Radiological features of brain hemorrhage through automated segmentation from computed tomography in stroke and traumatic brain injury.

Introduction: Radiological assessment is necessary to diagnose spontaneous intracerebral hemorrhage (ICH) and traumatic brain injury intracranial hemorrhage (TBI-bleed). Artificial intelligence (AI) deep learning tools provide a means for decision support. This study evaluates the hemorrhage segmentations produced from three-dimensional deep learning AI model that was developed using non-contrast computed tomography (CT) imaging data external to the current study. Methods: Non-contrast CT imaging data from 1263 patients were accessed across seven data sources (referred to as sites) in Norway and Sweden. Patients were included based on ICH, TBI-bleed, or mild TBI diagnosis. Initial non-contrast CT images were available for all participants. Hemorrhage location frequency maps were generated. The number of estimated haematoma clusters was correlated with the total haematoma volume. Ground truth expert annotations were available for one ICH site; hence, a comparison was made with the estimated haematoma volumes. Segmentation volume estimates were used in a receiver operator characteristics (ROC) analysis for all samples (i.e., bleed detected) and then specifically for one site with few TBI-bleed cases. Results: The hemorrhage frequency maps showed spatial patterns of estimated lesions consistent with ICH or TBI-bleed presentations. There was a positive correlation between the estimated number of clusters and total haematoma volume for each site (correlation range: 0.45-0.74; each p-value < 0.01) and evidence of ICH between-site differences. Relative to hand-drawn annotations for one ICH site, the VIOLA-AI segmentation mask achieved a median Dice Similarity Coefficient of 0.82 (interquartile range: 0.78 and 0.83), resulting in a small overestimate in the haematoma volume by a median of 0.47 mL (interquartile range: 0.04 and 1.75 mL). The bleed detection ROC analysis for the whole sample gave a high area-under-the-curve (AUC) of 0.92 (with sensitivity and specificity of 83.28% and 95.41%); however, when considering only the mild head injury site, the TBI-bleed detection gave an AUC of 0.70. Discussion: An open-source segmentation tool was used to visualize hemorrhage locations across multiple data sources and revealed quantitative hemorrhage site differences. The automated total hemorrhage volume estimate correlated with a per-participant hemorrhage cluster count. ROC results were moderate-to-high. The VIOLA-AI tool had promising results and might be useful for various types of intracranial hemorrhage.

[Metformin-associated lactic acidosis]. / Metforminassosiert laktacidose.

BACKGROUND: Metformin accumulation is associated with lactic acidosis and haemodynamic instability. CASE PRESENTATION: A woman in her seventies with diabetes, renal failure and hypertension presented unresponsive with severe acidosis, lactataemia, bradycardia and hypotension. After the initial survey, hypotension and bradycardia were noted before she went into cardiac arrest. After resuscitation and intubation, she was moved to the intensive care unit for dialysis and supportive care. After seven hours of dialysis, her hypotension persisted despite treatment with high levels of aminopressors. Methylene blue was given, and within hours the haemodynamic situation stabilised. She was successfully extubated the next day and has fully recovered. INTERPRETATION: Methylene blue might be a valuable adjunct to dialysis in patients with metformin accumulation and lactic acidosis where other vasopressors cannot provide adequate peripheral vascular resistance.

International Survey on Extracorporeal Membrane Oxygenation Transport.

Extracorporeal membrane oxygenation (ECMO) is a lifesaving therapy for severe respiratory and circulatory failure. It is best performed in high-volume centers to optimize resource utilization and outcomes. Regionalization of ECMO might require the implementation of therapy before and during transfer to the high-volume center. The aim of this international survey was to describe the manner in which interhospital ECMO transport care is organized at experienced centers. Fifteen mobile ECMO centers from nine countries participated in this survey. Seven (47%) of them operated under the "Hub-and-Spoke" model. Transport team composition varies from three to nine members, with at least one ECMO specialist (i.e., nurse or perfusionist) participating in all centers, although intensivists and surgeons were present in 69% and 50% of the teams, respectively. All centers responded that the final decision to initiate ECMO is multidisciplinary and made bedside at the referring hospital. Most centers (75%) have a quality control system; all teams practice simulation and water drills. Considering the variability in ECMO transport teams among experienced centers, continuous education, training and quality control within each organization itself are necessary to avoid adverse events and maintain a low mortality rate. A specific international ECMO Transport platform to share data, benchmark outcomes, promote standardization, and provide quality control is required.