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.

Transcranial direct current stimulation as an additional treatment to selective serotonin reuptake inhibitors in adults with major depressive disorder in Germany (DepressionDC): a triple-blind, randomised, sham-controlled, multicentre trial.

BACKGROUND: Transcranial direct current stimulation (tDCS) has been proposed as a feasible treatment for major depressive disorder (MDD). However, meta-analytic evidence is heterogenous and data from multicentre trials are scarce. We aimed to assess the efficacy of tDCS versus sham stimulation as an additional treatment to a stable dose of selective serotonin reuptake inhibitors (SSRIs) in adults with MDD. METHODS: The DepressionDC trial was triple-blind, randomised, and sham-controlled and conducted at eight hospitals in Germany. Patients being treated at a participating hospital aged 18-65 years were eligible if they had a diagnosis of MDD, a score of at least 15 on the Hamilton Depression Rating Scale (21-item version), no response to at least one antidepressant trial in their current depressive episode, and treatment with an SSRI at a stable dose for at least 4 weeks before inclusion; the SSRI was continued at the same dose during stimulation. Patients were allocated (1:1) by fixed-blocked randomisation to receive either 30 min of 2 mA bifrontal tDCS every weekday for 4 weeks, then two tDCS sessions per week for 2 weeks, or sham stimulation at the same intervals. Randomisation was stratified by site and baseline Montgomery-Åsberg Depression Rating Scale (MADRS) score (ie, <31 or ≥31). Participants, raters, and operators were masked to treatment assignment. The primary outcome was change on the MADRS at week 6, analysed in the intention-to-treat population. Safety was assessed in all patients who received at least one treatment session. The trial was registered with ClinicalTrials.gov (NCT02530164). FINDINGS: Between Jan 19, 2016, and June 15, 2020, 3601 individuals were assessed for eligibility. 160 patients were included and randomly assigned to receive either active tDCS (n=83) or sham tDCS (n=77). Six patients withdrew consent and four patients were found to have been wrongly included, so data from 150 patients were analysed (89 [59%] were female and 61 [41%] were male). No intergroup difference was found in mean improvement on the MADRS at week 6 between the active tDCS group (n=77; -8·2, SD 7·2) and the sham tDCS group (n=73; -8·0, 9·3; difference 0·3 [95% CI -2·4 to 2·9]). Significantly more participants had one or more mild adverse events in the active tDCS group (50 [60%] of 83) than in the sham tDCS group (33 [43%] of 77; p=0·028). INTERPRETATION: Active tDCS was not superior to sham stimulation during a 6-week period. Our trial does not support the efficacy of tDCS as an additional treatment to SSRIs in adults with MDD. FUNDING: German Federal Ministry of Education and Research.

Shape-specific characterization of colorectal adenoma growth and transition to cancer with stochastic cell-based models.

Colorectal adenoma are precursor lesions on the pathway to cancer. Their removal in screening colonoscopies has markedly reduced rates of cancer incidence and death. Generic models of adenoma growth and transition to cancer can guide the implementation of screening strategies. But adenoma shape has rarely featured as a relevant risk factor. Against this backdrop we aim to demonstrate that shape influences growth dynamics and cancer risk. Stochastic cell-based models are applied to a data set of 197,347 Bavarian outpatients who had colonoscopies from 2006-2009, 50,649 patients were reported with adenoma and 296 patients had cancer. For multi-stage clonal expansion (MSCE) models with up to three initiating stages parameters were estimated by fits to data sets of all shapes combined, and of sessile (70% of all adenoma), peduncular (17%) and flat (13%) adenoma separately for both sexes. Pertinent features of adenoma growth present themselves in contrast to previous assumptions. Stem cells with initial molecular changes residing in early adenoma predominantly multiply within two-dimensional structures such as crypts. For these cells mutation and division rates decrease with age. The absolute number of initiated cells in an adenoma of size 1 cm is small around 103, related to all bulk cells they constitute a share of about 10-5. The notion of very few proliferating stem cells with age-decreasing division rates is supported by cell marker experiments. The probability for adenoma transiting to cancer increases with squared linear size and shows a shape dependence. Compared to peduncular and flat adenoma, it is twice as high for sessile adenoma of the same size. We present a simple mathematical expression for the hazard ratio of interval cancers which provides a mechanistic understanding of this important quality indicator. We conclude that adenoma shape deserves closer consideration in screening strategies and as risk factor for transition to cancer.

