Continuous Risk Estimation of Acute Kidney Failure with Dense Temporal Data for ICU Patients.

Acute kidney failure is a dangerous complication for ICU patients, and it is difficult to identify at early stage with conventional medical analysis. In recent years, machine learning approaches have been applied to tackle medical diagnosis tasks with great performance. In this work, we deploy machine learning models for early detection of acute kidney failure that can handle static, temporal, sparse and dense data of ICU patients. We investigate different pre-processing methods for patient data to achieve higher prediction performance and how they influence the contribution of different physiological signals in the prediction process.

Risk Estimation for ICU Patients with Personalized Anomaly-Encoded Bedside Patient Data.

We propose a novel framework to estimate intensive care unit patients' health risk continuously with anomaly-encoded patient data. This framework consists of two modules. In the first module, we use Gaussian process models to learn change trend and day-night circulation in temporal patient data and annotate abnormal data. Such models provide dynamically adaptable bedside patient monitoring instead of conventional threshold-based monitoring. In the second module, we use the abnormal data together with the learned Gaussian models to estimate patients' risk level by predicting their in-hospital mortality and remaining length of stay in ICU ward. We show that prediction models with anomaly-encoded data have better performance than those with raw patient measurements, and they are comparable with state-of-art prediction models.

Study protocol of a phase 2, randomised, placebo-controlled, double-blind, adaptive, parallel group clinical study to evaluate the efficacy and safety of recombinant alpha-1-microglobulin in subjects at high risk for acute kidney injury following open-chest cardiac surgery (AKITA trial).

INTRODUCTION: Acute kidney injury (AKI) is a common complication after cardiac surgery (CS) and is associated with adverse short-term and long-term outcomes. Alpha-1-microglobulin (A1M) is a circulating glycoprotein with antioxidant, heme binding and mitochondrial-protective mechanisms. RMC-035 is a modified, more soluble, variant of A1M and has been proposed as a novel targeted therapeutic protein to prevent CS-associated AKI (CS-AKI). RMC-035 was considered safe and generally well tolerated when evaluated in four clinical phase 1 studies. METHODS AND ANALYSIS: This is a phase 2, randomised, double-blind, adaptive design, parallel group clinical study that evaluates RMC-035 compared with placebo in approximately 268 cardiac surgical patients at high risk for CS-AKI. RMC-035 is administered as an intravenous infusion. In total, five doses will be given. Dosing is based on presurgery estimated glomerular filtration rate (eGFR), and will be either 1.3 or 0.65 mg/kg.The primary study objective is to evaluate whether RMC-035 reduces the incidence of postoperative AKI, and key secondary objectives are to evaluate whether RMC-035 improves postoperative renal function compared with placebo. A blinded interim analysis with potential sample size reassessment is planned once 134 randomised subjects have completed dosing. An independent data monitoring committee will evaluate safety and efficacy data at prespecified intervals throughout the trial. The study is a global multicentre study at approximately 30 sites. ETHICS AND DISSEMINATION: The trial was approved by the joint ethics committee of the physician chamber Westfalen-Lippe and the University of Münster (code '2021-778 f-A') and subsequently approved by the responsible ethics committees/relevant institutional review boards for the participating sites. The study is conducted in accordance with Good Clinical Practice, the Declaration of Helsinki and other applicable regulations. Results of this study will be published in a peer-reviewed scientific journal. TRIAL REGISTRATION NUMBER: NCT05126303.

Data Provenance in Biomedical Research: Scoping Review.

BACKGROUND: Data provenance refers to the origin, processing, and movement of data. Reliable and precise knowledge about data provenance has great potential to improve reproducibility as well as quality in biomedical research and, therefore, to foster good scientific practice. However, despite the increasing interest on data provenance technologies in the literature and their implementation in other disciplines, these technologies have not yet been widely adopted in biomedical research. OBJECTIVE: The aim of this scoping review was to provide a structured overview of the body of knowledge on provenance methods in biomedical research by systematizing articles covering data provenance technologies developed for or used in this application area; describing and comparing the functionalities as well as the design of the provenance technologies used; and identifying gaps in the literature, which could provide opportunities for future research on technologies that could receive more widespread adoption. METHODS: Following a methodological framework for scoping studies and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, articles were identified by searching the PubMed, IEEE Xplore, and Web of Science databases and subsequently screened for eligibility. We included original articles covering software-based provenance management for scientific research published between 2010 and 2021. A set of data items was defined along the following five axes: publication metadata, application scope, provenance aspects covered, data representation, and functionalities. The data items were extracted from the articles, stored in a charting spreadsheet, and summarized in tables and figures. RESULTS: We identified 44 original articles published between 2010 and 2021. We found that the solutions described were heterogeneous along all axes. We also identified relationships among motivations for the use of provenance information, feature sets (capture, storage, retrieval, visualization, and analysis), and implementation details such as the data models and technologies used. The important gap that we identified is that only a few publications address the analysis of provenance data or use established provenance standards, such as PROV. CONCLUSIONS: The heterogeneity of provenance methods, models, and implementations found in the literature points to the lack of a unified understanding of provenance concepts for biomedical data. Providing a common framework, a biomedical reference, and benchmarking data sets could foster the development of more comprehensive provenance solutions.

