Diagnostic performance of a cardiopulmonary point-of-care ultrasound algorithm for identification of acute heart failure in patients with acute dyspnea

Candice Vauthier, Margaux Chabannon, Thibaut Markarian, Yann Taillandy, Kevin Guillemet, Hugo Krebs, Florian Bazalgette, Laurent Muller, Pierre-Géraud Claret, Xavier Bobbia


Rationale: Lung ultrasound (US) is effective for diagnosing lung interstitial syndrome; however, its performance is less effective in asserting the cardiogenic cause of lung edema. We evaluated the diagnostic performance of a cardiopulmonary point-of-care US (POCUS) algorithm for the diagnosis of acute heart failure (AHF) in emergency department patients admitted for acute dyspnea.

Method: A convenience sample of acute dyspneic patients was prospectively evaluated in two centers. The POCUS algorithm tested included a lung US for a B-profile, and three echocardiographic measurements performed on a single apical four-cavity view. The US techniques used for echocardiographic measurements were: mitral annular plan systolic excursion (MAPSE), mitral flow Doppler and tissue Doppler at the lateral mitral ring. The final diagnosis was made by two physicians blind to each other and to the US results. An adjudication committee was convened in case of disagreement.

Results: A total of 103 adult patients were included, mean age 73 ± 17 years, 51 (50%) females. The final diagnosis was AHF for 42 (41%). Expert agreement was good for the AHF diagnosis (κ = 0.82). The algorithm gave a response in 76 (74%) patients, 57 (85%) patients in sinus rhythm. For patients for whom the algorithm judged the diagnosis of AHF, AUC was 0.94, 95% CI [0.88; 1.00]: Se = 96% 95% CI [78%; 100%], Sp = 93% 95% CI [82%; 98%], PPV = 85% 95% CI [67%; 100%], NPV 98% 95% CI [88%; 100%], PLR 12.7 95% CI [4.9; 32.7], NLR 0.05 95% CI [0.01; 0.32].

Conclusion: The performance of a POCUS cardiopulmonary algorithm assessing pulmonary edema and cardiac congestion was good for the diagnosis of AHF in emergency department dyspneic patients. The algorithm successfully diagnosed 74% of the patients.

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