Open Access

Predicting survival of patients with idiopathic pulmonary fibrosis using GAP score: a nationwide cohort study

  • Sang Hoon Lee1,
  • Song Yee Kim2,
  • Dong Soon Kim3,
  • Young Whan Kim4,
  • Man Pyo Chung5,
  • Soo Taek Uh6,
  • Choon Sik Park7,
  • Sung Hwan Jeong8,
  • Yong Bum Park9,
  • Hong Lyeol Lee10,
  • Jong Wook Shin11,
  • Eun Joo Lee12,
  • Jin Hwa Lee13,
  • Yangin Jegal14,
  • Hyun Kyung Lee15,
  • Yong Hyun Kim16,
  • Jin Woo Song3,
  • Sung Woo Park7 and
  • Moo Suk Park2Email author
Respiratory Research201617:131

https://doi.org/10.1186/s12931-016-0454-0

Received: 15 July 2016

Accepted: 14 October 2016

Published: 18 October 2016

Abstract

Background

The clinical course of idiopathic pulmonary fibrosis (IPF) varies widely. Although the GAP model is useful for predicting mortality, survivals have not yet been validated for each GAP score. We aimed to elucidate how prognosis is related to GAP score and GAP stage in IPF patients.

Methods

The Korean Interstitial Lung Disease Study Group conducted a national survey to evaluate various characteristics in IPF patients from 2003 to 2007. Patients were diagnosed according to the 2002 criteria of the ATS/ERS. We enrolled 1,685 patients with IPF; 1,262 had undergone DLCO measurement. Patients were stratified based on GAP score (0–7): GAP score Group 0 (n = 26), Group 1 (n = 150), Group 2 (n = 208), Group 3 (n = 376), Group 4 (n = 317), Group 5 (n = 138), Group 6 (n = 39), and Group 7 (n = 8).

Results

Higher GAP score and GAP stage were associated with a poorer prognosis (p < 0.001, respectively). Survival time in Group 3 was lower than those in Groups 1 and 2 (p = 0.043 and p = 0.039, respectively), and higher than those in groups 4, 5, and 6 (p = 0.043, p = 0.032, and p = 0.003, respectively). Gender, age, and DLCO (%) differed significantly between Groups 2 and 3. All four variables in the GAP model differed significantly between Groups 3 and 4.

Conclusion

The GAP system showed significant predictive ability for mortality in IPF patients. However, prognosis in IPF patients with a GAP score of 3 were significantly different from those in the other stage I groups and stage II groups of Asian patients.

Keywords

Idiopathic pulmonary fibrosis GAP stage Prognosis

Background

Idiopathic pulmonary fibrosis (IPF) is a specific form of diffuse interstitial lung disease (DILD) that mainly occurs in adults over the age of 50 [1]. It is a chronic, progressive, irreversible, fibrosing interstitial pneumonia, characterized by limited to the lungs [2]. While the etiology of IPF is unknown, it is related to a histological and/or radiological “usual interstitial pneumonia” (UIP) pattern [1]. Morbidity and mortality are high in IPF—the median survival time is only 2.5 to 3.5 years—and the clinical course and prognosis vary widely among individual patients [3]. This high variability makes predicting prognosis difficult, which in turn causes problems with treatment planning. Therefore, physicians must be better equipped to predict the clinical course of IPF if they are to provide precise prognoses and adequate treatment to patients.

Previous studies have shown that age, gender, lung function change, radiological pattern, histological variability, dyspnoea, cough, pulmonary artery hypertension, amount of elastic fiber, and some molecular biomarkers are associated with prognosis [410]. Some investigators have attempted to predict clinical course using these prognostic factors [11]. However, none of these predictive models have been widely adopted, as they are difficult to use or lack external validation. In 2012, Ley et al. suggested a novel system for staging IPF that is similar to those used in asthma, chronic obstructive pulmonary disease (COPD), and lung cancer [12]. The so-called GAP index and staging system uses of four variables: gender (G), age (A), and two pulmonary physiological parameters (P)—percentage predicted forced vital capacity (FVC [%]), and percentage predicted diffusion capacity of the lungs for carbon monoxide (DLCO [%]). These four variables are commonly measured at the initial visit and are easily followed up. This system has helped clinicians to predict prognosis and decide on management strategies. Although this GAP model is simple-to-use for predicting mortality, prognoses have not yet been evaluated for each GAP score. The purpose of our study was to validate, using national survey data, how prognosis is related to GAP score and GAP stage in patients with IPF.

