Despite important advances in the linkage of residents' Medicare claims and Minimum Data Set (MDS) information, the data infrastructure for long-term care remains inadequate for public health surveillance and clinical research. It is widely known that the evidence base supporting treatment decisions for older nursing home residents is scant as residents are systematically excluded from clinical trials. Electronic health records (EHRs) hold the promise to improve this population's representation in clinical research, especially with the more timely and detailed clinical information available in EHRs that are lacking in claims and MDS. The COVID-19 pandemic shined a spotlight on the data gap in nursing homes. To address this need, the National Institute on Aging funded the Long-Term Care (LTC) Data Cooperative, a collaboration among providers and stakeholders in academia, government, and the private sector. The LTC Data Cooperative assembles residents' EHRs from major specialty vendors and facilitates linkage of these data with Medicare claims to create a comprehensive, longitudinal patient record. These data serve 4 key purposes: (1) health care operations and population health analytics; (2) public health surveillance; (3) observational, comparative effectiveness research; and (4) clinical research studies, including provider and patient recruitment into Phase 3 and Phase 4 randomized trials. Federally funded researchers wanting to conduct pragmatic trials can now enroll their partnering sites in this Cooperative to more easily access the clinical data needed to close the evidence gaps in LTC. Linkage to Medicare data facilitates tracking patients' long-term outcomes after being discharged back to the community. As of August 2022, nearly 1000 nursing homes have joined, feedback reports to facilities are being piloted, algorithms for identifying infections are being tested, and proposals for use of the data have been reviewed and approved. This emerging EHR system is a substantial innovation in the richness and timeliness of the data infrastructure of the nursing home population.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9742312 | PMC |
http://dx.doi.org/10.1016/j.jamda.2022.09.006 | DOI Listing |
Aim: This study was conducted to evaluate the in vitro effects of hydroxychloroquine (HCQ) on histone deacetylase (HDAC) enzyme activity and interleukin (IL)-6, IL-10, and tumor necrosis factor-alpha (TNF-α) expression. HDAC enzyme activity and the expression of inflammation markers were tested, with the presence of the HDAC inhibitor valproic acid, in human primary cell cultures prepared from two different tissues.
Material And Methods: Primary cell cultures were prepared.
Aim: Latissimus dorsi is a multi-purpose muscle that can be used to repair defects in many areas of the body. The current study aims to investigate latissimus dorsi morphometry, innervation, vascularization, and variational situations in fetuses.
Material And Methods: Forty-nine fetuses, aged between 15 and 40 weeks of gestation, were examined for the morphological development of the latissimus dorsi.
Aim: Many combinations of inflammation-based markers have been reported their prognostic ability. The prognostic value of albumin-to-gama-glutamyltransferase ratio (AGR), an inflammation-related index, has been identified for several cancers. However, the predictive value of AGR for high-grade glioma patients remains unclear.
View Article and Find Full Text PDFAim: This study aims to assess the clinicopathological and prognostic significance of Tim-3, an immune checkpoint molecule, and Rel-B, an NF-κB subunit, in grade 4 diffuse glioma samples and their relationship with each other.
Material And Methods: The demographic, radiologic, prognostic, and treatment data of patients diagnosed with grade 4 diffuse glioma between 2016 and 2019 were reviewed and recorded. Tim-3 and Rel-B were applied to the paraffin-embedded tissues by immunohistochemistry method.
BJOG
January 2025
Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Objective: To determine the diagnostic performance and clinical utility of the M4 prediction model and the NICE algorithm managing women with pregnancy of unknown location (PUL).
Design: The study has a superiority design regarding specificity for non-ectopic pregnancy for M4, given that the primary outcome of sensitivity for ectopic pregnancy (EP) is non-inferior in comparison with the NICE algorithm.
Setting: Emergency gynaecology units in Sweden.
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