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. To provide a systematic overview, we conducted a "Cochrane review" that assessed 75 published prediction models using relevant checklists (CHARMS, PROBAST, TRIPOD). We have summarized the relevant points from this analysis here so that the use of prognostic models for therapy decisions in clinical routine can be successful in the future.

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. However, the integration of less informative or redundant data types could potentially hinder this performance. Therefore, identifying the most effective combinations of omics data types that enhance predictive performance is critical for cost-effective and accurate predictions. METHODS: In this study, we systematically evaluated the predictive performance of all 31 possible combinations including at least one of five genomic data types (mRNA, miRNA, methylation, DNAseq, and copy number variation) using 14 cancer datasets with right-censored survival outcomes, publicly available from the TCGA database. We employed various prediction methods and up-weighted clinical data in every model to leverage their predictive importance. Harrell's C-index and the integrated Brier Score were used as performance measures. To assess the robustness of our findings, we performed a bootstrap analysis at the level of the included datasets. Statistical testing was conducted for key results, limiting the number of tests to ensure a low risk of false positives. RESULTS: Contrary to expectations, we found that using only mRNA data or a combination of mRNA and miRNA data was sufficient for most cancer types. For some cancer types, the additional inclusion of methylation data led to improved prediction results. Far from enhancing performance, the introduction of more data types most often resulted in a decline in performance, which varied between the two performance measures. CONCLUSIONS: Our findings challenge the prevailing notion that combining multiple omics data types in multi-omics survival prediction improves predictive performance. Thus, the widespread approach in multi-omics prediction of incorporating as many data types as possible should be reconsidered to avoid suboptimal prediction results and unnecessary expenditure.

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 report on their quantification, balancing, and evaluation in the literature. It will focus on computational methods used for incorporating patient preferences into algorithms and models at an individual level as well as the types of medical algorithms and models where 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. To address this issue, it is important to explore methods integrating patient preferences. INCLUSION CRITERIA: This review will investigate patient preferences and their integration into medical algorithms and models for individual-level clinical decision-making. The scoping review will include diverse sources, such as peer-reviewed articles, clinical practice guidelines, gray literature, government reports, guidelines, and expert opinions for a comprehensive investigation of the subject. METHODS: This scoping review will follow JBI methodology. A comprehensive search will be conducted in PubMed, Web of Science, ACM Digital Library, IEEE Xplore, the Cochrane Library, OpenGrey, the National Technical Reports Library, and the first 20 pages of Google Scholar. The search strategy will include keywords related to patient preferences, medical algorithms and models, decision-making, and software tools and frameworks. Data extraction and analysis will be guided by the JBI framework, which includes an explorative and qualitative analysis. REVIEW REGISTRATION: Open Science Framework https://osf.io/qg3b5.

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.) development, it is key to evaluate a trained model w.r.t. to its prognostic or predictive performance on new independent data. For binary classification, quantifying discrimination uses the receiver operating characteristics (ROC) and its area under the curve (AUC) as aggregation measure. We are interested to calculate both as well as basic indicators of calibration-in-the-large for a binary classification task using a distributed and privacy-preserving approach. METHODS: We employ DataSHIELD as the technology to carry out distributed analyses, and we use a newly developed algorithm to validate the prediction score by conducting distributed and privacy-preserving ROC analysis. Calibration curves are constructed from mean values over sites. The determination of ROC and its AUC is based on a generalized linear model (GLM) approximation of the true ROC curve, the ROC-GLM, as well as on ideas of differential privacy (DP). DP adds noise (quantified by the ℓ 2 sensitivity Δ 2 ( f ^ ) ) to the data and enables a global handling of placement numbers. The impact of DP parameters was studied by simulations. RESULTS: In our simulation scenario, the true and distributed AUC measures differ by Δ AUC < 0.01 depending heavily on the choice of the differential privacy parameters. It is recommended to check the accuracy of the distributed AUC estimator in specific simulation scenarios along with a reasonable choice of DP parameters. Here, the accuracy of the distributed AUC estimator may be impaired by too much artificial noise added from DP. CONCLUSIONS: The applicability of our algorithms depends on the ℓ 2 sensitivity Δ 2 ( f ^ ) of the underlying statistical/predictive model. The simulations carried out have shown that the approximation error is acceptable for the majority of simulated cases. For models with high Δ 2 ( f ^ ) , the privacy parameters must be set accordingly higher to ensure sufficient privacy protection, which affects the approximation error. This work shows that complex measures, as the AUC, are applicable for validation in distributed setups while preserving an individual's privacy.

