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A significant risk following a kidney transplantation is graft loss. The Screen Reject Project has developed a Clinical Data Warehouse (CDWH) as a foundation for a clinical decision support system designed to improve the diagnosis of graft rejections. The CDWH integrates patient data and event records of n = 141 kidney transplant patients. These data are not directly comparable within the cohort as they consist of irregular time series, particularly of laboratory values. Therefore, a pre-processing routine was developed which divides a relative time window before the last biopsy (the relevant end event of the reference period for subsequent machine learning procedures) into equal time intervals for each patient. For each of these intervals a representative value is calculated from the contained laboratory values. These representative values are used to train models for predicting kidney rejection. The comparison with an existing study from the project, in which a classification model was developed without considering the temporal dependencies, shows an improved sensitivity and specificity in predicting kidney rejection for the harmonised data using the same random forest model.
Background: Prior studies have documented that patients with colorectal cancer (CRC) are at an increased risk of cardiovascular disease (CVD).
Objectives: To examine coronary artery calcium (CAC) as a marker of subclinical atherosclerosis and its association with major adverse cardiovascular events (MACE) in patients with CRC across the cancer treatment trajectory.
Methods: Adults with newly diagnosed CRC were enrolled in the prospective ColoCare study from 2017 to 2024. CAC was measured from routine diagnostic computed tomography (CT) and positron emission tomography‐CT scans at CRC diagnosis until 5 years post‐diagnosis. Atherosclerosis was defined as the presence of CAC. We used multivariable‐adjusted Fine and Gray models to assess the association between CAC and MACE risk, accounting for competing risks.
Results: Among 300 CRC patients, the most common CVD risk factors at cancer diagnosis were hypertension (37%), hyperlipidemia (24%), and diabetes (14%). During follow‐up (median = 5.3 years), 75 (25%) individuals experienced MACE: stroke (3%), new/worsening HF (9%), HF exacerbation requiring hospitalization (2%), coronary revascularization (3%), and death (19%). Among individuals with imaging at baseline (n = 101), 37 (36.6%) had CAC, and statins were not prescribed in 11 (55.0%) patients with moderate/high CAC. For those with serial imaging (n = 61), 31.1% showed worsening CAC and 3% developed new CAC. Baseline CAC conferred a higher risk of MACE (HR = 4.79; 95% CI: 1.05–21.75, p = 0.04) after accounting for cancer‐related deaths as a competing risk.
Conclusions: Subclinical atherosclerosis and MACE are common among patients with CRC. Integrating CAC from routine cancer imaging can identify patients who may benefit from cardio‐preventive treatment.
Das niedersachsenweite Zukunftslabor Gesundheit entwickelt Methoden und
Werkzeuge für eine bessere Gesundheitsversorgung und -forschung. Dabei steht die sichere gemeinsame Nutzung von Gesundheitsdaten – von elektronischen Behandlungsdaten bis hin zu Sensorik in patientennaher Umgebung – für innovative praxisnahe Lösungen im Zentrum. Angebote umfassen Beratung, digitale Kompetenzvermittlung und technische Lösungen.
There has been an increase in discussion concerning the integration of sexuality education and the prevention of sexual violence. Furthermore, this is a concern at the level of different pedagogical professions in Germany, since sexuality education and sexual violence prevention have developed as largely separate fields. Both sexuality educators and sexual violence professionals work with a broad target group to prevent sexual violence, including children, young people, as well as parents and professionals working in social work or education. They collaborate at times, but they also engage in debates about their respective pedagogical approaches. Based on group discussions with 12 teams specializing in the two fields, this article analyzes how their tacitly shared knowledge (collective orientation) underpins their different pedagogical strategies. This should be considered to improve their long-term inter-professional cooperation.
Der vorliegende Beitrag befasst sich vor dem Hintergrund der aktuellen Diskussion über einen Fachkräftemangel in der Pflege mit der Zuverlässigkeit der Abbildung der Pflegeberufe in amtlichen Statistiken und methodischen Problemen bisheriger Vorausberechnungen. Daten zur Zahl der Beschäftigten in Pflegeberufen bieten mehrere amtliche Statistiken. Ein Vergleich dieser Statistiken zeigt jedoch zum Teil erhebliche Unterschiede in den Datenangaben sowohl für einzelne Pflegeberufe als auch für die Gesamtzahl des Pflegepersonals. Auf Grundlage einer Analyse der jeweiligen Methodiken kommt der Beitrag zu dem Schluss, dass eine Zusammenführung der Daten der Krankenhausstatistik, der Statistik der Vorsorge- und Rehabilitationseinrichtungen und der Pflegestatistik differenziertere und auch zuverlässigere Daten bietet als die Arbeitsmarktstatistik oder Gesundheitspersonalrechnung.
For people with physical disabilities, it is often desirable to regain control over their personal environment and communication tools. This paper introduces a novel Human-Machine Interface (HMI) using one-shot learning for individualized control signals without extensive training or specialized hardware. Our work suggests a modular system that utilizes common, easily accessible devices like webcams to interpret user-defined gestures and commands through a single demonstration. As a feasibility study on healthy volunteers, we investigate the control of a computer mouse by head movements only. We demonstrate the technical details of the HMI and discuss its potential applications in enhancing the autonomy and interaction capabilities of users with disabilities. By combining usercentric design principles with the advancements in one-shot learning, we aim to forge a more inclusive, accessible path forward in the development of assistive technologies.
Background: Falls are a common problem experienced by people living with HIV yet predictive models specific to this population remain underdeveloped. We aimed to identify, assess and stratify the predictive strength of various physiological, behavioral, and HIV-specific factors associated with falls among people living with HIV and inform a predictive model for fall prevention.
