Citations

AbstractBackgroundPrecursor plasma cell disorders such as monoclonal gammopathy of undetermined significance (MGUS) always precede the development of active malignancies such as multiple myeloma (MM). There is a need for novel biomarkers to identify those patients with such precursor plasma cell disorders who rapidly progress to MM. Plasma‐derived extracellular vesicles (EVs) may serve as a reservoir of potential biomarkers that can shed light on the pathogenesis and disease biology of MM.MethodsThis study isolated small EVs (SEVs) and large EVs (LEVs) from the platelet‐poor peripheral blood plasma of MGUS (n = 9) and MM (n = 12) patients using the size exclusion chromatography‐based method and evaluated their proteome using a label‐free proteomics workflow.ResultsIn total, 2055 proteins were identified in SEVs, while 2794 proteins were identified in LEVs. The transferrin receptor (or CD71) protein was upregulated in both populations of EVs derived from MM patients compared to MGUS patients and was of prognostic significance. Similarly, three isoforms of serum amyloid A (SAA) protein, SAA1, SAA2, and SAA4, were also highly upregulated in SEVs within MM patients relative to MGUS patients. Finally, CD40 expression was also higher in the LEVs derived from MM patients than in MGUS patients.ConclusionsThis study demonstrates the feasibility of successfully isolating both SEVs and LEVs from the peripheral blood of patients with plasma cell disorders and quantifying protein biomarkers within these EVs that could be of prognostic and diagnostic interest.

Background: Left atrial (LA) size increases in atrial fibrillation (AF). How changes in mitral annulus and atrial volume over time relate to severity of mitral regurgitation (MR) is unknown. Methods: This cohort study included 160 patients with a history of permanent AF who had at least 2 transthoracic echocardiograms (TTE) performed between 2005 and 2010 at Vancouver General Hospital (British Columbia, Canada). Patients with structural mitral valve disease, congenital heart disease, more than mild-moderate mitral/tricuspid regurgitation on their first TTE were excluded from analyses. LA volume was determined using biplane area-length method, averaged over 3 cardiac cycles. The mitral annulus diameter (MAD), defined as the distance between the hinge points of the anterior and posterior mitral leaflets from the apical 4-chamber view, was measured during end-systole and end-diastole. Linear mixed effects modeling was used in analyzing the above measurements for change over time. Mitral regurgitation was classified into 3 grades (mild, moderate, or severe) using standard color Doppler and PISA easurements where available. Results: Of the 160 subjects included in this analysis, 100 were men (62.5%). The mean age at study entry was 74±10 years. The median number of TTE performed was 3 (range 2 - 6). All 160 patients showed a progressive increase in MAD over time. There was a significant increase in MAD at end-systole and end-diastole over a mean follow-up of 3.2 ± 2.8 years; the increases were 0.48 mm/year (95% CI 0.124 -0.75, P=0.0037) and 0.45 mm/year (95% CI 0.102-0.716, P=0.0029) respectively. LA volume increased from a mean of 43 mL/m2 to 75 mL/m2 for the cohort, or 10 mL/m2 per year. Adjusting for age, sex, and left ventricular ejection fraction, an increase in MR severity by at least 1 grade was strongly and independently associated with an increase in MAD. A 15% increment in MAD was associated with an increase in MR severity by 1 grade. Conclusion: Permanent AF was associated with significant increases in MAD and LA volume over time. Severity of MR also significantly increased over time, and was independently related to the change in MAD. Further investigations as to whether this represents a mechanism for left heart failure in patients with permanent AF are warranted.

Background Patients who experience in-hospital ST-segment elevation myocardial infarction (iSTEMI) represent a uniquely high-risk cohort owing to delays in diagnosis, prolonged time to reperfusion and increased mortality. Quality initiatives aimed at improving the care of this vulnerable, yet understudied population are needed. Methods This study included consecutive patients with iSTEMI treated with percutaneous coronary intervention (PCI) between 1 January 2011 and 15 July 2019 at a single, tertiary referral centre. A comprehensive iSTEMI protocol (CSP) was implemented on 15 July 2014, incorporating: (1) cardiology fellow activation of the catheterisation lab using standardised criteria, (2) nursing chest pain protocol, (3) improved electronic access to electrocardiographic studies, (4) checklist for initial triage and management, (5) 24/7/365 catheterisation lab readiness and (6) radial-first PCI approach. Key metrics and clinical outcomes were compared before and after CSP implementation. Results Among 125 total subjects, the post-CSP cohort (n=81) was younger, had more males and were more likely to be hospitalised for cardiac-related reasons relative to the pre-CSP cohort (n=44) who were more likely hospitalised for operative-related aetiologies. After CSP adoption, median ECG-to-first-device-activation time decreased from 113 min to 64 min (p<0.001), goal ECG-to-first-device-activation time increased from 36% to 76% of patients (p<0.001), administration of guideline-directed medical therapy prior to PCI increased from 27.3% to 65.4% (p<0.001), trans-radial access increased from 16% to 70% (p<0.001) and rates of discharge home increased from 56.8% to 76.5% (p=0.04). Statistically insignificant numerical reductions were observed post-CSP in in-hospital mortality (18.2% vs 9.9%, p=0.30), 30-day mortality (15.9% vs 12.3%, p=0.78) and 1-year mortality (27.3% vs 21.0%, p=0.57). Conclusions The implementation of a CSP was associated with marked enhancements in key care metrics among patients with iSTEMI. Among a larger cohort, the use of a CSP yielded a significant reduction in ECG-to-first-device-activation time in a particularly vulnerable population at high risk of death.

