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.

The aim of this study was to explore concentrations differences of soluble amyloid precursor protein (sAPP) α and β in blood plasma in patients with probable Alzheimer’s disease (AD) and cognitively healthy age-matched control subjects, as well as patients with behavioural variant frontotemporal dementia (bvFTD). Concentrations of sAPPα and β were measured using enzyme-linked immunosorbent assay technology in 80 patients with probable AD, 37 age-matched control subjects and 14 patients with bvFTD. Concentration differences were explored using parametric tests. Significantly decreased plasma concentrations in the AD group compared with both the control group and the bvFTD group were detected for sAPPβ (P=0.03 for both group comparisons), but not for sAPPα. The study provides a further piece of evidence in support of sAPPβ as a promising new biomarker of AD, which may potentially improve the diagnostic accuracy of existing markers and also enable a less invasive diagnostic workup. Further research is required to establish normal ranges and to replicate the results in independent cohorts including larger numbers of participants covering a wider spectrum of cognitive impairment.