Prognosticating Prosthetic Ambulation Ability in People With Lower Limb Amputation in Early Post-Operative Phase.

OBJECTIVE: To formulate a prognostication model in the early post-operation phase of lower limb amputation to predict patient's ability to ambulate with a prosthesis post rehabilitation. DESIGN: Retrospective cohort study, using data collected from electronic medical records. Predictive factors and prosthetic ambulation outcomes post rehabilitation were used to develop prognostic models via machine learning techniques. SETTING: Regional hospital's ambulatory rehabilitation clinic. PARTICIPANTS: Patients with major lower limb amputation (N=329). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: The outcome of prosthetic ambulation ability post rehabilitation collected was categorized in 3 groups: non-ambulant with prosthesis, homebound ambulant with prosthesis (AP), and community AP. RESULTS: In a 2-class model of non-ambulant and AP (homebound and community), the model with highest accuracy of prediction included ethnicity, total Functional Comorbidity Index (FCI), level of amputation, being community ambulant prior to amputation, and age. The f1-score and area under receiver operator curve (AUROC) of the model is 0.78 and 0.82. In a 3-class model consisting of all 3 groups of outcomes, the model with highest accuracy of prediction required 10 factors. The additional factors from the 2-class model include presence of caregiver, history of congestive heart failure, diabetes, visual impairment, and stroke. The 3-class model has a moderate accuracy with a f1-score and AUROC of 0.60 and 0.79. CONCLUSION: The 2-class prognostication model has a high accuracy which can be used early post-amputation to predict if patient would be ambulant with a prosthesis post rehabilitation. The 3-class prognostication model has moderate accuracy and is able to further differentiate the walking ability to either homebound or community ambulation with a prosthesis, which can assist in prosthetic prescription and setting realistic rehabilitation goals.

Risk Prediction of Diabetic Foot Amputation Using Machine Learning and Explainable Artificial Intelligence.

BACKGROUND: Diabetic foot ulcers (DFUs) are serious complications of diabetes which can lead to lower extremity amputations (LEAs). Risk prediction models can identify high-risk patients who can benefit from early intervention. Machine learning (ML) methods have shown promising utility in medical applications. Explainable modeling can help its integration and acceptance. This study aims to develop a risk prediction model using ML algorithms with explainability for LEA in DFU patients. METHODS: This study is a retrospective review of 2559 inpatient DFU episodes in a tertiary institution from 2012 to 2017. Fifty-one features including patient demographics, comorbidities, medication, wound characteristics, and laboratory results were reviewed. Outcome measures were the risk of major LEA, minor LEA and any LEA. Machine learning models were developed for each outcome, with model performance evaluated using receiver operating characteristic (ROC) curves, balanced-accuracy and F1-score. SHapley Additive exPlanations (SHAP) was applied to interpret the model for explainability. RESULTS: Model performance for prediction of major, minor, and any LEA event achieved ROC of 0.820, 0.637, and 0.756, respectively, with XGBoost, XGBoost, and Gradient Boosted Trees algorithms demonstrating best results for each model, respectively. Using SHAP, key features that contributed to the predictions were identified for explainability. Total white cell (TWC) count, comorbidity score and red blood cell count contributed highest weightage to major LEA event. Total white cell, eosinophils, and necrotic eschar in the wound contributed most to any LEA event. CONCLUSIONS: Machine learning algorithms performed well in predicting the risk of LEA in a patient with DFU. Explainability can help provide clinical insights and identify at-risk patients for early intervention.

Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients.

Post-stroke depression and anxiety, collectively known as post-stroke adverse mental outcome (PSAMO) are common sequelae of stroke. About 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with PSAMO have poorer health outcomes with higher mortality and greater functional disability. In this study, we aimed to develop a machine learning (ML) model to predict the risk of PSAMO. We retrospectively studied 1780 patients with stroke who were divided into PSAMO vs. no PSAMO groups based on results of validated depression and anxiety questionnaires. The features collected included demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. Recursive feature elimination was used to select features to input in parallel to eight ML algorithms to train and test the model. Bayesian optimization was used for hyperparameter tuning. Shapley additive explanations (SHAP), an explainable AI (XAI) method, was applied to interpret the model. The best performing ML algorithm was gradient-boosted tree, which attained 74.7% binary classification accuracy. Feature importance calculated by SHAP produced a list of ranked important features that contributed to the prediction, which were consistent with findings of prior clinical studies. Some of these factors were modifiable, and potentially amenable to intervention at early stages of stroke to reduce the incidence of PSAMO.