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Preoperative risk assessment is essential for shared decision-making and adequate perioperative care. Common scores provide limited predictive quality and lack personalized information. The aim of this study was to create an interpretable machine-learning-based model to assess the patient's individual risk of postoperative mortality based on preoperative data to allow analysis of personal risk factors. After ethical approval, a model for prediction of postoperative in-hospital mortality based on preoperative data of 66,846 patients undergoing elective non-cardiac surgery between June 2014 and March 2020 was created with extreme gradient boosting. Model performance and the most relevant parameters were shown using receiver operating characteristic (ROC-) and precision-recall (PR-) curves and importance plots. Individual risks of index patients were presented in waterfall diagrams. The model included 201 features and showed good predictive abilities with an area under receiver operating characteristic (AUROC) curve of 0.95 and an area under precision-recall curve (AUPRC) of 0.109. The feature with the highest information gain was the preoperative order for red packed cell concentrates followed by age and c-reactive protein. Individual risk factors could be identified on patient level. We created a highly accurate and interpretable machine learning model to preoperatively predict the risk of postoperative in-hospital mortality. The algorithm can be used to identify factors susceptible to preoperative optimization measures and to identify risk factors influencing individual patient risk., (© 2023. The Author(s).)

Seizures are a common reason for calling emergency medical services. A lack of guidelines on prehospital treatment in Germany leads to high transportation rates and reduced confidence in decision making by professionals. Our aim was to investigate the reasons for hospitalization and evaluate their necessity. A retrospective analysis of all emergency medical services records in Munich, Germany was performed in order to examine the reasons for hospitalization of patients with seizures and to evaluate their trajectory following admission to a university hospital. 8882 records were analyzed with 415 records reporting seizures (4.9%). Primary endpoint was transportation to hospital. In 380 cases (92%) patients were transported, of which 177 patients (47%) had known epilepsy; 35 patients (8%) were left at scene. Older patients and patients with higher amounts of administered medication at the scene were hospitalized significantly more often (p = 0.032 and p = 0.004, respectively). Median hospital length of stay was 1 night [IQR 1-2]. In patients with out-of-hospital seizures, high hospital transportation rates were evident, most of which could be considered as not indicated. One possible reason is the lack of guidelines in Germany, which leads to uncertainty among medical staff. This results in distress for the patients, their caregivers and consequently high costs., (© 2022. The Author(s).)

Diaphragm weakness affects up to 60% of ventilated patients leading to muscle atrophy, reduction of muscle fiber force via muscle fiber injuries and prolonged weaning from mechanical ventilation. Electromagnetic stimulation of the phrenic nerve can induce contractions of the diaphragm and potentially prevent and treat loss of muscular function. Recommended safety distance of electromagnetic coils is 1 m. The aim of this study was to investigate the magnetic flux density in a typical intensive care unit (ICU) setting. Simulation of magnetic flux density generated by a butterfly coil was performed in a Berlin ICU training center with testing of potential disturbance and heating of medical equipment. Approximate safety distances to surrounding medical ICU equipment were additionally measured in an ICU training center in Bern. Magnetic flux density declined exponentially with advancing distance from the stimulation coil. Above a coil distance of 300 mm with stimulation of 100% power the signal could not be distinguished from the surrounding magnetic background noise. Electromagnetic stimulation of the phrenic nerve for diaphragm contraction in an intensive care unit setting seems to be safe and feasible from a technical point of view with a distance above 300 mm to ICU equipment from the stimulation coil., (© 2021. The Author(s).)

In a pandemic with a novel disease, disease-specific prognosis models are available only with a delay. To bridge the critical early phase, models built for similar diseases might be applied. To test the accuracy of such a knowledge transfer, we investigated how precise lethal courses in critically ill COVID-19 patients can be predicted by a model trained on critically ill non-COVID-19 viral pneumonia patients. We trained gradient boosted decision tree models on 718 (245 deceased) non-COVID-19 viral pneumonia patients to predict individual ICU mortality and applied it to 1054 (369 deceased) COVID-19 patients. Our model showed a significantly better predictive performance (AUROC 0.86 [95% CI 0.86-0.87]) than the clinical scores APACHE2 (0.63 [95% CI 0.61-0.65]), SAPS2 (0.72 [95% CI 0.71-0.74]) and SOFA (0.76 [95% CI 0.75-0.77]), the COVID-19-specific mortality prediction models of Zhou (0.76 [95% CI 0.73-0.78]) and Wang (laboratory: 0.62 [95% CI 0.59-0.65]; clinical: 0.56 [95% CI 0.55-0.58]) and the 4C COVID-19 Mortality score (0.71 [95% CI 0.70-0.72]). We conclude that lethal courses in critically ill COVID-19 patients can be predicted by a machine learning model trained on non-COVID-19 patients. Our results suggest that in a pandemic with a novel disease, prognosis models built for similar diseases can be applied, even when the diseases differ in time courses and in rates of critical and lethal courses.