Background:Acute myocardial infarction (AMI) mortality has declined over the past decade, but sex- and race/ethnic differences remain. We assessed trends in incident AMI mortality stratified by sex and race/ethnicity across separate time periods following hospitalization, which could help target efforts to reduce disparities in care. Method and results:We identified 578,274 Medicare fee-for-service beneficiaries hospitalized with incident AMI from 2008 to 2018 (mean age 80.94, 47.17% men, 86.81% White) and assessed annual mortality for the following periods: in-hospital, post-discharge (0-30 days post-discharge), intermediate-term (31 to 365 days post-discharge), and longer-term (1-3 years post-discharge). Patients who died during one period were not counted in subsequent periods. Risk-adjusted mortality ratios stratified by sex and race/ethnicity were calculated using mixed-effects generalized linear models. The 2008 mortality for men (compared to women) and White patients (compared to Black and Hispanic patients) was considered the baseline. Unadjusted mortality declined across all time periods and for all subgroups from 2008 to 2018. In adjusted analyses, in-hospital and post-discharge mortality was similar for women compared to men and for Black and Hispanic patients compared to White patients. However, women compared to men and Black patients compared to White patients had higher mortality for the intermediate-term and longer-term follow-up periods for all years studied (e.g., 2008 longer-term mortality for men 1.00 (95% CI 0.97-1.02) and for women 1.10 (95% CI 1.06-1.14); 2016 longer-term mortality for men 0.77 (95% CI 0.75-0.8) and for women 0.87 (95% CI 0.84-0.9); 2008 longer-term mortality for White patients 1.00 (95% CI 0.98-1.01) and for Black patients 1.22 (95% CI 1.15-1.30); 2016 longer-term mortality for White patients 0.78 (95% CI 0.70-0.80) and for Black patients 1.03 (95% CI 0.96-1.10)). Conclusions:Incident AMI mortality declined from 2008 to 2018 across all subgroups and follow-up periods. However women and Black patients had persistently higher mortality compared to men and White patients from 31 days to 3 years post-discharge. Opportunities to reduce disparities in AMI care might be particularly effective in longitudinal outpatient care following hospital discharge.

BACKGROUND Cardiologists are increasingly moving from independent practice to direct employment by hospitals. Hospital employment has the potential to improve care coordination and delivery, but little is known about its effect on care quality and outcomes. OBJECTIVES In this study, we sought to assess the association between hospital employment of cardiologists and patient outcomes, care quality, and utilization among patients hospitalized with incident acute myocardial infarction (AMI) or heart failure (HF). METHODS We used a sample of Medicare fee-for-service beneficiaries hospitalized with incident AMI or HF from 2008 to 2019. We identified the accountable cardiologists that cared for these patients and determined their employment status by means of tax identification numbers. We used difference-in-differences methods to compare clinical outcomes, quality measures, and utilization for patients treated by hospital-employed cardiologists after switching from independent to hospital-employed practice, to outcomes for patients treated by cardiologists who remained independent. Models were adjusted for time trends and patient, hospital, and cardiologist characteristics. Patient outcomes were in- hospital mortality, 30-day mortality, and 30-day readmission. Quality measures were receipt of: 1) a guideline- recommended test to assess cardiac function; and 2) a 30-day follow-up clinic visit. Utilization measures were length of stay and, for AMI patients, the proportion receiving coronary revascularization. RESULTS The proportion of U.S. cardiologists employed by hospitals increased from 26% in 2008 to 63% in 2019. We identified 186,052 AMI and 259,849 HF patients cared for by cardiologists who switched to hospital employment and 168,052 AMI and 245,769 HF patients cared for by independent cardiologists. Patient characteristics were similar (mean age 80.8 years; 47% men). We found no significant differences in outcomes (eg, adjusted difference in 30-day mortality 0.03% [95% CI:-0.39% to 0.45%] for AMI patients and-0.05% [95% CI:-0.37% to 0.27%] for HF patients); no differences in most quality metrics except a small increase in the proportion of HF patients with 30-day follow-up (adjusted difference: 1.04%; 95% CI: 0.46%-1.62%); and no differences in utilization between patients treated by hospital-employed cardiologists (postswitch) vs independent cardiologists. CONCLUSIONS Among U.S. cardiologists, there has been a large shift from independent practice to direct employment by hospitals. We found minimal evidence that cardiologist employment by hospitals improves care quality or outcomes. (JACC. 2025;85:352-361) (c) 2025 by the American College of Cardiology Foundation.

ImportancePoor medication adherence is common. Text messaging is increasingly used to change patient behavior but often not rigorously tested. ObjectiveTo compare different types of text messaging strategies with usual care to improve medication refill adherence among patients nonadherent to cardiovascular medications. Design, Setting, and ParticipantsPatient-level randomized pragmatic trial between October 2019 to April 2022 at 3 US health care systems, with last follow-up date of April 11, 2023. Adult (18 to <90 years) patients were eligible based on diagnosis of 1 or more cardiovascular condition(s) and prescribed medication to treat the condition. Patients who did not opt out and had a 7-day refill gap were randomized to 1 of 4 study groups. Intervention(s)Generic text message refill reminders (generic reminder); behavioral nudge text refill reminders (behavioral nudge); behavioral nudge text refill reminders plus a fixed-message chatbot (behavioral nudge + chatbot); usual care. Main Outcomes and MeasuresPrimary outcome was refill adherence based on pharmacy data using proportion of days covered at 12 months. Secondary outcomes were clinical events of emergency department visits, hospitalizations, and mortality. ResultsAmong 9501 enrolled patients, baseline characteristics across the 4 groups were comparable (mean age, 60 years; 47% female [n = 4351]; 16% Black [n = 1517]; 49% Hispanic [n = 4564]). At 12 months, the mean proportion of days covered was 62.0% for generic reminder, 62.3% for behavioral nudge, 63.0% for behavioral nudge + chatbot, and 60.6% for usual care (P = .06). In adjusted analysis, when compared with usual care, mean proportion of days covered was 2.2 percentage points (95% CI, 0.3-4.2; P = .02) higher for generic reminder, 2.0 percentage points (95% CI, 0.1-3.9; P = .04) higher for behavioral nudge, and 2.3 percentage points (95%, 0.4-4.2; P = .02) higher for behavioral nudge + chatbot, none of which were statistically significant after multiple comparisons correction. There were no differences in clinical events between study groups. Conclusions and RelevanceText message reminders targeting patients who delay refilling their cardiovascular medications did not improve medication adherence based on pharmacy refill data or reduce clinical events at 12 months. Poor medication adherence may be due to multiple factors. Future interventions may need to be designed to address the multiple factors influencing adherence. Trial RegistrationClinicalTrials.gov Identifier: NCT03973931