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.

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.

[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.

Cost-Effectiveness of a Specialized Breathlessness Service Versus Usual Care for Patients With Advanced Diseases.

OBJECTIVES: The Munich Breathlessness Service (MBS) significantly improved control of breathlessness measured by the Chronic Respiratory Questionnaire (CRQ) Mastery in a randomized controlled fast track trial with waitlist group design spanning 8 weeks in Germany. This study aimed to assess the within-trial cost-effectiveness of MBS from a societal perspective. METHODS: Data included generic (5-level version of EQ-5D) health-related quality of life and disease-specific CRQ Mastery. Quality-adjusted life years (QALYs) were calculated based on 5-level version of EQ-5D utilities valued with German time trade-off. Direct medical costs and productivity loss were calculated based on standardized unit costs. Incremental cost-effectiveness ratios (ICER) and cost-effectiveness-acceptance curves were calculated using adjusted mean differences (AMD) in costs (gamma-distributed model) and both effect parameters (Gaussian-distributed model) and performing 1000 simultaneous bootstrap replications. Potential gender differences were investigated in stratified analyses. RESULTS: Between March 2014 and April 2019, 183 eligible patients were enrolled. MBS intervention demonstrated significantly better effects regarding generic (AMD of QALY gains of 0.004, 95% confidence interval [CI] 0.0003 to 0.008) and disease-specific health-related quality of life at nonsignificantly higher costs (AMD of €605 [95% CI -1109 to 2550]). At the end of the intervention, the ICER was €152 433/QALY (95% CI -453 545 to 1 625 903) and €1548/CRQ Mastery point (95% CI -3093 to 10 168). Intervention costs were on average €357 (SD = 132). Gender-specific analyses displayed dominance for MBS in males and higher effects coupled with significantly higher costs in females. CONCLUSIONS: Our results show a high ICER for MBS. Considering dominance for MBS in males, implementing MBS on approval within the German health care system should be considered.

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.

PREDICT-juvenile-stroke: PRospective evaluation of a prediction score determining individual clinical outcome three months after ischemic stroke in young adults - a study protocol.

BACKGROUND: Although of high individual and socioeconomic relevance, a reliable prediction model for the prognosis of juvenile stroke (18-55 years) is missing. Therefore, the study presented in this protocol aims to prospectively validate the discriminatory power of a prediction score for the 3 months functional outcome after juvenile stroke or transient ischemic attack (TIA) that has been derived from an independent retrospective study using standard clinical workup data. METHODS: PREDICT-Juvenile-Stroke is a multi-centre (n = 4) prospective observational cohort study collecting standard clinical workup data and data on treatment success at 3 months after acute ischemic stroke or TIA that aims to validate a new prediction score for juvenile stroke. The prediction score has been developed upon single center retrospective analysis of 340 juvenile stroke patients. The score determines the patient's individual probability for treatment success defined by a modified Rankin Scale (mRS) 0-2 or return to pre-stroke baseline mRS 3 months after stroke or TIA. This probability will be compared to the observed clinical outcome at 3 months using the area under the receiver operating characteristic curve. The primary endpoint is to validate the clinical potential of the new prediction score for a favourable outcome 3 months after juvenile stroke or TIA. Secondary outcomes are to determine to what extent predictive factors in juvenile stroke or TIA patients differ from those in older patients and to determine the predictive accuracy of the juvenile stroke prediction score on other clinical and paraclinical endpoints. A minimum of 430 juvenile patients (< 55 years) with acute ischemic stroke or TIA, and the same number of older patients will be enrolled for the prospective validation study. DISCUSSION: The juvenile stroke prediction score has the potential to enable personalisation of counselling, provision of appropriate information regarding the prognosis and identification of patients who benefit from specific treatments. TRIAL REGISTRATION: The study has been registered at https://drks.de on March 31, 2022 ( DRKS00024407 ).