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. METHODS: We retrospectively integrated and analyzed clinical and outcome data of juvenile stroke and TIA patients treated at the LMU University Hospital, LMU Munich, Munich. Good outcome was defined as a modified Rankin Scale of 0-2 or return to baseline of function. We analyzed candidate predictors and developed a predictive model. Predictive abilities were inspected using Area Under the ROC curve (AUROC) and visual representation of the calibration. The model was validated internally. RESULTS: 346 patients were included in the analysis. We observed a good outcome in n = 293 patients (84.7%). The prediction model for an unfavourable outcome had an AUROC of 89.1% (95% CI 83.3-93.1%). The model includes age NIHSS, ASPECTS, blood glucose and type of vessel occlusion as predictors for the individual patient outcome. CONCLUSIONS: Here, we introduce the highly accurate PREDICT-score for the 3-month outcome after juvenile stroke derived from clinical routine data. The PREDICT-score might be helpful in guiding individual patient decisions and designing future studies but needs further prospective validation which is already planned. Trial registration The study has been registered at https://drks.de (DRKS00024407) on March 31, 2022.

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. Patients with ICD-10-GM codes D66 and HA medication were included in descriptive analyses. Severity levels were categorized according to HA medication consumption. In total, 257 patients were identified: mild HA, 104 patients (mean age: 40.0 years; SD: 22.9); moderate HA, 17 patients, (51.2 years; SD: 24.5); severe HA, 128 patients, (34.2 years; SD: 18.5). There were eight patients categorized with inhibitors (37.8 years; SD: 29.6). Psychotherapy was reported among 28.8% (mild) to 32.8% (severe) of patients. Joint disease was documented for 46.2% (mild) to 61.7% (severe) of patients. Mean direct costs per patient per year were 1.34× for mild, 11× for moderate, 81× higher for severe HA patients and 223× higher for inhibitor patients than the mean annual expenditure per AOK Bayern insurant (2019). German SHI data provide comprehensive information. The patient burden in HA is significant with respect to joint disease and psychological stress regardless of the HA severity level. The cost of HA care for patients is high. Large cost ranges suggest that the individual situation of a patient must be considered when interpreting costs. The main limitation of SHI data analysis for HA was the lack of granularity of ICD codes.

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. METHODS: In patients from the French OFSEP registry, we independently built and validated models predicting being free of relapse and free of confirmed disability progression (CDP), following the methodological roadmap and predictors reported by Stühler. Hierarchical Bayesian models were fit to predict the outcomes under 6 disease-modifying treatments given the individual disease course up to the moment of treatment change. Data was temporally split on 2017, and models were developed in patients treated earlier (n = 5517). Calibration curves, discrimination, mean squared error (MSE) and relative percentage of root MSE (RMSE%) were assessed by external validation of models in more-recent patients (n = 3768). Non-Bayesian fixed-effects GLMs were also applied and their outcomes were compared to these of the Bayesian ones. For both, we modelled the number of on-therapy relapses with a negative binomial distribution, and CDP occurrence with a binomial distribution. RESULTS: The performance of our temporally-validated relapse model (MSE: 0.326, C-Index: 0.639) is potentially superior to that of Stühler's (MSE: 0.784, C-index: 0.608). Calibration plots revealed miscalibration. Our CDP model (MSE: 0.072, C-Index: 0.777) was also better than its counterpart (MSE: 0.131, C-index: 0.554). Results from non-Bayesian fixed-effects GLM models were similar to the Bayesian ones. CONCLUSIONS: The relapse and CDP models rebuilt and externally validated in independent data could compare and strengthen the credibility of the Stühler models. Their model-building strategy was replicable.

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. Prescription of betahistine can, therefore, be used as a proxy estimate for prevalence. We used openly available claims data from the French health insurance data warehouse, defining annual prevalence of vestibular disease as the number of people who received at least one betahistine prescription that year. Dosage and pack size of each prescribed formulation were extracted to calculate the sum of betahistine in mg and the Defined Daily Dose (DDD) for age and sex strata and in total. To estimate the relative impact of one landmark trial, the BEMED study, we compared prescriptions from the years 2014/2015 to prescriptions in 2019/2022. A total of 735,121 (2014), 694,705 (2015), 614,431 (2019), and 562,476 (2022) persons filled in a prescription of betahistine. Patients were predominantly older and female. Average amount dispensed per year and per person increased from 4422.54 mg during the pre-BEMED period to 4736.90 mg during the post-BEMED period. DDD decreased from 130 Mio per year in 2014/2015 to 116 Mio per year in 2019/2022. Total costs for betahistine decreased by 42% from 21,615,037 Euro in 2014 to 12,894,249 Euro in 2022. Vestibular disease is frequent in France and has a relevant impact on population health. Despite conflicting clinical evidence, betahistine continues to be prescribed widely in medical practice.

