1 Department of Pulmonary and Critical Care Medicine, Mayo ... · 2 Mayo Clinic Robert D. and...
Transcript of 1 Department of Pulmonary and Critical Care Medicine, Mayo ... · 2 Mayo Clinic Robert D. and...
Clinical Effectiveness of the Anti-Fibrotic Medications for Idiopathic Pulmonary Fibrosis
Timothy M. Dempsey, MD1, Lindsey R. Sangaralingham, MPH2,3, Xiaoxi Yao, PhD4, Darshak Sanghavi,
MD3, Nilay D. Shah, PhD2,4, Andrew H. Limper, MD1,2,5
1 Department of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, Minnesota
2 Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery,
Mayo Clinic, Rochester, Minnesota
3 OptumLabs®, Cambridge, Massachusetts
4 Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
5 Correspondence: Dr. Andrew Limper, Gonda 18-South, Mayo Clinic, Rochester, MN 55905; Tel.: (507) 284-8484; Fax: (507) 284-5421; E-mail address: [email protected]
Author Contributions: TMD, AHL, LRS: developed concept and study design. LRS, XY, DS,
NDS acquired and evaluated the claims data. TMD: drafted initial manuscript. All authors
reviewed and approved final manuscript.
Sources of Support: This study was funded by the Mayo Clinic Robert D. and Patricia E. Kern
Center for the Science of Health Care Delivery, which receives no industry funding.
Running Head: Clinical Effectiveness of Anti-Fibrotics
Descriptor: 9.23 Interstitial Lung Disease
Disclosures/Disclaimers:
In the past 36 months, Dr. Shah has received research support through Mayo Clinic from the
Food and Drug Administration to establish Yale-Mayo Clinic Center for Excellence in
Regulatory Science and Innovation (CERSI) program (U01FD005938), from the Centers of
Medicare and Medicaid Innovation under the Transforming Clinical Practice Initiative (TCPI),
from the Agency for Healthcare Research and Quality (R01HS025164; R01HS025402;
R03HS025517; 1U19HS024075), from the National Heart, Lung and Blood Institute of the
National Institutes of Health (NIH) (R56HL130496; R01HL131535), National Science
Foundation, and from the Patient Centered Outcomes Research Institute (PCORI) to develop a
Clinical Data Research Network (LHSNet). ). In the past 36 months, Dr. Limper has also
received research support through Mayo Clinic for the study of interstitial lung diseases from the
Hurvis Foundation, The Annenberg Foundation, and the Rosemary Clooney Pulmonary Research
Fund. All others have no disclosures in regard to this manuscript.
FOOTNOTES (first page): This study was funded by the Mayo Clinic Robert D. and Patricia E.
Kern Center for the Science of Health Care Delivery, which receives no industry funding.
Word Count: 3179
Clinical impact: This is the first real-world analysis in the United States of the clinical
effectiveness of the anti-fibrotic medications pirfenidone and nintedanib. Using a large
insurance database to perform a retrospective cohort analysis, we observed that the medications
had an association with a reduced risk of all-cause mortality for up to two years of follow-up in
patients with idiopathic pulmonary fibrosis compared to an untreated matched cohort. Due to the
high mortality associated with idiopathic pulmonary fibrosis, this is a significant finding for
patients with the disease and the clinicians who treat it.
“At a Glance Commentary”
Scientific Knowledge on the Subject: Idiopathic pulmonary fibrosis is a progressive lung disease with high mortality that had no effective medical treatment options until the approval of the anti-fibrotic medications pirfenidone and nintedanib in 2014. While the randomized trials leading to their approval demonstrated the medications slow the decline in lung function and may have a trend toward decreased mortality, to date there has been no analysis of their effect on clinically important outcomes such as mortality and hospitalizations in every day clinical practice.
What This Study Adds to the Field: This is the first real-world analysis of the clinical effectiveness of pirfenidone and nintedanib. Using a large insurance database to perform a retrospective cohort analysis, it was observed that the medications had an association with a reduced risk of all-cause mortality and hospitalizations for up to two years of follow-up compared to an untreated matched cohort. There were no significant differences between the two drugs in all-cause mortality. The findings in this study support the randomized trial data suggesting these medications have an impact on clinical outcomes in patients with idiopathic pulmonary fibrosis.
