Background - COVID-19 has led to over 1 million deaths worldwide and has been associated with cardiac complications including cardiac arrhythmias. The incidence and pathophysiology of these manifestations remain elusive. In this worldwide survey of patients hospitalized with COVID-19 who developed cardiac arrhythmias, we describe clinical characteristics associated with various arrhythmias, as well as global differences in modulations of routine electrophysiology practice during the pandemic. Methods - We conducted a retrospective analysis of patients hospitalized with COVID-19 infection worldwide with and without incident cardiac arrhythmias. Patients with documented atrial fibrillation (AF), atrial flutter (AFL), supraventricular tachycardia (SVT), non-sustained or sustained ventricular tachycardia (VT), ventricular fibrillation (VF), atrioventricular block (AVB), or marked sinus bradycardia (HR<40bpm) were classified as having arrhythmia. De-identified data was provided by each institution and analyzed. Results - Data was collected for 4,526 patients across 4 continents and 12 countries, 827 of whom had an arrhythmia. Cardiac comorbidities were common in patients with arrhythmia: 69% had hypertension, 42% diabetes mellitus, 30% had heart failure and 24% coronary artery disease. Most had no prior history of arrhythmia. Of those who did develop an arrhythmia, the majority (81.8%) developed atrial arrhythmias, 20.7% developed ventricular arrhythmias, and 22.6% had bradyarrhythmia. Regional differences suggested a lower incidence of AF in Asia compared to other continents (34% vs. 63%). Most patients in in North America and Europe received hydroxychloroquine, though the frequency of hydroxychloroquine therapy was constant across arrhythmia types. Forty-three percent of patients who developed arrhythmia were mechanically ventilated and 51% survived to hospital discharge. Many institutions reported drastic decreases in electrophysiology procedures performed. Conclusions - Cardiac arrhythmias are common and associated with high morbidity and mortality among patients hospitalized with COVID-19 infection. There were significant regional variations in the types of arrhythmias and treatment approaches.

Background Despite regular colonoscopy, interval colorectal cancer (CRC) may occur. Long-term studies examining CRC rates in patients with previous colonoscopy are lacking. Objective We examined the rate of interval CRC in the Polyp Prevention Trial-Continued Follow-up Study (PPT-CFS), an observational study of participants which began after the Polyp Prevention Trial (PPT) ended. Design Prospective Setting A national U.S. community-based polyp prevention trial Main Outcome Measurements Medical records of CRC were collected, reviewed, and abstracted in a standardized fashion. Results Among 2,079 PPT participants, 1,297 (62.4%) agreed to participate in the PPT-CFS. They were followed for a median of 6.2 years after 4.3 years of median follow up in the main PPT. Nine cases of CRC were diagnosed over 7,626 person years of observation (PYO) for an incidence rate of 1.2/1000 PYO. The ratio of CRCs observed compared to that expected by SEER was 0.64 (95% CI 0.28–1.06). Including all CRCs (N=22) since the beginning of the PPT trial, the observed compared to expected rate by SEER was 0.74 (95% CI 0.47–1.05). Of patients who developed CRC in the PPT-CFS, 78% had a history of an advanced adenoma compared to only 43% among patients who remained cancer-free (p=0.04). Limitations A relatively small number of interval cancers were detected. Conclusions Despite frequent colonoscopy during the PPT trial, in the years subsequent to the trial, there was a persistent ongoing risk for cancer. Subjects with a history of advanced adenoma are at increased risk for subsequent cancer and should be followed closely with continued surveillance.