Dummy run for planning of isotoxic dose-escalated radiation therapy for glioblastoma used in the PRIDE trial (NOA-28; ARO-2024-01; AG-NRO-06).

Background: The PRIDE trial (NOA-28; ARO-2024-01; AG-NRO-06; NCT05871021) is designed to determine whether a dose escalation with 75.0 Gy in 30 fractions can enhance the median overall survival (OS) in patients with methylguanine methyltransferase (MGMT) promotor unmethylated glioblastoma compared to historical median OS rates, while being isotoxic to historical cohorts through the addition of concurrent bevacizumab (BEV). To ensure protocol-compliant irradiation planning with all study centers, a dummy run was planned and the plan quality was evaluated. Methods: A suitable patient case was selected and the computed tomography (CT), magnetic resonance imaging (MRI) and O-(2-[18F]fluoroethyl)-L-tyrosine (FET) positron emission tomography (PET) contours were made available. Participants at the various intended study sites performed radiation planning according to the PRIDE clinical trial protocol. The treatment plans and dose grids were uploaded as Digital Imaging and Communications in Medicine (DICOM) files to a cloud-based platform. Plan quality and protocol adherence were analyzed using a standardized checklist, scorecards and indices such as Dice Score (DSC) and Hausdorff Distance (HD). Results: Median DSC was 0.89, 0.90, 0.88 for PTV60, PTV60ex (planning target volume receiving 60.0 Gy for the standard and the experimental plan, respectively) and PTV75 (PTV receiving 75.0 Gy in the experimental plan), respectively. Median HD values were 17.0 mm, 13.9 mm and 12.1 mm, respectively. These differences were also evident in the volumes: The PTV60 had a volume range of 219.1-391.3 cc (median: 261.9 cc) for the standard plans, while the PTV75 volumes for the experimental plans ranged from 71.5-142.7 cc (median: 92.3 cc). The structures with the largest deviations in Dice score were the pituitary gland (median 0.37, range 0.00-0.69) and the right lacrimal gland (median 0.59, range 0.42-0.78). Conclusions: The deviations revealed the necessity of systematic trainings with appropriate feedback before the start of clinical trials in radiation oncology and the constant monitoring of protocol compliance throw-out the study. Trial registration: NCT05871021.

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. METHODS: ProVal-MS (Prospective study to validate a multidimensional decision score that predicts treatment outcome at 24 months in untreated patients with clinically isolated syndrome or early Relapsing-Remitting-MS) is a prospective cohort study in patients with clinically isolated syndrome (CIS) or Relapsing-Remitting (RR)-MS (McDonald 2017 criteria), diagnosed within the last two years, conducted at five academic centers in Southern Germany. The collection of clinical, laboratory, imaging, and paraclinical data as well as biosamples is harmonized across centers. The primary goal is to validate (discrimination and calibration) the previously published DIFUTURE MS-Treatment Decision score (MS-TDS). The score supports clinical decision-making regarding the options of early (within 6 months after study baseline) platform medication (Interferon beta, glatiramer acetate, dimethyl/diroximel fumarate, teriflunomide), or no immediate treatment (> 6 months after baseline) of patients with early RR-MS and CIS by predicting the probability of new or enlarging lesions in cerebral magnetic resonance images (MRIs) between 6 and 24 months. Further objectives are refining the MS-TDS score and providing data to identify new markers reflecting disease course and severity. The project also provides a technical evaluation of the ProVal-MS cohort within the IT-infrastructure of the DIFUTURE consortium (Data Integration for Future Medicine) and assesses the efficacy of the data sharing techniques developed. PERSPECTIVE: Clinical cohorts provide the infrastructure to discover and to validate relevant disease-specific findings. A successful validation of the MS-TDS will add a new clinical decision tool to the armamentarium of practicing MS neurologists from which newly diagnosed MS patients may take advantage. Trial registration ProVal-MS has been registered in the German Clinical Trials Register, `Deutsches Register Klinischer Studien` (DRKS)-ID: DRKS00014034, date of registration: 21 December 2018; https://drks.de/search/en/trial/DRKS00014034.

