Abstract
Background: The role of macrolide/β-lactam combination therapy in community-acquired pneumonia (CAP) of moderate severity is a matter of debate. Macrolides expand the coverage to atypical pathogens and attenuate pulmonary inflammation, but have been associated with cardiovascular toxicity and drug interactions. We developed a decision tree based on aetiological and clinical parameters, which are available ex ante to support a personalised decision for or against macrolides for the best clinical outcome of the individual patient. Methods: We employed machine learning in a cross-validation scheme based on a well-balanced selection of 4898 patients after propensity score matching to data available on admission of 6440 hospitalised patients with moderate severity (non-intensive care unit patients) from the observational, prospective, multinational CAPNETZ study. We aimed to improve the primary outcome of 180-day survival. Results: We found a simple decision tree of patient characteristics comprising chronic cardiovascular and chronic respiratory comorbidities as well as leukocyte counts in the respiratory secretion at enrolment. Specifically, we found that patients without cardiovascular or patients with respiratory comorbidities and high leukocyte counts in the respiratory secretion benefit from macrolide treatment. Patients identified to be treated in compliance with our treatment suggestion had a lower mortality of 27% (OR 1.83, 95% CI 1.48-2.27; p<0.001) compared to the observed standard of care. Conclusion: Stratifying macrolide treatment in patients following a simple treatment rule may lead to considerably reduced mortality in CAP. A future randomised controlled trial confirming our result is necessary before implementing this rule into the clinical routine.
| Original language | English |
|---|---|
| Article number | 1900824 |
| Journal | European Respiratory Journal |
| Volume | 54 |
| Issue number | 6 |
| ISSN | 0903-1936 |
| DOIs | |
| Publication status | Published - 01.12.2019 |
Funding
Support statement: This work was supported by the project CSCC (01EO1002, 01EO1502) of the German Federal Ministry of Education and Research (BMBF), the project CancerTelSys (01ZX1302B, 01ZX1602B) in the e:Med programme and the project KO 3678/5-1 funded by the Deutsche Forschungsgemeinschaft (DFG). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Funding information for this article has been deposited with the Crossref Funder Registry.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Research Areas and Centers
- Academic Focus: Center for Infection and Inflammation Research (ZIEL)
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