Elucidation of the genetic causes of bicuspid aortic valve disease.

AIMS: The present study aims to characterize the genetic risk architecture of bicuspid aortic valve (BAV) disease, the most common congenital heart defect. METHODS AND RESULTS: We carried out a genome-wide association study (GWAS) including 2236 BAV patients and 11 604 controls. This led to the identification of a new risk locus for BAV on chromosome 3q29. The single nucleotide polymorphism rs2550262 was genome-wide significant BAV associated (P = 3.49 × 10-08) and was replicated in an independent case-control sample. The risk locus encodes a deleterious missense variant in MUC4 (p.Ala4821Ser), a gene that is involved in epithelial-to-mesenchymal transformation. Mechanistical studies in zebrafish revealed that loss of Muc4 led to a delay in cardiac valvular development suggesting that loss of MUC4 may also play a role in aortic valve malformation. The GWAS also confirmed previously reported BAV risk loci at PALMD (P = 3.97 × 10-16), GATA4 (P = 1.61 × 10-09), and TEX41 (P = 7.68 × 10-04). In addition, the genetic BAV architecture was examined beyond the single-marker level revealing that a substantial fraction of BAV heritability is polygenic and ∼20% of the observed heritability can be explained by our GWAS data. Furthermore, we used the largest human single-cell atlas for foetal gene expression and show that the transcriptome profile in endothelial cells is a major source contributing to BAV pathology. CONCLUSION: Our study provides a deeper understanding of the genetic risk architecture of BAV formation on the single marker and polygenic level.

EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation.

BACKGROUND: Modern biomedical research is data-driven and relies heavily on the re-use and sharing of data. Biomedical data, however, is subject to strict data protection requirements. Due to the complexity of the data required and the scale of data use, obtaining informed consent is often infeasible. Other methods, such as anonymization or federation, in turn have their own limitations. Secure multi-party computation (SMPC) is a cryptographic technology for distributed calculations, which brings formally provable security and privacy guarantees and can be used to implement a wide-range of analytical approaches. As a relatively new technology, SMPC is still rarely used in real-world biomedical data sharing activities due to several barriers, including its technical complexity and lack of usability. RESULTS: To overcome these barriers, we have developed the tool EasySMPC, which is implemented in Java as a cross-platform, stand-alone desktop application provided as open-source software. The tool makes use of the SMPC method Arithmetic Secret Sharing, which allows to securely sum up pre-defined sets of variables among different parties in two rounds of communication (input sharing and output reconstruction) and integrates this method into a graphical user interface. No additional software services need to be set up or configured, as EasySMPC uses the most widespread digital communication channel available: e-mails. No cryptographic keys need to be exchanged between the parties and e-mails are exchanged automatically by the software. To demonstrate the practicability of our solution, we evaluated its performance in a wide range of data sharing scenarios. The results of our evaluation show that our approach is scalable (summing up 10,000 variables between 20 parties takes less than 300 s) and that the number of participants is the essential factor. CONCLUSIONS: We have developed an easy-to-use "no-code solution" for performing secure joint calculations on biomedical data using SMPC protocols, which is suitable for use by scientists without IT expertise and which has no special infrastructure requirements. We believe that innovative approaches to data sharing with SMPC are needed to foster the translation of complex protocols into practice.

Uncovering the molecular identity of cardiosphere-derived cells (CDCs) by single-cell RNA sequencing.

