Abstract
There is significant heterogeneity in survival time among patients with idiopathic pulmonary fibrosis. Studies of baseline clinical and physiologic variables as predictors of survival time have reported inconsistent results. We evaluated the predictive value of changes in clinical and physiologic variables over time for survival time in 81 patients with biopsy-proven idiopathic pulmonary fibrosis. Six-month changes in dyspnea score, total lung capacity, thoracic gas volume, FVC, FEV1, diffusing capacity of carbon monoxide, partial pressure of arterial oxygen, oxygen saturation, and alveolar–arterial oxygen gradient were predictive of survival time even after adjustment for baseline values. Analyses were repeated on 51 patients with 12-month change data. Twelve-month changes in dyspnea score, total lung capacity, FVC, partial pressure of arterial oxygen, oxygen saturation, and alveolar–arterial oxygen gradient were predictive of survival time after adjustment for baseline values. Evaluation of changes in clinical and physiological variables over 6 and 12 months may provide clinicians with more accurate prognostic information than baseline values alone.
Idiopathic pulmonary fibrosis (IPF) has undergone important redefinition in the last several years, based largely on revised histopathologic classification criteria (1). What was once a diagnosis applied to a variety of chronic idiopathic interstitial pneumonias is now reserved for a specific idiopathic interstitial pneumonia with the histopathologic pattern of usual interstitial pneumonia (2). This has important implications for clinicians, as IPF as currently defined has a poor response to traditional therapies and a significantly worse prognosis than other forms of idiopathic interstitial pneumonia (3–5). Median survival is generally reported as 2–3 years from the time of diagnosis (1).
Although IPF carries a uniquely poor prognosis, there is substantial heterogeneity in survival among patients (1). Despite significant attempts, it has proven difficult to predict survival time in individual patients with IPF. Over a dozen published studies attempting to identify clinical and physiologic predictors of survival time have yielded inconsistent and at times contradictory results (6–16). Many of these studies were published before the current diagnostic criteria for IPF were well established and likely included patients with other forms of idiopathic interstitial pneumonia.
Reliable predictors of survival time in patients who meet the current definition of IPF could be extremely useful for clinicians, improving prognostication and facilitating earlier referral for transplantation. Most studies to date have looked at the predictive value of baseline clinical and physiologic values. We hypothesized that change over time in easily measured clinical and physiologic variables may provide for more reliable and potent prediction of survival than baseline measurements alone. In this study, we report the predictive value of sequential measurement over 6 and 12 months of clinical and physiologic variables in a large, well-defined population of patients with biopsy-proven IPF.
The 6-month group consisted of 81 patients with IPF prospectively enrolled into a longitudinal study at National Jewish Medical and Research Center between 1982 and 1996. Most patients have been previously reported as part of a larger cohort (6). The diagnosis of IPF was made based on established clinicopathologic criteria (1). Baseline demographic information is detailed in Table 1
Characteristic | Value* |
|---|---|
| Age, yr | 61.5 (10.3) |
| Sex | |
| Male | 51 |
| Female | 30 |
| Smoking status | |
| Never | 30 |
| Former | 36 |
| Current | 15 |
| Dyspnea score | 10.2 (4.4) |
| Treatment at time of referral | |
| None | 51 |
| Corticosteroid alone | 26 |
| Corticosteroid/cytotoxic agent combined | 4 |
Patients were questioned regarding the amount of exertion required to precipitate dyspnea. The degree of dyspnea was scored from 0 to 20, with a higher score indicating more severe dyspnea (17). Pulmonary function testing and arterial blood gas sampling included measurement of thoracic gas volume, residual volume, total lung capacity (TLC), FVC, FEV1, single-breath diffusing capacity for carbon monoxide, arterial pH, partial pressure of oxygen, partial pressure of carbon dioxide, serum bicarbonate, and oxygen saturation (6). The alveolar–arterial oxygen gradient (AaPo2) was calculated from the simplified alveolar air equation (18).
All patients underwent surgical lung biopsy. All biopsies had been previously reviewed by an expert pulmonary pathologist and the diagnosis of usual interstitial pneumonia was confirmed. Other histopathologic patterns, including nonspecific interstitial pneumonia, organizing pneumonia, diffuse alveolar damage, respiratory bronchiolitis, desquamative interstitial pneumonia, and lymphocytic interstitial pneumonia, were excluded.
