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mTOR-Is positively influence the occurrence and course of certain tumors after solid organ transplantation. The effect of mTOR-Is on the overall incidence of tumors irrespective of their origin is not entirely clear. Furthermore, conflicting data have been shown on mortality under mTOR-Is. The current literature was searched for prospective randomized controlled renal transplantation trials. There were 1415 trials screened of which 13 could be included (pts. = 5924). A minimum follow-up of 24 months was mandatory for inclusion. Incidence of malignancies and patient survival was assessed in meta-analyses. The average follow-up of all trials was 40.6 months. Malignancy was significantly reduced under mTOR-Is compared to CNIs (RR 0.70, CI 0.49-0.99, p = 0.046). This effect remained stable when combined with CNIs (RR 0.58, CI 0.34-1.00, p = 0.05). When NMSCs were excluded the risk for malignancy remained significantly reduced under mTOR-I therapy (mono and combi) (RR 0.43, CI 0.24-0.77, p = 0.0046). Graft survival was minimally decreased under mTOR-Is (RR 0.99, CI 0.98-1.00, p = 0.054). This effect was abrogated when mTOR-Is were combined with CNIs (RR 0.99, CI 0.97-1.02, p = 0.50). Patient survival was not different (RR 1.00, CI 0.99-1.01, p = 0.54). Posttransplant patients have a lower incidence of malignancy when treated with an mTOR-I no matter if it is used in combination with CNIs or not. This beneficial effect remains significant even when NMSCs are excluded. With currently used mTOR-I-based regimen patient and graft survival is not different compared to CNI therapies.

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.

Background Algorithms and models increasingly support clinical and shared decision-making. However, they may be limited in effectiveness, accuracy, acceptance, and comprehensibility if they fail to consider patient preferences. Addressing this gap requires exploring methods to integrate patient preferences into model-based clinical decision-making. Objectives This scoping review aimed to identify and map applications of computational methods for incorporating patient preferences into individualized medical decision models and to report on the types of models where these methods are applied. Inclusion Criteria This review includes articles without restriction on publication date or language, focusing on practical applications. It examines the integration of patient preferences in models for individualized clinical decision-making, drawing on diverse sources, including both white and gray literature, for comprehensive insights. Methods Following the Joanna Briggs Institute (JBI) methodology, a comprehensive search was conducted across databases such as PubMed, Web of Science, ACM Digital Library, IEEE Xplore, Cochrane Library, OpenGrey, National Technical Reports Library, and the first 20 pages of Google Scholar. Keywords related to patient preferences, medical models, decision-making, and software tools guided the search strategy. Data extraction and analysis followed the JBI framework, with an explorative analysis. Results From 7074 identified and 7023 screened articles, 45 publications on specific applications were reviewed, revealing significant heterogeneity in incorporating patient preferences into decision-making tools. Clinical applications primarily target neoplasms and circulatory diseases, using methods like Multi-Criteria Decision Analysis (MCDA) and statistical models, often combining approaches. Studies show that incorporating patient preferences can significantly impact treatment decisions, underscoring the need for shared and personalized decision-making. Conclusion This scoping review highlights a wide range of approaches for integrating patient preferences into medical decision models, underscoring a critical gap in the use of cohesive frameworks that could enhance consistency and clinician acceptance. While the flexibility of current methods supports tailored applications, the limited use of existing frameworks constrains their potential. This gap, coupled with minimal focus on clinician and patient engagement, hinders the real-world utility of these tools. Future research should prioritize co-design with clinicians, real-world testing, and impact evaluation to close this gap and improve patient-centered care.

Objective To revise the German guidelines and recommendations for ensuring Good Epidemiological Practice (GEP) that were developed in 1999 by the German Society for Epidemiology (DGEpi), evaluated and revised in 2004, supplemented in 2008, and updated in 2014. Methods The executive board of the DGEpi tasked the third revision of the GEP. The revision was arrived as a result of a consensus-building process by a working group of the DGEpi in collaboration with other working groups of the DGEpi and with the German Association for Medical Informatics, Biometry and Epidemiology , the German Society of Social Medicine and Prevention (DGSMP), the German Region of the International Biometric Society (IBS-DR), the German Technology, Methods and Infrastructure for Networked Medical Research (TMF), and the German Network for Health Services Research (DNVF). The GEP also refers to related German Good Practice documents (e.g. Health Reporting, Cartographical Practice in the Healthcare System, Secondary Data Analysis). Results The working group modified the 11 guidelines (after revision: 1 ethics, 2 research question, 3 study protocol and manual of operations, 4 data protection, 5 sample banks, 6 quality assurance, 7 data storage and documentation, 8 analysis of epidemiological data, 9 contractual framework, 10 interpretation and scientific publication, 11 communication and public health) and modified and supplemented the related recommendations. All participating scientific professional associations adopted the revised GEP. Conclusions The revised GEP are addressed to everyone involved in the planning, preparation, execution, analysis, and evaluation of epidemiological research, as well as research institutes and funding bodies.

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.

Prognostic scores support clinicians in selecting risk-adjusted treatments and in comparatively assessing different results. For patients with chronic-phase chronic myeloid leukemia (CML), four baseline prognostic scores are commonly used. Our aim was to compare the prognostic performance of the scores and to arrive at an evidence-based score recommendation. In 2949 patients not involved in any score development, higher hazard ratios and concordance indices in any comparison demonstrated the best discrimination of long-term survival with the ELTS score. In a second step, of 5154 patients analyzed to investigate risk group classification differences, 23% (n = 1197) were allocated to high-risk by the Sokal score. Of the 1197 Sokal high-risk patients, 56% were non-high-risk according to the ELTS score and had a significantly more favorable long-term survival prognosis than the 526 high-risk patients according to both scores. The Sokal score identified too many patients as high-risk and relatively few (40%) as low-risk (versus 60% with the ELTS score). Inappropriate risk classification jeopardizes optimal treatment selection. The ELTS score outperformed the Sokal score, the Euro, and the EUTOS score regarding risk group discrimination. The recent recommendation of the European LeukemiaNet for preferred use of the ELTS score was supported with significant statistical evidence.

