Pirfenidone vs. nintedanib in patients with idiopathic pulmonary fibrosis: a retrospective cohort study

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Abstract

Background

Two antifibrotic drugs, pirfenidone and nintedanib, are licensed for the treatment of patients with idiopathic pulmonary fibrosis (IPF). However, there is neither evidence from prospective data nor a guideline recommendation, which drug should be preferred over the other. This study aimed to compare pirfenidone and nintedanib-treated patients regarding all-cause mortality, all-cause and respiratory-related hospitalizations, and overall as well as respiratory-related health care costs borne by the Statutory Health Insurance (SHI).

Methods

A retrospective cohort study with SHI data was performed, including IPF patients treated either with pirfenidone or nintedanib. Stabilized inverse probability of treatment weighting (IPTW) based on propensity scores was applied to adjust for observed covariates. Weighted Cox models were estimated to analyze mortality and hospitalization. Weighted cost differences with bootstrapped 95% confidence intervals (CI) were applied for cost analysis.

Results

We compared 840 patients treated with pirfenidone and 713 patients treated with nintedanib. Both groups were similar regarding two-year all-cause mortality (HR: 0.90 95% CI: 0.76; 1.07), one-year all cause (HR: 1.09, 95% CI: 0.95; 1.25) and respiratory-related hospitalization (HR: 0.89, 95% CI: 0.72; 1.08). No significant differences were observed regarding total (€− 807, 95% CI: €− 2977; €1220) and respiratory-related (€− 1282, 95% CI: €− 3423; €534) costs.

Conclusion

Our analyses suggest that the patient-related outcomes mortality, hospitalization, and costs do not differ between the two currently available antifibrotic drugs pirfenidone and nintedanib. Hence, the decision on treatment with pirfenidone versus treatment with nintedanib ought to be made case-by-case taking clinical characteristics, comorbidities, comedications, individual risk of side effects, and patients’ preferences into account.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-021-01857-y.

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Most cited references 31

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An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

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The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.

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Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

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Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.

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The central role of the propensity score in observational studies for causal effects

Paul R. Rosenbaum, Donald B Rubin (1983)

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Author and article information

Contributors

Pavo Marijic:

ORCID: http://orcid.org/0000-0002-6321-7296

pavo.marijic@helmholtz-muenchen.de

Journal

Journal ID (nlm-ta): Respir Res

Journal ID (iso-abbrev): Respir Res

Title: Respiratory Research

Publisher: BioMed Central (London )

ISSN (Print): 1465-9921

ISSN (Electronic): 1465-993X

Publication date (Electronic): 19 October 2021

Publication date PMC-release: 19 October 2021

Publication date (Print): 2021

Volume: 22

Electronic Location Identifier: 268

Affiliations

[1 ]GRID grid.4567.0, ISNI 0000 0004 0483 2525, Institute of Health Economics and Health Care Management, , Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), ; Neuherberg, Germany

[2 ]Pettenkoffer School of Public Health, Munich, Germany

[3 ]GRID grid.5252.0, ISNI 0000 0004 1936 973X, Institute for Medical Information Processing, Biometry and Epidemiology - IBE, , LMU Munich, ; Munich, Germany

[4 ]GRID grid.452624.3, Comprehensive Pneumology Center Munich (CPC-M), Member of the German Center for Lung Research (DZL), ; Munich, Germany

[5 ]GRID grid.417840.e, ISNI 0000 0001 1017 4547, IFT-Institut Fuer Therapieforschung, ; Munich, Germany

[6 ]GRID grid.9018.0, ISNI 0000 0001 0679 2801, Department of Economics, , Martin Luther University Halle-Wittenberg, ; Halle, Germany

[7 ]AOK Research Institute - WIdO, Berlin, Germany

[8 ]GRID grid.7700.0, ISNI 0000 0001 2190 4373, Center for Interstitial and Rare Lung Diseases, Pneumology and Respiratory Critical Care Medicine, Thoraxklinik, , University of Heidelberg, Member of the German Center for Lung Research (DZL), ; Heidelberg, Germany

Author information

Pavo Marijic http://orcid.org/0000-0002-6321-7296

Article

Publisher ID: 1857

DOI: 10.1186/s12931-021-01857-y

PMC ID: 8527681

PubMed ID: 34666765

SO-VID: bdf01c63-aa7d-4618-9e08-88a5eee164fa

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

History

Date received : 15 July 2021

Date accepted : 3 October 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100008334, stiftung oskar-helene-heim;

Funded by: Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH) (4209)

Open Access :

Open Access funding enabled and organized by Projekt DEAL.

Custom metadata

ScienceOpen disciplines: Respiratory medicine

Keywords: idiopathic pulmonary fibrosis,mortality,hospitalization,health care costs,administrative data,drugs,statutory health insurance

Data availability:

ScienceOpen disciplines: Respiratory medicine

Keywords: idiopathic pulmonary fibrosis, mortality, hospitalization, health care costs, administrative data, drugs, statutory health insurance

Pirfenidone vs. nintedanib in patients with idiopathic pulmonary fibrosis: a retrospective cohort study

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Abstract

Background

Methods

Results

Conclusion

Supplementary Information

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Most cited references 31

An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

The central role of the propensity score in observational studies for causal effects

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