosis

In total, there were 61 events; 13 patients achieved the endpoint via lung transplantation (8, 4, and 1 in the definite, possible and inconsistent groups, respectively), and 48 died without a lung transplantation (18, 25, and 5 in the definite, possible and inconsistent groups, respectively). Figure 2 shows Kaplan-Meier event-free survival curves for each HRCT pattern group with pairwise p-values. Those with definite UIP lived an average of 3.57 years over 7 years of follow up (associated median survival 2.27 years), significantly shorter than those with possible UIP, who lived 5.21 years on average (associated median survival 6.95 years) during this same period (p=0.002). The inconsistent with UIP group was intermediate, with a 7-year restricted mean survival time of 4.75 years (median survival time greater than 7.19 years), and this was not significantly different from Definite UIP or Possible UIP (pairwise p>0.15).

Figure 2. Kaplan-Meier Lung-transplant-free Survival by HRCT Pattern.

Figure 2

Those with definite UIP (dashed line) had a mean survival time of 3.57 years, significantly shorter than those with possible UIP (dotted line), who had a mean survival time of 5.21 years (p=0.002). The inconsistent with UIP group (solid line) was intermediate, with a mean survival time of 4.75 years, and this was not significantly different from definite UIP or possible UIP (p>0.15). On the plot, events are denoted by steps and censored non-events by crosses (“+”).

Univariable Cox proportional hazards analysis on the combined lung-transplant or death endpoint (displayed in Table 2) revealed significantly increased hazards for those with lower FVC and DLCO %-predicted, and GAP stage 2 or 3 (versus 1). Individual HRCT features significantly associated with increased hazard of death or lung transplant were honeycombing (HR 2.04 CI 95% 1.23–3.41 p=0.006) and the traction bronchiectasis score (HR 1.24 per each additional lobe having traction bronchiectasis, CI 95% 1.05–1.46, p=0.01). Having ground glass was associated with reduced hazard of death or lung transplant (HR 0.54, CI 95% 0.30–0.98, p=0.04). Compared to the definite UIP HRCT pattern, a possible UIP pattern was protective against death/transplant (HR=0.44, CI 95% 0.26–0.76, p=0.003).

Table 2.

Univariable Cox Prorportional Hazards of Death or Lung Transplantation for baseline Clinical and HRCT Characteristics

HR CI 95% P C-statistic

Age (per 1-year) 1.02 0.99–1.05 0.33 0.52

Male Gender 1.19 0.69–2.04 0.53 0.50

FVC %-Predicted (per 10% increase) 0.64 0.53–0.76 <0.001 0.70

DLCO %-Predicted (per 10% increase) 0.73 0.61–0.88 <0.001 0.66

GAP Stage 0.62
 1 (Score 0–3) Ref Ref Ref
 2 (Score 4–5) 2.03 1.12–3.68 0.02
 3 (Score 6–8) 5.50 2.36–12.82 <0.001

Ever-smoker 0.88 0.52–1.48 0.62 0.52

HRCT Pattern 0.62
 Definite UIP Ref Ref Ref
 Possible UIP 0.44 0.26–0.76 0.003
 Inconsistent with UIP 0.56 0.23–1.37 0.21

HRCT Features

 Honeycombing 2.04 1.23–3.41 0.006 0.61

 Septal Thickening 0.87 0.31–2.40 0.78 0.50

 Traction Bronchiectasis (categorical) 1.01 0.51–2.00 0.98 0.51

 Traction Bronchiectasis Score* 1.24 1.05–1.46 0.01 0.62

 Ground Glass 0.54 0.30–0.98 0.04 0.55

 Mosaic Attenuation 1.92 0.87–4.23 0.11 0.53

 Air Trapping 1.01 0.25–4.17 0.99 0.51

HRCT Craniocaudal Distribution 0.54
 Lower Ref Ref Ref
 Upper 2.42 0.74–7.86 0.14
 Diffuse 1.34 0.72–2.51 0.36
 None/Missing 1.24 0.38–4.04 0.72

HRCT Axial Distribution 0.52
 Peripheral Ref Ref Ref
 Central 0.69 0.10–5.01 0.71
 Diffuse 0.58 0.21–1.61 0.30
 None/Missing 1.07 0.33–3.43 0.91
*

The Traction Bronchiectasis Score is the number of lobes having bronchiectasis (range 0–5), and the hazard is per 1-lobe increase.