Methods

Patient selection

The study involved patients who had been diagnosed with idiopathic interstitial pneumonia (IIP) at 54 university and teaching hospitals between January 1, 2003 and December 31, 2007. At each hospital, pulmonary specialists (pulmonologists, chest radiologists, and pathologists) had confirmed the diagnoses, and data were reviewed by the Scientific Committee at the Korean Academy of Tuberculosis and Respiratory Diseases. IPF was diagnosed on the basis of the 2002 criteria of the American Thoracic Society/European Respiratory Society (ATS/ERS) [13]. Initially, we excluded patients who had a history of connective tissue disease, pneumoconiosis, or ingestion of either a cytotoxic agent or amiodarone, and all of which are well-known to cause interstitial lung disease. Additionally, we excluded patients with suspected chronic hypersensitivity pneumonitis; such decisions were made on the basis of history, laboratory data, and committee conference.

In total, 2,186 patients with idiopathic interstitial pneumonia (IIP) were registered; of these, patients with other forms of ILD than IPF (n = 501) were excluded from the study, as were patients who had not undergone pulmonary function testing (PFT) that included DLCO measurement (n = 423). Ultimately, 1,262 patients were included in the study: 760 at GAP stage I, 455 at stage II, and 47 at stage III (Fig. 1). We reviewed the clinical, radiological, and physiological data of all the included patients. With regard to physiological data, we investigated FVC, FVC (%), forced expiratory volume in one second (FEV1), percentage predicted FEV1 (FEV1 [%]), total lung capacity (TLC), percentage predicted TLC (TLC [%]), DLCO, and percentage predicted DLCO (DLCO [%]). In addition, we evaluated patients’ C-reactive protein (CRP) levels, and examined their blood for the positivity of antinuclear antibody (ANA) and rheumatoid factor (RF). The composite physiologic index (CPI), which is a predictive model for IPF prognosis, was calculated as Well et al. reported [14]. All hospital data were entered into the ILD web-based registry (http://www.ild.or.kr/).
Fig. 1

Flow chart showing inclusion and exclusion of patients in the study. A total of 1262 IPF patients were analysed in this study, excluding 501 with other interstitial lung disease and 423 who had not undergone pulmonary function testing that had included DLCO. Note: Groups with a total GAP score of 0 and 7 were excluded because they contained too few patients and because the baseline characteristics of patients with GAP score 0 were significantly different (all women, never smokers). No patients with a GAP score of 8 were included, because the “unable to perform” category in DLCO was not checked in this study. Definition of abbreviations: IIP, idiopathic interstitial pneumonia; ILD, interstitial lung disease; AIP, acute interstitial pneumonia; BOOP, bronchiolitis obliterans organizing pneumonia; DIP, desquamative interstitial pneumonia; LIP, lymphocytic interstitial pneumonia; NSIP, non-specific interstitial pneumonia; RB-ILD, respiratory bronchiolitis-associated interstitial lung disease

GAP model

Total GAP score was calculated using the method suggested by Ley et al [12] (Table 1). All four clinical variables were examined: gender (woman: 0 points, man: 1 point), age (0–2 points), FVC (%) (0–2 points), and DLCO (%) (0–3 points). We then divided the patients on the basis of GAP score (Groups 0–7): Group 0 (n = 26), Group 1 (n = 150), Group 2 (n = 208), Group 3 (n = 376), Group 4 (n = 317), Group 5 (n = 138), Group 6 (n = 39), and Group 7 (n = 8). In the physiological category, the “cannot perform” classification (3 points) of DLCO measurement had not been recorded in the data used. For this reason, the total GAP score of 8 was not investigated in the current study. Additionally, we excluded patients with total GAP scores of 0 (n = 26), and 7 (n = 8), as these two groups contained much fewer patients than the other groups. The characteristics in Group 0, which contained only women who had never smoked, were significantly different from those in the other groups.
Table 1

GAP index and number (%) of patients

Variables

GAP Points

No. of patients

Gender

 Female

0

315 (25.7)

 Male

1

913 (74.3)

Age, yr

 ≤60

0

263 (21.4)

 61–65

1

208 (16.9)

 >65

2

757 (61.6)

Physiology

 FVC, % predicted

  >75

0

626 (50.9)

  50–75

1

540 (44.0)

  <50

2

62 (5.0)

 DLCO, % predicted

  

  >55

0

735 (59.9)

  36–55

1

399 (32.5)

  ≤35

2

94 (7.6)

  Can not perform

3

-

GAP stage

 Stage I

0–3

760 (60.2)

 Stage II

4–5

455 (36.1)

 Stage III

6–8

47 (3.7)

Note: Values in parentheses are percentages

GAP gender, age, and 2 lung physiology variables (FVC and DLCO)

Statistical analysis

Information was obtained from web-based questionnaires and medical records; it was stored and analysed using the Excel™ computer program. Analysis of variance (ANOVA) was used to compare continuous variables, and Bonferroni’s correction was used for post-hoc analysis. Pearson’s chi-squared test or Fisher’s exact test were used to compare categorical variables. Continuous variables were presented as mean ± standard deviation, or proportions within each group as a percentage.

