# Blood RNA Signatures Enable Accurate Discrimination of Stroke Subtype and Onset Time at Hospital Admission

## TL;DR

Blood RNA signatures can quickly identify stroke types and onset time, helping doctors decide on urgent treatments.

## Contribution

A machine learning model using blood RNA signatures accurately classifies stroke subtypes and predicts treatment eligibility.

## Key findings

- A three-transcript panel perfectly distinguishes hemorrhagic from non-hemorrhagic stroke.
- A four-transcript panel achieves 97% accuracy in validation for stroke classification.
- RNA signatures correlate with stroke severity and time of onset.

## Abstract

Despite advances in thrombolytic therapy (IVT) and mechanical thrombectomy (MT) for acute ischemic stroke, their benefit is strongly time-dependent and contingent on rapid exclusion of intracranial hemorrhage (ICH), where thrombolysis can be fatal. Here we evaluated whether a peripheral blood transcriptome–based machine learning model could rapidly identify hemorrhagic stroke, distinguish ischemic stroke from mimics, and predict eligibility for thrombolysis. Whole-blood samples (n=314) were collected from acute stroke patients admitted at emergency department of Grady Memorial Hospital (Atlanta, GA). Two independent training (n=192) and validation cohorts (n=122) were sequenced, aligned to GRCh38, and quantified with StringTie2. Differentially expressed transcripts were used to train hierarchical machine-learning models (caret, hidden Markov models [HMMs]) to classify hemorrhagic stroke, then distinguish ischemic stroke from stroke mimics, and further predict thrombolysis eligibility (≤3.5 hours from onset) and stroke severity (NIHSS). HMM-based classifiers demonstrated robust performance: a three-transcript panel perfectly discriminated hemorrhagic from non-hemorrhagic stroke (100% accuracy), and a four-transcript panel achieved 97% accuracy with 100% sensitivity and 96% specificity in validation. Ischemic stroke panels accurately distinguished patients from stroke mimics, time-associated transcripts identified individuals within the thrombolysis window, and severity-associated RNA signatures strongly correlated with NIHSS scores. The findings indicate that RNA profiling at admission can rapidly identify stroke subtypes, time of stroke onset, and stroke severity, supporting point-of-care triage and timely thrombolytic therapy.