As shown in Table 3, multivariable analysis adjusting for baseline age, gender, %predicted FVC, and %-predicted DLCO via the GAP Stage, extent of fibrosis (via the traction bronchiectasis score) and ever-smoker status, possible UIP HRCT pattern continued to be associated with reduced hazard of death or lung transplant (HR=0.42, CI 95% 0.23–0.78, p=0.006). The c-statistic for this model was 0.710 (where 0.50 indicates model discrimination no better than chance and 1.0 is perfect discrimination18). We also evaluated a multivariable model including GAP Stage, extent of fibrosis (traction bronchiectasis score) and ever-smoker status, and obtained a c-statistic of 0.681, indicating that addition of the HRCT pattern variable improves model discrimination. The complete model without HRCT pattern is shown in the e-appendix e-Table 1. We also evaluated multivariable models adjusted for baseline age, gender, %-predicted FVC, and %-predicted DLCO, with these variables in their native form (i.e. not summarized by the GAP Stage), extent of fibrosis (via the traction bronchiectasis score) and ever-smoker status, and found that possible UIP pattern continued to be associated with reduced hazard of death or lung transplantation (see e-Table 2).

Table 3.

Multivariable Cox Proportional Hazards of HRCT Pattern for Death or Lung Transplantation, adjusted for GAP stage, smoking status, and extent of fibrosis

c-statistic = 0.710
n=133
HR CI 95% P

GAP Stage
 1 (Score 0–3) Ref Ref Ref
 2 (Score 4–5) 1.62 0.88–3.00 0.12
 3 (Score 6–8) 4.77 1.97–11.53 0.001

Ever-Smoker 0.64 0.35–1.14 0.13

HRCT Pattern
 Definite UIP Ref Ref Ref
 Possible UIP 0.42 0.23–0.78 0.006
 Inconsistent with UIP 0.56 0.23–1.38 0.21

Traction Bronchiectasis Score* 1.12 0.95–1.32 0.18
*

The Traction Bronchiectasis Score is the number of lobes having bronchiectasis (range 0–5), and the hazard is per 1-lobe increase.

In a separate multivariable model adjusted for GAP stage, extent of fibrosis, and ever-smoker status, presence of ground glass was independently associated with reduced hazard of death or lung transplantation (HR 0.43, CI 95% 0.23–0.80, p=0.008). The results presented here include multiple imputations; see e-Tables 3 and 5 in the e-appendix for un-imputed Cox proportional hazards analysis results.

Because the Kaplan-Meier transplant-free survival plot indicates a potential issue with crossing hazards that could affect Cox proportional hazards model estimates (see Figure 2 and also e-Figure 1 for a C log-log plot in the e-appendix), the analysis was repeated using pseudo-observation models for estimating restricted mean lifetimes; restricted mean models don’t rely on a proportional hazards assumption in making inferences. Complete results are shown in e-Tables 4, 5, 7, and 8 for imputed and un-imputed analyses. Results were in line with those seen in the Cox models. Importantly, after adjusting for the baseline GAP stage, smoking status, and fibrosis extent, a possible UIP HRCT pattern versus definite UIP pattern was associated with 31% longer life expectancy (Exp[Beta]=1.31, CI 95% 0.99–1.72, p=0.058), consistent with findings obtained via the Cox proportional hazards analysis.

