Initial evidence indicates that preoperatively initiated administration of omega-3 fatty acids (FAs) attenuates the postoperative inflammatory reaction. The effects of immunonutrition containing omega-3 FAs, such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), on the inflammatory response to abdominal surgery continues to be unclear, although improved outcomes have been reported. Therefore, we determined the effectiveness of preoperatively initiated omega-3 FAs administration on postoperative inflammation defined as CRP (C-Reactive Protein), IL-6 (Interleukin 6), and WBC (White Blood Count) and potential effects on postoperative length of hospital stay (LOS) due to an improved inflammatory response. Methods: a literature search of Cochrane Library was conducted to identify all randomized controlled trials (RCTs) investigating the effects of preoperatively initiated omega-3 to standard care, placebo, or other immunonutrients excluding omega-3 FAs in patients undergoing abdominal surgery until the end of December 2022. Results: a total of 296 articles were found during the initial search. Thirteen RCTs involving 950 patients were identified that met the search criteria. These were successively analyzed and included in this meta-analysis. There was no significant difference between the groups with respect to inflammatory markers IL-6: −0.55 [−1.22; 0.12] p = 0.10, CRP: −0.14 [−0.67; 0.40] p = 0.55, WBC: −0.58 [−3.05; 1.89] p = 0.42, or hospital stay −0.5 [−1.43; 0.41] p = 0.2. Conclusion: although reduced inflammatory markers were observed, preoperative administration of omega-3 FAs immunonutrients had no significant effect on the postoperative inflammatory response in patients undergoing abdominal surgeries. Yet, results obtained from this study are inconclusive, likely attributed to the limited number of trials and patients included. Further studies are required to obtain a better educated verdict.

We present a new procedure for enhanced variable selection for component-wise gradient boosting. Statistical boosting is a computational approach that emerged from machine learning, which allows to fit regression models in the presence of high-dimensional data. Furthermore, the algorithm can lead to data-driven variable selection. In practice, however, the final models typically tend to include too many variables in some situations. This occurs particularly for low-dimensional data ($p<n$ ), where we observe a slow overfitting behavior of boosting. As a result, more variables get included into the final model without altering the prediction accuracy. Many of these false positives are incorporated with a small coefficient and therefore have a small impact, but lead to a larger model. We try to overcome this issue by giving the algorithm the chance to deselect base-learners with minor importance. We analyze the impact of the new approach on variable selection and prediction performance in comparison to alternative methods including boosting with earlier stopping as well as twin boosting. We illustrate our approach with data of an ongoing cohort study for chronic kidney disease patients, where the most influential predictors for the health-related quality of life measure are selected in a distributional regression approach based on beta regression.

We develop a model‐based boosting approach for multivariate distributional regression within the framework of generalized additive models for location, scale, and shape. Our approach enables the simultaneous modeling of all distribution parameters of an arbitrary parametric distribution of a multivariate response conditional on explanatory variables, while being applicable to potentially high‐dimensional data. Moreover, the boosting algorithm incorporates data‐driven variable selection, taking various different types of effects into account. As a special merit of our approach, it allows for modeling the association between multiple continuous or discrete outcomes through the relevant covariates. After a detailed simulation study investigating estimation and prediction performance, we demonstrate the full flexibility of our approach in three diverse biomedical applications. The first is based on high‐dimensional genomic cohort data from the UK Biobank, considering a bivariate binary response (chronic ischemic heart disease and high cholesterol). Here, we are able to identify genetic variants that are informative for the association between cholesterol and heart disease. The second application considers the demand for health care in Australia with the number of consultations and the number of prescribed medications as a bivariate count response. The third application analyses two dimensions of childhood undernutrition in Nigeria as a bivariate response and we find that the correlation between the two undernutrition scores is considerably different depending on the child's age and the region the child lives in.

