Bayesian network analysis of antidepressant treatment trajectories

It is currently difficult to successfully choose the correct type of antidepressant for individual patients. To discover patterns in patient characteristics, treatment choices and outcomes, we performed retrospective Bayesian network analysis combined with natural language processing (NLP). This study was conducted at two mental healthcare facilities in the Netherlands. Adult patients admitted and treated with antidepressants between 2014 and 2020 were included. Outcome measures were antidepressant continuation, prescription duration and four treatment outcome topics: core complaints, social functioning, general well-being and patient experience, extracted through NLP of clinical notes. Combined with patient and treatment characteristics, Bayesian networks were constructed at both facilities and compared. Antidepressant choices were continued in 66% and 89% of antidepressant trajectories. Score-based network analysis revealed 28 dependencies between treatment choices, patient characteristics and outcomes. Treatment outcomes and prescription duration were tightly intertwined and interacted with antipsychotics and benzodiazepine co-medication. Tricyclic antidepressant prescription and depressive disorder were important predictors for antidepressant continuation. We show a feasible way of pattern discovery in psychiatry data, through combining network analysis with NLP. Further research should explore the found patterns in patient characteristics, treatment choices and outcomes prospectively, and the possibility of translating these into a tool for clinical decision support

Outcome prediction of electroconvulsive therapy for depression

Introduction: We developed and tested a Bayesian network(BN) model to predict ECT remission for depression, with non-response as a secondary outcome. Methods: We performed a systematic literature search on clinically available predictors. We combined these predictors with variables from a dataset of clinical ECT trajectories (performed in the University Medical Center Utrecht) to create priors and train the BN. Temporal validation was performed in an independent sample. Results: The systematic literature search yielded three meta-analyses, which provided prior knowledge on outcome predictors. The clinical dataset consisted of 248 treatment trajectories in the training set and 44 trajectories in the test set at the same medical center. The AUC for the primary outcome remission estimated on an independent validation set was 0.686 (95%CI 0.513–0.859) (AUC values of 0.505 – 0.763 observed in 5-fold cross validation of the model within the train set). Accuracy 0.73 (balanced accuracy 0.67), sensitivity 0.55, specificity 0.79, after temporal validation in the independent sample. Prior literature information marginally reduced CI width. Discussion: A BN model comprised of prior knowledge and clinical data can predict remission of depression after ECT with reasonable performance. This approach can be used to make outcome predictions in psychiatry, and offers a methodological framework to weigh additional information, such as patient characteristics, symptoms and biomarkers. In time, it may be used to improve shared decision-making in clinical practice

WHO global research priorities for antimicrobial resistance in human health

The WHO research agenda for antimicrobial resistance (AMR) in human health has identified 40 research priorities to be addressed by the year 2030. These priorities focus on bacterial and fungal pathogens of crucial importance in addressing AMR, including drug-resistant pathogens causing tuberculosis. These research priorities encompass the entire people-centred journey, covering prevention, diagnosis, and treatment of antimicrobial-resistant infections, in addition to addressing the overarching knowledge gaps in AMR epidemiology, burden and drivers, policies and regulations, and awareness and education. The research priorities were identified through a multistage process, starting with a comprehensive scoping review of knowledge gaps, with expert inputs gathered through a survey and open call. The priority setting involved a rigorous modified Child Health and Nutrition Research Initiative approach, ensuring global representation and applicability of the findings. The ultimate goal of this research agenda is to encourage research and investment in the generation of evidence to better understand AMR dynamics and facilitate policy translation for reducing the burden and consequences of AMR

Generic E-variables for exact sequential k-sample tests that allow for optional stopping

We develop E-variables for testing whether two or more data streams come from the same source or not, and more generally, whether the difference between the sources is larger than some minimal effect size. These E-variables lead to exact, nonasymptotic tests that remain safe, i.e., keep their type-I error guarantees, under flexible sampling scenarios such as optional stopping and continuation. In special cases our E-variables also have an optimal ‘growth’ property under the alternative. While the construction is generic, we illustrate it through the special case of k×2 contingency tables, i.e. k Bernoulli streams, allowing for the incorporation of different restrictions on the composite alternative. Comparison to p-value analysis in simulations and a real-world 2 × 2 contingency table example show that E-variables, through their flexibility, often allow for early stopping of data collection — thereby retaining similar power as classical methods — while also retaining the option of extending or combining data afterwards

Bayesian network analysis of antidepressant treatment trajectories

Abstract It is currently difficult to successfully choose the correct type of antidepressant for individual patients. To discover patterns in patient characteristics, treatment choices and outcomes, we performed retrospective Bayesian network analysis combined with natural language processing (NLP). This study was conducted at two mental healthcare facilities in the Netherlands. Adult patients admitted and treated with antidepressants between 2014 and 2020 were included. Outcome measures were antidepressant continuation, prescription duration and four treatment outcome topics: core complaints, social functioning, general well-being and patient experience, extracted through NLP of clinical notes. Combined with patient and treatment characteristics, Bayesian networks were constructed at both facilities and compared. Antidepressant choices were continued in 66% and 89% of antidepressant trajectories. Score-based network analysis revealed 28 dependencies between treatment choices, patient characteristics and outcomes. Treatment outcomes and prescription duration were tightly intertwined and interacted with antipsychotics and benzodiazepine co-medication. Tricyclic antidepressant prescription and depressive disorder were important predictors for antidepressant continuation. We show a feasible way of pattern discovery in psychiatry data, through combining network analysis with NLP. Further research should explore the found patterns in patient characteristics, treatment choices and outcomes prospectively, and the possibility of translating these into a tool for clinical decision support

