Citations

Background: Asymptomatic recurrences of atrial fibrillation (AF) are common after ablation of AF. Objective: We aimed to analyze the performance of the mobile ECG device using artificial intelligence (AI) algorithm in detection of AF after ablation. Method: A randomized controlled trial of AF screening using a handheld single-lead ECG monitor (BigThumb®) or a traditional follow-up strategy was conducted in patients with non-valvular AF after catheter ablation. Consecutive patients were randomized to either BigThumb Group (BT Group) or Traditional Follow-up Group (TF Group). Monitoring data was collected and analyzed. The ECGs collected by BigThumb were compared using the automated AF detection algorithm, AI algorithm and cardiologists’ manual review. Subsequent changes in adherence on oral anticoagulation of patients were also recorded. Result: We studied 218 patients (109 in BT Group, 109 in TF Group). After a follow-up of 345.4±60.2 days, AF-free survival rate was 64.2% in BT Group and 78.9% in TF Group (P=0.0163), with more adherence on oral anticoagulation in BT Group (P=0.0052). The participants in the BT Group recorded 26133 ECGs during the follow-up, among which 3299 (12.6%) were diagnosed as AF by cardiologists’ manual review. The sensitivity and specificity of the AI algorithm were 94.4% and 98.5% respectively, which are significantly higher than the automated AF detection algorithm (90.7% and 96.2%). Conclusion: We found that follow-up after AF ablation using BigThumb leads to a more frequent detection of AF recurrence and more adherence on oral anticoagulation. Artificial intelligence algorithm improves the accuracy of ECG diagnosis.

AbstractObjectiveTo identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications.Materials and MethodsUsing EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics.ResultsUsing the computational approaches, we derived three depression subphenotypes: Phenotype_A (n = 2791; 31.35%) included patients who were the oldest (mean (SD) age, 72.55 (14.93) years), had the most comorbidities, and took the most medications. The most common comorbidities in this cluster of patients were hyperlipidemia, hypertension, and diabetes. Phenotype_B (mean (SD) age, 68.44 (19.09) years) was the largest cluster (n = 4687; 52.65%), and included patients suffering from moderate loss of body function. Asthma, fibromyalgia, and Chronic Pain and Fatigue (CPF) were common comorbidities in this subphenotype. Phenotype_C (n = 1452; 16.31%) included patients who were younger (mean (SD) age, 63.47 (18.81) years), had the fewest comorbidities, and took fewer medications. Anxiety and tobacco use were common comorbidities in this subphenotype.ConclusionComputationally deriving depression subtypes can provide meaningful insights and improve understanding of depression as a heterogeneous disorder. Further investigation is needed to assess the utility of these derived phenotypes to inform clinical trial design and interpretation in routine patient care.

Abstract Among 2,186 U.S. adults with invasive cancer and laboratory-confirmed SARS-CoV-2 infection, we examined the association of COVID-19 treatments with 30-day all-cause mortality and factors associated with treatment. Logistic regression with multiple adjustments (e.g., comorbidities, cancer status, baseline COVID-19 severity) was performed. Hydroxychloroquine with any other drug was associated with increased mortality versus treatment with any COVID-19 treatment other than hydroxychloroquine or untreated controls; this association was not present with hydroxychloroquine alone. Remdesivir had numerically reduced mortality versus untreated controls that did not reach statistical significance. Baseline COVID-19 severity was strongly associated with receipt of any treatment. Black patients were approximately half as likely to receive remdesivir as white patients. Although observational studies can be limited by potential unmeasured confounding, our findings add to the emerging understanding of patterns of care for patients with cancer and COVID-19 and support evaluation of emerging treatments through inclusive prospective controlled trials. Significance: Evaluating the potential role of COVID-19 treatments in patients with cancer in a large observational study, there was no statistically significant 30-day all-cause mortality benefit with hydroxychloroquine or high-dose corticosteroids alone or in combination; remdesivir showed potential benefit. Treatment receipt reflects clinical decision-making and suggests disparities in medication access. This article is highlighted in the In This Issue feature, p. 1426

