An SGO commentary: U.S. News and World Report gynecologic oncology procedural ratings-Do they reflect high-quality care?

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Missed opportunities in hospital quality measurement during the COVID-19 pandemic: a retrospective investigation of US hospitals' CMS Star Ratings and 30-day mortality during the early pandemic

OBJECTIVES

In the USA and UK, pandemic-era outcome data have been excluded from hospital rankings and pay-for-performance programmes. We assessed the relationship between US hospitals' pre-pandemic Centers for Medicare and Medicaid Services (CMS) Overall Hospital Star ratings and early pandemic 30-day mortality among both patients with COVID and non-COVID to understand whether pre-existing structures, processes and outcomes related to quality enabled greater pandemic resiliency.

DESIGN AND DATA SOURCE

A retrospective, claim-based data study using the 100% Inpatient Standard Analytic File and Medicare Beneficiary Summary File including all US Medicare Fee-for-Service inpatient encounters from 1 April 2020 to 30 November 2020 linked with the CMS Hospital Star Ratings using six-digit CMS provider IDs.

OUTCOME MEASURE

The outcome was risk-adjusted 30-day mortality. We used multivariate logistic regression adjusting for age, sex, Elixhauser mortality index, US Census Region, month, hospital-specific January 2020 CMS Star rating (1-5 stars), COVID diagnosis (U07.1) and COVID diagnosis×CMS Star Rating interaction.

RESULTS

We included 4 473 390 Medicare encounters from 2533 hospitals, with 92 896 (28.2%) mortalities among COVID-19 encounters and 387 029 (9.3%) mortalities among non-COVID encounters. There was significantly greater odds of mortality as CMS Star Ratings decreased, with 18% (95% CI 15% to 22%; p<0.0001), 33% (95% CI 30% to 37%; p<0.0001), 38% (95% CI 34% to 42%; p<0.0001) and 60% (95% CI 55% to 66%; p<0.0001), greater odds of COVID mortality comparing 4-star, 3-star, 2-star and 1-star hospitals (respectively) to 5-star hospitals. Among non-COVID encounters, there were 17% (95% CI 16% to 19%; p<0.0001), 24% (95% CI 23% to 26%; p<0.0001), 32% (95% CI 30% to 33%; p<0.0001) and 40% (95% CI 38% to 42%; p<0.0001) greater odds of mortality at 4-star, 3-star, 2-star and 1-star hospitals (respectively) as compared with 5-star hospitals.

CONCLUSION

Our results support a need to further understand how quality outcomes were maintained during the pandemic. Valuable insights can be gained by including the reporting of risk-adjusted pandemic era hospital quality outcomes for high and low performing hospitals.

Hospital quality reporting in the pandemic era: to what extent did hospitals' COVID-19 census burdens impact 30-day mortality among non-COVID Medicare beneficiaries?

OBJECTIVES

Highly visible hospital quality reporting stakeholders in the USA such as the US News & World Report (USNWR) and the Centers for Medicare & Medicaid Services (CMS) play an important health systems role via their transparent public reporting of hospital outcomes and performance. However, during the pandemic, many such quality measurement stakeholders and pay-for-performance programmes in the USA and Europe have eschewed the traditional risk adjustment paradigm, instead choosing to pre-emptively exclude months or years of pandemic era performance data due largely to hospitals' perceived COVID-19 burdens. These data exclusions may lead patients to draw misleading conclusions about where to seek care, while also masking genuine improvements or deteriorations in hospital quality that may have occurred during the pandemic. Here, we assessed to what extent hospitals' COVID-19 burdens (proportion of hospitalised patients with COVID-19) were associated with their non-COVID 30-day mortality rates from March through November 2020 to inform whether inclusion of pandemic-era data may still be appropriate.

DESIGN

This was a retrospective cohort study using the 100% CMS Inpatient Standard Analytic File and Master Beneficiary Summary File to include all US Medicare inpatient encounters with admission dates from 1 April 2020 through 30 November 2020, excluding COVID-19 encounters. Using linear regression, we modelled the association between hospitals' COVID-19 proportions and observed/expected (O/E) ratios, testing whether the relationship was non-linear. We calculated alternative hospital O/E ratios after selective pandemic data exclusions mirroring the USNWR data exclusion methodology.

SETTING AND PARTICIPANTS

We analysed 4 182 226 consecutive Medicare inpatient encounters from across 2601 US hospitals.

RESULTS

The association between hospital COVID-19 proportion and non-COVID O/E 30-day mortality was statistically significant (p<0.0001), but weakly correlated (r²=0.06). The median (IQR) pairwise relative difference in hospital O/E ratios comparing the alternative analysis with the original analysis was +3.7% (-2.5%, +6.7%), with 1908/2571 (74.2%) of hospitals having relative differences within ±10%.

CONCLUSIONS

For non-COVID patient outcomes such as mortality, evidence-based inclusion of pandemic-era data is methodologically plausible and must be explored rather than exclusion of months or years of relevant patient outcomes data.

Abstract P403: In Situ Physiological and Behavioral Monitoring With Digital Sensors for Cerebrovascular Disease: A Scoping Review

Background: Cerebrovascular disease is a life-threatening neurological event and a leading cause of long-term disability and death worldwide. Early detection of characteristic behavioral and physiological changes associated with cerebrovascular disease is critical to improving patient outcomes and quality of life measures. The growing prevalence of remote monitoring tools, from wearable devices to smartphone applications, that facilitate in situ observation of patients and the environments of daily life holds promise for more timely recognition and possible prevention of cerebrovascular accidents (CVA) like stroke.

Objective: The goal of this scoping review is to examine and establish categories of innovation with digital sensors that monitor physiological and behavioral variables in situ to augment the current screening and diagnostic processes for patients with cerebrovascular disease.

Methods: Guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist, a robust search strategy for spanning multiple databases from 2012 to September 30, 2022, excluding review articles, articles including interventions, and articles not published in English, was implemented. Among the databases searched were Web of Science; Scopus; Ovid Embase; Ovid Cochrane Central Register of Controlled Trials; and Ovid MEDLINE and Epub ahead of print, in-process and other nonindexed citations, and daily.

Results: This search strategy aggregated 689 articles, of which 101 (14.7%) articles met the inclusion criteria for this scoping review. Articles were divided into two categories based on their focus: physiological and behavioral. Articles with a physiological focus were sorted into one of nine subcategories according to the signal(s) monitored: motor function, heart rhythm, heart rate, kinematic analysis, physical activity, blood pressure, sensory deficit, electrodermal activity, and intracranial pressure. Articles focusing on behavioral variables were sorted into two subcategories: mood and fatigue. Most studies used an ECG-enabled smartwatch, like an Apple Watch 3, or passive smartphone sensors.

Conclusions: This scoping review identified disparate methods and conclusions associated with the use of digital sensors for in situ physiological and behavioral monitoring of cerebrovascular disease patients. While most articles evaluated pilot validation and feasibility trials, the lack of randomized controlled trials is a critical literature gap specific to this evolving research area.

Real-world utilization of SARS-CoV-2 serological testing in RNA positive patients across the United States

BACKGROUND

As diagnostic tests for COVID-19 were broadly deployed under Emergency Use Authorization, there emerged a need to understand the real-world utilization and performance of serological testing across the United States.

METHODS

Six health systems contributed electronic health records and/or claims data, jointly developed a master protocol, and used it to execute the analysis in parallel. We used descriptive statistics to examine demographic, clinical, and geographic characteristics of serology testing among patients with RNA positive for SARS-CoV-2.

RESULTS

Across datasets, we observed 930,669 individuals with positive RNA for SARS-CoV-2. Of these, 35,806 (4%) were serotested within 90 days; 15% of which occurred <14 days from the RNA positive test. The proportion of people with a history of cardiovascular disease, obesity, chronic lung, or kidney disease; or presenting with shortness of breath or pneumonia appeared higher among those serotested compared to those who were not. Even in a population of people with active infection, race/ethnicity data were largely missing (>30%) in some datasets-limiting our ability to examine differences in serological testing by race. In datasets where race/ethnicity information was available, we observed a greater distribution of White individuals among those serotested; however, the time between RNA and serology tests appeared shorter in Black compared to White individuals. Test manufacturer data was available in half of the datasets contributing to the analysis.

