Otto ME, Atik FA, Moreira MDN, Ribeiro LCM, Mello BCRD, Lima JGE, Ribeiro MS, Domingues ACPM, Calzada RP, Jreige A, Schloicka LL, Pibarot P. Determinants of Aortic Prosthesis Mismatch in a Brazilian Public Health System Hospital: Big Patients or Small Prosthesis?
Arq Bras Cardiol 2019;
114:12-22. [PMID:
31664320 PMCID:
PMC7025311 DOI:
10.5935/abc.20190231]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 04/10/2019] [Indexed: 11/25/2022] Open
Abstract
Background
Prosthesis-patient mismatch (PPM) is associated with worse outcomes.
Objective
Determine the frequency and evaluate preoperatory variables independently associated with severe PPM in a tertiary hospital focused on Public Health Care.
Methods
A total of 316 patients submitted to aortic valve replacement, who had echocardiography performed within the first 30 days after surgery, were retrospectively analyzed. The indexed effective orifice area (iEOA) of the prosthesis was used to classify the patients into three groups, according to PPM, considering body mass index (BMI): severe PPM (iEOA) < 0.65 cm2/m2), mild to moderate PPM (iEOA, 0.65 cm2/m2 - 0.85 cm2/m2) and without PPM (iEOA > 0.85 cm2/m2) for a BMI < 30 kg/m2 and severe PPM (iEOA) < 0.55 cm2/m2), mild to moderate (iEOA, 0.55 cm2/m2- 0.70 cm2/m2) and without PPM (iEOA > 0.7 cm2/m2) for a BMI > 30 kg/m2. Statistical significance was considered when p < 0.05.
Results
iEOA was obtained in 176 patients. The frequency of severe and moderate PPM was 33.4% and 36.2%, respectively. Severe PPM patients were younger and had larger BMI, but smaller left ventricular outflow tract diameter (LVOTD). The independent variables used to predict severe PPM were male gender, BMI > 25 kg/m2, age < 60 years, LVOTD < 21 mm, and rheumatic etiology with an area under the ROC curve of 0.82.
Conclusion
The frequency of severe PPM is high in a Brazilian population representative of the Public Health System, and it is possible to predict PPM from preoperative variables such as rheumatic valvular disease, gender, BMI, age and LVOTD.
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