Citations

AbstractThe European Society of Gastrointestinal Endoscopy (ESGE) presents a short list of performance measures for colonoscopy in inflammatory bowel disease (IBD) patients. Current performance measures for colonoscopy mainly focus on detecting (pre)malignant lesions. However, these performance measures are not relevant for all colonoscopy indications in IBD patients. Therefore, our aim was to provide endoscopy services across Europe and other interested countries with a tool for quality monitoring and improvement in IBD colonoscopy. Eight key performance measures and one minor performance measure were recommended for measurement and evaluation in daily endoscopy practice.

BACKGROUND: The MBSAQIP has been used to evaluate risk and create risk models. Previous studies have determined what complications matter most. STUDY DESIGN: This study was a registry-based analysis of patients who underwent laparoscopic Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG) between January 1, 2015, and December 31, 2019, at centers participating in MBSAQIP. Cases were identified using Current Procedural Terminology. Patients less than 18 years old were excluded. A chi-square test was used to assess differences in the prevalence of complications and effects across years. Using a multivariable Poisson regression model with a link log and robust estimation, prevalence ratio estimates and 95% CI were obtained. RESULTS: A total of 690,770 observations met the inclusion criteria. Mean (SD) age was 44.5 (±12.0) years, and mean (SD) BMI was 45.2 kg/m2 (±7.9). SG cases accounted for 73.45% of all observations. There was a significant relative reduction in readmissions, end-organ dysfunction, and all-cause mortality from 2015 to 2019 in the SG subgroup. There were also noticeable reductions in 30-day readmission, ICU admission, and end-organ dysfunction in the RYGB subgroup. For complications, bleeding, postoperative pneumonia, and stroke were significantly decreased in the RYGB subgroup, but only bleeding was significantly decreased in the SG subgroup during the 5-year study period. CONCLUSIONS: There has been a continuous improvement in several (but not all) outcomes through the years that the MBSAQIP has been collecting data. Different outcomes were reduced between the SG and RYGB subgroups, and the decrease in all-cause mortality was only noted in the SG subgroup.

Abstract Aims Some artificial intelligence models applied in medical practice require ongoing retraining, introduce unintended racial bias, or have variable performance among different subgroups of patients. We assessed the real-world performance of the artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction with respect to multiple patient and electrocardiogram variables to determine the algorithm’s long-term efficacy and potential bias in the absence of retraining. Methods and results Electrocardiograms acquired in 2019 at Mayo Clinic in Minnesota, Arizona, and Florida with an echocardiogram performed within 14 days were analyzed (n = 44 986 unique patients). The area under the curve (AUC) was calculated to evaluate performance of the algorithm among age groups, racial and ethnic groups, patient encounter location, electrocardiogram features, and over time. The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction had an AUC of 0.903 for the total cohort. Time series analysis of the model validated its temporal stability. Areas under the curve were similar for all racial and ethnic groups (0.90–0.92) with minimal performance difference between sexes. Patients with a ‘normal sinus rhythm’ electrocardiogram (n = 37 047) exhibited an AUC of 0.91. All other electrocardiogram features had areas under the curve between 0.79 and 0.91, with the lowest performance occurring in the left bundle branch block group (0.79). Conclusion The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction is stable over time in the absence of retraining and robust with respect to multiple variables including time, patient race, and electrocardiogram features.

Background: Artificial intelligence (AI)-based analysis of the electrocardiogram (ECG) can estimate the probability of having atrial fibrillation (AF) while in sinus rhythm. We evaluated the association between the probability of AF using AI-ECG and incident ischemic stroke (I-Stroke) and cardiovascular (CV) mortality. Methods: We evaluated a community-based cohort of consecutive patients that sought primary care in Olmsted County, MN, from the years 1998 to 2000 followed-up through March 2016. Inclusion criteria were ages 40-79, an electrocardiogram within 2 years and complete CHA2DS2-VASc data. We excluded those with known atherosclerotic CV disease or AF. The AI network was developed in 180,000 patients (649,931 ECG). The primary endpoint was incident I-Stroke; secondary endpoint was I-Stroke or CV mortality; validated in duplicate. Multivariate cox models adjusted for age, sex, history of hypertension, diabetes, heart failure, and in CHA2DS2-VASc Score subgroups (categorized as low=0, moderate=1, and high risk ≥2). Results: We included 14,601 patients, mean age 53.2±13.08 (51% women, 95% whites), CHA2DS2-VASc score 1.3±1.2, median follow-up of 16.3 years, IQR 14.5-17.5, 727 (4.9%) had an I-Stroke and 1309 (8.9%) had I-Stroke or died from CV causes. When comparing the 5 th vs the 1 st quintile, those with high AI-ECG AF probability had increased risk for I-Stroke (HR 3.25, 95%CI 2.55-4.14, P-for trend <.0001 Figure-A ) and ischemic stroke or CV mortality (HR 5.73, 95%CI 4.69-6.99, P-for trend <.0001 Figure-B ). This remained significant after adjustments. Furthermore, AI-ECG AF probability was associated with I-Stroke across CHA2DS2-VASc risk levels (HR 2.43 for high, 2.33 for moderate, 1.98 for low CHA2DS2-VASc risk, all P<0.01). Conclusion: The AI-ECG AF algorithm predicts long-term I-Stroke and CV mortality and may refine CHA2DS2-VASc. These results highlight the utility of the AI enabled ECG when estimating I-Stroke or CV mortality risk.