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ABSTRACT
Rationale: Since their approval, there has been no real-world or randomized trial evidence
evaluating the effect of the anti-fibrotic medications pirfenidone and nintedanib on clinically
important outcomes like mortality and hospitalizations.
Objectives: To evaluate the clinical effectiveness of the anti-fibrotic medications in patients with
idiopathic pulmonary fibrosis.
Methods: Using a large United States insurance database, we identified 8098 patients with
idiopathic pulmonary fibrosis between October 1, 2014 and March 1, 2018. A one-to-one
propensity score matched cohort was created to compare those treated with anti-fibrotic
medications (n=1255) to those not on treatment (n=1255). The primary outcome was all-cause
mortality. The secondary outcome was acute hospitalizations. Subgroup analysis was performed
to evaluate mortality differences by drug.
Measurements and Main Results: The use of anti-fibrotic medications was associated with a
decreased risk of all-cause mortality (Hazard Ratio 0.77; 95% confidence interval, 0.62 to 0.98; p
value=0.034). However, this association was present only through the first two years of
treatment. There was also a decrease in acute hospitalizations in the treated cohort (Hazard Ratio
0.70; 95% confidence interval, 0.61 to 0.80; p value<0.001). There was no significant difference
in all-cause mortality between patients receiving pirfenidone and those on nintedanib (Hazard
Ratio 1.14; 95% CI, 0.79 to 1.65; p=0.471).
Conclusions: Among patients with idiopathic pulmonary fibrosis, anti-fibrotic agents may be
associated with a lower risk of all-cause mortality and hospitalizations compared to no treatment.
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Future research should test the hypothesis that these treatments reduce early but not long-term
mortality as demonstrated in our study.
Abstract word count: 250
3 to 5 key words: pirfenidone; nintedanib; idiopathic pulmonary fibrosis; mortality; acute
hospitalizations
Abbreviations: CI- confidence interval; FDA- Food and Drug Administration; ICD-9-
International Classification of Diseases, 9th edition; ICD-10- International Classification of
Diseases, 10th edition ; IPF- idiopathic pulmonary fibrosis; HR- Hazard Ratio; RCT- randomized
controlled trial; SD- standard deviation; US-United States
3
INTRODUCTION: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrosing lung disease with high
mortality that is associated with the usual interstitial pneumonia pattern on computed
tomography scan and/or biopsy.1 Over the years, several medical therapies have been tested for
the treatment of patients with IPF, though they have largely failed to show benefit and some have
even demonstrated harm.1-3 In October 2014, the anti-fibrotic medications pirfenidone and
nintedanib were concurrently approved by the Food and Drug Administration (FDA) following
the results of two phase III, multicenter, placebo-controlled randomized trials (ASCEND and
INPULSIS 1 and 2) that demonstrated the medications slowed the decline in lung function in
patients with IPF.4,5 There was also a non-statistically significant trend toward improved survival
for both medications, though there was no change in respiratory symptoms, patient-reported
outcomes, or quality of life with either treatment. This prompted a “conditional
recommendation” for the use of the two medications in the 2015 American Thoracic Society IPF
clinical practice guideline statement, as well as much anticipation from physicians given the
belief that there were now multiple effective treatment options for patients with IPF.1, 6
Since approval, there have been several systematic reviews and pooled data analyses which have
largely confirmed the outcomes of the randomized controlled trials (RCTs).7,8 There have also
been network meta-analyses indirectly comparing the two medications that have suggested there
were no clear outcome differences between the treatments.9-11 Given these comparable results in
clinical trials and systematic reviews (and lack of direct comparisons), it is believed that most
physicians who treat patients with IPF ultimately make the decision for therapy following a
shared-decision making discussion with patients, largely based on the possible adverse effects of
the chosen medication as well as out-of-pocket costs.
4
Despite the initial excitement surrounding the approval of the anti-fibrotics, there are several
questions that remain, including whether these medications actually work in general clinical
practice to improve survival and decrease hospitalizations. To date, there has been no analysis of
the available real-world data of IPF patients on therapy to answer these clinically important
questions. Thus, in this study, we aim to assess the treatment benefits of the medications
compared to patients with IPF not receiving treatment as well as compare the effectiveness of the
two medications head-to-head using individuals enrolled in United States (US) commercial and
Medicare Advantage health plans. Some of the results of this study have been previously
reported in the form of an abstract.12
METHODS:
Data Source: This analysis was a retrospective cohort study using de-identified administrative
claims data from the OptumLabs® Data Warehouse, which includes data for commercially
insured and Medicare Advantage enrollees in a large US health plan.13 The database contains
claims-based information on individuals from all 50 states comprising all ages, ethnicities, and
racial groups. The health plan provides comprehensive insurance coverage for physician,
hospital, and outpatient prescription services. Pursuant to the Health Insurance Portability and
Accountability Act, the use of de-identified data does not require Institutional Review Board
Approval.