Abstract Registry-based epidemiologic studies suggest associations between chronic inflammatory intestinal diseases and pancreatic ductal adenocarcinoma (PDAC). As genetic susceptibility contributes to a large proportion of chronic inflammatory intestinal diseases, we hypothesize that the genomic regions surrounding established genome-wide associated variants for these chronic inflammatory diseases are associated with PDAC. We examined the association between PDAC and genomic regions (±500 kb) surrounding established common susceptibility variants for ulcerative colitis, Crohn's disease, inflammatory bowel disease, celiac disease, chronic pancreatitis, and primary sclerosing cholangitis. We analyzed summary statistics from genome-wide association studies data for 8,384 cases and 11,955 controls of European descent from two large consortium studies using the summary data-based adaptive rank truncated product method to examine the overall association of combined genomic regions for each inflammatory disease group. Combined genomic susceptibility regions for ulcerative colitis, Crohn disease, inflammatory bowel disease, and chronic pancreatitis were associated with PDAC at P values < 0.05 (0.0040, 0.0057, 0.011, and 3.4 × 10−6, respectively). After excluding the 20 PDAC susceptibility regions (±500 kb) previously identified by GWAS, the genomic regions for ulcerative colitis, Crohn disease, and inflammatory bowel disease remained associated with PDAC (P = 0.0029, 0.0057, and 0.0098, respectively). Genomic regions for celiac disease (P = 0.22) and primary sclerosing cholangitis (P = 0.078) were not associated with PDAC. Our results support the hypothesis that genomic regions surrounding variants associated with inflammatory intestinal diseases, particularly, ulcerative colitis, Crohn disease, inflammatory bowel disease, and chronic pancreatitis are associated with PDAC. Significance: The joint effects of common variants in genomic regions containing susceptibility loci for inflammatory bowel disease and chronic pancreatitis are associated with PDAC and may provide insights to understanding pancreatic cancer etiology.

The absence of neuroendocrine (NE) cells in the intestinal mucosa in autoimmune enteropathy (AIE) has been occasionally reported. However, the status of NE cells has not been studied in detail in AIE. Small bowel and colonic biopsies were retrospectively retrieved from 18 AIE patients (26 baseline [18 small bowel and 8 colon]; and 15 follow-up [11 duodenum and 4 colon] biopsies in 11 patients). Thirty-three common variable immunodeficiency (CVID) patients (30 small bowel and 16 colon), 15 inflammatory bowel disease patients (5 duodenum and 10 colon), 13 immunoglobulinA deficiency patients (13 duodenum and 5 colon), and 10 normal controls (5 colon and 5 duodenum) were selected as control groups. Histologic features (villous atrophy, intraepithelial lymphocytosis, acute inflammation, crypt apoptosis, and absence or presence of goblet cells, Paneth cells and plasma cells) were recorded. Chromogranin immunostain was performed and chromogranin-positive NE cells were counted per 10 consecutive, well-oriented crypts. On the basis of the number of chromogranin-positive NE cells, cases were graded as being absent (≤3 NE cells), markedly decreased (≤15), and intact (>15). The NE cell status correlated with histologic features. The median age of 18 AIE patients was 38.5 years (range: 11 to 74 y) and 14 patients were male. Fourteen of 18 (78%) patients showed loss (absent or markedly decreased) of NE cells in the small bowel and/or colon in the baseline biopsies including 12 (of 18) small bowel and 6 (of 8) colon biopsies. Follow-up biopsy was available in 11 patients. Six of 7 (85%) patients who showed loss of NE cells in the baseline biopsies regained NE cells in the follow-up biopsies, and 1 patient continued to show loss of NE cells. Four patients who showed intact NE cells in the baseline remained unchanged in the follow-up. Among the control groups, 3 of 33 (9%) CVID patients showed loss of NE cells. NE cells were not lost in the biopsies of all 15 and 13 patients with inflammatory bowel disease and immunoglobulinA deficiency, respectively, or the 10 normal controls. In all 41 biopsies (26 baseline plus 15 follow-up) with AIE, NE cell loss was significantly associated with increased crypt apoptosis and loss of goblet cells (P=0.001, both) but not with other histologic findings. In conclusion, our study suggests that NE cells may also be the target cells in AIE and commonly lost in the intestinal crypts in AIE, and consequently loss of NE cells can be used as an adjunct histologic feature for diagnosis of AIE.

