Aspirin for Secondary Prevention of Cardiovascular Disease in 51 Low-, Middle-, and High-Income Countries.

Importance: Aspirin is an effective and low-cost option for reducing atherosclerotic cardiovascular disease (CVD) events and improving mortality rates among individuals with established CVD. To guide efforts to mitigate the global CVD burden, there is a need to understand current levels of aspirin use for secondary prevention of CVD. Objective: To report and evaluate aspirin use for secondary prevention of CVD across low-, middle-, and high-income countries. Design, Setting, and Participants: Cross-sectional analysis using pooled, individual participant data from nationally representative health surveys conducted between 2013 and 2020 in 51 low-, middle-, and high-income countries. Included surveys contained data on self-reported history of CVD and aspirin use. The sample of participants included nonpregnant adults aged 40 to 69 years. Exposures: Countries' per capita income levels and world region; individuals' socioeconomic demographics. Main Outcomes and Measures: Self-reported use of aspirin for secondary prevention of CVD. Results: The overall pooled sample included 124 505 individuals. The median age was 52 (IQR, 45-59) years, and 50.5% (95% CI, 49.9%-51.1%) were women. A total of 10 589 individuals had a self-reported history of CVD (8.1% [95% CI, 7.6%-8.6%]). Among individuals with a history of CVD, aspirin use for secondary prevention in the overall pooled sample was 40.3% (95% CI, 37.6%-43.0%). By income group, estimates were 16.6% (95% CI, 12.4%-21.9%) in low-income countries, 24.5% (95% CI, 20.8%-28.6%) in lower-middle-income countries, 51.1% (95% CI, 48.2%-54.0%) in upper-middle-income countries, and 65.0% (95% CI, 59.1%-70.4%) in high-income countries. Conclusion and Relevance: Worldwide, aspirin is underused in secondary prevention, particularly in low-income countries. National health policies and health systems must develop, implement, and evaluate strategies to promote aspirin therapy.

A survey of internists' recommendations for aspirin in older adults and barriers to evidence-based use.

Recent trials suggest that aspirin for primary prevention may do more harm than good for some, including adults over 70 years of age. We sought to assess how primary care providers (PCPs) use aspirin for the primary prevention in older patients and to identify barriers to use according to recent guidelines, which recommend against routine use in patients over age 70. We surveyed PCPs about whether they would recommend aspirin in clinical vignettes of a 75-year-old patient with a 10-year atherosclerotic cardiovascular disease risk of 25%. We also queried perceived difficulty following guideline recommendations, as well as perceived barriers and facilitators. We obtained responses from 372 PCPs (47.9% response). In the patient vignette, 45.4% of clinicians recommended aspirin use, which did not vary by whether the patient was using aspirin initially (p = 0.21); 41.7% believed aspirin was beneficial. Perceived barriers to guideline-based aspirin use included concern about patients being upset (41.6%), possible malpractice claims (25.0%), and not having a strategy for discussing aspirin use (24.5%). The estimated adjusted probability of rating the guideline as "hard to follow" was higher in clinicians who believed aspirin was beneficial (29.4% vs. 8.0%; p < 0.001) and who worried the patient would be upset if told to stop aspirin (26.7% vs. 12.5%; p = 0.001). Internists vary considerably in their recommendations for aspirin use for primary prevention in older patients. A high proportion of PCPs continue to believe aspirin is beneficial in this setting. These results can inform de-implementation efforts to optimize evidence-based aspirin use.

Interpretability, credibility, and usability of hospital-specific template matching versus regression-based hospital performance assessments; a multiple methods study.

BACKGROUND: Hospital-specific template matching (HS-TM) is a newer method of hospital performance assessment. OBJECTIVE: To assess the interpretability, credibility, and usability of HS-TM-based vs. regression-based performance assessments. RESEARCH DESIGN: We surveyed hospital leaders (January-May 2021) and completed follow-up semi-structured interviews. Surveys included four hypothetical performance assessment vignettes, with method (HS-TM, regression) and hospital mortality randomized. SUBJECTS: Nationwide Veterans Affairs Chiefs of Staff, Medicine, and Hospital Medicine. MEASURES: Correct interpretation; self-rated confidence in interpretation; and self-rated trust in assessment (via survey). Concerns about credibility and main uses (via thematic analysis of interview transcripts). RESULTS: In total, 84 participants completed 295 survey vignettes. Respondents correctly interpreted 81.8% HS-TM vs. 56.5% regression assessments, p < 0.001. Respondents "trusted the results" for 70.9% HS-TM vs. 58.2% regression assessments, p = 0.03. Nine concerns about credibility were identified: inadequate capture of case-mix and/or illness severity; inability to account for specialized programs (e.g., transplant center); comparison to geographically disparate hospitals; equating mortality with quality; lack of criterion standards; low power; comparison to dissimilar hospitals; generation of rankings; and lack of transparency. Five concerns were equally relevant to both methods, one more pertinent to HS-TM, and three more pertinent to regression. Assessments were mainly used to trigger further quality evaluation (a "check oil light") and motivate behavior change. CONCLUSIONS: HS-TM-based performance assessments were more interpretable and more credible to VA hospital leaders than regression-based assessments. However, leaders had a similar set of concerns related to credibility for both methods and felt both were best used as a screen for further evaluation.

