Cholestasis impairs liver regeneration following partial liver resection (PHx). Bile acid receptor farnesoid X-receptor (FXR) is a key mediator of liver regeneration. The effects of FXR agonist obeticholic acid (OCA) on liver (re)growth were therefore studied in cholestatic rats. Animals underwent sham surgery or reversible bile duct ligation (rBDL). PHx with concurrent internal biliary drainage was performed 7 days after rBDL. Animals were untreated or received OCA (10 mg/kg/day) per oral gavage from rBDL until sacrifice. After 7 days of OCA treatment, dry liver weight increased in the rBDL + OCA group, indicating OCA-mediated liver growth. Enhanced proliferation in the rBDL + OCA group prior to PHx concurred with a rise in Ki67-positive hepatocytes, elevated hepatic Ccnd1 and Cdc25b expression, and an induction of intestinal fibroblast growth factor 15 expression. Liver regrowth after PHx was initially stagnant in the rBDL + OCA group, possibly due to hepatomegaly prior to PHx. OCA increased hepatobiliary injury markers during BDL, which was accompanied by upregulation of the bile salt export pump. There were no differences in histological liver injury. In conclusion, OCA induces liver growth in cholestatic rats prior to PHx but exacerbates biliary injury during cholestasis, likely by forced pumping of bile acids into an obstructed biliary tree.

ObjectiveDevelop and validate models that predict mortality of patients diagnosed with COVID-19 admitted to the hospital.DesignRetrospective cohort study.SettingA multicentre cohort across 10 Dutch hospitals including patients from 27 February to 8 June 2020.ParticipantsSARS-CoV-2 positive patients (age ≥18) admitted to the hospital.Main outcome measures21-day all-cause mortality evaluated by the area under the receiver operator curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. The predictive value of age was explored by comparison with age-based rules used in practice and by excluding age from the analysis.Results2273 patients were included, of whom 516 had died or discharged to palliative care within 21 days after admission. Five feature sets, including premorbid, clinical presentation and laboratory and radiology values, were derived from 80 features. Additionally, an Analysis of Variance (ANOVA)-based data-driven feature selection selected the 10 features with the highest F values: age, number of home medications, urea nitrogen, lactate dehydrogenase, albumin, oxygen saturation (%), oxygen saturation is measured on room air, oxygen saturation is measured on oxygen therapy, blood gas pH and history of chronic cardiac disease. A linear logistic regression and non-linear tree-based gradient boosting algorithm fitted the data with an AUC of 0.81 (95% CI 0.77 to 0.85) and 0.82 (0.79 to 0.85), respectively, using the 10 selected features. Both models outperformed age-based decision rules used in practice (AUC of 0.69, 0.65 to 0.74 for age >70). Furthermore, performance remained stable when excluding age as predictor (AUC of 0.78, 0.75 to 0.81).ConclusionBoth models showed good performance and had better test characteristics than age-based decision rules, using 10 admission features readily available in Dutch hospitals. The models hold promise to aid decision-making during a hospital bed shortage.

Clinical and pathological diagnoses were compared in a prospective study of 145 dogs. A diagnostic work up had been performed on all dogs of which 36 (24.8%) died and 109 (75.2%) were euthanatized. In 119 dogs (82.1%) both a clinical and patholical diagnosis was made, in 20 dogs (13.8%) no pathological diagnosis could be made and in 6 dogs (4.1%) no clinical diagnosis was established. In the 119 dogs the agreement level between clinical and pathological diagnosis was scored by the referring veterinarian together with a pathologist. Total agreement was found in 61 cases (51.3%) and disagreement in 31 cases (26.0%). In the remaining cases (27=22.7%) the pathological diagnosis further specified the clinical diagnosis. Consecutive submission appeared difficult to achieve by the participating veterinarians. However, no major differences in agreement level was present between the veterinarian which succeeded in almost consecutive submissions and the other veterinarians. At necropsy 42 cases were diagnosed as neoplasia, of which 52.4% had been diagnosed clinically. As to infectious diseases 55.0% of these diseases diagnosed at necropsy had been diagnosed clinically. In about 20% of the cases the differences were of clinical significance according to the referring veterinarians. In addition, it was indicated by the clinicians that about 50% of the necropsies revealed findings which could amend future patient care. The results of the study stress the relevance of postmortem examination as crucial part of continuing education and of quality monitoring and assurance in veterinary medicine.

ObjectiveValidated clinical risk scores are needed to identify patients with COVID-19 at risk of severe disease and to guide triage decision-making during the COVID-19 pandemic. The objective of the current study was to evaluate the performance of early warning scores (EWS) in the ED when identifying patients with COVID-19 who will require intensive care unit (ICU) admission for high-flow-oxygen usage or mechanical ventilation.MethodsPatients with a proven SARS-CoV-2 infection with complete resuscitate orders treated in nine hospitals between 27 February and 30 July 2020 needing hospital admission were included. Primary outcome was the performance of EWS in identifying patients needing ICU admission within 24 hours after ED presentation.ResultsIn total, 1501 patients were included. Median age was 71 (range 19–99) years and 60.3% were male. Of all patients, 86.9% were admitted to the general ward and 13.1% to the ICU within 24 hours after ED admission. ICU patients had lower peripheral oxygen saturation (86.7% vs 93.7, p≤0.001) and had a higher body mass index (29.2 vs 27.9 p=0.043) compared with non-ICU patients. National Early Warning Score 2 (NEWS2) ≥ 6 and q-COVID Score were superior to all other studied clinical risk scores in predicting ICU admission with a fair area under the receiver operating characteristics curve of 0.740 (95% CI 0.696 to 0.783) and 0.760 (95% CI 0.712 to 0.800), respectively. NEWS2 ≥6 and q-COVID Score ≥3 discriminated patients admitted to the ICU with a sensitivity of 78.1% and 75.9%, and specificity of 56.3% and 61.8%, respectively.ConclusionIn this multicentre study, the best performing models to predict ICU admittance were the NEWS2 and the Quick COVID-19 Severity Index Score, with fair diagnostic performance. However, due to the moderate performance, these models cannot be clinically used to adequately predict the need for ICU admission within 24 hours in patients with SARS-CoV-2 infection presenting at the ED.