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Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.
Die Covid-19-Pandemie hat das universitäre Leben seit dem Sommersemester 2020 auf den Kopf gestellt. Digitales Arbeiten von zu Hause aus, e-Learning und Video-Konferenzen prägen seither Forschung, Studium und Lehre. Wir haben Studierende, Lehrende, Mitarbeiterinnen und Mitarbeiter aus ganz unterschiedlichen Gebieten unseres Fachbereichs drei Fragen zu ihrem Arbeitsalltag zwischen Ausnahmezustand und „neuer Normalität“ gestellt.
1. Inwieweit hat die Pandemie Ihren (Arbeits-/Studien-) Alltag verändert?
2. Welche Rolle spielen dabei digitale Medien? (auch im Vergleich zur Zeit vor der Pandemie)
3. Für die Zeit „nach Corona“: Was nehmen Sie mit? Worauf freuen Sie sich?
Charts are used to measure relative success for a large variety of cultural items. Traditional music charts have been shown to follow self-organizing principles with regard to the distribution of item lifetimes, the on-chart residence times. Here we examine if this observation holds also for (a) music streaming charts (b) book best-seller lists and (c) for social network activity charts, such as Twitter hashtags and the number of comments Reddit postings receive. We find that charts based on the active production of items, like commenting, are more likely to be influenced by external factors, in particular by the 24 h day–night cycle. External factors are less important for consumption-based charts (sales, downloads), which can be explained by a generic theory of decision-making. In this view, humans aim to optimize the information content of the internal representation of the outside world, which is logarithmically compressed. Further support for information maximization is argued to arise from the comparison of hourly, daily and weekly charts, which allow to gauge the importance of decision times with respect to the chart compilation period.
Scores to identify patients at high risk of progression of coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), may become instrumental for clinical decision-making and patient management. We used patient data from the multicentre Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) and applied variable selection to develop a simplified scoring system to identify patients at increased risk of critical illness or death. A total of 1946 patients who tested positive for SARS-CoV-2 were included in the initial analysis and assigned to derivation and validation cohorts (n = 1297 and n = 649, respectively). Stability selection from over 100 baseline predictors for the combined endpoint of progression to the critical phase or COVID-19-related death enabled the development of a simplified score consisting of five predictors: C-reactive protein (CRP), age, clinical disease phase (uncomplicated vs. complicated), serum urea, and D-dimer (abbreviated as CAPS-D score). This score yielded an area under the curve (AUC) of 0.81 (95% confidence interval [CI]: 0.77–0.85) in the validation cohort for predicting the combined endpoint within 7 days of diagnosis and 0.81 (95% CI: 0.77–0.85) during full follow-up. We used an additional prospective cohort of 682 patients, diagnosed largely after the “first wave” of the pandemic to validate the predictive accuracy of the score and observed similar results (AUC for the event within 7 days: 0.83 [95% CI: 0.78–0.87]; for full follow-up: 0.82 [95% CI: 0.78–0.86]). An easily applicable score to calculate the risk of COVID-19 progression to critical illness or death was thus established and validated.
Investigation of the sympathetic regulation in delayed onset muscle soreness: results of an RCT
(2021)
Sports-related pain and injury is directly linked to tissue inflammation, thus involving the autonomic nervous system (ANS). In the present experimental study, we disable the sympathetic part of the ANS by applying a stellate ganglion block (SGB) in an experimental model of delayed onset muscle soreness (DOMS) of the biceps muscle. We included 45 healthy participants (female 11, male 34, age 24.16 ± 6.67 years [range 18–53], BMI 23.22 ± 2.09 kg/m2) who were equally randomized to receive either (i) an SGB prior to exercise-induced DOMS (preventive), (ii) sham intervention in addition to DOMS (control/sham), or (iii) SGB after the induction of DOMS (rehabilitative). The aim of the study was to determine whether and to what extent sympathetically maintained pain (SMP) is involved in DOMS processing. Focusing on the muscular area with the greatest eccentric load (biceps distal fifth), a significant time × group interaction on the pressure pain threshold was observed between preventive SGB and sham (p = 0.034). There was a significant effect on pain at motion (p = 0.048), with post hoc statistical difference at 48 h (preventive SGB Δ1.09 ± 0.82 cm VAS vs. sham Δ2.05 ± 1.51 cm VAS; p = 0.04). DOMS mediated an increase in venous cfDNA -as a potential molecular/inflammatory marker of DOMS- within the first 24 h after eccentric exercise (time effect p = 0.018), with a peak at 20 and 60 min. After 60 min, cfDNA levels were significantly decreased comparing preventive SGB to sham (unpaired t-test p = 0.008). At both times, 20 and 60 min, cfDNA significantly correlated with observed changes in PPT. The 20-min increase was more sensitive, as it tended toward significance at 48 h (r = 0.44; p = 0.1) and predicted the early decrease of PPT following preventive stellate blocks at 24 h (r = 0.53; p = 0.04). Our study reveals the broad impact of the ANS on DOMS and exercise-induced pain. For the first time, we have obtained insights into the sympathetic regulation of pain and inflammation following exercise overload. As this study is of a translational pilot character, further research is encouraged to confirm and specify our observations.