Attitudes, Knowledge, and Risk Perceptions of Patients who Received Elective Genomic Testing as a Clinical Service.

BACKGROUND: Elective genomic testing (EGT) is increasingly available clinically. Limited real world evidence exists about attitudes and knowledge of EGT recipients. METHODS: After web-based education, patients who enrolled in an EGT program at a rural nonprofit healthcare system completed a survey that assessed attitudes, knowledge, and risk perceptions. RESULTS: From August 2020 to April 2022, 5,920 patients completed the survey and received testing. Patients most frequently cited interest in learning their personal disease risks as their primary motivation. Patients most often expected results to guide medication management (74.0%), prevent future disease (70.4%), and provide information about risks to offspring (65.4%). Patients were "very concerned" most frequently about the privacy of genetic information (19.8%) and how well testing predicted disease risks (18.0%). On average, patients answered 6.7 of 11 knowledge items correctly (61.3%). They more often rated their risks for colon and breast cancers as lower rather than higher than the average person, but more often rated their risk for a heart attack as higher rather than lower than the average person (all p<0.001). CONCLUSION: Patients pursued EGT because of the utility expectations, but often misunderstood the test's capabilities.

Identifying factors and causal chains associated with optimal implementation of Lynch syndrome tumor screening: An application of coincidence analysis.

PURPOSE: This study compared Lynch syndrome universal tumor screening (UTS) across multiple health systems (some of which had 2 or more distinct UTS programs) to understand multilevel factors that may affect the successful implementation of complex programs. METHODS: Data from 66 stakeholder interviews were used to conduct multivalue coincidence analysis and identify key factors that consistently make a difference in whether UTS programs were implemented and optimized at the system level. RESULTS: The selected coincidence analysis model revealed combinations of conditions that distinguish 4 optimized UTS programs, 10 nonoptimized programs, and 4 systems with no program. Fully optimized UTS programs had both a maintenance champion and a positive inner setting. Two independent paths were unique to nonoptimized programs: (1) positive attitudes and a mixed inner setting or (2) limited planning and engaging among stakeholders. Negative views about UTS evidence or lack of knowledge about UTS led to a lack of planning and engaging, which subsequently prevented program implementation. CONCLUSION: The model improved our understanding of program implementation in health care systems and informed the creation of a toolkit to guide UTS implementation, optimization, and changes. Our findings and toolkit may serve as a use case to increase the successful implementation of other complex precision health programs.

Variation in completeness of coding external cause of injuries under ICD-10-CM.

INTRODUCTION: Information about causes of injury is key for injury prevention efforts. Historically, cause-of-injury coding in clinical practice has been incomplete due to the need for extra diagnosis codes in the International Classification of Diseases-Ninth Revision-Clinical Modification (ICD-9-CM) coding. The transition to ICD-10-CM and increased use of clinical support software for diagnosis coding is expected to improve completeness of cause-of-injury coding. This paper assesses the recording of external cause-of-injury codes specifically for those diagnoses where an additional code is still required. METHODS: We used electronic health record and claims data from 10 health systems from October 2015 to December 2021 to identify all inpatient and emergency encounters with a primary diagnosis of injury. The proportion of encounters that also included a valid external cause-of-injury code is presented. RESULTS: Most health systems had high rates of cause-of-injury coding: over 85% in emergency departments and over 75% in inpatient encounters with primary injury diagnoses. However, several sites had lower rates in both settings. State mandates were associated with consistently high external cause recording. CONCLUSIONS: Completeness of cause-of-injury coding improved since the adoption of ICD-10-CM coding and increased slightly over the study period at most sites. However, significant variation remained, and completeness of cause-of-injury coding in any diagnosis data used for injury prevention planning should be empirically determined.

Disparities in the risk of heart disease death among people with chronic lower respiratory disease.

