Quintile Of Risk Research Articles

The Association of Dietary Polyamines with Mortality and the Risk of Cardiovascular Disease: A Prospective Study in UK Biobank.

Polyamines, including spermidine (SPD), spermine (SPM) and putrescine (PUT), are essential for cellular physiology and various cellular processes. This study aimed to examine the associations of dietary polyamines intake and all-cause mortality and incident cardiovascular disease (CVD). This prospective cohort study included 184,732 participants without CVD at baseline from the UK Biobank who had completed at least one dietary questionnaire. Diet was assessed using Oxford WebQ, a web-based 24 h recall questionnaire, with polyamines intakes estimated from previous studies. Cox proportional models with restricted cubic splines were employed to investigate nonlinear associations. The primary endpoint was all-cause mortality or incident CVD (including CVD death, coronary heart disease and stroke). During a median follow-up period of 11.5 years, 7348 (3.9%) participants died and 12,316 (6.5%) developed incident CVD. Polyamines intake showed nonlinear associations with all-cause mortality and incident CVD (P for nonlinear < 0.01). Compared to the lowest quintile group of dietary polyamines intake (≤17.4 mg/day), the quintile 2 to 5 groups demonstrated a reduced risk of all-cause mortality, with the lowest risk in quintile 2 group (>17.4-22.3 mg/day) (HR:0.82, 95% CI: 0.76-0.88). Similar results were observed for incident CVD, with the lowest risk in the quintile 4 group (>27.1-33.5 mg/day) (HR: 0.86, 95% CI: 0.82-0.92). We found that dietary polyamines intake was associated with a lower risk of all-cause mortality or incident CVD. Furthermore, our study identified an optimal range of dietary polyamines intake.

Comparing five-year and ten-year predicted cardiovascular disease risk in Aotearoa New Zealand: national data linkage study of 1.7 million adults.

There is no consensus on the optimal time horizon for predicting cardiovascular disease (CVD) risk to inform treatment decisions. New Zealand and Australia recommend 5 years, whereas most countries recommend 10 years. We compared predicted risk and treatment-eligible groups using 5-year and 10-year equations. Individual-level linked administrative datasets identified 1,746,665 New Zealanders without CVD, aged 30-74 years in 2006, with follow-up to 2018. Participants were randomly allocated to derivation and validation cohorts. Sex-specific 5-year and 10-year risk prediction models were developed in the derivation cohort and applied in the validation cohort. 28,116 (3.2%) and 62,027 (7.1%) first CVD events occurred during 5-years and 10-years follow-up respectively (cumulative risk, derivation cohort). Median predicted 10-year CVD risk (3.8%) was approximately 2.5 times 5-year risk (1.6%) and 95% of individuals in the top quintile of 5-year risk were also in the top quintile of 10-year risk, across age/gender groups (validation cohort). Using common guideline-recommended treatment thresholds (5% 5-year, 10% 10-year risk), approximately 14% and 28% of women and men respectively were identified as treatment-eligible applying 5-year equations compared to 17% and 32% of women and men applying 10-year equations. Older age was the major contributor to treatment eligibility in both sexes. Predicted 10-year CVD risk was approximately 2.5 times 5-year risk. Both equations identified mostly the same individuals in the highest risk quintile. Conversely, commonly used treatment thresholds identified more treatment-eligible individuals using 10-year equations and both equations identified approximately twice as many treatment-eligible men as women. The treatment threshold, rather than the risk horizon, is the main determinant of treatment eligibility.

Polygenic risk, aspirin and primary prevention of coronary artery disease.

Recent aspirin primary prevention trials failed to identify a net benefit of aspirin for preventing cardiovascular disease versus the harms of bleeding. This study aimed to investigate whether a high-risk subgroup, individuals with elevated genetic predisposition to coronary artery disease (CAD), might derive more benefit than harm with aspirin, compared to those with lower genetic risk. We performed genetic risk stratification of the Aspirin in Reducing Events in the Elderly (ASPREE) randomized controlled trial using a CAD polygenic risk score (GPSMult). For 12,031 genotyped participants (5,974 aspirin, 6,057 placebo) overall, we stratified them by GPSMult quintiles (q1-5), then examined risk of CAD (composite of myocardial infarction and coronary heart disease death) and bleeding events using Cox models. During a median 4.6 years of follow-up with randomization to 100 mg/day aspirin versus placebo, 234 (1.9%) participants had CAD and 373 (3.1%) had bleeding events. In the overall cohort, aspirin resulted in higher bleeding risk (adjusted Hazard Ratio [aHR]=1.30 [1.06-1.61], P=0.01) but no significant CAD reduction (aHR=0.84 [0.64-1.09], P=0.19). However, among the highest quintile of polygenic risk (q5, top 20% of the GPSMult distribution), there was a 47% reduction in risk of CAD events with aspirin (aHR=0.53 [0.31-0.90], P=0.02) without increased bleeding risk (aHR=1.05 [0.60-1.82], P=0.88). Interaction between the GPSMult and aspirin was significant for CAD (q5 versus q1, P=0.02) but not bleeding (P=0.80). The balance between net benefit and harm on aspirin in the primary prevention setting shifts favourably in individuals with an elevated genetic predisposition.

