Citations

AbstractNeurological complications after cardiac surgery and percutaneous cardiac interventions are not uncommon. These include periprocedural stroke, postoperative cognitive dysfunction after cardiac surgery, contrast-induced encephalopathy after percutaneous interventions, and seizures. In this article, we review the incidence, pathophysiology, diagnosis, and management of these complications. Improved understanding of these complications could lead to their prevention, faster detection, and facilitation of diagnostic workup and appropriate treatment.

Background: Braak and others have proposed that Lewy-type α-synucleinopathy in Parkinson’s disease (PD) may arise from an exogenous pathogen that passes across the gastric mucosa and then is retrogradely transported up the vagus nerve to the medulla. Objective: We tested this hypothesis by immunohistochemically staining, with a method specific for p-serine 129 α-synuclein (pSyn), stomach and vagus nerve tissue from an autopsy series of 111 normal elderly subjects, 33 with incidental Lewy body disease (ILBD) and 53 with PD. Methods: Vagus nerve samples were taken adjacent to the carotid artery in the neck. Stomach samples were taken from the gastric body, midway along the greater curvature. Formalin-fixed paraffin-embedded sections were immunohistochemically stained for pSyn, shown to be highly specific and sensitive for α-synuclein pathology. Results: Median disease duration for the PD group was 13 years. In the vagus nerve none of the 111 normal subjects had pSyn in the vagus, while 12/26 ILBD (46%) and 32/36 PD (89%) subjects were pSyn-positive. In the stomach none of the 102 normal subjects had pSyn while 5/30 (17%) ILBD and 42/52 (81%) of PD subjects were pSyn-positive. Conclusion: As there was no pSyn in the vagus nerve or stomach of subjects without brain pSyn, these results support initiation of pSyn in the brain. The presence of pSyn in the vagus nerve and stomach of a subset of ILBD cases indicates that synucleinopathy within the peripheral nervous system may occur, within a subset of individuals, at preclinical stages of Lewy body disease.

e19003 Background: Acute panmyelosis with myelofibrosis (APMF) is a rare subtype of acute myeloid leukemia characterized by acute panmyeloid proliferation with increased blasts, cytopenias with bone marrow fibrosis, and absence of splenomegaly. There is a paucity of population-based studies of APMF. Methods: We queried the United States Surveillance, Epidemiology, and End Results (SEER) database and National Cancer Database (NCDB) using the ICD-O-3 code 9931/3. The SEER 17 registries (2004-2015) were used to find data on incidence. For NCDB data, comorbid disease burden was calculated using the Charlson-Deyo Score (CDS). Hazard ratios (HR) with confidence intervals (CI) were calculated using a Cox proportional hazards model. Overall survival (OS) was estimated with the Kaplan-Meier method. Variables significant in univariable analysis were included in a multivariable analysis. Results: We identified 260 APMF patients using the SEER database. Incidence was 0.3 cases/million individuals and did not change significantly from 2004-2015. With a median follow up of 6.9 years (95% CI 6.1-7.8), the median OS was 2.3 years (95% CI 1.7-2.8). We identified 530 APMF patients using the NCDB. The median age at diagnosis was 67 years (range 22-90) and 311 (59%) were male. With a median follow up of 5.0 years (95% CI 3.0-7.6), the median OS was 2.3 years (95% CI: 0.8-6.5). OS was 69%, 31%, and 18% at 1-, 5-, and 10- years, respectively. Patients diagnosed in 2012-2015 had a significantly improved OS compared to those diagnosed in 2004-2007 (HR 0.65, 95% CI 0.49-0.85; p=0.002). 271 patients (53%) patients received chemotherapy. The OS for those that received chemotherapy was 70% at 1 year and 30% at 5 years versus 70% and 32% at 1- and 5- years for those who did not ( p=0.99). The median time to chemotherapy from time of diagnosis was 25 days (range 0-532 days). 