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. Many researchers have turned to prognostic models to help predict clinical outcomes in people with MS; however, to our knowledge, no widely accepted prognostic model for MS is being used in clinical practice yet. OBJECTIVES: To identify and summarise multivariable prognostic models, and their validation studies for quantifying the risk of clinical disease progression, worsening, and activity in adults with MS. SEARCH METHODS: We searched MEDLINE, Embase, and the Cochrane Database of Systematic Reviews from January 1996 until July 2021. We also screened the reference lists of included studies and relevant reviews, and references citing the included studies. SELECTION CRITERIA: We included all statistically developed multivariable prognostic models aiming to predict clinical disease progression, worsening, and activity, as measured by disability, relapse, conversion to definite MS, conversion to progressive MS, or a composite of these in adult individuals with MS. We also included any studies evaluating the performance of (i.e. validating) these models. There were no restrictions based on language, data source, timing of prognostication, or timing of outcome. DATA COLLECTION AND ANALYSIS: Pairs of review authors independently screened titles/abstracts and full texts, extracted data using a piloted form based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), assessed risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST), and assessed reporting deficiencies based on the checklist items in Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). The characteristics of the included models and their validations are described narratively. We planned to meta-analyse the discrimination and calibration of models with at least three external validations outside the model development study but no model met this criterion. We summarised between-study heterogeneity narratively but again could not perform the planned meta-regression. MAIN RESULTS: We included 57 studies, from which we identified 75 model developments, 15 external validations corresponding to only 12 (16%) of the models, and six author-reported validations. Only two models were externally validated multiple times. None of the identified external validations were performed by researchers independent of those that developed the model. The outcome was related to disease progression in 39 (41%), relapses in 8 (8%), conversion to definite MS in 17 (18%), and conversion to progressive MS in 27 (28%) of the 96 models or validations. The disease and treatment-related characteristics of included participants, and definitions of considered predictors and outcome, were highly heterogeneous amongst the studies. Based on the publication year, we observed an increase in the percent of participants on treatment, diversification of the diagnostic criteria used, an increase in consideration of biomarkers or treatment as predictors, and increased use of machine learning methods over time. Usability and reproducibility All identified models contained at least one predictor requiring the skills of a medical specialist for measurement or assessment. Most of the models (44; 59%) contained predictors that require specialist equipment likely to be absent from primary care or standard hospital settings. Over half (52%) of the developed models were not accompanied by model coefficients, tools, or instructions, which hinders their application, independent validation or reproduction. The data used in model developments were made publicly available or reported to be available on request only in a few studies (two and six, respectively). Risk of bias We rated all but one of the model developments or validations as having high overall risk of bias. The main reason for this was the statistical methods used for the development or evaluation of prognostic models; we rated all but two of the included model developments or validations as having high risk of bias in the analysis domain. None of the model developments that were externally validated or these models' external validations had low risk of bias. There were concerns related to applicability of the models to our research question in over one-third (38%) of the models or their validations. Reporting deficiencies Reporting was poor overall and there was no observable increase in the quality of reporting over time. The items that were unclearly reported or not reported at all for most of the included models or validations were related to sample size justification, blinding of outcome assessors, details of the full model or how to obtain predictions from it, amount of missing data, and treatments received by the participants. Reporting of preferred model performance measures of discrimination and calibration was suboptimal. AUTHORS' CONCLUSIONS: The current evidence is not sufficient for recommending the use of any of the published prognostic prediction models for people with MS in clinical routine today due to lack of independent external validations. The MS prognostic research community should adhere to the current reporting and methodological guidelines and conduct many more state-of-the-art external validation studies for the existing or newly developed models.

[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.

