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Key Points: Question: How many US adults are estimated to experience changes in treatment eligibility when evaluated using the American Heart Association Predicting Risk of cardiovascular disease EVENTs (PREVENT) equations vs the pooled cohort equations? Findings: In this nationally representative study of 7765 US adults aged 30 to 79 years, it was estimated that using PREVENT would decrease the number of US adults receiving or recommended for statin therapy by 14.3 million and antihypertensive therapy by 2.62 million. Over 10 years, reductions in treatment eligibility could result in an estimated 107 000 additional occurrences of myocardial infarction or stroke. Meaning: Application of the PREVENT equations to current risk-based treatment thresholds could reduce eligibility for statin and antihypertensive therapy among 15.8 million US adults, with substantial implications for public health. Importance: Since 2013, the American College of Cardiology (ACC) and American Heart Association (AHA) have recommended the pooled cohort equations (PCEs) for estimating the 10-year risk of atherosclerotic cardiovascular disease (ASCVD). An AHA scientific advisory group recently developed the Predicting Risk of cardiovascular disease EVENTs (PREVENT) equations, which incorporated kidney measures, removed race as an input, and improved calibration in contemporary populations. PREVENT is known to produce ASCVD risk predictions that are lower than those produced by the PCEs, but the potential clinical implications have not been quantified. Objective: To estimate the number of US adults who would experience changes in risk categorization, treatment eligibility, or clinical outcomes when applying PREVENT equations to existing ACC and AHA guidelines. Design, Setting, and Participants: Nationally representative cross-sectional sample of 7765 US adults aged 30 to 79 years who participated in the National Health and Nutrition Examination Surveys of 2011 to March 2020, which had response rates ranging from 47% to 70%. Main Outcomes and Measures: Differences in predicted 10-year ASCVD risk, ACC and AHA risk categorization, eligibility for statin or antihypertensive therapy, and projected occurrences of myocardial infarction or stroke. Results: In a nationally representative sample of 7765 US adults aged 30 to 79 years (median age, 53 years; 51.3% women), it was estimated that using PREVENT equations would reclassify approximately half of US adults to lower ACC and AHA risk categories (53.0% [95% CI, 51.2%-54.8%]) and very few US adults to higher risk categories (0.41% [95% CI, 0.25%-0.62%]). The number of US adults receiving or recommended for preventive treatment would decrease by an estimated 14.3 million (95% CI, 12.6 million-15.9 million) for statin therapy and 2.62 million (95% CI, 2.02 million-3.21 million) for antihypertensive therapy. The study estimated that, over 10 years, these decreases in treatment eligibility could result in 107 000 additional occurrences of myocardial infarction or stroke. Eligibility changes would affect twice as many men as women and a greater proportion of Black adults than White adults. Conclusion and Relevance: By assigning lower ASCVD risk predictions, application of the PREVENT equations to existing treatment thresholds could reduce eligibility for statin and antihypertensive therapy among 15.8 million US adults. This cross-sectional study investigates the effects of evaluating atherosclerotic cardiovascular disease risk using pooled cohort equations or PREVENT equations on treatment eligibility of a nationally representative sample of US adults. [ABSTRACT FROM AUTHOR]

The document titled "THE AUTHORS REPLY" is a correspondence published in the New England Journal of Medicine. The authors discuss the influence of commercial interests on AI models and the need for third-party review, disclosure, and regulatory guidelines. They emphasize the importance of addressing the global alignment problem and suggest efforts such as the Responsible AI for Social and Ethical Healthcare (RAISE) symposium. The authors also acknowledge the value of clinical experience in assessing and preventing tragic outcomes, while recognizing the potential of AI to democratize access to clinical expertise without replacing the doctor-patient relationship. [Extracted from the article]

Objective: To establish a polyexposure score (PXS) for type 2 diabetes (T2D) incorporating 12 nongenetic exposures and examine whether a PXS and/or a polygenic risk score (PGS) improves diabetes prediction beyond traditional clinical risk factors.Research Design and Methods: We identified 356,621 unrelated individuals from the UK Biobank of White British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 nongenetically ascertained exposure and lifestyle variables for the PXS in prospective T2D. We computed the clinical risk score (CRS) and PGS by taking a weighted sum of eight established clinical risk factors and >6 million single nucleotide polymorphisms, respectively.Results: In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Individuals in the top 10% of PGS, PXS, and CRS had 2.00-, 5.90-, and 9.97-fold greater risk, respectively, compared to the remaining population. Addition of PGS and PXS to CRS improved T2D classification accuracy, with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively.Conclusions: For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. However, the concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models. [ABSTRACT FROM AUTHOR]