52 patients (10%) underwent transplantation and the OS of those patients was 90% at 1 year and 45% at 5 years versus 67% and 29% at 1- and 5- years for those who did not (HR: 1.7 (95% CI: 1.2-2.6), p=0.006). In univariable analysis, factors predicting inferior OS were age ≥ 65 years old at diagnosis (HR 1.8, 95% CI 1.5-2.3; p<0.001), male sex (HR 1.5, 95% CI 1.2-1.8; p<0.001), CDS ≥ 1 (HR: 1.5 (95% CI: 1.2-2.0), p<0.001), government insurance (HR 1.8, 95% CI 1.4-2.3; p<0.001), diagnosis at a non-academic facility (HR 1.6, 95% CI 1.2-2.0, p<0.001), and not receiving a hematologic transplant (HR 1.7, 95% CI 1.2-2.6; p=0.006). Multivariable-adjusted analysis is shown in the Table. Conclusions: The overall incidence of APMF has not changed between 2004 and 2015, but OS for 2012-2015 was improved compared to 2004-2007. Age ≥ 65 years old, male sex, CDS ≥ 1, and diagnosis at a nonacademic facility predicted inferior OS. Multivariable-adjusted analysis.[Table: see text]

Abstract BACKGROUND Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) has important management implications. OBJECTIVE To develop and evaluate machine-learning approaches to differentiate benign from malignant PNSTs. METHODS We identified PNSTs treated at 3 institutions and extracted high-dimensional radiomics features from gadolinium-enhanced, T1-weighted magnetic resonance imaging (MRI) sequences. Training and test sets were selected randomly in a 70:30 ratio. A total of 900 image features were automatically extracted using the PyRadiomics package from Quantitative Imaging Feature Pipeline. Clinical data including age, sex, neurogenetic syndrome presence, spontaneous pain, and motor deficit were also incorporated. Features were selected using sparse regression analysis and retained features were further refined by gradient boost modeling to optimize the area under the curve (AUC) for diagnosis. We evaluated the performance of radiomics-based classifiers with and without clinical features and compared performance against human readers. RESULTS A total of 95 malignant and 171 benign PNSTs were included. The final classifier model included 21 imaging and clinical features. Sensitivity, specificity, and AUC of 0.676, 0.882, and 0.845, respectively, were achieved on the test set. Using imaging and clinical features, human experts collectively achieved sensitivity, specificity, and AUC of 0.786, 0.431, and 0.624, respectively. The AUC of the classifier was statistically better than expert humans ( P = .002). Expert humans were not statistically better than the no-information rate, whereas the classifier was ( P = .001). CONCLUSION Radiomics-based machine learning using routine MRI sequences and clinical features can aid in evaluation of PNSTs. Further improvement may be achieved by incorporating additional imaging sequences and clinical variables into future models.

Introduction: The Mayo Clinic Cardiac Intensive Care Unit Admission Risk Score (M-CARS) has been shown to predict hospital mortality in the CICU population better than conventional critical care scoring models. We tested the hypothesis that M-CARS could similarly be used to predict one-year mortality. Methods: We retrospectively reviewed adult CICU patients admitted from 2007 to 2018. M-CARS was calculated using admission data. Groups were compared using Wilcoxon test for continuous variables and chi-squared test for categorical variables. Results: This study included 12428 unique patients with a mean age of 67 ± 15 years (37% females), of whom 11279 (90.8%) survived hospitalization and 2839 patients (22.8%) died within 1 year of admission. The 1-year survival decreased incrementally as a function of increasing M-CARS ( Figure 1A , p<0.0001), and all components of M-CARS were significant predictors of 1-year mortality (p<0.001). 1690 hospital survivors (15.0%) died within 1 year. The 1-year survival decreased incrementally as a function of increasing M-CARS for scores <3 (all p<0.001); however, there was no further adverse 1-year survival for patients with M-CARS ≥3 ( Figure 1B, p=0.99). M-CARS components associated with 1-year mortality among hospital survivors included blood urea nitrogen, red blood cell distribution width, Braden score, and respiratory failure (all p<0.001), while cardiac arrest, cardiogenic shock, and anion gap were not. Conclusions: M-CARS accurately predicts 1-year mortality among CICU admissions, with a plateau effects at high M-CARS scores ≥3. Significant predictors of 1-year mortality among CICU survivors include frailty markers rather than admission severity.