Population Performance and Individual Agreement of Coronary Artery Disease Polygenic Risk Scores.

Polygenic risk scores (PRSs) for coronary artery disease (CAD) are a growing clinical and commercial reality. Whether existing scores provide similar individual-level assessments of disease liability is a critical consideration for clinical implementation that remains uncharacterized. Characterize the reliability of CAD PRSs that perform equivalently at the population level at predicting individual-level risk. Cross-sectional Study. All of Us Research Program (AOU), Penn Medicine Biobank (PMBB), and UCLA ATLAS Precision Health Biobank. Volunteers of diverse genetic backgrounds enrolled in AOU, PMBB, and UCLA with available electronic health record and genotyping data. Polygenic risk for CAD from previously published PRSs and new PRSs developed separately from the testing cohorts. Sets of CAD PRSs that perform population prediction equivalently were identified by comparing calibration and discrimination (Brier score and AUROC) of generalized linear models of prevalent CAD using Bayesian analysis of variance. Among equivalently performing scores, individual-level agreement between risk estimates was tested with intraclass correlation (ICC) and Light's Kappa, measures of inter-rater reliability. 50 PRSs were calculated for 171,095 AOU participants. When included in a model of prevalent CAD, 48 scores had practically equivalent Brier scores and AUROCs (region of practical equivalence = 0.02). Across these scores, 84% of participants had at least one score in both the top and bottom risk quintile. Continuous agreement of individual risk predictions from the 48 scores was poor, with an ICC of 0.351 (95% CI; 0.349, 0.352). Agreement between two statistically equivalent scores was moderate, with an ICC of 0.649 (95% CI; 0.646, 0.652). Light's Kappa, used to evaluate consistency of assignment to high-risk thresholds, did not exceed 0.56 (interpreted as 'fair') across statistically and practically equivalent scores. Repeating the analysis among 41,193 PMBB and 50,748 UCLA participants yielded different sets of statistically and practically equivalent scores which also lacked strong individual agreement. Across three diverse biobanks, CAD PRSs that performed equivalently at the population level produced unreliable individual risk estimates. Approaches to clinical implementation of CAD PRSs must consider the potential for discordant individual risk estimates from otherwise indistinguishable scores.

Integration of Patient Reported Quality-of-life Data into Risk Assessment in Heart Failure

Optimal management of outpatients with heart failure (HF) requires serially updating the estimates of their risk for adverse clinical outcomes to guide treatment. Patient-reported outcomes (PROs) are becoming increasingly used in clinical care. The purpose of this study was to determine whether the inclusion of PROs can improve the risk prediction for HF hospitalization and death in ambulatory patients with HF. We included consecutive patients with HF with reduced ejection fraction (HFrEF) and HF with preserved EF (HFpEF) seen in a HF clinic between 2015 and 2019 who completed PROs as part of routine care. Cox regression with a least absolute shrinkage and selection operator regularization and gradient boosting machine analyses were used to estimate risk for a combined outcome of HF hospitalization, heart transplant, left ventricular assist device implantation, or death. The performance of the prediction models was evaluated with the time-dependent concordance index (Cτ). Among 1165 patients with HFrEF (mean age 59.1 ± 16.1, 68% male), the median follow-up was 487 days. Among 456 patients with HFpEF (mean age 64.2 ± 16.0 years, 55% male) the median follow-up was 494 days. Gradient boosting regression that included PROs had the best prediction performance - Cτ 0.73 for patients with HFrEF and 0.74 in patients with HFpEF, and showed very good stratification of risk by time to event analysis by quintile of risk. The Kansas City Cardiomyopathy Questionnaire overall summary score, visual analogue scale and Patient Reported Outcomes Measurement Information System dimensions of satisfaction with social roles and physical function had high variable importance measure in the models. PROs improve risk prediction in both HFrEF and HFpEF, independent of traditional clinical factors. Routine assessment of PROs and leveraging the comprehensive data in the electronic health record in routine clinical care could help more accurately assess risk and support the intensification of treatment in patients with HF.

Point-based risk score for the risk stratification and prediction of hepatocellular carcinoma: a population-based random survival forest modeling study