Cardiosphere-derived cells (CDCs) generated from human cardiac biopsies have been shown to have disease-modifying bioactivity in clinical trials. Paradoxically, CDCs' cellular origin in the heart remains elusive. We studied the molecular identity of CDCs using single-cell RNA sequencing (sc-RNAseq) in comparison to cardiac non-myocyte and non-hematopoietic cells (cardiac fibroblasts/CFs, smooth muscle cells/SMCs and endothelial cells/ECs). We identified CDCs as a distinct and mitochondria-rich cell type that shared biological similarities with non-myocyte cells but not with cardiac progenitor cells derived from human-induced pluripotent stem cells. CXCL6 emerged as a new specific marker for CDCs. By analysis of sc-RNAseq data from human right atrial biopsies in comparison with CDCs we uncovered transcriptomic similarities between CDCs and CFs. By direct comparison of infant and adult CDC sc-RNAseq data, infant CDCs revealed GO-terms associated with cardiac development. To analyze the beneficial effects of CDCs (pro-angiogenic, anti-fibrotic, anti-apoptotic), we performed functional in vitro assays with CDC-derived extracellular vesicles (EVs). CDC EVs augmented in vitro angiogenesis and did not stimulate scarring. They also reduced the expression of pro-apoptotic Bax in NRCMs. In conclusion, CDCs were disclosed as mitochondria-rich cells with unique properties but also with similarities to right atrial CFs. CDCs displayed highly proliferative, secretory and immunomodulatory properties, characteristics that can also be found in activated or inflammatory cell types. By special culture conditions, CDCs earn some bioactivities, including angiogenic potential, which might modify disease in certain disorders.

Surgical Aortic Valve Replacement-Age-Dependent Choice of Prosthesis Type.

BACKGROUND: Recently, the use of surgically implanted aortic bioprostheses has been favoured in younger patients. We aimed to analyse the long-term survival and postoperative MACCE (Major Adverse Cardiovascular and Cerebral Event) rates in patients after isolated aortic valve replacement. METHODS: We conducted a single-centre observational retrospective study, including all consecutive patients with isolated aortic valve replacement. 1:1 propensity score matching of the preoperative baseline characteristics was performed. RESULTS: A total of 2172 patients were enrolled in the study. After propensity score matching the study included 428 patients: 214 biological vs. 214 mechanical prostheses, divided into two subgroups: group A < 60 years and group B > 60 years. The mean follow-up time was 7.6 ± 3.9 years. Estimated survival was 97 ± 1.9% and 89 ± 3.4% at 10 years for biological and mechanical prosthesis, respectively in group A (p = 0.06). In group B the survival at 10 years was 79.1 ± 5.8% and 69.8 ± 4.4% for biological and mechanical prosthesis, respectively (p = 0.83). In group A, patients with a bioprosthesis exhibited a tendency for higher cumulative incidence MACCE rates compared to patients with a mechanical prosthesis, p = 0.83 (bio 7.3 ± 5.3% vs. mech 4.6 ± 2.2% at 10 years). In group B, patients with a mechanical prosthesis showed a tendency for higher cumulative incidence MACCE rates compared to patients with bioprosthesis, p = 0.86 (bio 4.3 ± 3.1% vs. mech 9.1 ± 3.1% at 10 years). CONCLUSIONS: Long-term survival after surgical aortic valve replacement is similar in patients with a biological and mechanical prosthesis, independent of the patients' age. Moreover, younger patients (<60 years) with bioprosthesis showed a survival benefit, compared to patients with mechanical prosthesis in this age group.

Privacy-preserving data sharing infrastructures for medical research: systematization and comparison.

BACKGROUND: Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research participants remain anonymous when data is shared. However, privacy protection typically comes at a cost, e.g. restrictions regarding the types of analyses that can be performed on shared data. What is lacking is a systematization making the trade-offs taken by different approaches transparent. The aim of the work described in this paper was to develop a systematization for the degree of privacy protection provided and the trade-offs taken by different data sharing methods. Based on this contribution, we categorized popular data sharing approaches and identified research gaps by analyzing combinations of promising properties and features that are not yet supported by existing approaches. METHODS: The systematization consists of different axes. Three axes relate to privacy protection aspects and were adopted from the popular Five Safes Framework: (1) safe data, addressing privacy at the input level, (2) safe settings, addressing privacy during shared processing, and (3) safe outputs, addressing privacy protection of analysis results. Three additional axes address the usefulness of approaches: (4) support for de-duplication, to enable the reconciliation of data belonging to the same individuals, (5) flexibility, to be able to adapt to different data analysis requirements, and (6) scalability, to maintain performance with increasing complexity of shared data or common analysis processes. RESULTS: Using the systematization, we identified three different categories of approaches: distributed data analyses, which exchange anonymous aggregated data, secure multi-party computation protocols, which exchange encrypted data, and data enclaves, which store pooled individual-level data in secure environments for access for analysis purposes. We identified important research gaps, including a lack of approaches enabling the de-duplication of horizontally distributed data or providing a high degree of flexibility. CONCLUSIONS: There are fundamental differences between different data sharing approaches and several gaps in their functionality that may be interesting to investigate in future work. Our systematization can make the properties of privacy-preserving data sharing infrastructures more transparent and support decision makers and regulatory authorities with a better understanding of the trade-offs taken.