Methods: Systematic review and meta-analysis were conducted to explore predictors of falls in people living with HIV. Data was sourced, screened, extracted, and analyzed by two independent reviewers from eight databases up to January 2nd, 2024, following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Evidence quality and bias were assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) and the Mixed Method Appraisal Tool (MMAT), respectively. Pooled odds ratios (OR) with 95% confidence intervals (CI) were computed using random-effects models to establish associations between predictors and falls risk. We applied established criteria (Bradford Hill’s criteria, Rothman’s and Nweke’s viewpoints) to stratify risk factors and create a weighted predictive algorithm.
Results: This review included 12 studies on falls/balance dysfunction in 117,638 participants (54,513 people living with HIV), with varying ages (45–50 years), sample sizes (32 − 26,373), study durations (6 months to 15 years), disease stages (CD4 + counts 347.2 cells/mm³ to ≥ 500 cells/µL) and fall definitions (self-reported histories to real-time reporting). Some predictors of falls in people living with HIV including depression, cannabis use, cognitive impairment/neurocognitive adverse effects (NCAE), hypertension, and stavudine—showed perfect risk responsiveness (Ri = 1), indicating their strong association with falls. Notably, cannabis use demonstrated the highest risk weight (Rw = 3.0, p < 0.05, 95%CI:1.51–5.82), followed by NCAE (Rw = 2.3, p < 0.05, 95%CI:1.66–3.21) and frailty with a broad confidence interval (Rw = 2.2, p < 0.05, 95%CI:0.73–14.40). Other significant predictors included hypertension (Rw = 1.8, p < 0.05, 95%CI:1.33–2.33), depression (Rw = 1.6, p < 0.05, 95%CI:1.22–2.18), stavudine use (Rw = 1.5, p < 0.05, 95%CI: 0.95–2.25), neuropathy (Rw = 1.3, p < 0.05, 95%CI:1.26–2.11), and polypharmacy (Rw = 1.2, p < 0.05, 95%CI:1.16–1.96). The fall risk threshold score was 12.8, representing the 76th percentile of the specific and sufficient risk weight.
Conclusion: Our meta-analysis identifies predictors of falls in people living with HIV, emphasizing physiological, behavioral, and HIV-specific factors. Integrating these into clinical practice could mitigate falls-related sequelae. We propose a novel approach to falls risk prediction using a novel clinical index, resulting in a HIV-specific falls risk assessment tool.
Background: Cachexia accounts for about 20% of all cancer‐related deaths and indicates poor prognosis. The impact of Fusobacterium nucleatum (Fn), a microbial risk factor for colorectal cancer (CRC), on the development of cachexia in CRC has not been established.
Methods: We evaluated the association between Fn abundance in pre‐surgical stool samples and onset of cachexia at 6 months post‐surgery in n = 87 patients with stages I–III CRC in the ColoCare Study.
Results: High fecal Fn abundance compared to negative/low fecal Fn abundance was associated with 4‐fold increased risk of cachexia onset at 6 months post‐surgery (OR = 4.82, 95% CI = 1.15, 20.10, p = 0.03).
Conclusion: Our findings suggest that high fecal Fn abundance was associated with an increased risk of cachexia at 6 months post‐surgery in CRC patients. This is the first study to link Fn abundance with cachexia in CRC patients, offering novel insights into biological mechanisms and potential management of cancer cachexia. Due to the small sample size, our results should be interpreted with caution. Future studies with larger sample sizes are needed to validate these findings.
The development of methods for the meta‐analysis of diagnostic test accuracy (DTA) studies is still an active area of research. While methods for the standard case where each study reports a single pair of sensitivity and specificity are nearly routinely applied nowadays, methods to meta‐analyze receiver operating characteristic (ROC) curves are not widely used. This situation is more complex, as each primary DTA study may report on several pairs of sensitivity and specificity, each corresponding to a different threshold. In a case study published earlier, we applied a number of methods for meta‐analyzing DTA studies with multiple thresholds to a real‐world data example (Zapf et al., Biometrical Journal. 2021; 63(4): 699–711). To date, no simulation study exists that systematically compares different approaches with respect to their performance in various scenarios when the truth is known. In this article, we aim to fill this gap and present the results of a simulation study that compares three frequentist approaches for the meta‐analysis of ROC curves. We performed a systematic simulation study, motivated by an example from medical research. In the simulations, all three approaches worked partially well. The approach by Hoyer and colleagues was slightly superior in most scenarios and is recommended in practice.
Introduction: The integration of Patient-Reported Experience Measures (PREM) alongside traditional clinical outcomes is crucial for improving quality of care. Although PREMs are frequently measured in inpatient treatment settings, they are rarely employed in digitally supported care processes or longitudinal assessment of care pathways.
Methods: To gain an overview of PREMs used to cover patients’ experiences with digitally supported care processes in heart failure (HF), a scoping review was conducted in Medline.
Results: Out of 538 publications, 29 were identified that focus on PREMs in digitally supported care processes across 9 unspecific and 14 disease-specific groups, with 5 manuscripts focusing on HF. PREMs were mostly assessed using self-developed, study-specific questionnaires lacking standardization and validity. In total, 9 PREM dimensions and 25 sub-dimensions were identified. This included care delivery, privacy, physician-patient relationship, involvement, administration, information, knowledge, technology, and experiences in general.
Conclusion: The findings suggest that the relevance of different dimensions assessed depends largely on the type of care rather than the underlying chronic disease.