To compare the GAP score groups in terms of survival times, Kaplan-Meier survival analysis and the log-rank test were used. In addition, multivariate analysis was conducted with Cox proportional hazard model. C-statistic was also performed for the GAP model at 1-year, 2-year, and 3-year. When performing the survival analysis, we censored the following conditions: (1) still alive at last visit (at last visit date), (2) lost to follow-up loss and (3) had undergone lung transplantation (at surgery date). Statistics were analysed using SPSS™ Version 20 (SPSS, Chicago, IL, USA). An adjusted p-value less than 0.05 was regarded as statistically significant.

Results

Demographic characteristics

There were 1,228 patients with a GAP score from 1 to 6. The baseline characteristics of these patients are summarized in Table 2. The mean age of the study population was 67.5 ± 9.3 years and was lowest in Group 1. The highest proportion of men occurred in Group 6 (p < 0.001). Although the patients in Group 1 had experienced the longest duration of respiratory symptoms at diagnosis, and those in Group 6 had experienced the shortest, this was not statistically significant (p = 0.133). With regard to smoking status, 83.3 % of patients in Group 6 were ever-smokers; the equivalent values in Groups 1 and 2 were 58.7 and 50.5 %, respectively. Furthermore, smoking duration and amount were higher in Group 6 than in the other score groups (p < 0.001 and p = 0.024, respectively). Patients with a higher GAP score tended to have been diagnosed using the clinical method rather than surgical lung biopsy. Specifically, the proportion of clinically diagnosed patients was 87.2 % in Group 6, whereas it was 22.0 % Group 1. The percentages of ANA and RF positivity did not differ significantly among the groups (p = 0.580 and p = 0.177, respectively). Increased CRP level was significantly associated with higher GAP score (p < 0.001). CPI also tended to increase as GAP score increased (p < 0.001). The mean value of CPI was significantly different between Group 3 and Group 4, although there was no significant difference between Group 2 and Group 3 after Bonferroni’s correction. The mean follow-up duration of the study population was 19.0 ± 16.0 months.
Table 2

Baseline characteristics of study population according to total GAP score

Variable

Total GAP score (n = 1,228)

p-value

1 (n = 150)

2 (n = 208)

3 (n = 376)

4 (n = 317)

5 (n = 138)

6 (n = 39)

Age, yr

56.2 ± 5.7

62.5 ± 8.9

69.0 ± 7.8

71.7 ± 7.6

72.4 ± 7.5

71.8 ± 5.9

<0.001a

Sex, male (%)

91 (60.7)

114 (54.8)

281 (74.7)

269 (84.9)

123 (89.1)

35 (89.7)

<0.001

Duration of symptoms at diagnosis (Month)

15.9 ± 27.9

9.8 ± 15.0

10.7 ± 20.6

10.8 ± 21.8

9.5 ± 17.8

5.4 ± 12.8

0.133

Smoking

      

<0.001

 Non-smoker

57 (41.3)

98 (49.5)

106 (31.7)

80 (28.3)

33 (27.3)

6 (16.7)

 

 Former

36 (26.1)

49 (24.7)

139 (41.6)

126 (44.5)

59 (48.8)

19 (52.8)

 

 Current

45 (32.6)

51 (25.8)

89 (26.6)

77 (27.2)

29 (24.0)

11 (30.6)

 

Smoking duration (yrs)

29.1 ± 9.1

29.8 ± 11.0

37.6 ± 11.6

38.0 ± 13.0

37.4 ± 15.7

41.3 ± 9.0

<0.001a

Smoking amounts (PYrs)

32.1 ± 18.2

30.7 ± 19.5

37.0 ± 18.0

36.6 ± 20.1

38.6 ± 25.7

40.0 ± 17.9

0.024a

Diagnostic method (%)

      

<0.001

 Clinical

33 (22.0)

86 (41.3)

235 (62.5)

244 (77.0)

119 (86.2)

34 (87.2)

 

 Surgical

117 (78.0)

122 (58.7)

141 (37.5)

73 (23.0)

19 (13.8)

5 (12.8)

 

Outcome

      

<0.001

 Alive

92 (61.3)

118 (56.7)

167 (44.4)

114 (36.0)

36 (26.1)

10 (25.6)

 

 Dead

24 (16.0)

31 (14.9)

82 (21.8)

83 (26.2)

35 (25.4)

15 (38.5)

 

 Loss

34 (22.7)

59 (28.4)

127 (33.8)

120 (37.9)

67 (48.6)

14 (35.9)