Introduction: Mycobacterium Chimaera (MC) infections following cardiovascular surgery are challenging to diagnose given their insidious presentation. We therefore reviewed the various imaging modalities used to diagnose these infections at Mayo Clinic. Methods: Cases from 01/01/2010-06/01/2020 were identified using electronic medical records. Demographics and clinical history were collected. Imaging studies, including transthoracic echocardiogram (TTE), transesophageal echocardiogram (TEE), positron emission tomography / computed tomography (PET/CT), cardiac CT (CCT), and cardiac magnetic resonance (CMR) were reviewed. Results: A total of 7 patients (85.7% male) were found. 6 underwent aortic valve replacement and 1 received an aortic composite valve conduit. Surgical dates ranged from 01/2010-12/2018. Mean age at presentation was 63.3 years. Mean time from surgery to symptom onset was 28.0 months. All patients underwent TTE and TEE; prosthetic valve endocarditis was identified in 6 cases between both, while CMR established the diagnosis in 1 case. TTE showed prosthetic valve obstruction in 2 cases and an anterior pseudoaneurysm in 1 case. TEE findings included thickened prosthesis and/or vegetations (n=5), thickened posterior root (n=4), and root abscess (n=3). Among the 3 patients who underwent PET/CT, 2 demonstrated increased fluorodeoxyglucose (FDG) uptake around the aortic prosthesis; in addition, 1 had elevated FDG uptake in the myocardium suggesting myocarditis. 1 patient did not have aortic prosthesis FDG uptake. In the 2 patients who had CCT, 1 showed a pseudoaneurysm that prompted suspicion for endocarditis, and the other revealed a fluid collection adjacent to the aortic valve conduit. 2 patients underwent CMR; 1 had aortic prosthesis thickening and patchy areas of myocardial delayed enhancement suspicious for myocarditis, whereas the other showed vegetation and an aortic root abscess. Conclusion: TTE plus TEE successfully identified MC prosthetic valve endocarditis in most cases with TEE having higher specificity. Advanced imaging techniques are helpful to support the diagnosis and assess for myocardial and/or aortic involvement. Combining these modalities is therefore crucial in unveiling this elusive organism.

There are growing concerns that the COVID-19 pandemic has facilitated a sedentary shift in our physical activity habits. A reduction in physical activity during the pandemic may be secondary to restrictive policies implemented at the government-level, typically those policies which limit interpersonal contact; for example, physical/social distancing. It is without question that social distancing is a necessary measure to mitigate community transmission of the novel virus; however, these policies often limit the public’s opportunities to engage in physical activity. The strictest enforcement of social distancing occurs during an authority-mandated ‘lockdown’ (also known as a ‘shelter-in-place’ or ‘stay-at-home’ order). This Viewpoint focuses on the current evidence demonstrating that physical activity declines during a COVID-19 ‘lockdown’. We highlight the point that most of the available evidence stems from investigations using non-validated, self-reported measures of physical activity and discuss the caveats therewith. This Viewpoint explores whether current evidence reflects an ‘actual’ or rather a ‘perceived’ reduction in physical activity, and raises the question of whether this distinction matters in the end.

Abstract BACKGROUND: Recurrent isocitrate dehydrogenase (IDH) 1 mutations are observed in 6-10% of patients with acute myeloid leukemia (AML). Ivosidenib (AG-120), a potent, selective, oral, small-molecule inhibitor of the mutant IDH1 (mIDH1) protein, is a promising therapeutic candidate for the treatment of patients with mIDH1 AML. Through inhibition of mIDH1, ivosidenib suppresses the abnormal production of the oncometabolite 2-hydroxyglutarate (2-HG), leading to clinical responses via differentiation of malignant cells. AIM: To report safety and efficacy data from the first-in-human phase 1 study of ivosidenib in patients with mIDH1 advanced hematologic malignancies including relapsed/refractory (R/R) AML (NCT02074839). This is the first report of data from the 4 expansion