Optimizing hepatitis B virus screening in the United States using a simple demographics-based model.

BACKGROUND AND AIMS: Chronic hepatitis B (CHB) affects >290 million persons globally, and only 10% have been diagnosed, presenting a severe gap that must be addressed. We developed logistic regression (LR) and machine learning (ML; random forest) models to accurately identify patients with HBV, using only easily obtained demographic data from a population-based data set. APPROACH AND RESULTS: We identified participants with data on HBsAg, birth year, sex, race/ethnicity, and birthplace from 10 cycles of the National Health and Nutrition Examination Survey (1999-2018) and divided them into two cohorts: training (cycles 2, 3, 5, 6, 8, and 10; n = 39,119) and validation (cycles 1, 4, 7, and 9; n = 21,569). We then developed and tested our two models. The overall cohort was 49.2% male, 39.7% White, 23.2% Black, 29.6% Hispanic, and 7.5% Asian/other, with a median birth year of 1973. In multivariable logistic regression, the following factors were associated with HBV infection: birth year 1991 or after (adjusted OR [aOR], 0.28; p < 0.001); male sex (aOR, 1.49; p = 0.0080); Black and Asian/other versus White (aOR, 5.23 and 9.13; p < 0.001 for both); and being USA-born (vs. foreign-born; aOR, 0.14; p < 0.001). We found that the ML model consistently outperformed the LR model, with higher area under the receiver operating characteristic values (0.83 vs. 0.75 in validation cohort; p < 0.001) and better differentiation of high- and low-risk persons. CONCLUSIONS: Our ML model provides a simple, targeted approach to HBV screening, using only easily obtained demographic data.

The other COVID-19 survivors: Timing, duration, and health impact of post-acute sequelae of SARS-CoV-2 infection.

AIMS AND OBJECTIVES: To determine the frequency, timing, and duration of post-acute sequelae of SARS-CoV-2 infection (PASC) and their impact on health and function. BACKGROUND: Post-acute sequelae of SARS-CoV-2 infection is an emerging major public health problem that is poorly understood and has no current treatment or cure. PASC is a new syndrome that has yet to be fully clinically characterised. DESIGN: Descriptive cross-sectional survey (n = 5163) was conducted from online COVID-19 survivor support groups who reported symptoms for more than 21 days following SARS-CoV-2 infection. METHODS: Participants reported background demographics and the date and method of their covid diagnosis, as well as all symptoms experienced since onset of covid in terms of the symptom start date, duration, and Likert scales measuring three symptom-specific health impacts: pain and discomfort, work impairment, and social impairment. Descriptive statistics and measures of central tendencies were computed for participant demographics and symptom data. RESULTS: Participants reported experiencing a mean of 21 symptoms (range 1-93); fatigue (79.0%), headache (55.3%), shortness of breath (55.3%) and difficulty concentrating (53.6%) were the most common. Symptoms often remitted and relapsed for extended periods of time (duration M = 112 days), longest lasting symptoms included the inability to exercise (M = 106.5 days), fatigue (M = 101.7 days) and difficulty concentrating, associated with memory impairment (M = 101.1 days). Participants reported extreme pressure at the base of the head, syncope, sharp or sudden chest pain, and "brain pressure" among the most distressing and impacting daily life. CONCLUSIONS: Post-acute sequelae of SARS-CoV-2 infection can be characterised by a wide range of symptoms, many of which cause moderate-to-severe distress and can hinder survivors' overall well-being. RELEVANCE TO CLINICAL PRACTICE: This study advances our understanding of the symptoms of PASC and their health impacts.

Gastroenterology Providers Spend an Additional 45-50 Minutes on Electronic Health Records Per Hour of Scheduled Time.

Nearly 50% of US physicians exhibit signs of burnout,1 a condition associated with dissatisfaction with work-life balance, substance abuse, reduced quality of care, and decreased productivity.2 Burnout and its consequences are also prevalent among physician assistants and nurse practitioners, collectively referred to as nonphysician providers.3.

Validity of International Classification of Diseases, Tenth Revision, Codes for Cirrhosis.

INTRODUCTION: Accurate identification of patients with cirrhosis is important for research using administrative databases. We aimed to examine the accuracy of several major ICD-10 codes for cirrhosis diagnosis in a large and diverse patient cohort; there is little existing research on this topic. METHODS: Using data from 3,396 patients with chronic liver disease (hepatitis B or C or nonalcoholic fatty liver disease) from 1 university and several community medical centers, we calculated sensitivity, specificity, positive predictive value (PPV), negative predictive value, and area under the receiver operating characteristic curve (AUROC) for several major ICD-10 codes for cirrhosis, which was verified by individual chart review. We performed a secondary validation in a general cohort of 1,560 randomly selected patients. RESULTS: While each of the individual study ICD-10 codes were specific (98.08-100%), none of the codes were sufficiently sensitive (0.27-55.70%). PPVs were high in the chronic liver disease cohort (88.41-100%) but lower in the general population (55.53-66.76%). The AUROC for having at least 1 code was higher (0.79) than any code alone (0.50-0.65). DISCUSSION/CONCLUSION: Individual ICD-10 codes are suboptimal for identifying patients with cirrhosis in the general patient population. We recommend conditioning ICD-10 code searches with a chronic liver disease diagnosis code and/or combining diagnostic codes to maximize performance.

A distinct symptom pattern emerges for COVID-19 long-haul: a nationwide study.

Long-haul COVID-19, also called post-acute sequelae of SARS-CoV-2 (PASC), is a new illness caused by SARS-CoV-2 infection and characterized by the persistence of symptoms. The purpose of this cross-sectional study was to identify a distinct and significant temporal pattern of PASC symptoms (symptom type and onset) among a nationwide sample of PASC survivors (n = 5652). The sample was randomly sorted into two independent samples for exploratory (EFA) and confirmatory factor analyses (CFA). Five factors emerged from the EFA: (1) cold and flu-like symptoms, (2) change in smell and/or taste, (3) dyspnea and chest pain, (4) cognitive and visual problems, and (5) cardiac symptoms. The CFA had excellent model fit (x2 = 513.721, df = 207, p < 0.01, TLI = 0.952, CFI = 0.964, RMSEA = 0.024). These findings demonstrate a novel symptom pattern for PASC. These findings can enable nurses in the identification of at-risk patients and facilitate early, systematic symptom management strategies for PASC.