ImportanceDespite advances in treatment and care quality for patients hospitalized with heart failure (HF), minimal improvement in mortality has been observed after HF hospitalization since 2010.ObjectiveTo evaluate trends in mortality rates across specific intervals after hospitalization.Design, Setting, and ParticipantsThis cohort study evaluated a random sample of Medicare fee-for-service beneficiaries with incident HF hospitalization from January 1, 2008, to December 31, 2018. Data were analyzed from February 2023 to May 2024.Main Outcomes and MeasuresUnadjusted mortality rates were calculated by dividing the number of all-cause deaths by the number of patients with incident HF hospitalization for the following periods: in-hospital, 30 days (0-30 days after hospital discharge), short term (31 days to 1 year after discharge), intermediate term (1-2 years after discharge), and long term (2-3 years after discharge). Each period was considered separately (ie, patients who died during one period were not counted in subsequent periods). Annual unadjusted and risk-adjusted mortality ratios were calculated (using logistic regression to account for differences in patient characteristics), defined as observed mortality divided by expected mortality based on 2008 rates.ResultsA total of 1 256 041 patients (mean [SD] age, 83.0 [7.6] years; 56.0% female; 86.0% White) were hospitalized with incident HF. There was a substantial decrease in the mortality ratio for the in-hospital period (unadjusted ratio, 0.77; 95% CI, 0.67-0.77; risk-adjusted ratio, 0.74; 95% CI, 0.71-0.76). For subsequent periods, mortality ratios increased through 2013 and then decreased through 2018, resulting in no reductions in unadjusted postdischarge mortality during the full study period (30-day mortality ratio, 0.94; 95% CI, 0.82-1.06; short-term mortality ratio, 1.02; 95% CI, 0.87-1.17; intermediate-term mortality ratio, 0.99; 95% CI, 0.79-1.19; and long-term mortality ratio, 0.96; 95% CI, 0.76-1.16) and small reductions in risk-adjusted postdischarge mortality during the full study period (30-day mortality ratio, 0.88; 95% CI, 0.86-0.90; short-term mortality ratio, 0.94; 95% CI, 0.94-0.95; intermediate-term mortality ratio, 0.94; 95% CI, 0.92-0.95; and long-term mortality ratio, 0.95; 95% CI, 0.93-0.96).Conclusions and RelevanceIn this study of Medicare fee-for-service beneficiaries, there was a substantial decrease in in-hospital mortality for patients hospitalized with incident HF from 2008 to 2018, but little to no reduction in mortality for subsequent periods up to 3 years after hospitalization. These results suggest opportunities to improve longitudinal outpatient care for patients with HF after hospital discharge.

OBJECTIVE:To develop an accurate and reproducible measure of vertical integration between physicians and hospitals (defined as hospital or health system employment of physicians), which can be used to assess the impact of integration on healthcare quality and spending. DATA SOURCES AND STUDY SETTING:We use multiple data sources including from the Internal Revenue Service, the Centers for Medicare and Medicaid Services, and others to determine the Tax Identification Numbers (TINs) that hospitals and physicians use to bill Medicare for services, and link physician billing TINs to hospital-related TINs. STUDY DESIGN:We developed a new measure of vertical integration, based on the TINs that hospitals and physicians use to bill Medicare, using a broad set of sources for hospital-related TINs. We considered physicians as hospital-employed if they bill Medicare primarily or exclusively using hospital-related TINs. We assessed integration status for all physicians who billed Medicare from 1999 to 2019. We compared this measure with others used in the existing literature. We conducted a simulation study which highlights the importance of accurately identifying integrated physicians when study the effects of integration. DATA COLLECTION/EXTRACTION METHODS:We extracted physician and hospital-related TINs from multiple sources, emphasizing specificity (a small proportion of nonintegrated physicians identified as integrated). PRINCIPAL FINDINGS:We identified 12,269 hospital-related TINs, used for billing by 546,775 physicians. We estimate that the percentage of integrated physicians rose from 19% in 1999 to 43% in 2019. Our approach identifies many additional physician practices as integrated; a simpler TIN measure, comparable with prior work, identifies only 30% (3877) of the TINs we identify. A service location measure, used in prior work, has both many false positives and false negatives. CONCLUSION:We developed a new measure of hospital-physician integration. This measure is reproducible and identifies many additional physician practices as integrated.

Introduction: U.S. healthcare has experienced substantial growth in vertical integration (physicians employed by hospitals), but little is known about its effect on patient care. Aims: To assess the association between cardiologist vertical integration and patient outcomes, care quality, and utilization among patients hospitalized with incident acute myocardial infarction (AMI) or heart failure (HF). Methods: We used a sample of all Medicare fee-for-service beneficiaries hospitalized with incident AMI and a 75% sample of beneficiaries hospitalized with incident HF between 2008-2019. We identified the accountable cardiologists that cared for these patients and determined their integration status using tax identification numbers. We used difference-in-differences methods to compare outcomes for patients treated by integrated cardiologists after switching from non-integrated to integrated practice, to outcomes for patients treated by cardiologists who remained non-integrated. Patient outcomes were in-hospital mortality, 30-day mortality, and 30-day readmission. Quality measures were the proportion of patients that received 1) a guideline-recommended test to assess cardiac function and 2) a 30-day follow-up clinic visit. Utilization measures were length of stay and the proportion of AMI patients receiving percutaneous coronary intervention. Models were adjusted for time trends and patient, hospital, and cardiologist characteristics. Results: The proportion of U.S. cardiologists employed by hospitals increased from 26% in 2008 to 61% in 2018. We identified 186,052 AMI patients and 259,849 HF patients cared for cardiologists who switched to integrated practice and 168,052 AMI patients and 245,769 HF patients cared for by non-integrated cardiologists. Patients were similar in age, sex, race/ethnicity, and comorbidities. We found no significant differences in clinical outcomes (e.g., adjusted difference in 30-day mortality 0.03% [95% CI = -0.39%, 0.45%] for AMI and -0.05% [-0.37%, 0.27%] for HF patients); no differences in most quality metrics, and no differences in utilization between patients treated by integrated versus non-integrated cardiologists. Conclusions: We found minimal evidence that cardiologist employment by hospitals improves care quality or outcomes. Regulation of vertical integration should focus on other effects of integration such as higher prices paid by insurers.

