Sordi Chara, Beatriz

INTRODUCTION: Building on prior results, we hypothesized that an electrocardiogram (ECG)-enabled machine learning (ML) model could be used to detect advanced chronic liver disease (CLD). METHODS: A cohort with CLD and 12-lead ECGs was matched with controls from electronic health records. A ML model was trained as a binary classifier. RESULTS: There are 12,930 patients with CLD and 64,577 controls in the cohort. The model's discriminative ability to classify CLD showed an area under the receiver-operating characteristic curve 0.858 (95% confidence interval: 0.850–0.866), and at the chosen threshold, CLD ECGs had 12 times higher odds of being classified as CLD (diagnostic odds ratio 12.33, 95% confidence interval: 11.16–13.63). DISCUSSION: An ECG-enabled ML model affords great promise in identifying advanced CLD in low resource areas.

Background: Remote patient monitoring (RPM) is an emerging focus in health care, and specialized programs may reduce medical costs, supplement in-office visits, and improve patient satisfaction. In this study, we describe the development, feasibility, and early outcomes of an RPM program for patients with decompensated cirrhosis. Methods: Forty-six patients were offered enrollment at the time of hospital discharge in the cirrhosis RPM program (CiRPM), of which 41 completed at least 30 days of monitoring. Participants were mailed remote monitoring equipment and a tablet to be used for patient-reported outcomes. Alerts were continuously monitored by virtual nursing staff who could perform targeted interventions. A cohort of historical controls (n = 74) was created for comparison using inverse probability of treatment weighting. Results: Patients were enrolled in the program for a mean of 83.9 days, with 28 (68%) completing the full 90-day program. Participants uploaded vital signs and responded to symptom-based questionnaires on 93% of the monitored days. On end-of-program surveys, over 75% of patients expressed satisfaction with the program. Gender, age, and MELD-Na were similar between CiRPM and weighted control groups. The 90-day readmission rate was 34% in CiRPM and 47% in weighted controls. In the CiRPM group, 12% of subjects had 2 or more admissions, compared to 37% in the weighted control group. Conclusion: This study demonstrates the feasibility of a cirrhosis-specific RPM program. Overall, patient satisfaction and utilization of the CiRPM was high. Future studies are needed to confirm the impact of RPM on the reduction of hospital readmissions in decompensated cirrhosis.

Abstract Background Unlike cigarette smoking, environmental tobacco smoke (ETS) has not been as well described as an environmental risk for Multiple sclerosis (MS) nor as a risk factor for disease progression. Objective We systematically reviewed the association between ETS and the risk of onset and/or progression of MS. Methods We systematically screened MedLine/PubMed, Science Direct, LILACs, and SciELO searching for publications between January 1st, 2010, and July 5, 2021, with the following keywords: “multiple sclerosis and smoking”; “multiple sclerosis and passive smoking”; “multiple sclerosis and secondhand smoking”. Results Fifteen articles were included in this review, which consisted of systematic reviews with meta-analysis (N = 2), systematic reviews (N = 2), and observational studies (N = 11). Both meta-analyses reported an impact of ETS on MS onset among secondhand smokers. One of the systematic reviews selected two observational studies showing the association between ETS and MS development, and one study that did not find a significant association between ETS and the risk of MS development. The other systematic review identified selected eight articles showing a relationship between ETS and MS. Seven observational studies reported higher odds of MS onset when associated with ETS. Four observational studies did not show a relationship between ETS and MS onset or progression. Conclusion Most articles showed a positive association between ETS exposure and the risk of developing MS. On the other hand, an association between ETS and a higher risk for MS progression could not be established.

INTRODUCTION: Management of hepatic encephalopathy relies on self-titration of lactulose. In this feasibility trial, we assess an artificial intelligence–enabled tool to guide lactulose use through a smartphone application. METHODS: Subjects with hepatic encephalopathy on lactulose captured bowel movement pictures during lead-in and intervention phases. During the intervention phase, daily feedback on lactulose titration was delivered through the application. Goals were determined according to number of bowel movement and Bristol Stool Scale reports. RESULTS: Subjects completed the study with more than 80% satisfaction. In the lead-in phase, less compliant subjects achieved Bristol Stool Scale goal on 62/111 (56%) of days compared with 107/136 (79%) in the intervention phase ( P = 0.041), while the most compliant subjects showed no difference. Severe/recurrent hepatic encephalopathy group achieved Bristol Stool Scale goal on 80/104 (77%) days in the lead-in phase and 90/110 (82%) days in the intervention phase ( P = NS), compared with 89/143 (62%) days and 86/127 (68%) days in the stable group. DISCUSSION: Dieta application is a promising tool for objective Bowel Movement/Bristol Stool Scale tracking for hepatic encephalopathy and may potentially be used to assist with lactulose titration.