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
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Agenda
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Study
• 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
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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
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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 = 91.0 - (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
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PFT parameters per patient
indicates an AE event
Data available for statistical analysis:
- 9 patients with AE (1 to 6 observations)
- 24 patients without AE (1 to 11 observations)
- 1 patient was excluded from the statistical
analysis as a potential outlier (from AE group)
- 1 patient did not have data prior to AE
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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
II
I
III
?
Died
CPI
CPI
Survived
Mortality
AE – acute exacerbation
No AE
AE event
No AE
AE event
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Statistical Analysis Plan
 Explore prognostic accuracy of PFT for AEs:
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 Cox proportional hazards regression with time-varying covariates:
o Repeated measures of clinical predictors
o Unequal measurement of time-points
o Right censoring
 Inspect characteristics of the data:
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 Univariate (fixed and mixed models)
 Multivariate logistic models (stepwise selection, Likelihood ratio test)
 Multivariate GLM with repeated measurements
AE – acute exacerbation
PFT – pulmonary function test
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Patient profiles for %FVC
FVC – forced vital capacity
AE – acute exacerbation
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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
24
mean
18
mean
12
mean
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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
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Exploratory analysis
 Summary Measures: Logistic regression
 Models were adjusted for time on SOC and SOC indicator
 CPI vs a combination of %FVC, %FEV1, %TLCO
Half year means:
6 to 0 months
12 to 6 months
18 to 12 months
24 to 18 months
CPI
(Forward +
Backward)
selection
3 PFTs
Slope
Change from Baseline
 Repeated measures: GLM
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
CPI (6 to 0 m)
CPI (12 to 6 m)
%FVC (6 to 0 m)
%FVC (18 to 12 m)
%TLCO(12 to 6 m)
Likelihood
ratio test
CPI (6 to 0 m)
CPI (12 to 6 m)
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Primary analysis : Cox proportional hazard regression with timevarying covariates
 Stepwise selection:
 Hazard ratio summary:
Model 1:
HR = 1.108 (CI: 1.041, 1.180) =>
1-unit increase in CPI leads to 10.8% increase in risk of having an AE
Model 2:
HR = 0.888 (CI: 0.842 , 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
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Discrimination ability assessment
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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
24
18
12
6
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
n = 22
Patients’
survival
time
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ROCs for CPI
N = 22
N = 14
CPI – Composite Physiologic Index
N = 17
N = 25
N = 17
N = 22
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ROCs: CPI vs 3PFTs
N = 22
CPI – Composite Physiologic Index
PFT – pulmonary function test
N = 17
N = 17
N = 14
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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)
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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
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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)
Thank you
Any Questions?
Back up
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2x2 tables
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Patient profiles for CPI
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Patient profiles for %FEV1
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Patient profiles for %TLCO
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Exploratory analysis
 Logistic regression using summary measures:
(Forward + Backward)
Likelihood ratio test results
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ROCs: CPI vs FVC
N = 22
N = 17
N = 17
N = 14