Translating ethical and quality principles for the effective, safe and fair development, deployment and use of artificial intelligence technologies in healthcare.

OBJECTIVE: The complexity and rapid pace of development of algorithmic technologies pose challenges for their regulation and oversight in healthcare settings. We sought to improve our institution's approach to evaluation and governance of algorithmic technologies used in clinical care and operations by creating an Implementation Guide that standardizes evaluation criteria so that local oversight is performed in an objective fashion. MATERIALS AND METHODS: Building on a framework that applies key ethical and quality principles (clinical value and safety, fairness and equity, usability and adoption, transparency and accountability, and regulatory compliance), we created concrete guidelines for evaluating algorithmic technologies at our institution. RESULTS: An Implementation Guide articulates evaluation criteria used during review of algorithmic technologies and details what evidence supports the implementation of ethical and quality principles for trustworthy health AI. Application of the processes described in the Implementation Guide can lead to algorithms that are safer as well as more effective, fair, and equitable upon implementation, as illustrated through 4 examples of technologies at different phases of the algorithmic lifecycle that underwent evaluation at our academic medical center. DISCUSSION: By providing clear descriptions/definitions of evaluation criteria and embedding them within standardized processes, we streamlined oversight processes and educated communities using and developing algorithmic technologies within our institution. CONCLUSIONS: We developed a scalable, adaptable framework for translating principles into evaluation criteria and specific requirements that support trustworthy implementation of algorithmic technologies in patient care and healthcare operations.

Novel Prediction Equations for Absolute Risk Assessment of Total Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic Health: A Scientific Statement From the American Heart Association.

Cardiovascular-kidney-metabolic (CKM) syndrome is a novel construct recently defined by the American Heart Association in response to the high prevalence of metabolic and kidney disease. Epidemiological data demonstrate higher absolute risk of both atherosclerotic cardiovascular disease (CVD) and heart failure as an individual progresses from CKM stage 0 to stage 3, but optimal strategies for risk assessment need to be refined. Absolute risk assessment with the goal to match type and intensity of interventions with predicted risk and expected treatment benefit remains the cornerstone of primary prevention. Given the growing number of therapies in our armamentarium that simultaneously address all 3 CKM axes, novel risk prediction equations are needed that incorporate predictors and outcomes relevant to the CKM context. This should also include social determinants of health, which are key upstream drivers of CVD, to more equitably estimate and address risk. This scientific statement summarizes the background, rationale, and clinical implications for the newly developed sex-specific, race-free risk equations: PREVENT (AHA Predicting Risk of CVD Events). The PREVENT equations enable 10- and 30-year risk estimates for total CVD (composite of atherosclerotic CVD and heart failure), include estimated glomerular filtration rate as a predictor, and adjust for competing risk of non-CVD death among adults 30 to 79 years of age. Additional models accommodate enhanced predictive utility with the addition of CKM factors when clinically indicated for measurement (urine albumin-to-creatinine ratio and hemoglobin A1c) or social determinants of health (social deprivation index) when available. Approaches to implement risk-based prevention using PREVENT across various settings are discussed.

Mitigating Racial And Ethnic Bias And Advancing Health Equity In Clinical Algorithms: A Scoping Review.

In August 2022 the Department of Health and Human Services (HHS) issued a notice of proposed rulemaking prohibiting covered entities, which include health care providers and health plans, from discriminating against individuals when using clinical algorithms in decision making. However, HHS did not provide specific guidelines on how covered entities should prevent discrimination. We conducted a scoping review of literature published during the period 2011-22 to identify health care applications, frameworks, reviews and perspectives, and assessment tools that identify and mitigate bias in clinical algorithms, with a specific focus on racial and ethnic bias. Our scoping review encompassed 109 articles comprising 45 empirical health care applications that included tools tested in health care settings, 16 frameworks, and 48 reviews and perspectives. We identified a wide range of technical, operational, and systemwide bias mitigation strategies for clinical algorithms, but there was no consensus in the literature on a single best practice that covered entities could employ to meet the HHS requirements. Future research should identify optimal bias mitigation methods for various scenarios, depending on factors such as patient population, clinical setting, algorithm design, and types of bias to be addressed.

Cardiovascular Disease Risk Assessment Using Traditional Risk Factors and Polygenic Risk Scores in the Million Veteran Program.

