The protective effect of tumor necrosis factor-alpha inhibitors in COVID-19 in patients with inflammatory rheumatic diseases compared to the general population: a comparison of two German registries

Objectives: To investigate, whether inflammatory rheumatic diseases (IRD) inpatients are at higher risk to develop a severe course of SARS-CoV-2 infections compared to the general population, data from the German COVID-19 registry for IRD patients and data from the Lean European Survey on SARS-CoV-2 (LEOSS) infected patients covering inpatients from the general population with SARS-CoV-2 infections were compared. Methods: 4310 (LEOSS registry) and 1139 cases (IRD registry) were collected in general. Data were matched for age and gender. From both registries, 732 matched inpatients (LEOSS registry: n = 366 and IRD registry: n = 366) were included for analyses in total. Results: Regarding the COVID-19 associated lethality, no significant difference between both registries was observed. Age > 65°years, chronic obstructive pulmonary disease, diabetes mellitus, rheumatoid arthritis, spondyloarthritis and the use of rituximab were associated with more severe courses of COVID-19. Female gender and the use of tumor necrosis factor-alpha inhibitors (TNF-I) were associated with a better outcome of COVID-19. Conclusion: Inflammatory rheumatic diseases (IRD) patients have the same risk factors for severe COVID-19 regarding comorbidities compared to the general population without any immune-mediated disease or immunomodulation. The use of rituximab was associated with an increased risk for severe COVID-19. On the other hand, the use of TNF-I was associated with less severe COVID-19 compared to the general population, which might indicate a protective effect of TNF-I against severe COVID-19 disease

The Life of Comedy after the Death of Plautus: The Palliata in Roman Life and Letters

This dissertation examines Roman comedy (comoedia palliata) and its influence from the stage onto the pages of Latin literature. I argue that the plays of Plautus and Terence (and increasingly also their Greek model, Menander) continued to be performed during the late Roman Republic and early Empire. Orators like Cicero impressed their audiences by tapping into fond memories of such performances, and from Catullus onwards, a new generation of authors experimented with ways of ‘updating’ the plays. One popular solution was to have allusions to comedy contrast with neighboring references to other attractions at the Roman festival, ranging from pantomime dances to gladiatorial combats. Especially under the Empire, authors like Horace, Propertius, Ovid, and Juvenal came to blend comedy with elements from darker dramas, such as tragedy or mime. Comedy thus emerged as an indispensible component in the creation of ‘new’ genres like Roman love elegy and Imperial satire, or the new Ovidian branch of Latin epic. In closing, I suggest that the vicarious experience provided by episodic television shows (as described by David Foster Wallace and Umberto Eco) can help explain this enduring popularity of Roman comedy: TV viewers and theatrical audiences both find themselves transported into a world whose rules are slightly easier to grasp than those of their own, and they fantasize about navigating their lives as efficiently as a comedic trickster

The Life of Comedy after the Death of Plautus: The Palliata in Roman Life and Letters

This dissertation examines Roman comedy (comoedia palliata) and its influence from the stage onto the pages of Latin literature. I argue that the plays of Plautus and Terence (and increasingly also their Greek model, Menander) continued to be performed during the late Roman Republic and early Empire. Orators like Cicero impressed their audiences by tapping into fond memories of such performances, and from Catullus onwards, a new generation of authors experimented with ways of ‘updating’ the plays. One popular solution was to have allusions to comedy contrast with neighboring references to other attractions at the Roman festival, ranging from pantomime dances to gladiatorial combats. Especially under the Empire, authors like Horace, Propertius, Ovid, and Juvenal came to blend comedy with elements from darker dramas, such as tragedy or mime. Comedy thus emerged as an indispensible component in the creation of ‘new’ genres like Roman love elegy and Imperial satire, or the new Ovidian branch of Latin epic. In closing, I suggest that the vicarious experience provided by episodic television shows (as described by David Foster Wallace and Umberto Eco) can help explain this enduring popularity of Roman comedy: TV viewers and theatrical audiences both find themselves transported into a world whose rules are slightly easier to grasp than those of their own, and they fantasize about navigating their lives as efficiently as a comedic trickster

Two-step trajectory visualization for robot-assisted spine radiofrequency ablations

Radiofrequency Ablations (RFAs) can be employed for the treatment of spine metastases. Instruments are therefor inserted through the vertebra’s pedicle into cancerous tissue within the vertebral body. This requires high precision during interventions. We present a two-step method to increase risk awareness during intervention planning and execution of manual and robot-assisted spine RFAs. Three medical experts evaluated our method and stated that it yields two advantages: First, improved visualizations for manual interventions and second, increased safety in hand-guided, robot-assisted setups

Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning

Purpose While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization. Methods We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16). Results The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 +/- 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface. Conclusion We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19

Update on the “Choosing Wisely” initiative in infectious diseases in Germany

Purpose!#!The Choosing Wisely!##!Methods!#!The recommendations of the DGI are part of the 'Klug entscheiden' initiative of the German Society of Internal Medicine (DGIM). Topics for the new items were suggested by members of the DGI, checked for scientific evidence and consented within the DGI and the DGIM before publication.!##!Results!#!The new recommendations are: (1) individuals with immune-suppression, advanced liver cirrhosis or renal insufficiency should receive a dual pneumococcal vaccination. (2) In case of positive blood cultures with Candida spp. thorough diagnostics and treatment should be initiated. (3) In case of suspected meningitis, adult patients should receive dexamethasone and antibiotics immediately after venipuncture for blood cultures and before potential imaging. (4) In case of suspected meningitis a CT scan before lumbar puncture should not be ordered-except for symptoms indicating high CSF pressure or focal brain pathology or in cases of severe immune-suppression. (5) In patients with suspected severe infections, a minimum of two pairs of blood cultures should be drawn using separate venipunctures prior to antibiotic therapy-regardless of body temperature. There is no need of a minimum time interval in between the blood draws.!##!Conclusion!#!Applying these new Choosing Wisel

Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning

Purpose!#!While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization.!##!Methods!#!We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16).!##!Results!#!The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface.!##!Conclusion!#!We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19