Psychological and Biomechanical Factors for Secondary ACL Injury Risk Prediction

Shengjie Xiong's dataset, last updated in May 2026, contains retrospective clinical data from 487 patients who underwent primary anterior cruciate ligament (ACL) reconstruction. It includes 30 predictor variables spanning demographic, MRI, gait analysis, isokinetic strength, and psychological domains collected at a 6-month postoperative follow-up. The data was used to develop a multi-domain machine learning model predicting secondary ACL injury risk, with 64 patients (13.1%) experiencing a secondary injury.

Use Cases

• Predicting secondary ACL injury risk based on multi-domain postoperative data.
• Evaluating the incremental predictive value of psychological factors like kinesiophobia (TSK score) and depressive symptoms (PHQ-9) in clinical models.
• Comparing the performance of different machine learning algorithms (e.g., logistic regression, random forest) for a clinical prediction task.
• Investigating the relationship between biomechanical factors (gait analysis, isokinetic strength) and long-term surgical outcomes.

Strengths

• Includes data from 487 patients, providing a substantial cohort for analysis.
• Encompasses 30 predictor variables across five distinct clinical domains (demographic, MRI, gait, strength, psychological).
• Model performance metrics are reported, including an AUC of 0.739 and a Brier score of 0.106 for the best-performing algorithm.
• Released under a permissive CC-BY-4.0 license for open reuse.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for certain modeling tasks.
• The retrospective study design may introduce biases not present in prospective data.

Provenance

Source

figshare

Collection Method

Retrospective cohort study of patients who underwent primary ACL reconstruction.

Freshness

Last updated 2026-05-15 04:28:12; freshness should be verified.