Ten-Year Multicenter Retrospective Study Utilizing Machine Learning Algorithms to Identify Patients at High Risk of Venous Thromboembolism After Radical Gastrectomy

Liu, Yuan; Chen, Song; Tian, Zhiqiang; Shen, Wei

doi:10.2147/ijgm.s408770

Cited by 2 publications

(5 citation statements)

References 43 publications

(56 reference statements)

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“…Until recently, physicians estimated this risk based on clinical gestalt and/or use of CPMs, such as IMPROVE 8 and CAPRINI. 26 Overall, 22 studies 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 on various patient groups (e.g., asthma, trauma, intensive care unit [ICU], acute pancreatitis, etc.) that explored in- and post-hospital VTE at different time periods, were included (see Supplementary Table S1 , available in the online version).…”

Section: Resultsmentioning

confidence: 99%

“…31 33 36 49 Interestingly, methodological pitfalls in the ML architecture that can lead to data leakage have been identified, such as handling of missing values with imputation methods before splitting the dataset into training and testing subsets 49 as well as not clear reporting on individual test set. 27 29 30 34 38 39 41 51 Performance metrics are not consistently reported, making the head-to-head comparison of these studies challenging. Only one study performed external validation.…”

Section: Resultsmentioning

confidence: 99%

“…125 Nevertheless, if it is not performed properly, data leakage can happen. In every case, an unseen part of the dataset must be kept for internal validation purposes and this was not clear in many 27 28 29 30 34 38 39 40 41 51 53 54 56 57 58 61 62 64 66 77 84 89 of the reviewed studies.…”

Section: Discussionmentioning

confidence: 99%

“…splitting the dataset into training and testing subsets 49 as well as not clear reporting on individual test set 27,29,30,34,38,39,41,51 . Performance metrics are not consistently reported, making the head-to-head comparison of these studies challenging.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Nevertheless, if it is not performed properly data leakage can happen. In every case an unseen part of the dataset must be kept for internal validation purposes and this was not clear in many [28][29][30]34,[38][39][40][41]51,53,54,[56][57][58]61,62,64,77,84,89,126,127 of the reviewed studies. This review highlighted a significant problem in AI-related research, namely the lack of standardized reporting methods.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

See 4 more Smart Citations

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

Danilatou,

Dimopoulos,

Kostoulas

et al. 2024

Thromb Haemost

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Background: Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific Clinical Prediction Models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records (EHRs). We aimed to explore ML-CPMs applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment. Methods: Three databases were searched, PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included. Results: Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison. Conclusion: ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of AI in VTE.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

See 3 more Smart Citations

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

Danilatou,

Dimopoulos,

Kostoulas

et al. 2024

Thromb Haemost

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Prediction, stratification, and explanation of the risk of Venous Thromboembolism in post-operative patients using Electronic Health Records (Preprint)

Edakalavan,

Andraska,

Myers

et al. 2024

Preprint

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BACKGROUND Postoperative venous thromboembolic events (VTE), encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE), are preventable but can cause severe morbidity and mortality in post-surgical patients. Around 1 in 1000 individuals annually experience VTE, making prompt diagnosis and therapy vital, as untreated VTE carries a 30% mortality rate. These events result in $1 billion in hospital costs every year. While prophylaxis for preventing VTE is crucial, some patients may face challenges that limit their ability to take prophylactic measures, such as the risk of bleeding, allergies or adverse reactions, recent surgery or trauma, severe liver or kidney disease, and drug interactions. Of the patients who develop VTE after surgery, around 40% occur during the initial hospital stay , whereas 60% of the patients develop VTE within 30-90 days after discharge from the hospital following surgery. Existing VTE risk scoring systems, such as Caprini and Rogers sub-optimal and can be cumbersome and difficult to translate to patient-specific clinical decision-making. Though machine learning approaches have been used to predict VTE risk in specific surgery types, no validated predictive models are currently used in clinical practice for VTE risk calculation under the broader umbrella of post-operative surgery patients. OBJECTIVE The study seeks to use both the static and the time-varying EHR patient data (aggregated per visit) to 1) predict the VTE risk for a surgical patient within 30 days post-discharge using machine learning (ML); 2) stratify the VTE risk of the patient into high, medium, and low-risk categories; and 3) compare the performance of individual department-specific ML models to that of a unified model 4) determine which variables are most strongly associated with postoperative VTE METHODS The structured EHR data from post-operative patients from our multi-center hospital system between 2013 and 2019 were used. Various machine learning algorithms, including linear regression with L1/L2 regularization, random forest, and eXtreme Gradient Boosting (XGBoost), were evaluated. Different output probability thresholds were chosen to stratify the patients into low, medium, and high-risk categories. Department-specific ML models were developed, and their performance was compared with the unified model to determine the viability of constructing individual models versus unified models. Feature importance techniques were subsequently applied to identify the most influential features, followed by Rank Biased Overlap (RBO) analysis to quantify the level of overlap among the department-specific models. RESULTS Our findings demonstrate the efficiency of ML models in predicting and categorizing VTE risk among post-operative patients. The top-performing ML model achieved an F1 score of 0.76 with an area under the receiver operating characteristic curve (AUROC) of 0.83 and an area under the precision recall curve (AUPRC) of 0.89. A threshold of less than 0.3 in ML output probability was utilized to designate patients as low risk, while probabilities exceeding 0.7 indicated high risk. Interestingly, our investigation revealed that department-specific models failed to surpass the performance of the unified model. Analysis of feature importance correlation highlighted the length of stay (LOS) as the most influential predictor, followed by various laboratory results such as blood urea nitrogen (BUN), white blood cell count (WBC), vital signs like heart rate and temperature, as well as the patient’s age and body mass index (BMI). Our model integrates the dynamic aspects of a patient’s condition, incorporating changes in laboratory values and vital signs into its assessments. This represents a critical improvement over static models, which fail to account for the fluctuating clinical features of patients. CONCLUSIONS We have demonstrated the utility of machine learning models in predicting and stratifying VTE risk among post-operative patients. Accurate risk assessment plays a pivotal role in VTE prevention, enabling the development of personalized VTE prophylaxis strategies and monitoring plans. Our research marks the initial phase in creating a decision support tool to provide automated risk assessments, guiding tailored screening and prophylaxis approaches for individual patients.

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Ten-Year Multicenter Retrospective Study Utilizing Machine Learning Algorithms to Identify Patients at High Risk of Venous Thromboembolism After Radical Gastrectomy

Cited by 2 publications

References 43 publications

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

Prediction, stratification, and explanation of the risk of Venous Thromboembolism in post-operative patients using Electronic Health Records (Preprint)

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