Impact of Persistent Iatrogenic Atrial Septal Defect following MitraClip.

BACKGROUND: Prior studies have reported conflicting results of persistent iatrogenic atrial septal defect (iASD) and its impact following a transcatheter edge-to-edge repair (TEER) procedure. This study aims to evaluate the incidence of iASD and its clinical and hemodynamic impact after a TEER. METHODS: Consecutive patients who underwent a TEER procedure from June 2014 to September 2020 at the Mayo Clinic were identified. The presence of iASD was retrospectively identified on post-procedure transthoracic echocardiography (TTE) to group patients into an iASD+ group and an iASD- group for comparison of prognosis and cardiac function. RESULTS: A total of 316 patients were included; the mean age was 79.1 ± 9.1 years, and 67.7% were male. Persistent iASD was diagnosed in 108 (34.2%) patients. There was no difference concerning all-cause mortality, heart failure hospitalization, and stroke/TIA between groups at follow-up (median follow-up of 9 months). Post-procedure TTE demonstrated no differences regarding right ventricle (RV) and left ventricle (LV) dimensions and function, including TAPSE (15.2 ± 4.6 vs. 15.4 ± 5.5 mm, p = 0.875), and LV ejection fraction (51.1 ± 14.0% vs. 51.3 ± 13.9%, p = 0.933). However, patients with iASD had higher RV systolic pressure (48.7 ± 14.4 vs. 45.5 ± 14.5 mmHg, p = 0.042) compared with patients without iASD. CONCLUSION: Notwithstanding higher RV pressures, patients with persistent iASD had no hemodynamic compromise and an equal prognosis compared with those without a residual atrial defect after a TEER procedure. These findings support the mid-term safety of procedures in which an interatrial defect needs to be created and would argue against the need for interventional closure.

Baseline thrombocytopenia in acute coronary syndrome: The lower, the worse.

BACKGROUND: Patients with baseline thrombocytopenia can have increased mortality and morbidity, but are typically excluded from randomized clinical trials studying acute coronary syndromes (ACS). We sought to better define the effect thrombocytopenia on clinical outcomes in ACS patients. METHODS: Patients identified from the NCDR Chest Pain registry at Mayo Clinic Arizona from Oct 2015 to Sep 2018 were retrospectively classified into two groups: TP (platelet <150 × 103 µL) and control (platelet ≥150 × 103 µL). The groups were analyzed for the clinical outcome (all-cause mortality, major adverse cardiac events (MACE), and bleeding events). The TP group was divided into moderate-severe thrombocytopenia (TPmod; platelet 50-100 × 103 µL) and mild thrombocytopenia (TPmild; platelet 100-150 × 103 µL) for further analysis. P-value <0.05 is considered significant. RESULTS: Five hundred and thirty-six patients were identified, and 72 patients (13%) had thrombocytopenia. The median follow-up time was 1.1 years. The TP group was older (TP vs. control: mean age 73 ± 13 years vs. 70 ± 13 years; P = 0.026). In patients discharged on dual-antiplatelet therapy, the TP group had higher all-cause mortality (23% vs. 7.3%; P = 0.007) but not major bleeding events (11% vs. 5.0%; P = 0.123). Only all-cause mortality increased with the severity of thrombocytopenia (TPmod vs. TPmild vs. control: 33% vs. 24% vs. 7.3%; P = 0.007). CONCLUSIONS: In patients with ACS, baseline thrombocytopenia is associated with increased all-cause mortality and all bleeding events without net MACE benefit. Further study is needed to identify the optimal antiplatelet strategy in this higher risk population.

Machine Learning on High-Dimensional Data to Predict Bleeding Post Percutaneous Coronary Intervention.

INTRODUCTION: The purpose of the current study is to determine the accuracy of machine learning in predicting bleeding outcomes post percutaneous coronary intervention (PCI) in comparison with the American College of Cardiology CathPCI bleeding risk (ACC-BR) model. METHODS: Mayo Clinic CathPCI registry data were retrospectively analyzed from January, 2003 to June, 2018, including 15,603 patients who underwent PCI. The cohort was randomly divided into a training sample of 11,703 patients (75%) and a unique test sample of 3900 patients (25%) prior to model generation. The risk-prediction model was generated utilizing a boosted classification tree algorithm of 105 unique variables to predict the risk of major and minor bleeding complications within 72 hours after PCI or before hospital discharge. The receiver operating characteristic (ROC) curves and areas under the curve (AUC) for the boosted classification tree algorithm (AI-BR) model and ACC-BR model were compared for the test cohort. RESULTS: The mean age of the patient cohort was 67 ± 12.7 years, and women constituted 30% of the cohort. The rate of major bleeding complications in the entire cohort was 1.8%. The sensitivity and specificity of the AIBR model were 77.3% and 80.9%, respectively. The ROC-AUC for the AI-BR model (0.873) was superior vs the ACC-BR model (0.764; P=.02) in predicting major bleeding for the test cohort. CONCLUSION: The AI-BR model accurately predicts bleeding post PCI and outperforms the ACC-BR model in predicting the risk of bleeding in patients undergoing PCI.

Vasculitis on brain angiography is not always vasculitis: intravascular large B-cell lymphoma mimicking central nervous system vasculitis.

A 68-year-old man, with a history of non-Hodgkin's lymphoma in remission, was admitted for homonymous hemianopsia, headaches and subacute progressive cognitive decline. Imaging revealed brain infarcts and angiography suggested vasculitis. A brain biopsy, however, revealed an intravascular large B-cell lymphoma (IVLBL). Central nervous system (CNS) vasculitis and IVLBL of the brain are extremely rare diseases that can have an almost identical clinical presentation. Angiographic findings are very similar but usually are reported as compatible with vasculitis. Brain biopsy or a random skin biopsy are crucial in diagnosing IVLBL as the accuracy of angiographic findings for CNS vasculitis is low.

Distinct Phenotypic Clusters of Glioblastoma Growth and Response Kinetics Predict Survival.

PURPOSE: Despite the intra- and intertumoral heterogeneity seen in glioblastoma multiforme (GBM), there is little definitive data on the underlying cause of the differences in patient survivals. Serial imaging assessment of tumor growth allows quantification of tumor growth kinetics (TGK) measured in terms of changes in the velocity of radial expansion seen on imaging. Because a systematic study of this entire TGK phenotype-growth before treatment and during each treatment to recurrence -has never been coordinately studied in GBMs, we sought to identify whether patients cluster into discrete groups on the basis of their TGK. PATIENTS AND METHODS: From our multi-institutional database, we identified 48 patients who underwent maximally safe resection followed by radiotherapy with imaging follow-up through the time of recurrence. The patients were then clustered into two groups through a k-means algorithm taking as input only the TGK before and during treatment. RESULTS: There was a significant survival difference between the clusters ( P = .003). Paradoxically, patients among the long-lived cluster had significantly larger tumors at diagnosis ( P = .027) and faster growth before treatment ( P = .003) but demonstrated a better response to adjuvant chemotherapy ( P = .048). A predictive model was built to identify which cluster patients would likely fall into on the basis of information that would be available to clinicians immediately after radiotherapy (accuracy, 90.3%). CONCLUSION: Dichotomizing the heterogeneity of GBMs into two populations-one faster growing yet more responsive with increased survival and one slower growing yet less responsive with shorter survival-suggests that many patients who receive standard-of-care treatments may get better benefit from select alternative treatments.