Benchmark study of feature selection strategies for multi-omics data.

BACKGROUND: In the last few years, multi-omics data, that is, datasets containing different types of high-dimensional molecular variables for the same samples, have become increasingly available. To date, several comparison studies focused on feature selection methods for omics data, but to our knowledge, none compared these methods for the special case of multi-omics data. Given that these data have specific structures that differentiate them from single-omics data, it is unclear whether different feature selection strategies may be optimal for such data. In this paper, using 15 cancer multi-omics datasets we compared four filter methods, two embedded methods, and two wrapper methods with respect to their performance in the prediction of a binary outcome in several situations that may affect the prediction results. As classifiers, we used support vector machines and random forests. The methods were compared using repeated fivefold cross-validation. The accuracy, the AUC, and the Brier score served as performance metrics. RESULTS: The results suggested that, first, the chosen number of selected features affects the predictive performance for many feature selection methods but not all. Second, whether the features were selected by data type or from all data types concurrently did not considerably affect the predictive performance, but for some methods, concurrent selection took more time. Third, regardless of which performance measure was considered, the feature selection methods mRMR, the permutation importance of random forests, and the Lasso tended to outperform the other considered methods. Here, mRMR and the permutation importance of random forests already delivered strong predictive performance when considering only a few selected features. Finally, the wrapper methods were computationally much more expensive than the filter and embedded methods. CONCLUSIONS: We recommend the permutation importance of random forests and the filter method mRMR for feature selection using multi-omics data, where, however, mRMR is considerably more computationally costly.

Validating the knowledge bank approach for personalized prediction of survival in acute myeloid leukemia: a reproducibility study.

Reproducibility is not only essential for the integrity of scientific research but is also a prerequisite for model validation and refinement for the future application of predictive algorithms. However, reproducible research is becoming increasingly challenging, particularly in high-dimensional genomic data analyses with complex statistical or algorithmic techniques. Given that there are no mandatory requirements in most biomedical and statistical journals to provide the original data, analytical source code, or other relevant materials for publication, accessibility to these supplements naturally suggests a greater credibility of the published work. In this study, we performed a reproducibility assessment of the notable paper by Gerstung et al. (Nat Genet 49:332-340, 2017) by rerunning the analysis using their original code and data, which are publicly accessible. Despite an open science setting, it was challenging to reproduce the entire research project; reasons included: incomplete data and documentation, suboptimal code readability, coding errors, limited portability of intensive computing performed on a specific platform, and an R computing environment that could no longer be re-established. We learn that the availability of code and data does not guarantee transparency and reproducibility of a study; paradoxically, the source code is still liable to error and obsolescence, essentially due to methodological and computational complexity, a lack of reproducibility checking at submission, and updates for software and operating environment. The complex code may also hide problematic methodological aspects of the proposed research. Building on the experience gained, we discuss the best programming and software engineering practices that could have been employed to improve reproducibility, and propose practical criteria for the conduct and reporting of reproducibility studies for future researchers.

Predicting the earliest deviation in weight gain in the course towards manifest overweight in offspring exposed to obesity in pregnancy: a longitudinal cohort study.

