Causal AI Quantifies CV Risk, Providing Patient-Specific Goals

NEW ORLEANS — Causal artificial intelligence (AI) can translate polygenic scores (PGS) and other genetic information into risk reduction strategies for coronary artery disease (CAD) that is tailored for each individual patient, according to an analysis presented at the joint scientific sessions of the American College of Cardiology and the World Heart Federation.

Tested for LDL cholesterol (LDL-C) and systolic blood pressure (SBP), causal AI explained how much each of these risk factors must improve at the level of each individual patient “to overcome overall inherited risk,” reported Brian Ference, MD, MPhil, director of translational therapeutics, University of Cambridge (England).

Unlike the “black box” risk assessments common to machine learning, which relies on disparate forms of information of often unknown relative significance, causal AI explains cause and effect. In the case of CAD, its ability to encode the biological causes means that it can “both predict outcomes and prescribe specific actions to change those outcomes,” Dr. Ference explained.

The concept is testable against observed biology using randomized evidence, which was the objective of the study Dr. Ference presented in the late-breaker session.

Causal AI trained on nearly 2 million patients

This study employed a causal AI platform trained on roughly 1.3 million participants in Mendelian randomization studies, as well as more than 500,000 participants in randomized clinical trials. The PGS estimate of inherited risk was constructed from almost 4.1 million variants from genomewide association studies.

To test the ability of causal AI to reveal how much LDL-C or SBP had to be reduced to overcome the inherited risk of CAD based on PGS, it was applied to 445,765 participants of European ancestry in the UK Biobank. The goal was to determine how much those with greater than average risk would need to lower their LDL-C or SBP to achieve average CAD risk.

When validated against observed rates of events, causal AI accurately characterized risk before estimating what reductions in LDL-C, SBP, or both would attenuate that risk.

Providing examples, Dr. Ference explained that a PGS in the 80th percentile can be overcome by lowering LDL-C by 14 mg/dL. Alternatively, the 80th percentile risk could also be overcome by simultaneously lowering LDL-C and SBP by 7 mg/dL and 2.5 mm Hg, respectively.

Required risk factor reductions increase with age because of the increased risk of the events. For example, while a 14.8 mg/dL reduction in LDL-C would be adequate to overcome risk defined by a PGS in the 80th percentile at age 35, reductions of 18.2 mg/dL, 28.9 mg/dL, and 42.6 mg/dL would be required, respectively, at ages 45, 55, and 65 years. The values climb similarly for SBP.

Family history of CAD adds an independent variable that further contributes to the ability of causal AI to estimate risk and the degree of risk factor attenuation to overcome the risk.

Even though family history is equivalent to having PGS above the 95th percentile, it is an independent and additive variable, according to Dr. Ference. As a result, inherited risk of CAD depends on both.

Still when family history is factored into the analysis, “causal AI accurately estimated the magnitude of lower LDL-C, SBP, or both needed to overcome overall inherited risk at all levels of higher or lower PGS,” he reported.

According to Dr. Ference, the value of causal AI is that it can generate very specific goals for each patient regarding modifiable risk factors. Causal effects of risk factors encoded in time units of exposure allow the patient and the clinician to understand the biology and the basis of the disease burden.

Treatments become understandable to patients

“Encoding biology creates algorithms that are deeply explainable because they reveal why a person is at risk, how to reduce that risk, and how much each person will benefit from specific actions to reduce risk,” Dr. Ference said.

A real-world, randomized trial to confirm that the information from causal AI can reduce the risk of CAD is expected to start in 2023, but Dr. Ference thinks that causal AI for managing CAD risk, independent of this planned trial, is essentially inevitable. PGS, which he thinks will be performed routinely in all individuals within 10 years, is only likely to improve. He foresees large advantages of this form of personalized medicine.

mi Bhatt, MD, chief innovation officer for the American College of Cardiology, Washington, agreed, seeing a direct relationship between precision health as the pathway to improvements in population health.

By explaining risk factors in terms of mechanisms and specific goals to ameliorate these risks, it “engages our patients with agency,” said Dr. Bhatt. She suggested that the information provided by causal AI has the potential to empower patients while creating a collaborative approach with clinicians to CAD prevention.

With patient-specific information provided in the context of the disease biology, “you increase the sense of transparency,” Dr. Bhatt said.

She suggested this direction of research is wholly consistent with initiatives such as those from the World Health Organization to improve precision medicine as a step toward equipping patients to manage their own health.

Dr. Ference reported financial relationships with Amgen, AstraZeneca, CiVi Pharma, Daiichi Sankyo, DalCOR, Esperion, Eli Lilly, Ionis Pharmaceuticals, KrKA, Medicines Company, Merck, Mylan, Novo Nordisk, Novartis, and Sanofi, and Viatris. Dr. Bhatt reported no potential conflicts of interest.

This article originally appeared on, part of the Medscape Professional Network.

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