BackgroundThe determination of rivaroxaban and apixaban from serum samples of patients may be beneficial in specific clinical situations when additional blood sampling for plasma and thus the determination of factor Xa activity is not feasible or results are not plausible.Materials and methodsThe primary aim of this study was to compare the concentrations of rivaroxaban and apixaban in serum with those measured in plasma. Secondary aims were the performance of three different chromogenic methods and concentrations in patients on treatment with rivaroxaban 10 mg od (n = 124) or 20 mg od (n = 94) or apixaban 5 mg bid (n = 52) measured at different time.ResultsConcentrations of rivaroxaban and apixaban in serum were about 20–25% higher compared with plasma samples with a high correlation (r = 0·79775–0·94662) using all assays (all P < 0·0001). The intraclass correlation coefficients were about 0·90 for rivaroxaban and 0·55 for apixaban. Mean rivaroxaban concentrations were higher at 2 and 3 h compared with 1 and 12 h after administration measured from plasma and serum samples (all P-values < 0·05) and were not different between 1 vs. 12 h (plasma and serum).ConclusionsThe results indicate that rivaroxaban and apixaban concentrations can be determined specifically from serum samples.

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Purpose Surgical oncologists are frequently confronted with the question of expected long-term prognosis. The aim of this study was to apply machine learning algorithms to optimize survival prediction after oncological resection of gastroesophageal cancers. Methods Eligible patients underwent oncological resection of gastric or distal esophageal cancer between 2001 and 2020 at Heidelberg University Hospital, Department of General Surgery. Machine learning methods such as multi-task logistic regression and survival forests were compared with usual algorithms to establish an individual estimation. Results The study included 117 variables with a total of 1360 patients. The overall missingness was 1.3%. Out of eight machine learning algorithms, the random survival forest (RSF) performed best with a concordance index of 0.736 and an integrated Brier score of 0.166. The RSF demonstrated a mean area under the curve (AUC) of 0.814 over a time period of 10 years after diagnosis. The most important long-term outcome predictor was lymph node ratio with a mean AUC of 0.730. A numeric risk score was calculated by the RSF for each patient and three risk groups were defined accordingly. Median survival time was 18.8 months in the high-risk group, 44.6 months in the medium-risk group and above 10 years in the low-risk group. Conclusion The results of this study suggest that RSF is most appropriate to accurately answer the question of long-term prognosis. Furthermore, we could establish a compact risk score model with 20 input parameters and thus provide a clinical tool to improve prediction of oncological outcome after upper gastrointestinal surgery.