cohorts, with a total of 258 patients treated on study. METHODS: The ongoing phase 1 study assesses the safety, maximum tolerated dose (MTD), pharmacokinetics, pharmacodynamics, and clinical activity of ivosidenib in mIDH1 hematologic malignancies. Enrollment was completed on May 8, 2017. During dose escalation, patients received ivosidenib as a single agent orally once daily (QD) or twice daily (BID) in 28-day cycles. The MTD was not reached and 500 mg QD was selected as the recommended dose to be tested in 4 expansion cohorts: R/R AML (Arms 1 and 4, where Arm 1 patients are those with relapse after transplantation, second or later relapse, resistance to initial induction or reinduction treatment, or relapse within 1 year of initial treatment, and Arm 4 patients have R/R AML but are not eligible for Arm 1); untreated AML (Arm 2); and other advanced hematologic malignancies including myelodysplastic syndrome (MDS) (Arm 3). Updated safety data will be presented for all patients. Efficacy outcomes will be presented for all R/R AML patients treated at 500 mg QD across the dose escalation and expansion cohorts who received their first dose of ivosidenib at least 6 months prior to the analysis cut-off date of May 12, 2017, as well as for the poorest prognosis Arm 1 subset. Efficacy data for all treated patients from the other expansion cohorts (untreated AML and other advanced hematologic malignancies including MDS) will also be presented. RESULTS: In all, 258 patients (78 in dose escalation, 180 in expansion) were treated with ivosidenib. As of May 12, 2017, 62 of 258 (24%) patients were continuing on treatment. The median duration of exposure to ivosidenib was 3.5 months (range 0.1-33.5). Twenty-two (8.5%) patients discontinued treatment to proceed to allogeneic stem cell transplantation. Treatment was well tolerated; the most common adverse events (AEs) (n=258) of any grade irrespective of causality occurring in ≥20% of patients were diarrhea (33%), leukocytosis (30%), nausea (30%), fatigue (29%), febrile neutropenia (25%), dyspnea (24%), anemia (23%), QT prolongation (23%), peripheral edema (22%), pyrexia (21%), and decreased appetite (20%). The majority of these AEs were grades 1-2 and reported as unrelated to treatment. Differentiation syndrome (DS) was observed in 29 of 258 (11.2%) patients, including grade ≥3 DS in 14 (5.4%); study drug was held owing to DS in 11 patients (4.3%), and no instances of DS led to permanent treatment discontinuation or death. The primary efficacy endpoint for R/R AML is the CR+CRh rate, i.e., the rate of complete remission (CR according to modified IWG 2003 criteria plus CR with partial hematologic recovery, defined as CR except absolute neutrophil count &gt;0.5 × 109/L [500/µL] and platelet count &gt;50 × 109/L [50,000/µL]). Among 125 Arm 1 R/R AML patients receiving ivosidenib 500 mg QD across dose escalation and expansion who received their first dose at least 6 months prior to the analysis cutoff date, the CR+CRh rate was 30.4% (95% CI 22.5%, 39.3%), including CR in 27 (21.6%) and CRh in 11 (8.8%) patients. Median duration of CR+CRh was 8.2 months (95% CI 5.5, 12.0), and duration of CR was 9.3 months (95% CI 5.6, 18.3). The overall response rate (CR+CRi/CRp+PR+MLFS) was 41.6% (95% CI 32.9%, 50.8%) (52/125 patients). CONCLUSION: Ivosidenib monotherapy is well tolerated in patients with mIDH1 AML and other advanced hematologic malignancies. In a high-risk, molecularly defined R/R AML patient population with unmet medical need, ivosidenib induced durable remissions and improved patient outcomes. These findings support the role of ivosidenib as an effective, oral, targeted treatment for patients with mIDH1 AML. Disclosures DiNardo: Celgene: Honoraria, Research Funding; AbbVie: Honoraria, Research Funding; Agios: Honoraria, Research Funding; Daiichi-Sankyo: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. De Botton: Pfizer: Honoraria; Novartis: Honoraria; Celgene: Honoraria; Servier: Honoraria; Agios: Honoraria, Research Funding. Stein: GSK: Other: Advisory Board, Research Funding; Constellation Pharma: Research Funding; Seattle Genetics: Research Funding; Agios Pharmaceuticals, Inc.: Consultancy, Research Funding; Celgene Corporation: Consultancy, Other: Travel