Lumbar Injection Satisfaction — Data-Driven Analysis

Jul 2025 · 1 min read
projects

Data-driven project that retrospectively identifies which chronic low-back-pain (CLBP) patients benefit from lumbar steroid injections, using the clinical, demographic and patient-reported data of the TREXI study. The aim is to find key predictors of treatment satisfaction and to establish clinically meaningful pain-reduction thresholds.

Study design

212 participants completed questionnaires directly before (T0) and two weeks after (T1) the injection, covering pain intensity, patient-reported outcomes (COMI, PSEQ), and demographic and clinical variables.

Study design — baseline and post-treatment data collection

Methodology

Missing values were imputed with Random Forest (numeric) and K-Nearest-Neighbours (categorical); features were standardised or encoded by type. Nested cross-validation trained Random Forest, Logistic Regression and Gradient Boosting classifiers, with the best model optimised through Bayesian hyperparameter tuning. SHAP values interpreted the predictions and ROC analysis derived the pain-reduction thresholds.

Model development pipeline

Key results

A Random Forest model reached 0.865 average precision in predicting treatment satisfaction. SHAP analysis identified pain self-efficacy — coping mechanisms and maintained daily-activity performance — as the strongest predictors. A 2.03-point absolute (or 30 % relative) drop on the pain scale was found to be clinically meaningful.

Classification results

Published in Scientific Reports (Nature), 2025. Supported by the PHRT Strategic Focus Area of the ETH Domain.

Medical-Imaging Deep-Learning Data-Tools
Maria Monzon
Authors
Maria Monzon (she/her)
Computer Vision & Medical AI Researcher
PhD candidate at ETH Zurich developing robust and trustworthy deep learning for medical image analysis — spine and cardiac MRI, multimodal biomedical data, and uncertainty quantification. Previously a computer-vision researcher at BASF, where I deployed models to production in regulated, GLP-certified environments. I care about efficient code and reproducible research.