Plasma microRNA-21 for the early prediction of acute kidney injury in patients undergoing major cardiac surgery.

BACKGROUND: Acute kidney injury (AKI) is a complication after major cardiac surgery that is associated with higher rates of morbidity and mortality. MicroRNA-21 (miR-21) has been described as an early biomarker for AKI. We investigated whether miR-21 is predictive of AKI and long-term mortality after cardiac surgery. METHODS: Consecutive patients (n = 115) undergoing major cardiac surgery were included. Serum creatinine was measured prior to, 4 h after, and 1, 4 and 7 days after extracorporeal circulation. Diagnosis of post-operative AKI was made in accordance with the international Kidney Disease: Improving Global Outcomes definition of AKI. Serum cystatin C and miR-21 were measured prior to and 4 h after surgery. miR-21 was determined by quantitative RT-PCR and was normalized to miRNA-39 from Caenorhabditis elegans. The median follow-up time was 2.9 years. RESULTS: AKI occurred in 36.5% (n = 42) of all patients. Baseline miR-21 was significantly lower in patients developing cardiac surgery-associated AKI (CSA-AKI) than in patients without CSA-AKI [0.27 (interquartile range, IQR, 0.14-0.30) versus 0.44 (IQR 0.25-0.75); P < 0.01]. Baseline miR-21 predicted CSA-AKI Stage 2/3 with an area under the curve of 0.701 [95% confidence interval (CI) 0.59-0.82; P = 0.007]. Baseline miR-21 <0.31 showed a hazard ratio of 3.11 (95% CI: 1.33-11.26) for CSA-AKI Stage 2/3. Patients with AKI Stage 2/3 had a significantly higher mortality (50 versus 10%; P = 0.0001) and dialysis rate (27 versus 11%; P = 0.038) within the 2.9-year follow-up. CONCLUSIONS: Our results indicate that miR-21 has the potential to identify patients at higher risk for CSA-AKI. This predictive value might be helpful in pre-procedural risk assessment and peri-procedural diagnosis and treatment.

Unlocking the Potential of Secondary Data for Public Health Research: Retrospective Study With a Novel Clinical Platform.

BACKGROUND: Clinical routine data derived from university hospitals hold immense value for health-related research on large cohorts. However, using secondary data for hypothesis testing necessitates adherence to scientific, legal (such as the General Data Protection Regulation, federal and state protection legislations), technical, and administrative requirements. This process is intricate, time-consuming, and susceptible to errors. OBJECTIVE: This study aims to develop a platform that enables clinicians to use current real-world data for testing research and evaluate advantages and limitations at a large university medical center (542,944 patients in 2022). METHODS: We identified requirements from clinical practitioners, conceptualized and implemented a platform based on the existing components, and assessed its applicability in clinical reality quantitatively and qualitatively. RESULTS: The proposed platform was established at the University Medical Center Hamburg-Eppendorf and made 639 forms encompassing 10,629 data elements accessible to all resident scientists and clinicians. Every day, the number of patients rises, and parts of their electronic health records are made accessible through the platform. Qualitatively, we were able to conduct a retrospective analysis of Parkinson disease over 777 patients, where we provide additional evidence for a significantly higher proportion of action tremors in patients with rest tremors (340/777, 43.8%) compared with those without rest tremors (255/777, 32.8%), as determined by a chi-square test (P<.001). Quantitatively, our findings demonstrate increased user engagement within the last 90 days, underscoring clinicians' increasing adoption of the platform in their regular research activities. Notably, the platform facilitated the retrieval of clinical data from 600,000 patients, emphasizing its substantial added value. CONCLUSIONS: This study demonstrates the feasibility of simplifying the use of clinical data to enhance exploration and sustainability in scientific research. The proposed platform emerges as a potential technological and legal framework for other medical centers, providing them with the means to unlock untapped potential within their routine data.

Introducing Attribute Association Graphs to Facilitate Medical Data Exploration: Development and Evaluation Using Epidemiological Study Data.

