Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology. Dis Markers. 2017;2017:8781379. [PMID: 29104344 PMCID: PMC5623790 DOI: 10.1155/2017/8781379]

Machine Learning as a Diagnostic and Prognostic Tool for Predicting Thrombosis in Cancer Patients: A Systematic Review

Khorana score (KS) is an established risk assessment model for predicting cancer-associated thrombosis. However, it ignores several risk factors and has poor predictability in some cancer types. Machine learning (ML) is a novel technique used for the diagnosis and prognosis of several diseases, including cancer-associated thrombosis, when trained on specific diagnostic modalities. Consolidating the literature on the use of ML for the prediction of cancer-associated thrombosis is necessary to understand its diagnostic and prognostic abilities relative to KS. This systematic review aims to evaluate the current use and performance of ML algorithms to predict thrombosis in cancer patients. This study was conducted per Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Databases Medline, EMBASE, Cochrane, and ClinicalTrials.gov, were searched from inception to September 15, 2023, for studies evaluating the use of ML models for the prediction of thrombosis in cancer patients. Search terms "machine learning," "artificial intelligence," "thrombosis," and "cancer" were used. Studies that examined adult cancer patients using any ML model were included. Two independent reviewers conducted study selection and data extraction. Three hundred citations were screened, of which 29 studies underwent a full-text review, and ultimately, 8 studies with 22,893 patients were included. Sample sizes ranged from 348 to 16,407 patients. Thrombosis was characterized as venous thromboembolism (n = 6) or peripherally inserted central catheter thrombosis (n = 2). The types of cancer included breast, gastric, colorectal, bladder, lung, esophageal, pancreatic, biliary, prostate, ovarian, genitourinary, head-neck, and sarcoma. All studies reported outcomes on the ML's predictive capacity. The extreme gradient boosting appears to be the best-performing model, and several models outperform KS in their respective datasets.

DNA liquid biopsy-based prediction of cancer-associated venous thromboembolism

Cancer-associated venous thromboembolism (VTE) is a major source of oncologic cost, morbidity and mortality. Identifying high-risk patients for prophylactic anticoagulation is challenging and adds to clinician burden. Circulating tumor DNA (ctDNA) sequencing assays ('liquid biopsies') are widely implemented, but their utility for VTE prognostication is unknown. Here we analyzed three plasma sequencing cohorts: a pan-cancer discovery cohort of 4,141 patients with non-small cell lung cancer (NSCLC) or breast, pancreatic and other cancers; a prospective validation cohort consisting of 1,426 patients with the same cancer types; and an international generalizability cohort of 463 patients with advanced NSCLC. ctDNA detection was associated with VTE independent of clinical and radiographic features. A machine learning model trained on liquid biopsy data outperformed previous risk scores (discovery, validation and generalizability c-indices 0.74, 0.73 and 0.67, respectively, versus 0.57, 0.61 and 0.54 for the Khorana score). In real-world data, anticoagulation was associated with lower VTE rates if ctDNA was detected (n = 2,522, adjusted hazard ratio (HR) = 0.50, 95% confidence interval (CI): 0.30-0.81); ctDNA- patients (n = 1,619) did not benefit from anticoagulation (adjusted HR = 0.89, 95% CI: 0.40-2.0). These results provide preliminary evidence that liquid biopsies may improve VTE risk stratification in addition to clinical parameters. Interventional, randomized prospective studies are needed to confirm the clinical utility of liquid biopsies for guiding anticoagulation in patients with cancer.

Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot

The goal of machine learning (ML) is to create informative signals and useful tasks by leveraging large datasets to derive computational algorithms. ML has the potential to revolutionize the healthcare industry by boosting productivity, enhancing safe and effective patient care, and lightening the load on clinicians. In addition to gaining mechanistic insights into cancer-associated thrombosis (CAT), ML can be used to improve patient outcomes, streamline healthcare delivery, and spur innovation. Our review paper delves into the present and potential applications of this cutting-edge technology, encompassing three areas: i) computer vision-assisted diagnosis of thromboembolism from radiology data; ii) case detection from electronic health records using natural language processing; iii) algorithms for CAT prediction and risk stratification. The availability of large, well-annotated, high-quality datasets, overfitting, limited generalizability, the risk of propagating inherent bias, and a lack of transparency among patients and clinicians are among the challenges that must be overcome in order to effectively develop ML in the health sector. To guarantee that this powerful instrument can be utilized to maximize innovation in CAT, clinicians can collaborate with stakeholders such as computer scientists, regulatory bodies, and patient groups.