Study Populations: Two cohorts of patients with IPF were created for this study: an untreated
cohort and a treated group. For the treated cohort, we identified all adult patients (≥18 years) that
filled a prescription for either pirfenidone or nintedanib between October 1, 2014 and March 1,
2018. We required treated individuals to have a diagnosis of idiopathic pulmonary fibrosis using
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the International Classification of Diseases, 9th edition (ICD-9) and International Classification
of Diseases, 10th edition (ICD-10) billing codes for IPF (9th Edition, 516.31; 10th Edition,
J84.112) in the 180 days prior to their first fill for either drug. For the untreated cohort, we
identified individuals with a diagnosis of IPF (based on the above codes) who did not fill a
prescription during our study period. For each patient, we defined the index date as the date of
first fill for either pirfenidone or nintedanib or the first IPF encounter in the study period after
patients met the 180 days enrollment requirement. We required all patients to have at least 180
days of continuous enrollment before the index date in order to have sufficient data to capture
patient’s baseline medical history. For patients not receiving anti-fibrotic medications, we
required patients to have an additional claim for the above codes for IPF to increase the
specificity of patient identification (including those patients with a single inpatient claim or at
least 2 outpatient claims on different dates).
Because there has been controversy over the specificity of billing codes used to classify the
disease in prior epidemiological analyses, a local cohort validation study using chart and imaging
examination by expert reviewers was performed by a separate set of clinical pulmonologists to
find the most specific billing codes (unpublished data), which were found to be ICD-9 516.31
and ICD-10 J84.112. Other codes (including those used in prior studies such as post-
inflammatory fibrosis [ICD-9 515, ICD-10 J84.10]) were found to be non-specific for the
diagnosis of IPF and were not included in our analysis.14
Independent Variables: Independent variables of interest at index included age, sex,
race/ethnicity, residence region, steroid use, oxygen use, health care utilization, and
comorbidities (as captured by ICD-9 and ICD-10 codes on claims in any position in the 6 months
before index date). Comorbidity burden was assessed using the Elixhauser sum of conditions.15
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Prior hospitalizations and pulmonologist office visits in the 6-month baseline period were
captured using medical claims. Prior steroid use was defined as having a filled prescription
within 6 months prior to the index date. Oxygen use was identified using Healthcare Common
Procedure Coding System indicating oxygen supplies during the baseline period.
Follow-up: Follow-up started from the index date and continued until the end of treatment, end
of enrollment in health insurance plan, death, or end of study period (March 1, 2018). End of
treatment was defined as discontinuation of medication (not refilling a prescription within 30
days of the end of supply).
Study Outcomes: The primary outcome was all-cause mortality. Mortality was identified based
on the Social Security Death Master file and discharge status of the expired.16 The secondary
outcome was all-cause hospitalizations. Subgroup analysis was performed comparing
differences in all-cause mortality by drug.
Statistical Analysis: We used propensity score matching to balance the differences in baseline
characteristics between the treated and untreated cohorts. A propensity score was estimated using
logistic regression based on sociodemographic characteristics, medical history, prior steroid use,
year of index, oxygen use, and utilization. Specifically, we used one-to-one nearest-neighbor
caliper matching to match patients based on the logit of the propensity score using a caliper equal
to 0.2 of the standard deviation of the logit of the propensity score.17 We evaluated the
standardized difference to assess the balance of covariates after matching, and a standardized
difference less than 10% was considered acceptable.18 Cox proportional hazards regression was
then used to compare patients treated to those untreated for mortality and hospitalizations in the
propensity score-matched cohort, with robust sandwich estimates to account for the clustering
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within matched sets.19 All analyses were conducted using SAS 9.4 (SAS Institute Inc., Cary, NC)
and Stata version 14.1 (StataCorp).
