Ranking of stroke and cardiovascular risk factors for an optimal risk calculator design: Logistic regression approach

Elisa Cuadrado-Godia1, Ankush D Jamthikar2, Deep Gupta2

  • 1Department of Neurology, IMIM - Hospital Del Mar, Barcelona, Spain.

Insights

A new cardiovascular risk calculator, AtheroEdge composite risk score (AECRS1.0), incorporating carotid ultrasound image-based phenotypes (CUSIP) significantly improved cardiovascular disease risk prediction compared to conventional methods.

Area of Science:

  • Cardiovascular Medicine
  • Medical Imaging
  • Biostatistics

Background:

  • Conventional cardiovascular risk factors (CCVRFs) and carotid ultrasound image-based phenotypes (CUSIP) are independently linked to cardiovascular disease (CVD) risk.
  • Integrating CCVRFs with CUSIP may enhance CVD risk prediction accuracy.

Purpose of the Study:

  • To rank 26 cardiovascular risk (CVR) factors, combining CCVRFs and CUSIP.
  • To design and benchmark an optimal risk calculator, AtheroEdge composite risk score (AECRS1.0), against seven conventional CVR calculators.

Main Methods:

  • Ranked 26 CVR factors using multivariate logistic regression to compute odds ratios (OR).
  • Evaluated eight types of 10-year risk calculators using receiver operating characteristic (ROC) curves.
  • Validated results using SPSS, MEDCALC, and STATA software.

Main Results:

  • Carotid phenotypes, specifically intima-media thickness variability (IMTV) and IMTV10yr, were highly influential for left and right common carotid arteries (CCA).
  • AECRS1.0 and AECRS1.010yr showed the most significant OR for mean CCA.
  • AECRS1.010yr achieved the highest area under the curve (AUC) of 0.904, outperforming seven conventional calculators.

Conclusions:

  • The AECRS1.010yr calculator demonstrated superior performance in cardiovascular risk prediction.
  • The integration of CUSIP within the AECRS1.010yr model was key to its enhanced predictive capability and top ranking.
Abstract

Related Concept Videos

Factors Affecting the Risk of Infection01:26

Factors Affecting the Risk of Infection

The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
13.3K
Relative Risk01:12

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
2.0K
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
292
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.9K
Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
10.3K
Ranks01:02

Ranks

Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
478