Background-Presurgical planning of mitral valve (MV) repair in patients with Barlow disease (BD) and fibroelastic deficiency (FED) is challenging because of the inability to assess accurately the complexity of MV prolapse. We hypothesized that the etiology of degenerative MV disease (DMVD) could be objectively and accurately ascertained using parameters of MV geometry obtained by morphological analysis of real-time 3D echocardiographic (RT3DE) images. Methods and Results-Seventy-seven patients underwent transesophageal RT3DE study: 57 patients with DMVD studied intraoperatively (28 BD, 29 FED classified during surgery) and 20 patients with normal MV who were used as control subjects (NL). MVQ software (Philips) was used to measure parameters of annular dimensions and geometry and leaflet surface area, including billowing volume and height. The Student t test and multinomial logistic regression was performed to identify parameters best differentiating DMVD patients from normal as well as FED from BD. Morphological analysis in the DMVD group revealed a progressive increase in multiple parameters from NL to FED to BD, allowing for accurate diagnosis of these entities. The strongest predictors of the presence of DMVD included billowing height and volume. Three-dimensional billowing height with a cutoff value of 1.0 mm differentiated DMVD from NL without overlap, and billowing volume with a cutoff value 1.15 mL differentiated between FED and BD without overlap. Conclusions-Morphological analysis as a form of decision support in assessing MV billowing revealed significant quantifiable differences between NL, FED, and BD patients, allowing accurate classification of the etiology of MV prolapse and determination of the anticipated complexity of repair. (Circ Cardiovasc Imaging. 2011;4:24-32.)
Abstract. Coarctation of aorta (CoA) is a critical congenital heart defect (CCHD) that requires accurate and immediate diagnosis and treatment. Current newborn screening methods to detect CoA lack both in sensitivity and specificity, and when suspected in a newborn, it must be confirmed using specialized imaging and expert diagnosis, both of which are usually unavailable at tertiary birthing centers. We explore the feasibility of applying machine learning methods to reliably determine the presence of this difficult-to-diagnose cardiac abnormality from ultrasound image data. We propose a framework that uses deep learning-based machine learning methods for fully automated detection of CoA from two-dimensional ultrasound clinical data acquired in the parasternal long axis view, the apical four chamber view, and the suprasternal notch view. On a validation set consisting of 26 CoA and 64 normal patients our algorithm achieved a total error rate of 12.9% (11.5% false-negative error and 13.6% false-positive error) when combining decisions of classifiers over three standard echocardiographic view planes. This compares favorably with published results that combine clinical assessments with pulse oximetry to detect CoA (71% sensitivity).
OBJECTIVES:
ICUs have had to deal with a large number of patients with acute respiratory distress syndrome COVID-19, a significant number of whom received prone ventilation, which is a substantial consumer of care time. The selection of patients that we have to ventilate in prone position seems interesting. We evaluate the correlation between the percentage of collapsed dependent lung areas in the supine position, monitoring by electrical impedance tomography and the oxygenation response (change in Pa
o
2
/F
io
2
ratio) to prone position.
DESIGN:
An observational prospective study.
SETTING:
From October 21, 2020, to 30 March 30, 2021. At the Sainte Anne military teaching Hospital and the Timone University Hospital.
PATIENTS:
Fifty consecutive patients admitted in our ICUs, with COVID-19 acute respiratory distress syndrome and required mechanical, were included. Twenty-four (48%) received prone ventilation. Fifty-eight prone sessions were investigated.
INTERVENTIONS:
An electrical impedance tomography recording was made in supine position, daily and repeated just before and just after the prone session. The daily dependent area collapse was calculated in relation to the previous electrical impedance tomography recording. Prone ventilation response was defined as a Pa
o
2
/F
io
2
ratio improvement greater than 20%.
MEASUREMENT AND MAIN RESULTS:
The main outcome was the correlation between dependent area collapse and the oxygenation response to prone ventilation. Dependent area collapse was correlated with oxygenation response to prone ventilation (
R
2
= 0.49) and had a satisfactory prediction accuracy of prone response with an area under the curve of 0.94 (95% CI, 0.87–1.00;
p
< 0.001). Best Youden index was obtained for a dependent area collapse greater than 13.5 %. Sensitivity of 92% (95% CI, 78–97), a specificity of 91% (95% CI, 72–97), a positive predictive value of 94% (95% CI, 88–100), a negative predictive value of 87% (95% CI, 78–96), and a diagnostic accuracy of 91% (95% CI, 84–98).
CONCLUSIONS:
Dependent lung areas collapse (> 13.5%), monitored by electrical impedance tomography, has an excellent positive predictive value (94%) of improved oxygenation during prone ventilation.
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