BACKGROUND: Heart disease remains the leading cause of death in the United States, while chronic lower respiratory diseases (CLRD) are the sixth leading cause of death. Patients with CLRD have been shown to have an elevated risk of heart disease death. However, less is known regarding how this risk varies across demographic groups. METHODS: We used the Multiple Cause of Death database from the Centers for Disease Control Wide-ranging ONline Data for Epidemiologic Research to obtain 1999-2020 information on deaths with heart disease as a primary cause of death and CLRD as a contributing cause. We calculated age-adjusted mortality rates (AAMR) over time and for demographic subgroups. RESULTS: During 1999-2020, there were 1,178,048 heart disease deaths related to CLRD among people aged 45+. The AAMR for CLRD-associated heart disease deaths was 45.713 per 100,000 people. AAMR was highest among those aged 65+ (108.56 per 100,000). Elevated rates were seen among males (AAMR ratio = 1.744, 95% CI: 1.741-1.748), people living in the Midwest (AAMR ratio = 1.196, 95% CI: 1.190-1.202), and among people in rural areas (AAMR ratio = 1.309, 95% CI: 1.304-1.313) compared to their corresponding counterparts. Between 1999 and 2004 and 2016-2020 rates decreased among all demographic subgroups, except for among people aged 45-64, among whom deaths increased (AAMR ratio = 1.016, 95% CI: 1.003-1.030). CONCLUSION: Rates of CLRD-associated heart disease deaths have declined over time, but significant disparities remain. Enhanced interventions particularly among older people (65+), people living in rural areas, people living in the Midwest, and men may reduce CLRD-associated heart disease deaths in the United States.

Social determinants of health Z-code documentation practices in mental health settings: a scoping review.

Mental health remains an urgent global priority, alongside efforts to address underlying social determinants of health (SDoH) that contribute to the onset or exacerbate mental illness. SDoH factors can be captured in the form of International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM), SDoH Z codes. In this scoping review, we describe current SDoH Z-code documentation practices, with a focus on mental health care contexts. Among 2 743 061 374 health care encounters noted across 12 studies in the United States, SDoH Z-code documentation rates ranged from 0.5% to 2.4%. Documentation often involved patients under 64 years of age who are publicly insured and experience comorbidities, including depression, bipolar disorder and schizophrenia, chronic pulmonary disease, and substance abuse disorders. Documentation varied across hospital types, number of beds per facility, patient race/ethnicity, and geographic region. Variation was observed regarding patient sex/gender, although SDoH Z codes were more frequently documented for males. Documentation was most observed in government, nonfederal, and private not-for-profit hospitals. From these insights, we offer policy and practice recommendations, as well as considerations for patient data privacy, security, and confidentiality, to incentivize more routine documentation of Z codes to better assist patients with complex mental health care needs.

National trends in pneumonia-related mortality in the United States, 1999-2019.

INTRODUCTION: Pneumonia is one of the most common causes of hospital admissions in the United States and remains a major cause of death. However, less is known regarding the mortality burden from pneumonia in the United States and how this burden has changed over time. METHODS: Death rates from causes related to pneumonia were determined using the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) data from 1999-2019. Pneumonia deaths were calculated for the overall population as well as for sociodemographic subgroups. We also analysed changes in death rates over time. RESULTS: Overall, 2.1% of total US deaths during the period between 1999 and 2019 were due to pneumonia (2.6% in 1999 and 1.5% in 2019). Mortality declined over time for both men and women, and across most age cohorts, as well as all racial, urbanisation, and regional categories. Rates of pneumonia deaths were higher among males as compared to females (age-adjusted mortality rate ratio (AAMRR) = 1.35; 95% CI: 1.34-1.35). Compared to White Americans, Black Americans had the highest pneumonia-related mortality rates of any racial group (AAMRR = 1.11; 95% CI: 1.10-1.11). CONCLUSIONS: Rates of pneumonia-related death have decreased in the United States in recent decades. However, significant racial and gender disparities remain, indicating the need for more equitable care.

Taipei Medical University Clinical Research Database: a collaborative hospital EHR database aligned with international common data standards.

