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Contusion expansion (CE) is a potentially treatable outcome predictor in traumatic brain injury (TBI), and a suitable end-point for hemostatic therapy trials. However, there is no consensus on the definition of clinically relevant CE, both in terms of measurement criteria (absolute vs. relative volume increase) and cutoff values. In light of this, the aim of this study was to assess the predictive abilities of different CE definitions on outcome. We performed a multi-center observational cohort study of adults with moderate-to-severe TBI treated in an intensive care unit. The exposure of interest was CE, defined as the absolute and relative volume change between the first and second computed tomography scan. The primary outcome was the Glasgow Outcome Scale (GOS) at 6–12 months post-injury, dichotomized into unfavorable (GOS ≤3) or favorable (GOS ≥4). The secondary outcome was all-cause mortality. In total, 798 patients were included, with a median duration of 7.0 h between the first and second CT scan. The median absolute and relative CE was 1.5 mL (interquartile range [IQR] 0.1–8.3 mL) and 100% (IQR 10–530%), respectively. Both CE forms were independently associated with unfavorable GOS. Absolute CE outperformed relative CE in predicting both unfavorable GOS (area under the curve [AUC]: 0.65 vs. 0.60, p = 0.002) and all-cause mortality (AUC: 0.66 vs. 0.60, p = 0.003). For dichotomized CE, absolute cutoffs of 1–10 mL yielded the best results. We conclude that absolute CE demonstrates stronger outcome correlation than relative CE. In studies focusing on lesion progression in TBI, it may be advantageous to use absolute CE as the primary outcome metric. For dichotomized outcomes, cutoffs between 1 and 10 mL are suggested, depending on the desired sensitivity-specificity balance., De två första författarna delar förstaförfattarskapet

BACKGROUND: While the Glasgow coma scale (GCS) is one of the strongest outcome predictors, the current classification of traumatic brain injury (TBI) as 'mild', 'moderate' or 'severe' based on this fails to capture enormous heterogeneity in pathophysiology and treatment response. We hypothesized that data-driven characterization of TBI could identify distinct endotypes and give mechanistic insights. METHODS: We developed an unsupervised statistical clustering model based on a mixture of probabilistic graphs for presentation (< 24 h) demographic, clinical, physiological, laboratory and imaging data to identify subgroups of TBI patients admitted to the intensive care unit in the CENTER-TBI dataset (N = 1,728). A cluster similarity index was used for robust determination of optimal cluster number. Mutual information was used to quantify feature importance and for cluster interpretation. RESULTS: Six stable endotypes were identified with distinct GCS and composite systemic metabolic stress profiles, distinguished by GCS, blood lactate, oxygen saturation, serum creatinine, glucose, base excess, pH, arterial partial pressure of carbon dioxide, and body temperature. Notably, a cluster with 'moderate' TBI (by traditional classification) and deranged metabolic profile, had a worse outcome than a cluster with 'severe' GCS and a normal metabolic profile. Addition of cluster labels significantly improved the prognostic precision of the IMPACT (International Mission for Prognosis and Analysis of Clinical trials in TBI) extended model, for prediction of both unfavourable outcome and mortality (both p < 0.001). CONCLUSIONS: Six stable and clinically distinct TBI endotypes were identified by probabilistic unsupervised clustering. In addition to presenting neurology, a profile of biochemical derangement was found to be an important distinguishing feature that was both biologically plausible and associated with outcome. Our work motivates refining current TBI classifications wi