BACKGROUND: Interpretability and intuitive visualization facilitate medical knowledge generation through big data. In addition, robustness to high-dimensional and missing data is a requirement for statistical approaches in the medical domain. A method tailored to the needs of physicians must meet all the abovementioned criteria. OBJECTIVE: This study aims to develop an accessible tool for visual data exploration without the need for programming knowledge, adjusting complex parameterizations, or handling missing data. We sought to use statistical analysis using the setting of disease and control cohorts familiar to clinical researchers. We aimed to guide the user by identifying and highlighting data patterns associated with disease and reveal relations between attributes within the data set. METHODS: We introduce the attribute association graph, a novel graph structure designed for visual data exploration using robust statistical metrics. The nodes capture frequencies of participant attributes in disease and control cohorts as well as deviations between groups. The edges represent conditional relations between attributes. The graph is visualized using the Neo4j (Neo4j, Inc) data platform and can be interactively explored without the need for technical knowledge. Nodes with high deviations between cohorts and edges of noticeable conditional relationship are highlighted to guide the user during the exploration. The graph is accompanied by a dashboard visualizing variable distributions. For evaluation, we applied the graph and dashboard to the Hamburg City Health Study data set, a large cohort study conducted in the city of Hamburg, Germany. All data structures can be accessed freely by researchers, physicians, and patients. In addition, we developed a user test conducted with physicians incorporating the System Usability Scale, individual questions, and user tasks. RESULTS: We evaluated the attribute association graph and dashboard through an exemplary data analysis of participants with a general cardiovascular disease in the Hamburg City Health Study data set. All results extracted from the graph structure and dashboard are in accordance with findings from the literature, except for unusually low cholesterol levels in participants with cardiovascular disease, which could be induced by medication. In addition, 95% CIs of Pearson correlation coefficients were calculated for all associations identified during the data analysis, confirming the results. In addition, a user test with 10 physicians assessing the usability of the proposed methods was conducted. A System Usability Scale score of 70.5% and average successful task completion of 81.4% were reported. CONCLUSIONS: The proposed attribute association graph and dashboard enable intuitive visual data exploration. They are robust to high-dimensional as well as missing data and require no parameterization. The usability for clinicians was confirmed via a user test, and the validity of the statistical results was confirmed by associations known from literature and standard statistical inference.

Evaluating REDCap as the Central Data Collection Tool for the Hamburg City Health Study.

INTRODUCTION: The collection of examination data for large clinical studies is often done with proprietary systems, which are accompanied by several disadvantages such as high cost and low flexibility. With the use of open-source tools, these disadvantages can be overcome and thereby improve data collection as well as data quality. Here we exemplary use the data collection process of the Hamburg City Health Study (HCHS), carried out at the University Medical Center Hamburg-Eppendorf (UKE). We evaluated how the recording of the examination data can be converted from an established, proprietary electronic healthcare record (EHR) system to the free-to-use Research Electronic Data Capture (REDCap) software. METHODS: For this purpose, a technical conversion of the EHR system is described first. Metafiles derived from the EHR system were used for REDCap electronic case report form (eCRF) building. The REDCap system was tested by HCHS study assistants via completion of self-developed tasks mimicking their everyday study life. Usability was quantitatively evaluated via the IBM Computer System Usability Questionnaire (CSUQ) and qualitatively assessed with a semi-structured interview. RESULTS: With the IBM CSUQ, the study assistants rated the usage of the basic REDCap system for HCHS examination data collection with an overall score of 4.39, which represents a medium acceptance. The interview feedback was used to formulate user stories to subsequently increase the administrative sovereignty and to conceptualize a REDCap HCHS information technology (IT) infrastructure. CONCLUSION: Our work aims to serve as a template for evaluating the feasibility of a conversion from a proprietary to a free-to-use data collection tool for large clinical studies such as the HCHS. REDCap has great potential, but extensions and an integration to the current IT infrastructure are required.

Development of an immediate release excipient composition for 3D printing via direct powder extrusion in a hospital.

3D printing offers the possibility to prepare personalized tablets on demand, making it an intriguing technology for hospital pharmacies. For the implementation of 3D-printed tablets into the digital Closed Loop Medication Management system, the required tablet formulation and development of the manufacturing process as well as the pharmaceutical validation were conducted. The goal of the formulation development was to enable an optimal printing process and rapid dissolution of the printed tablets for the selected model drugs Levodopa/Carbidopa. The 3D printed tablets were prepared by direct powder extrusion. Printability, thermal properties, disintegration, dissolution, physical properties and storage stability were investigated by employing analytical methods such as HPLC-UV, DSC and TGA. The developed formulation shows a high dose accuracy and an immediate drug release for Levodopa. In addition, the tablets exhibit high crushing strength and very low friability. Unfortunately, Carbidopa did not tolerate the printing process. This is the first study to develop an immediate release excipient composition via direct powder extrusion in a hospital pharmacy setting. The developed process is suitable for the implementation in Closed-Loop Medication Management systems in hospital pharmacies and could therefore contribute to medication safety.