The precise associations between common clinical biomarkers and hepatocellular carcinoma (HCC) risk remain unclear but hold valuable insights for HCC risk stratification and prediction. We examined the linear and nonlinear associations between the baseline levels of 32 circulating biomarkers and HCC risk in the England cohort of UK Biobank (UKBB) (n=397,702). The participants were enrolled between 2006 and 2010 and followed up to 31st October 2022. The primary outcome is incident HCC cases. We then employed random survival forests (RSF) to select the top ten most informative biomarkers, considering their association with HCC, and developed a point-based risk score to predict HCC. The performance of the risk score was evaluated in three validation sets including UKBB Scotland and Wales cohort (n=52,721), UKBB non-White-British cohort (n=29,315), and the Taizhou Longitudinal Study in China (n=17,269). Twenty-five biomarkers were significantly associated with HCC risk, either linearly or nonlinearly. Based on the RSF model selected biomarkers, our point-based risk score showed a concordance index of 0.866 in the England cohort and varied between 0.814 and 0.849 in the three validation sets. HCC incidence rates ranged from 0.95 to 30.82 per 100,000 from the lowest to the highest quintiles of the risk score in the England cohort. Individuals in the highest risk quintile had a 32-73 times greater risk of HCC compared to those in the lowest quintile. Moreover, over 70% of HCC cases were detected in individuals within the top risk score quintile across all cohorts. Our simple risk score enables the identification of high-risk individuals of HCC in the general population. However, including some biomarkers, such as insulin-like growth factor 1, not routinely measured in clinical practice may increase the model's complexity, highlighting the need for more accessible biomarkers that can maintain or improve the predictive accuracy of the risk score. This work was supported by the National Natural Science Foundation of China (grant numbers: 82204125) and the Science and Technology Support Program of Taizhou (TS202224).

How many future dementia cases would be missed by a high-risk screening program? A retrospective cohort study in a population-based cohort.

Risk prediction models aim to identify those at high risk to receive targeted interventions. We aimed to identify the proportion of future dementia cases that would be missed by a high-risk screening program. We identified validated dementia risk prediction models from systematic reviews. We applied these to European Prospective Investigation of Cancer Norfolk, a large population-based cohort of 30,387 individuals with 29 years of linked healthcare data. A maximum of 16.0% (14.7,17.2) and 31.9% (30.2,33.5) of cases arose from the highest risk decile and quintiles, respectively. For every 1000 people considered to be at high risk, a maximum of 235 (215, 255) developed dementia. Seven in every 10 cases of dementia arose from people at normal risk, and eight in every 10 people at high risk did not develop dementia. Individual-level prevention approaches targeted at high-risk groups are unlikely to produce large reductions in disease incidence at the population level. Dementia, a significant public health challenge, is not an inevitability of aging; risk reduction is possible. Several dementia risk prediction models have been validated in the general population, and these aim to identify people at high risk of the disease who can then be targeted with primary prevention interventions. An alternative prevention approach is to focus on interventions that reduce risk across the population, irrespective of risk status. In our study, seven out of every ten people who developed dementia during 29 year follow-up were classed as 'normal-risk' (rather than 'high risk') at baseline. Eight out of every ten people who were at high risk at baseline did not go on to develop dementia. Even if effective, dementia risk reduction efforts based upon targeted high-risk approaches are unlikely to reduce incidence of disease at the population level.

The risk and benefit profiles of US-eligible lung cancer screening attendees vs nonattendees.

The US Preventive Services Task Force (USPSTF) recommend lung cancer screening for individuals aged 50-80 years with at least 20 pack-years and no more than 15 quit-years, but uptake is low. The risk and benefit profiles of screening attendees are unknown; consequently, the impact and lost opportunity of ongoing lung cancer screening in the United States remains unclear. We estimated lung cancer death risk (using the Lung Cancer Death Risk Assessment Tool) and life gained from screening (using the Life Years Gained From Screening-Computed Tomography model) for individuals aged 50-79 years who ever-smoked in the US representative 2022 Behavioral Risk Factor Surveillance System. We compared lung cancer death risk and life gained among USPSTF-eligible individuals by screening status (self-reported screened vs not screened in past year) and estimated the number of lung cancer deaths averted and life-years gained under current screening levels and if everyone eligible was screened. USPSTF eligibility was 33.7% (95% confidence interval [CI] = 33.1% to 34.4%), of whom 17.9% (95% CI = 17.0% to 18.8%) self-reported screening. Screening uptake increased with increasing lung cancer death risk quintile (Q1 = 5.2%, 95% CI = 3.0% to 8.8%; Q5 = 21.8%, 95% CI = 20.3% to 23.3%) and life-gain from screening quintile (Q1 = 6.2%, 95% CI = 3.8% to 9.9%; Q5 = 20.8%, 95% CI = 19.5% to 22.2%). Screened individuals had higher lung cancer death risk (risk ratio [RR] = 1.35, 95% CI = 1.26 to 1.46) and life-years gained (RR = 1.19, 95% CI = 1.12 to 1.25) than unscreened individuals. Currently, screening averts 19 306 lung cancer deaths and gains 237 564 life-years; screening everyone eligible would additionally avert 56 956 lung cancer deaths and gain 751 850 life-years. Two-thirds of USPSTF lung-eligible women were up to date with breast cancer screening, but only 17.3% attended lung screening in the past year. Eligible screening attendees had higher lung cancer death risk and benefit from screening. Higher rates of screening could substantially increase the number of lung cancer deaths prevented.

Risk-stratified analysis of long-term clinical outcomes and cumulative costs in Finnish patients with recent acute coronary syndrome or coronary revascularization: a 5-year real-world study using electronic health records.

