Abstract
Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, ...containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies
Abstract Objective To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app “listener” that accesses ...standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API). Methods We advance a model for scalable, federated, data sharing and learning. Cumulus software is designed to address key technology and policy desiderata including local utility, control, and administrative simplicity as well as privacy preservation during robust data sharing, and artificial intelligence (AI) for processing unstructured text. Results Cumulus relies on containerized, cloud-hosted software, installed within a healthcare organization’s security envelope. Cumulus accesses EHR data via the Bulk FHIR interface and streamlines automated processing and sharing. The modular design enables use of the latest AI and natural language processing tools and supports provider autonomy and administrative simplicity. In an initial test, Cumulus was deployed across 5 healthcare systems each partnered with public health. Cumulus output is patient counts which were aggregated into a table stratifying variables of interest to enable population health studies. All code is available open source. A policy stipulating that only aggregate data leave the institution greatly facilitated data sharing agreements. Discussion and Conclusion Cumulus addresses barriers to data sharing based on (1) federally required support for standard APIs, (2) increasing use of cloud computing, and (3) advances in AI. There is potential for scalability to support learning across myriad network configurations and use cases.
Computationally derived ("synthetic") data can enable the creation and analysis of clinical, laboratory, and diagnostic data as if they were the original electronic health record data. Synthetic data ...can support data sharing to answer critical research questions to address the COVID-19 pandemic.
We aim to compare the results from analyses of synthetic data to those from original data and assess the strengths and limitations of leveraging computationally derived data for research purposes.
We used the National COVID Cohort Collaborative's instance of MDClone, a big data platform with data-synthesizing capabilities (MDClone Ltd). We downloaded electronic health record data from 34 National COVID Cohort Collaborative institutional partners and tested three use cases, including (1) exploring the distributions of key features of the COVID-19-positive cohort; (2) training and testing predictive models for assessing the risk of admission among these patients; and (3) determining geospatial and temporal COVID-19-related measures and outcomes, and constructing their epidemic curves. We compared the results from synthetic data to those from original data using traditional statistics, machine learning approaches, and temporal and spatial representations of the data.
For each use case, the results of the synthetic data analyses successfully mimicked those of the original data such that the distributions of the data were similar and the predictive models demonstrated comparable performance. Although the synthetic and original data yielded overall nearly the same results, there were exceptions that included an odds ratio on either side of the null in multivariable analyses (0.97 vs 1.01) and differences in the magnitude of epidemic curves constructed for zip codes with low population counts.
This paper presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in collaborative research for faster insights.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
To evaluate the real-world performance of the SMART/HL7 Bulk Fast Health Interoperability Resources (FHIR) Access Application Programming Interface (API), developed to enable push button access to ...electronic health record data on large populations, and required under the 21st Century Cures Act Rule.
We used an open-source Bulk FHIR Testing Suite at 5 healthcare sites from April to September 2023, including 4 hospitals using electronic health records (EHRs) certified for interoperability, and 1 Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across 6 types of FHIR.
Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1555-2500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12 000 resources/min.
The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases.
To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes.
Background and Purpose
We recently found that PKCε was required for spinal analgesic synergy between two GPCRs, δ opioid receptors and α2A adrenoceptors, co‐located in the same cellular ...subpopulation. We sought to determine if co‐delivery of μ and δ opioid receptor agonists would similarly result in synergy requiring PKCε.
Experimental Approach
Combinations of μ and δ opioid receptor agonists were co‐administered intrathecally by direct lumbar puncture to PKCε‐wild‐type (PKCε‐WT) and ‐knockout (PKCε‐KO) mice. Antinociception was assessed using the hot‐water tail‐flick assay. Drug interactions were evaluated by isobolographic analysis.
Key Results
All agonists produced comparable antinociception in both PKCε‐WT and PKCε‐KO mice. Of 19 agonist combinations that produced analgesic synergy, only 3 required PKCε for a synergistic interaction. In these three combinations, one of the agonists was morphine, although not all combinations involving morphine required PKCε. Morphine + deltorphin II and morphine + deltorphin I required PKCε for synergy, whereas a similar combination, morphine + deltorphin, did not. Additionally, morphine + oxymorphindole required PKCε for synergy, whereas a similar combination, morphine + oxycodindole, did not.
Conclusions and Implications
We discovered biased agonism for a specific signalling pathway at the level of spinally co‐delivered opioid agonists. As the bias is only revealed by an appropriate ligand combination and cannot be accounted for by a single drug, it is likely that the receptors these agonists act on are interacting with each other. Our results support the existence of μ and δ opioid receptor heteromers at the spinal level in vivo.
Linked Articles
This article is part of a themed section on Opioids: New Pathways to Functional Selectivity. To view the other articles in this section visit http://dx.doi.org/10.1111/bph.2015.172.issue-2
Abstract
Objective
Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are ...largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers.
Materials and Methods
The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics.
Results
Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access.
Conclusions
The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.
•Chemotherapy induces microbiome disruption, inflammation, and cognitive decline.•The resulting microbiome disruption relates to cognitive decline and inflammation.•Those cognitively impaired have ...unique chemotherapy-induced microbiome alterations.
