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Abstract Background The coronavirus disease 2019 (COVID-19) pandemic demands reliable prognostic models for estimating the risk of long COVID. We developed and validated a prediction model to estimate the probability of known common long COVID symptoms at least 60 days after acute COVID-19. Methods The prognostic model was built based on data from a multicentre prospective Swiss cohort study. Included were adult patients diagnosed with COVID-19 between February and December 2020 and treated as outpatients, at ward or intensive/intermediate care unit. Perceived long-term health impairments, including reduced exercise tolerance/reduced resilience, shortness of breath and/or tiredness (REST), were assessed after a follow-up time between 60 and 425 days. The data set was split into a derivation and a geographical validation cohort. Predictors were selected out of twelve candidate predictors based on three methods, namely the augmented backward elimination (ABE) method, the adaptive best-subset selection (ABESS) method and model-based recursive partitioning (MBRP) approach. Model performance was assessed with the scaled Brier score, concordance c statistic and calibration plot. The final prognostic model was determined based on best model performance. Results In total, 2799 patients were included in the analysis, of which 1588 patients were in the derivation cohort and 1211 patients in the validation cohort. The REST prevalence was similar between the cohorts with 21.6% (n = 343) in the derivation cohort and 22.1% (n = 268) in the validation cohort. The same predictors were selected with the ABE and ABESS approach. The final prognostic model was based on the ABE and ABESS selected predictors. The corresponding scaled Brier score in the validation cohort was 18.74%, model discrimination was 0.78 (95% CI: 0.75 to 0.81), calibration slope was 0.92 (95% CI: 0.78 to 1.06) and calibration intercept was −0.06 (95% CI: −0.22 to 0.09). Conclusion The proposed model was validated to identify COVID-19-infected patients at high risk for REST symptoms. Before implementing the prognostic model in daily clinical practice, the conduct of an impact study is recommended.

Abstract Background The sublingual microcirculation presumably exhibits disease-specific changes in function and morphology. Algorithm-based quantification of functional microcirculatory hemodynamic variables in handheld vital microscopy (HVM) has recently allowed identification of hemodynamic alterations in the microcirculation associated with COVID-19. In the present study we hypothesized that supervised deep machine learning could be used to identify previously unknown microcirculatory alterations, and combination with algorithmically quantified functional variables increases the model’s performance to differentiate critically ill COVID-19 patients from healthy volunteers. Methods Four international, multi-central cohorts of critically ill COVID-19 patients and healthy volunteers (n = 59/n = 40) were used for neuronal network training and internal validation, alongside quantification of functional microcirculatory hemodynamic variables. Independent verification of the models was performed in a second cohort (n = 25/n = 33). Results Six thousand ninety-two image sequences in 157 individuals were included. Bootstrapped internal validation yielded AUROC(CI) for detection of COVID-19 status of 0.75 (0.69–0.79), 0.74 (0.69–0.79) and 0.84 (0.80–0.89) for the algorithm-based, deep learning-based and combined models. Individual model performance in external validation was 0.73 (0.71–0.76) and 0.61 (0.58–0.63). Combined neuronal network and algorithm-based identification yielded the highest externally validated AUROC of 0.75 (0.73–0.78) (P

Abstract Background Necrotizing soft-tissue infections are infections with high mortality. The use of immunoglobulins within a combination therapy including broad-spectrum antibiotics has been debated. We assessed potential benefits of immunoglobulins and hypothesized that they were associated with a treatment benefit in a high-resource setting. Methods Patients with necrotizing soft-tissue infection hospitalized in the tertiary intensive care unit of the University Hospital of Zurich, Switzerland, between 2008 and 2020 were included retrospectively. The association between immunoglobulin administration and in-hospital survival, intensive care unit length of stay, the incidences of acute renal failure, acute respiratory distress syndrome and septic shock were analyzed. Results After adjustment for confounders, no difference for in-hospital survival (hazard ratio 2.20, 95% confidence interval [CI] 0.24–20.20, p = 0.5), intensive care unit length of stay (subhazard ratio [SHR] 0.90, CI 0.41–1.98, p = 0.8) and the development of acute respiratory distress syndrome (SHR 1.2, CI 0.36–4.03, p = 0.77) was observed in patients with or without immunoglobulin treatment. The Simplified Acute Physiology Score II, the risk of developing acute renal failure (SHR 2.86, CI 1.33–6.15, p = 0.01) and septic shock (SHR 1.86, CI 1.02–3.40, p = 0.04) was higher in patients treated with immunoglobulins, possibly reflecting a higher disease severity beyond measured confounders. Conclusions No clear evidence for a benefit of immunoglobulins in our cohort with consistent antibiotic use was found. Patients receiving immunoglobulins appeared more severely ill. Complementary to high treatment standards and appropriate antibiotics including beta lactams and protein synthesis inhibitors, immunoglobulins should be administered on a case-to-case basis, at least while more evidence from larger randomized controlled trials is missing.

Abstract Background In intensive care units (ICUs) treating patients with Coronavirus disease 2019 (COVID-19) invasive ventilation poses a high risk for aerosol and droplet formation. Surface contamination of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) or bacteria can result in nosocomial transmission. Methods Two tertiary care COVID-19 intensive care units treating 53 patients for 870 patient days were sampled after terminal cleaning and preparation for regular use to treat non-COVID-19 patients. Results A total of 176 swabs were sampled of defined locations covering both ICUs. No SARS-CoV-2 ribonucleic acid (RNA) was detected. Gram-negative bacterial contamination was mainly linked to sinks and siphons. Skin flora was isolated from most swabbed areas and Enterococcus faecium was detected on two keyboards. Conclusions After basic cleaning with standard disinfection measures no remaining SARS-CoV-2 RNA was detected. Bacterial contamination was low and mainly localised in sinks and siphons.

Abstract Background A higher-than-usual resistance to standard sedation regimens in COVID-19 patients suffering from acute respiratory distress syndrome (ARDS) has led to the frequent use of the second-line anaesthetic agent ketamine. Simultaneously, an increased incidence of cholangiopathies in mechanically ventilated patients receiving prolonged infusion of high-dose ketamine has been noted. Therefore, the objective of this study was to investigate a potential dose–response relationship between ketamine and bilirubin levels. Methods Post hoc analysis of a prospective observational cohort of patients suffering from COVID-19-associated ARDS between March 2020 and August 2021. A time-varying, multivariable adjusted, cumulative weighted exposure mixed-effects model was employed to analyse the exposure–effect relationship between ketamine infusion and total bilirubin levels. Results Two-hundred forty-three critically ill patients were included into the analysis. Ketamine was infused to 170 (70%) patients at a rate of 1.4 [0.9–2.0] mg/kg/h for 9 [4–18] days. The mixed-effects model revealed a positively correlated infusion duration–effect as well as dose–effect relationship between ketamine infusion and rising bilirubin levels (p