Background: Accountable care organizations (ACOs) often assess provider care quality by using attribution algorithms to determine the provider most responsible for a patient’s care. However, patients with ischemic heart disease (IHD) may receive care from more than one physician, and secondary prevention for IHD is often provided by both primary care physicians (PCPs) and cardiologists. Aim: To assess the performance of two commonly used claims-based ACO attribution methods for identifying the provider responsible for managing IHD-related outpatient care for patients in the year following incident acute myocardial infarction (AMI). Methods: Retrospective analysis of Medicare claims data for 491,391 AMI patients who survived for at least 1-year. Assignment of responsibility was made to the physician who saw the patient for the 1) plurality of PCP visits (PCP-method) or 2) plurality of PCP or cardiologist visits (PCP-Cardiologist method). For each method we assessed a) the proportion of patients that could be attributed to a physician, and b) how often the attributed physician and non-attributed physicians provided IHD care. IHD visits were identified using a previously validated list of IHD diagnosis codes. Results: The PCP-method attributed 352,261 patients (72%) and the PCP-Cardiologist method attributed 396,618 patients (81%) to a physician. The PCP-method attributed physician did not see the patient for an IHD visit in 38% of cases and did not provide the plurality of IHD visits in 67% of cases. The PCP-cardiologist method attributed physician did not see the patient for an IHD visit in 30% of cases and did not provide the plurality of IHD visits in 35% of cases. Overall, the proportion of IHD visits provided by other, non-attributed physicians was 69% for the PCP-method and 39% for the PCP-Cardiologist method. Conclusions: Patients with IHD often obtain care from multiple physicians. Commonly used ACO attribution methods to identify a single physician responsible for a patient’s IHD care results in a significant number of patients being unattributed. Attribution methods that focus on visit frequency alone often attribute patients to physicians that do not provide any IHD care or less than a plurality of IHD care. Multi-attribution methods which attribute patients to more than one physician and consider both visit frequency and clinical context could result in a more appropriate appraisal and distribution of the responsibility of IHD care.

ImportanceMany physicians believe that most medical malpractice claims are random events. This study assessed the association of prior paid claims (including a single prior claim) with future paid claims; whether public disclosure of prior paid claims affects future paid claims; and whether the association of prior and future paid claims decayed over time.ObjectiveTo examine the association of 1 or more prior paid medical malpractice claims with future paid claims.Design, Setting, and ParticipantsThis study assessed the association between prior paid claims (including a single prior claim) with future claims; whether public disclosure of prior claims affects future paid claims; and whether the association of prior and future paid claims decayed over time. This retrospective case-control study included all 881 876 licensed physicians in the US. All data analysis took place between July, 2018 and January, 2023.ExposurePaid medical malpractice claims.Main Outcome and MeasuresAssociation between a prior paid medical malpractice claim and likelihood of a paid claim in a future period, compared with simulated results expected if paid claims are random events. Using the same outcomes, we also assessed whether public disclosure of paid claims affects future paid claim rates.ResultsThis study included all 881 876 physicians licensed to practice in the US at the time of the study. Overall, 3.3% of the 841 961 physicians with 0 paid claims in the prior period had 1 or more claims in the future period vs 12.4% of the 34 512 physicians with 1 paid claim in the prior period; 22.4% of the 4189 physicians with 2 paid claims in the prior period; and 37% of the 1214 physicians with 3 paid claims in the prior period. The association between prior claims and future claims was similar for high-medical-malpractice-risk and lower-risk specialties; 1 prior-period claim was associated with a 3.1 times higher likelihood of a future-period claim for high-risk specialties (95% CI, 2.8-3.4) vs a 4.2 times higher likelihood for lower-risk specialties (95% CI, 3.8-4.6). The predictive power of a prior paid claim for future claims declined gradually as the time since the prior claim increased, for prior or future periods up to 10 years. Public disclosure did not affect the association between prior and future paid claims.Conclusions and RelevanceIn this study of paid medical malpractice claims for all US physicians, a single prior paid claim was associated with substantial, long-lived higher future claim risk, independent of whether a physician was practicing in a high- or low-risk specialty, or whether a state publicly disclosed paid claims. Timely, noncoercive intervention, including education, has the potential to reduce future claims.

Importance The Million Hearts Model paid health care organizations to assess and reduce cardiovascular disease (CVD) risk. Model effects on long-term outcomes are unknown. Objective To estimate model effects on first-time myocardial infarctions (MIs) and strokes and Medicare spending over a period up to 5 years. Design, Setting, and Participants This pragmatic cluster-randomized trial ran from 2017 to 2021, with organizations assigned to a model intervention group or standard care control group. Randomized organizations included 516 US-based primary care and specialty practices, health centers, and hospital-based outpatient clinics participating voluntarily. Of these organizations, 342 entered patients into the study population, which included Medicare fee-for-service beneficiaries aged 40 to 79 years with no previous MI or stroke and with high or medium CVD risk (a 10-year predicted probability of MI or stroke [ie, CVD risk score] ≥15%) in 2017-2018. Intervention Organizations agreed to perform guideline-concordant care, including routine CVD risk assessment and cardiovascular care management for high-risk patients. The Centers for Medicare & Medicaid Services paid organizations to calculate CVD risk scores for Medicare fee-for-service beneficiaries. CMS further rewarded organizations for reducing risk among high-risk beneficiaries (CVD risk score ≥30%). Main Outcomes and Measures Outcomes included first-time CVD events (MIs, strokes, and transient ischemic attacks) identified in Medicare claims, combined first-time CVD events from claims and CVD deaths (coronary heart disease or cerebrovascular disease deaths) identified using the National Death Index, and Medicare Parts A and B spending for CVD events and overall. Outcomes were measured through 2021. Results High- and medium-risk model intervention beneficiaries (n = 130 578) and standard care control beneficiaries (n = 88 286) were similar in age (median age, 72-73 y), sex (58%-59% men), race (7%-8% Black), and baseline CVD risk score (median, 24%). The probability of a first-time CVD event within 5 years was 0.3 percentage points lower for intervention beneficiaries than control beneficiaries (3.3% relative effect; adjusted hazard ratio [HR], 0.97 [90% CI, 0.93-1.00]; P = .09). The 5-year probability of combined first-time CVD events and CVD deaths was 0.4 percentage points lower in the intervention group (4.2% relative effect; HR, 0.96 [90% CI, 0.93-0.99]; P = .02). Medicare spending for CVD events was similar between the groups (effect estimate, -$1.83 per beneficiary per month [90% CI, -$3.97 to -$0.30]; P = .16), as was overall Medicare spending including model payments (effect estimate, $2.11 per beneficiary per month [90% CI, -$16.66 to $20.89]; P = .85). Conclusions and Relevance The Million Hearts Model, which encouraged and paid for CVD risk assessment and reduction, reduced first-time MIs and strokes. Results support guidelines to use risk scores for CVD primary prevention. Trial Registration ClinicalTrials.gov Identifier: NCT04047147.

