Prenatal diagnosis and outcome of fetal urinomas in relation to the underlying etiology.

OBJECTIVE: Evaluation of course and outcome of pregnancies with prenatally diagnosed fetal urinoma in a single center over a 20-year period. METHODS: Retrospective study including all prenatally suspected or diagnosed fetal urinomas. Cases were analyzed by ultrasound findings during pregnancy, comparing urinomas caused by lower urinary tract obstruction (LUTO) and ureteropelvic or ureterovesical junction obstruction (UPJO/UVJO). Course of pregnancy and outcomes were compared according to the underlying etiology. RESULTS: 87 cases of fetal urinomas were identified between 2002 and 2022. The underlying etiology was LUTO in 56.3% and UPJO/UVJO in 43.7%. Complications mainly included oligo- or anhydramnios, thoracic hypoplasia as well as associated renal abnormalities. Postnatally, loss of function (LOF) in kidneys affected by urinomas was seen in 78.6% of children overall, but significantly more often in cases of UPJO/UVJO than in LUTO (86.2% vs. 70.2%, p < 0.05). CONCLUSION: Association of prenatal urinoma and postnatal LOF of the affected kidney is high, especially in cases caused by UPJO/UVJO. In survivors with urinoma caused by UPJO/UVJO, the general prognosis is excellent if the contralateral kidney was normal. In survivors with urinomas caused by LUTO, long-term prognosis depends on postnatal renal and pulmonary function.

Boosting multivariate structured additive distributional regression models.

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.

Nonnormative Eating Behaviors and Eating Disorders and Their Associations With Weight Loss and Quality of Life During 6 Years Following Obesity Surgery.

Importance: Individuals with severe obesity presenting for obesity surgery (OS) frequently show nonnormative eating behaviors (NEBs) and eating disorders (EDs), but the long-term course and prospective associations with weight loss and health-related quality of life (HRQOL) remain unclear. Objective: To examine the prevalence and prospective relevance of presurgical and postsurgical NEBs and EDs according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, diagnosed through clinical interview, for weight loss and HRQOL up to 6 years following OS. Design, Setting, and Participants: In the prospective, multicenter Psychosocial Registry for Obesity Surgery cohort study, patients seeking OS were recruited at 6 OS centers in Germany and assessed at baseline before surgery and at 6 months and 1 to 6 years after surgery. From a consecutive sample of 1040 volunteers with planned OS from March 1, 2012, to December 31, 2020, a total of 748 (71.92%) were included in this study. Across follow-up, 93 of the 748 patients (12.43%) dropped out. Data were analyzed from April to November 2021. Interventions: Laparoscopic Roux-en-Y gastric bypass or sleeve gastrectomy. Main Outcomes and Measures: Both NEBs and EDs were identified using the Eating Disorder Examination interview. Main outcomes were the percentage of total body weight loss (%TBWL) and HRQOL (Impact of Weight on Quality of Life-Lite; range, 0-100, with 0 indicating worst and 100 indicating best). Results: In 748 patients undergoing OS (mean [SD] age, 46.26 [11.44] years; mean [SD] body mass index [calculated as weight in kilograms divided by height in meters squared], 48.38 [8.09]; 513 [68.58%] female), the mean (SD) %TBWL was 26.70% (9.61%), and the mean (SD) HRQOL improvement was 35.41 (20.63) percentage points across follow-up. Both NEBs and EDs were common before surgery, with postsurgical improvements of varying degrees. Whereas NEBs and EDs did not reveal significant prospective associations with %TBWL, loss-of-control eating at follow-up was concurrently associated with lower %TBWL (estimate, -0.09; 95% CI, -0.14 to -0.04). Loss-of-control eating (estimate, -0.10; 95% CI, -0.17 to -0.03 percentage points) and binge-eating disorder of low frequency and/or limited duration (estimate, -6.51; 95% CI, -12.69 to -0.34 percentage points) at follow-up showed significant prospective associations with lower HRQOL. Conclusions and Relevance: This cohort study found prospective relevance of loss-of-control eating and binge-eating disorder of low frequency and/or limited duration for reduced long-term HRQOL following OS. These findings underline the importance of monitoring both NEBs, especially loss-of-control eating, and EDs in the long term postsurgically to identify patients in need of targeted prevention or psychotherapy.

Deselection of base-learners for statistical boosting-with an application to distributional regression.

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

Detecting vision loss in intermediate age-related macular degeneration: A comparison of visual function tests.

The purpose of the study was to evaluate the diagnostic accuracy of visual function tests in intermediate age-related macular degeneration (iAMD). A total of 62 subjects (38 patients with iAMD and 24 controls) were included and underwent several functional assessments: Best-corrected visual acuity (BCVA), low luminance visual acuity (LLVA), visual acuity (VA) measured with the Moorfields Vanishing Optotypes Acuity Charts (MAC), contrast sensitivity with the Pelli-Robson test, reading speed using the International Reading Speed texts (IReST) and mesopic and dark-adapted microperimetry (S-MAIA, CenterVue, Padova, Italy). Groups were compared using non-parametric Wilcoxon rank sum tests and ROC analyses. Linear regression was used to control for confounding. Results showed that all visual function test performances except the IReST were significantly reduced in iAMD patients compared to controls (p < 0.05). These effects did not alter after controlling for age and sex. Best discrimination between iAMD and controls yield the combination of LLVA and contrast sensitivity as well as MAC-VA and contrast sensitivity (ROC area under the curve 0.95 and 0.93, respectively). Our results suggest that LLVA, MAC-VA, contrast sensitivity and mesopic and dark-adapted microperimetry can capture visual impairment characteristic for iAMD. Best discrimination against iAMD is achieved with a combination of two tests.