 

ANA positivity

34 (33.7)

31 (28.4)

63 (37.3)

38 (29.9)

20 (35.1)

4 (23.5)

0.580

RF positivity

21 (20.4)

18 (16.5)

42 (24.1)

36 (28.3)

14 (29.2)

2 (11.1)

0.177

CRP (mg/L)

1.3 ± 3.4

2.2 ± 4.3

4.0 ± 11.5

5.9 ± 15.9

7.6 ± 22.1

14.2 ± 38.7

<0.001a

CPI

28.0 ± 10.8

34.9 ± 12.3

35.8 ± 14.9

42.8 ± 12.8

55.7 ± 8.0

62.9 ± 6.8

<0.001a

Note: Values in parentheses are percentages.

CPI = 91.0 – (0.65 apercent predicted DLCO) – (0.53 apercent predicted FVC) + (0.34 apercentage predicted FEV1)

GAP gender, age, and 2 lung physiology variables (FVC and DLCO), ANA antinuclear antibody, RF rheumatoid factor, CPI composite physiologic score

athe following post hoc comparisons were significant at the p = 0.05 level; all other comparisons were non-significant: Score 1 group versus Score 2, 3, 4, 5, 6 groups, Score 2 group versus Score 3, 4, 5, 6 groups, and Score 3 group versus Score 4, 5 groups (age); Score 1 group versus Score 3, 4, 5, 6 groups and Score 2 group versus Score 3, 4, 5, 6 groups (smoking duration); Score 1 group versus Score 6 group, Score 2 group versus Score 6 group, and Score 3 group versus Score 6 group (CRP); Score 1 group versus Score 2, 3, 4, 5, 6 groups, Score 2 group versus Score 4, 5, 6 groups, Score 3 group versus Score 4, 5, 6 groups, Score 4 group versus Score 5, 6 groups and Score 5 group versus Score 6 group (CPI)

Physiological and radiological parameters

We investigated pulmonary function, ABGA results, and HRCT findings in IPF patients (Table 3). In Group 1, FVC (%) and DLCO (%) were, respectively, 85.6 and 75.8 %, while in Group 6 the values were 55.5 and 31.9 %. ABGA also differed significantly among groups. Resting pulmonary oxygen tension (PaO2) was highest in Group 1, and higher GAP score was significantly associated with lower pulmonary oxygen tension (p < 0.001). In terms of radiological findings, the groups did not differ in any parameter other than reticular pattern.
Table 3

Initial physiologic and radiologic characteristics according to total GAP score

Variable

Total GAP score (n = 1,228)

p-value

1 (n = 150)

2 (n = 208)

3 (n = 376)

4 (n = 317)

5 (n = 138)

6 (n = 39)

Pulmonary function test

 FVC (%)

85.6 ± 13.4

81.9 ± 17.4

81.3 ± 17.2

71.4 ± 15.7

63.2 ± 15.1

55.5 ± 12.9

<0.001a

 FEV1 (%)

93.1 ± 15.0

91.4 ± 21.2

92.5 ± 19.5

82.9 ± 16.8

74.4 ± 16.2

64.9 ± 14.9

<0.001a

 TLC (%)

90.8 ± 19.7

84.8 ± 19.7

87.2 ± 18.5

80.2 ± 18.6

72.0 ± 15.6

67.1 ± 23.9

<0.001a

 DLCO (%)

75.8 ± 15.8

67.4 ± 17.1

67.1 ± 21.3

59.2 ± 19.9

41.7 ± 13.6

31.9 ± 11.1

<0.001a

Resting PaO2 mm Hg

90.5 ± 21.2

85.8 ± 19.6

82.5 ± 22.3

78.6 ± 18.7

74.5 ± 21.5

69.5 ± 13.7

<0.001a

Resting PaCO2 mm Hg

39.3 ± 8.2

39.2 ± 6.5

37.4 ± 7.8

36.5 ± 6.2

35.1 ± 6.9

36.7 ± 6.4

<0.001a

Radiologic finding

 Reticular pattern

108 (75.5)

144 (73.5)

214 (60.5)

185 (65.4)

82 (65.6)

22 (62.9)

0.008

 Honeycombing change

105 (77.2)

141 (71.9)

282 (78.6)

241 (81.4)

110 (84.0)

31 (81.6)

0.101

 Ground glass opacities

98 (68.5)

132 (68.4)

206 (58.9)

155 (58.5)

67 (56.3)

22 (68.8)

0.052

 Nodular lesions

26 (20.3)

37 (19.9)

85 (25.5)

58 (23.6)

23 (21.9)

8 (25.8)