[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]. / Ergebnisse von PCR-Pooltestungen an bayerischen Grund- und Förderschulen im Schuljahr 2021/2022 zur Surveillance im Präsenzunterricht während der SARS-CoV-2-Pandemie.

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. The data were obtained from classes in elementary and special needs schools, involving pupils aged 6 to 11 years, who participated in the Bavaria-wide PCR pool testing from 09/20/21 to 04/08/22. Samples were collected twice weekly, consisting of PCR pool samples and individual PCR samples, which were only evaluated in case of a positive pool test. A class was considered positive if at least one individual sample from that class was positive within a calendar week (CW). A school (class) was considered to be infection-prone if three or more classes in that school (students in that class) were positive within a CW. The data included 2,430 elementary schools (339 special needs schools) with 23,021 (2,711) classes and 456,478 (29,200) children. A total of 1,157,617 pools (of which 3.37% were positive) and 724,438 individual samples (6.76% positive) were analyzed. Larger schools exhibited higher PR compared to smaller schools. From January 2022, the Omicron variant led to a massive increase in PR across Bavaria. The incidence rates per 100,000 person-weeks within the individual school samples were significantly lower than the concurrently reported age-specific and general infection incidences in the overall Bavarian population. PCR pool testing revealed relatively few positive pools, with an average of four children per one hundred pools testing positive. Schools and classes were rarely considered infection-prone, even during periods of high incidences outside of schools. The combination of PCR pool testing and hygiene measures allowed for a largely safe in-person education for pupils in primary and special needs schools in the school year 2021/22.

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. To address dynamic changes occurring over long periods of time, we account for the spread of new variants, seasonality, and time-varying differences in host susceptibility. We implement a Markov chain Monte Carlo algorithm to conduct Bayesian inference and illustrate the proposed approach with data on COVID-19 from 20 European countries. The approach shows good performance on simulated data and produces posterior predictions that show a good fit to reported cases, deaths, hospital, and intensive care occupancy.

Endovascular Treatment for Acute Isolated Internal Carotid Artery Occlusion : A Propensity Score Matched Multicenter Study.

PURPOSE: The benefit of endovascular treatment (EVT) in patients with acute symptomatic isolated occlusion of the internal carotid artery (ICA) without involvement of the middle and anterior cerebral arteries is unclear. We aimed to compare clinical and safety outcomes of best medical treatment (BMT) versus EVT + BMT in patients with stroke due to isolated ICA occlusion. METHODS: We conducted a retrospective multicenter study involving patients with isolated ICA occlusion between January 2016 and December 2020. We stratified patients by BMT versus EVT and matched the groups using propensity score matching (PSM). We assessed the effect of treatment strategy on favorable outcome (modified Rankin scale ≤ 2) 90 days after treatment and compared reduction in NIHSS score at discharge, rates of symptomatic intracranial hemorrhage (sICH) and 3­month mortality. RESULTS: In total, we included 149 patients with isolated ICA occlusion. To address imbalances, we matched 45 patients from each group using PSM. The rate of favorable outcomes at 90 days was 56% for EVT and 38% for BMT (odds ratio, OR 1.89, 95% confidence interval, CI 0.84-4.24; p = 0.12). Patients treated with EVT showed a median reduction in NIHSS score at discharge of 6 points compared to 1 point for BMT patients (p = 0.02). Rates of symptomatic intracranial hemorrhage (7% vs. 4%; p = 0.66) and 3­month mortality (11% vs. 13%; p = 0.74) did not differ between treatment groups. Periprocedural complications of EVT with early neurological deterioration occurred in 7% of cases. CONCLUSION: Although the benefit on functional outcome did not reach statistical significance, the results for NIHSS score improvement, and safety support the use of EVT in patients with stroke due to isolated ICA occlusion.