OBJECTIVE: The objective of this paper is to provide a comprehensive overview of the development and features of the Taipei Medical University Clinical Research Database (TMUCRD), a repository of real-world data (RWD) derived from electronic health records (EHRs) and other sources. METHODS: TMUCRD was developed by integrating EHRs from three affiliated hospitals, including Taipei Medical University Hospital, Wan-Fang Hospital and Shuang-Ho Hospital. The data cover over 15 years and include diverse patient care information. The database was converted to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) for standardisation. RESULTS: TMUCRD comprises 89 tables (eg, 29 tables for each hospital and 2 linked tables), including demographics, diagnoses, medications, procedures and measurements, among others. It encompasses data from more than 4.15 million patients with various medical records, spanning from the year 2004 to 2021. The dataset offers insights into disease prevalence, medication usage, laboratory tests and patient characteristics. DISCUSSION: TMUCRD stands out due to its unique advantages, including diverse data types, comprehensive patient information, linked mortality and cancer registry data, regular updates and a swift application process. Its compatibility with the OMOP CDM enhances its usability and interoperability. CONCLUSION: TMUCRD serves as a valuable resource for researchers and scholars interested in leveraging RWD for clinical research. Its availability and integration of diverse healthcare data contribute to a collaborative and data-driven approach to advancing medical knowledge and practice.

Therapeutic Drug Monitoring and Biomarkers; towards Better Dosing of Antimicrobial Therapy.

Due to variability in pharmacokinetics and pharmacodynamics, clinical outcomes of antimicrobial drug therapy vary between patients. As such, personalised medication management, considering both pharmacokinetics and pharmacodynamics, is a growing concept of interest in the field of infectious diseases. Therapeutic drug monitoring is used to adjust and individualise drug regimens until predefined pharmacokinetic exposure targets are achieved. Minimum inhibitory concentration (drug susceptibility) is the best available pharmacodynamic parameter but is associated with many limitations. Identification of other pharmacodynamic parameters is necessary. Repurposing diagnostic biomarkers as pharmacodynamic parameters to evaluate treatment response is attractive. When combined with therapeutic drug monitoring, it could facilitate making more informed dosing decisions. We believe the approach has potential and justifies further research.

Short report: Transition to International Classification of Diseases, 10th Revision and the prevalence of autism in a cohort of healthcare systems.

LAY ABSTRACT: Currently, the prevalence of autism spectrum disorder (henceforth "autism") is 1 in 36, an increasing trend from previous estimates. In 2015, the United States adopted a new version (International Classification of Diseases, 10th Revision) of the World Health Organization coding system, a standard for classifying medical conditions. Our goal was to examine how the transition to this new coding system impacted autism diagnoses in 10 healthcare systems. We obtained information from electronic medical records and insurance claims data from July 2014 through December 2016 for each healthcare system. We used member enrollment data for 30 consecutive months to observe changes 15 months before and after adoption of the new coding system. Overall, the rates of autism per 1000 enrolled members was increasing for 0- to 5-year-olds before transition to International Classification of Diseases, 10th Revision and did not substantively change after the new coding was in place. There was variation observed in autism diagnoses before and after transition to International Classification of Diseases, 10th Revision for other age groups. The change to the new coding system did not meaningfully affect autism rates at the participating healthcare systems. The increase observed among 0- to 5-year-olds is likely indicative of an ongoing trend related to increases in screening for autism rather than a shift associated with the new coding.

Returning personalized, genetic health test results to individuals of African descent or ancestry in precision medicine research.

Today, many epidemiological studies and biobanks are offering to disclose individual genetic results to their participants, including the National Institutes of Health's All of Us Research Program. Returning hereditary disease risks and pharmacogenetic test results to study participants from racial/ethnic groups that are historically underrepresented in biomedical research poses specific challenges to those participants and the health system writ large. For example, individuals of African descent are underrepresented in research about drug-gene interactions and have a relatively higher proportion of variants of unknown significance, affecting their ability to take clinical action following return of results. In this brief report, we summarize studies published to date concerning the perspectives and/or attitudes of African Americans engaged in genetic research programs to anticipate factors in disclosure protocols that would minimize risks and maximize benefits. A thematic analysis of studies identified (n = 6) lends to themes centered on motivations to engage or disengage in the return of results and integrating research and care. Actionable strategies determined in reaction to these themes center on ensuring adequate system and health education support for participants and personalizing the process for participants engaging in return of results. Overall, we offer these themes and actionable strategies as early guidance to research programs, and provide recommendations to policy makers focused on fair and equitable return of genetic research results to underrepresented research participants.

Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases.

Background: Previous studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods. Objective: This study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19. Methods: Data were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity. Results: The study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902. Conclusion: This study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.