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- Bilchick, K. C., et al.
(author)
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Seattle Heart Failure and Proportional Risk Models Predict Benefit From Implantable Cardioverter-Defibrillators
- 2017
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In: Journal of the American College of Cardiology. - : Elsevier BV. - 0735-1097 .- 1558-3597. ; 69:21, s. 2606-2618
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Journal article (peer-reviewed)abstract
- BACKGROUND Recent clinical trials highlight the need for better models to identify patients at higher risk of sudden death. OBJECTIVES The authors hypothesized that the Seattle Heart Failure Model (SHFM) for overall survival and the Seattle Proportional Risk Model (SPRM) for proportional risk of sudden death, including death from ventricular arrhythmias, would predict the survival benefit with an implantable cardioverter-defibrillator (ICD). METHODS Patients with primary prevention ICDs from the National Cardiovascular Data Registry (NCDR) were compared with control patients with heart failure (HF) without ICDs with respect to 5-year survival using multivariable Cox proportional hazards regression. RESULTS Among 98,846 patients with HF (87,914 with ICDs and 10,932 without ICDs), the SHFM was strongly associated with all-cause mortality (p < 0.0001). The ICD-SPRM interaction was significant (p < 0.0001), such that SPRM quintile 5 patients had approximately twice the reduction in mortality with the ICD versus SPRM quintile 1 patients (adjusted hazard ratios [HR]: 0.602; 95% confidence interval [CI]: 0.537 to 0.675 vs. 0.793; 95% CI: 0.736 to 0.855, respectively). Among patients with SHFM-predicted annual mortality <= 5.7%, those with a SPRM-predicted risk of sudden death below the median had no reduction in mortality with the ICD (adjusted ICD HR: 0.921; 95% CI: 0.787 to 1.08; p = 0.31), whereas those with SPRM above the median derived the greatest benefit (adjusted HR: 0.599; 95% CI: 0.530 to 0.677; p < 0.0001). CONCLUSIONS The SHFM predicted all-cause mortality in a large cohort with and without ICDs, and the SPRM discriminated and calibrated the potential ICD benefit. Together, the models identified patients less likely to derive a survival benefit from primary prevention ICDs. (J Am Coll Cardiol 2017;69:2606-18) (C) 2017 by the American College of Cardiology Foundation.
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- Rhodes, Olin E., et al.
(author)
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Integration of ecosystem science into radioecology : A consensus perspective
- 2020
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In: Science of the Total Environment. - : Elsevier BV. - 0048-9697 .- 1879-1026. ; 740
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Journal article (peer-reviewed)abstract
- In the Fall of 2016 a workshop was held which brought together over 50 scientists from the ecological and radiological fields to discuss feasibility and challenges of reintegrating ecosystem science into radioecology. There is a growing desire to incorporate attributes of ecosystem science into radiological risk assessment and radioecological research more generally, fueled by recent advances in quantification of emergent ecosystem attributes and the desire to accurately reflect impacts of radiological stressors upon ecosystem function. This paper is a synthesis of the discussions and consensus of the workshop participant's responses to three primary questions, which were: 1) How can ecosystem science support radiological risk assessment? 2) What ecosystem level endpoints potentially could be used for radiological risk assessment? and 3) What inference strategies and associated methods would be most appropriate to assess the effects of radionuclides on ecosystem structure and function? The consensus of the participants was that ecosystem science can and should support radiological risk assessment through the incorporation of quantitative metrics that reflect ecosystem functions which are sensitive to radiological contaminants. The participants also agreed that many such endpoints exit or are thought to exit and while many are used in ecological risk assessment currently, additional data need to be collected that link the causal mechanisms of radiological exposure to these endpoints. Finally, the participants agreed that radiological risk assessments must be designed and informed by rigorous statistical frameworks capable of revealing the causal inference tying radiological exposure to the endpoints selected for measurement.
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