Patient Stratification is a stratification analysis used to identify predictive biomarkers. The patient subpopulations are defined using on one or more biomarkers. For example, markers able to predict the response to a drug. These markers are fundamental to precision medicine approaches and are the basis of companion diagnostic tests.
In addressing the question: What is Patient Stratification? It should be understood that patient stratification is different from risk stratification and one should not be mistaken for the other.
Patient Stratification addresses certain types of problems. Health organizations too often lack visibility into complete, longitudinal patient data that will help them manage their populations. Those that do attempt to identify high-risk individuals spend enormous time and valuable resources, often looking at fragmented source data that doesn’t provide a holistic view of individual risk. Some of the key but often missing elements include identification of patients with the highest risk of requiring high-cost care using multiple models, comparing in and out-of-network claims, grouping or analyzing by care programs, encounter details, diagnosis codes, providers, age cohorts, and zip codes.
While on the other hand, Risk stratification is the process of using tools to identify and predict which patients are at high risk or likely to be at high risk and prioritizing the management and care of these patients in order to prevent deadly outcomes such as amputation, blindness or death. Risk stratification is an intentionally planned and proactive process carried out to effectively target clinic services to patients. Put simply, to risk-stratify patients is to sort them into high, moderate and low health risk tiers. Risk stratification aligns a practice’s very limited time and resources to prioritize the needs of its patient population. It can and should involve sophisticated algorithms and robust registries, but it relies just as much on patient experience and the physician’s judgment.
In the general understanding of the term stratification, you can say that stratification can be used to ensure equal allocation of subgroups of participants to each experimental condition. This may be done by gender, age, or other demographic factors. Stratification can be used to control for confounding variables, that is variables other than those the researcher is maybe studying, thereby making it easier for the research to detect and interpret relationships between variables. An example of using stratification in healthcare, if doing a study of fitness where age or gender was expected to influence the outcomes, participants could be stratified into groups by the confounding variable. A limitation of this method is that it requires knowledge of what variables need to be controlled. Stratification of clinical trials is the partitioning of subjects and results by a factor other than the treatment given.
When carrying out clinical trials, one of the most important logistical and statistical challenges is ensuring that your data accurately reflects the population you are setting out to study. None of us has the resources to test a drug on the entire human race, but if we want to have confidence in the results then we need to test it on a group that reflects the drug’s potential patients. That’s when the importance of stratification comes in.
Stratification is the division of your potential patient group into subgroups, also referred to as ‘strata’ or ‘blocks’. Each stratum represents a particular section of your patient population. For example, patients could be divided up according to age, gender, ethnicity, social background, medical history, or any other factor that you consider relevant. With patient stratification, different approaches can be taken to identify suitable test subjects. Groups of subjects are then included in the clinical trial to match each of these groups within the patient population. So a study into the intersection of genetics & medicine that is immunotherapy might need to include groups from different ethnic backgrounds and genders, to take into account genetic variations.