Importance: Primary prevention of atherosclerotic cardiovascular disease (ASCVD) relies on risk stratification. Genome-wide polygenic risk scores (PRSs) are proposed to improve ASCVD risk estimation. Objective: To determine whether genome-wide PRSs for coronary artery disease (CAD) and acute ischemic stroke improve ASCVD risk estimation with traditional clinical risk factors in an ancestrally diverse midlife population. Design, Setting, and Participants: This was a prognostic analysis of incident events in a retrospectively defined longitudinal cohort conducted from January 1, 2011, to December 31, 2018. Included in the study were adults free of ASCVD and statin naive at baseline from the Million Veteran Program (MVP), a mega biobank with genetic, survey, and electronic health record data from a large US health care system. Data were analyzed from March 15, 2021, to January 5, 2023. Exposures: PRSs for CAD and ischemic stroke derived from cohorts of largely European descent and risk factors, including age, sex, systolic blood pressure, total cholesterol, high-density lipoprotein (HDL) cholesterol, smoking, and diabetes status. Main Outcomes and Measures: Incident nonfatal myocardial infarction (MI), ischemic stroke, ASCVD death, and composite ASCVD events. Results: A total of 79 151 participants (mean [SD] age, 57.8 [13.7] years; 68 503 male [86.5%]) were included in the study. The cohort included participants from the following harmonized genetic ancestry and race and ethnicity categories: 18 505 non-Hispanic Black (23.4%), 6785 Hispanic (8.6%), and 53 861 non-Hispanic White (68.0%) with a median (5th-95th percentile) follow-up of 4.3 (0.7-6.9) years. From 2011 to 2018, 3186 MIs (4.0%), 1933 ischemic strokes (2.4%), 867 ASCVD deaths (1.1%), and 5485 composite ASCVD events (6.9%) were observed. CAD PRS was associated with incident MI in non-Hispanic Black (hazard ratio [HR], 1.10; 95% CI, 1.02-1.19), Hispanic (HR, 1.26; 95% CI, 1.09-1.46), and non-Hispanic White (HR, 1.23; 95% CI, 1.18-1.29) participants. Stroke PRS was associated with incident stroke in non-Hispanic White participants (HR, 1.15; 95% CI, 1.08-1.21). A combined CAD plus stroke PRS was associated with ASCVD deaths among non-Hispanic Black (HR, 1.19; 95% CI, 1.03-1.17) and non-Hispanic (HR, 1.11; 95% CI, 1.03-1.21) participants. The combined PRS was also associated with composite ASCVD across all ancestry groups but greater among non-Hispanic White (HR, 1.20; 95% CI, 1.16-1.24) than non-Hispanic Black (HR, 1.11; 95% CI, 1.05-1.17) and Hispanic (HR, 1.12; 95% CI, 1.00-1.25) participants. Net reclassification improvement from adding PRS to a traditional risk model was modest for the intermediate risk group for composite CVD among men (5-year risk >3.75%, 0.38%; 95% CI, 0.07%-0.68%), among women, (6.79%; 95% CI, 3.01%-10.58%), for age older than 55 years (0.25%; 95% CI, 0.03%-0.47%), and for ages 40 to 55 years (1.61%; 95% CI, -0.07% to 3.30%). Conclusions and Relevance: Study results suggest that PRSs derived predominantly in European samples were statistically significantly associated with ASCVD in the multiancestry midlife and older-age MVP cohort. Overall, modest improvement in discrimination metrics were observed with addition of PRSs to traditional risk factors with greater magnitude in women and younger age groups.

Observability and its impact on differential bias for clinical prediction models.

OBJECTIVE: Electronic health records have incomplete capture of patient outcomes. We consider the case when observability is differential across a predictor. Including such a predictor (sensitive variable) can lead to algorithmic bias, potentially exacerbating health inequities. MATERIALS AND METHODS: We define bias for a clinical prediction model (CPM) as the difference between the true and estimated risk, and differential bias as bias that differs across a sensitive variable. We illustrate the genesis of differential bias via a 2-stage process, where conditional on having the outcome of interest, the outcome is differentially observed. We use simulations and a real-data example to demonstrate the possible impact of including a sensitive variable in a CPM. RESULTS: If there is differential observability based on a sensitive variable, including it in a CPM can induce differential bias. However, if the sensitive variable impacts the outcome but not observability, it is better to include it. When a sensitive variable impacts both observability and the outcome no simple recommendation can be provided. We show that one cannot use observed data to detect differential bias. DISCUSSION: Our study furthers the literature on observability, showing that differential observability can lead to algorithmic bias. This highlights the importance of considering whether to include sensitive variables in CPMs. CONCLUSION: Including a sensitive variable in a CPM depends on whether it truly affects the outcome or just the observability of the outcome. Since this cannot be distinguished with observed data, observability is an implicit assumption of CPMs.

