Tailoring Risk Prediction Models to Local Populations.

Importance: Risk estimation is an integral part of cardiovascular care. Local recalibration of guideline-recommended models could address the limitations of existing tools. Objective: To provide a machine learning (ML) approach to augment the performance of the American Heart Association's Predicting Risk of Cardiovascular Disease Events (AHA-PREVENT) equations when applied to a local population while preserving clinical interpretability. Design, Setting, and Participants: This cohort study used a New England-based electronic health record cohort of patients without prior atherosclerotic cardiovascular disease (ASCVD) who had the data necessary to calculate the AHA-PREVENT 10-year risk of developing ASCVD in the event period (2007-2016). Patients with prior ASCVD events, death prior to 2007, or age 79 years or older in 2007 were subsequently excluded. The final study population of 95 326 patients was split into 3 nonoverlapping subsets for training, testing, and validation. The AHA-PREVENT model was adapted to this local population using the open-source ML model (MLM) Extreme Gradient Boosting model (XGBoost) with minimal predictor variables, including age, sex, and AHA-PREVENT. The MLM was monotonically constrained to preserve known associations between risk factors and ASCVD risk. Along with sex, race and ethnicity data from the electronic health record were collected to validate the performance of ASCVD risk prediction in subgroups. Data were analyzed from August 2021 to February 2024. Main Outcomes and Measures: Consistent with the AHA-PREVENT model, ASCVD events were defined as the first occurrence of either nonfatal myocardial infarction, coronary artery disease, ischemic stroke, or cardiovascular death. Cardiovascular death was coded via government registries. Discrimination, calibration, and risk reclassification were assessed using the Harrell C index, a modified Hosmer-Lemeshow goodness-of-fit test and calibration curves, and reclassification tables, respectively. Results: In the test set of 38 137 patients (mean [SD] age, 64.8 [6.9] years, 22 708 [59.5]% women and 15 429 [40.5%] men; 935 [2.5%] Asian, 2153 [5.6%] Black, 1414 [3.7%] Hispanic, 31 400 [82.3%] White, and 2235 [5.9%] other, including American Indian, multiple races, unspecified, and unrecorded, consolidated owing to small numbers), MLM-PREVENT had improved calibration (modified Hosmer-Lemeshow P > .05) compared to the AHA-PREVENT model across risk categories in the overall cohort (χ23 = 2.2; P = .53 vs χ23 > 16.3; P < .001) and sex subgroups (men: χ23 = 2.1; P = .55 vs χ23 > 16.3; P < .001; women: χ23 = 6.5; P = .09 vs. χ23 > 16.3; P < .001), while also surpassing a traditional recalibration approach. MLM-PREVENT maintained or improved AHA-PREVENT's calibration in Asian, Black, and White individuals. Both MLM-PREVENT and AHA-PREVENT performed equally well in discriminating risk (approximate ΔC index, ±0.01). Using a clinically significant 7.5% risk threshold, MLM-PREVENT reclassified a total of 11.5% of patients. We visualize the recalibration through MLM-PREVENT ASCVD risk charts that highlight preserved risk associations of the original AHA-PREVENT model. Conclusions and Relevance: The interpretable ML approach presented in this article enhanced the accuracy of the AHA-PREVENT model when applied to a local population while still preserving the risk associations found by the original model. This method has the potential to recalibrate other established risk tools and is implementable in electronic health record systems for improved cardiovascular risk assessment.

Assessing the dyslipidemias: to fast or not to fast?

PURPOSE OF REVIEW: Nonfasting lipid testing has been introduced into several guidelines over the past decade or so however, the uptake into clinical practice has not been universal. This review highlights some of the prevalent reasons for provider reluctance to use nonfasting testing and the evidence to support nonfasting testing for routine screening in most patients. RECENT FINDINGS: Several studies have found nonfasting lipids to be as, or more, strongly associated with cardiovascular disease (CVD) risk prediction. In particular, nonfasting tests improve system efficiency, are safe for patients with diabetes, the elderly, children, and in the vast majority of patients, do not need to be followed up with fasting studies due to severe hypertriglyceridemia. SUMMARY: Nonfasting lipids are a convenient first test for screening that offers equivalent, if not improved CVD risk prediction. Common misconceptions about nonfasting tests are not supported by the evidence.

Anti-Inflammatory HDL Function, Incident Cardiovascular Events, and Mortality: A Secondary Analysis of the JUPITER Randomized Clinical Trial.