Background and objectives The progression of polycystic liver disease is not well understood. The purpose of the study is to evaluate the associations of polycystic liver progression with other disease progression variables and classify liver progression on the basis of patient's age, height-adjusted liver cystic volume, and height-adjusted liver volume. Design, setting, participants, & measurements Prospective longitudinal magnetic resonance images from 670 patients with early autosomal dominant polycystic kidney disease for up to 14 years of follow-up were evaluated to measure height-adjusted liver cystic volume and height-adjusted liver volume. Among them, 245 patients with liver cyst volume >50 ml at baseline were included in the longitudinal analysis. Linear mixed models on log-transformed height-adjusted liver cystic volume and height-adjusted liver volume were fitted to approximate mean annual rate of change for each outcome. The association of sex, body mass index, genotype, baseline height-adjusted total kidney volume, and Mayo imaging class was assessed. We calculated height-adjusted liver cystic volume ranges for each specific age and divided them into five classes on the basis of annual percentage increase in height-adjusted liver cystic volume. Results The mean annual growth rate of height-adjusted liver cystic volume was 12% (95% confidence interval, 11.1% to 13.1%; P <0.001), whereas that for height-adjusted liver volume was 2% (95% confidence interval, 1.9% to 2.6%; P <0.001). Women had higher baseline height-adjusted liver cystic volume than men, but men had higher height-adjusted liver cystic volume growth rate than women by 2% (95% confidence interval, 0.4% to 4.5%; P =0.02). Whereas the height-adjusted liver cystic volume growth rate decreased in women after menopause, no decrease was observed in men at any age. Body mass index, genotype, and baseline height-adjusted total kidney volume were not associated with the growth rate of height-adjusted liver cystic volume or height-adjusted liver volume. According to the height-adjusted liver cystic volume growth rate, patients were classified into five classes (number of women, men in each class): A (24, six); B (44, 13); C (43, 48); D (28, 17); and E (13, nine). Conclusions Compared with height-adjusted liver volume, the use of height-adjusted liver cystic volume showed greater separations in volumetric progression of polycystic liver disease. Similar to the Mayo imaging classification for the kidney, the progression of polycystic liver disease may be categorized on the basis of patient’s age and height-adjusted liver cystic volume.

Ideal cardiovascular health (CVH) is defined for the presence of ideal behavioral and health metrics known to prevent cardiovascular disease (CVD). The association of circulatory phospho- and sphingo-lipids to primary reduction in cardiovascular risk is unclear. Our aim was to determine the association of CVH metrics with the circulating lipid profile of a population-based cohort. Serum sphingolipid and phospholipid species were extracted from 461 patients of the randomly selected prospective Kardiovize study based on Brno, Czech Republic. Lipids species were measured by a hyphenated mass spectrometry technique, and were associated with poor CVH scores, as defined by the American Heart Association. Phosphatidylcholine (PC), phosphatidylethanolamine (PE), lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE) species were significantly lower in ideal and intermediate scores of health dietary metric, blood pressure, total cholesterol and blood fasting glucose compared to poor scores. Current smokers presented higher levels of PC, PE and LPE individual species compared to non-smokers. Ceramide (Cer) d18:1/14:0 was altered in poor blood pressure, total cholesterol and fasting blood glucose metrics. Poor cardiovascular health metric is associated with a specific phospho- and sphingolipid pattern. Circulatory lipid profiling is a potential biomarker to refine cardiovascular health status in primary prevention strategies.