Cox's proportional hazards models were used to determine the effect of 6- and 12-month change variables on survival time, adjusting for age and stratified by smoking status. The predictive value of each change variable was evaluated individually with and without adjustment for baseline value. In an effort to determine which of the clinical and physiologic parameters best predicted survival time, −2 log likelihood statistics were calculated on a subset of 68 subjects (see online supplement for more details). Data analyses were performed using SAS version 8.2 (SAS Institute Inc., Cary, NC). All tests were two-sided and were performed at a significance level of 0.05. Further details of the statistical analysis can be found in the online supplement.
There were 81 patients with 6-month follow-up data. Their clinical and physiologic data are summarized in Tables 1 and 2
Characteristic | Value* |
|---|---|
| Baseline pulmonary function testing† | |
| TLC | 77.6 (15.7) |
| RV | 91.6 (36.4) |
| Vtg | 71.2 (17.2) |
| FVC | 67.2 (18.0) |
| FEV1 | 77.1 (20.4) |
| DLCO | 52.4 (15.7) |
| DLCO/VA | 78.6 (23.1) |
| Baseline arterial blood gas values | |
| pH | 7.44 (0.03) |
| PaO2, mm Hg | 64.1 (11.4) |
| PaCO2, mm Hg | 34.0 (3.6) |
| HCO3, mEq/L | 23.2 (2.5) |
| O2 saturation, % | 89.7 (5.7) |
| AaPO2, mm Hg | 16.7 (10.5) |
The median survival in the 6-month group was 4.3 years (the 25th and 75th percentiles were 1.7 and 9.7 years, respectively). There were 54 deaths due to IPF during the study period. Twenty-seven patients were censored, including 17 who were alive at analysis, 5 who had undergone lung transplantation, and 5 who died of causes unrelated to IPF. The median survival in the 12-month group was 6.2 years (25th and 75th percentiles were 3.1 and 10.6 years, respectively).
The baseline dyspnea score was predictive of survival time, as previously reported (6). The change in dyspnea score over 6 months was predictive of survival time and remained so after adjusting for baseline score (see Table E1 in the online supplement). Change in dyspnea score over 12 months was also predictive of survival time. The estimated 5-year survival rates for an average patient (average age and baseline dyspnea score) with a clinically relevant increase (2 points or more), decrease (2 points or more), or no change (less than two point increase or decrease) in dyspnea score over 6 months are shown in Table 3
Smoking History | 6-Month Change | Percentage Surviving 5 Years | 95% Confidence Interval |
|---|---|---|---|
| Never | −2 | 53.5 | (37.2, 77.0) |
| 0 | 44.2 | (28.8, 67.8) | |
| 2 | 34.5 | (20.3, 58.6) | |
| Former | −2 | 60.2 | (44.4, 81.6) |
| 0 | 51.6 | (35.9, 74.1) | |
| 2 | 42.1 | (26.7, 66.5) | |
| Current | −2 | 60.3 | (40.2, 90.3) |
| 0 | 51.6 | (30.7, 86.8) | |
| 2 | 42.2 | (21.3, 83.6) |
Figure 1. Kaplan-Meier survival estimates for change in dyspnea score. (A) Six-month change in dyspnea score. Improved indicates a decrease of two points or greater (n = 15). Stable indicates changes of less than two points (n = 33), and decline indicates an increase of two points or greater (n = 31). (B) Twelve-month change in dyspnea score. Improved indicates a decrease of two points or greater (n = 13). Stable indicates a change of less than 2 points (n = 18), and decline indicates an increase of two points or greater (n = 20).
[More] [Minimize]Baseline measurements of TLC percentage predicted, thoracic gas volume percentage predicted, FVC percentage predicted, FEV1 percentage predicted, and single-breath diffusing capacity for carbon monoxide percentage predicted were predictive of survival time as previously reported (6). The changes in TLC percentage predicted, thoracic gas volume percentage predicted, FVC percentage predicted, FEV1 percentage predicted, and diffusing capacity for carbon monoxide percentage predicted over 6 months were predictive of survival time and remained so when adjusted for baseline values (see Table E1 in the online supplement). Changes in TLC percentage predicted and FVC percentage predicted over 12 months were also predictive of survival time. Of these variables, FVC percentage predicted was the best predictor based on model fit comparison (see Table E2 in the online supplement). The estimated 5-year survival rates for an average patient (average age and baseline FVC percentage predicted) with a clinically relevant increase (10% or more), decrease (10% or more), or no change (less than 10% increase or decrease) in FVC percentage predicted over 6 months are shown in Table 4
Smoking History | 6-Month Change | Percentage Surviving 5 Years | 95% Confidence Interval |
|---|---|---|---|
| Never | −10 | 22.0 | (9.7, 49.7) |
| 0 | 46.4 | (30.1, 71.4) | |
| 10 | 67.7 | (51.4, 89.3) | |
| Former | −10 | 17.8 | (6.7, 47.0) |
| 0 | 41.6 | (26.3, 65.8) | |
| 10 | 64.1 | (48.3, 85.2) | |
| Current | −10 | 18.2 | (3.8, 88.2) |
| 0 | 42.2 | (19.8, 90.0) | |
| 10 | 64.6 | (43.2, 96.5) |
Figure 2. Kaplan-Meier survival estimates for change in FVC % predicted. (A) Six-month change in FVC % predicted. Improved indicates an increase in percent predicted FVC of 10 or greater (n = 9). Stable indicates that the change in percentage predicted FVC was less than 10 (n = 50), and decline indicates a decrease in percentage predicted FVC of 10 or greater (n = 22). (B) Twelve-month change in FVC % predicted. Improved indicates an increase in percent predicted FVC of 10 or greater (n = 13). Stable indicates the change in percentage predicted FVC was less than 10 (n = 23), and a decline indicates a decrease in percent predicted FVC of 10 or greater (n = 15).
