Diagnostic Methods for Pulmonary Congestion and Pleural Effusion: A Protocol for a Systematic Review and Meta-Analysis.

Olesen, Anne Sophie Overgaard; Miger, Kristina; Thune, Jens Jakob; Nielsen, Olav Wendelboe; Jakobsen, Janus Christian; Grand, Johannes · Acta Anaesthesiol Scand · 2025

systematic_review · Level I

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Abstract

Pulmonary congestion and pleural effusion are key manifestations in patients with acute heart failure, contributing to over one million emergency department admissions annually in, respectively, the United States and Europe. Accurate and timely diagnosis is critical for initiating early treatment and optimizing patient outcomes. Routinely used diagnostic tools, such as chest radiography, have limitations in sensitivity and specificity. Emerging imaging modalities, including lung ultrasound, computed tomography (CT), and remote dielectric sensing (ReDS), may offer improved diagnostic accuracy, but their comparative effectiveness remains unclear. We will conduct a systematic review and meta-analysis following PRISMA-P guidelines to assess the diagnostic accuracy of chest radiography, lung ultrasound, CT, and ReDS for pulmonary congestion and pleural effusion. We will include prospective diagnostic studies comparing these modalities to a reference standard of pulmonary congestion or pleural effusion. Our search will cover MEDLINE, Embase, CENTRAL, and other major databases, without language restrictions. We will begin our search in April 2025. Sensitivity, specificity, and likelihood ratios will be pooled using a hierarchical summary receiver operating characteristic model. Risk of bias will be assessed using QUADAS-2. This protocol defines the detailed methodology and approach used for a systematic review that will provide a comprehensive assessment of current diagnostic modalities for pulmonary congestion and pleural effusion. By comparing their diagnostic accuracy, we aim to guide clinicians in selecting the most effective tools for clinical practice. Additionally, identifying gaps and the risk of bias in existing research may inform future studies and advancements in acute heart failure diagnostics.

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