CONCLUSION

Our results inform the underlying context of serotesting during the first year of the COVID-19 pandemic and differences observed between claims and EHR data sources-a critical first step to understanding the real-world accuracy of serological tests. Incomplete reporting of race/ethnicity data and a limited ability to link test manufacturer data, lab results, and clinical data challenge the ability to assess the real-world performance of SARS-CoV-2 tests in different contexts and the overall U.S. response to current and future disease pandemics.

Real-world performance of SARS-Cov-2 serology tests in the United States, 2020

BACKGROUND

Real-world performance of COVID-19 diagnostic tests under Emergency Use Authorization (EUA) must be assessed. We describe overall trends in the performance of serology tests in the context of real-world implementation.

METHODS

Six health systems estimated the odds of seropositivity and positive percent agreement (PPA) of serology test among people with confirmed SARS-CoV-2 infection by molecular test. In each dataset, we present the odds ratio and PPA, overall and by key clinical, demographic, and practice parameters.

RESULTS

A total of 15,615 people were observed to have at least one serology test 14-90 days after a positive molecular test for SARS-CoV-2. We observed higher PPA in Hispanic (PPA range: 79-96%) compared to non-Hispanic (60-89%) patients; in those presenting with at least one COVID-19 related symptom (69-93%) as compared to no such symptoms (63-91%); and in inpatient (70-97%) and emergency department (93-99%) compared to outpatient (63-92%) settings across datasets. PPA was highest in those with diabetes (75-94%) and kidney disease (83-95%); and lowest in those with auto-immune conditions or who are immunocompromised (56-93%). The odds ratios (OR) for seropositivity were higher in Hispanics compared to non-Hispanics (OR range: 2.59-3.86), patients with diabetes (1.49-1.56), and obesity (1.63-2.23); and lower in those with immunocompromised or autoimmune conditions (0.25-0.70), as compared to those without those comorbidities. In a subset of three datasets with robust information on serology test name, seven tests were used, two of which were used in multiple settings and met the EUA requirement of PPA ≥87%. Tests performed similarly across datasets.

CONCLUSION

Although the EUA requirement was not consistently met, more investigation is needed to understand how serology and molecular tests are used, including indication and protocol fidelity. Improved data interoperability of test and clinical/demographic data are needed to enable rapid assessment of the real-world performance of in vitro diagnostic tests.

A Multisite Assessment of Inpatient Safety Event Rates During the Coronavirus Disease 2019 Pandemic

To date, there has been a notable lack of peer-reviewed or publicly available data documenting rates of hospital quality outcomes and patient safety events during the coronavirus disease 2019 pandemic era. The dearth of evidence is perhaps related to the US health care system triaging resources toward patient care and away from reporting and research and also reflects that data used in publicly reported hospital quality rankings and ratings typically lag 2-5 years. At our institution, a learning health system assessment is underway to evaluate how patient safety was affected by the pandemic. Here we share and discuss early findings, noting the limitations of self-reported safety event reporting, and suggest the need for further widespread investigations at other US hospitals. During the 2-year study period from January 1, 2020, through December 31, 2021 across 3 large US academic medical centers at our institution, we documented an overall rate of 25.8 safety events per 1000 inpatient days. The rate of events meeting "harm" criteria was 12.4 per 1000 inpatient days, the rate of nonharm events was 11.1 per 1000 inpatient days, and the fall rate was 2.3 per 1000 inpatient days. This descriptive exploratory analysis suggests that patient safety event rates at our institution did not increase over the course of the pandemic. However, increasing health care worker absences were nonlinearly and strongly associated with patient safety event rates, which raises questions regarding the mechanisms by which patient safety event rates may be affected by staff absences during pandemic peaks.

A Simple and Interpretable Mortality-Based Value Metric for Condition- or Procedure-Specific Hospital Performance Reporting

Objective

To develop a simple, interpretable value metric (VM) to assess the value of care of hospitals for specific procedures or conditions by operationalizing the value equation: Value = Quality/Cost.

Patients and Methods

The present study was conducted on a retrospective cohort from 2015 to 2018 drawn from the 100% US sample of Medicare inpatient claims. The final cohort comprised 637,341 consecutive inpatient encounters with a cancer-related Medicare Severity-Diagnosis Related Grouping and 13,307 consecutive inpatient encounters with the International Classification of Diseases, Ninth Revision or International Classification of Diseases, Tenth Revision procedure code for partial or total gastrectomy. Claims-based demographic and clinical variables were used for risk adjustment, including age, sex, year, dual eligibility, reason for Medicare entitlement, and binary indicators for each of the Elixhauser comorbidities used in the Elixhauser mortality index. Risk-adjusted 30-day mortality and risk-adjusted encounter-specific costs were combined to form the VM, which was calculated as follows: number needed to treat = 1/(Mortality_national - Mortality_hospital), and VM = number needed to treat × risk-adjusted cost per encounter.

Results

Among hospitals with better-than-average 30-day cancer mortality rates, the cost to prevent 1 excess 30-day mortality for an inpatient cancer encounter ranged from $71,000 (best value) to $1.4 billion (worst value), with a median value of $543,000. Among hospitals with better-than-average 30-day gastrectomy mortality rates, the cost to prevent 1 excess 30-day mortality for an inpatient gastrectomy encounter ranged from $710,000 (best value) to $95 million (worst value), with a median value of $1.8 million.

Conclusion

This simple VM may have utility for interpretable reporting of hospitals' value of care for specific conditions or procedures. We found substantial inter- and intrahospital variation in value when defined as the costs of preventing 1 excess cancer or gastrectomy mortality compared with the national average, implying that hospitals with similar quality of care may differ widely in the value of that care.

State and Government Administrative Databases: Medicare, National Inpatient Sample (NIS), and State Inpatient Databases (SID) Programs

The availability of large state and federally run administrative health-care databases provides potentially comprehensive population-wide information that can dramatically impact both medical and health-policy decision-making. Specific opportunities and important limitations exist with all administrative databases based on what information is collected and how reliably specific data elements are reported. Access to patient identifiable-level information can be critical for certain long-term outcome studies but can be difficult (although not impossible) due to patient privacy protections, while more easily available de-identified information can provide important insights that may be more than sufficient for some short-term operative or in-hospital outcome questions. The first section of this paper by Sarah K. Meier and Benjamin D. Pollock discusses Medicare and the different data files available to health-care researchers. They describe what is and is not generally available from even the most granular Medicare Standard Analytic Files, and provide an analysis of the strengths and weaknesses of Medicare administrative data as well as the resulting best and inappropriate uses of these data. In the second section, the Nationwide Inpatient Sample and complementary State Inpatient Database programs are reviewed by Steven M. Kurtz and Edmund Lau, with insights into the origins of these programs, the data elements that are recorded relating to the operative procedure and hospital stay, and examples of the types of studies that optimally utilize these data sources. They also detail the limitations of these databases and identify studies that they are not well-suited for, especially those involving linkage or longitudinal studies over time. Both sections provide useful guidance on the best uses and pitfalls related to these important large representative national administrative data sources.

A Learning Health System Approach to Hospital Quality Performance Benchmarking: The Composite Hospital Quality Index

US hospital quality rankings and ratings use disparate methodologies and are weakly correlated. This causes confusion for patients and hospital quality staff. At the authors' institution, a Composite Hospital Quality Index (CHQI) was developed to combine hospital quality ratings. This approach is described and a calculator is shared here for other health systems to explore their performance. Among the US News and World Report Top 50 Hospitals, hospital-specific numeric summary scores were aggregated from the 2021 Centers for Medicare and Medicaid Services (CMS) Hospital Overall Star Rating, the Spring 2021 Leapfrog Safety Grade, and the April 2021 Hospital Consumer Assessment of Healthcare Providers and Systems Star Rating. The CHQI is the hospital-specific sum of the national percentile-rankings across these 3 ratings. In this example, mean (SD) percentiles were as follows: CMS Stars 74 (19), Hospital Consumer Assessment of Healthcare Providers and Systems 63 (19), Leapfrog 65 (24), with mean (SD) CHQI of 202 (49). The CHQI is used at the authors' institution to identify improvement opportunities and ensure that high-quality care is delivered across the health system.

Low antispike antibody levels correlate with poor outcomes in COVID-19 breakthrough hospitalizations

BACKGROUND

While COVID-19 immunization programs attempted to reach targeted rates, cases rose significantly since the emergence of the delta variant. This retrospective cohort study describes the correlation between antispike antibodies and outcomes of hospitalized, breakthrough cases during the delta variant surge.

