Personalized lab test models to quantify disease potentials in healthy individuals.

Area of Science:

• Biomedical Informatics
• Clinical Pathology
• Data Science in Healthcare

Background:

• Standardized laboratory tests are crucial for patient assessment, diagnosis, and treatment.
• Current interpretation lacks quantitative and personalized metrics, limiting clinical utility.
• Vast amounts of lab data exist but are underutilized for personalized health insights.

Purpose of the Study:

• To develop and validate personalized models for interpreting laboratory test results.
• To assess the variance in lab test results explained by patient history versus demographics.
• To establish a quantitative basis for patient evaluation and disease risk stratification.

Main Methods:

• Modeled 2.1 billion lab measurements from 2.8 million adults over 18 years.
• Applied unsupervised filtering for 131 chronic conditions and 5,223 drug-test interactions.
• Developed personalized models using patient historical data to analyze lab test distributions.

Main Results:

• Age and sex explained <10% of variance in 89/92 tests.
• Personalized models explained >60% of variance for 17 tests and >36% for half of the tests.
• Demonstrated ability to stratify risk for future abnormal lab levels and disease emergence.

Conclusions:

• Multivariate modeling of within-normal lab tests offers quantitative patient evaluation.
• Personalized models significantly enhance the interpretation of laboratory data.
• This approach facilitates early risk identification and proactive healthcare management.