[More] [Minimize]Baseline measurement of partial pressure of oxygen (PaO2), O2 saturation, and AaPo2 were predictive of survival time as previously reported (6). The changes in PaO2, O2 saturation, and AaPo2 over 6 months were predictive of survival time after adjustment for baseline value (see Table E1 in the online supplement). Changes in PaO2, O2 saturation, and AaPo2 over 12 months were also predictive of survival time. Of these variables, AaPo2 was the best predictor based on model fit comparison (see Table E2 in the online supplement). The estimated 5-year survival rates for an average patient (average age and baseline AaPo2) with a clinically relevant increase (5 mm Hg or more), decrease (5 mm Hg or more), or no change (less than 5 mm Hg increase or decrease) in AaPo2 over 6 months are shown in Table 5
Smoking History | 6-Month Change | Percentage Surviving 5 Years | 95% Confidence Interval |
|---|---|---|---|
| Never | −5 | 55.4 | (37.1, 82.6) |
| 0 | 42.8 | (25.9, 70.6) | |
| 5 | 29.6 | (14.8, 59.0) | |
| Former | −5 | 61.9 | (44.6, 86.0) |
| 0 | 50.3 | (33.5, 75.3) | |
| 5 | 37.2 | (21.7, 63.9) | |
| Current | −5 | 76.8 | (59.8, 98.7) |
| 0 | 68.5 | (48.9, 96.0) | |
| 5 | 58.1 | (36.1, 93.5) |
Figure 3. Kaplan-Meier survival estimates for change in alveolar–arterial oxygen gradient (AaPO2). (A) Six-month change in AaPO2. Improved indicates a decrease in AaPO2 of 5 mm Hg or greater (n = 12). Stable indicates the change in AaPO2 of less than 5 mm Hg (n = 30), and decline indicates an increase in AaPO2 of 5 mm Hg or greater (n = 29). (B) Twelve-month change in AaPO2. Improved indicates a decrease in AaPO2 of 5 mm Hg or greater (n = 7). Stable indicates the change in AaPO2 of less than 5 mm Hg (n = 24), and decline indicates an increase in AaPO2 of 5 mm Hg or greater (n = 18).
[More] [Minimize]Changes in several easily measured clinical and physiologic variables over 6 and 12 months are statistically significant predictors of survival time in IPF. Symptom-based dyspnea scores, pulmonary function testing, and arterial blood gas analysis are widely available and highly reproducible. Measurement of these variables can be obtained in most practices quickly and can provide the practicing clinician with powerful prognostic information. Importantly, changes in these variables predict survival even after adjustment for the variables' baseline measurements, suggesting that the rate of progression, independent of the initial degree of disability, is important for determining prognosis. Based on model fit comparison analysis and clinical utility, the most powerful predictors appear to be dyspnea score, FVC percentage predicted, and AaPo2.
Predicting survival time in patients with IPF has been the focus of much study over the last 30 years (6–16). Interpretation of this literature is difficult for several reasons. First, IPF has recently been redefined as a clinicopathologic condition requiring the histopathologic pattern of usual interstitial pneumonia. Many older studies contain patient populations that include a heterogeneous mixture of idiopathic interstitial pneumonias no longer characterized as IPF, most commonly nonspecific interstitial pneumonia. These conditions generally have better survival when compared with IPF, making any predictors derived from populations containing patients with these conditions suspect. Second, many studies contain a sizable number of patients without biopsy-proven IPF. Although expert clinical and radiographic assessment has been shown to identify many patients with IPF accurately (19, 20), it is unlikely that all studies employed such rigorous methods, further adding to heterogeneity in patient populations. Finally, accumulating large cohorts of patients with IPF is difficult, and several published studies have relatively small numbers.
