Lower extremity fracture surgery: Who is at risk for chronic pain? :- Medznat
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Chronic pain after leg fracture surgery: New predictive model identifies high-risk patients

Chronic postsurgical pain Chronic postsurgical pain
Chronic postsurgical pain Chronic postsurgical pain

What's new?

A high-accuracy machine learning model predicts chronic postsurgical pain after lower extremity fracture surgery, enabling early risk stratification and personalized pain management.

Chronic pain after fracture repair is more common than many clinicians expect—and now, a new study offers a smarter way to predict it before patients even leave the hospital. Using a machine learning–enhanced model, Yangzi Zhu and other researchers have identified key risk factors for chronic postsurgical pain (CPSP) in patients with distal lower extremity fractures, paving the way for earlier and more personalized intervention.

Analyzing 818 patients with 1-year follow-up, the study found that 38.4% developed CPSP, highlighting a substantial postoperative burden. Notably, 18.2% of those patients went on to develop neuropathic pain, a more severe and persistent form that can markedly impair quality of life. To tackle this, the authors built a robust predictive model using a three-step approach—LASSO regression, information gain analysis, and multivariable logistic regression.

The result: a high-performing tool with an area under the curve (AUC) of 0.872 in the development cohort and 0.838 in validation, illustrating strong accuracy and reliability in identifying at-risk patients. The model pinpoints several clinically actionable predictors, including:

  • Postoperative analgesic technique
  • Surgical fixation type
  • Preoperative clinical management
  • Pain intensity at hospital visit
  • Pain intensity on postoperative day 1 (via numerical rating scale [NRS])

These insights reinforce that early pain control and surgical decisions play a critical role in long-term outcomes. What sets this cohort analysis apart is its real-world application. Researchers translated the model into a web-based risk calculator, allowing clinicians to assess CPSP risk at discharge and act early. By combining machine learning precision with the interpretability of traditional regression, the tool bridges the gap between data science and bedside care.

The takeaway is clear: CPSP is both common and predictable. With the right tools, clinicians can move from reactive treatment to proactive, personalized pain care, potentially mitigating complications like depression, disability, and reduced quality of life.

Source:

Anesthesia & Analgesia

Article:

Risk Factors and Prediction of Chronic Postsurgical Pain Among Patients With Distal Lower Extremity Fracture: Cohort Analysis

Authors:

Yangzi Zhu et al.

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