1. Anastasiia Raievska (Veramed)
Prognostic accuracy of pulmonary function tests, in Idiopathic Pulmonary Fibrosis (IPF), to select patients at high risk of Acute Exacerbations: A Retrospective Case-Control Study
Anastasiia Raievska (Veramed)
15 May 2017

2. Agenda Study PFT Data Statistical Analysis Conclusions Indication
Population and data
PFT Data
Availability
Aim of the statistical analysis
Patient Profiles
Statistical Analysis
Statistical analysis plan
Results
Conclusions
Limitations
Future plans
PFT – Pulmonary function test

3. Idiopathic Pulmonary Fibrosis (IPF)
Scarring/Fibrosis of lungs
Chronic irreversible and ultimately fatal disease characterized by a progressive decline in lung function
The cause of IPF is unknown but certain environmental factors and exposures have been shown to increase the risk of getting IPF
Estimated median survival time of 2 to 5 years following diagnosis
Standard of care (SOC):
Pirfenidone
Nintedanib

4. Study population and parameters
Retrospective observational case control study in IPF
35 patients with probable diagnosis of IPF:
8 patients had 1 acute exacerbation (AE), 3 patients had 2 AEs
24 patients did not have an AE
Data extracted from the medical history records:
Date of birth, Gender, Number of AEs, Date of AE, survival status and date of death (if applicable)
Monocyte count and a date of sampling, Fibrosis score and a date of sampling
Pulmonary function test (PFT) parameters and dates of sampling:
%FVC
%FEV1
%TLCO
CPI, derived as CPI = (0.65 x %TLCO) - (0.53 x %FVC) + (0.34 x %FEV1)
SOC taken and duration on SOC
SOC – Standard of care
FVC – Forced Vital Capacity
FEV1 – Forced Expiratory Volume in 1 second
TLCO – Transfer factor of the lung for carbon monoxide
CPI – Composite Physiologic Index

5. PFT parameters per patient
Data available for statistical analysis:
- 9 patients with AE (1 to 6 observations)
- 24 patients without AE (1 to 11 observations)
indicates an AE event
- 1 patient was excluded from the statistical analysis as a potential outlier (from AE group)
- 1 patient did not have data prior to AE

6. Aim of the statistical analysis
Aim: find predictors of AE in IPF patients
Patients with AE progress faster than patients without AE
Predictor of AE can be used as:
Inclusion criterion (enrichment with fast progresors) => shorter duration of the trial!
Stratifier
Endpoint ? I II
III
Died
CPI
CPI
Survived
Mortality
No AE
AE event
No AE
AE event
AE – acute exacerbation

7. Statistical Analysis Plan2
Explore prognostic accuracy of PFT for AEs:
Cox proportional hazards regression with time-varying covariates:
Repeated measures of clinical predictors
Unequal measurement of time-points
Right censoring
Inspect characteristics of the data:
Univariate (fixed and mixed models)
Multivariate logistic models (stepwise selection, Likelihood ratio test)
Multivariate GLM with repeated measurements 1
AE – acute exacerbation
PFT – pulmonary function test

8. Patient profiles for %FVC
FVC – forced vital capacity
AE – acute exacerbation

9. Preparation for Primary and Exploratory analysis
Derivations
CPI vs a combination of %FVC, %FEV1 and %TLCO
How early can we predict an AE?
Different summary measures
Changes from “Baseline”
Slopes
Means
mean
mean
mean
mean 24 18 12 6 AE
Patient’s survival time
FVC – Forced Vital Capacity
FEV1 – Forced Expiratory Volume in 1 second
TLCO – Transfer factor of the lung for carbon monoxide
AE – acute exacerbation

10. (Forward + Backward) selection1
Exploratory analysis
Summary Measures: Logistic regression
Models were adjusted for time on SOC and SOC indicator
CPI vs a combination of %FVC, %FEV1, %TLCO
Repeated measures: GLM
(Forward + Backward) selection
Likelihood ratio test
Half year means:
6 to 0 months
12 to 6 months
18 to 12 months
24 to 18 months
CPI
CPI (6 to 0 m)
CPI (12 to 6 m)
CPI (6 to 0 m)
CPI (12 to 6 m)
3 PFTs
%FVC (6 to 0 m)
%FVC (18 to 12 m)
%TLCO(12 to 6 m)
Slope
Change from Baseline
FVC – Forced Vital Capacity
FEV1 – Forced Expiratory Volume in 1 second
TLCO – Transfer factor of the lung for carbon monoxide
CPI – Composite Physiologic Index
SOC – standard of care

11. 2
Primary analysis : Cox proportional hazard regression with time-varying covariates
Stepwise selection:
Hazard ratio summary:
Model 1: HR = (CI: 1.041, 1.180) =>
1-unit increase in CPI leads to 10.8% increase in risk of having an AE
Model 2: HR = (CI: , 1.180) =>
1-unit increase in %FVC leads to 11.2% decrease in risk of having an AE
FVC – Forced Vital Capacity
CPI – Composite Physiologic Index
AE – acute exacerbation

12. Discrimination ability assessment
ROC and AUC results:
CPI (AUC=0.80)
FVC (AUC=0.79)
CPI (AUC=0.58)
FVC (AUC=0.56)
CPI (AUC=0.70)
FVC (AUC=0.87)
CPI (AUC=0.77)
FVC (AUC=0.61)
n = 14
n = 17
n = 17
n = 22 24 18 12 6
Patients’ survival time AE
n = 22
CPI (AUC=0.40)
FVC (AUC=0.65)
n = 25
CPI (AUC=0.79)
FVC (AUC=0.77)
FVC – Forced Vital Capacity
CPI – Composite Physiologic Index
AE – acute exacerbation

13. TOO LATE? ROCs for CPI N = 17 N = 17 N = 22 N = 14 N = 25 N = 22
CPI – Composite Physiologic Index

14. ROCs: CPI vs 3PFTs N = 17 N = 17 N = 22 N = 14
CPI – Composite Physiologic Index
PFT – pulmonary function test

15. Limitations Small retrospective study
Assumption that data is not left censored
Poor quality of SOC data (missing and incomplete)
Sparse data
Convenience (“All comers”) sample (no inclusion/exclusion criteria)

16. Conclusions and future plans
Cox PH model with time-varying covariates can be used for the modelling of sparse data
CPI is highly predictive of an AE event
%FVC is a main predictive component of CPI
Differences in PFT parameters between groups get larger as we approach an AE event
CPI itself offers a desired level of association with an AE and can be used on its own as a main predictor of an AE event
Conclusion from ROCs:
Reasonable predictive accuracy up to 18 months
Good sensitivity up to 12 months
Loss of sensitivity, but retention of specificity after 12 months
Repeat the analysis on the new data from similar retrospective case control study (n = 200)
Identification of the risk cut off – patients with values above will be considered to be at risk of having an AE

17. Acknowledgments
Dr Irene Rebollo Mesa (Associate Director Exploratory Statistics, UCB, Slough, UK)
Dr Ling-Pei Ho (Principal Investigator, University of Oxford/Weatherall Institute of Molecular Medicine, Oxford, UK)
Emily Fraser (University of Oxford)

18. Thank you Any Questions?

19. Back up

20. 2x2 tables

21. Patient profiles for CPI

22. Patient profiles for %FEV1

23. Patient profiles for %TLCO

24. Exploratory analysis Logistic regression using summary measures: 1
(Forward + Backward)
Likelihood ratio test results

25. ROCs: CPI vs FVC
N = 17
N = 22
N = 17
N = 14