Background High-density lipoprotein (HDL) cholesterol has inverse association with cardiovascular disease. HDL possesses anti-inflammatory properties in vitro, but it is unknown whether this may be protective in individuals with inflammation. Methods and Results The functional capacity of HDL to inhibit oxidation of oxidized low-density lipoprotein (ie, the HDL inflammatory index; HII) was measured at baseline and 12 months after random allocation to rosuvastatin or placebo in a nested case-control study of the JUPITER (Justification for the Use of Statins in Prevention: An Intervention Evaluating Rosuvastatin) trial. There were 517 incident cases of cardiovascular disease and all-cause mortality compared to 517 age- and sex-matched controls. Multivariable conditional logistic regression was used to examine associations of HII with events. Median baseline HII was 0.54 (interquartile range, 0.50-0.59). Twelve months of rosuvastatin decreased HII by a mean of 5.3% (95% CI, -8.9% to -1.7%; P=0.005) versus 1.3% (95% CI, -6.5% to 4.0%; P=0.63) with placebo (P=0.22 for between-group difference). HII had a nonlinear relationship with incident events. Compared with the reference group (HII 0.5-1.0) with the lowest event rates, participants with baseline HII ≤0.5 had significantly increased risk of cardiovascular disease/mortality (adjusted hazard ratio, 1.53; 95% CI, 1.06-2.21; P=0.02). Furthermore, there was significant (P=0.002) interaction for HDL particle number with HII, such that having more HDL particles was associated with decreased risk only when HDL was anti-inflammatory. Conclusions In JUPITER participants recruited on the basis of chronic inflammation, HII was associated with incident cardiovascular disease/mortality, with an optimal anti-inflammatory HII range between 0.5 and 1.0. This nonlinear relationship of anti-inflammatory HDL function with risk may account in part for the HDL paradox. Registration URL: https://www.clini​caltr​ials.gov; Unique identifier: NCT00239681.

Comparison of nonfasting and fasting lipoprotein subfractions and size in 15,397 apparently healthy individuals: An analysis from the VITamin D and OmegA-3 TriaL.

BACKGROUND: Elevated postprandial triglycerides reflect a proatherogenic milieu, but underlying mechanisms are unclear. OBJECTIVE: We examined differences between fasting and nonfasting profiles of directly measured lipoprotein size and subfractions to assess if postprandial triglycerides reflected increases in very low density lipoprotein (VLDL), intermediate density lipoprotein (IDL) and remnants, or small dense lipid depleted LDL (sdLDL) particles. METHODS: We conducted a cross-sectional analysis of 15,397 participants (10,135 fasting; 5262 nonfasting [<8 hours since last meal]) from the VITamin D and OmegA-3 TriaL. Baseline cholesterol subfractions were measured by the vertical auto profile method and particle subfractions by ion mobility. We performed multivariable linear regression adjusting for cardiovascular and lipoprotein-modifying risk factors. RESULTS: Mean age (SD) was 68.0 years (±7.0), with 50.9% women. Adjusted mean triglyceride concentrations were higher nonfasting by 17.8 ± 1.3%, with higher nonfasting levels of directly measured VLDL cholesterol (by 3.5 ± 0.6%) and total VLDL particles (by 2.0 ± 0.7%), specifically large VLDL (by 12.3 ± 1.3%) and medium VLDL particles (by 5.3 ± 0.8%), all P < .001. By contrast, lower concentrations of low density lipoprotein (LDL) and IDL cholesterol and particles were noted for nonfasting participants. sdLDL cholesterol levels and particle concentrations showed no statistically significant difference by fasting status (-1.3 ± 2.1% and 0.07 ± 0.6%, respectively, P > .05). CONCLUSIONS: Directly measured particle and cholesterol concentrations of VLDL, not sdLDL, were higher nonfasting and may partly contribute to the proatherogenicity of postprandial hypertriglyceridemia. These differences, although statistically significant, were small and may not fully explain the increased risk of postprandial hypertriglyceridemia.

Premature Myocardial Infarction in the Middle East and North Africa: Rationale for the Gulf PREVENT Study.

The Middle East and North Africa (MENA) region has a high burden of morbidity and mortality due to premature (≤55 years in men; ≤65 years in women) myocardial infarction (MI) and acute coronary syndrome (ACS). Despite this, the prevalence of risk factors in patients presenting with premature MI or ACS is incompletely described. We compared lifestyle, clinical risk factors, and biomarkers associated with premature MI/ACS in the MENA region with selected non-MENA high-income countries. We identified English-language, peer-reviewed publications through PubMed (up to March 2018). We used the World Bank classification system to categorize countries. Patients with premature MI/ACS in the MENA region had a higher prevalence of smoking than older patients with MI/ACS but a lower prevalence of diabetes, hypertension, and dyslipidemia. Men with premature MI/ACS had a higher prevalence of smoking than women but a lower prevalence of diabetes and hypertension. The MENA region had sparse data on lifestyle, diet, psychological stress, and physical activity. To address these knowledge gaps, we initiated the ongoing Gulf Population Risks and Epidemiology of Vascular Events and Treatment (Gulf PREVENT) case-control study to improve primary and secondary prevention of premature MI in the United Arab Emirates, a high-income country in the MENA region.