Addressing treatment switching in the ALTA-1L trial with g-methods: exploring the impact of model specification.

Background: Treatment switching in randomized clinical trials introduces challenges in performing causal inference. Intention To Treat (ITT) analyses often fail to fully capture the causal effect of treatment in the presence of treatment switching. Consequently, decision makers may instead be interested in causal effects of hypothetical treatment strategies that do not allow for treatment switching.

Deep Learning-Based Detection of Carotid Plaques Informs Cardiovascular Risk Prediction and Reveals Genetic Drivers of Atherosclerosis.

Atherosclerotic cardiovascular disease, the leading cause of global mortality, is driven by lipid accumulation and plaque formation within arterial walls. Carotid plaques, detectable via ultrasound, are a well-established marker of subclinical atherosclerosis. In this study, we trained a deep learning model to detect plaques in 177,757 carotid ultrasound images from 19,499 UK Biobank (UKB) participants (aged 47-83 years) to assess the prevalence, risk factors, prognostic significance, and genetic architecture of carotid atherosclerosis in a large population-based cohort.

Prevention of allergies and infections by minimally processed milk in infants-The MARTHA feasibility and safety trial.

Background: Consumption of raw cow's milk has repeatedly been shown to protect from asthma, allergies, and respiratory infections. As raw milk bears potential health hazards, it cannot be recommended for prevention. Therefore, we performed an intervention study with microbially safe but otherwise minimally processed cow's milk.

Prognostic models in multiple sclerosis: progress and challenges in clinical integration.

As a chronic inflammatory disease of the central nervous system, multiple sclerosis (MS) is of great individual health and socio-economic significance. To date, there is no prognostic model that is used in routine clinical care to predict the very heterogeneous course of the disease. Despite several research groups working on different prognostic models using traditional statistics, machine learning and/or artificial intelligence approaches, the use of published models in clinical decision making is limited because of poor model performance, lack of transferability and/or lack of validated models.

Does combining numerous data types in multi-omics data improve or hinder performance in survival prediction? Insights from a large-scale benchmark study.

Background: Predictive modeling based on multi-omics data, which incorporates several types of omics data for the same patients, has shown potential to outperform single-omics predictive modeling. Most research in this domain focuses on incorporating numerous data types, despite the complexity and cost of acquiring them. The prevailing assumption is that increasing the number of data types necessarily improves predictive performance.

Distributed non-disclosive validation of predictive models by a modified ROC-GLM.

Background: Distributed statistical analyses provide a promising approach for privacy protection when analyzing data distributed over several databases. Instead of directly operating on data, the analyst receives anonymous summary statistics, which are combined into an aggregated result. Further, in discrimination model (prognosis, diagnosis, etc.

Methods to incorporate patient preferences into medical decision algorithms and models, and their quantification, balancing, and evaluation: a scoping review protocol.

Objective: The objective of this scoping review is to identify and map methods used to incorporate patient preferences into medical algorithms and models as well as to report on their quantification, balancing, and evaluation in the literature. The review will focus on computational methods for incorporating patient preferences into algorithms and models at an individual level as well as the types of medical algorithms and models in which these methods have been applied.

Introduction: Medical algorithms and models are increasingly being used to support clinical and shared decision-making; however, their effectiveness, accuracy, acceptance, and comprehension may be limited if patients' preferences are not considered.

A novel prediction score determining individual clinical outcome 3 months after juvenile stroke (PREDICT-score).

Background: Juvenile strokes (< 55 years) account for about 15% of all ischemic strokes. Structured data on clinical outcome in those patients are sparse. Here, we aimed to fill this gap by systematically collecting relevant data and modeling a juvenile stroke prediction score for the 3-month functional outcome.

Can German Health Insurance Claims Data Fill Information Gaps in Rare Chronic Diseases: Use Case of Haemophilia A.

Claims data are increasingly discussed to evaluate health care for rare diseases (resource consumption, outcomes and costs). Using haemophilia A (HA) as a use case, this analysis aimed to generate evidence for the aforementioned information using German Statutory Health Insurance (SHI) claims data. Claims data (2017-2019) from the German SHI 'AOK Bayern - Die Gesundheitskasse' were used.

Framework for personalized prediction of treatment response in relapsing-remitting multiple sclerosis: a replication study in independent data.

Background: Individualizing and optimizing treatment of relapsing-remitting multiple sclerosis patients is a challenging problem, which would benefit from a clinically valid decision support. Stühler et al. presented black box models for this aim which were developed and internally evaluated in a German registry but lacked external validation.

Prevalence of vestibular disease in France: analysis of prescription data from a national health insurance database.

Vestibular problems are frequent reasons for primary care consultations. However, there is considerable uncertainty about the prevalence and cost of vestibular disorders. Despite ambiguous effectiveness data, the histamine analogue betahistine is widely and almost exclusively used for treatment of vertigo.

Prospective study validating a multidimensional treatment decision score predicting the 24-month outcome in untreated patients with clinically isolated syndrome and early relapsing-remitting multiple sclerosis, the ProVal-MS study.

Introduction: In Multiple Sclerosis (MS), patients´ characteristics and (bio)markers that reliably predict the individual disease prognosis at disease onset are lacking. Cohort studies allow a close follow-up of MS histories and a thorough phenotyping of patients. Therefore, a multicenter cohort study was initiated to implement a wide spectrum of data and (bio)markers in newly diagnosed patients.