Risk assessment is essential in the prevention of cardiovascular disease. In patients with recent acute coronary syndrome (ACS) or coronary revascularization, risk prediction tools, like the European Society of Cardiology guideline recommended SMART-REACH risk score, are increasingly used to predict the risk of recurrent cardiovascular events enabling risk-based personalized prevention. However, little is known about the association between risk stratification and the social and healthcare costs at a population level. This study evaluated the associations between baseline SMART-REACH risk scores, long-term recurrent clinical events, cumulative costs, and post-index event LDL-C goal attainment in patients with recent ACS and/or revascularization. This retrospective study used electronic health records and was conducted in the North Karelia region of Finland. The study cohort included all patients aged 45-85 admitted to a hospital for ACS or who underwent percutaneous coronary intervention or coronary artery bypass surgery between 1 January 2017 and 31 December 2021. Patients were divided into quintiles based on their baseline SMART-REACH risk scores to examine the associations between predicted 5-year scores and selected clinical and economic outcomes. In addition, simple age-based stratification was conducted as a sensitivity analysis. The observed 5-year cumulative incidence of recurrent events ranged from 20% in the lowest to 41% in the highest risk quintile, whereas the corresponding predicted risks ranged from 13% to 51%, and cumulative 5-year mean total costs per patient ranged from 15 827 to 46 182€, respectively. Both monitoring and attainment of low LDL-C values were suboptimal. The use of the SMART-REACH quintiles as a population-level risk stratification tool successfully stratified patients into subgroups with different cumulative numbers of recurrent events and cumulative total costs. However, more research is needed to define clinically and economically optimal threshold values for a population-level stratification.

Hip Fracture Risk Assessment Tools for Adults Aged 80 Years and Older

While adults aged 80 years and older account for 70% of hip fractures in the US, performance of fracture risk assessment tools in this population is uncertain. To compare performance of the Fracture Risk Assessment Tool (FRAX), Garvan Fracture Risk Calculator, and femoral neck bone mineral density (FNBMD) alone in 5-year hip fracture prediction. Prognostic analysis of 3 prospective cohort studies including participants attending an index examination (1997 to 2016) at age 80 years or older. Data were analyzed from March 2023 to April 2024. Participants contacted every 4 or 6 months after index examination to ascertain incident hip fractures and vital status. Predicted 5-year hip fracture probabilities calculated using FRAX and Garvan models incorporating FNBMD and FNBMD alone. Model discrimination assessed by area under receiver operating characteristic curve (AUC). Model calibration assessed by comparing observed vs predicted hip fracture probabilities within predicted risk quintiles. A total of 8890 participants were included, with a mean (SD) age at index examination of 82.6 (2.7) years; 4906 participants (55.2%) were women, 866 (9.7%) were Black, 7836 (88.1%) were White, and 188 (2.1%) were other races and ethnicities. During 5-year follow-up, 321 women (6.5%) and 123 men (3.1%) experienced a hip fracture; 818 women (16.7%) and 921 men (23.1%) died before hip fracture. Among women, AUC was 0.69 (95% CI, 0.67-0.72) for FRAX, 0.69 (95% CI, 0.66-0.72) for Garvan, and 0.72 (95% CI, 0.69-0.75) for FNBMD alone (FNBMD superior to FRAX, P = .01; and Garvan, P = .01). Among men, AUC was 0.71 (95% CI, 0.66-0.75) for FRAX, 0.76 (95% CI, 0.72-0.81) for Garvan, and 0.77 (95% CI, 0.72-0.81) for FNBMD alone (P < .001 Garvan and FNBMD alone superior to FRAX). Among both sexes, Garvan greatly overestimated hip fracture risk among individuals in upper quintiles of predicted risk, while FRAX modestly underestimated risk among those in intermediate quintiles of predicted risk. In this prognostic study of adults aged 80 years and older, FRAX and Garvan tools incorporating FNBMD compared with FNBMD alone did not improve 5-year hip fracture discrimination. FRAX modestly underpredicted observed hip fracture probability in intermediate-risk individuals. Garvan markedly overpredicted observed hip fracture probability in high-risk individuals. Until better prediction tools are available, clinicians should prioritize consideration of hip BMD, life expectancy, and patient preferences in decision-making regarding drug treatment initiation for hip fracture prevention in late-life adults.

