Population-Based Prevalence of Gastrointestinal Abnormalities at Colon Capsule Endoscopy.

BACKGROUND & AIMS: The population prevalence of gastrointestinal (GI) disease is unclear and difficult to assess in an asymptomatic population. The aim of this study was to determine prevalence of GI lesions in a largely asymptomatic population undergoing colon capsule endoscopy (CCE). METHODS: Participants aged between 50-75 years were retrieved from the Rotterdam Study, a longitudinal epidemiological study, between 2017-2019. Participants received CCE with bowel preparation. Abnormalities defined as clinically relevant were Barrett segment >3cm, severe ulceration, polyp >10 mm or ≥3 polyps in small bowel (SB) or colon, and cancer. RESULTS: Of 2800 invited subjects, 462 (16.5%) participants (mean age 66.8 years, female 53.5%) ingested the colon capsule. A total of 451 videos were analyzed, and in 94.7% the capsule reached the descending colon. At least 1 abnormal finding was seen in 448 (99.3%) participants. The prevalence of abnormalities per GI segment, and the most common type of abnormality, were as follows: Esophageal 14.8% (Barrett's esophagus <3 cm in 8.3%), gastric 27.9% (fundic gland polyps in 18.1%), SB abnormalities 33.9% (erosions in 23.8%), colon 93.3% (diverticula in 81.2%). A total of 54 participants (12%) had clinically relevant abnormalities, 3 (0.7%) in esophagus/stomach (reflux esophagitis grade D, Mallory Weiss lesion and severe gastritis), 5 (1.1%) in SB (polyps > 10 mm; n = 4, severe ulcer n = 1,) and 46 (10.2%) in colon (polyp > 10 mm or ≥3 polyps n = 46, colorectal cancer n = 1). CONCLUSIONS: GI lesions are very common in a mostly asymptomatic Western population, and clinically relevant lesions were found in 12% at CCE. These findings provide a frame of reference for the prevalence rates of GI lesions in the general population.

Review of Digitalized Patient Education in Cardiology: A Future Ahead?

INTRODUCTION: An increased focus on shared decision-making and patient empowerment in cardiology and on patient outcomes such as quality of life (QoL), depression, and anxiety underline the importance of high-quality patient education. Studies focusing on digital means of patient education performed in other disciplines of medicine demonstrated its positive effect in these areas. Therefore, a review of the current literature was performed to (i) evaluate the status of innovative, digitalized means of patient education in cardiology and (ii) assess the impact of digital patient education on outcome parameters (i.e., patient knowledge (or health literacy), QoL, depression, anxiety, and patient satisfaction). METHOD: A review of the current literature was performed to evaluate the effect of digitalized patient education for any purpose in the field of cardiology. Medline and EMBASE were searched for articles reporting any digital educational platform used for patient education up to May 2020. The articles were compared on their effect on patient knowledge or health literacy, QoL, depression or anxiety, and patient satisfaction. RESULTS: The initial search yielded 279 articles, 34 of which were retained after applying in, and exclusion criteria. After full-text analysis, the total number of articles remaining was 16. Of these, 6 articles discussed the use of smartphone or tablet applications as a means of patient education, whereas 3 reviewed web-based content, and 7 evaluated the use of video (2 three-dimensional videos, from which one on a virtual reality headset). CONCLUSION: This review demonstrates that digital patient education increases patient knowledge. Overall, digital education increases QoL and lowers feelings of depression and anxiety. The majority of patients express satisfaction with digital platforms. It remains important that developers of digital patient education platforms remain focused on clear, structured, and comprehensible information presentation.

Computer versus cardiologist: Is a machine learning algorithm able to outperform an expert in diagnosing a phospholamban p.Arg14del mutation on the electrocardiogram?

BACKGROUND: Phospholamban (PLN) p.Arg14del mutation carriers are known to develop dilated and/or arrhythmogenic cardiomyopathy, and typical electrocardiographic (ECG) features have been identified for diagnosis. Machine learning is a powerful tool used in ECG analysis and has shown to outperform cardiologists. OBJECTIVES: We aimed to develop machine learning and deep learning models to diagnose PLN p.Arg14del cardiomyopathy using ECGs and evaluate their accuracy compared to an expert cardiologist. METHODS: We included 155 adult PLN mutation carriers and 155 age- and sex-matched control subjects. Twenty-one PLN mutation carriers (13.4%) were classified as symptomatic (symptoms of heart failure or malignant ventricular arrhythmias). The data set was split into training and testing sets using 4-fold cross-validation. Multiple models were developed to discriminate between PLN mutation carriers and control subjects. For comparison, expert cardiologists classified the same data set. The best performing models were validated using an external PLN p.Arg14del mutation carrier data set from Murcia, Spain (n = 50). We applied occlusion maps to visualize the most contributing ECG regions. RESULTS: In terms of specificity, expert cardiologists (0.99) outperformed all models (range 0.53-0.81). In terms of accuracy and sensitivity, experts (0.28 and 0.64) were outperformed by all models (sensitivity range 0.65-0.81). T-wave morphology was most important for classification of PLN p.Arg14del carriers. External validation showed comparable results, with the best model outperforming experts. CONCLUSION: This study shows that machine learning can outperform experienced cardiologists in the diagnosis of PLN p.Arg14del cardiomyopathy and suggests that the shape of the T wave is of added importance to this diagnosis.