expenses, Research Funding; Pfizer: Consultancy, Other: Travel expenses; Novartis: Consultancy, Research Funding. Roboz: AbbVie, Agios, Amgen, Amphivena, Array Biopharma Inc., Astex, AstraZeneca, Celator, Celgene, Clovis Oncology, CTI BioPharma, Genoptix, Immune Pharmaceuticals, Janssen Pharmaceuticals, Juno, MedImmune, MEI Pharma, Novartis, Onconova, Pfizer, Roche Pharmace: Consultancy; Cellectis: Research Funding. Mims: Novartis: Honoraria. Pollyea: Takeda, Ariad, Alexion, Celgene, Pfizer, Pharmacyclics, Gilead, Jazz, Servier, Curis: Membership on an entity's Board of Directors or advisory committees; Agios, Pfizer: Research Funding. Altman: Syros: Consultancy; NCCN: Other: Educational speaker; BMS: Consultancy; Celgene: Consultancy; Astellas: Consultancy; Ceplene: Consultancy; Janssen Pharmaceuticals: Consultancy; Novartis: Consultancy; ASH: Other: Educational speaker. Collins: Celgene Corporation: Research Funding; Agios: Research Funding; Arog: Research Funding; BMS: Research Funding. Mannis: Curis: Honoraria; Juno: Research Funding; Agios: Research Funding; Amgen: Honoraria. Uy: GlycoMimetics: Consultancy; Novartis: Consultancy, Other: Travel Suppport; Boehringer Ingelheim: Consultancy. Fathi: Juno: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Research Funding; Agios: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Medimmune: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees. Stein: Amgen: Consultancy, Speakers Bureau; Stemline: Consultancy. Erba: Celgene: Consultancy, Other: Chair, Scientific Steering Committee , Speakers Bureau; Incyte: all research support paid to University of Alabama, Consultancy, Speakers Bureau; Jazz: Consultancy, Speakers Bureau; Novartis: Consultancy, Speakers Bureau; Amgen: Consultancy, Other: all research support paid to University of Alabama, Research Funding; Daiichi Sankyo: Consultancy, Other: all research support paid to University of Alabama, Research Funding; ImmunoGen: Consultancy, Other: all research support paid to University of Alabama, Research Funding; MacroGen: Consultancy; Ono: Consultancy; Pfizer: Consultancy; Seattle Genetics: Consultancy, Other: all research support paid to University of Alabama, Research Funding; Sunesis: Consultancy; Millennium/Takeda: Consultancy, Other: all research support paid to University of Alabama, Research Funding; Agios: Other: all research support paid to University of Alabama, Research Funding; Juno: Other: all research support paid to University of Alabama, Research Funding; Astellas: Other: all research support paid to University of Alabama, Research Funding; Celator: Other: all research support paid to University of Alabama, Research Funding; Janssen: Other: all research support paid to University of Alabama, Research Funding; Glycomimetics: Other: Chair, Data and Safety Monitoring Committee. Traer: ImmunoGen: Consultancy; Tolero: Consultancy; Notable Labs: Equity Ownership. Stuart: Pharmacyclics LLC, an AbbVie Company: Research Funding; Amgen: Consultancy, Honoraria; Agios: Research Funding; Celator/Jazz: Research Funding; Sunesis: Consultancy, Honoraria, Other: Travel Support, Research Funding; Bayer: Research Funding; Novartis: Research Funding; Incyte: Research Funding; ONO: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Research Funding; MedImmune: Research Funding; Cantex: Research Funding; Astellas: Research Funding. Arellano: Cephalon Oncology: Research Funding. Sekeres: Celgene: Membership on an entity's Board of Directors or advisory committees. Yen: Agios: Employment, Equity Ownership. Kapsalis: Agios: Employment, Equity Ownership. Liu: Agios Pharmaceuticals, Inc.: Employment, Equity Ownership. Goldwasser: Agios: Employment, Equity Ownership. Agresta: Agios Pharmaceuticals, Inc.: Employment, Equity Ownership. Attar: Agios: Employment, Equity Ownership. Stone: Novartis: Consultancy; Celgene: Consultancy; Amgen: Consultancy; Abbvie: Consultancy; Fuji Film: Consultancy; Jazz: Consultancy; Astellas: Consultancy; Pfizer: Consultancy; Arog: Consultancy; Ono: Consultancy; Agios: Consultancy; Sumitomo: Consultancy. Kantarjian: ARIAD: Research Funding; Bristol-Meyers Squibb: Research Funding; Delta-Fly Pharma: Research Funding; Amgen: Research Funding; Pfizer: Research Funding; Novartis: Research Funding.