Developing and validating an electronic health record-based frailty index in pre-operative settings using machine learning

Bai, Chen; Al-Ani, Mohammad; Amini, Shawna; Tighe, Patrick; Price, Catherine; Manini, Todd; Mardini, Mamoun

doi:10.1007/s10844-023-00818-9

Cited by 2 publications

(3 citation statements)

References 47 publications

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“…Considering all these biomarkers collectively, the ML approach shows promise in enhancing the capability to detect frailty with improved sensitivity and specificity, especially since frailty involves multiple biological systems. The feature importance of the BA and frailty predictors revealed that cystatin C, glycated hemoglobin, creatinine, hemoglobin and Creative protein are among the most important features, which is consistent with many previous findings [6,8,9,18,28,61].…”

Section: Advantages Of Machine Learning and Ba And Frailty Status Pre...supporting

confidence: 91%

“…Additionally, SHAP toolkit provides several visualization tools to depict the impact of features on the prediction model, such as the SHAP summary plot, waterfall plot, and partial dependence plot. These plots have been applied in many studies to explain the effects exerted on BA or frailty prediction models [18,23,[27][28][29].…”

Section: Machine Learning and Explainable Ai For Biomarker Discoverymentioning

confidence: 99%

“…The feature variables and candidate models used are the same as those employed in the BA predictor building. The choice of models was also based on publications and later XAI analysis [28,45].…”

Section: Frailty Status Predictor Modelingmentioning

confidence: 99%

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Explainable machine learning framework for biomarker discovery by combining biological age and frailty prediction (Preprint)

Wang,

2024

Preprint

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BACKGROUND Biological age (BA) and frailty are two distinct measures of health that offer insights into aging. The existing machine learning (ML) predictors of BA or frailty from blood-based biomarkers are incapable of comparing and analyzing these biomarkers, which may deepen our understanding of these two distinct pathways toward aging. OBJECTIVE This study aimed to develop a framework to compare and analyze biomarkers by combining BA and frailty ML predictors with eXplainable Artificial Intelligence (XAI) techniques. METHODS We utilized data from middle-aged and older Chinese adults (≥45 years) in the 2011/2012 wave (n=9702) and the 2015/2016 wave (n=9455, as external validation) of the China Health and Retirement Longitudinal Study (CHARLS). Sixteen blood-based biomarkers were used to predict BA and frailty. Four ML algorithms were employed in the training and validation, and performance metrics were compared to select the best models. Then, SHapley Additive exPlanations (SHAP) analysis was conducted on the selected models. RESULTS Gradient Boosting performed the best in the BA predictor, and CatBoost performed the best in the frailty predictor. Traditional ML feature importance identified cystatin C and glycated hemoglobin as the major contributors for their respective models. However, subsequent SHAP analysis demonstrated that only cystatin C was the primary contributor in both models, suggesting that it plays an important role in both pathways. CONCLUSIONS Our novel framework can compare and analyze biomarkers by integrating BA and frailty predictors and XAI techniques. The present approach leverages routine blood biomarkers and can easily incorporate additional biomarkers, providing a scalable and comprehensive toolset that offers a quantitative understanding of interesting biomarkers and complex physiological traits. CLINICALTRIAL N/A

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Section: Advantages Of Machine Learning and Ba And Frailty Status Pre...supporting

confidence: 91%

Section: Machine Learning and Explainable Ai For Biomarker Discoverymentioning

confidence: 99%

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Explainable machine learning framework for biomarker discovery by combining biological age and frailty prediction (Preprint)

Wang,

2024

Preprint

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Enhancing Frailty Assessments for Transcatheter Aortic Valve Replacement Patients Using Structured and Unstructured Data: Real-World Evidence Study

Mardini,

Bai,

Bavry

et al. 2024

JMIR Aging

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Background Transcatheter aortic valve replacement (TAVR) is a commonly used treatment for severe aortic stenosis. As degenerative aortic stenosis is primarily a disease afflicting older adults, a frailty assessment is essential to patient selection and optimal periprocedural outcomes. Objective This study aimed to enhance frailty assessments of TAVR candidates by integrating real-world structured and unstructured data. Methods This study analyzed data from 14,000 patients between January 2018 and December 2019 to assess frailty in TAVR patients at the University of Florida. Frailty was identified using the Fried criteria, which includes weight loss, exhaustion, walking speed, grip strength, and physical activity. Latent Dirichlet allocation for topic modeling and Extreme Gradient Boosting for frailty prediction were applied to unstructured clinical notes and structured electronic health record (EHR) data. We also used least absolute shrinkage and selection operator regression for feature selection. Model performance was rigorously evaluated using nested cross-validation, ensuring the generalizability of the findings. Results Model performance was significantly improved by combining unstructured clinical notes with structured EHR data, achieving an area under the receiver operating characteristic curve of 0.82 (SD 0.07), which surpassed the EHR-only model’s area under the receiver operating characteristic curve of 0.64 (SD 0.08). The Shapley Additive Explanations analysis found that congestive heart failure management, back problems, and atrial fibrillation were the top frailty predictors. Additionally, the latent Dirichlet allocation topic modeling identified 7 key topics, highlighting the role of specific medical treatments in predicting frailty. Conclusions Integrating unstructured clinical notes and structured EHR data led to a notable enhancement in predicting frailty. This method shows great potential for standardizing frailty assessments using real-world data and improving patient selection for TAVR.

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Developing and validating an electronic health record-based frailty index in pre-operative settings using machine learning

Cited by 2 publications

References 47 publications

Explainable machine learning framework for biomarker discovery by combining biological age and frailty prediction (Preprint)

Explainable machine learning framework for biomarker discovery by combining biological age and frailty prediction (Preprint)

Enhancing Frailty Assessments for Transcatheter Aortic Valve Replacement Patients Using Structured and Unstructured Data: Real-World Evidence Study

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