METHODS

All patients with positive SARS-CoV-2 polymerase chain reaction hospitalized at Mayo Clinic Florida from 19 June 2021 to 11 November 2021 were considered for analysis. Cases were analyzed by vaccination status. Breakthrough cases were then analyzed by low and high antibody titers against SARS-CoV-2 spike protein, with a cut-off value of ≥132 U/ml. Outcomes included hospital length of stay (LOS), need for intensive care unit (ICU), mechanical ventilation, and mortality. We used 1:1 nearest neighbor propensity score matching without replacement to assess for confounders.

RESULTS

Among 627 hospitalized patients with COVID-19, vaccine breakthrough cases were older with more comorbidities compared to unvaccinated. After propensity score matching, the unvaccinated patients had higher mortality (27 [28.4%] vs. 12 [12.6%], p = 0.002) and LOS (7 [1.0-57.0] vs. 5 [1.0-31.0] days, p = 0.011). In breakthrough cases, low-titer patients were more likely to be solid organ transplant recipients (16 [34.0%] vs. 9 [12.3%], p = 0.006), with higher need for ICU care (24 [51.1%] vs. 22 [11.0%], p = 0.034), longer hospital LOS (median 6 vs. 5 days, p = 0.013), and higher mortality (10 [21.3%] vs. 5 [6.8%], p = 0.025) than high-titer patients.

CONCLUSIONS

Hospitalized breakthrough cases were more likely to have underlying risk factors than unvaccinated patients. Low-spike antibody titers may serve as an indicator for poor prognosis in breakthrough cases admitted to the hospital.

The association of acute COVID-19 infection with Patient Safety Indicator-12 events in a multisite healthcare system

BACKGROUND

Patient Safety Indicator (PSI)-12, a hospital quality measure designed by Agency for Healthcare Research and Quality (AHRQ) to capture potentially preventable adverse events, captures perioperative venous thromboembolism (VTE). It is unclear how COVID-19 has affected PSI-12 performance.

OBJECTIVE

We sought to compare the cumulative incidence of PSI-12 in patients with and without acute COVID-19 infection.

DESIGN, SETTING, AND PARTICIPANTS

This was a retrospective cohort study including PSI-12-eligible events at three Mayo Clinic medical centers (4/1/2020-10/5/2021).

EXPOSURE, MAIN OUTCOMES, AND MEASURES

We compared the unadjusted rate and adjusted risk ratio (aRR) for PSI-12 events among patients with and without COVID-19 infection using Fisher's exact χ²  test and the AHRQ risk-adjustment software, respectively. We summarized the clinical outcomes of COVID-19 patients with a PSI-12 event.

RESULTS

Our cohort included 50,400 consecutive hospitalizations. Rates of PSI-12 events were significantly higher among patients with acute COVID-19 infection (8/257 [3.11%; 95% confidence interval {CI}, 1.35%-6.04%]) compared to patients without COVID-19 (210/50,143 [0.42%; 95% CI, 0.36%-0.48%]) with a PSI-12 event during the encounter (p < .001). The risk-adjusted rate of PSI-12 was significantly higher in patients with acute COVID-19 infection (1.50% vs. 0.38%; aRR, 3.90; 95% CI, 2.12-7.17; p < .001). All COVID-19 patients with PSI-12 events had severe disease and 4 died. The most common procedure was tracheostomy (75%); the mean (SD) days from surgical procedure to VTE were 0.12 (7.32) days.

CONCLUSION

Patients with acute COVID-19 infection are at higher risk for PSI-12. The present definition of PSI-12 does not account for COVID-19. This may impact hospitals' quality performance if COVID-19 infection is not accounted for by exclusion or risk adjustment.

Clinical and Laboratory Profiles of SARS-CoV-2 Delta Variant Compared to Pre-Delta Variants

BACKGROUND

The emergence of SARS-CoV-2 variants of concern has led to significant phenotypical changes in transmissibility, virulence, and public health measures. Our study used clinical data to compare characteristics between a Delta variant wave and a pre-Delta variant wave of hospitalized patients.

METHODS

This single-center retrospective study defined a wave as an increasing number of COVID-19 hospitalizations, which peaked and later decreased. Data from the United States Department of Health and Human Services was used to identify the waves' primary variant. Wave 1 (08/08/20-04/01/21) was characterized by heterogeneous variants, while Wave 2 (06/26/21-10/18/21) was predominantly Delta variant. Descriptive statistics, regression techniques, and machine learning approaches supported the comparisons between waves.

RESULTS

From the cohort(n=1318), Wave 2 patients(n=665) were more likely to be younger, have fewer comorbidities, require more ICU care, and show an inflammatory profile with higher C-reactive protein, lactate dehydrogenase, ferritin, fibrinogen, prothrombin time, activated thromboplastin time, and INR compared to Wave 1. The gradient boosting model showed an area under the ROC curve of 0.854(sensitivity 86.4%;specificity 61.5%;positive predictive value 73.8%; negative predictive value 78.3%).

CONCLUSIONS

Clinical and laboratory characteristics can be used to estimate the COVID-19 variant regardless of genomic testing availability. This finding has implications for variant-driven treatment protocols and further research.

Association of stay-at-home orders and COVID-19 incidence and mortality in rural and urban United States: a population-based study

OBJECTIVE

We examined the association between stay-at-home order implementation and the incidence of COVID-19 infections and deaths in rural versus urban counties of the United States.

DESIGN

We used an interrupted time-series analysis using a mixed effects zero-inflated Poisson model with random intercept by county and standardised by population to examine the associations between stay-at-home orders and county-level counts of daily new COVID-19 cases and deaths in rural versus urban counties between 22 January 2020 and 10 June 2020. We secondarily examined the association between stay-at-home orders and mobility in rural versus urban counties using Google Community Mobility Reports.

INTERVENTIONS

Issuance of stay-at-home orders.

PRIMARY AND SECONDARY OUTCOME MEASURES

Co-primary outcomes were COVID-19 daily incidence of cases (14-day lagged) and mortality (26-day lagged). Secondary outcome was mobility.

RESULTS

Stay-at-home orders were implemented later (median 30 March 2020 vs 28 March 2020) and were shorter in duration (median 35 vs 54 days) in rural compared with urban counties. Indoor mobility was, on average, 2.6%-6.9% higher in rural than urban counties both during and after stay-at-home orders. Compared with the baseline (pre-stay-at-home) period, the number of new COVID-19 cases increased under stay-at-home by incidence risk ratio (IRR) 1.60 (95% CI, 1.57 to 1.64) in rural and 1.36 (95% CI, 1.30 to 1.42) in urban counties, while the number of new COVID-19 deaths increased by IRR 14.21 (95% CI, 11.02 to 18.34) in rural and IRR 2.93 in urban counties (95% CI, 1.82 to 4.73). For each day under stay-at-home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981 to 0.982) in rural and 0.952 (95% CI, 0.951 to 0.953) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.977 (95% CI, 0.976 to 0.977) in rural counties and 0.935 (95% CI, 0.933 to 0.936) in urban counties. Each day after stay-at-home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994 to 0.995) in rural and 0.997 (95% CI, 0.995 to 0.999) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.969 (95% CI, 0.968 to 0.970) in rural counties and 0.928 (95% CI, 0.926 to 0.929) in urban counties.

CONCLUSION

Stay-at-home orders decreased mobility, slowed the spread of COVID-19 and mitigated COVID-19 mortality, but did so less effectively in rural than in urban counties. This necessitates a critical re-evaluation of how stay-at-home orders are designed, communicated and implemented in rural areas.

Do diets with higher carbon footprints increase the risk of mortality? A population-based simulation study using self-selected diets from the USA

OBJECTIVE

Are diets with a greater environmental impact less healthy? This is a key question for nutrition policy, but previous research does not provide a clear answer. To address this, our objective here was to test whether American diets with the highest carbon footprints predicted greater population-level mortality from diet-related chronic disease than those with the lowest.

DESIGN

Baseline dietary recall data were combined with a database of greenhouse gases emitted in the production of foods to estimate a carbon footprint for each diet. Diets were ranked on their carbon footprints and those in the highest and lowest quintiles were studied here. Preventable Risk Integrated Model (PRIME), an epidemiological modelling software, was used to assess CVD and cancer mortality for a simulated dietary change from the highest to the lowest impact diets. The diet-mortality relationships used by PRIME came from published meta-analyses of randomised controlled trials and prospective cohort studies.