686 Background: Immune checkpoint inhibitors (ICI) have been used to treat advanced muscle invasive and/or metastatic bladder cancer. However, ICIs are only effective in 30-40% of cases. In light of potentially significant IRAE’s, tools to predict individual patient level benefits are desirable. AI technology offers the opportunity to apply computational methods to predict outcomes from readily available digitized pathology slides. We investigated the utility of an AI platform integrating computational biomarkers based on morphological characterization of digitized H&E slide whole images (WSI) to predict response to ICI. Methods: We analyzed H&E-stained whole slide images (WSI) of bladder tumor tissue collected from transurethral resection of bladder tumor (TURBT) from 116 patients with advanced or metastatic bladder cancer at a single institution between 2015-2020. Adjacent multiplex IHC stained specimens were analyzed myeloid and lymphoid marker panels. 20 patients from the overall cohort treated with ICI were designated ‘responders’ (complete response, partial response, stable disease) and 20 patients were identified as non-responders (progressive disease) based on ICI best clinical/radiographic response. WSIs were divided into small non-overlapping image patches. These image tiles were processed into multiple AI encoder models to extract morphological features. The tile morphological features, represented by high dimensional vectors, were combined by an aggregation model to represent the whole slide morphology and then used to classify the patients into responders vs non-responders to immunotherapy. Area-under-the-receiver operating characteristic (AUC) was used to measure the performance of response prediction. Results: Our method shows AUC of 0.708 at classification of the patients (n=40) into responder and non-responder groups. With a cutoff that identifies the top 50% patients (n=20) of high probability of responding to immunotherapy predicted by our models, 75% of them are responders. In the bottom 50% patients (n=20) of the low scores, 75% of them are non-responders. . Multiplex IHC (myeloid and lymphoid panel) was performed using adjacent sections and were characterized yielding deeper mechanistic insight into the inflammatory populations driving predictive morphologic patterns. Conclusions: By applying innovative AI morphological analysis on patient WSIs, we generated a computational biomarker that shows promising ability to predict patient response to immunotherapy in bladder cancer. This approach merits future prospective validation and additional patient level analysis in broader cohorts. [Table: see text]

Background: Cardiac amyloidosis (CA) in conjunction with atrial fibrillation (AF) presents unique management challenges. CHA2DS2VASc score in these patients is believed to underestimate the risk of ischemic stroke, necessitating a better predictive model in these patients. Methods: Data was obtained from the National Readmission Database (NRD). Outcomes between CA-AF and no-CA-AF were compared using multivariate regression analysis to calculate adjusted odds ratios (aOR). AutoScore; an interpretable machine learning framework, was used to develop a stroke risk prediction model, the predictive accuracy of which was evaluated with an area under the curve (AUC) using the receiver operating characteristic analysis. Results: A total of 11,860,804 (CA-AF 22,687 [0.19%] and no-CA-AF 11,838,117) patients were identified from 2015-2019. The adjusted odds of mortality (aOR 1.41 and 1.29), stroke (aOR 1.78 and 1.74), non-intracranial hemorrhage (aOR 2.10 and aOR 1.85), and intracranial hemorrhage (aOR 14.4 and aOR 4.26) were significantly higher in CA-AF compared with non-CA-AF at both index admission and 30-days, respectively. The CHA 2 DS 2 VASc score had a poor discriminative accuracy for stroke at 30-days in CA-AF (AUC 49%, 95%CI 47%-51%, p=0.54). The machine learning autoscore integrative model revealed that the predictive ability of our newly proposed E-CHADS score (end-stage renal disease (ESRD), congestive heart failure, hypertension, active cancer, dementia, and diabetes mellitus) for 30-day risk of ischemic stroke in CA-AF was excellent (for a cutoff of 52 points random forest score) with an AUC of 80% (95%CI 74%-86%) Conclusion: Cardiac amyloidosis carries a high risk of ischemic stroke that is not accurately predicted by the CHA 2 DS 2 VASc score. Our proposed model (E-CHADS) identifies 3 new variables (ESRD, dementia, and cancer) that have higher discriminative accuracy for ischemic stroke in these patients.