Prevalence of multimodal treatment in children and adolescents with ADHD in Germany: a nationwide study based on health insurance data.

BACKGROUND: Attention-deficit hyperactivity disorder (ADHD) ranks top among neurodevelopmental disorders in children and adolescents. Due to a large number of unfavorable outcomes including psychiatric comorbidities, school problems, and lower socioeconomic status, early and effective treatment of ADHD is essential. Multimodal treatment has become the gold standard in ADHD management, comprising pharmacotherapy and psychosocial interventions, e.g., psychotherapy. Yet, little is known about the prevalence of multimodal treatment in routine care. METHODS: Based on German health claims data for the years 2009-2017, we identified children and adolescents aged 3-17 years diagnosed with ADHD and characterized them cross-sectionally (per calendar year) in terms of treatment status and psychiatric comorbidities. The detection of pharmacotherapy was based on dispensations of drugs to treat ADHD (e.g., methylphenidate); psychotherapeutic treatment was based on corresponding billing codes. Multimodal treatment was assumed if ADHD medication and psychotherapeutic treatment were coded within the same calendar year. Psychiatric comorbidities were based on outpatient and inpatient diagnoses. Prevalences of ADHD and proportions of different treatment options were calculated and standardized by age and sex. RESULTS: In 2017, 91,118 children met the study criteria for ADHD (prevalence: 42.8/1000). Of these, 25.2% had no psychiatric comorbidity, 28.8% had one, 21.6% had two, and 24.5% had three or more. Regarding overall treatment status, 36.2% were treated only pharmacologically, 6.5% received multimodal treatment, and 6.8% were treated with psychotherapy only (neither treatment: 50.2%). With increasing numbers of psychiatric comorbidities, the proportions of patients with multimodal treatment increased from 2.2% (no psychiatric comorbidities) to 11.1% (three or more psychiatric comorbidities) while the proportions of untreated (from 56.8% to 42.7%) or only pharmacologically treated patients (38.4% to 35.0%) decreased. From 2009 to 2017, prevalences were stable and the proportion of patients with only pharmacotherapy decreased from 48% to 36.5%. Concurrently, the proportion of patients with neither pharmacotherapy nor psychotherapy increased from 40.5% to 50.2%. The fraction of patients with multimodal treatment ranged between 6.5% (2017) and 7.4% (2013). CONCLUSIONS: Multimodal treatment, although recommended as the standard of treatment, is rather the exception than the rule. It is, however, increasingly common in ADHD patients with psychiatric comorbidities.

Examining the robustness of observational associations to model, measurement and sampling uncertainty with the vibration of effects framework.

BACKGROUND: The results of studies on observational associations may vary depending on the study design and analysis choices as well as due to measurement error. It is important to understand the relative contribution of different factors towards generating variable results, including low sample sizes, researchers' flexibility in model choices, and measurement error in variables of interest and adjustment variables. METHODS: We define sampling, model and measurement uncertainty, and extend the concept of vibration of effects in order to study these three types of uncertainty in a common framework. In a practical application, we examine these types of uncertainty in a Cox model using data from the National Health and Nutrition Examination Survey. In addition, we analyse the behaviour of sampling, model and measurement uncertainty for varying sample sizes in a simulation study. RESULTS: All types of uncertainty are associated with a potentially large variability in effect estimates. Measurement error in the variable of interest attenuates the true effect in most cases, but can occasionally lead to overestimation. When we consider measurement error in both the variable of interest and adjustment variables, the vibration of effects are even less predictable as both systematic under- and over-estimation of the true effect can be observed. The results on simulated data show that measurement and model vibration remain non-negligible even for large sample sizes. CONCLUSION: Sampling, model and measurement uncertainty can have important consequences for the stability of observational associations. We recommend systematically studying and reporting these types of uncertainty, and comparing them in a common framework.

Sampling uncertainty versus method uncertainty: A general framework with applications to omics biomarker selection.