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. Both benefits and harms increased with earlier start and shorter intervals of screening. When screening started before age 50, 32-36 CRC-related deaths per 1000 persons were avoided with colonoscopy and 29-34 with iFOBT screening, compared to 29-31 (colonoscopy) and 28-30 (iFOBT) CRC-related deaths per 1000 persons when starting age 50 or older, respectively. For iFOBT screening, the IHBRs expressed as additional colonoscopies per LYG were one (biennial, age 45-65 vs no screening), four (biennial, age 35-65), six (biennial, age 30-70) and 34 (annual, age 30-54; biennial, age 55-75). Corresponding IHBRs for 10-yearly colonoscopy were four (age 55-65), 10 (age 45-65), 15 (age 35-65) and 29 (age 30-70). Offering screening with colonoscopy or iFOBT to individuals with familial CRC risk before age 50 is expected to be beneficial. Depending on the accepted IHBR threshold, 10-yearly colonoscopy or alternatively biennial iFOBT from age 30 to 70 should be recommended for this target group.

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. A combination with the monoamine oxidase B (MAO-B) inhibitor, selegiline, may increase the bioavailability of betahistine to levels similar to the well-established combination of L-DOPA with carbidopa or benserazide in the treatment of Parkinson's disease. We investigated the effect of selegiline on betahistine pharmacokinetics and the safety of the combination in humans. Methods: In an investigator-initiated prospective, non-randomized, single-sequence, two-period titration, open label single-center phase 1 study, 15 healthy volunteers received three single oral dosages of betahistine (24, 48, and 96 mg in this sequence with at least 2 days' washout period) without and with selegiline (5 mg/d with a loading period of 7 days). Betahistine serum concentrations were measured over a period of 240 min at eight time points (area under the curve, AUC0-240 min). This trial is registered with EudraCT (2019-002610-39) and ClinicalTrials.gov. Findings: In all three single betahistine dosages, selegiline increased the betahistine bioavailability about 80- to 100-fold. For instance, the mean (±SD) of the area under curve for betahistine 48 mg alone was 0.64 (+/-0.47) h*ng/mL and for betahistine plus selegiline 53.28 (+/-37.49) h*ng/mL. The half-life time of around 30 min was largely unaffected, except for the 24 mg betahistine dosage. In total, 14 mild adverse events were documented. Interpretation: This phase 1 trial shows that the MAO-B inhibitor selegiline increases betahistine bioavailability by a factor of about 80 to 100. No safety concerns were detected. Whether the increased bioavailability has an impact on the preventive treatment of Menière's disease, acute vestibular syndrome, or post-BPPV residual dizziness has to be evaluated in placebo-controlled trials. Clinical trial registration: https://clinicaltrials.gov/study/NCT05938517?intr=betahistine%20and%20selegiline&rank=1, identifier: NCT05938517.

Antibiotic use in pediatric acute care hospitals: an analysis of antibiotic consumption data from Germany, 2013-2020.

BACKGROUND: Antimicrobial stewardship (AMS) programs are effective tools for improving antibiotic prescription quality. Their implementation requires the regular surveillance of antibiotic consumption at the patient and institutional level. Our study captured and analyzed antibiotic consumption density (ACD) for hospitalized pediatric patients. METHOD: We collected antibacterial drug consumption data for 2020 from hospital pharmacies at 113 pediatric departments of acute care hospitals in Germany. ACD was calculated as defined daily dose (DDD, WHO/ATC Index 2019) per 100 patient days (pd). In addition, we analyzed the trends in antibiotic use during 2013-2020. RESULTS: In 2020, median ACD across all participating hospitals was 26.7 DDD/100 pd, (range: 10.1-79.2 DDD/100 pd). It was higher at university vs. non-university hospitals (38.6 vs. 25.2 DDD/100 pd, p < 0.0001). The highest use densities were seen on oncology wards and intensive care units at university hospitals (67.3 vs. 38.4 DDD/100 pd). During 2013-2020, overall ACD declined (- 10%) and cephalosporin prescriptions also decreased (- 36%). In 2020, cephalosporins nevertheless remained the most commonly dispensed class of antibiotics. Interhospital variability in cephalosporin/penicillin ratio was substantial. Antibiotics belonging to WHO AWaRe "Watch" and "Reserve" categories, including broad-spectrum penicillins (+ 31%), linezolid (+ 121%), and glycopeptides (+ 43%), increased over time. CONCLUSION: Significant heterogeneity in ACD and prescription of different antibiotic classes as well as high prescription rates for cephalosporins and an increased use of reserve antibiotics indicate improvable antibiotic prescribing quality. AMS programs should urgently prioritize these issues to reduce antimicrobial resistance.