A Comprehensive Portal for Clinical and Translational Data Warehouses.

Data-driven methods in biomedical research can help to obtain new insights into the development, progression and therapy of diseases. Clinical and translational data warehouses such as Informatics for Integrating Biology and the Bedside (i2b2) and tranSMART are important solutions for this. From the well-known FAIR data principles, which are used to address the aspects of findability, accessibility, interoperability and reusability. In this paper, we focus on findability. For this purpose, we describe a portal solution that acts as a catalogue for a wide range of data warehouse instances, featuring a central access point and links to training material, such as user manuals and video tutorials. Moreover, the portal provides an overview of the status of multiple warehouses for developers and a set of statistics about the data currently loaded. Due to its modular design and the use of modern web technologies, the portal is easy to extend and customize to reflect different corporate designs and institutional requirements.

Congenital heart disease risk loci identified by genome-wide association study in European patients.

Genetic factors undoubtedly affect the development of congenital heart disease (CHD) but still remain ill defined. We sought to identify genetic risk factors associated with CHD and to accomplish a functional analysis of SNP-carrying genes. We performed a genome-wide association study (GWAS) of 4034 White patients with CHD and 8486 healthy controls. One SNP on chromosome 5q22.2 reached genome-wide significance across all CHD phenotypes and was also indicative for septal defects. One region on chromosome 20p12.1 pointing to the MACROD2 locus identified 4 highly significant SNPs in patients with transposition of the great arteries (TGA). Three highly significant risk variants on chromosome 17q21.32 within the GOSR2 locus were detected in patients with anomalies of thoracic arteries and veins (ATAV). Genetic variants associated with ATAV are suggested to influence the expression of WNT3, and the variant rs870142 related to septal defects is proposed to influence the expression of MSX1. We analyzed the expression of all 4 genes during cardiac differentiation of human and murine induced pluripotent stem cells in vitro and by single-cell RNA-Seq analyses of developing murine and human hearts. Our data show that MACROD2, GOSR2, WNT3, and MSX1 play an essential functional role in heart development at the embryonic and newborn stages.

Citizen-Centered Mobile Health Apps Collecting Individual-Level Spatial Data for Infectious Disease Management: Scoping Review.

BACKGROUND: The novel coronavirus SARS-CoV-2 rapidly spread around the world, causing the disease COVID-19. To contain the virus, much hope is placed on participatory surveillance using mobile apps, such as automated digital contact tracing, but broad adoption is an important prerequisite for associated interventions to be effective. Data protection aspects are a critical factor for adoption, and privacy risks of solutions developed often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches. OBJECTIVE: Our aim is to inform the current discussions and to support the development of solutions providing an optimal balance between privacy protection and pandemic control. To this end, we present a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis is that there are dependencies between the following dimensions: the use cases supported, the technology used to collect spatial data, the specific diseases focused on, and data protection measures implemented. METHODS: We searched PubMed and IEEE Xplore with a search string combining terms from the area of infectious disease management with terms describing spatial surveillance technologies to identify studies published between 2010 and 2020. After a two-step eligibility assessment process, 27 articles were selected for the final analysis. We collected data on the four dimensions described as well as metadata, which we then analyzed by calculating univariate and bivariate frequency distributions. RESULTS: We identified four different use cases, which focused on individual surveillance and public health (most common: digital contact tracing). We found that the solutions described were highly specialized, with 89% (24/27) of the articles covering one use case only. Moreover, we identified eight different technologies used for collecting spatial data (most common: GPS receivers) and five different diseases covered (most common: COVID-19). Finally, we also identified six different data protection measures (most common: pseudonymization). As hypothesized, we identified relationships between the dimensions. We found that for highly infectious diseases such as COVID-19 the most common use case was contact tracing, typically based on Bluetooth technology. For managing vector-borne diseases, use cases require absolute positions, which are typically measured using GPS. Absolute spatial locations are also important for further use cases relevant to the management of other infectious diseases. CONCLUSIONS: We see a large potential for future solutions supporting multiple use cases by combining different technologies (eg, Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented. Technologies currently used in this context can probably not offer enough protection. We, therefore, recommend that future solutions should consider the use of modern privacy-enhancing techniques (eg, from the area of secure multiparty computing and differential privacy).