Starting around 2006, the Centers for Medicare and Medicaid Services (CMS) progressively reduced Medicare Fee-for-Service (M-FFS) payments for the principal noninvasive cardiac tests, when performed in a cardiologist office (Office), yet kept payments flat to increasing for the same tests, performed in the hospital-based outpatient (HBO) setting. This produced a growing gap between HBO and Office payments for the same tests, and thus an incentive for hospitals to acquire cardiology practices in order to move cardiac tests to the HBO location and capture the HBO/Office payment differential. We use difference-in-differences analysis, in which we compare national M-FFS trends in cardiac test location to those for a control group of several large, integrated Medicare Advantage (M-Adv) health systems over 2005-2015, which were not affected by these reimbursement changes, and provide evidence that these reimbursement changes led to a large shift in testing from Office to HBO. This shift was concurrent with a sharp rise in hospital-cardiologist integration. The rise in integration and the proportion of testing in HBO varied greatly across states. Independent practice remains viable in very large states, but is endangered in many states, and is all but extinct in a growing number of states.

BackgroundAdvances in technology and care quality have transformed the care of acute myocardial infarction (AMI), but little is known about trends in mortality rates across separate time periods after hospitalization. Methods and ResultsWe identified all Medicare fee-for-service beneficiaries hospitalized with incident AMI from 2008 to 2018. We calculated unadjusted mortality rates by dividing the number of all-cause deaths by the number of patients with incident AMI for the following time periods: acute (in hospital), post acute (0-30 days after hospital discharge), short term (31 days to 1 year after discharge), intermediate term (1-2 years after discharge), and long term (2-3 years after discharge). Each period was considered separately (ie, patients who died during one period were not counted in subsequent periods). Using logistic regression to account for differences in patient characteristics, we calculated annual risk standardized mortality ratios defined as observed over expected mortality based on 2008 rates. Among 768 084 patients with incident AMI (mean age 81 years, 48% male, 87% White), declines in observed-to-expected mortality ratios were observed for each time period: acute (0.68 [95% CI, 0.66-0.71]), postacute (0.72 [95% CI, 0.71-0.75]), short term (0.77 [95% CI, 0.75-0.78]), intermediate term (0.79 [95% CI, 0.77-0.81]), and long term (0.77 [95% CI, 0.75-0.79]). Declines were observed both for patients with and without ST-segment-elevation AMI. ConclusionsFor patients with incident AMI, there have been improvements in mortality rates across periods spanning the hospital stay through 3 years after discharge, reflecting advances in AMI care from hospitalization through long-term outpatient follow-up.

Background: New medical technologies and advancements in care quality have transformed care of patients hospitalized with incident acute myocardial infarction (AMI). While some advances may impact short-term mortality (e.g., mechanical circulatory support), others may take much longer to impact mortality (e.g., cardiac rehabilitation). Little is known about trends in time-incremental AMI mortality rates. Methods: We identified all patients hospitalized with incident AMI using a 5% sample of Medicare fee-for-service beneficiaries from 2008 to 2017. We used a three-year look back to exclude patients with a prior AMI hospitalization. We calculated unadjusted in-hospital, 30-day, 1-year, 2-year, and 3-year mortality rates by dividing the total number of all-cause deaths during each period by the corresponding number of patients who were hospitalized. We used logistic regression models to adjust mortality rates for patient demographics (age, sex, race/ethnicity) and a variety of comorbid conditions. Using 2008 as the baseline, we calculated annual incremental mortality rates for each time period. We performed subgroup analyses among AMI patients with ST-segment and non-ST-segment elevation. Results: From 2008 to 2017, we identified 42,567 patients hospitalized with incident AMI (mean age 81 years, 44% male, 86% white). The largest incremental declines in mortality rates were observed for the in-hospital (2.9% [CI 2.7 to 3.1%]), 30-day (2.1% [CI 1.9 to 2.3%], and 1-year (2.9% [CI 2.6 to 3.2%] time periods. Mortality declines were smaller but persisted for the 2-year (1.5% [CI 1.2 to 1.8%]), and 3-year (0.5% [CI 0.2 to 0.8%]; Figure) time periods. Similar patterns were observed among both ST-segment and non-ST-segment elevation AMI patients. Conclusions: For patients hospitalized with incident AMI, advances in care have led to incremental declines in mortality across time periods spanning the hospital stay through 3 years after discharge.

The Million Hearts Cardiovascular Disease (CVD) Risk Reduction Model pays provider organizations for measuring and reducing Medicare patients' cardiovascular risk.To assess whether the model increases the initiation or intensification of antihypertensive medications or statins among patients with blood pressure or low-density lipoprotein (LDL) cholesterol levels above guideline thresholds for treatment intensification.This prespecified secondary analysis of a cluster-randomized, pragmatic trial included primary care and cardiology practices, health care centers, and hospital-based outpatient departments across the US. Participants included Medicare patients who were enrolled into the model in 2017 by participating organizations and who were at high risk and at medium risk of a myocardial infarction or stroke in 10 years. Patient outcomes were analyzed for 1 year postenrollment (through December 2018) using an intent-to-treat design. Analysis began November 2019.US Centers for Medicare & Medicaid Services paid organizations for risk stratifying Medicare patients and reducing CVD risk among high-risk patients through discussing risk scores, developing individualized risk reduction plans, and following up with patients twice yearly.Initiating or intensifying statin or antihypertensive therapy within 1 year of enrollment, measured in Medicare Part D claims, and LDL cholesterol and systolic blood pressure levels approximately 1 year after enrollment, measured in usual care and reported to Centers for Medicare & Medicaid Services via a data registry (data complete for 51% of high-risk enrollees). The study's primary outcome (incidence of first-time myocardial infarction and stroke) is not reported because the trial is ongoing.A total of 330 primary care and cardiology practices, health care centers, and hospital-based outpatient departments and 125 436 Medicare patients were included in this analysis. High-risk patients in the intervention group had a mean (SD) age of 74 (4.1), 15 213 (63%) were male, 21 657 (90%) were receiving antihypertensive medication at baseline, and 16 558 (69%) were receiving statins. Almost all (21 791 [91%]) high-risk intervention group patients had above-threshold systolic blood pressure level (>130 mm Hg), LDL cholesterol level (>70 mg/dL), or both. Patients in the intervention group with these risk factors were more likely than control patients (8127 [37.3%] vs 4753 [32.4%]; adjusted difference in percentage points, 4.8; 95% CI, 2.9-6.7; P < .001) to initiate or intensify statins or antihypertensive medication. Centers for Medicare & Medicaid Services did not pay for CVD risk reduction for medium-risk enrollees, but initiation or intensification rates for these enrollees were also higher in the intervention vs control groups (12 668 [27.9%] vs 7544 [24.8%]; adjusted difference in percentage points, 3.1; 95% CI, 1.9-4.3; P < .001). Among high-risk enrollees with clinical data approximately 1 year after enrollment, LDL cholesterol level was slightly lower in the intervention vs control groups (mean [SD], 89 [31.8] vs 91 [32.1] mg/dL; adjusted difference in percentage points, -1.8; 95% CI, -2.9 to -0.6; P = .002), as was systolic blood pressure (mean [SD], 133 [15.7] vs 135 [16.4] mm Hg; adjusted difference in percentage points, -1.7; 95% CI, -2.8 to -0.6; P = .003).In this study, a pay-for-performance model led to modest increases in the use of CVD medications in a range of organizations, despite high medication use at baseline.