Chemotherapy is notorious for causing behavioral side effects (e.g., cognitive decline). Notably, the gut microbiome has recently been reported to communicate with the brain to affect behavior, including cognition. Thus, the aim of this clinical longitudinal observational study was to determine whether chemotherapy-induced disruption of the gut microbial community structure relates to cognitive decline and circulating inflammatory signals. Fecal samples, blood, and cognitive measures were collected from 77 patients with breast cancer before, during, and after chemotherapy. Chemotherapy altered the gut microbiome community structure and increased circulating TNF-α. Both the chemotherapy-induced changes in microbial relative abundance and decreased microbial diversity were related to elevated circulating pro-inflammatory cytokines TNF-α and IL-6. Participants reported subjective cognitive decline during chemotherapy, which was not related to changes in the gut microbiome or inflammatory markers. In contrast, a decrease in overall objective cognition was related to a decrease in microbial diversity, independent of circulating cytokines. Stratification of subjects, via a reliable change index based on 4 objective cognitive tests, identified objective cognitive decline in 35% of the subjects. Based on a differential microbial abundance analysis, those characterized by cognitive decline had unique taxonomic shifts (Faecalibacterium, Bacteroides, Fusicatenibacter, Erysipelotrichaceae UCG-003, and Subdoligranulum) over chemotherapy treatment compared to those without cognitive decline. Taken together, gut microbiome change was associated with cognitive decline during chemotherapy, independent of chemotherapy-induced inflammation. These results suggest that microbiome-related strategies may be useful for predicting and preventing behavioral side effects of chemotherapy.
This multicenter, randomized, double-blind study compared the efficacy and safety of itraconazole oral solution and fluconazole tablets in the treatment of esophageal candidiasis. One hundred ...twenty-six immunocompromised patients with esophageal candidiasis were treated with itraconazole oral solution or fluconazole tablets (both at 100–200 mg) once daily for 3–8 weeks, for 2 weeks beyond the resolution of symptoms, and were then followed for 4 more weeks. Severity of symptoms was assessed weekly during treatment and every 2 weeks during follow-up. Patients treated with itraconazole oral solution had a rate of clinical response (cured or improved) comparable to that of patients treated with fluconazole (94% vs. 91%). The mycologic eradication rate was 92% for itraconazole and 78% for fluconazole. Both treatments were well tolerated. Results from treatment with once-daily itraconazole oral solution was clinically comparable to those with fluconazole and is an alternative for the treatment of esophageal candidiasis in immunocompromised patients.
Alpha-2-macroglobulin (alpha-2M; encoded by the gene A2M) is a serum pan-protease inhibitor that has been implicated in Alzheimer disease (AD) based on its ability to mediate the clearance and ...degradation of A beta, the major component of beta-amyloid deposits. Analysis of a deletion in the A2M gene at the 5' splice site of 'exon II' of the bait region (exon 18) revealed that inheritance of the deletion (A2M-2) confers increased risk for AD (Mantel-Haenzel odds ratio=3.56, P=0.001). The sibship disequilibrium test (SDT) also revealed a significant association between A2M and AD (P=0.00009). These values were comparable to those obtained for the APOE-epsilon4 allele in the same sample, but in contrast to APOE-epsilon4, A2M-2 did not affect age of onset. The observed association of A2M with AD did not appear to account for the previously published linkage of AD to chromosome 12, which we were unable to confirm in this sample. A2M, LRP1 (encoding the alpha-2M receptor) and the genes for two other LRP ligands, APOE and APP (encoding the amyloid beta-protein precursor), have now all been genetically linked to AD, suggesting that these proteins may participate in a common neuropathogenic pathway leading to AD.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The purpose of this study was to characterize the relationship between heart rate and post-discharge outcomes in patients with hospitalization for heart failure (HHF) with reduced ejection fraction ...(EF) in sinus rhythm.
A reduction in heart rate improves clinical outcomes in patients with chronic heart failure and in sinus rhythm, but the association between heart rate and post-discharge outcomes in patients with HHF is presently unclear.
This post-hoc analysis of the EVEREST (Efficacy of Vasopressin Antagonism in Heart Failure: Outcome Study With Tolvaptan) trial examined 1,947 patients with HHF and EF ≤40% not in atrial fibrillation/flutter or pacemaker dependent.
The median follow-up period was 9.9 months. At baseline, patients with a higher heart rate tended to be younger with lower EF and were more likely to have worse New York Heart Association functional class and higher natriuretic peptide levels. After adjustment for clinical risk factors, baseline heart rate was not predictive of all-cause mortality (p ≥ 0.066). However, at ≥70 beats/min, every 5-beat increase in 1-week post-discharge heart rate was independently associated with increased all-cause mortality (hazard ratio: 1.13 95% confidence interval: 1.05 to 1.22; p = 0.002). Similarly, every 5-beat increase ≥70 beats/min in 4-week post-discharge heart rate was predictive of all-cause mortality (hazard ratio: 1.12 95% confidence interval: 1.05 to 1.19; p = 0.001).
In this large cohort of patients with HHF with reduced EF and in sinus rhythm, baseline heart rate did not correlate with all-cause mortality. In contrast, at ≥70 beats/min, higher heart rate in the early post-discharge period was independently predictive of death during subsequent follow-up. Further study of post-discharge heart rate as a potential therapeutic target in this high-risk population is encouraged.
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