Optimizing Endoscopy Education in Gastroenterology Fellowship.

Education in endoscopy encompasses a wide breadth of topics and skills. Despite a shared interest in improving training in endoscopy, there is wide variation among programs, largely because of broad requirements put forth by the Accreditation Council on Graduate Medical Education. Historically, efforts to improve education in endoscopy were focused on numerics as a surrogate for competence. However, there is a role for "milestone" development goals to ensure trainees are on the right track to developing procedural competence. These milestones should encompass aspects of preprocedural assessment, intraprocedural technique, and postprocedural management and interpretation. Two important aspects of intraprocedural technique that are not universally emphasized among training programs but would be immensely beneficial to fellow education are (i) mucosal examination and (ii) device education. In this article, we will discuss the importance of developing the aforementioned skills and how we can approach a competency-based assessment of endoscopic skills during fellowship.

External validation of the COVID-19 4C mortality score in an urban United States cohort.

BACKGROUND: Identifying patients at risk for mortality from COVID-19 is crucial to triage, clinical decision-making, and the allocation of scarce hospital resources. The 4C Mortality Score effectively predicts COVID-19 mortality, but it has not been validated in a United States (U.S.) population. The purpose of this study is to determine whether the 4C Mortality Score accurately predicts COVID-19 mortality in an urban U.S. adult inpatient population. METHODS: This retrospective cohort study included adult patients admitted to a single-center, tertiary care hospital (Philadelphia, PA) with a positive SARS-CoV-2 PCR from 3/01/2020 to 6/06/2020. Variables were extracted through a combination of automated export and manual chart review. The outcome of interest was mortality during hospital admission or within 30 days of discharge. RESULTS: This study included 426 patients; mean age was 64.4 years, 43.4% were female, and 54.5% self-identified as Black or African American. All-cause mortality was observed in 71 patients (16.7%). The area under the receiver operator characteristic curve of the 4C Mortality Score was 0.85 (95% confidence interval, 0.79-0.89). CONCLUSIONS: Clinicians may use the 4C Mortality Score in an urban, majority Black, U.S. inpatient population. The derivation and validation cohorts were treated in the pre-vaccine era so the 4C Score may over-predict mortality in current patient populations. With stubbornly high inpatient mortality rates, however, the 4C Score remains one of the best tools available to date to inform thoughtful triage and treatment allocation.

Association of race/ethnicity and socioeconomic status with COVID-19 30-day mortality at a Philadelphia medical center using a retrospective cohort study.

COVID-19 has disproportionately affected low-income communities and people of color. Previous studies demonstrated that race/ethnicity and socioeconomic status (SES) are not independently correlated with COVID-19 mortality. The purpose of our study is to determine the effect of race/ethnicity and SES on COVID-19 30-day mortality in a diverse, Philadelphian population. This is a retrospective cohort study in a single-center tertiary care hospital in Philadelphia, PA. The study includes adult patients hospitalized with polymerase-chain-reaction-confirmed COVID-19 between March 1, 2020 and June 6, 2020. The primary outcome was a composite of COVID-19 death or hospice discharge within 30 days of discharge. The secondary outcome was intensive care unit (ICU) admission. The study included 426 patients: 16.7% died, 3.3% were discharged to hospice, and 20.0% were admitted to the ICU. Using multivariable analysis, race/ethnicity was not associated with the primary nor secondary outcome. In Model 4, age greater than 75 (odds ratio [OR]: 11.01; 95% confidence interval [CI]: 1.96-61.97) and renal disease (OR: 2.78; 95% CI: 1.31-5.90) were associated with higher odds of the composite primary outcome. Living in a "very-low-income area" (OR: 0.29; 95% CI: 0.12-0.71) and body mass index (BMI) 30-35 (OR: 0.24; 95% CI: 0.08-0.69) were associated with lower odds of the primary outcome. When controlling for demographics, SES, and comorbidities, race/ethnicity was not independently associated with the composite primary outcome. Very-low SES, as extrapolated from census-tract-level income data, was associated with lower odds of the composite primary outcome.