Abstract Background An artificial intelligence (AI) algorithm detecting age from 12-lead ECG has been suggested to signal “physiological age” of the individual. Importantly, increased physiological age gauged by an increased difference between ECG-age and chronological age has been associated with higher risk of cardiac events in non-transplant population. Purpose We sought to investigate the validity of the AI-derived ECG-age algorithm in patients who underwent heart transplantation and its relationship to major adverse cardiovascular events (MACE). Methods A total of 489 consecutive patients who had undergone heart transplantation in our institution between 1994 and 2018 were studied. AI-ECG age was calculated by a previously-trained artificial intelligence (AI) algorithm using a 12-lead ECG per patient. ECGs used in the training process of the algorithm were excluded. The average of the ECG-ages within one year before and one year after heart transplantation was used to represent pre- and post-transplant ECG-ages. MACE was defined as any incidence of revascularization, re-transplantation, and death. Results Pre-transplant ECG-age (mean 63±10 years) correlated significantly with recipient chronological age (mean 50±13 years, r=0.57, p&lt;0.0001), but this correlation between recipient and ECG-ages was weakened after transplantation (mean post-transplant ECG age of 55±10 years, r=0.34, p&lt;0.0001). Interestingly, post-transplant ECG-age correlated significantly with donor age (mean ECG age of 55±10 years vs. mean donor age of 32±13 years, r=0.42, p&lt;0.0001). During a median (IQR) follow-up of 9 (5, 14) years, 251 patients had MACE. Mean change in ECG age after transplantation compared to before was −8.8±12.7 years. Patients who had an increase in ECG-age after compared to before transplantation showed increased risk of MACE (HR: 1.53 [1.16, 2.01], p=0.002), independent of recipient and donor ages (adjusted HR: 1.68 [1.26, 2.25], p=0.001); whereas there were no significant differences in risk of MACE in patients who were transplanted with an older donor heart (HR: 1.07 [0.77, 1.50], p=0.66). In a Kaplan Meier survival analysis, those with increased ECG-age after transplantation had significantly lower MACE-free survival compared to those with decreased ECG-age. (Log-rank P=0.002; Wilcoxon P=0.001) (Figure) Conclusion Post-transplant ECG-age correlates more faithfully with the donor's than the recipient's chronological age, suggesting that ECG-age more closely reflects cardiac age than the patient age. Furthermore, ECG-age derived cardiac aging after transplantation is associated with higher risk of MACE. Funding Acknowledgement Type of funding sources: None.

Abstract Aims The current gold standard comprehensive assessment of coronary microvascular dysfunction (CMD) is through a limited-access invasive catheterization lab procedure. We aimed to develop a point-of-care tool to assist clinical guidance in patients presenting with chest pain and/or an abnormal cardiac functional stress test and with non-obstructive coronary artery disease (NOCAD). Methods and results This study included 1893 NOCAD patients (&lt;50% angiographic stenosis) who underwent CMD evaluation as well as an electrocardiogram (ECG) up to 1-year prior. Endothelial-independent CMD was defined by coronary flow reserve (CFR) ≤2.5 in response to intracoronary adenosine. Endothelial-dependent CMD was defined by a maximal percent increase in coronary blood flow (%ΔCBF) ≤50% in response to intracoronary acetylcholine infusion. We trained algorithms to distinguish between the following outcomes: CFR ≤2.5, %ΔCBF ≤50, and the combination of both. Two classes of algorithms were trained, one depending on ECG waveforms as input, and another using tabular clinical data. Mean age was 51 ± 12 years and 66% were females (n = 1257). Area under the curve values ranged from 0.49 to 0.67 for all the outcomes. The best performance in our analysis was for the outcome CFR ≤2.5 with clinical variables. Area under the curve and accuracy were 0.67% and 60%. When decreasing the threshold of positivity, sensitivity and negative predictive value increased to 92% and 90%, respectively, while specificity and positive predictive value decreased to 25% and 29%, respectively. Conclusion An artificial intelligence-enabled algorithm may be able to assist clinical guidance by ruling out CMD in patients presenting with chest pain and/or an abnormal functional stress test. This algorithm needs to be prospectively validated in different cohorts.

Abstract Myelofibrosis (MF) is a clonal stem cell neoplasm characterized by abnormal JAK-STAT signaling, chronic inflammation, cytopenias, and risk of transformation to acute leukemia. Despite improvements in the therapeutic options for patients with MF, allogeneic hematopoietic stem cell transplantation remains the only curative treatment. We previously demonstrated multiple immunosuppressive mechanisms in patients with MF, including increased expression of programmed cell death protein 1 (PD-1) on T cells compared with healthy controls. Therefore, we conducted a multicenter, open-label, phase 2, single-arm study of pembrolizumab in patients with Dynamic International Prognostic Scoring System category of intermediate-2 or greater primary, post-essential thrombocythemia or post-polycythemia vera myelofibrosis that were ineligible for or were previously treated with ruxolitinib. The study followed a Simon 2-stage design and enrolled a total of 10 patients, 5 of whom had JAK2V617mutation, 2 had CALR mutation, and 6 had additional mutations. Most patients were previously treated with ruxolitinib. Pembrolizumab treatment was well tolerated, but there were no objective clinical responses, so the study closed after the first stage was completed. However, immune profiling by flow cytometry, T-cell receptor sequencing, and plasma proteomics demonstrated changes in the immune milieu of patients, which suggested improved T-cell responses that can potentially favor antitumor immunity. The fact that these changes were not reflected in a clinical response strongly suggests that combination immunotherapeutic approaches rather than monotherapy may be necessary to reverse the multifactorial mechanisms of immune suppression in myeloproliferative neoplasms. This trial was registered at www.clinicaltrials.gov as #NCT03065400.