Assessment of ECG during hybrid comprehensive telerehabilitation in heart failure patients-Subanalysis of the Telerehabilitation in Heart Failure Patients (TELEREH-HF) randomized clinical trial.

BACKGROUND: Exercise training in heart failure (HF) patients should be monitored to ensure patients' safety. Electrocardiographic (ECG) telemonitoring was used to assess the safety of hybrid comprehensive telerehabilitation (HCTR). OBJECTIVE: Analysis of ECG recorded during HCTR in HF patients. METHODS: The TELEREH-HF multicenter, randomized, controlled trial enrolled 850 HF patients with New York Heart Association class I-III and left ventricular ejection fraction of ≤40%. This subanalysis focuses on 386 patients (aged 62 ± 11 years, LVEF 31 ± 7%) randomized to HCTR. HCTR was telemonitored with a device allowing to record 16-s fragments of ECG and to transmit the data via mobile phone network to the monitoring center. ResultsIn 386 patients, 16,622 HCTR sessions were recorded and 66,488 ECGs fragments were evaluated. Sinus rhythm was present in 320 (83%) and permanent atrial fibrillation (AF) in 66 (17%) patients, respectively. The most common arrhythmias were ventricular and atrial premature beats, recorded in 76.4% and 27.7% of the patients, respectively. Non-sustained ventricular tachycardia (21 episodes in 8 patients) and paroxysmal AF episodes (6 in 4 patients) were rare. None of the analyzed demographic and clinical characteristics was predictive for onset of the new arrhythmias on exercise. CONCLUSION: Telerehabilitation in HF patients was safe without the evidence for symptomatic arrhythmias requiring discontinuation of telerehabilitation. Only one mildly symptomatic paroxysmal AF episode led to the short-term suspension of the training program. The most common arrhythmias were atrial and ventricular premature beats. These arrhythmias did not result in any changes in rehabilitation and therapy regimens.

Impact of the COVID-19 pandemic on patterns of outpatient cardiovascular care.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic brought about abrupt changes in the way health care is delivered, and the impact of transitioning outpatient clinic visits to telehealth visits on processes of care and outcomes is unclear. METHODS: We evaluated ordering patterns during cardiovascular telehealth clinic visits in the Duke University Health System between March 15 and June 30, 2020 and 30-day outcomes compared with in-person visits in the same time frame in 2020 and in 2019. RESULTS: Within the Duke University Health System, there was a 33.1% decrease in the number of outpatient cardiovascular visits conducted in the first 15 weeks of the COVID-19 pandemic, compared with the same time period in 2019. As a proportion of total visits initially booked, 53% of visits were cancelled in 2020 compared to 35% in 2019. However, patients with cancelled visits had similar demographics and comorbidities in 2019 and 2020. Telehealth visits comprised 9.3% of total visits initially booked in 2020, with younger and healthier patients utilizing telehealth compared with those utilizing in-person visits. Compared with in-person visits in 2020, telehealth visits were associated with fewer new (31.6% for telehealth vs 44.6% for in person) or refill (12.9% vs 15.6%, respectively) medication prescriptions, electrocardiograms (4.3% vs 31.4%), laboratory orders (5.9% vs 21.8%), echocardiograms (7.3% vs 98%), and stress tests (4.4% vs 6.6%). When adjusted for age, race, and insurance status, those who had a telehealth visit or cancelled their visit were less likely to have an emergency department or hospital encounter within 30 days compared with those who had in-person visits (adjusted rate ratios (aRR) 0.76 [95% 0.65, 0.89] and aRR 0.71 [95% 0.65, 0.78], respectively). CONCLUSIONS: In response to the perceived risks of routine medical care affected by the COVID-19 pandemic, different phenotypes of patients chose different types of outpatient cardiology care. A better understanding of these differences could help define necessary and appropriate mode of care for cardiology patients.

Claims-based cardiovascular outcome identification for clinical research: Results from 7 large randomized cardiovascular clinical trials.

