Predictors of outcome of pleurodesis in patients with malignant pleural effusion: a systematic review and meta-analysis.
Maged HassanMohamed GadallahRachel M MercerElinor HarrissNajib M RahmanPublished in: Expert review of respiratory medicine (2020)
Objectives: Pleurodesis is an important management option to palliate breathlessness in patients with malignant pleural effusion (MPE). This systematic review aimed to examine available literature for studies investigating factors that predict pleurodesis outcome.Methods: The healthcare databases advanced search (HDAS) Medline and Embase in addition to Cochrane Database of Systematic Reviews were searched on for publications reporting on pleurodesis for MPE in English language. All study types reporting previously unpublished data on predictors of pleurodesis success were included. Thirty-four studies involving 4626 patients were included in the systematic review.Results: The most common pleurodesis agent used was talc which was used in 27 studies. Meta-analyses demonstrated that the strongest predictors of pleurodesis success were higher pleural fluid pH, smaller volume of effusion pre-pleurodesis and full lung re-expansion post effusion drainage. Shorter duration of tube drainage, higher pleural fluid glucose, lower LDH, and lower pleural tumor burden all seem to favor pleurodesis success, but with considerable statistical heterogeneity between studies. Available data do not suggest that chest tube size affects pleurodesis outcome.Conclusion: Overall, available results are difficult to interpret due to evidence quality. Prospective studies are needed to further explore these factors.Protocol registration: CRD42018115874 (Prospero database of systematic reviews).
Keyphrases
- systematic review
- meta analyses
- healthcare
- case control
- adverse drug
- end stage renal disease
- chronic kidney disease
- type diabetes
- newly diagnosed
- emergency department
- big data
- electronic health record
- ejection fraction
- machine learning
- single cell
- skeletal muscle
- prognostic factors
- artificial intelligence
- patient reported