0.702

Note: Values in parentheses are percentages

GAP gender, age, and 2 lung physiology variables (FVC and DLCO), FVC forced vital capacity, % pred percentage of the predicted value, FEV 1 forced expiratory volume, TLC total lung capacity, DL CO diffusing capacity of the lung for carbon monoxide, PaO 2 arterial oxygen tension, PaCO 2 arterial carbon dioxide tension

athe following post hoc comparisons were significant at the p = 0.05 level; all other comparisons were non-significant: Score 1 group versus Score 4, 5, 6 groups, Score 2 group versus Score 4, 5, 6 groups, Score 3 group versus Score 4, 5, 6 groups and Score 4 group versus Score 5, 6 groups (FVC (%)); Score 1 group versus Score 4, 5, 6 groups, Score 2 group versus Score 4, 5, 6 groups, Score 3 group versus Score 4, 5, 6 groups and Score 4 group versus Score 5, 6 groups (FEV1 (%)); Score 1 group versus Score 4, 5, 6 groups, Score 2 group versus Score 5, 6 groups, Score 3 group versus Score 4, 5, 6 groups, and Score 4 group versus Score 5, 6 groups (TLC (%)); Score 1 group versus Score 2, 3, 4, 5, 6 groups, Score 2 group versus Score 4, 5, 6 groups, Score 3 group versus Score 4, 5, 6 groups, and Score 4 versus Score 5,6 groups (DLCO (%)); Score 1 group versus Score 3, 4, 5, 6 groups, Score 2 group versus Score 4, 5, 6 groups, and Score 3 group versus Score 5, 6 groups (Resting PaO2); Score 1 group versus Score 4, 5 groups and Score 2 group versus Score 4, 5 groups (Resting PaCO2)

Comorbidities and initial respiratory symptoms

Co-morbidities and initial presenting respiratory symptoms are shown in Additional file 1: Tables S1 and S2. The most common co-morbidities were past history of tuberculosis, diabetes mellitus, and hypertension; specifically, past history of tuberculosis was in 147 patients (12.0 %), diabetes mellitus in 234 (19.1 %), and hypertension in 271 (22.1 %). Furthermore, 74 patients (6.0 %) had lung cancer. These co-morbidities were not significantly different among groups. Fourteen patients (1.1 %) had a family history of IPF (data not shown). Cough, sputum, and hemoptysis were significantly more frequent at higher GAP scores (p = 0.004, p < 0.001, and p = 0.021, respectively). Although the proportion of patients who suffered dyspnoea of exertion increased as GAP score increased, this association was not statistically significant.

Survival analysis on the basis of GAP score

All GAP variables showed significant association with prognosis except gender (G) (Table 4, Additional file 1: Table S3). The C-statistic values for the GAP stage at 1, 2, and 3 years were 0.59 (CI 0.537–0.638), 0.59 (CI 0.544–0.631), and 0.57 (CI 0.530–0.611), respectively. The GAP score showed a similar C statistic value with GAP stage. It was 0.61 (CI 0.556–0.653), 0.61 (CI 0.566–0.649), and 0.59 (0.549–0.627), respectively. Kaplan-Meier analysis was performed to compare survival among groups, as well as among GAP stages (Fig. 2a and b). Advanced GAP stage was associated with poor prognosis (p < 0.001). At GAP stages I and II (Groups 1–5), Group 3 differed significantly from all other groups in terms of cumulative survival (Group 3 vs. Group 1, p = 0.027; Group 3 vs. Group 2, p = 0.022; Group 3 vs. Group 4, p = 0.025; Group 3 vs. Group 5, p = 0.001). The causes of death are shown in Table 5. Respiratory failure (42.3 %) and infection (34.2 %) were the most common causes of death in study population.
Table 4

Survival analysis with Cox proportional hazard model

Variable

Univariate

Multivariate

 

HR

95 % CI

p-value

HR

95 % CI

p-value

Age

1.015

1.002–1.028

0.028

1.018

1.005–1.031

0.006

Sex (M/F)

1.184

0.890–1.575

0.245

1.264

0.949–1.684

0.109

FVC (%)

0.985

0.978–0.992

<0.001

0.986

0.979–0.993

<0.001

DLCO (%)

0.987

0.981–0.993

<0.001

0.989

0.983–0.995

0.001

FVC forced vital capacity, % pred percentage of the predicted value, DL CO, diffusing capacity of the lung for carbon monoxide

Fig. 2

Kaplan-Meier estimates of survival of IPF patients based on (a) GAP stage, and (b) total GAP score. a Advanced GAP stages were significantly associated with poor prognosis (p < 0.001). b Cumulative survival in GAP score group 3 was significantly different from that in the other GAP score groups: GAP score 3 vs. GAP score 1, p = 0.043; GAP score 3 vs. GAP score 2, p = 0.039; GAP score 3 vs. GAP score 4, p = 0.043; GAP score 3 vs. GAP score 5, p = 0.032; GAP score 3 vs. GAP score 6, p = 0.003). Definition of abbreviations: GAP, gender, age, and two pulmonary physiology variables (FVC and DLCO)

