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Optimizing cardiovascular imaging in Staphylococcus aureus endocarditis
Author(s) -
Shah Sangeeta,
Gupta Tripti,
White Christopher J,
Jain Surma,
Ramee Emily,
Qamruddin Salima,
Kemmerly Sandra A.
Publication year - 2021
Publication title -
echocardiography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 62
eISSN - 1540-8175
pISSN - 0742-2822
DOI - 10.1111/echo.15022
Subject(s) - medicine , bacteremia , infective endocarditis , endocarditis , algorithm , demographics , retrospective cohort study , health care , blood culture , staphylococcus aureus , emergency medicine , intensive care medicine , genetics , demography , sociology , computer science , bacteria , microbiology and biotechnology , economics , biology , economic growth , antibiotics
The shift toward value‐based health care drives physicians to examine opportunities to optimize use of healthcare resources. There is discordance between providers’ use of cardiovascular imaging (CVI) in assessing patients for infective endocarditis (IE) with Staphylococcus aureus bacteremia (SAB). An evidence‐based algorithm was created to minimize variation of CVI use. The primary objective was to ensure sensitivity of the algorithm to recommend CVI in patients suspected of IE. Methods A retrospective review evaluated patients at Ochsner Medical Center who developed SAB between 1/1/13 and 12/31/14. Predefined patient demographics, use of CVI, outcomes, and 12‐week follow‐up for readmission after first positive blood culture were collected from chart review. The created algorithm was applied retrospectively to determine its sensitivity and specificity in recommending the right CVI test. Results 181 patients admitted were admitted with SAB, of which 114 (63%) were male. There were 115 TTEs and 55 TEEs performed. Out of 15 patients diagnosed with IE, 3 were found on TTE and 12 were found on TEE. The algorithm would have recommended a TEE in all 15 patients who had high‐risk features for IE and a true diagnosis of IE, suggesting a sensitivity of 100% and specificity of 74.7% for the algorithm to have recommended a highly sensitive CVI modality. Conclusion This algorithm optimizes CVI for diagnosing IE in patients with SAB. As healthcare adapts to a value‐based system, use of best‐practice algorithms will promote consistency in practice among providers and help optimize patient outcomes and use of resources.