[Results of PCR Pool Testing In Primary and Special Needs Schools In Bavaria For The School Year 2021/2022: Sentinel Surveillance In Face-To-Face Teaching During The Sars-CoV-2 Pandemic].

In the school years 2019/20 and 2020/21, children were physically, psychologically, and socially stressed by school closures caused by the SARS-CoV-2 pandemic. To ensure attendance with optimal infection protection, PCR pool testing was conducted during the 2021/22 school year at Bavarian elementary schools and schools for pupils with special needs for timely detection of SARS-CoV-2 infection. This study analyzes the results of PCR pool testing over time stratified by region, school type, and age of children.

A Bayesian hierarchical approach to account for evidence and uncertainty in the modeling of infectious diseases: An application to COVID-19.

Infectious disease models can serve as critical tools to predict the development of cases and associated healthcare demand and to determine the set of nonpharmaceutical interventions (NPIs) that is most effective in slowing the spread of an infectious agent. Current approaches to estimate NPI effects typically focus on relatively short time periods and either on the number of reported cases, deaths, intensive care occupancy, or hospital occupancy as a single indicator of disease transmission. In this work, we propose a Bayesian hierarchical model that integrates multiple outcomes and complementary sources of information in the estimation of the true and unknown number of infections while accounting for time-varying underreporting and weekday-specific delays in reported cases and deaths, allowing us to estimate the number of infections on a daily basis rather than having to smooth the data.

Examination of betahistine bioavailability in combination with the monoamine oxidase B inhibitor, selegiline, in humans-a non-randomized, single-sequence, two-period titration, open label single-center phase 1 study (PK-BeST).

Background: Betahistine was registered in Europe in the 1970s and approved in more than 80 countries as a first-line treatment for Menière's disease. It has been administered to more than 150 million patients. However, according to a Cochrane systematic review of betahistine and recent meta-analyses, there is insufficient evidence to say whether betahistine has any effect in the currently approved dosages of up to 48 mg/d.

Effectiveness of an individually tailored complex intervention to improve activities and participation in nursing home residents with joint contractures (JointConEval): a multicentre pragmatic cluster-randomised controlled trial.

Objective: This study aims to examine the effects of the individually tailored complex intervention Participation Enabling Care in Nursing (PECAN) on activities and participation of residents with joint contractures.

Design: Multicentre pragmatic cluster-randomised controlled trial.

Setting: 35 nursing homes in Germany (August 2018-February 2020).

Effect of blood pressure-lowering agents on microvascular function in people with small vessel diseases (TREAT-SVDs): a multicentre, open-label, randomised, crossover trial.

Background: Hypertension is the leading risk factor for cerebral small vessel disease. We aimed to determine whether antihypertensive drug classes differentially affect microvascular function in people with small vessel disease.

Methods: We did a multicentre, open-label, randomised crossover trial with blinded endpoint assessment at five specialist centres in Europe.

Colorectal Cancer Screening for Persons With a Positive Family History—Evaluation of the FARKOR Program for the Secondary Prevention of Colorectal Cancer in Persons Aged 25 to 50.

Background: Persons with a positive family history of colorectal cancer (CRC) are more likely than others to develop CRC and are also younger at the onset of the disease. Nonetheless, the German Federal Joint Committee (G-BA, Gemeinsamer Bundes - ausschuss) recommends screening all persons aged 50 and above regardless of their family history. FARKOR was a project supported by the Innovation Fund of the G-BA to study the feasibility, efficacy, and safety of a risk-adapted early detection program for CRC among persons aged 25 to 50 without any specific past medical history.

[Newborn hearing screening in Germany-results of the 2011/2012 and 2017/2018 evaluations].

Background: Newborn hearing screening (NHS) was introduced nationwide by the Federal Joint Committee (Gemeinsamer Bundesausschuss, G‑BA) in 2009. In this process, quality targets were also set in the pediatrics directive. In order to review the quality NHS in Germany, the G‑BA commissioned a consortium to conduct an initial evaluation for the years 2011 and 2012 and a follow-up evaluation for 2017 and 2018.

Long-term benefits and harms of early colorectal cancer screening in German individuals with familial cancer risk.

Individuals with a family history of colorectal cancer (CRC) may benefit from early screening with colonoscopy or immunologic fecal occult blood testing (iFOBT). We systematically evaluated the benefit-harm trade-offs of various screening strategies differing by screening test (colonoscopy or iFOBT), interval (iFOBT: annual/biennial; colonoscopy: 10-yearly) and age at start (30, 35, 40, 45, 50 and 55 years) and end of screening (65, 70 and 75 years) offered to individuals identified with familial CRC risk in Germany. A Markov-state-transition model was developed and used to estimate health benefits (CRC-related deaths avoided, life-years gained [LYG]), potential harms (eg, associated with additional colonoscopies) and incremental harm-benefit ratios (IHBR) for each strategy.

Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis.

Background: Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system that affects millions of people worldwide. The disease course varies greatly across individuals and many disease-modifying treatments with different safety and efficacy profiles have been developed recently. Prognostic models evaluated and shown to be valid in different settings have the potential to support people with MS and their physicians during the decision-making process for treatment or disease/life management, allow stratified and more precise interpretation of interventional trials, and provide insights into disease mechanisms.