SETTING

USA.

PARTICIPANTS

Baseline diets came from adults (n 12 865) in the nationally representative 2005-2010 National Health and Nutrition Examination Survey.

RESULTS

A simulated change at the population level from the highest to the lowest carbon footprint diets resulted in 23 739 (95 % CI 20 349, 27 065) fewer annual deaths from CVD and cancer. This represents a 1·83 % (95 % CI 1·57 %, 2·08 %) decrease in total deaths. About 95 % of deaths averted were from CVD.

CONCLUSIONS

Diets with the highest carbon footprints were associated with a greater risk of mortality than the lowest, suggesting that dietary guidance could incorporate sustainability information to reinforce health messaging.

Real-world incidence of breakthrough COVID-19 hospitalization after vaccination versus natural infection in a large, local, empaneled primary care population using time-to-event analysis

We followed 106,349 primary care patients for 22,385,309 person-days across 21 calendar months. There were 69 breakthrough COVID-19 hospitalizations: 65/102,613(0.06%) among fully vaccinated, 3/11,047(0.03%) among those previously infected, and 1/7,313(0.01%) among those with both statuses. This data gives primary care providers real-world context regarding breakthrough COVID-19 hospitalization risk.

Impact of the Coronavirus Disease 2019 (COVID-19) Vaccine on Asymptomatic Infection Among Patients Undergoing Preprocedural COVID-19 Molecular Screening

BACKGROUND

Several vaccines are now available under emergency use authorization in the United States and have demonstrated efficacy against symptomatic COVID-19. Vaccine impact on asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is largely unknown.

METHODS

We conducted a retrospective cohort study of consecutive, asymptomatic adult patients (n = 39 156) within a large US healthcare system who underwent 48 333 preprocedural SARS-CoV-2 molecular screening tests between 17 December 2020 and 8 February 2021. The primary exposure of interest was vaccination with ≥1 dose of an mRNA COVID-19 vaccine. The primary outcome was relative risk (RR) of a positive SARS-CoV-2 molecular test among those asymptomatic persons who had received ≥1 dose of vaccine compared with persons who had not received vaccine during the same time period. RR was adjusted for age, sex, race/ethnicity, patient residence relative to the hospital (local vs nonlocal), healthcare system regions, and repeated screenings among patients using mixed-effects log-binomial regression.

RESULTS

Positive molecular tests in asymptomatic individuals were reported in 42 (1.4%) of 3006 tests and 1436 (3.2%) of 45 327 tests performed on vaccinated and unvaccinated patients, respectively (RR, .44; 95% CI, .33-.60; P < .0001). Compared with unvaccinated patients, risk of asymptomatic SARS-CoV-2 infection was lower among those >10 days after the first dose (RR, .21; 95% CI, .12-.37; P < .0001) and >0 days after the second dose (RR, .20; 95% CI, .09-.44; P < .0001) in the adjusted analysis.

CONCLUSIONS

COVID-19 vaccination with an mRNA-based vaccine showed a significant association with reduced risk of asymptomatic SARS-CoV-2 infection as measured during preprocedural molecular screening. Results of this study demonstrate the impact of the vaccines on reduction in asymptomatic infections supplementing the randomized trial results on symptomatic patients.

Coronavirus Disease 2019 Vaccine-Breakthrough Infections Requiring Hospitalization in Mayo Clinic Florida Through August 2021

We characterized coronavirus disease 2019 (COVID-19) breakthrough cases admitted to a single center in Florida. With the emergence of delta variant, an increased number of hospitalizations was seen due to breakthrough infections. These patients were older and more likely to have comorbidities. Preventive measures should be maintained even after vaccination.

Quantifying the Importance of COVID-19 Vaccination to Our Future Outlook

Predictive models have played a critical role in local, national, and international response to the COVID-19 pandemic. In the United States, health care systems and governmental agencies have relied on several models, such as the Institute for Health Metrics and Evaluation, Youyang Gu (YYG), Massachusetts Institute of Technology, and Centers for Disease Control and Prevention ensemble, to predict short- and long-term trends in disease activity. The Mayo Clinic Bayesian SIR model, recently made publicly available, has informed Mayo Clinic practice leadership at all sites across the United States and has been shared with Minnesota governmental leadership to help inform critical decisions during the past year. One key to the accuracy of the Mayo Clinic model is its ability to adapt to the constantly changing dynamics of the pandemic and uncertainties of human behavior, such as changes in the rate of contact among the population over time and by geographic location and now new virus variants. The Mayo Clinic model can also be used to forecast COVID-19 trends in different hypothetical worlds in which no vaccine is available, vaccinations are no longer being accepted from this point forward, and 75% of the population is already vaccinated. Surveys indicate that half of American adults are hesitant to receive a COVID-19 vaccine, and lack of understanding of the benefits of vaccination is an important barrier to use. The focus of this paper is to illustrate the stark contrast between these 3 scenarios and to demonstrate, mathematically, the benefit of high vaccine uptake on the future course of the pandemic.

Accuracy of Computable Phenotyping Approaches for SARS-CoV-2 Infection and COVID-19 Hospitalizations from the Electronic Health Record

Objective:

Real-world data have been critical for rapid-knowledge generation throughout the COVID-19 pandemic. To ensure high-quality results are delivered to guide clinical decision making and the public health response, as well as characterize the response to interventions, it is essential to establish the accuracy of COVID-19 case definitions derived from administrative data to identify infections and hospitalizations.

Methods:

Electronic Health Record (EHR) data were obtained from the clinical data warehouse of the Yale New Haven Health System (Yale, primary site) and 3 hospital systems of the Mayo Clinic (validation site). Detailed characteristics on demographics, diagnoses, and laboratory results were obtained for all patients with either a positive SARS-CoV-2 PCR or antigen test or ICD-10 diagnosis of COVID-19 (U07.1) between April 1, 2020 and March 1, 2021. Various computable phenotype definitions were evaluated for their accuracy to identify SARS-CoV-2 infection and COVID-19 hospitalizations.

Results:

Of the 69,423 individuals with either a diagnosis code or a laboratory diagnosis of a SARS-CoV-2 infection at Yale, 61,023 had a principal or a secondary diagnosis code for COVID-19 and 50,355 had a positive SARS-CoV-2 test. Among those with a positive laboratory test, 38,506 (76.5%) and 3449 (6.8%) had a principal and secondary diagnosis code of COVID-19, respectively, while 8400 (16.7%) had no COVID-19 diagnosis. Moreover, of the 61,023 patients with a COVID-19 diagnosis code, 19,068 (31.2%) did not have a positive laboratory test for SARS-CoV-2 in the EHR. Of the 20 cases randomly sampled from this latter group for manual review, all had a COVID-19 diagnosis code related to asymptomatic testing with negative subsequent test results. The positive predictive value (precision) and sensitivity (recall) of a COVID-19 diagnosis in the medical record for a documented positive SARS-CoV-2 test were 68.8% and 83.3%, respectively. Among 5,109 patients who were hospitalized with a principal diagnosis of COVID-19, 4843 (94.8%) had a positive SARS-CoV-2 test within the 2 weeks preceding hospital admission or during hospitalization. In addition, 789 hospitalizations had a secondary diagnosis of COVID-19, of which 446 (56.5%) had a principal diagnosis consistent with severe clinical manifestation of COVID-19 (e.g., sepsis or respiratory failure). Compared with the cohort that had a principal diagnosis of COVID-19, those with a secondary diagnosis had a more than 2-fold higher in-hospital mortality rate (13.2% vs 28.0%, P<0.001). In the validation sample at Mayo Clinic, diagnosis codes more consistently identified SARS-CoV-2 infection (precision of 95%) but had lower recall (63.5%) with substantial variation across the 3 Mayo Clinic sites. Similar to Yale, diagnosis codes consistently identified COVID-19 hospitalizations at Mayo, with hospitalizations defined by secondary diagnosis code with 2-fold higher in-hospital mortality compared to those with a primary diagnosis of COVID-19.

Conclusions:

COVID-19 diagnosis codes misclassified the SARS-CoV-2 infection status of many people, with implications for clinical research and epidemiological surveillance. Moreover, the codes had different performance across two academic health systems and identified groups with different risks of mortality. Real-world data from the EHR can be used to in conjunction with diagnosis codes to improve the identification of people infected with SARS-CoV-2.

