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Phenotypic Classification of Low Back Pain in Rheumatic and Spinal Disorders: A Data-Driven Approach Using Latent

Minh Quan Le Hoang¹, Dieu Dang Thi¹, Ngoc Nghia Nguyen Thi¹

¹University of Medicine and Pharmacy at Ho Chi Minh city, Ho Chi Minh City, Vietnam.

Journal of Integrative and Complementary Medicine

|June 9, 2026

Summary

Latent Tree Models identified three low back pain phenotypes that align with Traditional Medicine syndromes. This approach bridges traditional observation with modern data science for better LBP treatment strategies.

Keywords:

latent tree analysis pain phenotype rheumatology specific low back pain spinal disorders

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Area of Science:

Integrative Medicine
Data Science in Healthcare
Musculoskeletal Disorders

Background:

Low back pain (LBP) is a complex condition with heterogeneous causes.
Traditional Medicine (TM) offers diagnostic syndromes that may capture LBP variations.
Integrating data-driven approaches with TM could enhance LBP understanding and treatment.

Purpose of the Study:

To empirically link data-driven pain phenotypes with classical TM syndromes in LBP patients.
To utilize Latent Tree Models (LTM) for identifying distinct LBP phenotypes.
To evaluate the alignment of identified phenotypes with traditional diagnostic constructs.

Main Methods:

A cross-sectional study of 260 LBP patients with identifiable musculoskeletal/spinal disorders in Vietnam.
Latent Tree Models (LTM) applied to clinical symptom data to define pain phenotypes.
Multivariable logistic regression used to analyze risk factors for each phenotype.

Main Results:

LTM identified three key phenotypes: 'Stiffness/Cold/Heavy Pain', 'Dull, Localized Pain', and 'Sharp, Stabbing Pain'.
Phenotypes correlated with specific clinical findings (radiating pain, bilateral pain, smoking history) and diagnoses (herniated disc).
These data-driven clusters aligned with TM syndromes: Cold-Dampness, Kidney Deficiency, and Blood Stasis, respectively.

Conclusions:

Latent Tree Models (LTM) effectively identify data-verifiable clusters corresponding to TM syndromes in LBP.
This provides an evidence-based framework for stratifying LBP related to spinal disorders.
The approach bridges traditional observations with modern data science for mechanism-based therapeutic strategies.