Adding a New Medication Versus Maximizing Dose to Intensify Hypertension Treatment in Older Adults : A Retrospective Observational Study.

BACKGROUND: There are 2 approaches to intensifying antihypertensive treatment when target blood pressure is not reached, adding a new medication and maximizing dose. Which strategy is better is unknown. OBJECTIVE: To assess the frequency of intensification by adding a new medication versus maximizing dose, as well as the association of each method with intensification sustainability and follow-up systolic blood pressure (SBP). DESIGN: Large-scale, population-based, retrospective cohort study. Observational data were used to emulate a target trial with 2 groups, new medication and maximizing dose, who underwent intensification of their drug regimen. SETTING: Veterans Health Administration (2011 to 2013). PATIENTS: Veterans aged 65 years or older with hypertension, an SBP of 130 mm Hg or higher, and at least 1 antihypertensive medication at less than the maximum dose. MEASUREMENTS: The following 2 intensification approaches were emulated: adding a new medication, defined as a total dose increase with new medication, and maximizing dose, defined as a total dose increase without new medication. Inverse probability weighting was used to assess the observational effectiveness of the intensification approach on sustainability of intensified treatment and follow-up SBP at 3 and 12 months. RESULTS: Among 178 562 patients, 45 575 (25.5%) had intensification by adding a new medication and 132 987 (74.5%) by maximizing dose. Compared with maximizing dose, adding a new medication was associated with less intensification sustainability (average treatment effect, -15.2% [95% CI, -15.7% to -14.6%] at 3 months and -15.1% [CI, -15.6% to -14.5%] at 12 months) but a slightly larger reduction in mean SBP (-0.8 mm Hg [CI, -1.2 to -0.4 mm Hg] at 3 months and -1.1 mm Hg [CI, -1.6 to -0.6 mm Hg] at 12 months). LIMITATION: Observational data; largely male population. CONCLUSION: Adding a new antihypertensive medication was less frequent and was associated with less intensification sustainability but slightly larger reductions in SBP. Trials would provide the most definitive support for our findings. PRIMARY FUNDING SOURCE: National Institute on Aging and Veterans Health Administration.

Correction: Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials.

[This corrects the article DOI: 10.1371/journal.pmed.1002410.].

Optimal cholesterol treatment plans and genetic testing strategies for cardiovascular diseases.

Atherosclerotic cardiovascular disease (ASCVD) is among the leading causes of death in the US. Although research has shown that ASCVD has genetic elements, the understanding of how genetic testing influences its prevention and treatment has been limited. To this end, we model the health trajectory of patients stochastically and determine treatment and testing decisions simultaneously. Since the cholesterol level of patients is one controllable risk factor for ASCVD events, we model cholesterol treatment plans as Markov decision processes. We determine whether and when patients should receive a genetic test using value of information analysis. By simulating the health trajectory of over 64 million adult patients, we find that 6.73 million patients undergo genetic testing. The optimal treatment plans informed with clinical and genetic information save 5,487 more quality-adjusted life-years while costing $1.18 billion less than the optimal treatment plans informed with clinical information only. As precision medicine becomes increasingly important, understanding the impact of genetic information becomes essential.

Postcritical illness vulnerability.

PURPOSE OF REVIEW: Critical illness survivorship is associated with new and worsening physical, cognitive, and emotional status. Survivors are vulnerable to further health set-backs, most commonly because of infection and exacerbation of chronic medical conditions. Awareness of survivors' challenges are important given the anticipated rise in critical illness survivors because of SARS-CoV-2 viral sepsis. RECENT FINDINGS: Studies continue to document challenges of critical illness survivorship. Beyond the cognitive, physical, and mental health sequelae encompassed by postintensive case syndrome, patients commonly experience persistent immunosuppression, re-hospitalization, inability to resume prior employment, and reduced quality of life. Although recommended practices for enhancing recovery from sepsis are associated with better outcomes, only a minority of patients receive all recommended practices. ICU follow-up programs or peer support groups remain important interventions to learn about and address the multifaceted challenges of critical illness survivorship, but there is little evidence of benefit to date. SUMMARY: Survivors of sepsis and critical illness commonly experience impaired health status, reduced quality of life, and inability to return to prior employment. Although the challenges of critical illness survivorship are increasingly well documented, there are relatively few studies on enhancing recovery. Future studies must focus on identifying best practices for optimizing recovery and strategies to promote their implementation.