BACKGROUND: Obesity in pregnancy and related early-life factors place the offspring at the highest risk of being overweight. Despite convincing evidence on these associations, there is an unmet public health need to identify "high-risk" offspring by predicting very early deviations in weight gain patterns as a subclinical stage towards overweight. However, data and methods for individual risk prediction are lacking. We aimed to identify those infants exposed to obesity in pregnancy at ages 3 months, 1 year, and 2 years who likely will follow a higher-than-normal body mass index (BMI) growth trajectory towards manifest overweight by developing an early-risk quantification system. METHODS: This study uses data from the prospective mother-child cohort study Programming of Enhanced Adiposity Risk in CHildhood-Early Screening (PEACHES) comprising 1671 mothers with pre-conception obesity and without (controls) and their offspring. Exposures were pre- and postnatal risks documented in patient-held maternal and child health records. The main outcome was a "higher-than-normal BMI growth pattern" preceding overweight, defined as BMI z-score >1 SD (i.e., World Health Organization [WHO] cut-off "at risk of overweight") at least twice during consecutive offspring growth periods between age 6 months and 5 years. The independent cohort PErinatal Prevention of Obesity (PEPO) comprising 11,730 mother-child pairs recruited close to school entry (around age 6 years) was available for data validation. Cluster analysis and sequential prediction modelling were performed. RESULTS: Data of 1557 PEACHES mother-child pairs and the validation cohort were analyzed comprising more than 50,000 offspring BMI measurements. More than 1-in-5 offspring exposed to obesity in pregnancy belonged to an upper BMI z-score cluster as a distinct pattern of BMI development (above the cut-off of 1 SD) from the first months of life onwards resulting in preschool overweight/obesity (age 5 years: odds ratio [OR] 16.13; 95% confidence interval [CI] 9.98-26.05). Contributing early-life factors including excessive weight gain (OR 2.08; 95% CI 1.25-3.45) and smoking (OR 1.94; 95% CI 1.27-2.95) in pregnancy were instrumental in predicting a "higher-than-normal BMI growth pattern" at age 3 months and re-evaluating the risk at ages 1 year and 2 years (area under the receiver operating characteristic [AUROC] 0.69-0.79, sensitivity 70.7-76.0%, specificity 64.7-78.1%). External validation of prediction models demonstrated adequate predictive performances. CONCLUSIONS: We devised a novel sequential strategy of individual prediction and re-evaluation of a higher-than-normal weight gain in "high-risk" infants well before developing overweight to guide decision-making. The strategy holds promise to elaborate interventions in an early preventive manner for integration in systems of well-child care.

Associations of the vasoactive peptides CT-proET-1 and MR-proADM with incident type 2 diabetes: results from the BiomarCaRE Consortium.

BACKGROUND: Endothelin-1 (ET-1) and adrenomedullin (ADM) are commonly known as vasoactive peptides that regulate vascular homeostasis. Less recognised is the fact that both peptides could affect glucose metabolism. Here, we investigated whether ET-1 and ADM, measured as C-terminal-proET-1 (CT-proET-1) and mid-regional-proADM (MR-proADM), respectively, were associated with incident type 2 diabetes. METHODS: Based on the population-based Biomarkers for Cardiovascular Risk Assessment in Europe (BiomarCaRE) Consortium data, we performed a prospective cohort study to examine associations of CT-proET-1 and MR-proADM with incident type 2 diabetes in 12,006 participants. During a median follow-up time of 13.8 years, 862 participants developed type 2 diabetes. The associations were examined in Cox proportional hazard models. Additionally, we performed two-sample Mendelian randomisation analyses using published data. RESULTS: CT-proET-1 and MR-proADM were positively associated with incident type 2 diabetes. The multivariable hazard ratios (HRs) [95% confidence intervals (CI)] were 1.10 [1.03; 1.18], P = 0.008 per 1-SD increase of CT-proET-1 and 1.11 [1.02; 1.21], P = 0.016 per 1-SD increase of log MR-proADM, respectively. We observed a stronger association of MR-proADM with incident type 2 diabetes in obese than in non-obese individuals (P-interaction with BMI < 0.001). The HRs [95%CIs] were 1.19 [1.05; 1.34], P = 0.005 and 1.02 [0.90; 1.15], P = 0.741 in obese and non-obese individuals, respectively. Our Mendelian randomisation analyses yielded a significant association of CT-proET-1, but not of MR-proADM with type 2 diabetes risk. CONCLUSIONS: Higher concentrations of CT-proET-1 and MR-proADM are associated with incident type 2 diabetes, but our Mendelian randomisation analysis suggests a probable causal link for CT-proET-1 only. The association of MR-proADM seems to be modified by body composition.

Fusion gene detection by RNA-sequencing complements diagnostics of acute myeloid leukemia and identifies recurring NRIP1-MIR99AHG rearrangements.