BACKGROUND: Medicare insurance claims may provide an efficient means to ascertain follow-up of older participants in clinical research. We sought to determine the accuracy and completeness of claims- versus site-based follow-up with clinical event committee (+CEC) adjudication of cardiovascular outcomes. METHODS: We performed a retrospective study using linked Medicare and Duke Database of Clinical Trials data. Medicare claims were linked to clinical data from 7 randomized cardiovascular clinical trials. Of 52,476 trial participants, linking resulted in 5,839 (of 10,497 linkage-eligible) Medicare-linked trial participants with fee-for-service A and B coverage. Death, myocardial infarction (MI), stroke, and revascularization incidences were compared using Medicare inpatient claims only, site-reported events (+CEC) only, or a combination of the 2. Randomized treatment effects were compared as a function of whether claims-based, site-based (+CEC), or a combined system was used for event detection. RESULTS: Among the 5,839 study participants, the annual event rates were similar between claims- and site-based (+CEC) follow-up: death (overall rate 5.2% vs 5.2%; adjusted κ 0.99), MI (2.2% vs 2.3%; adjusted κ 0.96), stroke (0.7% vs 0.7%; adjusted κ 0.99), and any revascularization (7.4% vs 7.9%; adjusted κ 0.95). Of events detected by claims yet not reported by CEC, a minority were reported by sites but negatively adjudicated by CEC (39% of MIs and 18% of strokes). Differences in individual case concordance led to higher event rates when claims- and site-based (+CEC) systems were combined. Randomized treatment effects were similar among the 3 approaches for each outcome of interest. CONCLUSIONS: Claims- versus site-based (+CEC) follow-up identified similar overall cardiovascular event rates despite meaningful differences in the events detected. Randomized treatment effects were similar using the 2 methods, suggesting claims data could be used to support clinical research leveraging routinely collected data. This approach may lead to more effective evidence generation, synthesis, and appraisal of medical products and inform the strategic approaches toward the National Evaluation System for Health Technology.

Cost-effectiveness of Low-density Lipoprotein Cholesterol Level-Guided Statin Treatment in Patients With Borderline Cardiovascular Risk.

Importance: American College of Cardiology/American Heart Association cholesterol guidelines prioritize primary prevention statin therapy based on 10-year absolute risk (AR10) of atherosclerotic cardiovascular disease (ASCVD). However, given the same AR10, patients with higher levels of low-density lipoprotein cholesterol (LDL-C) experience greater absolute risk reduction from statin therapy. Objectives: To estimate the cost-effectiveness of expanding preventive statin treatment eligibility from standard care to patients at borderline risk (AR10, 5.0%-7.4%) for ASCVD and with high levels of LDL-C and to estimate cost-effectiveness of statin treatment across ranges of age, sex, AR10, and LDL-C levels. Design, Setting, and Participants: This study evaluated 100 simulated cohorts, each including 1 million ASCVD-free survey respondents (50% men and 50% women) aged 40 years at baseline. Cohorts were created by probabilistic sampling of the 1999-2014 US National Health and Nutrition Examination Surveys from the perspective of the US health care sector. The CVD Policy Model microsimulation version projected lifetime health and cost outcomes. Probability of first-ever coronary heart disease or stroke event was estimated by analysis of 6 pooled US cohort studies and recalibrated to match contemporary event rates. Other model variables were derived from national surveys, meta-analyses, and published literature. Data were analyzed from May 15, 2018, through June 10, 2019. Exposures: Four statin treatment strategies were compared: (1) treat all patients with AR10 of at least 7.5%, diabetes, or LDL-C of at least 190 mg/dL (standard care); (2) add treatment for borderline risk and LDL-C levels of 160 to 189 mg/dL; (3) add treatment for borderline risk and LDL-C levels of 130 to 159 mg/dL; and (4) add treatment for remainder of patients with AR10 of at least 5.0%. Statin treatment was also compared with no statin treatment in age, sex, AR10, and LDL-C strata. Main Outcomes and Measures: Lifetime quality-adjusted life-years (QALYs) and costs (2019 US dollars) were projected and discounted 3.0% annually. The primary outcome was the incremental cost-effectiveness ratio. Results: In these 100 simulated cohorts, each with 1 million patients aged 40 years at baseline (50% women and 50% men), adding preventive statins to individuals with borderline AR10 and LDL-C levels of 160 to 189 mg/dL would be cost-saving; further treating borderline AR10 and LDL-C levels of 130 to 159 mg/dL would also be cost-saving; and treating all individuals with AR10 of at least 5.0% would be highly cost-effective ($33 558/QALY) and would prevent the most ASCVD events. Within age, AR10, and sex categories, individuals with higher baseline LDL-C levels gained more QALYs from statin therapy. Cost-effectiveness increased with LDL-C level and AR10. Conclusions and Relevance: In this study, lifetime statin treatment of patients in a hypothetical cohort with borderline ASCVD risk and LDL-C levels of 160 to 189 mg/dL was found to be cost-saving. Results suggest that treating all patients at borderline risk regardless of LDL-C level would likely be highly cost-effective.