ImportanceThe Million Hearts Cardiovascular Disease (CVD) Risk Reduction Model pays provider organizations for measuring and reducing Medicare patients' cardiovascular risk.ObjectiveTo assess whether the model increases the initiation or intensification of antihypertensive medications or statins among patients with blood pressure or low-density lipoprotein (LDL) cholesterol levels above guideline thresholds for treatment intensification.Design, Setting, and ParticipantsThis prespecified secondary analysis of a cluster-randomized, pragmatic trial included primary care and cardiology practices, health care centers, and hospital-based outpatient departments across the US. Participants included Medicare patients who were enrolled into the model in 2017 by participating organizations and who were at high risk and at medium risk of a myocardial infarction or stroke in 10 years. Patient outcomes were analyzed for 1 year postenrollment (through December 2018) using an intent-to-treat design. Analysis began November 2019.InterventionsUS Centers for Medicare & Medicaid Services paid organizations for risk stratifying Medicare patients and reducing CVD risk among high-risk patients through discussing risk scores, developing individualized risk reduction plans, and following up with patients twice yearly.Main Outcomes and MeasuresInitiating or intensifying statin or antihypertensive therapy within 1 year of enrollment, measured in Medicare Part D claims, and LDL cholesterol and systolic blood pressure levels approximately 1 year after enrollment, measured in usual care and reported to Centers for Medicare & Medicaid Services via a data registry (data complete for 51% of high-risk enrollees). The study's primary outcome (incidence of first-time myocardial infarction and stroke) is not reported because the trial is ongoing.ResultsA total of 330 primary care and cardiology practices, health care centers, and hospital-based outpatient departments and 125 436 Medicare patients were included in this analysis. High-risk patients in the intervention group had a mean (SD) age of 74 (4.1), 15 213 (63%) were male, 21 657 (90%) were receiving antihypertensive medication at baseline, and 16 558 (69%) were receiving statins. Almost all (21 791 [91%]) high-risk intervention group patients had above-threshold systolic blood pressure level (>130 mm Hg), LDL cholesterol level (>70 mg/dL), or both. Patients in the intervention group with these risk factors were more likely than control patients (8127 [37.3%] vs 4753 [32.4%]; adjusted difference in percentage points, 4.8; 95% CI, 2.9-6.7; P < .001) to initiate or intensify statins or antihypertensive medication. Centers for Medicare & Medicaid Services did not pay for CVD risk reduction for medium-risk enrollees, but initiation or intensification rates for these enrollees were also higher in the intervention vs control groups (12 668 [27.9%] vs 7544 [24.8%]; adjusted difference in percentage points, 3.1; 95% CI, 1.9-4.3; P < .001). Among high-risk enrollees with clinical data approximately 1 year after enrollment, LDL cholesterol level was slightly lower in the intervention vs control groups (mean [SD], 89 [31.8] vs 91 [32.1] mg/dL; adjusted difference in percentage points, -1.8; 95% CI, -2.9 to -0.6; P = .002), as was systolic blood pressure (mean [SD], 133 [15.7] vs 135 [16.4] mm Hg; adjusted difference in percentage points, -1.7; 95% CI, -2.8 to -0.6; P = .003).Conclusions and RelevanceIn this study, a pay-for-performance model led to modest increases in the use of CVD medications in a range of organizations, despite high medication use at baseline.

Background Nearly half of patients do not take their cardiovascular medications as prescribed, resulting in increased morbidity, mortality, and healthcare costs. Mobile and digital technologies for health promotion and disease self-management offer an opportunity to adapt behavioral “nudges” using ubiquitous mobile phone technology to facilitate medication adherence. The Nudge pragmatic clinical trial uses population-level pharmacy data to deliver nudges via mobile phone text messaging and an artificial intelligent interactive chat bot with the goal of improving medication adherence and patient outcomes in three integrated healthcare delivery systems. Methods The Theory of mHealth, the Expanded RE-AIM/PRISM, and the PRECIS-2 frameworks were used for program planning, implementation, and evaluation, along with a focus on dissemination and cost considerations. During the planning phase, the Nudge study team developed and piloted a technology-based nudge message and chat bot of optimized interactive content libraries for a range of diverse patients. Inclusion criteria are very broad and include patients in one of three diverse health systems who take medications to treat hypertension, atrial fibrillation, coronary artery disease, diabetes, or hyperlipidemia. A target of approximately 10,000 participants will be randomized to one of 4 study arms: usual care (no intervention), generic nudge (text reminder), optimized nudge, and optimized nudge plus interactive AI chat bot. The PRECIS-2 tool indicated that the study protocol is very pragmatic, although there is variability across PRECIS-2 dimensions. Discussion The primary effectiveness outcome is medication adherence defined by the proportion of days covered (PDC) using pharmacy refill data. Implementation outcomes are assessed using the RE-AIM framework, with a particular focus on reach, consistency of implementation, adaptations, cost, and maintenance/sustainability. The project has limitations including limited power to detect some subgroup effects, medication complications (bleeding), and longer-term outcomes (myocardial infarction). Strengths of the study include the diverse healthcare systems, a feasible and generalizable intervention, transparent reporting using established pragmatic research and implementation science frameworks, strong stakeholder engagement, and planning for dissemination and sustainment. Trial registration ClinicalTrials.gov NCT03973931 . Registered on 4 June 2019. The study was funded by the NIH; grant number is 4UH3HL144163-02 issued 4/5/19.

