IL‐13, periostin and dipeptidyl‐peptidase‐4 reveal endotype‐phenotype associations in atopic dermatitis

Background: The heterogeneous (endo)phenotypes of atopic dermatitis (AD) require precision medicine. Currently, systemic therapy is recommended to patients with an Eczema Area and Severity Index (EASI) ≥ 16. Previous studies have demonstrated an improved treatment response to the anti‐interleukin (IL)‐13 antibody tralokinumab in AD subgroups with elevated levels of the IL‐13‐related biomarkers dipeptidyl‐peptidase (DPP)‐4 and periostin. Methods: Herein, 373 AD patients aged ≥12 years were stratified by IL‐13$^{high}$, periostin$^{high}$ and DPP‐4$^{high}$ endotypes using cross‐sectional data from the ProRaD cohort Bonn. “High” was defined as >80th quantile of 47 non‐atopic controls. We analyzed endotype‐phenotype associations using machine‐learning gradient boosting compared to logistic regression. Results: Atopic dermatitis severity and eosinophils correlated with IL‐13 and periostin levels. Correlations of IL‐13 with EASI were stronger in patients with increased (rs = 0.482) than with normal (rs = 0.342) periostin levels. We identified eosinophilia >6% and an EASI range of 5.5–17 dependent on the biomarker combination to be associated with increasing probabilities of biomarker$^{high}$ endotypes. Also patients with mild‐to‐low‐moderate severity (EASI < 16) featured increased biomarkers (IL‐13$^{high}$: 41%, periostin$^{high}$: 48.4%, DPP‐4$^{high}$: 22.3%). Herthoge sign (adjusted Odds Ratio (aOR) = 1.89, 95% Confidence Interval (CI) [1.14–3.14]) and maternal allergic rhinitis (aOR = 2.79–4.47) increased the probability of an IL‐13$^{high}$‐endotype, “dirty neck” (aOR = 2.83 [1.32–6.07]), orbital darkening (aOR = 2.43 [1.08–5.50]), keratosis pilaris (aOR = 2.21 [1.1–4.42]) and perleche (aOR = 3.44 [1.72–6.86]) of a DPP‐4$^{high}$‐endotype. Conclusions: A substantial proportion of patients with EASI < 16 featured high biomarker levels suggesting systemic impact of skin inflammation already below the current cut‐off for systemic therapy. Our findings facilitate the identification of patients with distinct endotypes potentially linked to response to IL‐13‐targeted therapy

Cost-of-illness studies based on massive data: a prevalence-based, top-down regression approach

Despite the increasing availability of routine data, no analysis method has yet been presented for cost-of-illness (COI) studies based on massive data. We aim, first, to present such a method and, second, to assess the relevance of the associated gain in numerical efficiency. We propose a prevalence-based, top-down regression approach consisting of five steps: aggregating the data; fitting a generalized additive model (GAM); predicting costs via the fitted GAM; comparing predicted costs between prevalent and non-prevalent subjects; and quantifying the stochastic uncertainty via error propagation. To demonstrate the method, it was applied to aggregated data in the context of chronic lung disease to German sickness funds data (from 1999), covering over 7.3 million insured. To assess the gain in numerical efficiency, the computational time of the innovative approach has been compared with corresponding GAMs applied to simulated individual-level data. Furthermore, the probability of model failure was modeled via logistic regression. Applying the innovative method was reasonably fast (19 min). In contrast, regarding patient-level data, computational time increased disproportionately by sample size. Furthermore, using patient-level data was accompanied by a substantial risk of model failure (about 80 % for 6 million subjects). The gain in computational efficiency of the innovative COI method seems to be of practical relevance. Furthermore, it may yield more precise cost estimates

Discrete-time survival forests with Hellinger distance decision trees

Random survival forests (RSF) are a powerful nonparametric method for building prediction models with a time-to-event outcome. RSF do not rely on the proportional hazards assumption and can be readily applied to both low- and higher-dimensional data. A remaining limitation of RSF, however, arises from the fact that the method is almost entirely focussed on continuously measured event times. This issue may become problematic in studies where time is measured on a discrete scale t=1,2,..., referring to time intervals [0,a1),[a1,a2),…. In this situation, the application of methods designed for continuous time-to-event data may lead to biased estimators and inaccurate predictions if discreteness is ignored. To address this issue, we develop a RSF algorithm that is specifically designed for the analysis of (possibly right-censored) discrete event times. The algorithm is based on an ensemble of discrete-time survival trees that operate on transformed versions of the original time-to-event data using tree methods for binary classification. As the outcome variable in these trees is typically highly imbalanced, our algorithm implements a node splitting strategy based on Hellinger’s distance, which is a skew-insensitive alternative to classical split criteria such as the Gini impurity. The new algorithm thus provides flexible nonparametric predictions of individual-specific discrete hazard and survival functions. Our numerical results suggest that node splitting by Hellinger’s distance improves predictive performance when compared to the Gini impurity. Furthermore, discrete-time RSF improve prediction accuracy when compared to RSF approaches treating discrete event times as continuous in situations where the number of time intervals is small

Sedation versus General Anesthesia for Cardiac Catheterization in Infants: A Retrospective, Monocentric, Cohort Evaluation

Background: Children with congenital heart disease require repeated catheterization. Anesthetic management influences the procedure and may influence outcome; however, data and recommendations are lacking for infants. We studied the influence of sedation versus general anesthesia (GA) on adverse events during catheterization for children p = 0.01) after GA or sedation, respectively. HSAE occurred in 75 (20%) versus 40 (9%) (p p = 0.05), smaller weights (p p = 0.02), two-ventricle physiology (OR 7.3 [2.7–20.2], p p p p = 0.02) and procedure-type risk category 4 (OR 28.9 [1.8–455.1], p = 0.02). Sedation did not increase the events rate and decreased the requirement for hemodynamic support (OR 5.2 [2.2–12.0], p < 0.01). Conclusion: Sedation versus GA for cardiac catheterization in children <2 years old is safe and effective with regard to HSAE. Sedation also decreases the requirement for hemodynamic support. Paradoxical effects (older age and two-ventricle physiology) on risk have been found for this specific age cluster