Table 5

Causes of death in the study population according to total GAP score

Variable

Total GAP score

Total

1 (n = 11)

2 (n = 21)

3 (n = 60)

4 (n = 63)

5 (n = 28)

6 (n = 13)

Respiratory failure

8 (72.7)

11 (52.4)

21 (35.0)

25 (39.7)

12 (42.9)

6 (46.2)

83

Infection

2 (18.2)

6 (28.6)

19 (31.7)

26 (41.3)

10 (35.7)

4 (30.8)

67

Heart failure

  

6 (1.0)

5 (7.9)

2 (7.1)

1 (7.7)

14

Lung cancer

1 (9.1)

4 (19.0)

9 (15.0)

5 (7.9)

2 (7.1)

1 (7.7)

22

Othersa

  

5 (8.3)

2 (3.2)

2 (7.1)

1 (7.7)

10

Note: Values in parentheses are percentages

The cause of death was investigated in 196 mortality cases

GAP gender, age, and two lung physiology variables (FVC and DLCO)

atrauma or malignancy other than lung cancer

Sub-analysis by GAP score

Table 6 shows the distribution of GAP points in each group in terms of predictive variables. Higher GAP scores were significantly associated with male predominance, aging, and poorer lung function, same as the original definition of the GAP model. Furthermore, gender, age, and DLCO (%) differed significantly between Groups 2 and 3, and all four variables in the GAP model differed significantly between Groups 3 and 4.
Table 6

Distribution of GAP points by each predictor according to total GAP score

Variable

GAP Points

Total GAP score (n = 1,228)

p-value

  

1 (n = 150)

2 (n = 208)

3 (n = 376)

4 (n = 317)

5 (n = 138)

6 (n = 39)

 

Gender

       

<0.001

 Female

0

59 (39.3)

94 (45.2)

95 (25.3)

48 (15.1)

15 (10.9)

4 (10.3)

 

 Male

1

91 (60.3)

114 (54.8)

281 (74.7)

269 (84.9)

123 (89.1)

35 (89.7)

 

Age, yr

       

<0.001

 ≤60

0

121 (80.7)

76 (36.5)

45 (12.0)

15 (4.7)

6 (4.3)

-

 

 61–65

1

29 (19.3)

76 (36.5)

66 (17.6)

24 (7.6)

12 (8.7)

1 (2.6)

 

 >65

2

-

56 (26.9)

265 (70.5)

278 (87.7)

120 (87.0)

38 (97.4)

 

Physiology

 FVC, % predicted

       

<0.001

  >75

0

127 (84.7)

134 (64.4)

249 (66.2)

94 (29.7)

22 (15.9)

-

 

  50-75

1

23 (15.3)

71 (34.1)

121 (32.2)

211 (66.6)

92 (66.7)

22 (56.4)

 

  <50

2

-

3 (1.4)

6 (1.6)

12 (3.8)

24 (17.4)

17 (43.6)

 

 DLCO, % predicted

       

<0.001

  >55

0

143 (95.3)

172 (82.7)

263 (69.9)

147 (46.4)

10 (7.2)

-

 

  36–55

1

7 (4.7)

35 (16.8)

108 (28.7)

156 (49.2)

81 (58.7)

12 (30.8)

 

  ≤35

2

-

1 (0.5)

5 (1.3)

14 (4.4)

47 (34.1)

27 (69.2)

 

Note: Values in parentheses are percentages

“Cannot perform” in DLCO was not recorded in this study

Total GAP score 3 group was compared with each group 2 and group 4 by Bonferroni adjustment. The following post hoc comparisons were significant at the adjusted p value = 0.05; Score 3 group versus Score 2 group (Gender, age, and DLCO, % predicted); Score 3 group versus Score 4 group (Gender, age, FVC, % predicted and DLCO, %predicted)

GAP gender, age, and 2 lung physiology variables (FVC and DLCO)

Discussion

The GAP model is simple to use in planning treatment or providing prognosis information to IPF patients. However, prognosis in relation to individual score groups have not been studied until now. This study attempted to undertake external validation of the GAP model in a relatively large cohort of IPF patients. Herein, we found that GAP score groups differed in terms of survival: in particular, survival in Group 3 patients differed from the other stage I groups, as well as the stage II groups.