Self-Reported Social Determinants of Health and Area-Level Social Vulnerability

Many health care systems are investing resources in identifying social determinants of health (SDoH) needs and facilitating interventions among the populations they serve. Because self-reported SDoH information is lacking, area-level measures are often used to estimate needs and direct resources. To describe the large-scale deployment of SDoH assessments by a health system and determine the extent to which self-reported SDoH needs identified therein are associated with census tract-level social vulnerability measured using the Social Vulnerability Index (SVI). This cross-sectional study assessed SDoH needs between January 1, 2020, and April 30, 2023, in both payer and clinical care settings. Modalities included telephonic outreach, face-to-face clinical interactions, self-entry into a tablet or kiosk, and web-based survey tools. Participants included individuals who responded to the assessment and had sufficient information for census tract identification. Respondents included both Highmark Health Plan members and nonmembers. Health plan members responded to the assessment through health plan programs or platforms, and both members and nonmembers responded to assessments during inpatient or outpatient encounters with the affiliated health system. Overall and domain-specific SDoH needs self-reported through assessments, and severity and complexity of needs identified. Residential social vulnerability measures included overall SVI and the 4 conceptual themes comprising overall SVI. In total, 841 874 assessments were recorded for 401 697 individuals (55.1% women; median [IQR] age, 55 [41-70] years). Social determinants of health needs were identified in 120 769 assessments (14.3%). Across all SDoH domains, increasing SVI was associated with a higher positivity rate (eg, 11.2% of those residing in the lowest-risk SVI quintile reported a need compared with 22.7% among those residing in the highest-risk quintile). Associations varied by SDoH domain and SVI theme. After adjusting for demographic and screening characteristics, odds of positive screening among those residing in the highest-risk SVI quintile were 1.74 (95% CI, 1.62-1.86) to 3.73 (95% CI, 3.48-4.00) times the odds among those residing in lowest risk quintile. In this cross-sectional study, the overall level of SDoH needs generally corresponded to area-level vulnerability. Some SDoH domains appeared far more sensitive to community characteristics than others. Notably, even among individuals from the highest-risk areas, the positive screening rate was roughly 1 in 4. These findings underscore the importance of individual-level SDoH data for service provision planning and health services research.

318 Discovering Subgroups with Supervised Machine Learning Models for Heterogeneity of Treatment Effect Analysis

OBJECTIVES/GOALS: The goal of the study is to provide insights into the use of machine learning methods as a means to predict heterogeneity of treatment effect (HTE) in participants of randomized clinical trials. METHODS/STUDY POPULATION: Using data from 2,441 participants enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) randomized controlled trial of daily low-dose aspirin vs placebo in the United States, we developed multivariable risk prediction models for the composite outcome of dementia, disability, or death. We used two machine learning techniques, decision trees and random forests, to develop novel non-parametric outcomes classifiers and generate risk-based subgroups. The comparator method was an extant semi-parametric proportional hazards predictive risk model. We then assessed HTE by examining the 5-year absolute risk reduction (ARR) of aspirin vs placebo in each risk subgroup. RESULTS/ANTICIPATED RESULTS: In the random forest classifier, the ARR at 5 years in the highest risk quintile was 13.7% (95% CI 3.1% to 24.4%). For the semi-parametric proportional hazards model, the ARR in the highest risk quintile was 15.1% (95% CI 4.0% to 26.3%). These results were comparable and provide evidence of the viability of internally developed parsimonious non-parametric machine learning models for HTE analysis. The decision tree model results (5-year ARR = 17.0%, 95% CI= -5.4% to 39.4% in the highest risk subgroup) exhibited more uncertainty in the results. DISCUSSION/SIGNIFICANCE: None of the models detected significant HTE on the relative scale; there was substantial HTE on the absolute scale in three of the models. Treatment benefit on the absolute scale may be regarded as bearing greater clinical importance and may be present even in the absence of benefit on the relative scale.

Polygenic risk for major depression, attention deficit hyperactivity disorder, neuroticism, and schizophrenia are correlated with experience of intimate partner violence

Research has suggested that mental illness may be a risk factor for, as well as a sequela of, experiencing intimate partner violence (IPV). The association between IPV and mental illness may also be due in part to gene-environment correlations. Using polygenic risk scores for six psychiatric disorders - attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BPD), major depressive disorder (MDD), neuroticism, and schizophrenia—and a combined measure of overall genetic risk for mental illness, we tested whether women’s genetic risk for mental illness was associated with the experience of three types of intimate partner violence. In this cohort of women of European ancestry (N = 11,095), participants in the highest quintile of genetic risk for ADHD (OR range: 1.38–1.49), MDD (OR range: 1.28–1.43), neuroticism (OR range: (1.18–1.25), schizophrenia (OR range: 1.30–1.34), and overall genetic risk (OR range: 1.30–1.41) were at higher risk for experiencing more severe emotional and physical abuse, and, except schizophrenia, more severe sexual abuse, as well as more types of abuse and chronic abuse. In addition, participants in the highest quintile of genetic risk for neuroticism (OR = 1.43 95% CI: 1.18, 1.72), schizophrenia (OR = 1.33 95% CI: 1.10, 1.62), and the overall genetic risk (OR = 1.40 95% CI: 1.15, 1.71) were at higher risk for experiencing intimate partner intimidation and control. Participants in the highest quintile of genetic risk for ADHD, ASD, MDD, schizophrenia, and overall genetic risk, compared to the lowest quintile, were at increased risk for experiencing harassment from a partner (OR range: 1.22–1.92). No associations were found between genetic risk for BPD with IPV. A better understanding of the salience of the multiple possible pathways linking genetic risk for mental illness with risk for IPV may aid in preventing IPV victimization or re-victimization.