Sensitivity Analyses: Subgroup analyses for mortality were performed among those treated
stratified by index drug for drug-by-drug comparisons. In this analysis, if a covariate was not
balanced, we examined whether including it in the regression affected results. Falsification
endpoint analyses were performed to test for residual confounding; two endpoints were selected:
acute myocardial infarction and fracture.
Lastly, we used inverse probability of treatment weighting instead of propensity score matching
to repeat the main analyses. Specifically, we used a weight of 1/propensity score for patients
receiving a treatment and 1/ (1 − propensity score) for untreated patients.20
RESULTS
Baseline Characteristics
A total of 1255 treated and 6843 untreated adults with IPF were identified. The final propensity-
matched cohort included 1255 matched pairs of patients with IPF treated or untreated. Baseline
characteristics were well balanced between the two groups with standardized differences less
than 10% as seen in Table 1. Mean duration of follow-up was 9.93 (standard deviation [SD]
9.35) months. The mean age of the treated and untreated cohorts was 72.2 and 72.4 years,
respectively. The percentage of female patients was 36.9 in the untreated cohort and 37.1 in the
treated group. Roughly half of patients in the untreated group (48.8 percent) received steroids in
the six months prior to diagnosis compared to 49.6 percent of patients in the treated cohort, who
received steroids in the six months prior to treatment initiation. Between both groups, 58.5
percent of individuals required home oxygen supplementation. The most frequent co-morbidities
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by diagnostic codes in the overall cohort were hypertension (66.4 percent), other chronic
pulmonary conditions (62.8 percent), and diabetes (32.9 percent).
The baseline characteristics of the subgroup analysis of patients treated with pirfenidone (n=593)
compared to nintedanib (n=662) are shown in Supplementary Table E1. There were no baseline
differences between patients taking the two medications in age, gender, steroid use, oxygen use,
or co-morbidities even without propensity score matching. The mean duration of treatment for
both medications was also similar (pirfenidone mean duration=241.32 days; nintedanib mean
duration=228.86 days).
All-Cause Mortality and Acute Hospitalizations
For the primary outcome of all-cause mortality, there was an association with reduced all-cause
mortality for those in the treated cohort compared to the untreated matched cohort (treated
cohort: 13.78 per 100 person-years versus untreated cohort: 16.34 per 100 person-years; Hazard
Ratio [HR] 0.77; 95% confidence interval [CI], 0.62 to 0.98; p value=0.034). This mortality
benefit was only present through the first two years of follow-up as seen in Figure 1A. For the
secondary outcome of acute hospitalizations, there was also a decrease in the treated cohort as
seen in Figure 1B (46.70 per 100 person-years versus 62.44 per 100 person-years; HR 0.70; 95%
confidence interval, 0.61 to 0.80; p value<0.001). The most common reason for hospitalization
identified in this cohort was respiratory failure (Supplementary Table E2).
In subgroup analysis, we observed no significant difference in all-cause mortality between
patients treated with pirfenidone and those on nintedanib as seen in Figure 2 (pirfenidone 12.83
per 100 person-years versus nintedanib 14.77 per 100 person-years; HR 1.14; 95% CI, 0.79 to
1.65; p=0.471).
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All sensitivity analyses performed to confirm our primary findings showed results similar to the
main analysis (Supplementary Table E3). The reasons for censoring are provided in
Supplementary Table E4.
DISCUSSION
This is the first real-world study of the clinical effectiveness of the anti-fibrotic medications
pirfenidone and nintedanib for patients with IPF since their approval in 2014. There are a
number of important findings from this cohort analysis, most notably the association with an all-
cause mortality benefit observed in the cohort treated with antifibrotic medications.
All-cause mortality has been proposed as the most clinically meaningful endpoint for both
patients and clinicians when considering treatment of IPF.21 Given the relative rarity of the
disease and its progressive and ultimately fatal nature, powering a trial and ensuring appropriate
follow-up to demonstrate a mortality effect is incredibly difficult.22-24 Despite this, even before
pirfenidone and nintedanib were approved, there was debate about whether the use of surrogate
endpoints in the phase three trials of the drugs was appropriate.21,25 While the FDA ultimately
deemed the primary outcome of the trials and their findings strong enough for regulatory
approval, questions have remained as to whether the trial data translate into meaningful results in
every day clinical practice.