Comparing reliability of ICD-10-based COVID-19 comorbidity data to manual chart review, a retrospective cross-sectional study.

International Statistical Classification of Disease and Related Health Problems, 10th Revision codes (ICD-10) are used to characterize cohort comorbidities. Recent literature does not demonstrate standardized extraction methods. OBJECTIVE: Compare COVID-19 cohort manual-chart-review and ICD-10-based comorbidity data; characterize the accuracy of different methods of extracting ICD-10-code-based comorbidity, including the temporal accuracy with respect to critical time points such as day of admission. DESIGN: Retrospective cross-sectional study. MEASUREMENTS: ICD-10-based-data performance characteristics relative to manual-chart-review. RESULTS: Discharge billing diagnoses had a sensitivity of 0.82 (95% confidence interval [CI]: 0.79-0.85; comorbidity range: 0.35-0.96). The past medical history table had a sensitivity of 0.72 (95% CI: 0.69-0.76; range: 0.44-0.87). The active problem list had a sensitivity of 0.67 (95% CI: 0.63-0.71; range: 0.47-0.71). On day of admission, the active problem list had a sensitivity of 0.58 (95% CI: 0.54-0.63; range: 0.30-0.68)and past medical history table had a sensitivity of 0.48 (95% CI: 0.43-0.53; range: 0.30-0.56). CONCLUSIONS AND RELEVANCE: ICD-10-based comorbidity data performance varies depending on comorbidity, data source, and time of retrieval; there are notable opportunities for improvement. Future researchers should clearly outline comorbidity data source and validate against manual-chart-review.

Gender Disparity in Host Responses to Hepatitis B-Related Hepatocellular Carcinoma: A Case Series.

Chronic hepatitis B virus (HBV) infection is one of the most common causes of hepatocellular carcinoma (HCC), a malignant tumor with high mortality worldwide. One remarkable clinical feature of HBV-related HCC is that the risk of development is higher in males and postmenopausal females compared to other females. Increasing evidence also indicates that the prognosis of HBV-associated HCC may involve gender disparity, with females having more favorable outcomes. The proposed mechanism of this gender disparity is thought to be complex and multifactorial. Attributions have been made to gender differences in behavioral risk factors, host stress, immune response, psychology, metabolic risk factors, tumor biology, and hormonal factors. Gender disparities in hormonal factors and stress with consequent incited inflammation and hepatocarcinogenesis in HBV-related HCC is a particularly burgeoning area of investigation. Clarifying these mechanisms could provide insight into HBV-related HCC pathogenesis, and potentially provide a target for prevention and treatment of this disease. Reported herein is a case series involving two families affected by vertically transmitted chronic hepatitis B, longitudinally observed over multiple decades, with family members demonstrating discordant outcomes related to HCC, with worse outcomes among affected males. As a supplement to this case, we review the currently available literature on gender differences in outcomes from HBV-related HCC. In reporting this case series, we aim to add our important observation to the current literature and highlight the need for further research in the mechanisms involved in gender disparity in the prognosis of HBV-related HCC.

Development and Validation of a Web-Based Severe COVID-19 Risk Prediction Model.

BACKGROUND: Coronavirus disease 2019 (COVID-19) carries high morbidity and mortality globally. Identification of patients at risk for clinical deterioration upon presentation would aid in triaging, prognostication, and allocation of resources and experimental treatments. RESEARCH QUESTION: Can we develop and validate a web-based risk prediction model for identification of patients who may develop severe COVID-19, defined as intensive care unit (ICU) admission, mechanical ventilation, and/or death? METHODS: This retrospective cohort study reviewed 415 patients admitted to a large urban academic medical center and community hospitals. Covariates included demographic, clinical, and laboratory data. The independent association of predictors with severe COVID-19 was determined using multivariable logistic regression. A derivation cohort (n=311, 75%) was used to develop the prediction models. The models were tested by a validation cohort (n=104, 25%). RESULTS: The median age was 66 years (Interquartile range [IQR] 54-77) and the majority were male (55%) and non-White (65.8%). The 14-day severe COVID-19 rate was 39.3%; 31.7% required ICU, 24.6% mechanical ventilation, and 21.2% died. Machine learning algorithms and clinical judgment were used to improve model performance and clinical utility, resulting in the selection of eight predictors: age, sex, dyspnea, diabetes mellitus, troponin, C-reactive protein, D-dimer, and aspartate aminotransferase. The discriminative ability was excellent for both the severe COVID-19 (training area under the curve [AUC]=0.82, validation AUC=0.82) and mortality (training AUC= 0.85, validation AUC=0.81) models. These models were incorporated into a mobile-friendly website. CONCLUSIONS: This web-based risk prediction model can be used at the bedside for prediction of severe COVID-19 using data mostly available at the time of presentation.

