Diagnostic algorithm for urgent causes of dyspnea based on lung auscultation and ultrasound: The CLINIC-LUS Prospective Study
Abstract
Objective: Dyspnea is a frequent reason for consultation in emergency departments (EDs). Ultrasound artifacts generated within lung tissue can be interpreted for diagnostic purposes. The primary endpoint of this study was to provide evidence on the feasibility and applicability of the CLINIC-LUS algorithm, based on lung auscultation and lung ultrasound, for diagnosing causes of acute dyspnea in adult patients presenting to hospital.
Methods: We conducted a prospective, multicenter feasibility study in patients evaluated for acute dyspnea in EDs. Lung auscultation and lung ultrasound patterns were assessed using a 12-point ultrasound protocol. Feasibility was evaluated by the time (in minutes) required to perform lung ultrasound and by the investigator's perception. Diagnostic accuracy (percentage of correct diagnoses) and the Cohen kappa coefficient for agreement of the algorithm were calculated.
Results: A total of 95 patients with acute dyspnea presenting to EDs from January 2024 through June 2025 were included and eventually analyzed. The time required to perform lung ultrasound [median (range)] was 12 (9 16) minutes. Investigators unanimously reported that use of the CLINIC-LUS algorithm was straightforward and did not pose difficulties in clinical practice. Diagnostic accuracy was 78.1% (95% CI, 67.5-86.4). The kappa coefficient was 0.73 (95% CI, 0.52-0.82).
Conclusions: Integrating physical examination with lung ultrasound into a single algorithm for diagnosing causes…
How strong is the evidence?
Evidence Hub reads this as a clinical trial, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 95 participants or records.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.