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Modelling of interflows in soils: a review. This review sums up the different schemes in hydrological modelling. Moreover, it underlines how the interflows are taken into account and draws the first lines to improve their physically based representation. In the mood of the change in agricultural practices, especially concerning the conventional tillage reduction in support of conservational tillage, it seems to be necessary to propose a better description of decisive parameters like horizontal hydraulic conductivity. More precisely, the dependence of this parameter with the water retention curve has to be described in order to represent fluxes at the plot scale and at the watershed scale. Under these circumstances, an approach with in situ measurements and physically based modelling is suggested.

Hydrology: a partner of geomorphopedology in the scope of a scale-through environmental management. Unsaturated soil and subsoil are often called "critical zone" considering their major interfacing role in our environment. Dealing with solute transfer or water and soil conservation, hydrologic research relies on pedologic descriptions. It is the case from micro to macro scale analysis. Hydrodynamic parameters are derived from pedologic information on soil. They allow hydrologists to quantify and spatially describe the dynamic exchanges between water, soil, crops and atmosphere. The modelling of water and solute transfer through soil and vadose zone also needs them. The paper presents some research highlights on soil behaviour, hydrological modelling and forecasting under climate change. Erosion is another major topic. Soil is a poorly renewable resource. Soil conservation and soft hydraulic management in watersheds deserve more attention. They can help limiting nutrient and sediment transfer to surface water. Again, pedologic information is the starting point of conceptualization and modelling. Furthermore, the Soil Map of Belgium includes information on geomorphology and landscape descriptions which date from decades. They are of first importance to calibrate and validate detachment, transport and sedimentation models. In the current context of high environmental concern, it is demonstrated how hydrology and pedology have to be partners in order to deal with such major issues.

In population pharmacokinetic (PK) models, interindividual variability is explained by implementation of covariates in the model. The widely used forward stepwise selection method is sensitive to bias, which may lead to an incorrect inclusion of covariates. Alternatives, such as the full fixed effects model, reduce this bias but are dependent on the chosen implementation of each covariate. As the correct functional forms are unknown, this may still lead to an inaccurate selection of covariates. Machine learning (ML) techniques can potentially be used to learn the optimal functional forms for implementing covariates directly from data. A recent study suggested that using ML resulted in an improved selection of influential covariates. However, how do we select the appropriate functional form for including these covariates? In this work, we use SHapley Additive exPlanations (SHAP) to infer the relationship between covariates and PK parameters from ML models. As a case‐study, we use data from 119 patients with hemophilia A receiving clotting factor VIII concentrate peri‐operatively. We fit both a random forest and a XGBoost model to predict empirical Bayes estimated clearance and central volume from a base nonlinear mixed effects model. Next, we show that SHAP reveals covariate relationships which match previous findings. In addition, we can reveal subtle effects arising from combinations of covariates difficult to obtain using other methods of covariate analysis. We conclude that the proposed method can be used to extend ML‐based covariate selection, and holds potential as a complete full model alternative to classical covariate analyses. [ABSTRACT FROM AUTHOR]

Nonlinear mixed effect (NLME) models are the gold standard for the analysis of patient response following drug exposure. However, these types of models are complex and time‐consuming to develop. There is great interest in the adoption of machine‐learning methods, but most implementations cannot be reliably extrapolated to treatment strategies outside of the training data. In order to solve this problem, we propose the deep compartment model (DCM), a combination of neural networks and ordinary differential equations. Using simulated datasets of different sizes, we show that our model remains accurate when training on small data sets. Furthermore, using a real‐world data set of patients with hemophilia A receiving factor VIII concentrate while undergoing surgery, we show that our model more accurately predicts a priori drug concentrations compared to a previous NLME model. In addition, we show that our model correctly describes the changing drug concentration over time. By adopting pharmacokinetic principles, the DCM allows for simulation of different treatment strategies and enables therapeutic drug monitoring. [ABSTRACT FROM AUTHOR]