Clinical Implications of Revised Pooled Cohort Equations for Estimating Atherosclerotic Cardiovascular Disease Risk.

Background: The 2013 pooled cohort equations (PCEs) are central in prevention guidelines for cardiovascular disease (CVD) but can misestimate CVD risk. Objective: To improve the clinical accuracy of CVD risk prediction by revising the 2013 PCEs using newer data and statistical methods. Design: Derivation and validation of risk equations. Setting: Population-based. Participants: 26 689 adults aged 40 to 79 years without prior CVD from 6 U.S. cohorts. Measurements: Nonfatal myocardial infarction, death from coronary heart disease, or fatal or nonfatal stroke. Results: The 2013 PCEs overestimated 10-year risk for atherosclerotic CVD by an average of 20% across risk groups. Misestimation of risk was particularly prominent among black adults, of whom 3.9 million (33% of eligible black persons) had extreme risk estimates (<70% or >250% those of white adults with otherwise-identical risk factor values). Updating these equations improved accuracy among all race and sex subgroups. Approximately 11.8 million U.S. adults previously labeled high-risk (10-year risk ≥7.5%) by the 2013 PCEs would be relabeled lower-risk by the updated equations. Limitations: Updating the 2013 PCEs with data from modern cohorts reduced the number of persons considered to be at high risk. Clinicians and patients should consider the potential benefits and harms of reducing the number of persons recommended aspirin, blood pressure, or statin therapy. Our findings also indicate that risk equations will generally become outdated over time and require routine updating. Conclusion: Revised PCEs can improve the accuracy of CVD risk estimates. Primary Funding Source: National Institutes of Health.

Detecting Heterogeneous Treatment Effects to Guide Personalized Blood Pressure Treatment: A Modeling Study of Randomized Clinical Trials.

BACKGROUND: Two recent randomized trials produced discordant results when testing the benefits and harms of treatment to reduce blood pressure (BP) in patients with cardiovascular disease (CVD). OBJECTIVE: To perform a theoretical modeling study to identify whether large, clinically important differences in benefit and harm among patients (heterogeneous treatment effects [HTEs]) can be hidden in, and explain discordant results between, treat-to-target BP trials. DESIGN: Microsimulation. DATA SOURCES: Results of 2 trials comparing standard (systolic BP target <140 mm Hg) with intensive (systolic BP target <120 mm Hg) BP treatment and data from the National Health and Nutrition Examination Survey (2013 to 2014). TARGET POPULATION: U.S. adults. TIME HORIZON: 5 years. PERSPECTIVE: Societal. INTERVENTION: BP treatment. OUTCOME MEASURES: CVD events and mortality. RESULTS OF BASE-CASE ANALYSIS: Clinically important HTEs could explain differences in outcomes between 2 trials of intensive BP treatment, particularly diminishing benefit with each additional BP agent (for example, adding a second agent reduces CVD risk [hazard ratio, 0.61], but adding a fourth agent to a third has no benefit) and increasing harm at low diastolic BP. RESULTS OF SENSITIVITY ANALYSIS: Conventional treat-to-target trial designs had poor (<5%) statistical power to detect the HTEs, despite large samples (n > 20 000), and produced biased effect estimates. In contrast, a trial with sequential randomization to more intensive therapy achieved greater than 80% power and unbiased HTE estimates, despite small samples (n = 3500). LIMITATIONS: The HTEs as a function of the number of BP agents only were explored. Simulated aggregate data from the trials were used as model inputs because individual-participant data were not available. CONCLUSION: Clinically important heterogeneity in intensive BP treatment effects remains undetectable in conventional trial designs but can be detected in sequential randomization trial designs. PRIMARY FUNDING SOURCE: National Institutes of Health and U.S. Department of Veterans Affairs.

Alternative Strategies to Achieve Cardiovascular Mortality Goals in China and India: A Microsimulation of Target- Versus Risk-Based Blood Pressure Treatment.