Deployment of an Interdisciplinary Predictive Analytics Task Force to Inform Hospital Operational Decision-Making During the COVID-19 Pandemic

In March 2020, our institution developed an interdisciplinary predictive analytics task force to provide coronavirus disease 2019 (COVID-19) hospital census forecasting to help clinical leaders understand the potential impacts on hospital operations. As the situation unfolded into a pandemic, our task force provided predictive insights through a structured set of visualizations and key messages that have helped the practice to anticipate and react to changing operational needs and opportunities. The framework shared here for the deployment of a COVID-19 predictive analytics task force could be adapted for effective implementation at other institutions to provide evidence-based messaging for operational decision-making. For hospitals without such a structure, immediate consideration may be warranted in light of the devastating COVID-19 third-wave which has arrived for winter 2020-2021.

Lower Survival After Coronary Artery Bypass in Patients Who Had Atrial Fibrillation Missed by Widely Used Definitions

Objective

To investigate the impact of limiting the definition of post-coronary artery bypass graft (CABG) atrial fibrillation (AF) to AF/flutter requiring treatment-as in the Society of Thoracic Surgeons' (STS) database- on the association with survival.

Patients and Methods

We assessed in-hospital incidence of post-CABG AF in 7110 consecutive isolated patients with CABG without preoperative AF at 4 hospitals (January 1, 2004 to December 31, 2010). Patients with ≥1 episode of post-CABG AF detected via continuous in-hospital electrocardiogram (ECG)/telemetry monitoring documented by physicians were assigned to the following: Group 1, identified as having post-CABG AF in STS data and Group 2, not identified as having post-CABG AF in STS data. Patients without documented post-CABG AF constituted Group 3. Survival was compared via a Cox model, adjusted for STS risk of mortality and accounting for site differences.

Results

Over 7 years' follow-up, 16.0% (295 of 1841) of Group 1, 18.7% (79 of 422) of Group 2, and 7.9% (382 of 4847) of Group 3 died. Group 2 had a significantly greater adjusted risk of death than both Group 1 (hazard ratio [HR]: 1.16; 95% confidence interval [CI], 1.02 to 1.33) and Group 3 (HR: 1.94; 95% CI, 1.69 to 2.22).

Conclusions

The statistically significant 16% higher risk of death for patients with AF post-CABG missed vs captured in STS data suggests treatment and postdischarge management should be investigated for differences. The historical misclassification of "missed" patients as experiencing no AF in the STS data weakens the ability to observe differences in risk between patients with and without post-CABG AF. Therefore, STS data should not be used for research examining post-CABG AF.

Association of Do-Not-Resuscitate Patient Case Mix With Publicly Reported Risk-Standardized Hospital Mortality and Readmission Rates

IMPORTANCE

The Centers for Medicare and Medicaid Services's (CMS's) 30-day risk-standardized mortality rate (RSMR) and risk-standardized readmission rate (RSRR) models do not adjust for do-not-resuscitate (DNR) status of hospitalized patients and may bias Hospital Readmissions Reduction Program (HRRP) financial penalties and Overall Hospital Quality Star Ratings.

OBJECTIVE

To identify the association between hospital-level DNR prevalence and condition-specific 30-day RSMR and RSRR and the implications of this association for HRRP financial penalty.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study obtained patient-level data from the Medicare Limited Data Set Inpatient Standard Analytical File and hospital-level data from the CMS Hospital Compare website for all consecutive Medicare inpatient encounters from July 1, 2015, to June 30, 2018, in 4484 US hospitals. Hospitalized patients had a principal diagnosis of acute myocardial infarction (AMI), heart failure (HF), stroke, pneumonia, or chronic obstructive pulmonary disease (COPD). Incoming acute care transfers, discharges against medical advice, and patients coming from or discharged to hospice were among those excluded from the analysis.

EXPOSURES

Present-on-admission (POA) DNR status was defined as an International Classification of Diseases, Ninth Revision diagnosis code of V49.86 (before October 1, 2015) or as an International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis code of Z66 (beginning October 1, 2015). Hospital-level prevalence of POA DNR status was calculated for each of the 5 conditions.

MAIN OUTCOMES AND MEASURES

Hospital-level 30-day RSMRs and RSRRs for 5 condition-specific cohorts (mortality cohorts: AMI, HF, stroke, pneumonia, and COPD; readmission cohorts: AMI, HF, pneumonia, and COPD) and HRRP financial penalty status (yes or no).

RESULTS

Included in the study were 4 884 237 inpatient encounters across condition-specific 30-day mortality cohorts (patient mean [SD] age, 78.8 [8.5] years; 2 608 182 women [53.4%]) and 4 450 378 inpatient encounters across condition-specific 30-day readmission cohorts (patient mean [SD] age, 78.6 [8.5] years; 2 349 799 women [52.8%]). Hospital-level median (interquartile range [IQR]) prevalence of POA DNR status in the mortality cohorts varied: 11% (7%-16%) for AMI, 13% (7%-23%) for HF, 14% (9%-22%) for stroke, 17% (9%-26%) for pneumonia, and 10% (5%-18%) for COPD. For the readmission cohorts, the hospital-level median (IQR) POA DNR prevalence was 9% (6%-15%) for AMI, 12% (6%-22%) for HF, 16% (8%-24%) for pneumonia, and 9% (4%-17%) for COPD. The 30-day RSMRs were significantly higher for hospitals in the highest quintiles vs the lowest quintiles of DNR prevalence (eg, AMI: 12.9 [95% CI, 12.8-13.1] vs 12.5 [95% CI, 12.4-12.7]; P < .001). The inverse was true among the readmission cohorts, with the highest quintiles of DNR prevalence exhibiting the lowest RSRRs (eg, AMI: 15.3 [95% CI, 15.1-15.5] vs 15.9 [95% CI, 15.7-16.0]; P < .001). A 1% absolute increase in risk-adjusted hospital-level DNR prevalence was associated with greater odds of avoiding HRRP financial penalty (odds ratio, 1.06; 95% CI, 1.04-1.08; P < .001).

CONCLUSIONS AND RELEVANCE

This cross-sectional study found that the lack of adjustment in CMS 30-day RSMR and RSRR models for POA DNR status of hospitalized patients may be associated with biased readmission penalization and hospital-level performance.

Postcoronary Artery Bypass Graft Atrial Fibrillation Event Count and Survival: Differences by Sex

BACKGROUND

New-onset atrial fibrillation (AF) after coronary artery bypass graft surgery (CABG) is associated with poor outcomes, but data on the effects of its characteristics are lacking and conflicting. We examined the effect number of post-CABG AF events has on long-term mortality risk, and whether this is sex dependent.

METHODS

Routinely collected Society of Thoracic Surgeons (STS) data were supplemented with details on new-onset post-CABG AF (detected in-hospital by continuous electrocardiogram/telemetry monitoring) and long-term survival for 9203 consecutive patients with isolated-CABG (2002-2010). With the use of Cox regression, we determined the propensity-adjusted (STS-recognized risk factors) effect of number of AF events on survival, testing for effect modification by sex and controlling for AF duration.

RESULTS

AF occurred in 739 women (29.4%) and 2157 men (32.3%) (P < .001). Adjusted results showed 2 or more AF events significantly (P < .001) increased 5-year mortality risk, independently of total AF duration. However, mortality risk differed between the sexes (P < .001): women with 2 AF episodes had the greatest increase (hazard ratio [HR] = 2.98; 95% confidence interval [CI], 1.43-4.83; versus women without AF), followed by women and men with 4 or more AF events (HR = 2.76 [95% CI, 1.27-4.55] and HR = 2.73 [95% CI, 2.30-3.19], respectively). A single post-CABG AF episode was not associated with increased mortality risk.

CONCLUSIONS

Both men and women who experienced 2 or more post-CABG AF episodes showed increased risk of 5-year mortality, independent of total AF duration. Although men's risk increased as the number of AF events increased, women's risk peaked at 2 AF events. Future research needs to determine whether this divergence stems from differences in treatment/management or underlying biology.

Preoperative β-Blockers as a Coronary Surgery Quality Metric: The Lack of Evidence of Efficacy

BACKGROUND

Two quality measures used in public reporting and value-based payment programs require β-blockers be administered less than 24 hours before isolated coronary artery bypass graft surgery to prevent atrial fibrillation and mortality. Questions have arisen about continued use of these measures.

