Amantadine for NeuroenhaNcement in acutE patients Study - a protocol for a prospective pilot proof of concept phase IIb study in intensive and intermediate care unit patients (ANNES)

Abstract Background Persisting coma is a common complication in (neuro)intensive care in neurological disease such as acute ischemic stroke, intracerebral hemorrhage or subarachnoid hemorrhage. Amantadine acts as a nicotinic receptor antagonist, dopamine receptor agonist and non-competitive N-Methyl-D-aspartate receptor antagonist. Amantadine is a long-known drug, originally approved for treatment of influenza A and Parkinson`s Disease. It has been proven effective in improving vigilance after traumatic brain injury. The underlying mechanisms remain largely unknown, albeit anti-glutamatergic and dopaminergic effects might be most relevant. With limited evidence of amantadine efficacy in non-traumatic pathologies, the aim of our study is to assess the effects of amantadine for neuroenhancement in non-traumatic neurointensive patients with persisting coma. Methods An investigator-initiated, monocenter, phase IIb proof of concept open-label pilot study will be carried out. Based on the Simon design, 43 adult (neuro)intensive care patients who meet the clinical criteria of persisting coma not otherwise explained and < 8 points on the Glasgow Coma Scale (GCS) will be recruited. Amantadine will be administered intravenously for five days at a dosage of 100 mg bid. The primary endpoint is an improvement of at least 3 points on the GCS. If participants present as non-responders (increase < 3 points or decrease on the GCS) within the first 48 h, the dosage will be doubled from day three to five. Secondary objectives aim to demonstrate that amantadine improves vigilance via alternative scales. Furthermore, the incidence of adverse events will be investigated and electroencephalography (EEG) will be recorded at baseline and end of treatment. Discussion The results of our study will help to systematically assess the clinical utility of amantadine for treatment of persisting coma in non-traumatic brain injury. We expect that, in the face of only moderate treatment risk, a relevant number of patients will benefit from amantadine medication by improved vigilance (GCS increase of at least 3 points) finally leading to a better rehabilitation potential and improved functional neurological outcome. Further, the EEG data will allow evaluation of brain network states in relation to vigilance and potentially outcome prediction in this study cohort. Trial Registration NCT05479032

Distinct immunological signatures discriminate severe COVID-19 from non-SARS-CoV-2-driven critical pneumonia

Immune profiling of COVID-19 patients has identified numerous alterations in both innate and adaptive immunity. However, whether those changes are specific to SARS-CoV-2 or driven by a general inflammatory response shared across severely ill pneumonia patients remains unknown. Here, we compared the immune profile of severe COVID-19 with non-SARS-CoV-2 pneumonia ICU patients using longitudinal, high-dimensional single-cell spectral cytometry and algorithm-guided analysis. COVID-19 and non-SARS-CoV-2 pneumonia both showed increased emergency myelopoiesis and displayed features of adaptive immune paralysis. However, pathological immune signatures suggestive of T cell exhaustion were exclusive to COVID-19. The integration of single-cell profiling with a predicted binding capacity of SARS-CoV-2 peptides to the patients' HLA profile further linked the COVID-19 immunopathology to impaired virus recognition. Toward clinical translation, circulating NKT cell frequency was identified as a predictive biomarker for patient outcome. Our comparative immune map serves to delineate treatment strategies to interfere with the immunopathologic cascade exclusive to severe COVID-19

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort

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

Fever and hypothermia represent two populations of sepsis patients and are associated with outside temperature

Background!#!Fever and hypothermia have been observed in septic patients. Their influence on prognosis is subject to ongoing debates.!##!Methods!#!We did a secondary analysis of a large clinical dataset from a quality improvement trial. A binary logistic regression model was calculated to assess the association of the thermal response with outcome and a multinomial regression model to assess factors associated with fever or hypothermia.!##!Results!#!With 6542 analyzable cases we observed a bimodal temperature response characterized by fever or hypothermia, normothermia was rare. Hypothermia and high fever were both associated with higher lactate values. Hypothermia was associated with higher mortality, but this association was reduced after adjustment for other risk factors. Age, community-acquired sepsis, lower BMI and lower outside temperatures were associated with hypothermia while bacteremia and higher procalcitonin values were associated with high fever.!##!Conclusions!#!Septic patients show either a hypothermic or a fever response. Whether hypothermia is a maladaptive response, as indicated by the higher mortality in hypothermic patients, or an adaptive response in patients with limited metabolic reserves under colder environmental conditions, remains an open question. Trial registration The original trial whose dataset was analyzed was registered at ClinicalTrials.gov (NCT01187134) on August 23, 2010, the first patient was included on July 1, 2011

Epidemiology of surgery associated acute kidney injury (EPIS-AKI): a prospective international observational multi-center clinical study

Purpose: The incidence, patient features, risk factors and outcomes of surgery-associated postoperative acute kidney injury (PO-AKI) across different countries and health care systems is unclear. Methods: We conducted an international prospective, observational, multi-center study in 30 countries in patients undergoing major surgery (&gt; 2-h duration and postoperative intensive care unit (ICU) or high dependency unit admission). The primary endpoint was the occurrence of PO-AKI within 72&nbsp;h of surgery defined by the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Secondary endpoints included PO-AKI severity and duration, use of renal replacement therapy (RRT), mortality, and ICU and hospital length of stay. Results: We studied 10,568 patients and 1945 (18.4%) developed PO-AKI (1236 (63.5%) KDIGO stage 1500 (25.7%) KDIGO stage 2209 (10.7%) KDIGO stage 3). In 33.8% PO-AKI was persistent, and 170/1945 (8.7%) of patients with PO-AKI received RRT in the ICU. Patients with PO-AKI had greater ICU (6.3% vs. 0.7%) and hospital (8.6% vs. 1.4%) mortality, and longer ICU (median 2 (Q1-Q3, 1-3) days vs. 3 (Q1-Q3, 1-6) days) and hospital length of stay (median 14 (Q1-Q3, 9-24) days vs. 10 (Q1-Q3, 7-17) days). Risk factors for PO-AKI included older age, comorbidities (hypertension, diabetes, chronic kidney disease), type, duration and urgency of surgery as well as intraoperative vasopressors, and aminoglycosides administration. Conclusion: In a comprehensive multinational study, approximately one in five patients develop PO-AKI after major surgery. Increasing severity of PO-AKI is associated with a progressive increase in adverse outcomes. Our findings indicate that PO-AKI represents a significant burden for health care worldwide