Evaluation of Mortality Data From the Social Security Administration Death Master File for Clinical Research.

Importance: Despite its documented undercapture of mortality data, the US Social Security Administration Death Master File (SSDMF) is still often used to provide mortality end points in retrospective clinical studies. Changes in death data reporting to SSDMF in 2011 may have further affected the reliability of mortality end points, with varying consequences over time and by state. Objective: To evaluate the reliability of mortality rates in the SSDMF in a cohort of patients with atherosclerotic cardiovascular disease (ASCVD). Design, Setting, and Participants: This observational analysis used the IBM MarketScan Medicare and commercial insurance databases linked to mortality information from the SSDMF. Adults with ASCVD who had a clinical encounter between January 1, 2012, and December 31, 2013, at least 2 years of follow-up, and from states with 1000 or more eligible adults with ASCVD were included in the study. Data analysis was conducted between April 18 and May 21, 2018. Main Outcomes and Measures: Kaplan-Meier analyses were conducted to estimate state-level mortality rates for adults with ASCVD, stratified by database (commercial or Medicare). Constant hazards of mortality by state were tested, and individual state Kaplan-Meier curves for temporal changes were evaluated. For states in which the hazard of death was constant over time, mortality rates for adults with ASCVD were compared with state-level, age group-specific overall mortality rates in 2012, as reported by the National Center for Health Statistics (NCHS). Results: This study of mortality data of 667 516 adults with ASCVD included 274 005 adults in the commercial insurance database cohort (171 959 male [62.8%] and median [interquartile range (IQR)] age of 58 [52-62] years) and 393 511 in the Medicare database cohort (245 366 male [62.4%] and median [IQR] age of 76 [70-83] years). Of the 41 states included, 11 states (26.8%) in the commercial cohort and 18 states (43.9%) in the Medicare cohort had a change in the hazard of death after 2012. Among states with constant hazard, state-level mortality rates using the SSDMF ranged widely, from 0.06 to 1.30 per 100 person-years (commercial cohort) and from 0.83 to 6.07 per 100 person-years (Medicare cohort). Variability between states in mortality estimates for adults with ASCVD using SSDMF data greatly exceeded variability in overall mortality from the NCHS. No correlation was found between NCHS mortality estimates and those from the SSDMF (ρ = 0.29 [P = .06] for age 55-64 years; ρ = 0.18 [P = .27] for age 65-74 years). Conclusions and Relevance: The SSDMF appeared to markedly underestimate mortality rates, with variable undercapture among states and over time; this finding suggests that SSDMF data are not reliable and should not be used alone by researchers to estimate mortality rates.

Statistical methods for building better biomarkers of chronic kidney disease.

The last two decades have witnessed an explosion in research focused on the development and assessment of novel biomarkers for improved prognosis of diseases. As a result, best practice standards guiding biomarker research have undergone extensive development. Currently, there is great interest in the promise of biomarkers to enhance research efforts and clinical practice in the setting of chronic kidney disease, acute kidney injury, and glomerular disease. However, some have questioned whether biomarkers currently add value to the clinical practice of nephrology. The current state of the art pertaining to statistical analyses regarding the use of such measures is critical. In December 2014, the National Institute of Diabetes and Digestive and Kidney Diseases convened a meeting, "Toward Building Better Biomarker Statistical Methodology," with the goals of summarizing the current best practice recommendations and articulating new directions for methodological research. This report summarizes its conclusions and describes areas that need attention. Suggestions are made regarding metrics that should be commonly reported. We outline the methodological issues related to traditional metrics and considerations in prognostic modeling, including discrimination and case mix, calibration, validation, and cost-benefit analysis. We highlight the approach to improved risk communication and the value of graphical displays. Finally, we address some "new frontiers" in prognostic biomarker research, including the competing risk framework, the use of longitudinal biomarkers, and analyses in distributed research networks.