Loss of Thrombomodulin in Placental Dysfunction in Preeclampsia

OBJECTIVE: Preeclampsia is a pregnancy-specific syndrome characterized by placental dysfunction and an angiogenic imbalance. Systemically, levels of thrombomodulin, an endothelium- and syncytiotrophoblast-bound protein that regulates coagulation, inflammation, apoptosis, and tissue remodeling, are increased. We aimed to investigate placental thrombomodulin dysregulation and consequent downstream effects in the pathogenesis of preeclampsia. APPROACH AND RESULTS: Placentas from 28 preeclampsia pregnancies, 30 uncomplicated pregnancies, and 21 pregnancies complicated by growth restriction as extra controls were included. Immunohistochemical staining of thrombomodulin, caspase-3, and fibrin was performed. Placental mRNA expression of thrombomodulin, inflammatory markers, matrix metalloproteinases 2 and 9, and soluble Flt-1 were measured with quantitative polymerase chain reaction. Thrombomodulin mRNA expression was determined in vascular endothelial growth factor-transfected trophoblast cell lines. Thrombomodulin protein and mRNA expression were decreased in preeclampsia as compared with both control groups (P=0.001). Thrombomodulin mRNA expression correlated with maternal body mass index (P<0.01) and diastolic blood pressure (P<0.05) in preeclampsia. An increase in placental apoptotic cells was associated with preeclampsia (P<0.001). Thrombomodulin expression correlated positively with matrix metalloproteinase expression (P<0.01) in preeclampsia, but not with fibrin deposits or inflammatory markers. Placental soluble Flt-1 expression correlated with decreased thrombomodulin expression. Vascular endothelial growth factor induced upregulation of thrombomodulin expression in trophoblast cells. CONCLUSIONS: Decreased thrombomodulin expression in preeclampsia may play a role in placental dysfunction in preeclampsia and is possibly caused by an angiogenic imbalance. Hypertension and obesity are associated with thrombomodulin downregulation. These results set the stage for further basic and clinical research on thrombomodulin in the pathogenesis of preeclampsia and other syndromes characterized by endothelial dysfunction

From Glomerular Endothelium to Podocyte Pathobiology in Preeclampsia : a Paradigm Shift

Preeclampsia is a pregnancy-specific syndrome characterized by renal dysfunction and high blood pressure. When evaluated with light microscopy, the renal lesion of preeclampsia is marked by endothelial cell swelling and the appearance of bloodless glomeruli. However, regarding the pathobiology of renal damage in preeclampsia, attention recently has shifted from the glomerular endothelial cells to the podocytes. The angiogenic imbalance in preeclampsia plays a key role in the development of both podocyte and endothelial damage in the glomerular filtration barrier. Here, we review the latest studies on the role of podocytes in the development of renal damage in preeclampsia and on podocytes as potential targets for diagnosis, treatment, and prevention of long-term complications of preeclampsia

Loss of placental thrombomodulin in oocyte donation pregnancies

Objective To investigate whether thrombomodulin dysregulation is involved in the development of preeclampsia after oocyte donation (OD). Women who become pregnant after OD are prone to develop preeclampsia, a syndrome characterized by an aberrant immunologic response, hypercoagulability, and endothelial dysfunction. A mediator of inflammation and coagulation is thrombomodulin, which has a possible role to play in this syndrome. Design Case-control study. Setting Not applicable. Patient(s) Placentas from 82 women with an uncomplicated pregnancy (48 naturally conceived, 21 IVF, and 33 OD pregnancies) and 9 women with an OD pregnancy complicated by preeclampsia have been studied. Intervention(s) None. Main Outcome Measure(s) Abundances of thrombomodulin protein and vitamin D receptor (VDR) were determined using immunohistochemistry; mRNA expression was determined using quantitative polymerase chain reaction. Result(s) Placental thrombomodulin protein abundance was lower in OD pregnancies (diffuse pattern in 45%) than in controls (diffuse pattern in 96%). Placental thrombomodulin mRNA expression was lower in OD pregnancies complicated by preeclampsia (0.72 ± 0.47) compared with in uncomplicated OD pregnancies (0.43 ± 0.18). Thrombomodulin expression correlated with inflammation and coagulation. VDR expression was decreased in OD pregnancies complicated by preeclampsia and was correlated with thrombomodulin mRNA. Conclusion(s) Pregnancies conceived through OD lose placental thrombomodulin expression. This loss is associated with an increased coagulation and inflammation and indicates that endothelial protection is diminished in OD pregnancies, which might be an explanation for the increased risk for preeclampsia. Vitamin D metabolism is dysregulated in OD pregnancies and might be a target for therapy