METHODS

We conducted a systematic search for randomized controlled trials (RCTs) examining the impact of preoperative β-blockers on atrial fibrillation or mortality after isolated coronary artery bypass graft surgery to determine what evidence of efficacy supports the measures.

RESULTS

We identified 11 RCTs. All continued β-blockers postoperatively, making it unfeasible to separate the benefits of preoperative vs postoperative administration. Meta-analysis was precluded by methodologic variation in β-blocker utilized, timing and dosage, and supplemental and comparison treatments. Of the eight comparisons of β-blockers/β-blocker plus digoxin versus placebo (n = 826 patients), six showed significant reductions in atrial fibrillation/supraventricular arrhythmias. Of the three comparisons (n = 444) of β-blockers versus amiodarone, two found no significant difference in atrial fibrillation; the third showed significantly lower incidence with amiodarone. One RCT compared β-blocker plus amiodarone versus each of those drugs separately; the combination reduced atrial fibrillation significantly better than the β-blocker alone, but not amiodarone alone. Seven RCTs reported short-term mortality, but this outcome was too rare and the sample sizes too small to provide any meaningful comparisons.

CONCLUSIONS

Existing RCT evidence does not support the structure of quality measures that require β-blocker administration specifically within 24 hours before coronary artery bypass graft surgery to prevent postoperative atrial fibrillation or short-term mortality. Quality measures should be revised to align with the evidence, and further studies conducted to determine optimal timing and method of prophylaxis.

Life course trajectories of cardiovascular risk: Impact on atherosclerotic and metabolic indicators

BACKGROUND AND AIMS

In this analysis, we estimated population-level trajectory groups of life course cardiovascular risk to explore their impact on mid-life atherosclerotic and metabolic outcomes.

METHODS

This prospective study followed n = 1269 Bogalusa Heart participants, each with at least 4 study visits from childhood in 1973 through adulthood in 2016. We used discrete mixture modeling to determine trajectories of cardiovascular risk percentiles from childhood to adulthood. Outcomes included mid-life subclinical atherosclerotic measures [(carotid intima-media thickness (cIMT), pulse wave velocity (PWV)], metabolic indicators [(diabetes and body mass index (BMI)], and short physical performance battery (SPPB).

RESULTS

Between the mean ages of 9.6-48.3 years, we estimated five distinct trajectory groups of life course cardiovascular risk (High-Low, High-High, Mid-Low, Low-Low, and Low-High). Adult metabolic and vascular outcomes were significantly determined by life course cardiovascular risk trajectory groups (all p < 0.01). Those in the High-Low group had lower risks of diabetes (20% vs. 28%, respectively; p = .12) and lower BMIs (32.4 kg/m²vs. 34.6 kg/m²; p = .06) than those who remained at high risk (High-High) throughout life. However, the High-Low group had better cIMT (0.89 mm vs. 1.05 mm; p < .0001) and PWV (7.8 m/s vs. 8.2 m/s; p = .03) than the High-High group. For all outcomes, those in the Low-Low group fared best.

CONCLUSIONS

We found considerable movement between low- and high-relative cardiovascular risk strata over the life course. Children who improved their relative cardiovascular risk over the life course achieved better mid-life atherosclerotic health despite maintaining relatively poor metabolic health through adulthood.

Categorizing body mass index biases assessment of the association with post-coronary artery bypass graft mortality

OBJECTIVES

The high prevalence of obesity makes accurately estimating the impact of anthropometric measures on cardiac surgery outcomes critical. The Society of Thoracic Surgeons coronary artery bypass graft (CABG) surgery risk model includes body surface area (as a continuous variable, using spline functions), but most studies apply various categorizations of body mass index (BMI)-contributing to the contradictory published findings. We assessed the association between BMI (modelled as a continuous variable without assumptions of linearity) and CABG operative mortality and examined the impact of applying previous studies' BMI modelling strategies.

METHODS

We identified 25 studies investigating the BMI-operative mortality association: 22 categorized BMI, 2 as a linear continuous variable,1 used spline functions. Our cohort of 12 715 consecutive patients underwent isolated CABG at 32 cardiac surgery programmes in North Texas from 1 January 2008-31 December 2012. BMI was modelled using restricted cubic spline functions in a propensity-adjusted model (controlling for Society of Thoracic Surgeons risk factors) estimating operative mortality. The analysis was repeated using each categorization identified and modelling BMI as a linear continuous variable.

RESULTS

BMI (modelled with a restricted cubic spline) was significantly associated with operative mortality (P < 0.0001). Risk was lowest for BMI near 30 kg/m2 and highest below 20 kg/m2 and above 40 kg/m2. No categorization, nor the linear continuous model, fully captured this association.

CONCLUSIONS

BMI is strongly associated with CABG operative mortality. Categorizing BMI (or assuming a linear relationship) heavily biases estimates of its association with post-CABG mortality. In general, smoothing techniques should be used for all continuous risk factors to avoid bias.

Differential sex effects of systolic blood pressure and low-density lipoprotein cholesterol on type 2 diabetes: Life course data from the Bogalusa Heart Study

BACKGROUND

There may be sex-specific cardiometabolic mechanisms early in life that affect the development of type 2 diabetes mellitus (T2DM) through mid-adulthood. However, few studies have examined whether early life course interactions between cardiometabolic risk factors and sex are associated with incident T2DM.

METHODS

This study followed 7725 children (3834 [49.6%] females, 3891 [50.4%] males) from the Bogalusa Heart Study through mid-adulthood to examine whether low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), body mass index (BMI), or systolic blood pressure (SBP) differentially affect the risk of T2DM for females versus males. Potential sex interactions were tested after adjusting for age, race, triglycerides, smoking, follow-up time, puberty stage, use of birth control, and enrollment year.

RESULTS

Mean (± SD) age at baseline was 9.4 ± 3.5 years. There were 176 cases of T2DM (cumulative incidence = 2.3%) during a median follow-up of 9.1 years. In females versus males, LDL-C and SBP were differentially associated with T2DM (P ≤ 0.001 and P = 0.017, respectively). The relationships of BMI and HDL-C with T2DM were non-differential between females and males (P = 0.79 and P = 0.27, respectively).

CONCLUSIONS

This study is the first to show evidence of sex-specific differential effects of LDL-C and SBP on the risk of T2DM from childhood to adulthood. Greater LDL-C places girls at disproportionally higher risk of T2DM as women, whereas greater SBP differentially exposes boys to a greater risk of T2DM as men. Additional studies within existing child cohorts are needed to confirm and investigate the mechanisms underlying these differential effects.

Insomnia with objective short sleep duration and risk of incident cardiovascular disease and all-cause mortality: Sleep Heart Health Study

Study Objectives

To quantify the association between insomnia or poor sleep with objective short sleep duration and incident cardiovascular disease (CVD) and mortality in the general population.

Methods

We conducted a time-to-event analysis of Sleep Heart Health Study data. Questionnaires and at-home polysomnography (PSG) were performed between 1994 and 1998. Participants were followed for a median of 11.4 years (Q1-Q3, 8.8-12.4 years) until death or last contact. The primary exposure was insomnia or poor sleep with short sleep defined as follows: difficulty falling asleep, difficulty returning to sleep, early morning awakenings, or sleeping pill use, 16-30 nights per month; and total sleep of <6 hr on PSG. We used proportional hazard models to estimate the association between insomnia or poor sleep with short sleep and CVD, as well as all-cause mortality.

Results

Among 4994 participants (mean age: 64.0 ± 11.1 years), 14.1 per cent reported insomnia or poor sleep, of which 50.3 per cent slept <6 hr. Among 4437 CVD-free participants at baseline, we observed 818 incident CVD events. After propensity adjustment, there was a 29 per cent higher risk of incident CVD in the insomnia or poor sleep with short sleep group compared with the reference group (HR: 1.29, 95% CI: 1.00, 1.66), but neither the insomnia or poor sleep only nor short sleep only groups were associated with higher incident CVD. Insomnia or poor sleep with objective short sleep was not associated with all-cause mortality (HR: 1.07, 95% CI: 0.86, 1.33).

Conclusions

Insomnia or poor sleep with PSG-short sleep was associated with higher risk of incident CVD. Future studies should evaluate the impact of interventions to improve insomnia with PSG-short sleep on CVD.