In this large cohort study of privately insured and Medicare Advantage patients, we have
attempted to start answering those questions by supplementing the trial data with real-world
evidence. We observed that patients treated with either pirfenidone or nintedanib had an
association with decreased risk of all-cause mortality and acute hospitalizations compared to a
matched cohort of untreated patients. This helps complement both the initial trial data for the
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medications as well as more recent studies, which have largely relied on pooled analyses of trial
data.4,5,7,8,26-28 Impressively, this benefit was found in a population that was older and appeared to
have more severe disease than those studied in the randomized trials, which enrolled patients
with mostly mild to moderate IPF. In our cohort, more than 58 percent of patients in both the
treated and non-treated groups required home oxygen supplementation. In ASCEND, the
number of patients requiring oxygen was around 28 percent.4 All-cause mortality in our cohort
was 14.85 percent at 52 week follow-up, while in the two trials combined all-cause mortality at
one year in the placebo and treatment arms ranged from 5.6 to 6.5 percent. At least in this
cohort, it would appear physicians were using pirfenidone and nintedanib in patients with more
severe disease who would have been excluded from the RCTs. Despite those differences in
severity, these medications are improving outcomes in patients, such as in our study.
When comparing the effectiveness of the two medications at reducing all-cause mortality, there
was no difference; however, as with the published trial data, pirfenidone had a slightly more
favorable trend. Comparing the demographics of patients treated with either medication (even
without matching), the populations had nearly identical age, race, and co-morbidity profiles.
Given these similarities, it would seem to corroborate the belief that physicians are currently
deciding which medication to use based largely on shared-decision making discussions
surrounding factors such as side effects and cost considerations. Due to the lack of significant
clinical differences observed in this cohort, physicians should continue to solicit patient input
into which medication they prefer for initial treatment while awaiting further confirmation of
these results. Additional comparisons are also needed on patient-important factors such as
quality of life, symptom control, medication tolerability, and exercise tolerance.
11
One of the more intriguing findings from this study is the decline in the benefit of therapy over
time. This is consistent with prior pooled data and survival modeling for pirfenidone suggesting
a mortality benefit for up to two years.7,26 The reason for this later decline in efficacy is
currently unclear. These medications are not curative, meaning that fibrosis continues to
accumulate over time, as do clinical events such as acute exacerbations. One hypothesis is that
as lung fibrosis progresses and vascular remodeling occurs, deposition of the drugs in the lungs
decreases.29-31 Another possibility is that acute exacerbations occur despite drug therapy, which
prior research has shown portends a worse prognosis.32,33 In pooled analyses of trial data and
indirect comparison via network meta-analysis, pirfenidone consistently has the stronger
mortality benefit, while nintedanib has a stronger impact on acute exacerbations.3,7,34 Given these
findings, it is possible that a sequential treatment model utilizing pirfenidone initially and then
switching to nintedanib following the first acute exacerbation would help prevent this decreased
efficacy over time.35 In addition, combination therapy, which has been effective in other
pulmonary diseases like asthma, emphysema, and pulmonary hypertension, may also help sustain
the mortality benefit beyond two years. Thus far, studies have shown that combination therapy
is tolerable and possibly more efficacious than single drug treatment.35-38 Further research should
aim to discover the etiology for the reduction in clinical benefit over time as well as methods to
best attenuate this finding. Given the reported cost of the medications, if combination therapy is
to be considered, studies should also focus on out-of-pocket costs to patients as well as the
overall cost effectiveness of these agents.
Despite the large cohort and key observations of this analysis, there are several important
limitations of this study to consider. First, the dataset analyzed only includes patients enrolled in
private and Medicare Advantage health plans with pharmaceutical benefits, which limits the
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generalizability of this study to other groups of patients such as the uninsured or those enrolled in
other government health plans such as Medicaid. These populations may have a different
prevalence of risk factors as well as lower socio-economic status than individuals with private or
Medicare Advantage coverage, which could lead to worse outcomes. These differences may be
mitigated in our cohort by the fact that insurance coverage rates are generally higher in older
Americans, the group most likely to be affected by IPF, thus making our findings largely
generalizable to most patients with the disease.39 Second, despite adequate matching and
adjustments across groups, confounding factors are still a distinct possibility given this study’s
observational nature. Additional sensitivity tests were undertaken and demonstrated similar
results to the main analysis, which further help to validate our main findings and to moderate
some of the concerns over confounding factors. Third, the treated cohort was identified by date
of first fill of a prescription for either anti-fibrotic medication. We acknowledge that filling a
prescription does not mean that a patient actually took the medication as prescribed. However,
the individuals in our cohort continued to fill their prescriptions; otherwise, they were censored.
