ERS Statement on Benign Pleural Effusions in Adults.
Anand SundaralingamElżbieta Magdalena GrabczakPatrizia BurraM Inês CostaVineeth GeorgeEli HarrissEwa JankowskaJulius P JanssenGeorgia KarpathiouChristian Borbjerg LaursenKornelija MaceviciuteNick MaskellFederico MeiBlin NagavciVasiliki PanouValentina PinelliJosé M PorcelSara RicciardiSamira ShojaeeHugh WelchAlberto ZanettoUdaya Prabhakar UdayarajGiuseppe CardilloNajib M RahmanPublished in: The European respiratory journal (2024)
The incidence of non-malignant pleural effusions (NMPE) far outweighs that of malignant pleural effusions (MPE) and is estimated to be at least 3-fold higher. These so called "benign" effusions do not follow a "benign course" in many cases, with mortality rates matching and sometimes exceeding that of MPEs. In addition to the impact on patients, healthcare systems are significantly affected, with recent US epidemiological data demonstrating that 75% of resource allocation for pleural effusion management is spent on NMPEs (excluding empyema). Despite this significant burden of disease, and by existing at the junction of multiple medical specialties, reflecting a heterogenous constellation of medical conditions, NMPEs are rarely the focus of research or the subject of management guidelines. With this ERS Taskforce, we assembled a multi-specialty collaborative across eleven countries and three continents to provide a Statement based on systematic searches of the medical literature to highlight evidence in the management of the following clinical areas: a diagnostic approach to transudative effusions, heart failure, hepatic hydrothorax, end stage renal failure, benign asbestos related pleural effusion, post-surgical effusion and non-specific pleuritis.
Keyphrases
- healthcare
- heart failure
- end stage renal disease
- systematic review
- ejection fraction
- newly diagnosed
- risk factors
- chronic kidney disease
- prognostic factors
- type diabetes
- cardiovascular events
- left ventricular
- coronary artery disease
- quality improvement
- peritoneal dialysis
- patient reported outcomes
- machine learning
- health insurance
- artificial intelligence