Objective: To investigate prevalence and recovery of olfactory dysfunction (OD) in COVID‐19 patients according to the disease severity. Methods: From 22 March to 3 June 2020, 2581 COVID‐19 patients were identified from 18 European hospitals. Epidemiological and clinical data were extracted at baseline and within the 2‐month post‐infection. Results: The prevalence of OD was significantly higher in mild form (85.9%) compared with moderate‐to‐critical forms (4.5–6.9%; P = 0.001). Of the 1916 patients with OD, 1363 completed the evaluations (71.1%). A total of 328 patients (24.1%) did not subjectively recover olfaction 60 days after the onset of the dysfunction. The mean duration of self‐reported OD was 21.6 ± 17.9 days. Objective olfactory evaluations identified hyposmia/anosmia in 54.7% and 36.6% of mild and moderate‐to‐critical forms, respectively (P = 0.001). At 60 days and 6 months, 15.3% and 4.7% of anosmic/hyposmic patients did not objectively recover olfaction, respectively. The higher baseline severity of objective olfactory evaluations was strongly predictive of persistent OD (P < 0.001). Conclusion: OD is more prevalent in mild COVID‐19 forms than in moderate‐to‐critical forms. OD disappeared in 95% of patients regarding objective olfactory evaluations at 6 months. [ABSTRACT FROM AUTHOR]

SUMMARY: Objectives: This study aims at identifying factors that disciplines consider when diagnosing and reporting transfusion‐associated circulatory overload ('TACO'). Background: TACO is a clinical diagnosis based mainly on subjective factors. Therefore, TACO could be an underreported complication of blood transfusion. Methods: A survey was conducted among critical care physicians, anaesthesiologists, haematologists, transfusion medicine physicians and haemovigilance officers using case vignettes and a questionnaire. Factors that may affect diagnosing TACO were investigated using conjoint analysis. A positive B‐coefficient indicates a positive preference for diagnosing TACO. Participants rated factors influencing reporting TACO on a 0‐ to 100‐point scale. Results: One hundred and seven surveys were returned (62%). Vignettes showed preferences in favour of diagnosing TACO with the onset of symptoms within 2 h [β 0·4(−0·1–1·0)], positive fluid balance [β 0·9(0·4–1·5)] and history of renal failure [β 0·6(0·1–1·2)]. Compared with transfusion of a single unit of red blood cells (RBC), respondents showed a preference for diagnosing TACO following a single unit of solvent/detergent (S/D) plasma or pooled platelet concentrate (PPC) [β 0·3(−0·2–0·7) resp. 0·5(−0·1–1·2)]. Multiple transfusion (6 RBC + 4 S/D plasma) was a strong preference for diagnosing TACO compared to 1 RBC and 1 S/D plasma [β 0·3(−0·8–1·3)]. Respondents did not fully take into account new hypertension and tachycardia when reporting TACO [median 70 (IQR 50–80) resp. 60 (IQR 50–80)]. No differences were observed between disciplines involved. Conclusion: When diagnosing and reporting TACO, physicians and haemovigilance officers do consider known risk factors for TACO. Reporting could be improved by increasing the awareness of haemodynamic variables in future education programmes. [ABSTRACT FROM AUTHOR]

Introduction: The bleeding assessment tool (BAT) has been developed to standardize and interpret bleeding history for mild bleeding disorders. However, a critical appraisal addressing the quality and results of validation studies is lacking. Aim: We performed a systematic review of diagnostic studies assessing the performance of the BAT in patients referred for evaluation of bleeding symptoms. Methods: The electronic database PubMed was searched from inception through July 27, 2017. Eligible publications were original studies that assessed and validated the diagnostic accuracy of bleeding questionnaires for identification of adults with mild bleeding disorders. For each study, sensitivity, specificity and diagnostic odds ratio (DOR) were calculated. Quality was assessed using the Quality Assessment of Diagnostic studies‐2 tool. To assess the influence of specific study characteristics on DOR, univariate meta‐regression analyses were performed. Results: Nine studies were included. Five studies investigating the ISTH‐BAT or other bleeding questionnaires had a moderate to low DOR. Four studies investigating Vicenza‐based BATs had a high DOR, with high specificity (>90%) and sensitivity of 59%‐85%. Study characteristics such as case‐control design, retrospective data collection and differences in reference standard were associated with optimistic estimates of diagnostic performance. Three of four studies with a high DOR had these study characteristics. Studies with good methodological quality mainly had a low DOR. Conclusion: The main advantage of the BAT is that it offers a complete and structured interview. However, the BAT is of limited diagnostic value to the workup of patients referred for bleeding evaluation in clinical practice. [ABSTRACT FROM AUTHOR]