Comparison of different machine learning classification models for predicting deep vein thrombosis in lower extremity fractures

Deep vein thrombosis (DVT) is a common complication in patients with lower extremity fractures. Once it occurs, it will seriously affect the quality of life and postoperative recovery of patients. Therefore, early prediction and prevention of DVT can effectively improve the prognosis of patients. This study constructed different machine learning models to explore their effectiveness in predicting DVT. Five prediction models were applied to the study, including Extreme Gradient Boosting (XGBoost) model, Logistic Regression (LR) model, RandomForest (RF) model, Multilayer Perceptron (MLP) model, and Support Vector Machine (SVM) model. Afterwards, the performance of the obtained prediction models was evaluated by area under the curve (AUC), accuracy, sensitivity, specificity, F1 score, and Kappa. The prediction performances of the models based on machine learning are as follows: XGBoost model (AUC = 0.979, accuracy = 0.931), LR model (AUC = 0.821, accuracy = 0.758), RF model (AUC = 0.970, accuracy = 0.921), MLP model (AUC = 0.830, accuracy = 0.756), SVM model (AUC = 0.713, accuracy = 0.661). On our data set, the XGBoost model has the best performance. However, the model still needs external verification research before clinical application.

Establishment of machine learning-based tool for early detection of pulmonary embolism

BACKGROUND AND OBJECTIVES

Pulmonary embolism (PE) is a complex disease with high mortality and morbidity rate, leading to increasing society burden. However, current diagnosis is solely based on symptoms and laboratory data despite its complex pathology, which easily leads to misdiagnosis and missed diagnosis by inexperienced doctors. Especially, CT pulmonary angiography, the gold standard method, is not widely available. In this study, we aim to establish a rapid and accurate screening model for pulmonary embolism using machine learning technology. Importantly, data required for disease prediction are easily accessed, including routine laboratory data and medical record information of patients.

METHODS

We extracted features from patients' routine laboratory results and medical records, including blood routine, biochemical group, blood coagulation routine and other test results, as well as symptoms and medical history information. Samples with a feature loss rate greater than 0.8 were deleted from the original database. Data from 4723 cases were retained, 231 of which were positive for pulmonary embolism. 50 features were retained through the positive and negative statistical hypothesis testing which was used to build the predictive model. In order to avoid identification as majority-class samples caused by the imbalance of sample proportion, we used the method of Synthetic Minority Oversampling Technique (SMOTE) to increase the amount of information on minority samples. Five typical machine learning algorithms were used to model the screening of pulmonary embolism, including Support Vector Machines, Logistic Regression, Random Forest, XGBoost, and Back Propagation Neural Networks. To evaluate model performance, sensitivity, specificity and AUC curve were analyzed as the main evaluation indicators. Furthermore, a baseline model was established using the characteristics of the pulmonary embolism guidelines as a comparison model.

RESULTS

We found that XGBoost showed better performance compared to other models, with the highest sensitivity and specificity (0.99 and 0.99, respectively). Moreover, it showed significant improvement in performance compared to the baseline model (sensitivity and specificity were 0.76 and 0.76 respectively). More important, our model showed low missed diagnosis rate (0.46) and high AUC value (0.992). Finally, the calculation time of our model is only about 0.05 s to obtain the possibility of pulmonary embolism.

CONCLUSIONS

In this study, five machine learning classification models were established to assess the likelihood of patients suffering from pulmonary embolism, and the XGBoost model most significantly improved the precision, sensitivity, and AUC for pulmonary embolism screening. Collectively, we have established an AI-based model to accurately predict pulmonary embolism at early stage.

