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May-Thurner syndrome (MTS) also known as Cockett’s syndrome is a rare condition responsible for 2%-3% of all cases of deep venous thrombosis (DVT). The thrombosis results from mechanical compression of the left common iliac vein against the body of the fifth lumbar vertebra by the right common iliac artery. Repetitive hyperplasia of the venous wall by compression results in spur formation that in turn causes venous flow obstruction and results in the DVT. Our case is a young female who had acute extensive proximal DVT due to MTS that was successfully managed using mechanical thrombectomy with a venous stent. MTS although a rare entity should be suspected especially in young patients with unilateral DVT with extensive clots especially on left lower extremity without any antecedent risk factors.

Right heart failure (RHF) remains a common and serious complication after durable left ventricular assist device (LVAD) implantation. We used explainable machine learning (ML) methods to derive novel insights into preimplant patient factors associated with RHF. Continuous-flow LVAD implantations from 2008 to 2017 in the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) were included. A total of 186 preimplant patient factors were analyzed and the primary outcome was 30 days of severe RHF. A boosted decision tree ML algorithm and an explainable ML method were applied to identify the most important factors associated with RHF, nonlinear relationships and interactions, and risk inflection points. Out of 19,595 patients, 19.1% developed severe RHF at 30 days. Thirty top predictors of RHF were identified with the top five being INTERMACS profile, Model for End-stage Liver Disease score, the number of inotropic infusions, hemoglobin, and race. Many top factors exhibited nonlinear relationships with key risk inflection points such as INTERMACS profile between 2 and 3, right atrial pressure of 15 mmHg, pulmonary artery pressure index of 3, and prealbumin of 23 mg/dl. Finally, the most important variable interactions involved INTERMACS profile and the number of inotropes. These insights could help formulate patient optimization strategies prior to LVAD implantation.

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BackgroundCardiac telemetry is an important tool to detect life-threatening conditions in hospitalized patients but is used widely and inappropriately. We sought to assess current usage and improve the appropriate use of telemetry in a community hospital.MethodsWe conducted a quality improvement project on patients who were admitted on telemetry floors between January and March 2017 (pre-intervention). The indication(s) and duration of telemonitor use, event(s) recorded on telemonitor and outcome of the event(s) were documented. A six-month educational intervention was undertaken and the effect of intervention was assessed among patients admitted between December 2017 and February 2018 (post-intervention).ResultsIn the pre-intervention group, 329 patients qualified for the study, with a median age of 78 years. The post-intervention group had 383 qualified patients with a median age of 77 years. Mean duration of telemonitor use was four days in both groups. In the pre-intervention group, 54% had class I, 32% had class II, and 14% had class III indications. In post-intervention group, 46% had class I, 42% had class II, and 12% had class III indications. The educational intervention resulted in a trend towards less inappropriate use of telemetry, particularly in teaching service. Telemonitor events were recorded in 22 (7%) of the pre-intervention patients and 13 (4%) of the post-intervention group. Two patients died in the pre-intervention group and one in the post-intervention group from non-cardiac causes.ConclusionOur results highlight that change in practice requires sustained education interventions.