Background CMS reimbursement guidelines for implantable cardioverter-defibrillators (ICDs) include mandated shared decision making (SDM), but without any manner of assessing the quality of decisions made. We developed and tested a scale meant to assess patients' knowledge of and preferences specific to ICDs. Such a tool would assess these constructs in the clinical environment, targeting resources and support for patients considering a primary prevention ICD. Methods Development of the ICD decision quality (ICD-DQ) scale included (1) item creation, (2) content validation using surveys of patients (n = 23) and clinicians (n = 31), and (3) examination of validity and reliability using a survey of patients who previously received an ICD (n = 295, response rate = 72%). Results The final scale consists of 12 knowledge and 8 preference items. With respect to content validity, clinician and patient respondents agreed on the importance of 19 of 24 candidate knowledge items (79%), and 9 of 11 treatment preference items (81%). Knowledge items exhibited moderate internal validity (alpha = 0.62, 1 factor), strong test-retest reliability (mean % correct at first administration = 59%, 62% at follow-up, P > .1) and discriminant validity (59% correct for patients, 93% among cardiologists). Short versions of the ICD-DQ were developed for clinical settings, the scores from both of which correlated with the long version in this cohort (11-item (r = 0.90) and a 5-item (r = 0.75)). Conclusions : The ICD-DQ fills a critical gap in measuring the quality of patients' ICD decisions. They may be used to evaluate the effectiveness of patient decision aids or the quality of SDM in clinical practice.

Telemedicine allows the remote exchange of medical data between patients and healthcare professionals. It is used to increase patients' access to care and provide effective healthcare services at a distance. During the recent coronavirus disease 2019 (COVID-19) pandemic, telemedicine has thrived and emerged worldwide as an indispensable resource to improve the management of isolated patients due to lockdown or shielding, including those with hypertension. The best proposed healthcare model for telemedicine in hypertension management should include remote monitoring and transmission of vital signs (notably blood pressure) and medication adherence plus education on lifestyle and risk factors, with video consultation as an option. The use of mixed automated feedback services with supervision of a multidisciplinary clinical team (physician, nurse, or pharmacist) is the ideal approach. The indications include screening for suspected hypertension, management of older adults, medically underserved people, high-risk hypertensive patients, patients with multiple diseases, and those isolated due to pandemics or national emergencies.

The 2020 American Heart Association (AHA) Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care is based on the extensive evidence evaluation performed in conjunction with the International Liaison Committee on Resuscitation. The Adult Basic and Advanced Life Support, Pediatric Basic and Advanced Life Support, Neonatal Life Support, Resuscitation Education Science, and Systems of Care Writing Groups drafted, reviewed, and approved recommendations, assigning to each recommendation a Class of Recommendation (ie, strength) and Level of Evidence (ie, quality). The 2020 Guidelines are organized in knowledge chunks that are grouped into discrete modules of information on specific topics or management issues. The 2020 Guidelines underwent blinded peer review by subject matter experts and were also reviewed and approved for publication by the AHA Science Advisory and Coordinating Committee and the AHA Executive Committee. The AHA has rigorous conflict-of-interest policies and procedures to minimize the risk of bias or improper influence during development of the guidelines. Anyone involved in any part of the guideline development process disclosed all commercial relationships and other potential conflicts of interest.