Introduction: The effects of sodium-glucose cotransporter 2 inhibitors (SGLT2I) and dipeptidyl peptidase-4 inhibitors (DPP4I) on new-onset cognitive dysfunction in type 2 diabetes mellitus remain unknown. This study aimed to evaluate the effects of the two novel antidiabetic agents on cognitive dysfunction by comparing the rates of dementia between SGLT2I and DPP4I users.Methods: This was a population-based cohort study of type 2 diabetes mellitus patients treated with SGLT2I and DPP4I between January 1, 2015 and December 31, 2019 in Hong Kong. Exclusion criteria were &lt;1-month exposure or exposure to both medication classes, or prior diagnosis of dementia or major neurological/psychiatric diseases. Primary outcomes were new-onset dementia, Alzheimer's, and Parkinson's. Secondary outcomes were all-cause, cardiovascular, and cerebrovascular mortality.Results: A total of 13,276 SGLT2I and 36,544 DPP4I users (total n = 51,460; median age: 66.3 years old [interquartile range (IQR): 58–76], 55.65% men) were studied (follow-up: 472 [120–792] days). After 1:2 matching (SGLT2I: n = 13,283; DPP4I: n = 26,545), SGLT2I users had lower incidences of dementia (0.19 vs. 0.78%, p &lt; 0.0001), Alzheimer's (0.01 vs. 0.1%, p = 0.0047), Parkinson's disease (0.02 vs. 0.14%, p = 0.0006), all-cause (5.48 vs. 12.69%, p &lt; 0.0001), cerebrovascular (0.88 vs. 3.88%, p &lt; 0.0001), and cardiovascular mortality (0.49 vs. 3.75%, p &lt; 0.0001). Cox regression showed that SGLT2I use was associated with lower risks of dementia (hazard ratio [HR]: 0.41, 95% confidence interval [CI]: [0.27–0.61], P &lt; 0.0001), Parkinson's (HR:0.28, 95% CI: [0.09–0.91], P = 0.0349), all-cause (HR:0.84, 95% CI: [0.77–0.91], P &lt; 0.0001), cardiovascular (HR:0.64, 95% CI: [0.49–0.85], P = 0.0017), and cerebrovascular (HR:0.36, 95% CI: [0.3–0.43], P &lt; 0.0001) mortality.Conclusions: The use of SGLT2I is associated with lower risks of dementia, Parkinson's disease, and cerebrovascular mortality compared with DPP4I use after 1:2 ratio propensity score matching.

Aims Heart failure (HF) involves complex remodelling leading to electrical and mechanical dysfunction. We hypothesized that machine learning approaches incorporating data obtained from different investigative modalities including atrial and ventricular measurements from electrocardiography and echocardiography, blood inflammatory marker [neutrophil-to-lymphocyte ratio (NLR)], and prognostic nutritional index (PNI) will improve risk stratification for adverse outcomes in HF compared to logistic regression. Methods and results Consecutive Chinese patients referred to our centre for transthoracic echocardiography and subsequently diagnosed with HF, between 1 January 2010 and 31 December 2016, were included in this study. Two machine learning techniques, multilayer perceptron and multi-task learning, were compared with logistic regression for their ability to predict incident atrial fibrillation (AF), transient ischaemic attack (TIA)/stroke, and all-cause mortality. This study included 312 HF patients [mean age: 64 (55-73) years, 75% male]. There were 76 cases of new-onset AF, 62 cases of incident TIA/stroke, and 117 deaths during follow-up. Univariate analysis revealed that age, left atrial reservoir strain (LARS) and contractile strain (LACS) were significant predictors of new-onset AF. Age and smoking predicted incident stroke. Age, hypertension, type 2 diabetes mellitus, chronic kidney disease, mitral or aortic regurgitation, P-wave terminal force in V1, the presence of partial inter-atrial block, left atrial diameter, ejection fraction, global longitudinal strain, serum creatinine and albumin, high NLR, low PNI, and LARS and LACS predicted all-cause mortality. Machine learning techniques achieved better prediction performance than logistic regression. Conclusions Multi-modality assessment is important for risk stratification in HF. A machine learning approach provides additional value for improving outcome prediction.