Identification of fusion genes in clinical routine is mostly based on cytogenetics and targeted molecular genetics, such as metaphase karyotyping, fluorescence in situ hybridization and reverse-transcriptase polymerase chain reaction. However, sequencing technologies are becoming more important in clinical routine as processing time and costs per sample decrease. To evaluate the performance of fusion gene detection by RNAsequencing compared to standard diagnostic techniques, we analyzed 806 RNA-sequencing samples from patients with acute myeloid leukemia using two state-of-the-art software tools, namely Arriba and FusionCatcher. RNA-sequencing detected 90% of fusion events that were reported by routine with high evidence, while samples in which RNA-sequencing failed to detect fusion genes had overall lower and inhomogeneous sequence coverage. Based on properties of known and unknown fusion events, we developed a workflow with integrated filtering strategies for the identification of robust fusion gene candidates by RNA-sequencing. Thereby, we detected known recurrent fusion events in 26 cases that were not reported by routine and found discrepancies in evidence for known fusion events between routine and RNA-sequencing in three cases. Moreover, we identified 157 fusion genes as novel robust candidates and comparison to entries from ChimerDB or Mitelman Database showed novel recurrence of fusion genes in 14 cases. Finally, we detected the novel recurrent fusion gene NRIP1- MIR99AHG resulting from inv(21)(q11.2;q21.1) in nine patients (1.1%) and LTN1-MX1 resulting from inv(21)(q21.3;q22.3) in two patients (0.25%). We demonstrated that NRIP1-MIR99AHG results in overexpression of the 3' region of MIR99AHG and the disruption of the tricistronic miRNA cluster miR-99a/let-7c/miR-125b-2. Interestingly, upregulation of MIR99AHG and deregulation of the miRNA cluster, residing in the MIR99AHG locus, are known mechanisms of leukemogenesis in acute megakaryoblastic leukemia. Our findings demonstrate that RNA-sequencing has a strong potential to improve the systematic detection of fusion genes in clinical applications and provides a valuable tool for fusion discovery.

Joint analysis of PK and immunogenicity outcomes using factorization model - a powerful approach for PK similarity study.

Biological products, whether they are innovator products or biosimilars, can incite an immunogenic response ensuing in the development of anti-drug antibodies (ADA). The presence of ADA's often affects the drug clearance, resulting in an increase in the variability of pharmacokinetic (PK) analysis and challenges in the design and analysis of PK similarity studies. Immunogenic response is a complex process which may be manifested by product and non-product-related factors. Potential imbalances in non-product-related factors between treatment groups may lead to differences in antibodies formation and thus in PK outcome. The current standard statistical approaches dismiss any associations between immunogenicity and PK outcomes. However, we consider PK and immunogenicity as the two correlated outcomes of the study treatment. In this research, we propose a factorization model for the simultaneous analysis of PK parameters (normal variable after taking log-transformation) and immunogenic response subgroup (binary variable). The central principle of the factorization model is to describe the likelihood function as the product of the marginal distribution of one outcome and the conditional distribution of the second outcome given the previous one. Factorization model captures the additional information contained in the correlation between the outcomes, it is more efficient than models that ignore potential dependencies between the outcomes. In our context, factorization model accounts for variability in PK data by considering the influence of immunogenicity. Based on our simulation studies, the factorization model provides more accurate and efficient estimates of the treatment effect in the PK data by taking into account the impact of immunogenicity. These findings are supported by two PK similarity clinical studies with a highly immunogenic biologic.

Missing data imputation in clinical trials using recurrent neural network facilitated by clustering and oversampling.

