Highlights SARS-CoV-2 serological assays have to be interpreted with caution and may need to be optimized to produce reliable results. we identified significant discrepancies in sensitivity and specificity between compared assays, especially when COVID-19 outpatients were tested. performance of the compared IgG assays was comparable, when cut-off values were optimized by ROC analysis. Assays for IgA and IgM demonstrated either a lack of specificity or sensitivity.

Background & aimThe association of circulating sphingosine-1-phosphate (S1P), a bioactive lipid involved in various cellular processes, and related metabolites such as sphinganine-1-phosphate (SA1P) and sphingosine (SPH) with mortality in patients with end-stage liver disease is investigated in the presented study. S1P as a bioactive lipid mediator, is involved in several cellular processes, however, in end-stage liver disease its role is not understood.MethodsThe study cohort consisted of 95 patients with end-stage liver disease and available information on one-year outcome. The median MELD (Model for end-stage liver disease) score was 12.41 (Range 6.43–39.63). The quantification of sphingolipids in citrated plasma specimen was performed after methanolic protein precipitation followed by hydrophilic interaction liquid chromatography and tandem mass spectrometric detection.ResultsS1P and SA1P displayed significant correlations with the MELD score. Patients with circulating S1P levels below the lowest tertile (110.68 ng/ml) showed the poorest one-year survival rate of only 57.1%, whereas one-year survival rate in patients with S1P plasma levels above 165.67 ng/ml was 93.8%. In a multivariate cox regression analysis including platelet counts, concentrations of hemoglobin and MELD score, S1P remained a significant predictor for three-month and one-year mortality.ConclusionsLow plasma S1P concentrations are highly significantly associated with prognosis in end-stage liver disease. This association is independent of the stage of liver disease. Further studies should be performed to investigate S1P, its role in the pathophysiology of liver diseases and its potential for therapeutic interventions.

BackgroundDelay in diagnosing sepsis results in potentially preventable deaths. Mainly due to their complexity or limited applicability, machine learning (ML) models to predict sepsis have not yet become part of clinical routines. For this reason, we created a ML model that only requires complete blood count (CBC) diagnostics.MethodsNon-intensive care unit (non-ICU) data from a German tertiary care centre were collected from January 2014 to December 2021. Patient age, sex, and CBC parameters (haemoglobin, platelets, mean corpuscular volume, white and red blood cells) were utilised to train a boosted random forest, which predicts sepsis with ICU admission. Two external validations were conducted using data from another German tertiary care centre and the Medical Information Mart for Intensive Care IV database (MIMIC-IV). Using the subset of laboratory orders also including procalcitonin (PCT), an analogous model was trained with PCT as an additional feature.FindingsAfter exclusion, 1,381,358 laboratory requests (2016 from sepsis cases) were available. The derived CBC model shows an area under the receiver operating characteristic (AUROC) of 0.872 (95% CI, 0.857–0.887) for predicting sepsis. External validations show AUROCs of 0.805 (95% CI, 0.787–0.824) and 0.845 (95% CI, 0.837–0.852) for MIMIC-IV. The model including PCT revealed a significantly higher performance (AUROC: 0.857; 95% CI, 0.836–0.877) than PCT alone (AUROC: 0.790; 95% CI, 0.759–0.821; p<0.001).InterpretationOur results demonstrate that routine CBC results could significantly improve diagnosis of sepsis when combined with ML. The CBC model can facilitate early sepsis prediction in non-ICU patients with high robustness in external validations. Its implementation in clinical decision support systems has strong potential to provide an essential time advantage and increase patient safety.FundingThe study was part of the AMPEL project (www.ampel.care), which is co-financed through public funds according to the budget decided by the Saxon State Parliament under the RL eHealthSax 2017/18 grant number 100331796.

Background The model of end-stage liver disease (MELD) score was established for the allocation of liver transplants. The score is based on the medical laboratory parameters: bilirubin, creatinine and the international normalized ratio (INR). A verification algorithm for the laboratory MELD diagnostic was established, and the results from the first six years were analyzed. Methods We systematically investigated the validity of 7,270 MELD scores during a six-year period. The MELD score was electronically requested by the clinical physician using the laboratory system and calculated and specifically validated by the laboratory physician in the context of previous and additional diagnostics. Results In 2.7% (193 of 7,270) of the cases, MELD diagnostics did not fulfill the specified quality criteria. After consultation with the sender, 2.0% (145) of the MELD scores remained invalid for different reasons and could not be reported to the transplant organization. No cases of deliberate misreporting were identified. In 34 cases the dialysis status had to be corrected and there were 24 cases of oral anticoagulation with impact on MELD diagnostics. Conclusion Our verification algorithm for MELD diagnostics effectively prevented invalid MELD results and could be adopted by transplant centers to prevent diagnostic errors with possible adverse effects on organ allocation.