Open Access

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.

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.

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.

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.

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.