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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.

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. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?Covariate selection in pharmacokinetic (PK) modeling is a complex process and is sensitive to bias. Machine-learning (ML) techniques might help to simplify and potentially improve this process, but are difficult to interpret as is.

Aim Currently, it is unknown which patient‐reported outcomes are important for patients with autosomal inherited bleeding disorders. Therefore, the purpose of this study is to systematically review the available literature assessing patient‐reported outcomes and their measurement methods in autosomal inherited bleeding disorders. Methods The Embase, Medline ALL, Web of Science Core Collection, Cochrane Central Register of Controlled Trails and Google Scholar databases were searched from inception until 14 August 2020. Studies on patient‐reported outcomes in patients with von Willebrand disease, inherited platelet function disorders and coagulation factor deficiencies were included. Results Twenty‐one articles met the inclusion criteria. Three studies were assessed as having poor quality, and therefore a high risk of bias. Nineteen studies had fair quality rating. Different measurements methods were used, ranging from predefined to self‐developed questionnaires. The majority of included studies focused on von Willebrand disease. Patients with von Willebrand disease reported lower health‐related quality of life compared to the general population. Overall, this trend was especially visible in the following domains: vitality, physical and social functioning and pain. Women with inherited bleeding disorders scored lower on health‐related quality of life compared to men, especially women with heavy menstrual bleeding. Patients with joint bleeds or heavy menstrual bleeding reported an increased level of pain. Conclusion Patients with autosomal inherited bleeding disorders report lower health related quality of life, especially those with joint bleeds or heavy menstrual bleeding. Numerous measurement methods are used in patients with autosomal inherited bleeding disorders, highlighting the need for studies using established, standardized measurement methods.

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