BACKGROUND: The World Health Organization aims to reduce mortality from chronic diseases including cardiovascular disease (CVD) by 25% by 2025. High blood pressure is a leading CVD risk factor. We sought to compare 3 strategies for treating blood pressure in China and India: a treat-to-target (TTT) strategy emphasizing lowering blood pressure to a target, a benefit-based tailored treatment (BTT) strategy emphasizing lowering CVD risk, or a hybrid strategy currently recommended by the World Health Organization. METHODS AND RESULTS: We developed a microsimulation model of adults aged 30 to 70 years in China and in India to compare the 2 treatment approaches across a 10-year policy-planning horizon. In the model, a BTT strategy treating adults with a 10-year CVD event risk of ≥ 10% used similar financial resources but averted ≈ 5 million more disability-adjusted life-years in both China and India than a TTT approach based on current US guidelines. The hybrid strategy in the current World Health Organization guidelines produced no substantial benefits over TTT. BTT was more cost-effective at $205 to $272/disability-adjusted life-year averted, which was $142 to $182 less per disability-adjusted life-year than TTT or hybrid strategies. The comparative effectiveness of BTT was robust to uncertainties in CVD risk estimation and to variations in the age range analyzed, the BTT treatment threshold, or rates of treatment access, adherence, or concurrent statin therapy. CONCLUSIONS: In model-based analyses, a simple BTT strategy was more effective and cost-effective than TTT or hybrid strategies in reducing mortality.

Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials.

BACKGROUND: Intensive blood pressure (BP) treatment can avert cardiovascular disease (CVD) events but can cause some serious adverse events. We sought to develop and validate risk models for predicting absolute risk difference (increased risk or decreased risk) for CVD events and serious adverse events from intensive BP therapy. A secondary aim was to test if the statistical method of elastic net regularization would improve the estimation of risk models for predicting absolute risk difference, as compared to a traditional backwards variable selection approach. METHODS AND FINDINGS: Cox models were derived from SPRINT trial data and validated on ACCORD-BP trial data to estimate risk of CVD events and serious adverse events; the models included terms for intensive BP treatment and heterogeneous response to intensive treatment. The Cox models were then used to estimate the absolute reduction in probability of CVD events (benefit) and absolute increase in probability of serious adverse events (harm) for each individual from intensive treatment. We compared the method of elastic net regularization, which uses repeated internal cross-validation to select variables and estimate coefficients in the presence of collinearity, to a traditional backwards variable selection approach. Data from 9,069 SPRINT participants with complete data on covariates were utilized for model development, and data from 4,498 ACCORD-BP participants with complete data were utilized for model validation. Participants were exposed to intensive (goal systolic pressure < 120 mm Hg) versus standard (<140 mm Hg) treatment. Two composite primary outcome measures were evaluated: (i) CVD events/deaths (myocardial infarction, acute coronary syndrome, stroke, congestive heart failure, or CVD death), and (ii) serious adverse events (hypotension, syncope, electrolyte abnormalities, bradycardia, or acute kidney injury/failure). The model for CVD chosen through elastic net regularization included interaction terms suggesting that older age, black race, higher diastolic BP, and higher lipids were associated with greater CVD risk reduction benefits from intensive treatment, while current smoking was associated with fewer benefits. The model for serious adverse events chosen through elastic net regularization suggested that male sex, current smoking, statin use, elevated creatinine, and higher lipids were associated with greater risk of serious adverse events from intensive treatment. SPRINT participants in the highest predicted benefit subgroup had a number needed to treat (NNT) of 24 to prevent 1 CVD event/death over 5 years (absolute risk reduction [ARR] = 0.042, 95% CI: 0.018, 0.066; P = 0.001), those in the middle predicted benefit subgroup had a NNT of 76 (ARR = 0.013, 95% CI: -0.0001, 0.026; P = 0.053), and those in the lowest subgroup had no significant risk reduction (ARR = 0.006, 95% CI: -0.007, 0.018; P = 0.71). Those in the highest predicted harm subgroup had a number needed to harm (NNH) of 27 to induce 1 serious adverse event (absolute risk increase [ARI] = 0.038, 95% CI: 0.014, 0.061; P = 0.002), those in the middle predicted harm subgroup had a NNH of 41 (ARI = 0.025, 95% CI: 0.012, 0.038; P < 0.001), and those in the lowest subgroup had no significant risk increase (ARI = -0.007, 95% CI: -0.043, 0.030; P = 0.72). In ACCORD-BP, participants in the highest subgroup of predicted benefit had significant absolute CVD risk reduction, but the overall ACCORD-BP participant sample was skewed towards participants with less predicted benefit and more predicted risk than in SPRINT. The models chosen through traditional backwards selection had similar ability to identify absolute risk difference for CVD as the elastic net models, but poorer ability to correctly identify absolute risk difference for serious adverse events. A key limitation of the analysis is the limited sample size of the ACCORD-BP trial, which expanded confidence intervals for ARI among persons with type 2 diabetes. Additionally, it is not possible to mechanistically explain the physiological relationships explaining the heterogeneous treatment effects captured by the models, since the study was an observational secondary data analysis. CONCLUSIONS: We found that predictive models could help identify subgroups of participants in both SPRINT and ACCORD-BP who had lower versus higher ARRs in CVD events/deaths with intensive BP treatment, and participants who had lower versus higher ARIs in serious adverse events.