AbstractIntroductionElectronic health record (EHR)‐driven phenotyping is a critical first step in generating biomedical knowledge from EHR data. Despite recent progress, current phenotyping approaches are manual, time‐consuming, error‐prone, and platform‐specific. This results in duplication of effort and highly variable results across systems and institutions, and is not scalable or portable. In this work, we investigate how the nascent Clinical Quality Language (CQL) can address these issues and enable high‐throughput, cross‐platform phenotyping.MethodsWe selected a clinically validated heart failure (HF) phenotype definition and translated it into CQL, then developed a CQL execution engine to integrate with the Observational Health Data Sciences and Informatics (OHDSI) platform. We executed the phenotype definition at two large academic medical centers, Northwestern Medicine and Weill Cornell Medicine, and conducted results verification (n = 100) to determine precision and recall. We additionally executed the same phenotype definition against two different data platforms, OHDSI and Fast Healthcare Interoperability Resources (FHIR), using the same underlying dataset and compared the results.ResultsCQL is expressive enough to represent the HF phenotype definition, including Boolean and aggregate operators, and temporal relationships between data elements. The language design also enabled the implementation of a custom execution engine with relative ease, and results verification at both sites revealed that precision and recall were both 100%. Cross‐platform execution resulted in identical patient cohorts generated by both data platforms.ConclusionsCQL supports the representation of arbitrarily complex phenotype definitions, and our execution engine implementation demonstrated cross‐platform execution against two widely used clinical data platforms. The language thus has the potential to help address current limitations with portability in EHR‐driven phenotyping and scale in learning health systems.

Abstract We generated eight multiple myeloma cell lines resistant to bortezomib; five acquired PSMB5 mutations. In 1,500 patients such mutations were rare clinically. To better understand disruption of proteasomes on multiple myeloma viability and drug sensitivity, we systematically deleted the major proteasome catalytic subunits. Multiple myeloma cells without PSMB5 were viable. Drug-resistant, PSMB5-mutated cell lines were resensitized to bortezomib by PSMB5 deletion, implying PSMB5 mutation is activating in its drug resistance function. In contrast, PSMB6 knockout was lethal to multiple myeloma cell lines. Depleting PSMB6 prevented splicing of the major catalytic subunits PSMB5, PSMB7, PSMB8, and PSMB10; however, PSMB6 engineered without splicing function or catalytic activity, also restored viability, inferring the contribution of PSMB6 to proteasome structure to be more important than functional activity. Supporting this, bortezomib sensitivity was restored in drug-resistant multiple myeloma cell lines by low level expression of mutated PSMB6 lacking splicing function. Loss of PSMB8 and PSMB9 was neither lethal nor restored bortezomib sensitivity. Significant codependency of PSMB5, PSMB6, and PSMB7 expression was observed. We demonstrated elevated levels of PSMB6 and 7, but not 8 and 9, in some, but not all, serial patient samples exposed to proteasome inhibitors. In summary, we show PSMB6 and PSMB7, but not PSMB5, to be essential for multiple myeloma cell survival, this dependency is structural and that upregulation or activating mutation of PSMB5, 6, and 7 confers proteasome inhibitor resistance, while depletion confers sensitivity. Implications: These findings support modulation of PSMB5, PSMB6, or PSMB7 expression as a new therapeutic strategy.