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Björn Ställberg,1 Karin Lisspers,1 Kjell Larsson,2 Christer Janson,3 Mario Müller,4 Mateusz Łuczko,5 Bine Kjøller Bjerregaard,6 Gerald Bacher,7 Björn Holzhauer,7 Pankaj Goyal,7 Gunnar Johansson1 1Department of Public Health and Caring Sciences, Family Medicine and Preventive Medicine, Uppsala University, Uppsala, Sweden; 2Integrative Toxicology, Karolinska Institutet, Stockholm, Sweden; 3Department of Medical Sciences: Respiratory, Allergy and Sleep Research, Uppsala University, Uppsala, Sweden; 4Department of Data Science and Advanced Analytics, IQVIA, Frankfurt Am Main, Germany; 5Department of Data Science and Advanced Analytics, IQVIA, Warsaw, Poland; 6Department of Real World Evidence Solutions, IQVIA, Copenhagen, Denmark; 7Department of Clinical Development and Analytics, Novartis Pharma AG, Basel, SwitzerlandCorrespondence: Björn StällbergDepartment of Public Health and Caring Sciences, Family Medicine and Preventive Medicine, Uppsala University, Box 564, Uppsala, SE-75122, SwedenTel +46-070-3149944Email b.stallberg@telia.comPurpose: Chronic obstructive pulmonary disease (COPD) exacerbations can negatively impact disease severity, progression, mortality and lead to hospitalizations. We aimed to develop a model that predicts a patient’s risk of hospitalization due to severe exacerbations (defined as COPD-related hospitalizations) of COPD, using Swedish patient level data.Patients and Methods: Patient level data for 7823 Swedish patients with COPD was collected from electronic medical records (EMRs) and national registries covering healthcare contacts, diagnoses, prescriptions, lab tests, hospitalizations and socioeconomic factors between 2000 and 2013. Models were created using machine-learning methods to predict risk of imminent exacerbation causing patient hospitalization due to COPD within the next 10 days. Exacerbations occurring within this period were considered as one event. Model performance was assessed using the Area under the Precision-Recall Curve (AUPRC). To compare performance with previous similar studies, the Area Under Receiver Operating Curve (AUROC) was also reported. The model with the highest mean cross validation AUPRC was selected as the final model and was in a final step trained on the entire training dataset.Results: The most important factors for predicting severe exacerbations were exacerbations in the previous six months and in whole history, number of COPD-related healthcare contacts and comorbidity burden. Validation on test data yielded an AUROC of 0.86 and AUPRC of 0.08, which was high in comparison to previously published attempts to predict COPD exacerbation.Conclusion: Our work suggests that clinically available information on patient history collected via automated retrieval from EMRs and national registries or directly during patient consultation can form the basis for future clinical tools to predict risk of severe COPD exacerbations.Keywords: COPD, machine learning, exacerbation, hospitalization

Background: The objectives of this study were to determine observer variation in the assessment of the histopathologic diagnosis of acute appendicitis. Methods: Two consultant pathologists independently evaluated 415 appendectomy specimens (set I). After a consensus conference at which the observers unified the criteria of the diagnosis of acute appendicitis, another 396 appendectomy specimens (set II) were evaluated. To calculate the intra-observer variation, one observer evaluated set II once more. Finally, using the consensus diagnosis as the final diagnosis, the diagnostic accuracy of the surgeon, the primary pathology report, and the two observer evaluations was calculated. Results: In set I no difference was found in the level of agreement on the diagnosis of acute appendicitis versus other diagnoses, and a κ value of 0.85 was obtained. In set II a κ value of 0.88 was obtained, which was not significantly different from the κ value in set I. However, the consensus conference did increase the level of agreement on the diagnosis of acute appendicitis. The intra-observer variation was calculated, and a κ value of 0.88 was obtained. The cases in which the observers disagreed on a positive diagnosis were cases of mild appendicitis. The question remains how many specimens would have shown these changes if all tissue had been developed for microscopy. The observers' diagnoses were the most reliable, and there was a significant decrease in the diagnostic accuracy compared with both the primary pathology report and the surgeon's opinion, the last one being the least accurate. Conclusions: These observations stress how important it is for the pathologist to discuss the diagnostic criteria of the diagnosis of acute appendicitis and for the surgeon to go back to the patient's record and look up the histopathologic diagnosis. [ABSTRACT FROM AUTHOR]

ABSTRACT- Five observers each examined 20 jaundiced patients, recording clinical signs and symptoms on a form which also gave the definitions used for the study. The balanced design of the study allowed examination for order effects, but none were found, except for a tendency for agreement on indicants with more than two categories to improve as the study progressed. Chance agreement was corrected by the use of kappa statistics which showed that 80% of the indicants showed agreement significantly greater than expected by chance. Certain indicants (dark urine, variability of jaundice, abdominal pain, character of liver edge and presence of spleen) showed no evidence of significant agreement, even though the indicants were frequently observed in both states - present or absent. The percentage of correct clinical diagnoses reached by the observers (without biochemical or any other information) varied between 65% and 84%. The consensus diagnosis was correct in 80% of cases. Agreement was higher if the diagnosis was simplified to a 'Medical' or 'Surgical' diagnosis, the observers' accuracy being between 90 and 100%. [ABSTRACT FROM AUTHOR]

Fourteen patients with liver cirrhosis received oral prednisone or prednisolone (0.3 mg per kg) randomised on two consecutive days. Serum prednisone and prednisolone were measured over the following four hours. Mean serum prednisolone concentration after oral prednisone decreased with impaired liver function estimated by galactose elimination capacity (r = 0.64, P less than 0.03). Mean serum prednisolone concentration after oral prednisone in the seven patients with severely impaired liver function was only 53% (P less than 0.05) of that observed in the seven patients with slightly impaired liver function. Conversely, mean serum prednisone concentration after oral prednisone in the patients with severely impaired liver function was 74% higher (P = 0.05) than in patients with slightly impaired liver function. Mean serum prednisolone after oral prednisolone was independent of liver function. As only prednisolone exerts glucocorticoid activity, our results indicate that prednisolone should be preferred to prednisone in the treatment of patients with impaired liver function. [ABSTRACT FROM PUBLISHER]