The Veterans Affairs Cardiac Risk Score: Recalibrating the Atherosclerotic Cardiovascular Disease Score for Applied Use.

BACKGROUND: Accurately estimating cardiovascular risk is fundamental to good decision-making in cardiovascular disease (CVD) prevention, but risk scores developed in one population often perform poorly in dissimilar populations. We sought to examine whether a large integrated health system can use their electronic health data to better predict individual patients' risk of developing CVD. METHODS: We created a cohort using all patients ages 45-80 who used Department of Veterans Affairs (VA) ambulatory care services in 2006 with no history of CVD, heart failure, or loop diuretics. Our outcome variable was new-onset CVD in 2007-2011. We then developed a series of recalibrated scores, including a fully refit "VA Risk Score-CVD (VARS-CVD)." We tested the different scores using standard measures of prediction quality. RESULTS: For the 1,512,092 patients in the study, the Atherosclerotic cardiovascular disease risk score had similar discrimination as the VARS-CVD (c-statistic of 0.66 in men and 0.73 in women), but the Atherosclerotic cardiovascular disease model had poor calibration, predicting 63% more events than observed. Calibration was excellent in the fully recalibrated VARS-CVD tool, but simpler techniques tested proved less reliable. CONCLUSIONS: We found that local electronic health record data can be used to estimate CVD better than an established risk score based on research populations. Recalibration improved estimates dramatically, and the type of recalibration was important. Such tools can also easily be integrated into health system's electronic health record and can be more readily updated.

Corrected ROC analysis for misclassified binary outcomes.

Creating accurate risk prediction models from Big Data resources such as Electronic Health Records (EHRs) is a critical step toward achieving precision medicine. A major challenge in developing these tools is accounting for imperfect aspects of EHR data, particularly the potential for misclassified outcomes. Misclassification, the swapping of case and control outcome labels, is well known to bias effect size estimates for regression prediction models. In this paper, we study the effect of misclassification on accuracy assessment for risk prediction models and find that it leads to bias in the area under the curve (AUC) metric from standard ROC analysis. The extent of the bias is determined by the false positive and false negative misclassification rates as well as disease prevalence. Notably, we show that simply correcting for misclassification while building the prediction model is not sufficient to remove the bias in AUC. We therefore introduce an intuitive misclassification-adjusted ROC procedure that accounts for uncertainty in observed outcomes and produces bias-corrected estimates of the true AUC. The method requires that misclassification rates are either known or can be estimated, quantities typically required for the modeling step. The computational simplicity of our method is a key advantage, making it ideal for efficiently comparing multiple prediction models on very large datasets. Finally, we apply the correction method to a hospitalization prediction model from a cohort of over 1 million patients from the Veterans Health Administrations EHR. Implementations of the ROC correction are provided for Stata and R. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Implications of Heterogeneity of Treatment Effect for Reporting and Analysis of Randomized Trials in Critical Care.

Randomized clinical trials (RCTs) are conducted to guide clinicians' selection of therapies for individual patients. Currently, RCTs in critical care often report an overall mean effect and selected individual subgroups. Yet work in other fields suggests that such reporting practices can be improved. Specifically, this Critical Care Perspective reviews recent work on so-called "heterogeneity of treatment effect" (HTE) by baseline risk and extends that work to examine its applicability to trials of acute respiratory failure and severe sepsis. Because patients in RCTs in critical care medicine-and patients in intensive care units-have wide variability in their risk of death, these patients will have wide variability in the absolute benefit that they can derive from a given therapy. If the side effects of the therapy are not perfectly collinear with the treatment benefits, this will result in HTE, where different patients experience quite different expected benefits of a therapy. We use simulations of RCTs to demonstrate that such HTE could result in apparent paradoxes, including: (1) positive trials of therapies that are beneficial overall but consistently harm or have little benefit to low-risk patients who met enrollment criteria, and (2) overall negative trials of therapies that still consistently benefit high-risk patients. We further show that these results persist even in the presence of causes of death unmodified by the treatment under study. These results have implications for reporting and analyzing RCT data, both to better understand how our therapies work and to improve the bedside applicability of RCTs. We suggest a plan for measurement in future RCTs in the critically ill.