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Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

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Prolonging Sample Stability with Machine Learning Expands the Catalog of Tests Available for Mail-In Capillary Blood

Connor P O'Meara^1,2, Elizabeth K Fletcher^1,2, Alamgir Khan^1,2

¹Drop Bio Health, University of New South Wales, Kensington, New South Wales 2052, Australia.

Analytical Chemistry

|February 23, 2026

Summary

At-home blood tests are challenging due to sample instability. A new model uses environmental data to calibrate results, enabling stable transport for up to 9 days, expanding remote diagnostics.

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Area of Science:

Biomedical Engineering
Clinical Chemistry
Data Science

Background:

At-home biospecimen self-collection offers convenience but faces challenges with sample instability during transit.
Degradation of blood samples during shipment leads to inaccurate and imprecise clinical test results.
Existing methods lack robust solutions for maintaining sample integrity outside of controlled laboratory settings.

Purpose of the Study:

To develop and validate a software-based approach to model and control sample instability in self-collected blood.
To enable accurate calibration of blood test results despite degradation during transport.
To expand the feasibility of remote blood testing for clinical diagnostics and public health initiatives.

Main Methods:

Trained a predictive model (Remote Control) using environmental sensor data from blood samples subjected to real-world transit conditions.
Validated the model's ability to calibrate test results, approximating the time-zero value.
Assessed performance across various conditions, including ambient transport for up to 9 days and different temperatures (3.4–47.4 °C).

Main Results:

The calibrated model achieved high agreement (98.1–100%) with CLIA Total Error (TEa) for lipid panels.
Demonstrated low bias (0.1–1.6%), acceptable coefficient of variation (2.2–4.9%), and high sigma metrics (3–8.8σ).
Performance generalized across diverse laboratories, equipment, patient demographics, and health statuses, including capillary blood samples.

Conclusions:

A software-based calibration method effectively mitigates sample instability, enabling reliable at-home blood testing.
This approach supports decentralized clinical trials, public health screening, and affordable at-home healthcare delivery.
Unprocessed whole blood can be transported for extended periods under ambient conditions with accurate results post-calibration.