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Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

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Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program.

Vitaly Lorman¹, L Charles Bailey¹, Xing Song²

¹Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.

PLOS Digital Health

|April 10, 2025

Summary

This summary is machine-generated.

Researchers identified distinct clinical presentations, or subphenotypes, of pediatric Long COVID using machine learning. Cardiorespiratory issues were most common, followed by musculoskeletal pain and neuropsychiatric conditions.

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Area of Science:

Pediatric medicine
Computational biology
Infectious disease epidemiology

Background:

Long COVID presents with diverse symptoms in children, but distinct clinical patterns (subphenotypes) remain unclear.
Understanding pediatric Long COVID subphenotypes is crucial for targeted management and research.
Previous studies have not comprehensively characterized Long COVID presentations in pediatric populations without complex chronic conditions.

Purpose of the Study:

To identify and characterize distinct clinical subphenotypes of pediatric Long COVID.
To utilize an unsupervised machine learning approach for subphenotype discovery in a large pediatric cohort.
To analyze electronic health record data for patterns in Long COVID presentations.

Main Methods:

An unsupervised machine learning approach, an extension of the Phe2Vec algorithm, was employed.
A cohort of pediatric patients (<21 years) with Long COVID and no prior complex chronic conditions was identified.
Electronic health record data from 38 institutions, encompassing tens of thousands of clinical concepts, were analyzed.

Main Results:

Cardiorespiratory presentations were the most frequent subphenotype, observed in 54% of patients.
Subsequent subphenotypes, in decreasing order of frequency, included musculoskeletal pain, neuropsychiatric conditions, gastrointestinal symptoms, headache, and fatigue.
The machine learning model successfully grouped patients into distinct clinical presentation clusters.

Conclusions:

Distinct clinical subphenotypes of pediatric Long COVID have been identified, with cardiorespiratory involvement being most prevalent.
The findings provide a foundation for further research into the specific mechanisms and treatments for different pediatric Long COVID presentations.
This study highlights the utility of machine learning in dissecting complex clinical phenotypes from large electronic health record datasets.