Evaluation of fetal cerebral blood flow perfusion using power Doppler Ultrasound Angiography (3D-PDA) in growth-restricted fetuses

Objective: to explore the potential of 3D Power Doppler Angiography (3D PDA) to evaluate the cerebral circulation in normal and growth restricted fetuses (IUGR). Study design: in a pilot study, we enrolled 51 appropriate for gestational age (AGA) pregnancies and 17 singleton pregnancies presenting IUGR, all between 22 and 38 weeks of gestation. Using 3D power Doppler ultrasound, a volume acquisition of the fetal brain was performed. Two regions of interest (ROI) were defined within the fetal brain. Zone 1 is anterior to the cavum septi pellucidi (CSP). Zone 2 is defined by a rectangle obtained tracing a contour between the temporal bones as wide as the CSP, corresponding to the area of the middle cerebral artery. The Flow Index (FI), the Vascularization Index (VI), the Vascularization and Flow Index (VFI) were determined in both areas in both IUGR and AGA fetuses by a single operator. IUGR fetuses were divided into three groups: Group 1, with normal pulsatility index (PI) of umbilical artery (UA), middle cerebral artery (MCA) and ductus venosus (DV); Group 2, IUGR fetuses with abnormal UA PI, normal MCA PI, normal DV PI; in Group 3, IUGR fetuses with abnormal UA PI, MCA PI and DV PI. Results: FI and VFI values of zone 1 were increased in Group 1.Values of VFI in zone 2 were increased in Group 2. Conclusions: Our findings are in line with recent studies in growth-restricted fetuses suggesting that the anterior cerebral artery shows Doppler signs of vasodilatation before these are observed in the MCA, demonstrating the “frontal brain sparing effect”

Gemelli decision tree Algorithm to Predict the need for home monitoring or hospitalization of confirmed and unconfirmed COVID-19 patients (GAP-Covid19): Preliminary results from a retrospective cohort study

OBJECTIVE: To develop a deep learning-based decision tree for the primary care setting, to stratify adult patients with confirmed and unconfirmed coronavirus disease 2019 (COVID-19), and to predict the need for hospitalization or home monitoring. PATIENTS AND METHODS: We performed a retrospective cohort study on data from patients admitted to a COVID hospital in Rome, Italy, between 5 March 2020 and 5 June 2020. A confirmed case was defined as a patient with a positive nasopharyngeal RT-PCR test result, while an unconfirmed case had negative results on repeated swabs. Patients’ medical history and clinical, laboratory and radiological findings were collected, and the dataset was used to train a predictive model for COVID-19 severity. RESULTS: Data of 198 patients were included in the study. Twenty-eight (14.14%) had mild disease, 62 (31.31%) had moderate disease, 64 (32.32%) had severe disease, and 44 (22.22%) had critical disease. The G2 value assessed the contribution of each collected value to decision tree building. On this basis, SpO2 (%) with a cut point at 92 was chosen for the optimal first split. Therefore, the decision tree was built using values maximizing G2 and LogWorth. After the tree was built, the correspondence between inputs and outcomes was validated. CONCLUSIONS: We developed a machine learning-based tool that is easy to understand and apply. It provides good discrimination in stratifying confirmed and unconfirmed COVID-19 patients with different prognoses in every context. Our tool might allow general practitioners visiting patients at home to decide whether the patient needs to be hospitalized

Evaluation of fetal cerebral blood flow perfusion using power Doppler ultrasound angiography (3D_PDA) in normal and growth-restricted fetuses (IUGR)

5nonenoneRossi A; Romanello I; Forzano L; Fachechi G; Marchesoni DRossi, Alberto; Romanello, Irene; Forzano, Leonardo; Fachechi, Giorgio; Marchesoni, Dieg