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.

Hommel's procedure in linear time.

Hommel's and Hochberg's procedures for familywise error control are both derived as shortcuts in a closed testing procedure with the Simes local test. Hommel's shortcut is exact but takes quadratic time in the number of hypotheses. Hochberg's shortcut takes only linear time after the P-values are sorted, but is conservative. In this paper, we present an exact shortcut in linear time on sorted P-values, combining the strengths of both procedures. The novel shortcut also applies to a robust variant of Hommel's procedure that does not require the assumption of the Simes inequality.

CD34+CD38- leukemic stem cell frequency to predict outcome in acute myeloid leukemia.

Current risk algorithms are primarily based on pre-treatment factors and imperfectly predict outcome in acute myeloid leukemia (AML). We introduce and validate a post-treatment approach of leukemic stem cell (LSC) assessment for prediction of outcome. LSC containing CD34+CD38- fractions were measured using flow cytometry in an add-on study of the HOVON102/SAKK trial. Predefined cut-off levels were prospectively evaluated to assess CD34+CD38-LSC levels at diagnosis (n = 594), and, to identify LSClow/LSChigh (n = 302) and MRDlow/MRDhigh patients (n = 305) in bone marrow in morphological complete remission (CR). In 242 CR patients combined MRD and LSC results were available. At diagnosis the CD34+CD38- LSC frequency independently predicts overall survival (OS). After achieving CR, combining LSC and MRD showed reduced survival in MRDhigh/LSChigh patients (hazard ratio [HR] 3.62 for OS and 5.89 for cumulative incidence of relapse [CIR]) compared to MRDlow/LSChigh, MRDhigh/LSClow, and especially MRDlow/LSClow patients. Moreover, in the NPM1mutant positive sub-group, prognostic value of golden standard NPM1-MRD by qPCR can be improved by addition of flow cytometric approaches. This is the first prospective study demonstrating that LSC strongly improves prognostic impact of MRD detection, identifying a patient subgroup with an almost 100% treatment failure probability, warranting consideration of LSC measurement incorporation in future AML risk schemes.

Molecular Minimal Residual Disease in Acute Myeloid Leukemia.

BACKGROUND: Patients with acute myeloid leukemia (AML) often reach complete remission, but relapse rates remain high. Next-generation sequencing enables the detection of molecular minimal residual disease in virtually every patient, but its clinical value for the prediction of relapse has yet to be established. METHODS: We conducted a study involving patients 18 to 65 years of age who had newly diagnosed AML. Targeted next-generation sequencing was carried out at diagnosis and after induction therapy (during complete remission). End points were 4-year rates of relapse, relapse-free survival, and overall survival. RESULTS: At least one mutation was detected in 430 out of 482 patients (89.2%). Mutations persisted in 51.4% of those patients during complete remission and were present at various allele frequencies (range, 0.02 to 47%). The detection of persistent DTA mutations (i.e., mutations in DNMT3A, TET2, and ASXL1), which are often present in persons with age-related clonal hematopoiesis, was not correlated with an increased relapse rate. After the exclusion of persistent DTA mutations, the detection of molecular minimal residual disease was associated with a significantly higher relapse rate than no detection (55.4% vs. 31.9%; hazard ratio, 2.14; P<0.001), as well as with lower rates of relapse-free survival (36.6% vs. 58.1%; hazard ratio for relapse or death, 1.92; P<0.001) and overall survival (41.9% vs. 66.1%; hazard ratio for death, 2.06; P<0.001). Multivariate analysis confirmed that the persistence of non-DTA mutations during complete remission conferred significant independent prognostic value with respect to the rates of relapse (hazard ratio, 1.89; P<0.001), relapse-free survival (hazard ratio for relapse or death, 1.64; P=0.001), and overall survival (hazard ratio for death, 1.64; P=0.003). A comparison of sequencing with flow cytometry for the detection of residual disease showed that sequencing had significant additive prognostic value. CONCLUSIONS: Among patients with AML, the detection of molecular minimal residual disease during complete remission had significant independent prognostic value with respect to relapse and survival rates, but the detection of persistent mutations that are associated with clonal hematopoiesis did not have such prognostic value within a 4-year time frame. (Funded by the Queen Wilhelmina Fund Foundation of the Dutch Cancer Society and others.).

Multiple Testing of Gene Sets from Gene Ontology: Possibilities and Pitfalls.

The use of multiple testing procedures in the context of gene-set testing is an important but relatively underexposed topic. If a multiple testing method is used, this is usually a standard familywise error rate (FWER) or false discovery rate (FDR) controlling procedure in which the logical relationships that exist between the different (self-contained) hypotheses are not taken into account. Taking those relationships into account, however, can lead to more powerful variants of existing multiple testing procedures and can make summarizing and interpreting the final results easier. We will show that, from the perspective of interpretation as well as from the perspective of power improvement, FWER controlling methods are more suitable than FDR controlling methods. As an example of a possible power improvement, we suggest a modified version of the popular method by Holm, which we also implemented in the R package cherry.

A multiple testing method for hypotheses structured in a directed acyclic graph.

We present a novel multiple testing method for testing null hypotheses that are structured in a directed acyclic graph (DAG). The method is a top-down method that strongly controls the familywise error rate and can be seen as a generalization of Meinshausen's procedure for tree-structured hypotheses. Just as Meinshausen's procedure, our proposed method can be used to test for variable importance, only the corresponding variable clusters can be chosen more freely, because the method allows for multiple parent nodes and partially overlapping hypotheses. An important application of our method is in gene set analysis, in which one often wants to test multiple gene sets as well as individual genes for their association with a clinical outcome. By considering the genes and gene sets as nodes in a DAG, our method enables us to test both for significant gene sets as well as for significant individual genes within the same multiple testing procedure. The method will be illustrated by testing Gene Ontology terms for evidence of differential expression in a survival setting and is implemented in the R package cherry.

A region-based multiple testing method for hypotheses ordered in space or time.

We present a multiple testing method for hypotheses that are ordered in space or time. Given such hypotheses, the elementary hypotheses as well as regions of consecutive hypotheses are of interest. These region hypotheses not only have intrinsic meaning but testing them also has the advantage that (potentially small) signals across a region are combined in one test. Because the expected number and length of potentially interesting regions are usually not available beforehand, we propose a method that tests all possible region hypotheses as well as all individual hypotheses in a single multiple testing procedure that controls the familywise error rate. We start at testing the global null-hypothesis and when this hypothesis can be rejected we continue with further specifying the exact location/locations of the effect present. The method is implemented in the R package cherry and is illustrated on a DNA copy number data set.

Efficient approximate k-fold and leave-one-out cross-validation for ridge regression.

In model building and model evaluation, cross-validation is a frequently used resampling method. Unfortunately, this method can be quite time consuming. In this article, we discuss an approximation method that is much faster and can be used in generalized linear models and Cox' proportional hazards model with a ridge penalty term. Our approximation method is based on a Taylor expansion around the estimate of the full model. In this way, all cross-validated estimates are approximated without refitting the model. The tuning parameter can now be chosen based on these approximations and can be optimized in less time. The method is most accurate when approximating leave-one-out cross-validation results for large data sets which is originally the most computationally demanding situation. In order to demonstrate the method's performance, it will be applied to several microarray data sets. An R package penalized, which implements the method, is available on CRAN.