Summary. Background: The life expectancy of non-severe hemophilia A (HA) patients equals the life expectancy of the non-hemophilic population. However, data on the effect of inhibitor development on mortality and on hemophilia-related causes of death are scarce. The development of neutralizing factor VIII antibodies in non-severe HA patients may dramatically change their clinical outcome due to severe bleeding complications. Objectives: We assessed the association between the occurrence of inhibitors and mortality in patients with non-severe HA. Methods: In this retrospective cohort study, clinical data and vital status were collected for 2709 non-severe HA patients (107 with inhibitors) who were treated between 1980 and 2011 in 34 European and Australian centers. Mortality rates for patients with and without inhibitors were compared. Results: During 64 200 patient-years of follow-up, 148 patients died (mortality rate, 2.30 per 1000 person-years; 95% confidence interval (CI), 1.96-2.70) at a median age of 64 years (interquartile range [IQR], 49-76). In 62 patients (42%) the cause of death was hemophilia related. Sixteen inhibitor patients died at a median age of 71 years (IQR,. In ten patients the inhibitor was present at time of death; seven of them died of severe bleeding complications. The all-cause mortality rate in inhibitor patients was > 5 times increased compared with that for those without inhibitors (ageadjusted mortality rate ratio, 5.6). Conclusion: Inhibitor development in non-severe hemophilia is associated with increased mortality. High rates of hemophilia-related mortality in this study indicate that non-severe hemophilia is not mild at all and stress the importance of close follow-up for these patients.

Gray platelet syndrome (GPS) is a rare recessive disorder caused by variants in NBEAL2 and characterized by bleeding symptoms, the absence of platelet alpha-granules, splenomegaly and bone marrow (BM) fibrosis. Due to its rarity, it has been difficult to fully understand the pathogenic processes that lead to these clinical sequelae. To discern the spectrum of pathological features, we performed a detailed clinical genotypic and phenotypic study of 47 GPS patients. We identified 33 new causal variants in NBEAL2. Our GPS patient cohort exhibited known phenotypes, including macrothrombocytopenia, BM fibrosis, megakaryocyte emperipolesis of neutrophils, splenomegaly, and elevated serum vitamin B12 levels. We also observed novel clinical phenotypes; these include reduced leukocyte counts and increased presence of autoimmune disease and positive autoantibodies. There were widespread differences in the transcriptome and proteome of GPS platelets, neutrophils, monocytes, and CD4-lymphocytes. Proteins less abundant in these cells were enriched for constituents of granules, supporting a role for Nbeal2 in the function of these organelles across a wide range of blood cells. Proteomic analysis of GPS plasma showed increased levels of proteins associated with inflammation and immune response. One quarter of plasma proteins increased in GPS are known to be synthesized outside of hematopoietic cells, predominantly in the liver. In summary, our data demonstrate that, in addition to the well-described platelet defects in GPS, there are also immune defects. The abnormal immune cells may be the drivers of systemic abnormalities, such as autoimmune disease.

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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.

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