Evidence Based Change to Improve Outcomes in Cardiac Patients

According to the Centers for Disease Control and Prevention, “heart disease is the leading cause of death in the United States” (CDC, 2022). Modern healthcare has made numerous advances in changing the prevalence of heart disease and has shifted resources to promoting prevention. When a patient requires admission to an acute care facility for heart disease exacerbation, the focus is more of treating the acute process and how to manage the condition moving forward. Although there have been a substantial number of resources that have been allocated to prevention in the primary care and outpatient setting, there is a need for additional resources in the acute care setting for prevention and management of a newly acquired heart condition. In October of 2012, Centers for Medicare & Medicaid Services (CMS) created the Hospital Readmissions Reduction Program (HRRP) which reduces payment to those facilities that have “excess readmissions” within a 30-day period for common health conditions that include acute myocardial infarction, heart failure, and coronary artery bypass graft surgery (CMS, 2023). There is a unique opportunity for nursing interventions to make significant changes in the outcomes for this patient population at the acute care level and beyond. The focus of this project is to have a nurse navigator in an acute care facility that primary focus is patients with coronary artery disease and a newly placed coronary stent during a recent hospital admission. In effort to reduce readmissions and improve outcomes for these patients. Therefore, it is recommended for acute care facilities to allocate resources for a nurse navigator to monitor, track, and lead an interdisciplinary committee to promote early intervention for those patients discharged with a newly acquired diagnosis of coronary artery diseas

Parent–Child Separation Due to Incarceration: Assessment, Diagnosis, and Treatment Considerations

Decades of research and clinical observations have demonstrated the harmful effects of parent-child separation on children's short- and long-term well-being (Society for Research in Child Development, 2018). Young children may be separated from their parents due to a variety of circumstances. This article provides recommendations for the assessment, diagnosis, and treatment of young children who experience trauma as a result of being separated from their parent due to incarceration. An example of a multidisciplinary health care clinic is highlighted to demonstrate how clinicians and community partners work together to provide evaluation and care coordination services for children in foster care

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online

Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery.

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy

Motivation Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia. Results We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified. Availability and implementation Datasets and scripts for reproduction of results are available through: Https://nalab.stanford.edu/multiomics-pregnancy/

Implementation of a Family Intervention for Individuals with Schizophrenia

Families are rarely included in clinical care despite research showing that family involvement has a positive effect on individuals with schizophrenia by reducing relapse, improving work functioning, and social adjustment. The VA QUERI study, EQUIP (Enhancing QUality of care In Psychosis), implemented family services for this population. At two VA medical centers, veterans with schizophrenia and their clinicians were interviewed separately at baseline and 15 months. A family intervention was implemented, and a process evaluation of the implementation was conducted. Veterans with schizophrenia (n = 173) and their clinicians (n = 29). Consent to contact family was obtained, mailers to engage families were sent, families were prioritized as high need for family services, and staff volunteers were trained in a brief three-session family intervention. Of those enrolled, 100 provided consent for family involvement. Seventy-three of the 100 were sent a mailer to engage them in care; none became involved. Clinicians were provided assessment data on their patients and notified of 50 patients needing family services. Of those 50, 6 families were already involved, 34 were never contacted, and 10 were contacted; 7 new families became involved in care. No families were referred to the family psychoeducational program. Uptake of the family intervention failed due to barriers from all stakeholders. Families did not respond to the mailer, patients were concerned about privacy and burdening family, clinicians had misperceptions of family-patient contact, and organizations did not free up time or offer incentives to provide the service. If a full partnership with patients and families is to be achieved, these barriers will need to be addressed, and a family-friendly environment will need to be supported by clinicians and their organizations. Applicability to family involvement in other disorders is discussed

2019 American College of Rheumatology/Arthritis Foundation Guideline for the Management of Osteoarthritis of the Hand, Hip, and Knee

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153772/1/acr24131.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153772/2/acr24131_am.pd

The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

How and why weight stigma drives the obesity 'epidemic' and harms health.

BACKGROUND:In an era when obesity prevalence is high throughout much of the world, there is a correspondingly pervasive and strong culture of weight stigma. For example, representative studies show that some forms of weight discrimination are more prevalent even than discrimination based on race or ethnicity. DISCUSSION:In this Opinion article, we review compelling evidence that weight stigma is harmful to health, over and above objective body mass index. Weight stigma is prospectively related to heightened mortality and other chronic diseases and conditions. Most ironically, it actually begets heightened risk of obesity through multiple obesogenic pathways. Weight stigma is particularly prevalent and detrimental in healthcare settings, with documented high levels of 'anti-fat' bias in healthcare providers, patients with obesity receiving poorer care and having worse outcomes, and medical students with obesity reporting high levels of alcohol and substance use to cope with internalized weight stigma. In terms of solutions, the most effective and ethical approaches should be aimed at changing the behaviors and attitudes of those who stigmatize, rather than towards the targets of weight stigma. Medical training must address weight bias, training healthcare professionals about how it is perpetuated and on its potentially harmful effects on their patients. CONCLUSION:Weight stigma is likely to drive weight gain and poor health and thus should be eradicated. This effort can begin by training compassionate and knowledgeable healthcare providers who will deliver better care and ultimately lessen the negative effects of weight stigma