Nausea often occurs in stressful situations, such as chemotherapy or surgery. Clinically relevant placebo effects in nausea have been demonstrated, but it remains unclear whether stress has an impact on these effects. The aim of this experimental study was to investigate the interplay between acute stress and placebo effects in nausea. 80 healthy female volunteers susceptible to motion sickness were randomly assigned to either the Maastricht Acute Stress Test or a non-stress control condition, and to either placebo treatment or no treatment. Nausea was induced by a virtual vection drum and behavioral, psychophysiological as well as humoral parameters were repeatedly assessed. Manipulation checks confirmed increased cortisol levels and negative emotions in the stressed groups. In the non-stressed groups, the placebo intervention improved nausea, symptoms of motion sickness, and gastric myoelectrical activity (normo-to-tachy (NTT) ratio). In the stressed groups, the beneficial effects of the placebo intervention on nausea and motion sickness remained unchanged, whereas no improvement of the gastric NTT ratio was observed. Results suggest that placebo effects on symptoms of nausea and motion sickness are resistant to experimentally-induced stress. Stress most likely interfered with the validity of the gastric NTT ratio to measure nausea and thus the gastric placebo effect.

Purpose Most randomized clinical trials evaluating second generation tyrosine kinase inhibitors (TKI) for the first-line treatment of Chronic Myeloid Leukemia used as comparator the ‘standard’ dose of 400 mg imatinib daily. Several studies showed higher rates of major molecular remission (MMR) at 12 months with 800 mg compared to 400 mg, suggesting that high-dose imatinib may be the appropriate comparator rather than 400 mg. Methods We systematically reviewed randomized trials comparing the two dosages, calculated a common estimator and compared the result to a common estimator of trials evaluating a second generation TKI in comparison with 400 mg imatinib daily. Results We identified three trials comparing 400–800 mg imatinib resulting in a common relative risk of 1.30 (1.13–1.49) and indicating a significantly higher rate of MMR in patients treated with 800 mg imatinib ( p  = 0.0003). We identified five trials comparing 400 mg imatinib daily to a second generation TKI. The common relative risk for MMR at 12 months was 1.69 (1.50–1.90, p  < 0.0001). Differences in the prognostic profiles precluded a direct comparison of the common efficacy estimates. Conclusions We conclude that imatinib was probably not licensed at the optimal dose initially. We suggest that in the future, new TKIs are compared with a higher dose of imatinib. In addition, high-dose imatinib should be considered more often for routine clinical decisions based on the characteristics of the individual patient.

Purpose The genomic break on the major breakpoint cluster region of chromosome 22 results in two BCR-ABL1 transcripts of different sizes, e14a2 and e13a2. Favorable survival probabilities of patients with chronic myeloid leukemia (CML) in combination with too small patient samples may yet have obstructed the observation of differences in overall survival of patients according to transcript type. To overcome potential power problems, overall survival (OS) probabilities and probabilities of CML-related death were analyzed in 1494 patients randomized to first-line imatinib treatment. Methods OS probabilities and probabilities of dying of CML were compared using the log-rank or Gray test whichever was appropriate. Both tests were stratified for the EUTOS long-term survival score. Results Between the groups with a single transcript, neither OS probabilities (stratified log-rank test: p  = 0.106) nor probabilities of CML-related death were significantly different (stratified Gray test: p  = 0.256). Regarding OS, the Cox hazard ratio (HR) of transcript type e13a2 ( n  = 565) to type e14a2 ( n  = 738) was 1.332 (95% CI 0.940–1.887). Considering probabilities of leukemia-related death, the corresponding subdistribution HR resulted in 1.284 (95% CI 0.758–2.176). Outcome did not change if patients with both transcripts ( n  = 191) were added to the 738 with type e14a2 only. Conclusions The prognostic association of transcript type and long-term survival outcome was weak and without clinical relevance. However, earlier reported differences in the rate and the depth of molecular response could be relevant for the chance of successfully discontinuing TKI treatment. The effect of transcript type on molecular relapse after discontinuation is unknown, yet.

Nowadays in many fields of medicine, prognostic scores are used to predict the outcome for individual patients. In chronic myeloid leukemia (CML), the Sokal, the Euro, and the EUTOS score are established prognostic scores which were addressed by the CML management recommendations of the European LeukemiaNet. This review provides a general definition of prognostic scores and explains their meaning. Main differences between the Sokal, the Euro, and the EUTOS score are highlighted. Due to the therapeutic success of tyrosine kinase inhibitors, the proportion of patients with causes of death unrelated to CML is growing. To assess the potential of a drug to prevent dying of CML, causes of death unrelated to CML need to be considered as competing risks. Supported by data of patients randomized to imatinib-based treatments within the German CML study IV, this review also explores the prognostic performance of the established scores if the primary event is death due to CML only and explains the implicit statistical particularities when treating other causes of death as competing risks. In the presence of competing risks, the application of both the cause-specific hazard model and the subdistribution hazard model is recommended when investigating the influence of prognostic factors on the event of interest. Another purpose of this work is to foster the ability of hematologists to interpret the outcome of a cause-specific hazard and a subdistribution hazard model and to understand the differences between them.