DISCUSSION AND CONCLUSIONS

In a retrospectively-assembled cohort of IPF patients with histopathologically-confirmed UIP, we evaluated the prognostic significance of the HRCT patterns outlined in the 2011 ATS/ERS/JRS/ALAT guidelines for diagnosis and management of IPF, and found that patients with a possible UIP pattern have a longer lung transplant-free survival compared to those with definite UIP, independent of age, baseline lung function, smoking status, and fibrosis extent. The inconsistent with UIP pattern appears intermediate to the definite and possible UIP patterns, but with just 15 patients (or 11% of the cohort) in this category, it is difficult to draw conclusions about this group. Further, addition of the HRCT pattern variable to a multivariable model including baseline age, baseline lung function, smoking status, and fibrosis extent resulted in improvement in the c-statistic, suggesting that knowledge of this variable improves model discrimination. The guidelines-based HRCT pattern is commonly referenced in HRCT reports, making it a readily available data point providing physicians and patients useful prognostic information beyond that encompassed by clinical variables3–5.

The extent of fibrotic HRCT features including overall fibrosis score, honeycombing, reticular pattern, and traction bronchiectasis have been associated with poor prognosis in IPF and other fibrotic idiopathic interstitial pneumonias (IIPs)2–5. In unselected patients with fibrotic IIP, a definite UIP HRCT pattern has been associated with significantly worse survival compared to possible or inconsistent with UIP patterns6,7. In contrast, a difference in survival by overall HRCT pattern has not been consistently observed in patients with IPF4,9,10. Sumikawa and colleagues evaluated similarly defined HRCT patterns (“Definite UIP”, “Consistent with UIP”, and “Suggestive of Alternative Diagnosis”) in 98 IPF patients with biopsy-confirmed UIP, and found no statistically significant difference in survival across these categories, although estimated survival times were longer for “Consistent with UIP” and “Suggestive of Alternative Diagnosis,” as compared to Definite UIP9. Yagihashi and colleagues evaluated IPF patients enrolled in clinical therapeutics trials and found no difference in survival among those with radiologically and histopathologically concordant findings (e.g. definite/probable UIP histopathology and definite UIP HRCT) compared to those with discordant findings (definite/probable UIP histopathology and Inconsistent with UIP HRCT)10. Arcadu and colleagues recently evaluated the association of 2011 guidelines-based HRCT patterns in IPF patients, and determined that after adjustment for age, gender, and baseline FVC and DLCO, survival was not significantly different between groups, despite “consistent with UIP” pattern having worse survival compared to possible/inconsistent patterns in unadjusted analysis11. There are several factors contributing to differences in our findings compared to prior reports. First, inclusion criteria differed. Arcadu and colleagues11 did not require histopathologic confirmation of a UIP pattern, and in fact only 54% in the possible UIP group had a diagnostic lung biopsy. Second, assignment of HRCT pattern likely differed, despite similarly described methodology. Presence of radiologic honeycombing is reported in 100%, 39%, and 24% of consistent, possible, and inconsistent groups in the Arcadu study11, compared to 95%, 4%, and 27% in our definite, possible, and inconsistent groups. If radiologic honeycombing is the defining feature of a phenotype with poor outcomes, the higher prevalence of honeycombing in Arcadu and colleague’s possible UIP group may obscure survival differences when comparing to definite UIP. Of note, the handling of lung transplantation, an important event competing with death in this population, is not specifically mentioned in several reports6,7,9, or was handled via censoring11. We utilized death or lung transplant as the primary endpoint, making direct comparison with these studies difficult.

Additionally, we elected to use a traction bronchiectasis score as a measure of fibrosis extent to adjust for baseline disease severity, along with baseline FVC and DLCO. This avoids problems with collinearity when including a combined fibrosis score20,21 (measures presence and extent of honeycombing, reticulation, and traction bronchiectasis), and HRCT pattern (defined by presence of honeycombing) in a single prediction model, as traction bronchiectasis is not integral in the distinction between the HRCT pattern categories. Traction bronchiectasis scores have been associated with increased risk of death in IPF patients9,22, suggesting it is an appropriate surrogate for disease extent. Although a summary score of the number of lobes having traction bronchiectasis as we use here has not been validated as a predictor of transplant-free survival time in other cohorts, it was a significant univariable predictor of transplant-free survival time in this cohort.