Clinical Risk and Outpatient Therapy Utilization for COVID-19 in the Medicare Population

Multiple therapies are available for outpatient treatment of COVID-19 that are highly effective at preventing hospitalization and mortality. Although racial and socioeconomic disparities in use of these therapies have been documented, limited evidence exists on what factors explain differences in use and the potential public health relevance of these differences. To assess COVID-19 outpatient treatment utilization in the Medicare population and simulate the potential outcome of allocating treatment according to patient risk for severe COVID-19. This cross-sectional study included patients enrolled in Medicare in 2022 across the US, identified with 100% Medicare fee-for-service claims. The primary outcome was any COVID-19 outpatient therapy utilization. Secondary outcomes included COVID-19 testing, ambulatory visits, and hospitalization. Differences in outcomes were estimated based on patient demographics, treatment contraindications, and a composite risk score for mortality after COVID-19 based on demographics and comorbidities. A simulation of reallocating COVID-19 treatment, particularly with nirmatrelvir, to those at high risk of severe disease was performed, and the potential COVID-19 hospitalizations and mortality outcomes were assessed. In 2022, 6.0% of 20 026 910 beneficiaries received outpatient COVID-19 treatment, 40.5% of which had no associated COVID-19 diagnosis within 10 days. Patients with higher risk for severe disease received less outpatient treatment, such as 6.4% of those aged 65 to 69 years compared with 4.9% of those 90 years and older (adjusted odds ratio [aOR], 0.64 [95% CI, 0.62-0.65]) and 6.4% of White patients compared with 3.0% of Black patients (aOR, 0.56 [95% CI, 0.54-0.58]). In the highest COVID-19 severity risk quintile, 2.6% were hospitalized for COVID-19 and 4.9% received outpatient treatment, compared with 0.2% and 7.5% in the lowest quintile. These patterns were similar among patients with a documented COVID-19 diagnosis, those with no claims for vaccination, and patients who are insured with Medicare Advantage. Differences were not explained by variable COVID-19 testing, ambulatory visits, or treatment contraindications. Reallocation of 2022 outpatient COVID-19 treatment, particularly with nirmatrelvir, based on risk for severe COVID-19 would have averted 16 503 COVID-19 deaths (16.3%) in the sample. In this cross-sectional study, outpatient COVID-19 treatment was disproportionately accessed by beneficiaries at lower risk for severe infection, undermining its potential public health benefit. Undertreatment was not driven by lack of clinical access or treatment contraindications.

Molecular states associated with dysfunction and graft loss in heart transplants

PurposeWe explored the changes in gene expression correlating with dysfunction and graft failure in endomyocardial biopsies (EMBs). MethodsGenome-wide microarrays (19462 genes) were used to define mRNA changes correlating with dysfunction (LVEF≤55) and risk of graft loss within 3 years post biopsy. LVEF data was available for 1013 biopsies, and survival data for 779 patients (74 losses). Molecular classifiers were built for predicting dysfunction (LVEF≤55) and post-biopsy 3-year survival. ResultsDysfunction correlated with dedifferentiation - decreased expression of normal heart transcripts e.g., solute carriers, along with increased expression of inflammation genes. Many genes with reduced expression in dysfunction were matrix genes e.g., fibulin1 and decorin. Gene ontology (GO) categories suggested matrix remodeling and inflammation, not rejection.Genes associated with risk of failure post-biopsy overlapped dysfunction genes, but also included genes affecting microcirculation e.g., arginase 2 (ARG2), which reduces NO production, and endothelin 1 (EDN1). GO terms also reflected increased glycolysis and response to hypoxia, but decreased VEGF and angiogenesis pathways. TCMR was associated with reduced survival and ABMR with relatively good survival, but the main determinants of survival were features of parenchymal injury.Both dysfunction and graft loss correlated with increased biopsy expression of BNP (gene NPPB).Survival probability classifiers divided hearts into risk quintiles, with actuarial 3-year post-biopsy survival >95% for the highest vs. 50% for the lowest. ConclusionsDysfunction in transplanted hearts reflects dedifferentiation, decreased matrix genes, injury and inflammation. Risk of short-term loss includes these changes but is also associated with microcirculation abnormalities, glycolysis, and response-to-hypoxia.