Most published studies in this area have looked at the predictive value of baseline variables. A number of baseline predictors of survival in IPF have been proposed: age, sex, smoking status, degree of dyspnea, FEV1, FVC, TLC, thoracic gas volume, residual volume, diffusing capacity for carbon monoxide, PaO2, AaPo2, degree of radiographic abnormality, exercise physiology, bronchoalveolar lavage constituency, and histopathologic features (6–16). However, there has been little consistency across studies. Our group has recently described a modified Clinical-Radiographic-Physiologic scoring system consisting of various baseline values to predict survival time in IPF (6). Although the modified Clinical-Radiographic-Physiologic score is an accurate predictor of survival time in IPF, it requires radiographic analysis and exercise physiologic measurements not readily available to many physicians. This may limit its practical utility as a predictor of survival time to those in general practice.
Only one study to date has looked at the predictive value of changes in variables over time for survival time in IPF (14). Hanson and colleagues retrospectively identified 58 patients with IPF seen between 1970 and 1991 (14). Diagnosis required a diffuse reticulonodular infiltrate on chest radiograph and transbronchial or surgical lung biopsy demonstrating “interstitial fibrosis” without evidence of granuloma, tumor, or infection. Patients with history suggestive of collagen–vascular disease, hypersensitivity pneumonitis, drug-induced lung disease, and radiation pneumonitis were excluded. The results identified change in FVC percentage predicted over 1 year of more than 10% (e.g., 50% predicted to below 40% predicted), a change in single breath diffusing capacity of more than 20%, or a change in both FVC percentage predicted and single breath diffusing capacity to be predictive of survival time. The predictive value of change in AaPo2 at 1 year was not statistically significant. This study was performed before the recent clinicopathologic redefinition of IPF, making extrapolation of these results to today's patients with IPF difficult. Nonetheless, its findings are consistent with the results of our study.
There are several potential sources of bias in our study. There is by definition a selection bias for healthier patients given the requirement that patients live to at least 6 (or 12) months of follow-up to be included in the study. This bias likely explains why the median survival time of this cohort (4.3 years for the 6-month group and 6.2 years for the 12-month group) is longer than that seen in a general population of IPF patients (2–3 years). Baseline values of TLC percentage predicted, FVC percentage predicted, and FEV1 percentage predicted were higher in this cohort when compared with a larger cohort of patients with biopsy-proven IPF that included patients who died before the 6-month follow-up (data not shown). Baseline age, smoking status, dyspnea score, and arterial blood gas values were not substantially different. A second possible bias is related to loss to follow-up. Although patients were asked to return for 6 and 12 month visits, there was clearly a proportion (increasing over time) that elected not to return, even when follow-up calls revealed that patients were still alive at the time in question. All patients were seen at a tertiary care center specializing in interstitial lung disease, adding potential referral bias.
Our study also censored patients dying from causes of death other than IPF. An argument can be made that the more clinically meaningful endpoint is “all-cause death” and not “death due to IPF.” To assess the sensitivity of our findings to censoring, statistical analyses were repeated for the recommended predictors (dyspnea score, FVC percentage predicted, and AaPo2) without censoring deaths from other causes. This resulted in no appreciable changes in either the significance of the predictors or the magnitude of their impact on survival time estimates (data not shown.)
The predictive value of serially performed, easily measured and reliable clinical and physiologic variables could improve the care of patients with IPF in several ways. It could allow clinicians to deliver more accurate prognostic information and may allow for a timelier referral of patients with limited prognoses for lung transplantation. Furthermore, changes in clinical and physiologic variables over time could be used as surrogate markers of survival time, allowing for improved monitoring of therapeutic efficacy in individual patients and in clinical trials of new therapies. Although further assessment of the validity of using changes in clinical and physiologic variables as surrogate endpoints for survival time is clearly needed, this would have significant implications for the care of patients with IPF and for future clinical trials.