Novel Insights in Venous Thromboembolism Risk Assessment Methods in Ambulatory Cancer Patients: From the Guidelines to Clinical Practice

Many cancer patients will experience venous thromboembolism (VTE) at some stage, with the highest rate in the initial period following diagnosis. Novel cancer therapies may further enhance the risk. VTE in a cancer setting is associated with poor prognostic, a decreased quality of life, and high healthcare costs. If thromboprophylaxis in hospitalized cancer patients and perioperative settings is widely accepted in clinical practice and supported by the guidelines, it is not the same situation in ambulatory cancer patient settings. The guidelines do not recommend primary thromboprophylaxis, except in high-risk cases. However, nowadays, risk stratification is still challenging, although many tools have been developed. The Khrorana score remains the most used method, but it has many limits. This narrative review aims to present the current relevant knowledge of VTE risk assessment in ambulatory cancer patients, starting from the guideline recommendations and continuing with the specific risk assessment methods and machine learning models approaches. Biomarkers, genetic, and clinical features were tested alone or in groups. Old and new models used in VTE risk assessment are exposed, underlining their clinical utility. Imaging and biomolecular approaches to VTE screening of outpatients with cancer are also presented, which could help clinical decisions.

Artificial intelligence in the prediction of venous thromboembolism: A systematic review and pooled analysis

BACKGROUND

Accurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking.

AIMS

To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models.

METHODS

A systematic literature search was performed using PubMed, MEDLINE, EMBASE, and Web of Science from inception to April 20, 2021. Search terms included "artificial intelligence" and "venous thromboembolism." Eligible criteria were original studies evaluating AI in the prediction of VTE in adults and reporting one of the following outcomes: sensitivity, specificity, positive predictive value, negative predictive value, or area under receiver operating curve (AUC). Risks of bias were assessed using the PROBAST tool. Unpaired t-test was performed to compare the mean AUC from AI versus conventional methods (RAMs or logistic regression models).

RESULTS

A total of 20 studies were included. Number of participants ranged from 31 to 111 888. The AI-based models included artificial neural network (six studies), support vector machines (four studies), Bayesian methods (one study), super learner ensemble (one study), genetic programming (one study), unspecified machine learning models (two studies), and multiple machine learning models (five studies). Twelve studies (60%) had both training and testing cohorts. Among 14 studies (70%) where AUCs were reported, the mean AUC for AI versus conventional methods were 0.79 (95% CI: 0.74-0.85) versus 0.61 (95% CI: 0.54-0.68), respectively (p < .001). However, the good to excellent discriminative performance of AI methods is unlikely to be replicated when used in clinical practice, because most studies had high risk of bias due to missing data handling and outcome determination.

CONCLUSION

The use of AI appears to improve the accuracy of diagnostic and prognostic prediction of VTE over conventional risk models; however, there was a high risk of bias observed across studies. Future studies should focus on transparent reporting, external validation, and clinical application of these models.

Assessing the risk of venous thromboembolism in patients with haematological cancers using three prediction models

PURPOSE

Assessment of individual VTE risk in cancer patients prior to chemotherapy is critical for determining necessity of interventions. Risk assessment models (RAM) are available but have not been validated for haematological malignancy. We aimed to assess the validity of the Vienna Cancer and Thrombosis Study (V-CATS) score in prediction of VTE in a variety of haematological malignancies.

METHODS

This is a prospective cohort study conducted on 81 newly diagnosed cancer patients undergoing chemotherapy. Demographic, clinical and cancer related data were collected, patients were followed up for 6 months, and VTE events were recorded. Khorana score (KS) was calculated. Plasma D-dimer and sP-selectin were measured, and then, V-CATS score was calculated. Receiver operator curve (ROC) was used to assess the sensitivity and specificity of RAMs. A modified V-CATS was generated and subsequently assessed by using new cut-off levels of d-dimer and sP-selectin based on ROC curve of the patients' results and compared the probability of VTE occurrence using all three RAMs.