A polygenic risk score predicts atrial fibrillation in patients with coronary artery disease without severe systolic dysfunction

Abstract Background Genome-wide polygenic risk scores (GPS) comprised of multiple common variants can identify individuals within the general population at significantly higher risk for developing atrial fibrillation AF; however, the clinical utility in general populations is limited by the low absolute risk of AF. Purpose To determine whether a genome-wide polygenic score for AF (GPSAF) can be utilized to identify a patient population at high absolute risk for the development of AF among older patients with established coronary artery disease (CAD) and left ventricular ejection fraction (LVEF) &amp;gt; 30-35%. Methods Participants in the PREDETERMINE study without a prior history of AF who had claims data available in Centers for Medicare and Medicaid Services (CMS) database were followed for incident AF defined as International Classification of Diseases codes 427.3 and I48. A recently validated GPSAF comprised of 1.1 million variants was generated and participants were divided into quintiles based upon the distribution of the GPSAF in the general population. The population was separated into 3 categories of genetic risk, with the top quintile classified as high risk, the second through fourth quintiles classified as intermediate risk, and the bottom quintile classified as low risk. Cumulative incidence curves for incident AF were plotted stratified by the GPSAF categories and the association between GPSAF and incident AF were estimated using Cox proportional hazard models with and without adjusting for established AF risk factors and biomarkers (N-terminal pro-B-type natriuretic peptide, high-sensitivity Troponin I, high sensitivity c-reactive protein). Results Over a median follow-up of 8.0 years, there were 482 incident AF events among 2272 study participants. Overall, median age was 67 [62,73] years and 73.5% were men. Baseline AF risk factors and biomarkers did not differ across GPSAF risk categories. The unadjusted cumulative incidence of AF over the course of the study was 39.2% (29.2%-47.8%) in the high GPSAF risk group, 32.5% (25.8%-38.5%) in the intermediate GPSAF risk group, and 21.6% (13.4%-29.9%) in the low GPSAF risk group (Figure 1). Compared with the participants with low GPSAF risk, participants with high and intermediate GPSAF risk had respectively t 2.32-fold (95% CI, 1.71-3.14; p=0.001) and 1.64-fold (95% CI, 1.27-2.10; p=0.001) increased risk for incident AF, adjusted for age and sex. Both remained significantly elevated after controlling for clinical factors, and biomarkers (Table 1). Conclusion A polygenic risk score for AF is strongly associated with incident AF risk among patients with CAD without severe systolic dysfunction, and clinically meaningful elevations in absolute risk of developing AF were observed in those in the highest quintile of risk. Future research is needed to determine if genetic risk assessment and targeted screening strategies will result in reduce morbidity from AF in this high-risk population.

Abstract 12085: Emergency Heart Failure Mortality Risk Grade (EHMRG) Score Quintiles Correlate With Short and Long-Term Healthcare Costs

Introduction: The use of the EHMRG model in the emergency department (ED) has been demonstrated to improve outcomes for patients presenting with acute heart failure (HF). It is unknown if the EHMRG model also correlates with healthcare costs, which has not been examined in other HF risk models. Hypothesis: We hypothesized that patients with higher EHMRG risk scores, i.e., sicker, would have higher costs of care in short-term (30-day) and longer-term (up to 2 year) time horizons. Methods: We examined direct costs of care from a health payer perspective using the original EHMRG derivation cohort of 11,857 patients hospitalized with acute HF in Ontario, Canada (from 2004) and linking to population-based case-costing databases. Costs were stratified by the quintiles of the EHMRG risk score (Q1 = lowest risk, Q5 = highest risk). Costs (in 2021 Canadian dollars) were categorized into hospital, physician, drug, home care, long term care (LTC), and other costs. Results: Patients in the lowest EHMRG risk quintile (Q1), compared to higher quintiles (Q2-Q5) had lower total cost of care in both the short-term ($8,113 vs $9,442, $10,557, $12,078 and $15,005, p &lt;0.001) and long-term ($46,661 vs $55,904, $58,904, $63,286 and $64,229, p&lt;0.001). This positive correlation between EHMRG risk quintile and cost of care was also observed for hospital costs ($26,388 [Q1] vs $35,659 [Q5], p&lt;.0001) and LTC costs ($1,236 [Q1] vs $6,316 [Q5], p&lt;.0001) at all time intervals, including at 2 years shown here. Physician costs and drug costs were positively correlated with EHMRG risk quintiles at 30 days (physician: $1,241 [Q1] vs $1,762 [Q5], p&lt;0.0001; drug: $219 [Q1] vs $282 [Q5], p&lt;.0001), and negatively correlated at 2 years (physician: $7,943 [Q1] vs $7,175 [Q5], p&lt;.0001; drug: $4,539 [Q1] vs $4,377 [Q5], p&lt;.0001). There was an increasing trend of costs irrespective of the formulation of the EHMRG risk score as 7-day risk quintile, 30-day risk quintile, or 30-day risk tertile. Conclusion: The EHMRG7 risk score was positively correlated with healthcare costs from 30 days to 2 years after initial ED presentation. This novel correlational study demonstrated that in the absence of an associated intervention, higher mortality risk in HF patients portends higher costs in short and long-term follow-up.

Colorectal Cancer Screening Receipt Does Not Differ by 10-Year Mortality Risk Among Older Adults.