HomeCirculationVol. 142, No. 16_suppl_2Part 6: Resuscitation Education Science: 2020 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Free AccessReview ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyRedditDiggEmail Jump toFree AccessReview ArticlePDF/EPUBPart 6: Resuscitation Education Science: 2020 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Adam Cheng, MD, Chair, David J. Magid, MD, MPH, Marc Auerbach, MD, MSCE, Farhan Bhanji, MD, MEd, Blair L. Bigham, MD, MSc, Audrey L. Blewer, PhD, MPH, Katie N. Dainty, MSc, PhD, Emily Diederich, MD, MS, Yiqun Lin, MD, MHSc, PhD, Marion Leary, RN, MSN, MPH, Melissa Mahgoub, PhD, Mary E. Mancini, RN, PhD, Kenneth Navarro, PhD(c) and Aaron Donoghue, MD, MSCE, Vice Chair Adam ChengAdam Cheng Search for more papers by this author , David J. MagidDavid J. Magid Search for more papers by this author , Marc AuerbachMarc Auerbach Search for more papers by this author , Farhan BhanjiFarhan Bhanji Search for more papers by this author , Blair L. BighamBlair L. Bigham Search for more papers by this author , Audrey L. BlewerAudrey L. Blewer Search for more papers by this author , Katie N. DaintyKatie N. Dainty Search for more papers by this author , Emily DiederichEmily Diederich Search for more papers by this author , Yiqun LinYiqun Lin Search for more papers by this author , Marion LearyMarion Leary Search for more papers by this author , Melissa MahgoubMelissa Mahgoub Search for more papers by this author , Mary E. ManciniMary E. Mancini Search for more papers by this author , Kenneth NavarroKenneth Navarro Search for more papers by this author and Aaron DonoghueAaron Donoghue Search for more papers by this author Originally published21 Oct 2020https://doi.org/10.1161/CIR.0000000000000903Circulation. 2020;142:S551–S579Top 10 Take-Home MessagesEffective education is an essential contributor to improved survival outcomes from cardiac arrest.Use of a deliberate practice and mastery learning model during resuscitation training improves skill acquisition and retention for many critical tasks.The addition of booster training to resuscitation courses is associated with improved cardiopulmonary resuscitation (CPR) skill retention over time and improved neonatal outcomes.Implementation of a spaced learning approach for resuscitation training improves clinical performance and technical skills compared with massed learning.The use of CPR feedback devices during resuscitation training promotes CPR skill acquisition and retention.Teamwork and leadership training, high-fidelity manikins, in situ training, gamified learning, and virtual reality represent opportunities to enhance resuscitation training that may improve learning outcomes.Self-directed CPR training represents a reasonable alternative to instructor-led CPR training for lay rescuers.Middle school– and high school–age children should be taught how to perform high-quality CPR because this helps build the future cadre of trained community-based lay rescuers.To increase bystander CPR rates, CPR training should be tailored to low–socioeconomic status neighborhoods and specific racial and ethnic communities, where there is currently a paucity of training opportunities.Future resuscitation education research should include outcomes of clinical relevance, establish links between performance outcomes in training and patient outcomes, describe cost-effectiveness of interventions, and explore how instructional design can be tailored to specific skills.PreambleEach year, millions of providers receive basic and advanced life support training with the aim of improving patient outcomes from cardiac arrest.1 Resuscitation training programs incorporate evidence-based content while providing opportunities for learners to practice lifesaving skills in individual and team-based clinical environments. While resuscitation training is widespread, learners frequently fall short of achieving the desired performance outcomes, resulting in skills that do not consistently translate to clinical care with real patients.1,2The International Liaison Committee on Resuscitation Formula for Survival (Figure) emphasizes 3 essential components influencing survival outcomes from cardiac arrest: guidelines based on current resuscitation science, effective education of resuscitation providers, and local implementation of guidelines during patient care.3 Greater emphasis on effective education will improve provider performance, enhance local implementation of guidelines, and potentially increase survival rates from cardiac arrest.Download figureDownload PowerPointFigure. Formula for Survival in Resuscitation: Key Elements Contributing to Educational Efficiency. ACLS indicates advanced cardiovascular life support; and CPR, cardiopulmonary resuscitation.These guidelines contain recommendations for the design and delivery of resuscitation training for lay rescuers and healthcare providers. The provision of effective education is highly dependent on the instructional design of educational programs because this determines how content is delivered to the learner. In this Part, we explore the evidence informing different instructional design features and discuss how social determinants of health (eg, socioeconomic status [SES], race) and individual factors (eg, practitioner experience) may influence clinical performance and patient outcomes.References1. Cheng A, Nadkarni VM, Mancini MB, Hunt EA, Sinz EH, Merchant RM, Donoghue A, Duff JP, Eppich W, Auerbach M, Bigham BL, Blewer AL, Chan PS, Bhanji F; American Heart Association Education Science Investigators; and on behalf of the American Heart Association Education Science and Programs Committee, Council on Cardiopulmonary, Critical Care, Perioperative and Resuscitation; Council on Cardiovascular and Stroke Nursing; and Council on Quality of Care and Outcomes Research. Resuscitation education science: educational strategies to improve outcomes from cardiac arrest: a scientific statement from the American Heart Association.Circulation. 2018; 138:e82–e122. doi: 10.1161/CIR.0000000000000583LinkGoogle Scholar2. Bhanji F, Donoghue AJ, Wolff MS, Flores GE, Halamek LP, Berman JM, Sinz EH, Cheng A. Part 14: education: 2015 American Heart Association Guidelines Update for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care.Circulation. 2015; 132(suppl 2):S561–e573. doi: 10.1161/CIR.0000000000000268LinkGoogle Scholar3. Søreide E, Morrison L, Hillman K, Monsieurs K, Sunde K, Zideman D, Eisenberg M, Sterz F, Nadkarni VM, Soar J, Nolan JPUtstein Formula for Survival Collaborators. The formula for survival in resuscitation.Resuscitation. 2013; 84:1487–1493. doi: 10.1016/j.resuscitation.2013.07.020CrossrefMedlineGoogle ScholarIntroductionScope of GuidelineCardiac arrest remains a major public health problem, with more than 600 000 cardiac arrests per year in the United States.1,2 Survival rates of patients with cardiac arrest remain low despite advancements in resuscitation science.3 Each year, millions of people receive basic and advanced life support training in an effort to improve the quality of care delivered to cardiac arrest patients.4 Resuscitation training programs are designed to convey evidence-based content and provide opportunities for learners (ie, those enrolled in resuscitation training programs) to apply knowledge and practice critical skills. These programs, however, frequently fall short of achieving the desired learning outcomes (eg, knowledge and skill acquisition), with performance that does not consistently translate over to the real-world clinical environment.4,5 For example, cardiopulmonary resuscitation (CPR) skills that are acquired immediately after basic life support (BLS) training often show decay by as early as 3 months, resulting in many BLS-trained healthcare providers—such as physicians, nurses, respiratory therapists, and other healthcare professionals—struggling to perform guideline-compliant CPR during simulated and real cardiac arrests.6–14 Additionally, current research on lay rescuer CPR training is lacking evidence describing the optimal methods to train bystanders to recognize cardiac arrest, initiate CPR, and use automated external defibrillators appropriately.15–17 A dedicated focus on instructional design is essential to ensure that knowledge and skills acquired during training are applied when caring for patients in cardiac arrest.4Improving survival from cardiac arrest is highly dependent on the quality of resuscitative care. Many key determinants of survival, such as immediate recognition of cardiac arrest, early initiation of CPR, early defibrillation, and high-quality chest compressions, are variables that can be targeted by resuscitation training programs to improve patient outcomes. Instructional design features are the key elements, or “active ingredients,” of resuscitation training programs that determine how content is delivered to the learner.18 A better understanding of the impact of instructional design features on learning outcomes will enable educators to design training programs that translate into outstanding clinical performance during cardiac arrests. Furthermore, appreciating how social determinants of health (eg, SES, race) and individual factors (eg, practitioner experience) influence the downstream impact of resuscitation education will help inform future policy and implementation strategies. In this Part, we describe the evidence supporting key elements of resuscitation education and provide recommendations aimed at improving learner outcomes