Of HRCT findings considered inconsistent with IPF, mosaic attenuation and air trapping were more prevalent among patients in the inconsistent with IPF group compared to the definite/possible IPF groups, as were diffuse axial and craniocaudal distributions of abnormality. Yagihashi and colleagues similarly identified mosaic attenuation/air trapping as a common reason for classification in the discordant radiologic/histopathologic group, and it was not significantly associated with increased risk of death in their report (unadjusted HR=1.194, CI 95% 0.265–4.006, p=0.79)10. Similarly, we found no association between presence of mosaic attenuation and risk of death or lung transplant (HR 1.92, CI 95% 0.87–4.23, p=0.11), but with just 10 patients (7.5% of the cohort) having mosaic attenuation, power to detect a difference in hazards is limited. Ground glass was prevalent across HRCT category groups, and in unadjusted and adjusted Cox models was associated with decreased hazard of death. The reason for this association is unclear, and is counter to the observation in several other studies where ground glass attenuation was associated with increased hazard of death22, or not associated with any difference in survival9,20. Ground glass opacities on CT scan are not consistently associated with any underlying histopathologic finding (e.g. fibrotic or inflammatory changes), a possible explanation for some of the variability in its association with outcomes.21 Understanding the clinical relevance of individual features considered inconsistent with UIP/IPF in the setting of biopsy-proved UIP has important diagnostic and treatment implications and requires further study.

Strengths of our study include availability of histopathology confirmation of UIP/IPF in all patients, and vital status confirmation via two sources (electronic medical record and Social Security Death Master File) to ensure complete capture of events. There were several weaknesses. Inclusion of patients collected from a single tertiary referral center, potentially limiting the generalizability of our findings. Additionally, only 95% of patients assigned by interpreting radiologists to the definite UIP HRCT category had honeycombing (and 5% did not have honeycombing), and conversely, 4% of patients assigned to the possible UIP HRCT category did have honeycombing, indicating that assignment of HRCT pattern based on guideline definitions was not perfect. Further, we had just one expert thoracic radiologist (of a panel of four) review each HRCT, rather than use consensus to determine presence of HRCT features and overall pattern, as was done in previous studies4,9–11. There is recognized variability in interpretation of HRCT findings, with inter-observer agreement on categorization of overall HRCT pattern as defined in the 2011 consensus guidelines reported as moderate (weighted kappa 0.48 and 0.52 for general and thoracic radiologists, respectively)23. We are unable to report on agreement across the various readers in this study.

An additional weakness of this study is the retrospective nature of patient collection. We do not prospectively document clinical characteristics such as non-pulmonary symptoms (e.g., gastroesophageal reflux disease, joint swelling, Raynaud phenomenon) as part of our multidisciplinary team review, and measurement of autoantibodies is at the discretion of the treating pulmonologist, rendering us unable to objectively compare these important clinical characteristics across HRCT groups. Second, we do not have comprehensive, prospectively collected data on treatments rendered during the study period. It is likely that some patients received immunosuppressant drugs or antifibrotic agents within or outside of clinical trials.24–26

In conclusion, we found that a possible UIP pattern on HRCT is associated with reduced hazard of death or lung transplant compared to definite UIP pattern, and this effect persists after adjusting for baseline age, pulmonary physiology, and extent of traction bronchiectasis. The 2011 consensus guideline-based HRCT pattern may represent a widely available radiologic measure informative of prognosis in IPF patients, and possibly driven by the presence of honeycombing in almost all patients categorized as definite UIP compared to very few in the possible UIP category. Validation of these findings in a separate cohort of patients is prudent prior to broad application in practice.

Supplementary Material

supplement

HIGHLIGHTS.

  • IPF is a progressive interstitial lung disease with poor prognosis.

  • We compared transplant-free survival by radiologic diagnosis category.

  • “Possible UIP” category has significantly longer survival than “definite UIP”.

Acknowledgments

Authorship Contributions: FJM and KRF conceptualized the work and participated in data analysis and acquisition. MLS, MX, SM, LBT, AMN, SLS, BHG, EAK, ARC, BS, JLM, AL, and NT participated in data analysis and acquisition. MLS, LBT, and KRF drafted the manuscript. MX, SM, AMN, SLS, BHG, EAK, ARC, BS, JLM, AL, FJM and NT critically revised the manuscript. All authors approved the final version to be published and take accountability for the work. MLS, LBT, and KRF are responsible for the integrity of the work, from inception to publication.