As subsequent fills were tracked, this makes significant lack of adherence unlikely.
Additionally, the possibility that those in the treated cohort did not use the anti-fibrotics as
intended would presumably strengthen the observations of our analysis, as we would expect the
outcomes to further trend toward the treated cohort with stricter adherence.
A fourth limitation of our study is the use of the Social Security Death Master File to obtain the
primary outcome of mortality. Due to various circumstances and policies, since 2011, this
database has been limited in its ability to capture up to one-third of deaths.40 We attempted to
minimize this gap by using discharge status (i.e. in-hospital death) to supplement the Social
Security Death Master File as the majority of deaths occur in a hospital setting. Using this
13
approach, an additional 30 percent of deaths are typically captured in addition to those obtained
through the Death Master File. While this accounts for the majority of deaths missed by the
Death Master File, we acknowledge that a small proportion of patients that die outside of the
hospital setting could be missing, though this number is likely not high enough to influence our
overall observations.
Finally, billing codes were used to identify patients with IPF. There are a multitude of factors
that make IPF a complex diagnostic decision including the need for a multidisciplinary team, the
importance of considering other fibrotic interstitial lung diseases, and the ability to diagnose
based solely on radiographs without tissue pathology in some cases.41 Such qualities make the
use of billing codes prone to misidentification, something prior epidemiological studies in IPF
have acknowledged.42-45 We attempted to mitigate this limitation by using a local cohort
validation to identify and use only the most accurate codes for IPF (unpublished data). We also
required two outpatient codes for IPF to exclude the possibility of an evolving diagnosis after the
completion of further diagnostic testing. Even with these steps aimed at ensuring cohort
specificity, the possibility for misdiagnosis or miscoding is still possible.
In summary, in this real-world analysis of the clinical impact of the anti-fibrotic medications
pirfenidone and nintedanib, patients on treatment for idiopathic pulmonary fibrosis had an
association with a reduced risk of all-cause mortality and acute hospitalizations compared to
untreated matched individuals. Compared head-to-head, there was no difference in all-cause
mortality between the medications. Further research is needed to test the hypothesis that these
treatments reduce early but not long-term mortality as demonstrated in our study.
14
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20
FIGURE LEGENDS:
Figure 1A: Mortality Cumulative Risk Percent. The cumulative risk of all-cause mortality in
patients on treatment for idiopathic pulmonary fibrosis (treated) compared to an untreated IPF
matched cohort (untreated) during 24-month follow-up. For the number of patients at risk during
each time interval, please see Supplementary Table E5.
Figure 1B. Hospitalization Cumulative Risk Percent. The cumulative risk of acute
hospitalizations in patients on treatment for idiopathic pulmonary fibrosis (treated) compared to
an untreated IPF matched cohort (untreated) during 24-month follow-up. For the number of
patients at risk during each time interval, please see Supplementary Table E5.
Figure 2. Comparison of Mortality Risk by Drug. The cumulative risk of all-cause mortality in
patients on pirfenidone compared to those on nintedanib during 24-month follow-up. For the
number of patients at risk during each time interval, please see Supplementary Table E5.