and patient outcomes from cardiac arrest.The following sections briefly describe the process of evidence review and guideline development. See “Part 2: Evidence Evaluation and Guidelines Development” in the 2020 ECC Guidelines for more details on this process.19Organization of the Resuscitation Education Science Writing GroupThe Resuscitation Education Science Writing Group comprised a diverse team of experts with backgrounds in resuscitation education, clinical medicine (ie, pediatrics, intensive care, emergency medicine), nursing, prehospital care, health services, and education research. Writing group members are American Heart Association (AHA) volunteers with an interest and recognized expertise in resuscitation and are selected by the AHA Emergency Cardiovascular Care (ECC) Committee. The AHA has rigorous conflict-of-interest policies and procedures to minimize the risk of bias and improper influence during development of the guidelines.20 Before appointment, writing group members and peer reviewers disclosed all commercial relationships and other potential (including intellectual) conflicts. Disclosure information for writing group members is listed in Appendix 1.Methodology and Evidence ReviewThis Part of the 2020 AHA Guidelines for CPR and ECC is based on the extensive evidence evaluation performed in conjunction with the International Liaison Committee on Resuscitation and affiliated International Liaison Committee on Resuscitation member councils. Three different types of evidence reviews (systematic reviews, scoping reviews, and evidence updates) were used in the 2020 process. Each of these resulted in a description of the literature that facilitated guideline development.21–25 Reviews were limited to the resuscitation education science literature, but many of the concepts reviewed have origins within other fields (eg, medical education, psychology).Class of Recommendation and Level of EvidenceThe AHA Resuscitation Education Science Writing Group reviewed all relevant and current AHA Guidelines for CPR and ECC5,26–37 and the relevant 2020 International Consensus on CPR and ECC Science With Treatment Recommendations27 to determine if current guidelines should be reaffirmed, revised, or retired and whether new recommendations were needed. The writing group then drafted, reviewed, and approved recommendations (by majority vote among members), assigning to each a Level of Evidence (LOE; ie, quality) and Class of Recommendation (COR; ie, strength; see Table 1, Applying COR and LOE to Clinical Strategies, Interventions, Treatments, or Diagnostic Testing in Patient Care).Table 1. Applying Class of Recommendation and Level of Evidence to Clinical Strategies, Interventions, Treatments, or Diagnostic Testing in Patient Care (Updated May 2019)*This table defines the Classes of Recommendation (COR) and Levels of Evidence (LOE). COR indicates the strength the writing group assigns the recommendation, and the LOE is assigned based on the quality of the scientific evidence. The outcome or result of the intervention should be specified (an improved clinical outcome or increased diagnostic accuracy or incremental prognostic information).Classes of RecommendationCOR designations include Class 1, a strong recommendation for which the potential benefit greatly outweighs the risk; Class 2a, a moderate recommendation for which benefit most likely outweighs the risk; Class 2b, a weak recommendation for which it’s unknown whether benefit will outweigh the risk; Class 3: No Benefit, a moderate recommendation signifying that there is equal likelihood of benefit and risk; and Class 3: Harm, a strong recommendation for which the risk outweighs the potential benefit.Suggested phrases for writing Class 1 recommendations includeIs recommendedIs indicated/useful/effective/beneficialShould be performed/administered/otherComparative-effectiveness phrases include treatment/strategy A is recommended/indicated in preference to treatment B, and treatment A should be chosen over treatment B.Suggested phrases for writing Class 2a recommendations includeIs reasonableCan be useful/effective/beneficialComparative-effectiveness phrases include treatment/strategy A is probably recommended/indicated in preference to treatment B, and it is reasonable to choose treatment A over treatment B.For comparative-effectiveness recommendations (COR 1 and 2a; LOE A and B only), studies that support the use of comparator verbs should involve direct comparisons of the treatments or strategies being evaluated.Suggested phrases for writing Class 2b recommendations includeMay/might be reasonableMay/might be consideredUsefulness/effectiveness is unknown/unclear/uncertain or not well-establishedSuggested phrases for writing Class 3: No Benefit recommendations (generally, LOE A or B use only) includeIs not recommendedIs not indicated/useful/effective/beneficialShould not be performed/administered/otherSuggested phrases for writing Class 3: Harm recommendations includePotentially harmfulCauses harmAssociated with excess morbidity/mortalityShould not be performed/administered/otherLevels of EvidenceFor LOEs, the method of assessing quality is evolving, including the application of standardized, widely-used, and preferably validated evidence grading tools; and for systematic reviews, the incorporation of an Evidence Review Committee. LOE designations include Level A, Level B-R, Level B-NR, Level C-LD, and Level C-EO.Those categorized as Level A are derived fromHigh-quality evidence from more than 1 randomized clinical trial, or RCTMeta-analyses of high-quality RCTsOne or more RCTs corroborated by high-quality registry studiesThose categorized as Level B-R (randomized) are derived fromModerate-quality evidence from 1 or more RCTsMeta-analyses of moderate-quality RCTsThose categorized as Level B-NR (nonrandomized) are derived fromModerate-quality evidence from 1 or more well-designed, well-executed nonrandomized studies, observational studies, or registry studiesMeta-analyses of such studiesThose categorized as Level C-LD (limited data) are derived fromRandomized or nonrandomized observational or registry studies with limitations of design or executionMeta-analyses of such studiesPhysiological or mechanistic studies in human subjectsThose categorized as Level C-EO (expert opinion) are derived fromConsensus of expert opinion based on clinical experienceCOR and LOE are determined independently (any COR may be paired with any LOE).A recommendation with LOE C does not imply that the recommendation is weak. Many important clinical questions addressed in guidelines do not lend themselves to clinical trials. Although RCTs are unavailable, there may be a very clear clinical consensus that a particular test or therapy is useful or effective.Table 1. Applying Class of Recommendation and Level of Evidence to Clinical Strategies, Interventions, Treatments, or Diagnostic Testing in Patient Care (Updated May 2019)*Importantly, applying Grading of Recommendations, Assessment, Development, and Evaluation (GRADE)38 to educational studies yields greater challenges than its application to clinical studies. Specific considerations for studies involving educational outcomes (eg, improved “outcomes” in simulated patient settings or improved performance on summative assessment tools) are not provided in GRADE methodology; the writing group frequently assigned LOE to these studies according to a combination of a typical review of study quality, perceived importance of underlying constructs in the context of educational science, and (where possible) extrapolation of findings to analogous clinical phenomena (eg, outcomes in real patients as opposed to simulated ones).Guideline StructureThe 2020 guidelines are organized into knowledge chunks, grouped into discrete modules of information on specific topics or management issues.39 Each modular knowledge chunk includes a table of recommendations using standard AHA nomenclature of COR and LOE. A brief introduction or short synopsis puts the recommendations into context with important background information and overarching management or treatment concepts. Recommendation-specific supportive text clarifies the rationale and key study data supporting the recommendations. Hyperlinked references are provided to facilitate quick access and review.Document Review and ApprovalThese guidelines were submitted for blinded peer review to subject matter experts nominated by the AHA. Peer reviewer feedback was provided for guidelines in draft format and again in final format. The guidelines were reviewed and approved for publication by the AHA Science Advisory and Coordinating Committee and the AHA Executive Committee. Disclosure information for peer reviewers is listed in Appendix 2.AbbreviationsAbbreviationMeaning/PhraseACLSadvanced cardiovascular life supportAHAAmerican Heart AssociationB-CPRbystander cardiopulmonary resuscitationBLSbasic life supportCORClass of RecommendationCPRcardiopulmonary resuscitationECCemergency cardiovascular careEMSemergency medical servicesEOexpert opinionLDlimited dataLOELevel of EvidenceNRnonrandomizedOHCAout-of-hospital cardiac arrestPALSpediatric advanced life supportRCTrandomized controlled trialROSCreturn of spontaneous circulationSESsocioeconomic statusVRvirtual realityReferences1. Andersen LW, Holmberg MJ, Berg KM, Donnino MW, Granfeldt A. In-hospital cardiac arrest: a review.JAMA. 2019; 321:1200–1210. doi: 10.1001/jama.2019.1696CrossrefMedlineGoogle Scholar2. 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