In clinical practice, the composition of missing data may be complex, for example, a mixture of missing at random (MAR) and missing not at random (MNAR) assumptions. Many methods under the assumption of MAR are available. Under the assumption of MNAR, likelihood-based methods require specification of the joint distribution of the data, and the missingness mechanism has been introduced as sensitivity analysis. These classic models heavily rely on the underlying assumption, and, in many realistic scenarios, they can produce unreliable estimates. In this paper, we develop a machine learning based missing data prediction framework with the aim of handling more realistic missing data scenarios. We use an imbalanced learning technique (i.e., oversampling of minority class) to handle the MNAR data. To implement oversampling in longitudinal continuous variable, we first perform clustering via k$k$ -mean trajectories. And use the recurrent neural network (RNN) to model the longitudinal data. Further, we apply bootstrap aggregating to improve the accuracy of prediction and also to consider the uncertainty of a single prediction. We evaluate the proposed method using simulated data. The prediction result is evaluated at the individual patient level and the overall population level. We demonstrate the powerful predictive capability of RNN for longitudinal data and its flexibility for nonlinear modeling. Overall, the proposed method provides an accurate individual prediction for both MAR and MNAR data and reduce the bias of missing data in treatment effect estimation when compared to standard methods and classic models. Finally, we implement the proposed method in a real dataset from an antidepressant clinical trial. In summary, this paper offers an opportunity to encourage the integration of machine learning strategies for handling of missing data in the analysis of randomized clinical trials.

[Regional and Temporal Trends in SARS-CoV-2-Associated Mortality in Bavaria: An Age-Stratified Analysis Over 5 Quarters for Persons Aged 50 and Older]. / Regionale und zeitliche Trends der SARS-CoV-2 assoziierten Sterblichkeit in Bayern: Eine altersstratifizierte Analyse über 5 Quartale für Personen ab 50 Jahren.

OBJECTIVE: The aim of this study was to investigate the influence of regional factors such as incidence rate, hospitalizations, socio-economic status and nursing homes on the regional and temporal heterogeneity of SARS-CoV-2-associated mortality in Bavaria. METHODOLOGY: Official Bavarian SARS-CoV-2 reporting data were considered for three age groups (50-64, 65-74,>74 years) between March 2020 and April 2021. Maps of regional standardized mortality rates were spatially smoothed using a Bayesian hierarchical model. RESULTS: The picture of regional mortality was heterogeneous with an increasing gradient toward the northeast. Adjustment for standardized incidence rates, hospitalizations of infected persons, and availability of care homes for the elderly levelled the heterogeneity. CONCLUSION: The north-east gradient in Bavarian SARS-CoV-2-specific mortality rates is clearly explained by the comparable gradient in regional incidence rates. Other regional factors show a less clear influence.

[Mapping Regional Differences in Infection Rates for the Coronavirus (COVID-19): Results of a Bayesian Approach to Administrative Districts of Bavaria]. / Kartografische Darstellung regionaler Unterschiede der Infektionsraten beim Coronavirus (COVID-19): Ergebnisse einer bayesianischen Glättung über Landkreise Bayerns.

BACKGROUND: Since the beginning of the COVID-19 pandemic, thematic maps showing the spread of the disease have been of great public interest. From the perspective of risk communication, those maps can be problematic, since random variation or extreme values may occur and cover up the actual regional patterns. One potential solution is applying spatial smoothing methods. The aim of this study was to show changes in incidence ratios over time in Bavarian districts using spatially smoothed maps. METHODS: Data on SARS-CoV-2 were provided by the Bavarian Health and Food Safety Authority on 29.10.2021 and 17.02.2022. The demographic data per district are derived from the Statistical Report of the Bavarian State Office for Statistics for 2019. Four age groups per sex (<18, 18-29, 30-64,>64 years) divided into 16 time periods (01/28/2020 to 12/31/2021) were included. Maps show standardized incidence ratios (SIR) spatially smoothed by Bayesian hierarchical modelling. RESULTS: The SIR varied remarkably between districts. Variations occurred for each time period, showing changing regional patterns over time. CONCLUSION: Smoothed health maps are suitable for showing trends in incidence ratios over time for COVID-19 in Bavaria and offer the advantage over traditional maps in giving more realistic estimates by including neighborhood relationships. The methodological approach can be seen as a first step to explain the regional heterogeneity in the pandemic, and to support improved risk communication.