A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis.

An increasingly important data source for the development of clinical risk prediction models is electronic health records (EHRs). One of their key advantages is that they contain data on many individuals collected over time. This allows one to incorporate more clinical information into a risk model. However, traditional methods for developing risk models are not well suited to these irregularly collected clinical covariates. In this paper, we compare a range of approaches for using longitudinal predictors in a clinical risk model. Using data from an EHR for patients undergoing hemodialysis, we incorporate five different clinical predictors into a risk model for patient mortality. We consider different approaches for treating the repeated measurements including use of summary statistics, machine learning methods, functional data analysis, and joint models. We follow up our empirical findings with a simulation study. Overall, our results suggest that simple approaches perform just as well, if not better, than more complex analytic approaches. These results have important implication for development of risk prediction models with EHRs. Copyright © 2017 John Wiley & Sons, Ltd.

Data monitoring committees: Promoting best practices to address emerging challenges.

BACKGROUND AND PURPOSE: Data monitoring committees are responsible for safeguarding the interests of study participants and assuring the integrity and credibility of clinical trials. The independence of data monitoring committees from sponsors and investigators is essential in achieving this mission. Creative approaches are needed to address ongoing and emerging challenges that potentially threaten data monitoring committees' independence and effectiveness. METHODS: An expert panel of representatives from academia, industry and government sponsors, and regulatory agencies discussed these challenges and proposed best practices and operating principles for effective functioning of contemporary data monitoring committees. RESULTS AND CONCLUSIONS: Prospective data monitoring committee members need better training. Options could include didactic instruction as well as apprenticeships to provide real-world experience. Data monitoring committee members should be protected against legal liability arising from their service. While avoiding breaches in confidentiality of interim data remains a high priority, data monitoring committees should have access to unblinded efficacy and safety data throughout the trial to enable informed judgments about risks and benefits. Because overly rigid procedures can compromise their independence, data monitoring committees should have the flexibility necessary to best fulfill their responsibilities. Data monitoring committee charters should articulate principles that guide the data monitoring committee process rather than list a rigid set of requirements. Data monitoring committees should develop their recommendations by consensus rather than through voting processes. The format for the meetings of the data monitoring committee should maintain the committee's independence and clearly establish the leadership of the data monitoring committee chair. The independent statistical group at the Statistical Data Analysis Center should have sufficient depth of knowledge about the study at hand and experience with trials in general to ensure that the data monitoring committee has access to timely, reliable, and readily interpretable insights about emerging evidence in the clinical trial. Contracts engaging data monitoring committee members for industry-sponsored trials should have language customized to the unique responsibilities of data monitoring committee members rather than use language appropriate to consultants for product development. Regulatory scientists would benefit from experiencing data monitoring committee service that does not conflict with their regulatory responsibilities.

Determining When to Add Nonstatin Therapy: A Quantitative Approach.

BACKGROUND: Costs and uncertainty about the benefits of nonstatin therapies limit their use. OBJECTIVES: The authors sought to identify patients who might benefit from the addition of a nonstatin to background statin therapy. METHODS: We performed systematic reviews of subgroup analyses from randomized trials and observational studies with statin-treated participants to determine estimated 10-year absolute risk of atherosclerotic cardiovascular disease (ASCVD) and to define high-risk and very high-risk patients. We used the relative risk reductions for the addition of a nonstatin to lower low-density lipoprotein (LDL-C) used to determine the number needed to treat (NNT) to prevent 1 ASCVD event over 5 years for each patient group and to allow comparisons with 5-year cost analyses. RESULTS: The 10-year ASCVD risk is at least 30% (very high risk) for statin-treated participants with clinical ASCVD and comorbidities, and 20% to 29% (high risk) for those with ASCVD without comorbidities or who have heterozygous familial hypercholesterolemia. Adding ezetimibe to reduce low-density LDL-C by 20% would provide a 5-year NNT ≤50 for very high-risk patients with LDL-C ≥130 mg/dl or for high-risk patients with LDL-C ≥190 mg/dl, and an NNT ≤30 for very high-risk patients with LDL-C ≥160 mg/dl. Adding a PCSK9 monoclonal antibody to lower LDL-C by at least 50% would provide an NNT ≤50 for very high-risk and high-risk patients with LDL-C ≥70 mg/dl, and an NNT ≤30 for very high-risk and high-risk patients with an LDL-C ≥130 mg/dl. CONCLUSIONS: Adding ezetimibe or PCSK9 monoclonal antibodies to maximally tolerated statin therapy may be cost effective in very high-risk and high-risk patients, depending on baseline LDL-C levels.