Funding Support: T32 HL007749-21, and K24 HL111316

ABBREVIATIONS LIST

DLCO

Diffusing Capacity of Lung for Carbon Monoxide

FVC

Forced Vital Capacity

HRCT

High Resolution Computed Tomography

IPF

Idiopathic Pulmonary Fibrosis

IRB

Institutional Review Board

SD

Standard deviation

UIP

Usual Interstitial Pneumonia

Footnotes

Some of these results were previously presented as an abstract at the American Thoracic Society International Conference, May 18–21, 2014, San Diego, CA.

This article has an online data supplement.

Conflicts of Interest:

Dr. Salisbury has nothing to disclose relevant to the submitted work.

Dr. Tolle reports personal fees from Boehringer-Ingelheim, personal fees from Genentech, outside the submitted work.

Ms. Xia has nothing to disclose relevant to the submitted work.

Dr. Murray has nothing to disclose relevant to the submitted work.

Ms. Tayob has nothing to disclose relevant to the submitted work.

Dr. Nambiar has nothing to disclose relevant to the submitted work.

Dr. Schmidt has nothing to disclose relevant to the submitted work.

Dr. Lagstein has nothing to disclose relevant to the submitted work.

Dr. Myers has nothing to disclose relevant to the submitted work.

Dr. Gross has nothing to disclose relevant to the submitted work.

Dr. Kazerooni has nothing to disclose relevant to the submitted work.

Dr. Sundaram has nothing to disclose relevant to the submitted work.

Dr. Chughtai has nothing to disclose relevant to the submitted work.

Dr. Martinez reports grants from National Institutes of Health, non-financial support from Bayer, non-financial support from Centocor, non-financial support from Gilead, non-financial support from Promedior, personal fees from Ikaria, personal fees from Genentech, personal fees from Nycomed/Takeda, personal fees from Pfizer, personal fees from Vertex, personal fees from American Thoracic Society, personal fees from Inova Health System, personal fees from MedScape, personal fees from Spectrum Health System, personal fees from University of Texas Southwestern, personal fees from Stromedix/Biogen, personal fees from Axon Communications, from Johnson & Johnson, from Genzyme, personal fees from National Association for Continuing Education, personal fees from Boehringer Ingelheim, personal fees from Veracyte, during the conduct of the study; personal fees from Forest, personal fees from Janssens, personal fees from GSK, personal fees from Nycomed/Takeda, personal fees from Actelion, personal fees from Amgen, personal fees from Astra Zeneca, personal fees from CSA Medical, personal fees from Ikaria/Bellerophon, personal fees from Forest, personal fees from Genentech, personal fees from GSK, personal fees from Janssens, personal fees from Merck, personal fees from Pearl, personal fees from Nycomed/Takeda, personal fees from Pfizer, personal fees from Roche, personal fees from Sudler & Hennessey, personal fees from American College of Chest Physicians, personal fees from CME Incite, personal fees from Center for Healthcare Education, personal fees from Inova Health System, personal fees from MedScape, personal fees from Miller Medical, personal fees from National Association for Continuing Education, personal fees from Paradigm, personal fees from Peer Voice, personal fees from Projects in Knowledge, personal fees from St. John’s Hospital, personal fees from St. Mary’s Hospital, personal fees from University of Illinois Chicago, personal fees from UpToDate, personal fees from Wayne State University, personal fees from GSK, personal fees from Boehringer Ingelheim, personal fees from GSK, personal fees from Ikaria, personal fees from Bayer, personal fees from Nycomed/Takeda, personal fees from Grey Healthcare, personal fees from Merion, personal fees from Informa, personal fees from Annenberg, personal fees from GSK, personal fees from Forest, outside the submitted work.