Uncertainty is a crucial issue in statistics which can be considered from different points of view. One type of uncertainty, typically referred to as sampling uncertainty, arises through the variability of results obtained when the same analysis strategy is applied to different samples. Another type of uncertainty arises through the variability of results obtained when using the same sample but different analysis strategies addressing the same research question. We denote this latter type of uncertainty as method uncertainty. It results from all the choices to be made for an analysis, for example, decisions related to data preparation, method choice, or model selection. In medical sciences, a large part of omics research is focused on the identification of molecular biomarkers, which can either be performed through ranking or by selection from among a large number of candidates. In this paper, we introduce a general resampling-based framework to quantify and compare sampling and method uncertainty. For illustration, we apply this framework to different scenarios related to the selection and ranking of omics biomarkers in the context of acute myeloid leukemia: variable selection in multivariable regression using different types of omics markers, the ranking of biomarkers according to their predictive performance, and the identification of differentially expressed genes from RNA-seq data. For all three scenarios, our findings suggest highly unstable results when the same analysis strategy is applied to two independent samples, indicating high sampling uncertainty and a comparatively smaller, but non-negligible method uncertainty, which strongly depends on the methods being compared.

Von Willebrand factor parameters as potential biomarkers for disease activity and coronary artery lesion in patients with Kawasaki disease.

Elevated von Willebrand factor (vWF):Antigen plasma levels have been observed in conjunction with cardiovascular diseases or vasculitis. The association of Kawasaki disease, a vascular inflammatory disease and vWF:Antigen, vWF:Collagen binding activity, and vWF multimers is unknown. We therefore investigated vWF parameters in 28 patients with acute Kawasaki disease in association with disease activity and coronary artery lesions. VWF:Antigen and vWF:Collagen binding activity were assessed via enzyme-linked immunoassay. The ratio of both (vWF:Collagen binding activity and VWF:Antigen) was calculated and vWF multimeric structure analysis performed. We analyzed the association between vWF parameters and our clinical data focusing on coronary artery outcome. VWF:Antigen and vWF:Collagen binding activity levels were significantly higher in the acute than in the disease's convalescence phase, and correlated positively with CRP levels. Neither variable was associated with coronary artery lesions. The vWF:Collagen binding activity/vWF:Antigen ratio, however, was significantly decreased in patients with a coronary artery lesion (z-score > 2; N = 10; mean ratio 0.96 vs. 0.64; p = 0.031) and even more so in those with a coronary artery aneurysm (z-score > 2.5; N = 8; mean ratio 0.94 vs. 0.55; p = 0.02). In a sub-analysis, those patients with a very low ratio in the acute phase presented a persistent coronary artery aneurysm at their 1-year follow-up.Conclusion: This study suggests that comprehensive analysis of vWF parameters may help to both monitor KD inflammation and facilitate the identification of those patients carrying an increased risk for coronary artery lesion.What is Known:• Von Willebrand factor (VWF)-parameters represent surrogate markers for vascular inflammation.• Kawasaki disease is a generalized vasculitis in children, which can be complicated by coronary artery lesions.What is New:• In those Kawasaki disease patients with coronary artery lesions, the vWF:CB/vWF:Ag ratio was significantly decreased.• VWF parameters may help to identify patients at risk for coronary artery lesions.

Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data.

BACKGROUND: The inclusion of high-dimensional omics data in prediction models has become a well-studied topic in the last decades. Although most of these methods do not account for possibly different types of variables in the set of covariates available in the same dataset, there are many such scenarios where the variables can be structured in blocks of different types, e.g., clinical, transcriptomic, and methylation data. To date, there exist a few computationally intensive approaches that make use of block structures of this kind. RESULTS: In this paper we present priority-Lasso, an intuitive and practical analysis strategy for building prediction models based on Lasso that takes such block structures into account. It requires the definition of a priority order of blocks of data. Lasso models are calculated successively for every block and the fitted values of every step are included as an offset in the fit of the next step. We apply priority-Lasso in different settings on an acute myeloid leukemia (AML) dataset consisting of clinical variables, cytogenetics, gene mutations and expression variables, and compare its performance on an independent validation dataset to the performance of standard Lasso models. CONCLUSION: The results show that priority-Lasso is able to keep pace with Lasso in terms of prediction accuracy. Variables of blocks with higher priorities are favored over variables of blocks with lower priority, which results in easily usable and transportable models for clinical practice.