Background: Decreased cerebral oximetry (rSO₂) in cardiac surgery is associated with postoperative delirium (POD). However, interventions optimizing intraoperative rSO₂ are inconclusive. Methods: In this prospective observational cohort study, the relationship between rSO₂, middle cerebral artery blood flow velocity (MCAV), and processed EEG was assessed in cardiac surgery patients with and without POD. MCAV was continuously recorded by transcranial Doppler sonography (TCD), together with continuous rSO₂ and bispectral index (BIS) monitoring. Cardiopulmonary bypass (CPB) flow rate was adjusted according to body surface area. The cohort was divided into the POD and control groups, according to the postoperative results of the confusion assessment method (CAM/CAM-ICU), the 4A’s test (4AT), and the Delirium Observation Scale (DOS). A mixed model analysis was performed for intraoperative raw data. The cerebral autoregulation index was calculated from TCD, rSO₂, and arterial pressure values. Differences in impaired autoregulation were compared using the Mann–Whitney U test. Results: A total of 41 patients were included in this study. A total of 13 patients (36.11%) developed postoperative delirium. There were no significant differences in the baseline characteristics of patients with or without POD. Patients with POD had lower BIS values during CPB (adjusted mean difference −4.449 (95% CI [−7.978, −0.925])). RSO₂ was not significantly reduced in POD, (adjusted mean difference: −5.320, 95% CI [−11.508, 0.874]). In contrast, MCAV was significantly increased in POD (10.655, 95% CI [0.491, 20.819]). The duration of cerebral autoregulation impairment did not differ significantly for TCD and cerebral oximetry-derived indices (p = 0.4528, p = 0.2715, respectively). Conclusions: Our results suggest that disturbed cerebral metabolism reflects a vulnerable brain which may be more susceptible to overperfusion during CPB, which can be seen in increased MCAV values. These phenomena occur irrespectively of cerebral autoregulation.

Objective: Obesity surgery (OS) results in substantial, albeit heterogeneous, long‐term improvements in weight and mental health, with unclear trajectories and their associations. This study examined multivariate trajectories of weight, psychopathology, and health‐related quality of life (HRQOL) after OS, and their prospective association with long‐term health outcomes. Method: In the prospective multicenter Psychosocial Registry of Obesity Surgery, N = 856 patients were classified into multivariate trajectory classes using latent class linear mixed models, based on assessments of weight, depression, eating disorder psychopathology, and HRQOL at baseline and annually 1–5 years following OS. The prognostic significance of trajectory classes for 6‐year follow‐up was examined. Multivariate trajectory modeling was compared with univariate weight trajectory modeling for concordance and prognostic significance. Results: We identified three trajectory classes of low (I, 2.8%), medium (II, 89.1%), and high (III, 8.1%) sustainability 1–5 years after OS, indicating high (I) or gradual deterioration (II) or further improvement (III) after initial improvement of indicators. The low sustainability class (I) reached nadir improvements earliest. Consistently, trajectory classes were prospectively associated with differential clinically significant improvement in weight and mental health at the 6‐year follow‐up. Multivariate trajectory modeling was discordant with univariate weight trajectory modeling and showed greater predictive value for health outcomes at the 6‐year follow‐up. Discussion: Patients who achieve nadir improvements in weight and mental health early may require clinical attention to prevent long‐term relapse. Monitoring changes in the first years after OS appears essential to identify patients in need of additional intervention, ideally using indicators beyond weight, such as mental health.

Survival analysis plays a pivotal role across disciplines, including engineering, economics, and social sciences—not just in biomedical research. In many of these applications, incomplete observations due to censoring are common, arising from limited follow-up periods, study dropouts, or administrative constraints. A standard assumption in such settings is that the censoring mechanism is independent of the survival process. This assumption primarily holds when censoring occurs at the end of the observation period. However, there may be dependence between event and censoring times. For example, if a patient’s health deteriorates and they withdraw due to poor prognosis, the time of censoring depends on their health status, leading to dependent censoring as sicker patients are censored earlier. To address such situations adequately in statistical analyses, we propose a model-based boosting approach using distributional copula regression. Our approach models the joint distribution of survival and censoring times by linking unknown marginal distributions through an unknown parametric copula. All distribution parameters of the resulting joint distribution are estimated simultaneously as functions of potentially different covariates. A key merit of the boosting approach is its data-driven variable selection, which is particularly important for such flexible models. Estimation remains feasible even for high-dimensional data with more covariates than observations, where classical estimation frameworks meet their limits. To investigate the performance of our method, we conduct a comprehensive simulation study, and demonstrate its practical application using a recent observational study analyzing the overall survival of patients with colon cancer. The data has a high proportion of right-censored observations without information on the cause of censoring.