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Predictive value of the 5-item modified frailty index in adverse outcomes in total elbow arthroplasty: A
Yasmin Alamdeen Shahzada1, Matthew Smith1, Pratiik Kaushik1
1Department of Orthopaedic Surgery, Virginia Commonwealth University Health System, Richmond, VA, USA.
Introduction:
The modified frailty index (mFI-5) is a five factor risk stratification tool predicated on functional status and key medical comorbidities. mFI-5 scores have already demonstrated potential for predicting adverse outcomes after common orthopaedic procedures. The aim of this study was to capitalize upon this potential and leverage machine learning analysis to (1) further interrogate the utility of the mFI-5 as a risk stratification tool, and (2) develop an algorithm with predictive value for adverse outcomes after total elbow arthroplasty (TEA).
Methods:
A retrospective review of patients who underwent TEA from 2010 to 2020 was conducted using the American College of Surgeons National Surgery Quality Improvement Program (NSQIP) database. Postoperative complications were analyzed as summative binary variables representing the rate of complications such as mortality, readmission, reoperation, extended hospital length of stay (LOS), and discharge to a non-home destination. Univariate and multivariate analysis were performed to determine the relationship between mFI-5 and postoperative complications at the p < 0.05 level. An XGBoost binary classifier was trained to predict significant associations identified in the multivariate regression. SHAP model explainability determined the relative importance of each mFI-5 component.
Results:
A total of 725 patients (mean age 65.7 13.1 years, 78 % female) were included. Higher mFI-5 scores were associated with longer hospital stays (p < 0.001) and non-home discharge (p < 0.001). The machine learning model receiver operating characteristic area under the curve was 0.85 for LOS and 0.78 for non-home discharge. SHAP analysis revealed hypertension as the primary driver of mFI-5 predictive power, whereas congestive heart failure was found to be the least important component.
Conclusion:
mFI-5 scores have high predictive value for longer hospital stays and non-home discharge after TEA. There is growing evidence for using frailty indices to stratify risk and the model developed in this study may serve as a prototype for future clinical decision-making tools.
Insights
The modified frailty index (mFI-5) accurately predicts longer hospital stays and non-home discharge after total elbow arthroplasty (TEA). This study developed a machine learning model to enhance risk stratification for adverse outcomes in orthopedic surgery.
Area of Science:
- Orthopedic Surgery
- Geriatric Medicine
- Health Services Research
Background:
- The modified frailty index (mFI-5) is a validated tool for risk stratification in surgical patients.
- Previous studies suggest mFI-5 predicts adverse outcomes in orthopedic procedures.
- Total elbow arthroplasty (TEA) outcomes can be influenced by patient-specific factors.
Purpose of the Study:
- To evaluate the utility of the mFI-5 for risk stratification in patients undergoing TEA.
- To develop a machine learning algorithm predicting adverse outcomes after TEA using mFI-5.
- To identify key components of the mFI-5 that drive predictive accuracy.
Main Methods:
- Retrospective review of 725 patients undergoing TEA (2010-2020) from the NSQIP database.
- Analysis of postoperative complications including mortality, readmission, reoperation, LOS, and discharge disposition.
- XGBoost binary classifier and SHAP analysis to predict outcomes and determine feature importance.
Main Results:
- Higher mFI-5 scores significantly correlated with longer hospital stays (p<0.001) and non-home discharge (p<0.001).
- Machine learning model achieved an AUC of 0.85 for LOS and 0.78 for non-home discharge.
- Hypertension was the most significant mFI-5 component, while congestive heart failure was least significant.
Conclusions:
- mFI-5 scores demonstrate high predictive value for prolonged hospital stays and non-home discharge post-TEA.
- The developed machine learning model shows promise as a clinical decision-making tool for TEA.
- Frailty indices are increasingly valuable for risk stratification in orthopedic surgery.
