Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association

Benjamin, Emelia J.; Blaha, Michael J.; Chiuve, Stephanie E; Cushman, Mary; Das, Sandeep R; Deo, Rajat; Ferranti, Sarah D. de; Floyd, James S.; Fornage, Myriam; Gillespie, Cathleen; Isasi, Carmen R.; Jim'nez, Monik C.; Jordan, Lori C.; Judd, Suzanne E.; Lackland, Daniel T.; Lichtman, Judith H.; Lisabeth, Lynda D.; Liu, Simin; Longenecker, Chris T.; Mackey, Rachel H.; Matsushita, Kunihiro; Mozaffarian, Dariush; Mussolino, Michael E.; Nasir, Khurram; Neumar, Robert W.; Palaniappan, Latha; Pandey, Dilip K.; Thiagarajan, Ravi R.; Reeves, Mathew J.; Ritchey, Matthew D.; Rodríguez, Carlos J.; Roth, Gregory A.; Rosamond, Wayne D.; Sasson, Comilla; Towfghi, Amytis; Tsao, Connie W.; Turner, Melanie B.; Virani, Salim S.; Voeks, Jenifer H.; Willey, Joshua Z.; Wilkins, John T.; Wu, Jason; Alger, Heather M.; Wong, Sally S.; Muntner, Paul

doi:10.1161/cir.0000000000000485

Cited by 7,718 publications

(5,410 citation statements)

References 2,027 publications

(1,464 reference statements)

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“…Using the method described by Elmer et al,26 we estimated the potential impact of addressing 3 of the identified themes that have been studied previously in out‐of‐hospital cardiac arrest (OHCA): telecommunicator CPR, improved neuroprognostication, and optimizing postarrest care (Table 3). Nationally, there are ≈167 000 emergency medical service–treated OHCAs in the United States, with a survival rate of ≈12% 1, 27. In systems in which telecommunicator CPR is optimized, all rhythm survival from OHCA is ≈37% 28.…”

Section: Discussionmentioning

confidence: 99%

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Delphi Analysis of Science Gaps in the 2015 American Heart Association Cardiac Arrest Guidelines

Panchal

Cash

Crowe

et al. 2018

JAHA

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BackgroundCurrent cardiac arrest guidelines have limited high‐quality scientific evidence to support recommendations for care. The quality of scientific evidence on which guidelines are based may correlate with improved patient outcomes and meaningful survival. We sought to develop a prioritized list of knowledge gaps in resuscitation to assist researchers, policy makers, and funding agencies in their decision‐making process.Methods and ResultsA 4‐stage modified Delphi method was used with a panel of cardiac arrest experts. Experts addressed the prompt: “What are the top 3 gaps in knowledge involving cardiac arrest care that should be research priorities for National Institutes of Health/American Heart Association funding to have the greatest impact on public health?” Knowledge gaps were identified in the initial round, rated in a second round, and rank ordered in the third round, and they underwent final review and consensus (final round). The outcome was 10 knowledge gaps, with prioritization of the top 3 gaps. A total of 61 gaps, with 19 distinct themes, were identified by participants. The 10 knowledge gaps most likely to affect public health identified by the expert panel included, in order, the following: telecommunicator cardiopulmonary resuscitation, hemodynamic monitoring for goal‐directed resuscitation, reasons why bystanders fail to respond, optimization of postarrest care, out‐of‐hospital cardiac arrest identification and response, individualizing resuscitation strategies, predicting patients at risk, tools for neuroprognostication, optimal airway management, and optimizing educational strategies.ConclusionsTen priorities for cardiac arrest research were identified, but consensus was not reached on the prioritized top 3. Future research should address these gaps to potentially improve resuscitation guideline evidence quality.

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Section: Discussionmentioning

confidence: 99%

“…In the United States, ≈500 000 people each year are victims of cardiac arrest, with only ≈12% to 24% surviving to hospital discharge 1. The low survival rate continues even with a national initiative for cardiopulmonary resuscitation (CPR) training and the development of systems to improve resuscitation care both in and out of hospital 2.…”

mentioning

confidence: 99%

Delphi Analysis of Science Gaps in the 2015 American Heart Association Cardiac Arrest Guidelines

Panchal

Cash

Crowe

et al. 2018

JAHA

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“…Myocardial IRI may lead to myocardial infarction, cardiac arrhythmias, and contractile dysfunction 11. The risk for developing myocardial IRI is higher for males, and also includes: diabetes mellitus, dyslipidemia, hypertension, insulin resistance, nutritional deficits, and physical inactivity; as well as smoking, high total cholesterol and low HDL, and high blood pressure 1, 12…”

Section: Contributing Pathologies Within the Cvd Spectrummentioning

confidence: 99%

Cannabinoids, the Heart of the Matter

Alfulaij

Meiners

Michalek

et al. 2018

JAHA

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“…The lifetime risk of developing heart failure is high, and it is among the most common discharge diagnoses in the United States, with frequent costly readmissions for decompensation once disease has been established 1. While invasively measured biomarkers have improved prognostication, prediction of new‐onset heart failure remains challenging 2.…”

Section: Introductionmentioning

confidence: 99%

Bioimpedance and New‐Onset Heart Failure: A Longitudinal Study of >500 000 Individuals From the General Population

Lindholm

Fukaya

Leeper

et al. 2018

JAHA

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BackgroundHeart failure constitutes a high burden on patients and society, but although lifetime risk is high, it is difficult to predict without costly or invasive testing. We aimed to establish new risk factors of heart failure, which potentially could enable early diagnosis and preemptive treatment.Methods and ResultsWe applied machine learning in the UK Biobank in an agnostic search of risk factors for heart failure in 500 451 individuals, excluding individuals with prior heart failure. Novel factors were then subjected to several in‐depth analyses, including multivariable Cox models of incident heart failure, and assessment of discrimination and calibration. Machine learning confirmed many known and putative risk factors for heart failure and identified several novel candidates. Mean reticulocyte volume appeared as one novel factor and leg bioimpedance another, the latter appearing as the most important new marker. Leg bioimpedance was lower in those who developed heart failure during an up to 9.8‐year follow‐up. When adjusting for known heart failure risk factors, leg bioimpedance was inversely related to heart failure (hazard ratio [95% confidence interval], 0.60 [0.48–0.73] and 0.75 [0.59–0.94], in age‐ and sex‐adjusted and fully adjusted models, respectively, comparing the upper versus lower quartile). A model including leg bioimpedance, age, sex, and self‐reported history of myocardial infarction showed good discrimination for future heart failure hospitalization (Concordance index [C‐index]=0.82) and good calibration.ConclusionsLeg bioimpedance is inversely associated with heart failure incidence in the general population. A simple model of exclusively noninvasive measures, combining leg bioimpedance with history of myocardial infarction, age, and sex provides accurate predictive capacity.

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Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association

Cited by 7,718 publications

References 2,027 publications

Delphi Analysis of Science Gaps in the 2015 American Heart Association Cardiac Arrest Guidelines

Delphi Analysis of Science Gaps in the 2015 American Heart Association Cardiac Arrest Guidelines

Cannabinoids, the Heart of the Matter

Bioimpedance and New‐Onset Heart Failure: A Longitudinal Study of >500 000 Individuals From the General Population

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