Hepatitis D virus (HDV) infection is highly prevalent in patients with chronic hepatitis B (CHB). AASLD guidelines recommend a risk-based screening approach. Our aim was to ascertain if the risk-based approach leads to appropriate HDV screening, identify targets to improve screening rates, and study HDV clinical burden. CHB patients screened for HDV from 01/2016 to 12/2021 were identified. Level of training and specialty of providers ordering HDV screening tests were determined. HDV seropositive (HDV+) patient charts were reviewed for the presence of individual risk factors per the AASLD guidelines to determine if they met screening criteria. The severity of liver disease at the time of HDV screening was compared between the HDV+ group and a matched (based on age, hepatitis B e antigen status, BMI and sex) HDV seronegative (HDV−) group. During the study period, 1444/11,190 CHB patients were screened for HDV. Most screening tests were ordered by gastroenterology (90.2%) specialists and attending physicians (80.5%). HDV+ rate was 88/1444 (6%), and 72 HDV+ patients had complete information for analysis. 18% of HDV+ patients would be missed by a risk-based screening approach due to unreported or negative risk factors (see Table ). A significantly higher number of HDV+ patients had developed significant fibrosis (p = 0.001) and cirrhosis (p < 0.01) by the time of screening than HDV− (n = 67) patients. In conclusion, targeted interventions are needed towards trainees and primary care clinics to improve screening rates. Current risk-based criteria do not appropriately screen for HDV. It is time for universal screening of HDV in CHB patients.

Introduction National Comprehensive Cancer Network (NCCN) 2019 Guidelines recommend universal germline (GL) testing for patients (pts) with pancreatic cancer (PC), given germline mutations (gMut) can occur at a similar rate irrespective of an individual’s family history of cancer. Molecular analysis of tumors in those with metastatic disease is also recommended. We aimed to determine rates of genetic testing at our institution, factors associated with testing, and outcomes of those tested. Methods Frequency of GL and somatic testing was examined in pts diagnosed with non-endocrine PC, with &gt;2 visits between June 2019 and June 2021 at the Mount Sinai Health System. The clinicopathological variables and treatment outcomes were also recorded. Results A total of 149 pts met the inclusion criteria. Sixty-six pts (44%) underwent GL testing: 42 (28%) at time of diagnosis with the remainder later in treatment. The rate of GL testing increased every year: 33% (2019), 44% (2020), and 61% (2021). A family history of cancer was the only variable associated with the decision to perform GL testing. Eight pts (12% of pts tested) had pathological gMut: BRCA1 (1), BRCA2 (1), ATM (2), PALB2 (2), NTHL1 (1), both CHEK2 and APC (1). Neither gBRCA pt received a PARP inhibitor, all except one received first-line platinum. Ninety-eight pts (65.7%) had molecular tumor testing (66.7% of patients with metastases). Two pts with BRCA2 somatic mut did not have GL testing. Three pts received targeted therapies. Conclusion Genetic testing based on provider discretion results in low rates of GL testing. Early results of genetic testing can have an impact on treatment decisions and trajectory of disease. Initiatives to increase testing are needed but must be feasible in real-world clinic settings.

Predictive models for key outcomes of coronavirus disease 2019 (COVID-19) can optimize resource utilization and patient outcome. We aimed to design and internally validate a web-based calculator predictive of hospitalization and length of stay (LOS) in a large cohort of COVID-19-positive patients presenting to the Emergency Department (ED) in a New York City health system. The study cohort consisted of consecutive adult (> 18 years) patients presenting to the ED of Mount Sinai Health System hospitals between March 2020 and April 2020, diagnosed with COVID-19. Logistic regression was utilized to construct predictive models for hospitalization and prolonged (> 3 days) LOS. Discrimination was evaluated using area under the receiver operating curve (AUC). Internal validation with bootstrapping was performed, and a web-based calculator was implemented. From 5859 patients, 65% were hospitalized. Independent predictors of hospitalization and extended LOS included older age, chronic kidney disease, elevated maximum temperature, and low minimum oxygen saturation (p < 0.001). Additional predictors of hospitalization included male sex, chronic obstructive pulmonary disease, hypertension, and diabetes. AUCs of 0.881 and 0.770 were achieved for hospitalization and LOS, respectively. Elevated levels of CRP, creatinine, and ferritin were key determinants of hospitalization and LOS (p < 0.05). A calculator was made available under the following URL: https:// covid 19-outco me-predi ction. shiny apps. io/ COVID 19_ Hospi taliz ation_ Calcu lator/. This study yielded internally validated models that predict hospitalization risk in COVID-19-positive patients, which can be used to optimize resource allocation. Predictors of hospitalization and extended LOS included older age, CKD, fever, oxygen desaturation, elevated C-reactive protein, creatinine, and ferritin.

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