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). PARTICIPANTS: 562 nursing home residents aged ≥65 years with ≥1 major joint contracture (303 intervention group, 259 control group). INTERVENTIONS: Nursing homes were randomised to PECAN (18 clusters) or optimised standard care (17 clusters) with researcher-concealed cluster allocation by facsimile. The intervention targeted impairments in activities and participation. Implementation included training and support for selected staff. Control group clusters received brief information. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary endpoint PaArticular Scales combined residents' activities and participation at 12 months. The secondary outcome comprised quality of life. Safety measures were falls, fall-related consequences and physical restraints. Residents, staff and researchers were unblinded. Data collection, data entry and statistical analysis were blinded. Primary analyses were intention-to-treat at cluster level and individual level using a generalised mixed-effect regression model and imputation of missing data. RESULTS: Primary outcome analyses included 301 intervention group residents and 259 control group residents. The mean change on the Activities Scale was -1.47 points (SD 12.2) in the intervention group and 0.196 points (SD 12.5) in the control group and -3.87 points (SD 19.7) vs -3.18 points (SD 20.8) on the Participation Scale. The mean differences of changes between the groups were not statistically significant: Activities Scale: -1.72 (97.5% CI -6.05 to 2.61); Participation Scale: -1.24 (97.5% CI -7.02 to 4.45). We found no significant difference in the secondary outcome and no effects on safety measures. CONCLUSION: The complex intervention did not improve the activities and participation of nursing home residents on the PaArticular Scales at 12 months. Current nursing conditions in Germany may hamper implementation. TRIAL REGISTRATION NUMBER: DRKS00015185.

[Newborn hearing screening in Germany-results of the 2011/2012 and 2017/2018 evaluations]. / Neugeborenen-Hörscreening in Deutschland ­ Ergebnisse der Evaluationen 2011/2012 und 2017/2018.

BACKGROUND: Newborn hearing screening (NHS) was introduced nationwide by the Federal Joint Committee (Gemeinsamer Bundesausschuss, G­BA) in 2009. In this process, quality targets were also set in the pediatrics directive. In order to review the quality NHS in Germany, the G­BA commissioned a consortium to conduct an initial evaluation for the years 2011 and 2012 and a follow-up evaluation for 2017 and 2018. METHODS: The evaluations were based on NHS screening parameters (Sammelstatistiken) that must be documented by all obstetrics and neonatology departments as NHS providers and can also be compiled through cooperation with hearing screening centers (HSCs). Additional data were collected through questionnaires and interviews and routine data were used to evaluate the screening process. RESULTS: In 13 federal states, a total of 15 HSCs are involved in the screening process. Across Germany, an NHS screening rate of 86.1% was documented in 2018 (82.4% in 2012), but this differed significantly between the federal states. The specified quality targets could not yet be implemented everywhere. For example, only less than half of the obstetric departments achieved the specified screening rate of over 95%. A comparison of data from the follow-up evaluation and the first evaluation showed that the structural quality of NHS had improved, while the process quality remained the same or had deteriorated. The refer rate (children who were discharged without passing the screening) increased from 5.3% to 6.0%. DISCUSSION: To improve the quality of NHS, HSCs should be established nationwide and a second screening should be carried out more consistently before discharge in the case of a refer result in the initial screening.

Colorectal Cancer Screening for Persons With a Positive Family History­Evaluation 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. METHODS: Physicians in private practice in Bavaria documented their activities relating to FARKOR online. The FARKOR process comprised a declaration of consent, a simplified family history for CRC, an optional, more comprehensive family history, a counseling session for participatory decision-making on further measures, and various modalities of screening (an immunological fecal occult blood test [iFOBT], colonoscopy, or no screening). Related physician activities outside the FARKOR process were assessed by record linkage between study data and data of the patients' health insurance carriers. RESULTS: The simplified family history was documented in 25 847 persons and positive for CRC in 5769 (22.3%). 3232 persons had a more comprehensive family history, among whom 2054 (63.6%) participated in screening measures. 1595 underwent colonoscopy; 278 persons who had already undergone colonoscopy in the preceding five years were excluded from the analysis. Colonoscopy revealed adenoma in 232 persons (17,6 %), advanced adenoma in 78 (5.9%) and carcinoma in 4 (0.3%). There were no serious complications. CONCLUSION: The detection rates in this study corresponded to those of persons aged 55 to 59 in the current early detection program. Despite numerous problems in the performance of the study (inconsistencies in documentation, external performance of screening measures on program participants), the results support the feasibility of a risk-adapted early detection program in the young target population with a family history of CRC.