21
TABLES: Table 1. Baseline Demographics of Patients with Idiopathic Pulmonary Fibrosis Before and After Propensity Score Matching
Non-matched 1:1 Matched
No-treatment
(N=6843) Treated
(N=1255) Std. Diff
No treatment (N=1255)
Treated (N=1255)
Std. Diff
Age, years
Mean (SD) 72.9 (10.8) 72.2 (8.6) 1.6% 72.4 (9.2) 72.2 (8.6) 0.5%
Median 74 73 73 73
Age group
18-54 417 (6.1%) 42 (3.3%) 13.3% 43 (3.4%) 42 (3.3%) 0.6%
55-64 885 (12.9%) 172 (13.7%) 2.4% 176 (14.0%) 172 (13.7%) 0.9%
65-74 2131 (31.1%) 507 (40.4%) 19.5% 501 (39.9%) 507 (40.4%) 1.0%
75+ 3410 (49.8%) 534 (42.5%) 14.7% 535 (42.6%) 534 (42.5%) 0.2%
Sex
Female 3364 (49.2%) 465 (37.1%) 24.6% 463 (36.9%) 465 (37.1%) 0.4%
Male 3479 (50.8%) 790 (62.9%) 24.6% 792 (63.1%) 790 (62.9%) 0.4%
Census Region
MIDWEST 1901 (27.8%) 372 (29.6%) 4.0% 352 (28.0%) 372 (29.6%) 3.5%
NORTHEAST 988 (14.4%) 187 (14.9%) 1.4% 182 (14.5%) 187 (14.9%) 1.1%
SOUTH 3301 (48.3%) 590 (47.0%) 2.6% 596 (47.5%) 590 (47.0%) 1.0%
WEST 653 (9.5%) 106 (8.4%) 3.9% 125 (10.0%) 106 (8.4%) 5.5%
Race
Asian 190 (2.8%) 36 (2.9%) 0.6% 39 (3.1%) 36 (2.9%) 1.2%
Black 836 (12.2%) 136 (10.8%) 4.4% 141 (11.2%) 136 (10.8%) 1.3%
Hispanic 893 (13.0%) 131 (10.4%) 8.1% 129 (10.3%) 131 (10.4%) 0.3%
22
Unknown 335 (4.9%) 50 (4.0%) 4.4% 53 (4.2%) 50 (4.0%) 1.0%
White 4589 (67.1%) 902 (71.9%) 10.4% 893 (71.2%) 902 (71.9%) 1.6%
Steroid Use (6m baseline) 2837 (41.5%) 623 (49.6%) 16.3% 612 (48.8%) 623 (49.6%) 1.6%
Pulmonary Office Visit 3646 (53.3%) 1045 (83.3%) 68.1% 1043 (83.1%) 1045 (83.3%) 0.5%
DME, Oxygen 2756 (40.3%) 738 (58.8%) 37.7% 731 (58.2%) 738 (58.8%) 1.2%
Conditions
Cardiac Arrhythmia 2241 (32.7%) 339 (27.0%) 12.5% 346 (27.6%) 339 (27.0%) 1.4%
Congestive Heart Failure 2016 (29.5%) 253 (20.2%) 21.6% 264 (21.0%) 253 (20.2%) 2.0%
Chronic Pulmonary Disease 4313 (63.0%) 800 (63.7%) 1.5% 778 (62.0%) 800 (63.7%) 3.5%
Depression 903 (13.2%) 149 (11.9%) 3.9% 137 (10.9%) 149 (11.9%) 3.2%
Diabetes 2237 (32.7%) 412 (32.8%) 0.2% 415 (33.1%) 412 (32.8%) 0.6%
Hypertension 4734 (69.2%) 827 (65.9%) 7.1% 842 (67.1%) 827 (65.9%) 2.5%
Pulmonary Circulation Disorder
1357 (19.8%) 261 (20.8%) 2.5% 270 (21.5%) 261 (20.8%) 1.7%
Renal Failure 1195 (17.5%) 153 (12.2%) 15.0% 149 (11.9%) 153 (12.2%) 0.9%
Rheumatoid Arthritis 1072 (15.7%) 106 (8.4%) 22.6% 113 (9.0%) 106 (8.4%) 2.1%
Solid Tumor without Metastasis
745 (10.9%) 95 (7.6%) 11.4% 96 (7.6%) 95 (7.6%) 0.0%
Valvular Disease 1370 (20.0%) 247 (19.7%) 0.8% 238 (19.0%) 247 (19.7%) 1.8%
Elixhauser Sum of Conditions
Mean (SD) 4.7 (2.9) 4.0 (2.5) 3.4% 4.0 (2.5) 4.0 (2.5) 0%
Median 4 4 4 4
Smoker 2390 (34.9%) 534 (42.5%) 15.6% 520 (41.4%) 534 (42.5%) 2.2%
Baseline hospitalizations
0 4568 (66.8%) 865 (68.9%) 4.5% 892 (71.1%) 865 (68.9%) 4.8%
23
1 1576 (23.0%) 295 (23.5%) 1.2% 281 (22.4%) 295 (23.5%) 2.6%
2+ 699 (10.2%) 95 (7.6%) 9.1% 82 (6.5%) 95 (7.6%) 4.3%