Health status and life expectancy are important considerations for assessing potential benefits and harms of colorectal cancer (CRC) screening programs, particularly among older adults. We examined receipt of past-year CRC screening according to predicted 10-year mortality risk among 25,888 community-dwelling adults aged 65-84 years who were not up-to-date with screening in the nationwide National Health Interview Survey. Ten-year mortality risk was estimated using a validated index; from the lowest to highest quintiles of the index, risk was 12%, 24%, 39%, 58%, and 79%, respectively. We also examined the proportion of screening performed among adults with life expectancy <10 years. The prevalence of past-year CRC screening was 39.5%, 40.6%, 38.7%, 36.4%, and 35.4%, from the lowest to highest quintile of 10-year mortality risk. Odds of CRC screening did not differ between adults in the lowest vs highest quintile (adjusted odds ratio 1.05, 95% confidence interval: 0.93-1.20). One-quarter (27.9%) of past-year CRC screening occurred in adults with life expectancy <10 years, and more than half (50.7%) of adults aged 75-84 years had 10-year mortality risk ≥50% at the time of screening. In an exploratory analysis, invasive but not noninvasive screening increased as 10-year mortality risk increased ( P < 0.05) among adults aged 70-79 years. Past-year CRC screening does not differ by predicted 10-year mortality risk. An age-based approach to CRC screening results in underscreening of older, healthier adults and overscreening of younger adults with chronic conditions. Personalized screening with incorporation of individual life expectancy may increase the value of CRC screening programs.

Financial hardship associated with catastrophic out-of-pocket spending tied to primary care services in low- and lower-middle-income countries: findings from a modeling study

BackgroundFinancial risk protection (FRP) is a key component of universal health coverage (UHC): all individuals must be able to obtain the health services they need without experiencing financial hardship. In many low-income and lower-middle-income countries, however, the health system fails to provide sufficient protection against high out-of-pocket (OOP) spending on health services. In 2018, OOP health spending comprised approximately 40% of current health expenditures in low-income and lower-middle-income countries.MethodsWe model the household risk of catastrophic health expenditures (CHE), conditional on having a given disease or condition—defined as OOP health spending that exceeds a threshold percentage (10, 25, or 40%) of annual income—for 29 health services across 13 disease categories (e.g., diarrheal diseases, cardiovascular diseases) in 34 low-income and lower-middle-income countries. Health services were included in the analysis if delivered at the primary care level and part of the Disease Control Priorities, 3rd edition “highest priority package.” Data were compiled from several publicly available sources, including national health accounts, household surveys, and the published literature. A risk of CHE, conditional on having disease, was modeled as depending on usage, captured through utilization indicators; affordability, captured via the level of public financing and OOP health service unit costs; and income.ResultsAcross all countries, diseases, and health services, the risk of CHE (conditional on having a disease) would be concentrated among poorer quintiles (6.8% risk in quintile 1 vs. 1.3% in quintile 5 using a 10% CHE threshold). The risk of CHE would be higher for a few disease areas, including cardiovascular disease and mental/behavioral disorders (7.8% and 9.8% using a 10% CHE threshold), while lower risks of CHE were observed for lower cost services.ConclusionsInsufficient FRP stands as a major barrier to achieving UHC, and risk of CHE is a major problem for health systems in low-income and lower-middle-income countries. Beyond its threat to the financial stability of households, CHE may also lead to worse health outcomes, especially among the poorest for whom both ill health and financial risk are most severe. Modeling the risk of CHE associated with specific disease areas and services can help policymakers set progressive health sector priorities. Decision-makers could explicitly include FRP as a criterion for consideration when assessing the health interventions for inclusion in national essential benefit packages.

Selecting cases of major psychiatric and substance use disorders in Swedish national registries on the basis of clinical features to maximize the strength or specificity of the genetic risk.

We investigate how selection of psychiatric cases by phenotypic criteria can alter the strength and specificity of their genetic risk by examining samples from national Swedish registries for five disorders: major depression (MD, N = 158,557), drug use disorder (DUD, N = 69,841), bipolar disorder (BD, N = 13,530)) ADHD (N = 54,996) and schizophrenia (N = 11,227)). We maximized the family genetic risk score (FGRS) for each disorder and then the specificity of the FGRS in six disorder pairs by univariable and multivariable regression. We use split-half methods to divide our cases for each disorder into deciles for prediction of genetic risk magnitude and quintiles for prediction of specificity by FGRS differences between two disorders. We utilized seven predictor groups: demography/sex, # registrations, site of diagnosis, severity, comorbidity, treatment, and educational/social variables. The ratio of the FGRS in the upper vs two lower deciles from our multivariable prediction model was, in order, DUD - 12.6, MD - 4.9, BD - 4.5, ADHD - 3.3 and schizophrenia 1.4. From the lowest to highest quintile, our measures of genetic specificity increased more than five-fold for i) MD vs. Anxiety Disorders, ii) MD vs BD, iii) MD versus alcohol use disorder (AUD), iv) BD vs schizophrenia and v) DUD vs AUD. This increase was nearly two-fold for ADHD vs DUD. We conclude that the level of genetic liability for our psychiatric disorders could be substantially enriched by selection of cases with our predictors. Specificity of genetic risk could also be substantially impacted by these same predictors.