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Purpose of review Premature intracellular activation of pancreatic zymogens leads to the initiation of pancreatitis, which in up to 25% leads to chronic tissue destruction, exocrine and endocrine organ failure, and a moderate increased risk of pancreatic cancer development. Whereas in many cases, the trigger of organ damage is identified, diagnostic workup in a significant number of patients does not reveal the underlying etiology of pancreatic inflammation. In these cases, alterations in different pancreatic susceptibility genes have been described to be directly or indirectly involved in disease development. In this review, we want to give an update on the most important pancreatitis risk genes and their impact on clinical diagnostics and risk stratification as well as possible treatment options. Recent findings Genetic testing is not routinely implemented in the diagnostic workup of acute or chronic pancreatitis, as most genetic variations are not considered causative for pancreatitis development but confer increased susceptibility and genetic testing rarely changes disease management. However, in patients with recurrent pancreatitis episodes of unknown etiology after intensive diagnostic work-up, in patients with a family history of pancreatitis, relatives of patients with hereditary pancreatitis, and patients with disease onset at young age, genetic testing and counseling is recommended. Besides well-established susceptibility genes such as PRSS1, SPINK1, CPA1, and CFTR, additional genes such as TRPV6 and rare genetic alterations in established risk genes have been recently identified which significantly contribute to the risk of pancreatitis, involving different molecular mechanisms. Summary When genetic testing is considered, we propose screening at least for PRSS1, SPINK1, CPA1, and CFTR gene variants. The emergence of next-generation sequencing methods could also render larger gene panels possible and clinically meaningful to detect rare variants with high-risk phenotypes. Here we summarize, evaluate, and convey in the form of practical recommendations the current level of knowledge with respect to definition, etiology, and genetic diagnostics of all forms of inherited pancreatitis.

Purpose Identification of patients at risk of complicated or more severe COVID-19 is of pivotal importance, since these patients might require monitoring, antiviral treatment, and hospitalization. In this study, we prospectively evaluated the SACOV-19 score for its ability to predict complicated or more severe COVID-19. Methods In this prospective multicenter study, we included 124 adult patients with acute COVID-19 in three German hospitals, who were diagnosed in an early, uncomplicated stage of COVID-19 within 72 h of inclusion. We determined the SACOV-19 score at baseline and performed a follow-up at 30 days. Results The SACOV-19 score’s AUC was 0.816. At a cutoff of > 3, it predicted deterioration to complicated or more severe COVID-19 with a sensitivity of 94% and a specificity of 55%. It performed significantly better in predicting complicated COVID-19 than the random tree-based SACOV-19 predictive model, the CURB-65, 4C mortality, or qCSI scores. Conclusion The SACOV-19 score is a feasible tool to aid decision making in acute COVID-19.

Background Pulmonary embolism (PE) is an important complication of Coronavirus disease 2019 (COVID-19). COVID-19 is associated with respiratory impairment and a pro-coagulative state, rendering PE more likely and difficult to recognize. Several decision algorithms relying on clinical features and D-dimer have been established. High prevalence of PE and elevated Ddimer in patients with COVID-19 might impair the performance of common decision algorithms. Here, we aimed to validate and compare five common decision algorithms implementing age adjusted Ddimer, the GENEVA, and Wells scores as well as the PEGeD- and YEARS-algorithms in patients hospitalized with COVID-19. Methods In this single center study, we included patients who were admitted to our tertiary care hospital in the COVID-19 Registry of the LMU Munich. We retrospectively selected patients who received a computed tomography pulmonary angiogram (CTPA) or pulmonary ventilation/perfusion scintigraphy (V/Q) for suspected PE. The performances of five commonly used diagnostic algorithms (age-adjusted D-dimer, GENEVA score, PEGeD-algorithm, Wells score, and YEARS-algorithm) were compared. Results We identified 413 patients with suspected PE who received a CTPA or V/Q confirming 62 PEs (15%). Among them, 358 patients with 48 PEs (13%) could be evaluated for performance of all algorithms. Patients with PE were older and their overall outcome was worse compared to patients without PE. Of the above five diagnostic algorithms, the PEGeD- and YEARS-algorithms performed best, reducing diagnostic imaging by 14% and 15% respectively with a sensitivity of 95.7% and 95.6%. The GENEVA score was able to reduce CTPA or V/Q by 32.2% but suffered from a low sensitivity (78.6%). Age-adjusted D-dimer and Wells score could not significantly reduce diagnostic imaging. Conclusion The PEGeD- and YEARS-algorithms outperformed other tested decision algorithms and worked well in patients admitted with COVID-19. These findings need independent validation in a prospective study.