Retinoblastoma in the United States: A 40-Year Incidence and Survival Analysis

PURPOSE

To determine the incidence of retinoblastoma in the United States from 1973 to 2012 (40 years) and characterize the 5-year overall survival rate of the included patients.

METHODS

Cases of retinoblastoma were derived from the Surveillance, Epidemiology, and End Results (SEER) Program (National Cancer Institute, Rockville, MD). Incidence rates were calculated using U.S. Census Bureau data as the standard population, and trends over time were determined using the chi-square test. Hazard ratios with a 95% confidence interval (CI) were estimated for variables associated with mortality using Cox regression models. Survival rates were calculated using the Kaplan-Meier method and compared among different clinical and demographic categories.

RESULTS

A total of 879 cases of retinoblastoma were derived from the SEER databases. The annual incidence rates of retinoblastoma for a period of 40 years were 12.14 (95% CI: 11.32 to 12.96) cases per 1 million children 4 years or younger and 0.49 (95% CI: 0.36 to 0.65) cases per 1 million children between the ages of 5 and 9 years. There was no significant trend for children 4 years or younger (P = .6324) or between the ages of 5 and 9 years (P = .7695). The 5-year overall survival rates were 97.6%, 92.7%, 91.1%, and 96.4% for children diagnosed at the first, second, third, and after the third year of life, respectively (P = .0136). The 5-year overall survival rates were 92.5% for bilateral and 96.3% for unilateral cases (P = .0116). The 5-year overall survival rates were 90.8%, 92.5%, 97.6%, 97.3% for increasing time intervals (1973 to 1979, 1980 to 1989, 1990 to 1999, and 2000 to 2012, respectively; P = .0017).

CONCLUSIONS

The incidence rate of retinoblastoma in the United States has remained stable for the past 40 years. Survival rate analysis indicates a significant effect of laterality of tumor, age at diagnosis, and decade of diagnosis. [J Pediatr Ophthalmol Strabismus. 2018;55(3):182-188.].

Novel renal biomarker evaluation for early detection of acute kidney injury after transcatheter aortic valve implantation

Acute kidney injury (AKI) following transcatheter aortic valve implantation (TAVI) is associated with increased morbidity and mortality. The biomarkers neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and interleukin-18 (IL-18) are predictive of AKI after cardiac surgery, but there is little data regarding these biomarkers after TAVI. We evaluated the associations between NGAL, KIM-1, and IL-18 levels and the incidence and severity of AKI and changes in serum creatinine after TAVI. This was a prospective pilot study of 66 TAVI cases. Urinary biomarkers were measured at baseline and at 2, 4, and 12 hours after TAVI. Demographics, procedural features, and renal function until discharge were compared between patients with and without subsequent AKI. Seventeen patients (25.8%) developed AKI postoperatively (stage 1, n = 14; stage 2, n = 1; stage 3, n = 2). There were no significant differences in unadjusted mean NGAL, KIM-1, and IL-18 levels between patients with and without AKI at 2, 4, and 12 hours following surgery. After adjusting for the Society of Thoracic Surgeons risk of mortality, this study of three urinary biomarkers showed no association with AKI or creatinine after TAVI. Ongoing efforts to predict and modify the risk of AKI after TAVI remain challenging.

Associations between Hunter Type A/B Personality and Cardiovascular Risk Factors from Adolescence through Young Adulthood

PURPOSE

Type A personality, characterized by action-oriented tendencies, has been linked to cardiovascular disease in middle-aged and elderly adults. Alternatively, limited research has tested whether personality type A/B and cardiovascular (CVD) risk are linked prior to adulthood. Therefore, we used the Hunter-Wolf A/B personality score to determine whether personality type A/B is associated with traditional CVD risk factors during adolescence, and more importantly if personality type, or its individual type A components, are associated with cardiovascular risk through young adulthood. This study is the first to assess personality type A/B on a continuous spectrum with regard to its relationship with cardiovascular disease risk, as well as the first to examine this association in a biracial, adolescent population.

METHODS

Subjects (3396) from the Bogalusa Heart Study were surveyed from 1984 to 1986, and multivariable regression was used to test adjusted, cross-sectional associations between personality type A/B, as determined by Hunter-Wolf A/B personality questionnaire, and CVD risk factors during adolescence. To test whether associations existed longitudinally, subjects were followed through 2007, and general estimating equation (GEE) models were used to examine the associations of personality type A/B with CVD risk factors, as well as with Framingham risk score as a global score of CVD risk. The component traits of type A personality (leadership, hard-driving, eagerness-energy, and impatience-aggression) were tested individually to determine their independent, longitudinal associations with global CVD risk.

RESULTS

Baseline mean (SD) age was 15.9(5.2). Mean( SD) Hunter-Wolf score in was 96.9 (11.6). After adjustment, more type A Hunter-Wolf scores were cross-sectionally associated with lower alcohol consumption (p = 0.03), female gender (p < 0.0001), and black race (p < 0.0001) in adolescence. After follow-up (median = 11 years), personality type A/B as the continuous Hunter-Wolf score was non-linearly associated with young adult BMI (p = 0.01), fasting blood glucose (p < 0.01), and Framingham score (p = 0.05). Of the type A components, leadership and hard-driving were non-linearly associated with Framingham risk at follow-up (both p < 0.0001).

CONCLUSIONS

Adolescent personality type A is associated with female gender and black race. Generally, type A children have higher CVD risk during young adulthood, though this relationship is non-linear. Additionally, adolescents exhibiting strong leadership-oriented personality traits have worse cardiovascular risk profiles in early adulthood, whereas hard-driving adolescent personalities are protective of young adult CVD risk. Our results warrant consideration of personality as a continuous, non-categorical, trait in studies of cardiovascular disease.

Cardiovascular Risk and the American Dream: Life Course Observations From the BHS (Bogalusa Heart Study)

BACKGROUND

Economic literature shows that a child's future earnings are predictably influenced by parental income, providing an index of "socioeconomic mobility," or the ability of a person to move towards a higher socioeconomic status from childhood to adulthood. We adapted this economic paradigm to examine cardiovascular risk mobility (CRM), or whether there is life course mobility in relative cardiovascular risk.

METHODS AND RESULTS

Participants from the BHS (Bogalusa Heart Study) with 1 childhood and 1 adult visit from 1973 to 2016 (n=7624) were considered. We defined population-level CRM as the rank-rank slope (β) from the regression of adult cardiovascular disease (CVD) risk percentile ranking onto childhood CVD risk percentile ranking (β=0 represents complete mobility; β=1 represents no mobility). After defining and measuring relative CRM, we assessed its correlation with absolute cardiovascular health using the American Heart Association's Ideal Cardiovascular Health metrics. Overall, there was substantial mobility, with black participants having marginally better CRM than whites (β_black=0.10 [95% confidence interval, 0.05-0.15]; β_white=0.18 [95% confidence interval, 0.14-0.22]; P=0.01). Having high relative CVD risk at an earlier age significantly reduced CRM (β_age×slope=-0.02; 95% confidence interval, -0.03 to -0.01; P<0.001). Relative CRM was strongly correlated with life course changes in Ideal Cardiovascular Health sum (r=0.62; 95% confidence interval, 0.60-0.65).

CONCLUSIONS

Results from this novel application of an economic mobility index to cardiovascular epidemiology indicated substantial CRM, supporting the paradigm that life course CVD risk is highly modifiable. High CRM implies that the children with the best relative CVD profiles may only maintain a slim advantage over their peers into adulthood.

Epidemiology of new-onset atrial fibrillation following coronary artery bypass graft surgery

OBJECTIVES

Postoperative atrial fibrillation (AF) following coronary artery bypass graft surgery (CABG) is significantly associated with reduced survival, but poor characterisation and inconsistent definitions present barriers to developing effective prophylaxis and management. We sought to address this knowledge gap.

METHODS

From 2002 to 2010, 11 239 consecutive patients without AF underwent isolated CABG at five sites. Clinical data collected for the Society of Thoracic Surgeons (STS) Database were augmented with details on AF detected via continuous in-hospital ECG/telemetry monitoring to assess new-onset post-CABG AF (adjusted for STS risk of mortality); time to first AF; durations of first and longest AF episodes; total in-hospital time in AF; number of in-hospital AF episodes; operative mortality; stroke; discharge in AF; and length of stay (LOS).