| 1. | American Thoracic Society. Idiopathic pulmonary fibrosis: diagnosis and treatment: International consensus statement: American Thoracic Society (ATS), and the European Respiratory Society (ERS). Am J Respir Crit Care Med 2000;161(2 Pt 1):646–64. |
| 2. | Katzenstein ALA, Myers JL. Idiopathic pulmonary fibrosis: clinical relevance of pathologic classification. Am J Respir Crit Care Med 1998;157:1301–1315. |
| 3. | Bjoraker JA, Ryu JH, Edwin MK, Myers JL, Tazelaar HD, Schroeder DR, Offord KP. Prognostic significance of histopathologic subsets in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med 1998;157:199–203. |
| 4. | Daniil ZD, Gilchrist FC, Nicholson AG, Hansell DM, Harris J, Colby TV, du Bois RM. A histologic pattern of nonspecific interstitial pneumonia is associated with a better prognosis than usual interstitial pneumonia in patients with cryptogenic fibrosing alveolitis. Am J Respir Crit Care Med 1999;160:899–905. |
| 5. | Nicholson A, Colby TV, du Bois RM, Hansell DM, Wells AU. The prognostic significance of the histologic pattern of interstitial pneumonia in patients with the clinical entity of cryptogenic fibrosing alveolitis. Am J Respir Crit Care Med 2000;162:2213–2217. |
| 6. | King TE Jr, Tooze JA, Schwarz MI, Brown KR, Cherniack RM. Predicting survival in idiopathic pulmonary fibrosis: scoring system and survival model. Am J Respir Crit Care Med 2001;164:1171–1181. |
| 7. | King TE Jr, Schwarz MI, Brown K, Tooze JA, Colby TV, Waldron JA Jr, Flint A, Thurlbeck W, Cherniack RM. Idiopathic pulmonary fibrosis: relationship between histopathologic features and mortality. Am J Respir Crit Care Med 2001;164:1025–1032. |
| 8. | Mogulkoc N, Brutsche MH, Bishop PW, Greaves SM, Horrocks AW, Egan JJ. Pulmonary function in idiopathic pulmonary fibrosis and referral for lung transplantation. Am J Respir Crit Care Med 2001;164:103–108. |
| 9. | Mogulkoc N, Brutsche MH, Bishop PW, Murby B, Greaves MS, Horrocks AW, Wilson M, McCullough C, Prescott M, Egan JJ. Pulmonary (99m)Tc-DTPA aerosol clearance and survival in usual interstitial pneumonia (UIP). Thorax 2001;56:916–923. |
| 10. | Mapel DW, Hunt WC, Utton R, Baumgartner KB, Samet JM, Coultas DB. Idiopathic pulmonary fibrosis: survival in population based and hospital based cohorts. Thorax 1998;53:469–476. |
| 11. | Gay SE, Kazerooni EA, Toews GB, Lynch J III, Gross BH, Cascade PN, Spizarny DL, Flint A, Schork MA, Whyte RI, et al. Idiopathic pulmonary fibrosis: predicting response to therapy and survival. Am J Respir Crit Care Med 1998;157:1063–1072. |
| 12. | Hubbard R, Johnston I, Britton J. Survival in patients with cryptogenic fibrosing alveolitis: a population-based cohort study. Chest 1998;113:396–400. |
| 13. | Erbes R, Schaberg T, Loddenkemper R. Lung function tests in patients with idiopathic pulmonary fibrosis: are they helpful for predicting outcome? Chest 1997;111:51–57. |
| 14. | Hanson D, Winterbauer RH, Kirtland SH, Wu R. Changes in pulmonary function test results after 1 year of therapy as predictors of survival in patients with idiopathic pulmonary fibrosis. Chest 1995;108:305–310. |
| 15. | McCormack FX, King TE Jr, Bucher BL, Nielsen L, Mason RJ. Surfactant protein A predicts survival in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med 1995;152:751–759. |
| 16. | Schwartz DA, Helmers RA, Galvin JR, Van Fossen DS, Frees KL, Dayton CS, Burmeister LF, Hunninkhake GW. Determinants of survival in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med 1994;149:450–454. |
| 17. | Watters LC, King TE, Schwarz MI, Waldron JA, Stanford RE, Cherniack RM. A clinical, radiographic, and physiologic scoring system for the longitudinal assessment of patients with idiopathic pulmonary fibrosis. Am Rev Respir Dis 1986;133:97–103. |
| 18. | Riley RL, Cournand A. Ideal alveolar air and the analysis of ventilation-perfusion relationships in the lungs. J Appl Physiol 1949;1:825–847. |
| 19. | Hunninghake GW, Zimmerman MB, Schwartz DA, King TE Jr, Lynch J, Hegele R, Waldron J, Colby T, Muller N, Lynch D, et al. Utility of a lung biopsy for the diagnosis of idiopathic pulmonary fibrosis. Am J Respir Crit Care Med 2001;164:193–196. |
| 20. | Raghu G, Mageto YN, Lockhart D, Schmidt RA, Wood DE, Godwin JD. The accuracy of the clinical diagnosis of new-onset idiopathic pulmonary fibrosis and other interstitial lung disease: a prospective study. Chest 1999;116:1168–1174. |
