Predictive and Causal Modeling of MCL Tear Risk in Lateral Approach Total Knee Arthroplasty
A pilot, hypothesis-generating framework using Random Forest, SHAP, logistic odds ratios, and causal modeling to identify risk signals and benchmark technique in lateral approach TKA.
The lateral approach to total knee arthroplasty is associated with high patient satisfaction but remains technically demanding. A rare but important complication is MCL tear, which can disrupt workflow, increase cost, and limit broader adoption of the technique.
Study Overview
This presentation explores whether predictive and causal models can help identify signals that reduce MCL tear risk during lateral approach TKA and improve procedural safety.
- Lateral approach TKA is technically demanding, especially exposure of the posteromedial tibial corner
- MCL tear is uncommon, but clinically important, occurring in fewer than 2% of cases
- An MCL tear may require conversion to a stabilized implant and increase cost and complexity
- The goal was not to make definitive claims, but to build a useful hypothesis-generating framework
Study Design
Cohort
Data were collected from 24 consecutive cases out of approximately 1,200 lateral approach TKAs performed at a surgery center in Orlando, Florida. The adverse outcome was rare and the dataset was small and highly imbalanced.
Predictive Model
An optimized Random Forest classifier was used to flag predictive features. Performance was modest, with an F1 score of 0.3, but the model was useful for identifying feature importance and risk patterns.
Interpretability
SHAP values and SHAP interactions were used to explain feature importance and make the model more understandable for orthopedic clinicians.
Key Predictive Findings
Logistic odds ratio analysis identified the highest-risk profile as:
These findings, combined with SHAP values, were intended to give clinicians clear targets for additional vigilance and procedural risk mitigation.
Interactive Dashboard
To make the model clinically interpretable, an interactive dashboard was built in Google Colab.
- Clinicians can move sliders for age, sex, BMI, alignment, and surgeon subgroup
- The dashboard simulates patient profiles and updates risk predictions in real time
- This interface is more intuitive for orthopedic clinicians than standard SHAP plots alone
- It allows recreation of high-risk profiles and exploration of how assumptions change predicted risk
Causal Analysis and Surgeon Benchmarking
Framework
A causal framework using an S-Learner treated surgeon subgroup as the “treatment” and estimated how surgeon technique changed MCL tear risk while adjusting for confounders such as age, sex, and pre-alignment.
Initial Signal
Initial stratification showed tear rates between 1.4% and 3% across surgeon subgroups. In high-risk valgus patients, Surgeon 1 had the lowest tear rate.
Estimated Effect
The causal estimate suggested Surgeon 1 reduced MCL tear risk by about 2 percentage points compared with Surgeon 0, with the confidence interval entirely below zero.
Practical Takeaway
Although a 2 percentage-point reduction may sound small, when the baseline MCL tear rate is less than 2%, this corresponds to a relative risk reduction greater than 80%.
The study suggests Surgeon 1’s technique may serve as a benchmark for safer surgery, especially in high-risk patients such as elderly females with severe valgus deformity and elevated BMI.
Future Directions
- Prospective collection of retractor usage and outcomes
- Formal study of a novel MCL retractor that appears promising anecdotally
- Testing whether optimized technique plus custom retractors can drive tear rates further toward zero
- Developing policy and training protocols to scale the lateral approach more safely
Important Limitations
These results are encouraging but not definitive. The estimates depend on a very small event sample, and uncertainty remains even with bias-corrected bootstrap analysis. This should be viewed as strong hypothesis-generating evidence rather than final proof.
Summary
This pilot study combined Random Forest, SHAP, logistic odds ratios, an interactive clinician dashboard, and stratified causal analysis to identify high-risk patients and benchmark safer technique in lateral approach TKA. The same high-risk profile identified by the predictive model also showed a significant causal treatment effect, reinforcing the importance of extra vigilance in this vulnerable subgroup.
Lateral Approach TKA Risk Modeling
Predictive modeling, clinician dashboards, and causal benchmarking to improve safety in high-risk cases