NDLI: Preoperative prediction model for risk of readmission after total joint replacement surgery: a random forest approach leveraging NLP and unfairness mitigation for improved patient care and cost-effectiveness

Content Provider	Springer Nature : BioMed Central
Author	Digumarthi, Varun Amin, Tapan Kanu, Samuel Mathew, Joshua Edwards, Bryan Peterson, Lisa A Lundy, Matthew E Hegarty, Karen E
Abstract	Background The Center for Medicare and Medicaid Services (CMS) imposes payment penalties for readmissions following total joint replacement surgeries. This study focuses on total hip, knee, and shoulder arthroplasty procedures as they account for most joint replacement surgeries. Apart from being a burden to healthcare systems, readmissions are also troublesome for patients. There are several studies which only utilized structured data from Electronic Health Records (EHR) without considering any gender and payor bias adjustments. Methods For this study, dataset of 38,581 total knee, hip, and shoulder replacement surgeries performed from 2015 to 2021 at Novant Health was gathered. This data was used to train a random forest machine learning model to predict the combined endpoint of emergency department (ED) visit or unplanned readmissions within 30 days of discharge or discharge to Skilled Nursing Facility (SNF) following the surgery. 98 features of laboratory results, diagnoses, vitals, medications, and utilization history were extracted. A natural language processing (NLP) model finetuned from Clinical BERT was used to generate an NLP risk score feature for each patient based on their clinical notes. To address societal biases, a feature bias analysis was performed in conjunction with propensity score matching. A threshold optimization algorithm from the Fairlearn toolkit was used to mitigate gender and payor biases to promote fairness in predictions. Results The model achieved an Area Under the Receiver Operating characteristic Curve (AUROC) of 0.738 (95% confidence interval, 0.724 to 0.754) and an Area Under the Precision-Recall Curve (AUPRC) of 0.406 (95% confidence interval, 0.384 to 0.433). Considering an outcome prevalence of 16%, these metrics indicate the model’s ability to accurately discriminate between readmission and non-readmission cases within the context of total arthroplasty surgeries while adjusting patient scores in the model to mitigate bias based on patient gender and payor. Conclusion This work culminated in a model that identifies the most predictive and protective features associated with the combined endpoint. This model serves as a tool to empower healthcare providers to proactively intervene based on these influential factors without introducing bias towards protected patient classes, effectively mitigating the risk of negative outcomes and ultimately improving quality of care regardless of socioeconomic factors.
Related Links	https://josr-online.biomedcentral.com/counter/pdf/10.1186/s13018-024-04774-0.pdf
Ending Page	17
Page Count	17
Starting Page	1
File Format	HTM / HTML
DOI	10.1186/s13018-024-04774-0
Journal	Journal of Orthopaedic Surgery and Research
Issue Number	1
Volume Number	19
Language	English
Publisher	BioMed Central
Publisher Date	2024-05-10
Access Restriction	Open
Subject Keyword	Orthopedics Surgical Orthopedics Predictive model Classification Fairlearn Natural language processing Orthopedic
Content Type	Text
Resource Type	Article
Subject	Surgery Orthopedics and Sports Medicine
Journal Impact Factor	2.8/2023
5-Year Journal Impact Factor	3/2023

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