Dr. Flaherty reports grants from NIH, during the conduct of the study; personal fees from Boehringer Ingelheim, personal fees from Fibrogen, personal fees from Genentech, personal fees from Gilead, personal fees from Ikaria, personal fees from ImmuneWorks, personal fees from MedImmune, personal fees from Novartis, personal fees from Takeda, personal fees from Vertex, personal fees from Veracyte, personal fees from Roche, personal fees from Pulmonary Fibrosis Foundation, grants from ImmuneWorks, grants and personal fees from Intermune, grants from Bristol-Myers Squibb, outside the submitted work.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

  • 1.Raghu G, Collard HR, Egan JJ, et al. An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management. American journal of respiratory and critical care medicine. 2011;183(6):788–824. doi: 10.1164/rccm.2009-040GL. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Ley B, Collard HR, King TE., Jr Clinical course and prediction of survival in idiopathic pulmonary fibrosis. American journal of respiratory and critical care medicine. 2011;183(4):431–440. doi: 10.1164/rccm.201006-0894CI. [DOI] [PubMed] [Google Scholar]
  • 3.Ley B, Elicker BM, Hartman TE, et al. Idiopathic pulmonary fibrosis: CT and risk of death. Radiology. 2014;273(2):570–579. doi: 10.1148/radiol.14130216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Lynch DA, Godwin JD, Safrin S, et al. High-resolution computed tomography in idiopathic pulmonary fibrosis: diagnosis and prognosis. American journal of respiratory and critical care medicine. 2005;172(4):488–493. doi: 10.1164/rccm.200412-1756OC. [DOI] [PubMed] [Google Scholar]
  • 5.Salisbury ML, Lynch DA, van Beek EJ, et al. Idiopathic Pulmonary Fibrosis: Adaptive Multiple Features Method Fibrosis Association with Outcomes. American journal of respiratory and critical care medicine. 2016 doi: 10.1164/rccm.201607-1385OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Flaherty KR, Thwaite EL, Kazerooni EA, et al. Radiological versus histological diagnosis in UIP and NSIP: survival implications. Thorax. 2003;58(2):143–148. doi: 10.1136/thorax.58.2.143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Romei C, Tavanti L, Sbragia P, et al. Idiopathic interstitial pneumonias: do HRCT criteria established by ATS/ERS/JRS/ALAT in 2011 predict disease progression and prognosis? Radiol Med. 2015;120(10):930–940. doi: 10.1007/s11547-015-0526-0. [DOI] [PubMed] [Google Scholar]
  • 8.Lee JW, Shehu E, Gjonbrataj J, et al. Clinical findings and outcomes in patients with possible usual interstitial pneumonia. Respir Med. 2015;109(4):510–516. doi: 10.1016/j.rmed.2015.02.008. [DOI] [PubMed] [Google Scholar]
  • 9.Sumikawa H, Johkoh T, Colby TV, et al. Computed tomography findings in pathological usual interstitial pneumonia: relationship to survival. American journal of respiratory and critical care medicine. 2008;177(4):433–439. doi: 10.1164/rccm.200611-1696OC. [DOI] [PubMed] [Google Scholar]
  • 10.Yagihashi K, Huckleberry J, Colby TV, et al. Radiologic-pathologic discordance in biopsy-proven usual interstitial pneumonia. The European respiratory journal. 2016;47(4):1189–1197. doi: 10.1183/13993003.01680-2015. [DOI] [PubMed] [Google Scholar]
  • 11.Arcadu A, Byrne SC, Pirina P, Hartman TE, Bartholmai BJ, Moua T. Correlation of pulmonary function and usual interstitial pneumonia computed tomography patterns in idiopathic pulmonary fibrosis. Respir Med. 2017;129:152–157. doi: 10.1016/j.rmed.2017.06.013. [DOI] [PubMed] [Google Scholar]