Background: While the Glasgow coma scale (GCS) is one of the strongest outcome predictors, the current classification of traumatic brain injury (TBI) as'mild" 'moderate'or'severe' based on this fails to capture enormous heterogeneity in pathophysiology and treatment response. We hypothesized that data-driven characterization of TBl could identify distinct endotypes and give mechanistic insights. Methods: We developed an unsupervised statistical clustering model based on a mixture of probabilistic graphs for presentation (<24 h) demographic, clinical, physiological, laboratory and imaging data to identify subgroups of TBl patients admitted to the intensive care unit in the CENTER-TBI dataset (N= 1,728). A cluster similarity index was used for robust determination of optimal cluster number. Mutual information was used to quantify feature importance and for cluster interpretation. Results: Six stable endotypes were identified with distinct GCS and composite systemic metabolic stress profiles, distinguished by GCS, blood lactate, oxygen saturation, serum creatinine, glucose, base excess, pH, arterial partial pressure of carbon dioxide, and body temperature. Notably, a cluster with 'moderate'TBI (by traditional classification) and deranged metabolic profile, had a worse outcome than a cluster with 'severe'GCS and a normal metabolic profile. Addition of cluster labels significantly improved the prognostic precision of the IMPACT (International Mission for Prognosis and Analysis of Clinical trials in TBI) extended model, for prediction of both unfavourable outcome and mortality (both p <0.001). Conclusions: Six stable and clinically distinct TBI endotypes were identified by probabilistic unsupervised clustering. In addition to presenting neurology, a profile of biochemical derangement was found to be an important distinguishing feature that was both biologically plausible and associated with outcome. Our work motivates refining current TBI classifications with factor, QC 20220811

Background: While the Glasgow coma scale (GCS) is one of the strongest outcome predictors, the current classification of traumatic brain injury (TBI) as ‘mild’, ‘moderate’ or ‘severe’ based on this fails to capture enormous heterogeneity in pathophysiology and treatment response. We hypothesized that data-driven characterization of TBI could identify distinct endotypes and give mechanistic insights. Methods: We developed an unsupervised statistical clustering model based on a mixture of probabilistic graphs for presentation (< 24 h) demographic, clinical, physiological, laboratory and imaging data to identify subgroups of TBI patients admitted to the intensive care unit in the CENTER-TBI dataset (N = 1,728). A cluster similarity index was used for robust determination of optimal cluster number. Mutual information was used to quantify feature importance and for cluster interpretation. Results: Six stable endotypes were identified with distinct GCS and composite systemic metabolic stress profiles, distinguished by GCS, blood lactate, oxygen saturation, serum creatinine, glucose, base excess, pH, arterial partial pressure of carbon dioxide, and body temperature. Notably, a cluster with ‘moderate’ TBI (by traditional classification) and deranged metabolic profile, had a worse outcome than a cluster with ‘severe’ GCS and a normal metabolic profile. Addition of cluster labels significantly improved the prognostic precision of the IMPACT (International Mission for Prognosis and Analysis of Clinical trials in TBI) extended model, for prediction of both unfavourable outcome and mortality (both p < 0.001). Conclusions: Six stable and clinically distinct TBI endotypes were identified by probabilistic unsupervised clustering. In addition to presenting neurology, a profile of biochemical derangement was found to be an important distinguishing feature that was both biologically plausible and associated with outcome. Our work motivates refining current TBI classifications

Background: This is the study plan of the Karolinska NeuroCOVID study, a study of neurocognitive impairment after severe COVID-19, relating post-intensive care unit (ICU) cognitive and neurological deficits to biofluid markers and MRI. The COVID-19 pandemic has posed enormous health challenges to individuals and health- systems worldwide. An emerging feature of severe COVID-19 is that of temporary and extended neurocognitive impairment, exhibiting a myriad of symptoms and signs. The causes of this symptomatology have not yet been fully elucidated. Methods: In this study, we aim to investigate patients treated for severe COVID-19 in the ICU, as to describe and relate serum-, plasma- and cerebrospinal fluid-borne molecular and cellular biomarkers of immune activity, coagulopathy, cerebral damage, neuronal inflammation, and degeneration, to the temporal development of structural and functional changes within the brain as evident by serial MRI and extensive cognitive assessments at 3-12 months after ICU discharge. Results: To date, we have performed 51 3-month follow-up MRIs in the ICU survivors. Of these, two patients (similar to 4%) have had incidental findings on brain MRI findings requiring activation of the Incidental Findings Management Plan. Furthermore, the neuropsychological and neurological examinations have so far revealed varying and mixed patterns. Several patients expressed cognitive and/or mental concerns and fatigue, complaints closely related to brain fog. Conclusion: The study goal is to gain a better understanding of the pathological mechanisms and neurological consequences of this new disease, with a special emphasis on neurodegenerative and neuroinflammatory processes, in order to identify targets of intervention and rehabilitation.