RESULTS

Unadjusted incidence of new-onset post-CABG AF was 29.5%. Risk-adjusted incidence was 33.1% and varied little over time (P=0.139). Among 3312 patients with post-CABG AF, adjusted median time to first AF was 52 (IQR: 48-55) hours; mean (SD) duration of first and longest events were 7.2 (5.3,9.1) and 13.1 (10.4,15.9) hours, respectively, and adjusted median total time in AF was 22 (IQR: 18-26) hours. Adjusted rates of operative mortality, stroke and discharge in AF did not vary significantly over time (P=0.156, P=0.965 and P=0.347, respectively). LOS varied (P=0.035), but in no discernible pattern.

CONCLUSIONS

Each year, ~800 000 people undergo CABG worldwide; >264 000 will develop post-CABG AF. Onset is typically 2-3 days post-CABG and episodes last, on average, several hours. Effective prophylaxis and management is urgently needed to reduce associated risks of adverse outcomes.

Efficacy and effectiveness of on- versus off-pump coronary artery bypass grafting: A meta-analysis of mortality and survival

BACKGROUND

Despite many studies comparing on- versus off-pump coronary artery bypass graft (CABG), there is no consensus as to whether one of these techniques offers patients better outcomes.

METHODS

We searched PubMed from inception to June 30, 2015, and identified additional studies from bibliographies of meta-analyses and reviews. We identified 42 randomized controlled trials (RCTs) and 31 rigorously adjusted observational studies (controlling for the Society of Thoracic Surgeons-recognized risk factors for mortality) reporting mortality for off-pump versus on-pump CABG at specified time points. Trial data were extracted independently by 2 researchers using a standardized form. Differences in probability of mortality (DPM) were estimated for the RCTs and observational studies separately and combined, for time points ranging from 30 days to 10 years.

RESULTS

RCT-only data showed no significant differences at any time point, whereas observational-only data and the combined analysis showed short-term mortality favored off-pump CABG (n = 1.2 million patients; 36 RCTs, 26 observational studies; DPM [95% confidence interval (CI)], -44.8% [-45.4%, -43.8%]) but that at 5 years it was associated with significantly greater mortality (n = 60,405 patients; 3 RCTs, 5 observational studies; DPM [95% CI], 10.0% [5.0%, 15.0%]). At 10 years, only observational data were available, and off-pump CABG showed significantly greater mortality (DPM [95% CI], 14.0% [11.0%, 17.0%]).

CONCLUSIONS

Evidence from RCTs showed no differences between the techniques, whereas rigorously adjusted observational studies (with >1.1 million patients) and the combined analysis indicated that off-pump CABG offers lower short-term mortality but poorer long-term survival. These results suggest that, in real-world settings, greater operative safety with off-pump CABG comes at the expense of lasting survival gains.

Underestimation of the incidence of new-onset post-coronary artery bypass grafting atrial fibrillation and its impact on 30-day mortality

OBJECTIVE

Inconsistent definitions of atrial fibrillation after coronary artery bypass grafting have caused uncertainty about its incidence and risk. We examined the extent to which limiting the definition to post-coronary artery bypass grafting atrial fibrillation events requiring treatment underestimates its incidence and impact on 30-day mortality.

METHODS

We assessed in-hospital atrial fibrillation and 30-day mortality in 9268 consecutive patients without preoperative atrial fibrillation who underwent isolated coronary artery bypass grafting at 5 US hospitals (2004-2010). Patients who experienced 1 or more episode of post-coronary artery bypass grafting atrial fibrillation detected via continuous in-hospital electrocardiogram/telemetry monitoring were divided into those for whom Society of Thoracic Surgeons data (applying the definition "atrial fibrillation/flutter requiring treatment") also indicated atrial fibrillation versus those for whom it did not. Risk-adjusted 30-day mortality was compared between these 2 groups and with patients without post-coronary artery bypass grafting atrial fibrillation.

RESULTS

Risk-adjusted incidence of post-coronary artery bypass grafting atrial fibrillation incidence was 33.4% (27.0% recorded in Society of Thoracic Surgeons data, 6.4% missed). Patients with post-coronary artery bypass grafting atrial fibrillation missed by Society of Thoracic Surgeons data had a significantly greater risk of 30-day mortality (odds ratio, 2.08, 95% confidence interval, 1.17-3.69) than those captured. By applying the significant underestimation of post-coronary artery bypass grafting atrial fibrillation incidence we observed (odds ratio [Society of Thoracic Surgeons vs missed], 0.78; 95% confidence interval, 0.72-0.83) to the approximately 150,000 patients undergoing isolated coronary artery bypass grafting in the United States each year estimates this increased risk of mortality is carried by 9600 patients (95% confidence interval, 9420-9780) annually.

CONCLUSIONS

Defining post-coronary artery bypass grafting atrial fibrillation as episodes requiring treatment significantly underestimates incidence and misses patients at a significantly increased risk for mortality. Further research is needed to determine whether this increased risk carries over into long-term outcomes and whether it is mediated by differences in treatment and management.

Utility of existing diabetes risk prediction tools for young black and white adults: Evidence from the Bogalusa Heart Study

AIMS

To evaluate several adult diabetes risk calculation tools for predicting the development of incident diabetes and pre-diabetes in a bi-racial, young adult population.

METHODS

Surveys beginning in young adulthood (baseline age ≥18) and continuing across multiple decades for 2122 participants of the Bogalusa Heart Study were used to test the associations of five well-known adult diabetes risk scores with incident diabetes and pre-diabetes using separate Cox models for each risk score. Racial differences were tested within each model. Predictive utility and discrimination were determined for each risk score using the Net Reclassification Index (NRI) and Harrell's c-statistic.

RESULTS

All risk scores were strongly associated (p<.0001) with incident diabetes and pre-diabetes. The Wilson model indicated greater risk of diabetes for blacks versus whites with equivalent risk scores (HR=1.59; 95% CI 1.11-2.28; p=.01). C-statistics for the diabetes risk models ranged from 0.79 to 0.83. Non-event NRIs indicated high specificity (non-event NRIs: 76%-88%), but poor sensitivity (event NRIs: -23% to -3%).

CONCLUSIONS

Five diabetes risk scores established in middle-aged, racially homogenous adult populations are generally applicable to younger adults with good specificity but poor sensitivity. The addition of race to these models did not result in greater predictive capabilities. A more sensitive risk score to predict diabetes in younger adults is needed.

Sex Differences in the Epidemiology of New-Onset In-Hospital Post-Coronary Artery Bypass Graft Surgery Atrial Fibrillation: A Large Multicenter Study

BACKGROUND

New-onset atrial fibrillation (AF) after coronary artery bypass graft surgery (CABG) is associated with increased morbidity and poorer long-term survival. Although many studies show differences in outcome in women versus men after CABG, little is known about the sex-specific incidence and characteristics of post-CABG AF.

METHODS AND RESULTS

Overall, 11 236 consecutive patients without preoperative AF underwent isolated CABG from 2002 to 2010 at 4 US academic medical centers and 1 high-volume specialty cardiac hospital. Data routinely collected for the Society of Thoracic Surgeons database were augmented with details on new-onset post-CABG AF events detected via continuous in-hospital ECG/telemetry monitoring. Unadjusted incidence of post-CABG AF was 29.5% (3312/11 236) overall, 30.2% (2485/8214) in men, and 27.4% (827/3022) in women. After adjustment for Society of Thoracic Surgeons-recognized risk factors, women had significantly lower risk for post-CABG AF (odds ratio [95% confidence interval]=0.75 [0.64-0.89]), shorter first, longest, and total duration of AF episodes (mean difference [95% confidence interval]=-2.7 [-4.7 to -0.8] hours; -4.1 [-6.9 to -1.2] hours; -2.4 [-2.5 to -2.3] hours, respectively). At 48 hours, AF-free probabilities were 77% for women and 72% for men (P<0.001). Number of episodes (P=0.18), operative mortality (P=0.048), stroke (P=0.126), and discharge in AF (P=0.234) did not differ significantly by sex.

CONCLUSIONS

These novel data on sex-specific characteristics of new-onset AF after isolated CABG show that women had lower adjusted risk for post-CABG AF and experienced shorter episodes. Investigation of sex-specific impacts on outcomes is needed to identify optimal strategies for prevention and management to ensure all patients achieve the best possible outcomes.