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Background: paediatric patients are vulnerable to blood loss and even a small loss of blood can be associated with severe shock. In emergency situations, a red blood cell (RBC) transfusion may become unavoidable, although it is associated with various risks. The aim of this trial was to identify independent risk factors for perioperative RBC transfusion in children undergoing surgery. Methods: to identify independent risk factors for perioperative RBC transfusion in children undergoing surgery and to access RBC transfusion rates and in-hospital outcomes (e.g., length of stay, mortality, and typical postoperative complication rates), a monocentric, retrospective, and observational study was conducted. Descriptive, univariate, and multivariate analyses were performed. Results: between 1 January 2010 and 31 December 2019, data from n = 14,248 cases were identified at the centre. Analysis revealed an RBC transfusion rate of 10.1% (n = 1439) in the entire cohort. The independent predictors of RBC transfusion were the presence of preoperative anaemia (p < 0.001; OR = 15.10 with preoperative anaemia and OR = 2.40 without preoperative anaemia), younger age (p < 0.001; ORs between 0.14 and 0.28 for children older than 0 years), female gender (p = 0.036; OR = 1.19 compared to male gender), certain types of surgery (e.g., neuro surgery (p < 0.001; OR = 10.14), vascular surgery (p < 0.001; OR = 9.93), cardiac surgery (p < 0.001; OR = 4.79), gynaecology (p = 0.014; OR = 3.64), visceral surgery (p < 0.001; OR = 2.48), and the presence of postoperative complications (e.g., sepsis (p < 0.001; OR = 10.16), respiratory dysfunction (p < 0.001; OR = 7.56), cardiovascular dysfunction (p < 0.001; OR = 4.68), neurological dysfunction (p = 0.029; OR = 1.77), and renal dysfunction (p < 0.001; OR = 16.17)). Conclusion: preoperative anaemia, younger age, female gender, certain types of surgery, and postoperative complications are independent predictors for RBC transfusion in children undergoing surgery. Future prospective studies are urgently required to identify, in detail, the potential risk factors and impact of RBC transfusion in children.
Background: SAMHD1 mediates resistance to anti-cancer nucleoside analogues, including cytarabine, decitabine, and nelarabine that are commonly used for the treatment of leukaemia, through cleavage of their triphosphorylated forms. Hence, SAMHD1 inhibitors are promising candidates for the sensitisation of leukaemia cells to nucleoside analogue-based therapy. Here, we investigated the effects of the cytosine analogue CNDAC, which has been proposed to be a SAMHD1 inhibitor, in the context of SAMHD1. Methods: CNDAC was tested in 13 acute myeloid leukaemia (AML) cell lines, in 26 acute lymphoblastic leukaemia (ALL) cell lines, ten AML sublines adapted to various antileukaemic drugs, 24 single cell-derived clonal AML sublines, and primary leukaemic blasts from 24 AML patients. Moreover, 24 CNDAC-resistant sublines of the AML cell lines HL-60 and PL-21 were established. The SAMHD1 gene was disrupted using CRISPR/Cas9 and SAMHD1 depleted using RNAi, and the viral Vpx protein. Forced DCK expression was achieved by lentiviral transduction. SAMHD1 promoter methylation was determined by PCR after treatment of genomic DNA with the methylation-sensitive HpaII endonuclease. Nucleoside (analogue) triphosphate levels were determined by LC-MS/MS. CNDAC interaction with SAMHD1 was analysed by an enzymatic assay and by crystallisation. Results: Although the cytosine analogue CNDAC was anticipated to inhibit SAMHD1, SAMHD1 mediated intrinsic CNDAC resistance in leukaemia cells. Accordingly, SAMHD1 depletion increased CNDAC triphosphate (CNDAC-TP) levels and CNDAC toxicity. Enzymatic assays and crystallisation studies confirmed CNDAC-TP to be a SAMHD1 substrate. In 24 CNDAC-adapted acute myeloid leukaemia (AML) sublines, resistance was driven by DCK (catalyses initial nucleoside phosphorylation) loss. CNDAC-adapted sublines displayed cross-resistance only to other DCK substrates (e.g. cytarabine, decitabine). Cell lines adapted to drugs not affected by DCK or SAMHD1 remained CNDAC sensitive. In cytarabine-adapted AML cells, increased SAMHD1 and reduced DCK levels contributed to cytarabine and CNDAC resistance. Conclusion: Intrinsic and acquired resistance to CNDAC and related nucleoside analogues are driven by different mechanisms. The lack of cross-resistance between SAMHD1/ DCK substrates and non-substrates provides scope for next-line therapies after treatment failure.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.