Clinical Experience With IV Angiotensin II Administration: A Systematic Review of Safety.

OBJECTIVE: Angiotensin II is an endogenous hormone with vasopressor and endocrine activities. This is a systematic review of the safety of IV angiotensin II. DATA SOURCES: PubMed, Medline, Scopus, and Cochrane. STUDY SELECTION: Studies in which human subjects received IV angiotensin II were selected whether or not safety was discussed. DATA EXTRACTION: In total, 18,468 studies were screened by two reviewers and one arbiter. One thousand one hundred twenty-four studies, in which 31,281 participants received angiotensin II (0.5-3,780 ng/kg/min), were selected. Data recorded included number of subjects, comorbidities, angiotensin II dose and duration, pressor effects, other physiologic and side effects, and adverse events. DATA SYNTHESIS: The most common nonpressor effects included changes in plasma aldosterone, renal function, cardiac variables, and electrolytes. Adverse events were infrequent and included headache, chest pressure, and orthostatic symptoms. The most serious side effects were exacerbation of left ventricular failure in patients with congestive heart failure and bronchoconstriction. One patient with congestive heart failure died from refractory left ventricular failure. Refractory hypotensive shock was fatal in 55 of 115 patients treated with angiotensin II in case studies, cohort studies, and one placebo-controlled study. One healthy subject died after a pressor dose of angiotensin II was infused continuously for 6 days. No other serious adverse events attributable to angiotensin II were reported. Heterogeneity in study design prevented meta-analysis. CONCLUSION: Adverse events associated with angiotensin II were infrequent; however, exacerbation of asthma and congestive heart failure and one fatal cerebral hemorrhage were reported. This systematic review supports the notion that angiotensin II has an acceptable safety profile for use in humans.

Furosemide Stress Test and Biomarkers for the Prediction of AKI Severity.

Clinicians have access to limited tools that predict which patients with early AKI will progress to more severe stages. In early AKI, urine output after a furosemide stress test (FST), which involves intravenous administration of furosemide (1.0 or 1.5 mg/kg), can predict the development of stage 3 AKI. We measured several AKI biomarkers in our previously published cohort of 77 patients with early AKI who received an FST and evaluated the ability of FST urine output and biomarkers to predict the development of stage 3 AKI (n=25 [32.5%]), receipt of RRT (n=11 [14.2%]), or inpatient mortality (n=16 [20.7%]). With an area under the curve (AUC)±SEM of 0.87±0.09 (P<0.0001), 2-hour urine output after FST was significantly better than each urinary biomarker tested in predicting progression to stage 3 (P<0.05). FST urine output was the only biomarker to significantly predict RRT (0.86±0.08; P=0.001). Regardless of the end point, combining FST urine output with individual biomarkers using logistic regression did not significantly improve risk stratification (ΔAUC, P>0.10 for all). When FST urine output was assessed in patients with increased biomarker levels, the AUC for progression to stage 3 improved to 0.90±0.06 and the AUC for receipt of RRT improved to 0.91±0.08. Overall, in the setting of early AKI, FST urine output outperformed biochemical biomarkers for prediction of progressive AKI, need for RRT, and inpatient mortality. Using a FST in patients with increased biomarker levels improves risk stratification, although further research is needed.