For a long time, clinicians who care for IPF patients have been struggling to make accurate prognoses, because IPF is a heterogeneous disease that lacks a validated predictive model [11, 15]. Many previous researchers have aimed to find an ideal model for predicting clinical outcome in IPF patients [14, 1622]. In 2001, for instance, King et al. [16] created an upgraded version of a previously existing clinical, radiological, and physiological scoring system, known as the “CRP system”, [17] to predict survival in IPF patients. This model took into account age, smoking status, clubbing of the fingertips, HRCT score, HRCT score for pulmonary hypertension score, TLC (%), and PaO2 at max exercise. However, it did not make clear that gender was significantly associated with mortality. Furthermore, it was too complex to use in a clinical setting, and cardio-pulmonary exercise testing was essential to calculating the score. Wells et al. [14] then proposed the composite physiological index (CPI), which used a combination of three factors to make a prediction—FVC (%), FEV1 (%), and DLCO (%); these factors are determined using pulmonary function testing (PFT). Physicians could calculate CPI using PFT results only, rendering CT findings unnecessary in predicting prognosis. Besides these models, du Bois et al. [21] developed a predictive system that was based on IPF diagnostic criteria, and Richards et al. [22] used biomarkers to create another predictive model. However, these models have also been criticized because they are complicated to use or lack external validation.

Ley et al. developed the GAP model in 2012. Its straightforward nature has allowed the GAP index to be widely studied, [2328] and it has been validated in the United States, Italy, and South Korea [12, 23]. In fact, the system showed robust predictive power in patients with chronic ILD (ILD-GAP model) and IPF related to occupational dust exposure [26, 28]. Furthermore, the model is more powerful and accurate when follow-up PFT results are taken into account, [26, 27] and it has been found that DLCO can be replaced by HRCT fibrosis score in the GAP model (CT-GAP model) [25].

Interestingly, the duration of respiratory symptoms at diagnosis was longest in Group 1 and shortest in Group 6, although this was not a significant difference. This may be due to variations in individual perception of respiratory symptoms [29]. Hiwatari et al. [30] reported that IPF patients with mucous hypersecretion had significantly poor prognosis. In our study, the high score group showed sputum production significantly more often than score 1 or 2 group. This could mean that the patients with a higher GAP score could be more vulnerable to respiratory infection, which could be a cause of death. In our study, patients with a score over 3 showed a higher mortality rate due to infection than score 1 or 2 group. Variables related to smoking were significantly related to GAP score in this study; the proportion of ever-smokers, as well as smoking amount, were highest in Group 6. In other studies however, results have conflicted regarding the association between smoking and prognosis in IPF. Such results are easily influenced by gender, as well as the “healthy smoker effect” [16, 31]. In our study, smoking was not significantly associated with mortality in both univariate and multivariate analyses (Additional file 1: Table S3). Some investigations have shown that elevated CRP levels are related to poor prognosis [2, 32]. In the present study, CRP levels were highest in Group 6, and GAP score was significantly associated with CRP level (p < 0.001).

The most common cause of death in IPF patients is respiratory failure, which results from the progression of lung fibrosis, rather than comorbidities [3]. Furthermore, our study revealed no significant differences among the groups in terms of comorbidities. This suggests that mortality in IPF can be predicted, because the majority of mortalities are caused by the IPF itself.

In the present study, prognosis in Group 3 differed significantly from that in the other score groups, as shown using Kaplan-Meier analysis. This result suggests that the GAP score of 3 could be divided from the other stage I scores, thus creating a more refined prognostic system. Although the GAP model is simple to use and has proven effective in other chronic ILDs, the staging system amounts basically to a rough grouping of the GAP scores (stage I: 0–3 points, stage II: 4–5 points, and stage III: 6–8 points); the GAP stages I, II, and III were designed to have lowest 40 % risk, middle 40 % risk, and highest 20 % risk, respectively. In our study, Group 3 differed significantly from the other stage I groups, and from the stage II groups, in terms of all four predictive variables that contribute to GAP score; the only exception was FVC (%), which did not differ between Groups 2 and 3. Although the mean value of lung function results was similar, age and gender composition were significantly different between Group 2 and 3. Ley et al. [12] mentioned that one of the limitations of the GAP model is its overestimation of risk in lower-risk groups, and this may be the reason for the lack of significant difference in FVC (%) mentioned between Group 2 and 3. Although the mean value of CPI significantly increased as GAP score increased, the difference of CPI between Groups 2 and 3 was not significant in our study, unlike GAP score. This might be explained by a difference in study design between the GAP model and CPI: GAP uses more clinical data in its model, such as age and gender, while CPI was created using only PFT results [12, 14].

