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Analgesia and Pain Management

Analgesia and Pain Management

Pain is critical to various clinical pathologies, provoking an urgent need for effective management. Pain, whether acute or chronic, is a complex neurochemical process. Its alleviation depends on the type, with nonopioid analgesics effective for mild to moderate pain, such as musculoskeletal or inflammatory pain, while neuropathic pain responds best to anticonvulsants, tricyclic antidepressants, or serotonin/norepinephrine reuptake inhibitors. For severe acute or chronic pain, opioids may be...

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Updated: Mar 2, 2026

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Published on: April 14, 2016

Predicting the course of high-impact chronic pain using machine learning algorithms.

Matthew C Morris¹, Hamidreza Moradi², Maryam Aslani³

¹Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, United States; Vanderbilt Center for Musculoskeletal Research, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Psychiatry and Human Behavior, University of Mississippi Medical Center, Jackson, MS, United States.

The Journal of Pain

|February 28, 2026

Summary

This summary is machine-generated.

Family income and physical health significantly predict high-impact chronic pain (HICP) outcomes, while depression severity is a key mental health factor. Understanding these biopsychosocial predictors can improve HICP management.

Keywords:

Biopsychosocial High-impact chronic pain Machine learning Pain transitions

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Area of Science:

Pain Medicine
Public Health
Health Informatics

Background:

High-impact chronic pain (HICP) affects millions, yet its predictors remain unclear.
Understanding HICP incidence, persistence, and recovery is crucial for effective management.

Purpose of the Study:

To identify and assess the relative importance of biopsychosocial factors in predicting HICP outcomes.
To evaluate machine learning models for HICP prediction accuracy.

Main Methods:

Utilized the National Health Interview Survey Longitudinal Cohort (2019-2020).
Applied machine learning algorithms, including Gradient Boosting Decision Trees, to predict HICP incidence, persistence, and recovery.
Employed Shapley Additive Explanation values to determine feature importance.

Main Results:

Gradient Boosting Decision Trees achieved high discriminatory power (macro-AUC=0.80).
Family income predicted HICP incidence and recovery; physical health predicted persistent HICP.
Depression severity emerged as the most significant mental health predictor across all outcomes.

Conclusions:

Biopsychosocial factors, including socioeconomic status, physical health, and mental health, have differential prognostic importance for HICP.
Findings can inform predictive frameworks, clinical risk screening, and targeted interventions for HICP.