ObjectivesBrugada syndrome (BrS) is an ion channelopathy that predisposes affected patients to spontaneous ventricular tachycardia/fibrillation (VT/VF) and sudden cardiac death. The aim of this study is to examine the predictive factors of spontaneous VT/VF.MethodsThis was a territory-wide retrospective cohort study of patients diagnosed with BrS between 1997 and 2019. The primary outcome was spontaneous VT/VF. Cox regression was used to identify significant risk predictors. Non-linear interactions between variables (latent patterns) were extracted using non-negative matrix factorisation (NMF) and used as inputs into the random survival forest (RSF) model.ResultsThis study included 516 consecutive BrS patients (mean age of initial presentation=50±16 years, male=92%) with a median follow-up of 86 (IQR: 45–118) months. The cohort was divided into subgroups based on initial disease manifestation: asymptomatic (n=314), syncope (n=159) or VT/VF (n=41). Annualised event rates per person-year were 1.70%, 0.05% and 0.01% for the VT/VF, syncope and asymptomatic subgroups, respectively. Multivariate Cox regression analysis revealed initial presentation of VT/VF (HR=24.0, 95% CI=1.21 to 479, p=0.037) and SD of P-wave duration (HR=1.07, 95% CI=1.00 to 1.13, p=0.044) were significant predictors. The NMF-RSF showed the best predictive performance compared with RSF and Cox regression models (precision: 0.87 vs 0.83 vs. 0.76, recall: 0.89 vs. 0.85 vs 0.73, F1-score: 0.88 vs 0.84 vs 0.74).ConclusionsClinical history, electrocardiographic markers and investigation results provide important information for risk stratification. Machine learning techniques using NMF and RSF significantly improves overall risk stratification performance.

Heart failure (HF) is a major epidemic with rising morbidity and mortality rates that encumber global healthcare systems. While some studies have demonstrated the value of CRP in predicting (i) the development of HFpEF and (ii) long-term clinical outcomes in HFpEF patients, others have shown no such correlation. As a result, we conducted the following systematic review and meta-analysis to assess both the diagnostic and prognostic role of CRP in HFpEF. PubMed and Embase were searched for studies that assess the relationship between CRP and HFpEF using the following search terms: (((C-reactive protein) AND ((preserved ejection fraction) OR (diastolic heart failure))). The search period was from the start of database to August 6, 2019, with no language restrictions. A total of 312 and 233 studies were obtained from PubMed and Embase respectively, from which 19 studies were included. Our meta-analysis demonstrated the value of a high CRP in predicting the development of not only new onset HFpEF (HR: 1.08; 95% CI: 1.00-1.16; P = 0.04; I 2 = 22%), but also an increased risk of cardiovascular mortality when used as a categorical (HR: 2.52; 95% CI: 1.61-3.96; P < 0.0001; I 2 = 19%) or a continuous variable (HR: 1.24; 95% CI: 1.04-1.47; P = 0.01; I 2 = 28%), as well as all-cause mortality when used as a categorical (HR: 1.78; 95% CI: 1.53-2.06; P < 0.00001; I 2 = 0%) or a continuous variable: (HR: 1.06; 95% CI: 1.02-1.06; P = 0.003; I 2 = 61%) in HFpEF patients. CRP can be used as a biomarker to predict the development of HFpEF and long-term clinical outcomes in HFpEF patients, in turn justifying its use as a simple, accessible parameter to guide clinical management in this patient population. However, more prospective studies are still required to not only explore the utility and dynamicity of CRP in HFpEF but also to determine whether risk stratification algorithms incorporating CRP actually provide a material benefit in improving patient prognosis.

Recent studies have reported numerous predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk scores available for prompt risk stratification. The objective is to develop a simple risk score for predicting severe COVID-19 disease using territory-wide data based on simple clinical and laboratory variables. Consecutive patients admitted to Hong Kong’s public hospitals between 1 January and 22 August 2020 and diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8 September 2020. An external independent cohort from Wuhan was used for model validation. COVID-19 testing was performed in 237,493 patients and 4442 patients (median age 44.8 years old, 95% confidence interval (CI): [28.9, 60.8]); 50% males) were tested positive. Of these, 209 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, diabetes mellitus, hypertension, atrial fibrillation, heart failure, ischemic heart disease, peripheral vascular disease, stroke, dementia, liver diseases, gastrointestinal bleeding, cancer, increases in neutrophil count, potassium, urea, creatinine, aspartate transaminase, alanine transaminase, bilirubin, D-dimer, high sensitive troponin-I, lactate dehydrogenase, activated partial thromboplastin time, prothrombin time, and C-reactive protein, as well as decreases in lymphocyte count, platelet, hematocrit, albumin, sodium, low-density lipoprotein, high-density lipoprotein, cholesterol, glucose, and base excess. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. The derived score system was evaluated with out-of-sample five-cross-validation (AUC: 0.86, 95% CI: 0.82–0.91) and external validation (N = 202, AUC: 0.89, 95% CI: 0.85–0.93). A simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results.

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