Risks with aneurysm surgery made clearer with mathematical model, researchers say
Even though operating on an abdominal aortic aneurysm can be risky, there are no patient-specific guidelines for deciding the optimal time for surgery. A mathematical model developed by Swedish researchers offers a way to guide doctors in making the right choices for individual patients.
A ruptured abdominal aortic aneurysm is almost always lethal, but it can be prevented if the aneurysm is discovered in time and operated on. Yet, even though the risks of surgery are affected by the patient’s age and health, there remains only one guideline for doctors to determine whether to proceed – that is, the aneurysm must reach a diameter of 5.5 cm.
Robert Mattila and Antti Siika, two researchers at KTH and Karolinska Institute, say the guideline is insufficient as a decision-making policy – since it offers no basis for deciding which patient should be exempt from the surgery.
Depending on the life expectancy, age and health of a patient, it may in fact be better to wait even if a 5.5 cm-diameter aorta is diagnosed, the researchers say.
In place of the standard rule the researchers recommend establishing new guidelines by use of a Markov decision process model - a tool routinely used in industrial applications for sequential decision-making in uncertain situations.
“We have used a mathematical model that offers a way to weigh the risks of an operation against the merits,” Mattila says. The new guideline takes into account previously-ignored factors such as life-expectancy and age-dependent surgical mortality.
The project represents another way in which Markov decision processes specifically – and mathematics in general – is becoming increasingly useful in life science. However obvious the result might seem – for example, it’s better to do the surgery when the patient is young – Mattila notes that up to now there has been no rigorous method to form the basis for such decision-making.
The model is intended to be used by a doctor in consultation with the patient, so that there is an optimal basis for decision-making and maximizing the patient’s life. It can be made more sophisticated and complex, so that other factors can play a role, such as different diseases the patient has or their gender, Siika says.
“The parameters in the model can be adjusted based on the patient who is in front of us right now, Siika says. “The risks of the surgery become clearer.”
The next steps for the project are to conduct a retrospective clinical study to evaluate the developed method and compare it with the decision-making support used today.
The research results were presented recently at the annual IEEE Multi-Conference on Systems and Control in Buenos Aires in September.
Peter Ardell/David Callahan
For more information, contact Robert Mattila at +46 76-201 40 56 / email@example.com or Antti Siika at +46 73-983 83 54 / firstname.lastname@example.org .