Researchers unveil tool to predict cardiac death risk

Identifying high-risk patients can guide treatment

An international group of researchers has created a risk-predicting tool that enables clinicians to calculate the chances that a particular patient will die within six months of going home from the hospital after a heart attack or unstable angina episode. Their work was detailed in an article in the June 9, 2004, issue of the Journal of the American Medical Association.1

The calculating tool, which can fit on a pocket card or be programmed into an ordinary handheld data device, is based on data from 22,645 patients treated at 94 hospitals in 14 countries.

The tool is more current and more broadly applicable than other tools developed in the past, asserts Kim A. Eagle, MD, cardiovascular center clinical director at the University of Michigan in Ann Arbor and leader of the research team.

"The tool was developed on patients accrued within the past five years, and tests included patients up to Dec. 31, 2003," he notes. "In terms of evaluating risk, it is fairly reflective of modern coronary care."

The tool also is greater in breadth, Eagle continues. "Other predictive models that have been used have sometimes focused on a specific group of patients with acute coronary syndrome (ACS) — such as unstable angina," he notes. "We wanted to create a model applicable to all ACS patients. Physicians might be more likely to use a broad model than one that is more narrowly focused; you can use this model with any patient you admit with MI or unstable angina."

The tool creates a score for each patient based on nine variables. The higher their score, the higher their chance of dying within six months of leaving the hospital.

Older age, a history of previous heart attack or heart failure, or a lack of angioplasty or stenting during hospitalization boost patients’ scores the most, but so do results from exams and blood tests conducted when they first arrive at the hospital: Patients with faster pulse rates, lower systolic blood pressures, certain electrocardiogram readings, and high levels of blood creatinine and cardiac enzymes score higher.

Amazing GRACE

The new tool is based on data from GRACE, the Global Registry of Acute Coronary Events, which pools information on people who have had heart attacks and unstable angina episodes, and allows researchers to analyze their in-hospital symptoms and care, medical history, demographics, and survival rates.

Eagle and his co-authors developed the GRACE model based on data from 15,007 patients who were discharged alive from the participating hospitals between April 1999 and March 2002, and followed for at least six months after leaving the hospital.

They used sophisticated statistical methods to determine which factors were common to those in this development group who lived through or died during that period, and how often those various factors occurred in each group.

The researchers then validated the tool by using it on 7,638 patients treated between April 2002 and December 2003. They found that the tool offered an excellent gauge of which patients were most at risk. The nine variables stood out as consistently different between those who died soon after leaving the hospital and those who didn’t. This allowed them to assign a hazard ratio or relative point value for each characteristic, and create the risk-prediction tool to allocate points to patients.

"The GRACE registry studies consecutive admissions and offers the potential of reflecting a real-world experience," Eagle observes.

"Plus, there are 94 hospitals enrolling patients all around the world. You can hear the physician asking, Does this really apply to my patients?’ We hope it can be used anywhere in the U.S. and be relevant," he adds.

Available online

The GRACE prediction model, as the new tool is called, is available on-line for free use by any clinician, at http://www.statcoder.com/grace.htm grace. It can be downloaded to a PDA.

What exactly can the tool tell a clinician?

"Actually, the risk tool gives an exact percentage estimate of six-month mortality risk," says Eagle, adding that there is no official cut-off point for low, medium, or high risk. "

What you might see as a low risk, others might not," he points out. "We leave it to the doc and the patient to decide if, say, a score of four feels like high-risk or low-risk."

The mean risk obtained in the study was 4.8%, he adds. "Obviously, if my doc told me mine was 1%, I’d feel like that was good, while 10% or 15% seems high to me, but the truth is, each patient is an individual."

For higher-risk patients — in the 10%, 15%, or 20% range, for example — "We’d have to say cardiac rehab is a good idea," Eagle says. "I’d be very careful to follow up to make sure everything is done just right."

In general, Eagle says he hopes the tool can help physicians evaluate patients while they are still in the hospital and determine how much of a post-hospitalization risk they face.

This, in turn, can guide treatment. For example, since patients who had angioplasty or stenting to open a clogged artery did better than those who didn’t, a physician may want to consider ordering this kind of revascularization procedure for patients who haven’t had it — if the patient is a good candidate. And physicians may want to pursue more aggressive drug treatment, post-hospital monitoring, and rehabilitation programs for patients who score high on the model.

Alternately, they may be able to reassure a patient who scores low that he or she has a low risk of dying in the next few months — and help that patient understand how diet, exercise, and medication can help keep that risk low.

The tool has practical applications for quality managers as well, Eagle says.

"I think one area where it would be potentially very usable is if, as part of discharge planning for patients with ACS, we not only go over plans for meds, lifestyle and so on, but also their estimated risk," he suggests.

"This can incent both the patient and the care team to lower a risk number that seems too high," Eagle adds.

Reference

1. Eagle KA, Lim MJ, Dabbous OH, et al. A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6-month post-discharge death in an international registry. JAMA 2004; 291:2,727-2,733.

Need More Information?

For more information, contact:

• Kim A. Eagle, MD, Clinical Director, University of Michigan Cardiovascular Center, Ann Arbor. Phone: (734) 936-5275. E-mail: keagle@umich.edu.