# Precision Rehabilitation After Youth Anterior Cruciate Ligament Reconstruction: Individualized Reinjury Risk Stratification and Modifiable Risk Factor Identification to Guide Late-Phase Rehabilitation

## TL;DR

This study creates a model to predict and reduce the risk of second ACL injuries in young athletes after surgery, using modifiable factors to guide personalized rehabilitation.

## Contribution

A clinician-informed ACL reinjury risk prediction model with interpretable modifiable risk factors for individualized rehabilitation.

## Key findings

- The model achieved 94% sensitivity and 76% specificity in predicting ACL reinjury risk.
- High-risk patients had 4.5 times greater risk of repeat ACL injuries compared to low-risk patients.
- The model includes 23 modifiable risk factors, prioritized for individualized rehabilitation planning.

## Abstract

After anterior cruciate ligament (ACL) reconstruction, adolescent athletes have a high risk of second ACL injuries, and revision ACL reconstruction is associated with increased medical costs, reduced activity levels, chronic knee pain, and higher rates of knee osteoarthritis, making the prevention of a reinjury a priority. While athlete clearance protocols and algorithms exist, the current methods of identifying the reinjury risk have limited predictive accuracy and are largely based on nonmodifiable risk factors, which limit their clinical application.

The goal of this study was to develop an ACL reinjury risk prediction (ACL-RRP) model capable of accurately classifying an individual patient’s risk, identifying modifiable risk factors, and ranking these factors in the order of importance and ability to be modified.

Cohort study (Diagnosis); Level of evidence, 2.

A clinician-informed approach was utilized to develop the prediction model and an interpretable output system. The primary outcome variable was the likelihood of sustaining a repeat ACL injury. The data were split into training (80% [n = 628]) and holdout (20% [n = 158]) datasets to train and subsequently validate the model. The accuracy of classification was identified by the sensitivity, specificity, positive/negative predictive values, and odds ratio.

The final model included 33 predictor variables, 23 of which are modifiable. The model adjusted the weight of the risk classification and risk factors (predictor variables) on a case-by-case basis. The model demonstrated a sensitivity of 94% and a specificity of 76%. Patients classified as being high risk had 4.5 times the risk of repeat ACL injuries compared with those classified as being low risk.

This clinician-informed ACL-RRP model demonstrated a high degree of accuracy when classifying patients as having a high or low risk of repeat ACL injuries and generated patient-specific modifiable risk factors to guide ongoing rehabilitation or patient education to achieve the goals of reducing the ACL reinjury risk.