Recommendations for Lung Ultrasound in Internal Medicine.
Natalia BudaWojciech KosiakMarcin WełnickiAgnieszka SkoczylasRobert OlszewskiJakub PiotrkowskiSzymon SkoczyńskiElżbieta RadzikowskaEwa JassemElżbieta Magdalena GrabczakPiotr KwaśniewiczGebhard MathisTudor P TomaPublished in: Diagnostics (Basel, Switzerland) (2020)
A growing amount of evidence prompts us to update the first version of recommendations for lung ultrasound in internal medicine (POLLUS-IM) that was published in 2018. The recommendations were established in several stages, consisting of: literature review, assessment of literature data quality (with the application of QUADAS, QUADAS-2 and GRADE criteria) and expert evaluation carried out consistently with the modified Delphi method (three rounds of on-line discussions, followed by a secret ballot by the panel of experts after each completed discussion). Publications to be analyzed were selected from the following databases: Pubmed, Medline, OVID, and Embase. New reports published as of October 2019 were added to the existing POLLUS-IM database used for the original publication of 2018. Altogether, 528 publications were systematically reviewed, including 253 new reports published between September 2017 and October 2019. The new recommendations concern the following conditions and issues: pneumonia, heart failure, monitoring dialyzed patients' hydration status, assessment of pleural effusion, pulmonary embolism and diaphragm function assessment. POLLUS-IM 2020 recommendations were established primarily for clinicians who utilize lung ultrasound in their everyday clinical work.
Keyphrases
- pulmonary embolism
- clinical practice
- heart failure
- magnetic resonance imaging
- end stage renal disease
- newly diagnosed
- chronic kidney disease
- systematic review
- adverse drug
- ejection fraction
- inferior vena cava
- peritoneal dialysis
- palliative care
- big data
- intensive care unit
- ultrasound guided
- prognostic factors
- computed tomography
- atrial fibrillation
- randomized controlled trial
- quality improvement
- extracorporeal membrane oxygenation
- deep learning
- meta analyses
- patient reported
- patient reported outcomes
- data analysis