Clinical Effectiveness of Statin Therapy After Ischemic Stroke: Primary Results From the Statin Therapeutic Area of the Patient-Centered Research Into Outcomes Stroke Patients Prefer and Effectiveness Research (PROSPER) Study.

BACKGROUND: In patients with ischemic stroke, data on the real-world effectiveness of statin therapy for clinical and patient-centered outcomes are needed to better inform shared decision making. METHODS AND RESULTS: Patient-Centered Research Into Outcomes Stroke Patients Prefer and Effectiveness Research (PROSPER) is a Patient-Centered Outcomes Research Institute-funded research program designed with stroke survivors to evaluate the effectiveness of poststroke therapies. We linked data on patients ≥65 years of age enrolled in the Get With The Guidelines-Stroke Registry to Medicare claims. Two-year to postdischarge outcomes of those discharged on a statin versus not on a statin were adjusted through inverse probability weighting. Our coprimary outcomes were major adverse cardiovascular events and home time (days alive and out of a hospital or skilled nursing facility). Secondary outcomes included all-cause mortality, all-cause readmission, cardiovascular readmission, and hemorrhagic stroke. From 2007 to 2011, 77 468 patients who were not taking statins at the time of admission were hospitalized with ischemic stroke; of these, 71% were discharged on statin therapy. After adjustment, statin therapy at discharge was associated with a lower hazard of major adverse cardiovascular events (hazard ratio, 0.91; 95% confidence interval, 0.87-0.94), 28 more home-time days after discharge (P<0.001), and lower all-cause mortality and readmission. Statin therapy at discharge was not associated with increased risk of hemorrhagic stroke (hazard ratio, 0.94; 95% confidence interval, 0.72-1.23). Among statin-treated patients, 31% received a high-intensity dose; after risk adjustment, these patients had outcomes similar to those of recipients of moderate-intensity statin. CONCLUSION: In older ischemic stroke patients who were not taking statins at the time of admission, discharge statin therapy was associated with lower risk of major adverse cardiovascular events and nearly 1 month more home time during the 2-year period after hospitalization.

Apolipoprotein B improves risk assessment of future coronary heart disease in the Framingham Heart Study beyond LDL-C and non-HDL-C.

AIMS: Analyses using conventional statistical methodologies have yielded conflicting results as to whether low-density lipoprotein cholesterol (LDL-C) or non-high-density lipoprotein cholesterol (non-HDL-C) or apolipoprotein B (apoB) is the best marker of the apoB-associated risk of coronary heart disease. The aim of this study was to determine the additional value of apoB beyond LDL-C or non-HDL-C as a predictor of coronary heart disease. METHODS AND RESULTS: For each patient from the Framingham Offspring Cohort aged 40-75 years (n = 2966), we calculated the extent to which the observed apoB differed from the expected apoB based on their LDL-C or non-HDL-C. We added this difference to a Cox model predicting new onset coronary heart disease over a maximum of 20 years adjusting for standard risk factors plus LDL-C or non-HDL. The difference between observed and expected apoB over LDL-C or non-HDL-C was highly prognostic of future coronary heart disease events: adjusted hazard ratios 1.26 (95% confidence interval: 1.15, 1.37) and 1.20 (1.11, 1.29), respectively, for each standard deviation increase beyond expected apoB levels. When this difference between observed and expected apoB was added to standard coronary heart disease prediction models including LDL-C or non-HDL-C, prediction improved significantly (likelihood ratio test p-values <0.0001) and discrimination c-statistics increased from 0.72 to 0.73. The corresponding relative integrated discrimination improvements were 11% and 8%, respectively. CONCLUSIONS: apoB improves risk assessment of future coronary heart disease events over and beyond LDL-C or non-HDL-C, which is consistent with coronary risk being more closely related to the number of atherogenic apoB particles than to the mass of cholesterol within them.