The concept of patient-years is used in many clinical studies and statistical assessments of risk. Viewing things in these terms allows researchers to look at a population more generally, rather than trying to separate out and process data from each individual member of a group. This concept often show up in news articles about long-term studies, although this precise term may not be specified. To obtain the number, researchers add together all of the years that patients in a study were followed, and then divide those years by the event of interest.
For example, if ten patients participated in a study on heart attacks for 15 years (i.e., 150 patient-years (10 x 15)), and three of them had heart attacks, there would be one heart attack for every 50 patient-years in the study. While it is important to look at individual data in any study, looking at things in these terms can reveal trends.
In the heart attack example above, the researchers might choose to follow several different populations and compare them at the end of the study. If our group above was a control group, there might be several research groups with different heart attack rates, like one heart attack for every three patient-years, or one heart attack for every seven. By looking at the general average, the researchers might be able to draw some conclusions about the various means to prevent heart attacks that are being studied.
Many studies on new medications also view things in these terms. For example, if one death is experienced for every 1,000 patient-years of a study, this might be viewed as an acceptable risk, while a high death rate might be cause to reconsider the validity of a medication. Contraindications for medications are also sometimes processed in this light; if one group being studied experiences a high rate of complications while on the new medication, researchers might decide that the medication is contraindicated for similar people in the general population.
In addition to being used in discussions of clinical studies, patient-years also sometimes crop up in long term morbidity and mortality reports. For example, the organ donor waiting list is usually carefully tracked to see how many patients die each year waiting for organs, and calculations using this concept sometimes become important in determining who is entitled to new organs. For instance, if a population of specific individuals waiting for lungs on the list experiences 300 deaths for every patient-year of waiting while another group waiting for lungs experiences 25 deaths, patients in the more high-risk group are probably going to get lungs first.
