Purpose Postoperative pancreatic fistulae (POPF) present a serious and life-threatening complication after pancreatic head resections (PD). Therefore, reliable risk stratification to identify those at risk is urgently needed. The aim of this study was to identify postoperative laboratory parameters for the prediction of POPF in the early postoperative period. Methods One hundred eighty-two patients who underwent PD from 2012 until 2017 were retrospectively analyzed. Multivariate logistic regression was performed using the GLM (general linear model) method for model building. Two nomograms were created based on the GLM models of postoperative day one and postoperative day one to five. A cohort of 48 patients operated between 2018 and 2019 served as internal validation. Results Clinically relevant pancreatic fistulae (CR-POPF) were present in 16% (n = 29) of patients. Patients with CR-POPF experienced significantly more insufficiencies of gastroenterostomies, delayed gastric emptying, and more extraluminal bleeding than patients without CR-POPF. Multivariate analysis revealed multiple postoperative predictive models, the best one including ASA, main pancreatic duct diameter, operation time, and serum lipase as well as leucocytes on day one. This model was able to predict CR-POPF with an accuracy of 90% and an AUC of 0.903. Two nomograms were created for easier use. Conclusion Clinically relevant fistula can be predicted using simple laboratory and clinical parameters. Not serum amylase, but serum lipase is an independent predictor of CR-POPF. Our simple nomograms may help in the identification of patients for early postoperative interventions.

Background The novel coronavirus (COVID-19) has presented a significant and urgent threat to global health and there has been a need to identify prognostic factors in COVID-19 patients. The aim of this study was to determine whether chest CT characteristics had any prognostic value in patients with COVID-19. Methods A retrospective analysis of COVID-19 patients who underwent a chest CT-scan was performed in four medical centers. The prognostic value of chest CT results was assessed using a multivariable survival analysis with the Cox model. The characteristics included in the model were the degree of lung involvement, ground glass opacities, nodular consolidations, linear consolidations, a peripheral topography, a predominantly inferior lung involvement, pleural effusion, and crazy paving. The model was also adjusted on age, sex, and the center in which the patient was hospitalized. The primary endpoint was 30-day in-hospital mortality. A second model used a composite endpoint of admission to an intensive care unit or 30-day in-hospital mortality. Results A total of 515 patients with available follow-up information were included. Advanced age, a degree of pulmonary involvement ≥ 50% (Hazard Ratio 2.25 [95% Cl: 1.378 to 3.671], p= 0.001), nodular consolidations and pleural effusions were associated with lower 30-day in-hospital survival rates. An exploratory subgroup analysis showed a 60.6% mortality rate in patients over 75 with ≥ 50% lung involvement on a CT-scan. Conclusions Chest CT findings such as the percentage of pulmonary involvement ≥ 50%, pleural effusion and nodular consolidation were strongly associated with 30-day mortality in COVID-19 patients. CT examinations are essential for the assessment of severe COVID-19 patients and their results must be considered when making care management decisions.

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