Machine Learning-Based Screening of Risk Factors and Prediction of Deep Vein Thrombosis and Pulmonary Embolism  After Hip Arthroplasty

Ding, Ruifeng; Ding, Yu; Zheng, Dongyu; Huang, Xingshuai; Dai, Jing-Ya; Jia, Hui; Deng, Mengqiu; Yuan, Hongbin; Zhang, Yijue; Fu, Hui‐Jun

doi:10.1177/10760296231186145

Cited by 11 publications

(4 citation statements)

References 37 publications

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“…While prognostic tools for venous thromboembolism exist for patients with various underlying conditions [16,17], to the best of our knowledge, this study represents the first attempt to utilize an AI-based tool for predicting the risk of developing DVT following EVTA. In a prior study focusing on the early diagnosis of DVT in patients undergoing hip arthroplasty, XGBoost performed as the top-performing model, achieving an impressive AUC of 0.982 [18].…”

Section: Discussionmentioning

confidence: 99%

“…Conventional approaches to identifying risk factors typically involve constructing risk models using univariate or multivariate regression techniques. However, these methods often fail to account for interactions and nonlinear relationships among variables [18]. In contrast, ML Other important predictors for DVT, preop platelet and WBC count, also demonstrate the clinical relevance of the model.…”

Section: Discussionmentioning

confidence: 99%

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An artificial-intelligence interpretable tool to predict risk of deep vein thrombosis after endovenous thermal ablation

Tabari,

Ma,

Alfonso

et al. 2024

Preprint

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IntroductionEndovenous thermal ablation (EVTA) stands as one of the primary treatments for superficial venous insufficiency. Concern exists about the potential for thromboembolic complications following this procedure. Although rare, those complications can be severe, necessitating early identification of patients prone to increased thrombotic risks. This study aims to leverage AI-based algorithms to forecast patients’ likelihood of developing deep vein thrombosis (DVT) within 30 days following EVTA.Materials and MethodsFrom 2007 to 2017, all patients who underwent EVTA were identified using the American College of Surgeons National Surgical Quality Improvement Program database. We developed and validated 4 machine learning models using demographics, comorbidities, and laboratory values to predict the risk of postoperative deep vein thrombosis: Classification and Regression Trees (CART), Optimal Classification Trees (OCT), Random Forests, and Extreme Gradient Boosting (XGBoost). The models were trained using all the available variables. SHAP analysis was adopted to interpret model outcomes and offer medical insights into feature importance and interactions.ResultsA total of 21,549 patients were included (mean age of 54 ± SD years, 67% female). In this cohort, 1.59% developed DVT. The XGBoost model had good discriminative power for predicting DVT risk with AUC of 0.711 in the hold-out test set for all-variable model. Stratification of the test set by age, BMI, preoperative white blood cell and platelet count shows that the model performs equally well across these groups.ConclusionWe developed and validated an interpretable model that enables physicians to predict which patients with superficial venous insufficiency has higher risk of developing deep vein thrombosis within 30 days following endovenous thermal ablation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

An artificial-intelligence interpretable tool to predict risk of deep vein thrombosis after endovenous thermal ablation

Tabari,

Ma,

Alfonso

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…In addition, models exist to explore the risk of general medical complications after orthopaedic surgery 30,129–147 (Table V). Using AI to effectively predict postoperative complications, physicians can attempt to minimize the individual risk of complications through targeted interventions, although this potential has been slow to become realized in clinical practice.…”

Section: Preoperative Prediction Of Postoperative Complicationsmentioning

confidence: 99%

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence

Huffman,

Pasqualini,

Khan

et al. 2024

JBJS Reviews

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» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.

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“…Profilaktyczne leczenie przeciwzakrzepowe jest standardowym działaniem u pacjentów poddawanych całkowitej endoprotezoplastyce stawu biodrowego i ma na celu zapobieganie żylnej chorobie zakrzepowo-zatorowej oraz zatorowości płucnej [42].…”

Section: Powikłaniaunclassified

Total hip arthroplasty; indications, historical overview, surgical techniques, complications, and outcomes

Kania,

Gonet,

Miszczyk

et al. 2023

J Educ Health Sport

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Introduction and purpose Total hip arthroplasty (THA) has revolutionized the treatment of various hip pathologies. This method of treatment leads to significant pain relief, improved function, and enhanced quality of life for patients worldwide. The following article provides an extensive overview of THA; covering indications, historical overview, surgical techniques, complications, as well as outcomes. Material and method The authors had analyzed both PubMed, as well as Google Scholar databases using the following key words: ‘THA’; ‘total hip replacement’; ‘total hip arthroplasty’; ‘hip’; ‘direct anterior approach’; ‘direct lateral approach’; ‘posterior approach’; ‘THA outcomes’. Conclusion THA is considered to be one of the most clinically successful and cost-effective medical procedures, which were developed during the last century. What is worth mentioning, the world’s highest-impact academic journal, The Lancet, called THA ‘the operation of the century’ in the year 2007. The historical perspective of total hip arthroplasty reveals the remarkable journey of medical pioneers who paved the way for modern techniques and materials. From the early days of simple hemiarthroplasty to the development of innovative prosthetic components and minimally invasive approaches, the field has undergone substantial progress, resulting in improved patient outcomes and longevity of the implants. Despite these advancements, the review highlights several challenges and complications associated with total hip arthroplasty. Through continued investment in research, and collaboration among orthopedic surgeons, engineers, and researchers, addressing these challenges can lead to further improvements and enhanced patient experiences.

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Machine Learning-Based Screening of Risk Factors and Prediction of Deep Vein Thrombosis and Pulmonary Embolism After Hip Arthroplasty

Cited by 11 publications

References 37 publications

An artificial-intelligence interpretable tool to predict risk of deep vein thrombosis after endovenous thermal ablation

An artificial-intelligence interpretable tool to predict risk of deep vein thrombosis after endovenous thermal ablation

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence

Total hip arthroplasty; indications, historical overview, surgical techniques, complications, and outcomes

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