Our study did have some limitations. Firstly, patients were diagnosed using the 2002 ATS/ERS guidelines, which place more importance on surgical lung biopsy results than do the 2011 updated guidelines. Also in this study, the HRCT findings were not quantified as scores, or classified according to updated guidelines. In addition, in radiologic findings, traction bronchiectasis was not investigated. However, Ley et al. [12] created the GAP model using a derivation cohort and validation cohort that had been diagnosed between 2000 and 2010. Additionally, Kim et al. [23] demonstrated that the GAP model was effective (except in predicting the 3-year risk of death) in Korean IPF patients who had been diagnosed between 2005 and 2009. Another limitation is that Groups 0 and 7 were excluded from the study because they contained much fewer patients than the other score groups. In fact, patients in Group 0 (all women, never smokers) differed significantly from the other score groups in terms of baseline characteristics. Furthermore, no patients were enrolled who had a GAP score of 8, which requires the inclusion of an “unable to perform” category in DLCO measurement. We also excluded patients who had not undergone PFT that included DLCO. This considerable number of excluded groups may have led to selection bias. Finally, the Korean ILD group did not investigate the radiologic scoring of fibrosis, dyspnea scale, and pulmonary artery hypertension, which could have provided more information on prognosis in IPF patients.

Conclusion

In summary, this study was designed as a national validation study to evaluate GAP scores in relation to the prognosis of patients with IPF. On the basis of our study results, we suggest that Group 3 could be separated from other GAP stage I patients and that reporting this score separately would improve mortality prediction.

Abbreviation

% pred: 

Percentage of the predicted value

ABGA: 

Arterial blood gas analysis

CPI: 

Composite physiologic index

DLCO

Diffusing capacity of the lung for carbon monoxide

FEV1

Forced expiratory volume

FVC: 

Forced vital capacity

GAP: 

Gender, age, and 2 lung physiology variables (FVC and DLCO)

HRCT: 

High-resolution computed tomography

ILD: 

Interstitial lung disease

IPF: 

Idiopathic pulmonary fibrosis

PaCO2

Arterial carbon dioxide tension

PaO2

Arterial oxygen tension

PFT: 

Pulmonary function test

TLC: 

Total lung capacity

Declarations

Acknowledgements

We are grateful to all the members of The Korean Interstitial Lung Disease Research Group, as well as to the pulmonologists, radiologists, and pathologists at the 54 hospitals who helped to gather the data for analysis.

Funding

None.

Availablity of data and materials

All data were available in the ILD web-based registry (www.ild.or.kr).

Authors’ contributions

MSP and SHL conceived and designed the study. All authors contributed to participant recruitment, and data collection/acquisition. SYK and DSK analyzed the data and performed the statistical analysis. MSP and SHL wrote the first draft of the manuscript. All authors critically evaluated the data, reviewed the manuscript, and approved the final version.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The study protocol was reviewed and approved by the Institutional Review Board (IRB) of Yonsei University Health Service, Severance Hospital (IRB approval number: 4-2009-0372); the IRB deemed that, because of the retrospective cohort nature of the study, informed consent was not necessary.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Internal Medicine, Seoul National University College of Medicine, Division of Pulmonary and Critical Care Medicine, Seoul National University Bundang Hospital
(2)
Division of Pulmonology, Department of Internal Medicine, Severance Hospital, Institute of Chest Diseases, Yonsei University College of Medicine
(3)
Division of Pulmonary and Critical Care Medicine, University of Ulsan College of Medicine, Asan Medical Center
(4)
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine and Lung Institute, Seoul National University College of Medicine
(5)
Division of Pulmonary and Critical Care Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine
(6)
Division of Allergy and Respiratory Medicine, Department of Internal Medicine, Soonchunhyang University Seoul Hospital
(7)
Division of Allergy and Respiratory Medicine, Department of Internal Medicine, Soonchunhyang University Bucheon Hospital
(8)
Division of Pulmonology, Department of Internal Medicine, Gachon University Gil Medical Center
(9)
Division of Pulmonary, Allergy & Critical Care Medicine, Department of Internal Medicine, Hallym University Kangdong Sacred Heart Hospital
(10)
Pulmonary Division, Department of Internal Medicine, Inha University Hospital
(11)
Division of Pulmonary Medicine, Department of Internal medicine, Chung Ang University College of Medicine
(12)
Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine
(13)
Department of Internal Medicine, Ewha Womans University School of Medicine, Ewha Medical Research Institute
(14)
Division of Pulmonary Medicine, Department of Internal Medicine, Ulsan University Hospital, University of Ulsan College of Medicine
(15)
Division of Critical Care and Pulmonary Medicine, Department of Internal Medicine, Inje University Pusan Paik Hospital
(16)
Division of Allergy and Pulmonology, Department of Internal Medicine, Bucheon St. Mary’s Hospital, The Catholic University of Korea School of Medicine

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Copyright

© The Author(s). 2016

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