  • 12.Tolle LBTN, Nambiar A, Schmidt SL, Flint A, Lagstein A, Myers JL, Schmidt LA, Gross B, Kazerooni E, Sundaram B, Chughtai A, Toews GB, Martinez FJ, Flaherty KR. The Role of High Resolution CT Patterns in Prognosticating IPF. American journal of respiratory and critical care medicine. 2014;189:A1494. [Google Scholar]
  • 13.Hansell DM, Bankier AA, MacMahon H, McLoud TC, Muller NL, Remy J. Fleischner Society: glossary of terms for thoracic imaging. Radiology. 2008;246(3):697–722. doi: 10.1148/radiol.2462070712. [DOI] [PubMed] [Google Scholar]
  • 14.Pepe MS, Fleming TR. Weighted Kaplan-Meier statistics: a class of distance tests for censored survival data. Biometrics. 1989;45(2):497–507. [PubMed] [Google Scholar]
  • 15.Cox DR. Regression models and life tables (with discussion) Journal of the Royal Statistical Society. 1972;34:187–220. [Google Scholar]
  • 16.Andersen PK, Hansen MG, Klein JP. Regression analysis of restricted mean survival time based on pseudo-observations. Lifetime Data Anal. 2004;10(4):335–350. doi: 10.1007/s10985-004-4771-0. [DOI] [PubMed] [Google Scholar]
  • 17.Van Buuren SG-OK. mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software. 2011;35(3):1–67. [Google Scholar]
  • 18.Terry M, Therneau aPMG. Modeling Survival Data: Extending the Cox Model. New York: Springer; 2000. [Google Scholar]
  • 19.Ley B, Ryerson CJ, Vittinghoff E, et al. A multidimensional index and staging system for idiopathic pulmonary fibrosis. Annals of internal medicine. 2012;156(10):684–691. doi: 10.7326/0003-4819-156-10-201205150-00004. [DOI] [PubMed] [Google Scholar]
  • 20.Best AC, Meng J, Lynch AM, et al. Idiopathic pulmonary fibrosis: physiologic tests, quantitative CT indexes, and CT visual scores as predictors of mortality. Radiology. 2008;246(3):935–940. doi: 10.1148/radiol.2463062200. [DOI] [PubMed] [Google Scholar]
  • 21.Kazerooni EA, Martinez FJ, Flint A, et al. Thin-section CT obtained at 10-mm increments versus limited three-level thin-section CT for idiopathic pulmonary fibrosis: correlation with pathologic scoring. AJR. American journal of roentgenology. 1997;169(4):977–983. doi: 10.2214/ajr.169.4.9308447. [DOI] [PubMed] [Google Scholar]
  • 22.Jacob J, Bartholmai BJ, Rajagopalan S, et al. Mortality prediction in idiopathic pulmonary fibrosis: evaluation of computer-based CT analysis with conventional severity measures. The European respiratory journal. 2017;49(1) doi: 10.1183/13993003.01011-2016. [DOI] [PubMed] [Google Scholar]
  • 23.Walsh SL, Calandriello L, Sverzellati N, Wells AU, Hansell DM, Consort UIPO. Interobserver agreement for the ATS/ERS/JRS/ALAT criteria for a UIP pattern on CT. Thorax. 2016;71(1):45–51. doi: 10.1136/thoraxjnl-2015-207252. [DOI] [PubMed] [Google Scholar]
  • 24.Raghu G, Anstrom KJ, King TE, Jr, Lasky JA, Martinez FJ Network IPFCR. Prednisone, azathioprine, and N-acetylcysteine for pulmonary fibrosis. The New England journal of medicine. 2012;366(21):1968–1977. doi: 10.1056/NEJMoa1113354. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.King TE, Jr, Bradford WZ, Castro-Bernardini S, et al. A phase 3 trial of pirfenidone in patients with idiopathic pulmonary fibrosis. The New England journal of medicine. 2014;370(22):2083–2092. doi: 10.1056/NEJMoa1402582. [DOI] [PubMed] [Google Scholar]
  • 26.Noble PW, Albera C, Bradford WZ, et al. Pirfenidone in patients with idiopathic pulmonary fibrosis (CAPACITY): two randomised trials. Lancet. 2011;377(9779):1760–1769. doi: 10.1016/S0140-6736(11)60405-4. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

supplement

RESOURCES