Mitral Regurgitation Severity Assessment after Transcutaneous Edge-to-Edge Mitral Valve Repair: Recommended Integration versus Volumetric Assessment Guidelines.

Objectives: This article aims to evaluate the accuracy of the color-Doppler-based technique to evaluate residual mitral regurgitation post TEER. Background: The evaluation of residual mitral regurgitation (MR) post-mitral transcutaneous edge-to-edge repair (mitral TEER) is a critical determinant in patients' outcomes. The common methods used today, based on the integration of color flow Doppler parameters, may be misleading because of the multiple jets and high velocities created by the TEER devices. Methods: Patients undergoing TEER at Hadassah hospital were recruited between 2015 and 2019. Post-procedural MR was evaluated using the integrated qualitative approach as recommended by the guidelines. In addition, the MR volume for each patient was calculated by subtracting the forward stroke volume (calculated by multiplying the LVOT area with the velocity time integral of the LVOT systolic flow) from the total stroke volume (calculated by the biplane Simpson method of discs). We compared the two methods for concordance. Results: Overall, 112 cases were enrolled. In 55.4% of cases, the volumetric residual MR was milder than the MR severity assessed by the guidelines' recommended method. In 25.1%, the MR severity was similar in both methods. In 16.2%, the MR severity was worse when calculated using the volumetric method (pValue < 0.001, Kappameasure of agreement = 0.053). The lower residual MR degree using the volumetric approach was mostly observed in patients classified as "moderate" by the integrated approach. Conclusions: MR severity after TEER is often overestimated by the guideline-recommended integrative method when compared with a volumetric method. Alternative methods should be considered to assess the MR severity after mitral TEER.

A Novel Prediction Tool for Endoscopic Intervention in Patients with Acute Upper Gastro-Intestinal Bleeding.

(1) Background: Predicting which patients with upper gastro-intestinal bleeding (UGIB) will receive intervention during urgent endoscopy can allow for better triaging and resource utilization but remains sub-optimal. Using machine learning modelling we aimed to devise an improved endoscopic intervention predicting tool. (2) Methods: A retrospective cohort study of adult patients diagnosed with UGIB between 2012−2018 who underwent esophagogastroduodenoscopy (EGD) during hospitalization. We assessed the correlation between various parameters with endoscopic intervention and examined the prediction performance of the Glasgow-Blatchford score (GBS) and the pre-endoscopic Rockall score for endoscopic intervention. We also trained and tested a new machine learning-based model for the prediction of endoscopic intervention. (3) Results: A total of 883 patients were included. Risk factors for endoscopic intervention included cirrhosis (9.0% vs. 3.8%, p = 0.01), syncope at presentation (19.3% vs. 5.4%, p < 0.01), early EGD (6.8 h vs. 17.0 h, p < 0.01), pre-endoscopic administration of tranexamic acid (TXA) (43.4% vs. 31.0%, p < 0.01) and erythromycin (17.2% vs. 5.6%, p < 0.01). Higher GBS (11 vs. 9, p < 0.01) and pre-endoscopy Rockall score (4.7 vs. 4.1, p < 0.01) were significantly associated with endoscopic intervention; however, the predictive performance of the scores was low (AUC of 0.54, and 0.56, respectively). A combined machine learning-developed model demonstrated improved predictive ability (AUC 0.68) using parameters not included in standard GBS. (4) Conclusions: The GBS and pre-endoscopic Rockall score performed poorly in endoscopic intervention prediction. An improved predictive tool has been proposed here. Further studies are needed to examine if predicting this important triaging decision can be further optimized.

Spine radiosurgery: lessons learned from the first 100 treatment sessions.

OBJECTIVE Local therapy to spine tumors has been shown to be effective in selected cases. Spinal radiosurgery (SRS) is an evolving radiotherapy regimen allowing for noninvasive, highly efficacious local treatment. The learning curve can compromise the results of any newly employed technology and should be studied to minimize its effects. In this paper the first 100 SRSs performed at several medical centers are presented and analyzed for the effects of the learning curve on outcome. METHODS A retrospective analysis was undertaken to evaluate data from patients treated with SRS at Sheba Medical Center and Assuta Medical Centers in the period from September 2011 to February 2016. Medical history, clinical and neurological findings, pathological diagnoses, SRS variables, complications, and follow-up data were collected and analyzed. Local control rates were calculated, and local treatment failure cases were qualitatively studied. RESULTS One hundred treatment sessions were performed for 118 lesions at 179 spinal levels in 80 patients. The complication rate was low and did not correlate with a learning curve. Mean follow-up time was 302 days, and the overall local control rate was 95%. The local control rate was dose dependent and increased from 87% (among 35 patients receiving a dose of 16 Gy) to 97% (among 65 patients receiving a dose of 18 Gy). The 6 treatment failure cases are discussed in detail. CONCLUSIONS Spinal radiosurgery is a safe and effective treatment. Comprehensive education of the treating team and continuous communication are essential to limit the effects of the learning curve on outcome.