A team from Acıbadem University with Norwegian researchers has developed a new algorithm to derive personalized reference intervals.
Physicians usually use reference intervals to interpret patients' laboratory test results for the diagnosis and monitoring of diseases. These reference intervals are derived from population data and are not specific to individuals. Due to the heterogeneity of the population, the population-based reference intervals are not appropriate for all individuals. It could lead to misdiagnosis of diseases, potentially resulting in significant medical errors and increased healthcare costs. To overcome this problem the researchers have developed a new algorithm to derive individual-specific reference intervals based on individuals’ own data rather than using population data. The new model uses individuals’ laboratory test results when the individual has no diagnosis for disease and derives individual-specific (personalized) reference intervals for laboratory tests. Since the new reference intervals are based on individuals' own data, they are specific to individuals’ clinical situations.
Within the last decade, the development of omics technologies and particularly pharmacogenomics made great contributions to personalized, precision, and preventing medicine (3P medicine). The new reference intervals will significantly enhance the personalization of laboratory data and promote the integration of new technologies and biomarkers in 3P medicine. The researchers are currently developing new algorithms to estimate the potential health risks and identify undiagnosed diseases using individuals' own data, even when their laboratory test results are normal and no disease has been diagnosed.
The paper can be accessed here.