RESULTS

Among the 81 patients included in this study, a total of 2.7% were diagnosed with advanced metastatic cancer. The most frequent cancer was non-Hodgkin lymphoma (39.5%), and 8 patients (9.8%) developed VTE events. The calculated probability of VTE occurrence using KS, V-CATS and modified V-CATS scores at cut-off levels ≥ 3 was 87.5%, 87.5% and 100%, respectively. The AUC in ROC curve of modified Vienna CATS score showed significant difference when compared to that of V-CATS and KS (P = 0.047 and 0.029, respectively).

CONCLUSION

The findings of our study highlight the value of three VTE risk assessment models in haematological malignancies. The modified V-CATS score demonstrated higher specificity compared to both V-CATS and KS, while all three scores exhibited similar sensitivity. We encourage the implementation of RAMs in haematological cancers for an appropriate use of thromboprophylaxis.

Machine-learning-based adverse drug event prediction from observational health data: A review

Adverse drug events (ADEs) are responsible for a significant number of hospital admissions and fatalities. Machine learning models have been developed to assess the individual patient risk of having an ADE. In this article, we have reviewed studies addressing the prediction of ADEs in observational health data with machine learning. The field of individualised ADE prediction is rapidly emerging through the increasing availability of additional data modalities (e.g., genetic data, screening data, wearables data) and advanced deep learning models such as transformers. Consequently, personalised adverse drug event predictions are becoming more feasible and tangible.

Development and validation of machine learning models for venous thromboembolism risk assessment at admission: a retrospective study

Introduction

Venous thromboembolism (VTE) risk assessment at admission is of great importance for early screening and timely prophylaxis and management during hospitalization. The purpose of this study is to develop and validate novel risk assessment models at admission based on machine learning (ML) methods.

Methods

In this retrospective study, a total of 3078 individuals were included with their Caprini variables within 24 hours at admission. Then several ML models were built, including logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB). The prediction performance of ML models and the Caprini risk score (CRS) was then validated and compared through a series of evaluation metrics.

Results

The values of AUROC and AUPRC were 0.798 and 0.303 for LR, 0.804 and 0.360 for RF, and 0.796 and 0.352 for XGB, respectively, which outperformed CRS significantly (0.714 and 0.180, P < 0.001). When prediction scores were stratified into three risk levels for application, RF could obtain more reasonable results than CRS, including smaller false positive alerts and larger lower-risk proportions. The boosting results of stratification were further verified by the net-reclassification-improvement (NRI) analysis.

Discussion

This study indicated that machine learning models could improve VTE risk prediction at admission compared with CRS. Among the ML models, RF was found to have superior performance and great potential in clinical practice.

Prediction of Venous Thromboembolism in Patients With Cancer Using Machine Learning Approaches: A Systematic Review and Meta-Analysis

PURPOSE

Recent studies have suggested that machine learning (ML) could be used to predict venous thromboembolism (VTE) in cancer patients with high accuracy.

METHODS

We aimed to evaluate the performance of ML in predicting VTE events in patients with cancer. PubMed, Web of Science, and EMBASE to identify studies were searched.

RESULTS

Seven studies involving 12,249 patients with cancer were included. The combined results of the different ML models demonstrated good accuracy in the prediction of VTE. In the training set, the global pooled sensitivity was 0.87, the global pooled specificity was 0.87, and the AUC was 0.91, and in the test set 0.65, 0.84, and 0.80, respectively.

CONCLUSION

The prediction ML models showed good performance to predict VTE. External validation to determine the result's reproducibility is necessary.

Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis

Tailored treatments for metastatic colorectal cancer (mCRC) have not yet completely evolved due to the variety in response to drugs. Therefore, artificial intelligence has been recently used to develop prognostic and predictive models of treatment response (either activity/efficacy or toxicity) to aid in clinical decision making. In this systematic review, we have examined the ability of learning methods to predict response to chemotherapy alone or combined with targeted therapy in mCRC patients by targeting specific narrative publications in Medline up to April 2022 to identify appropriate original scientific articles. After the literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. Our results show that all investigations conducted on this field have provided generally promising results in predicting the response to therapy or toxic side-effects. By a meta-analytic approach we found that the overall weighted means of the area under the receiver operating characteristic (ROC) curve (AUC) were 0.90, 95% C.I. 0.80-0.95 and 0.83, 95% C.I. 0.74-0.89 in training and validation sets, respectively, indicating a good classification performance in discriminating response vs. non-response. The calculation of overall HR indicates that learning models have strong ability to predict improved survival. Lastly, the delta-radiomics and the 74 gene signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Specifically, when we evaluated the predictive models with tests reaching 80% sensitivity (SE) and 90% specificity (SP), the delta radiomics showed an SE of 99% and an SP of 94% in the training set and an SE of 85% and SP of 92 in the test set, whereas for the 74 gene signatures the SE was 97.6% and the SP 100% in the training set.

Flowing through laboratory clinical data: the role of artificial intelligence and big data

During the last few years, clinical laboratories have faced a sea change, from facilities producing a high volume of low-cost test results, toward a more integrated and patient-centered service. Parallel to this paradigm change, the digitalization of healthcare data has made an enormous quantity of patients' data easily accessible, thus opening new scenarios for the utilization of artificial intelligence (AI) tools. Every day, clinical laboratories produce a huge amount of information, of which patients' results are only a part. The laboratory information system (LIS) may include other "relevant" compounding data, such as internal quality control or external quality assessment (EQA) results, as well as, for example, timing of test requests and of blood collection and exams transmission, these data having peculiar characteristics typical of big data, as volume, velocity, variety, and veracity, potentially being used to generate value in patients' care. Despite the increasing interest expressed in AI and big data in laboratory medicine, these topics are approaching the discipline slowly for several reasons, attributable to lack of knowledge and skills but also to poor or absent standardization, harmonization and problematic regulatory and ethical issues. Finally, it is important to bear in mind that the mathematical postulation of algorithms is not sufficient for obtaining useful clinical tools, especially when biological parameters are not evaluated in the appropriate context. It is therefore necessary to enhance cooperation between laboratory and AI experts, and to coordinate and govern processes, thus favoring the development of valuable clinical tools.

Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis

Deep venous thrombosis (DVT) is a disease that must be diagnosed quickly, as it can trigger the death of patients. Nowadays, one can find different ways to determine it, including clinical scoring, D-dimer, ultrasonography, etc. Recently, scientists have focused efforts on using machine learning (ML) and neural networks for disease diagnosis, progressively increasing the accuracy and efficacy. Patients with suspected DVT have no apparent symptoms. Using pattern recognition techniques, aiding good timely diagnosis, as well as well-trained ML models help to make good decisions and validation. The aim of this paper is to propose several ML models for a more efficient and reliable DVT diagnosis through its implementation on an edge device for the development of instruments that are smart, portable, reliable, and cost-effective. The dataset was obtained from a state-of-the-art article. It is divided into 85% for training and cross-validation and 15% for testing. The input data in this study are the Wells criteria, the patient’s age, and the patient’s gender. The output data correspond to the patient’s diagnosis. This study includes the evaluation of several classifiers such as Decision Trees (DT), Extra Trees (ET), K-Nearest Neighbor (KNN), Multi-Layer Perceptron Neural Network (MLP-NN), Random Forest (RF), and Support Vector Machine (SVM). Finally, the implementation of these ML models on a high-performance embedded system is proposed to develop an intelligent system for early DVT diagnosis. It is reliable, portable, open source, and low cost. The performance of different ML algorithms was evaluated, where KNN achieved the highest accuracy of 90.4% and specificity of 80.66% implemented on personal computer (PC) and Raspberry Pi 4 (RPi4). The accuracy of all trained models on PC and Raspberry Pi 4 is greater than 85%, while the area under the curve (AUC) values are between 0.81 and 0.86. In conclusion, as compared to traditional methods, the best ML classifiers are effective at predicting DVT in an early and efficient manner.

Cancer-associated venous thromboembolism

Cancer-associated thrombosis (including venous thromboembolism (VTE) and arterial events) is highly consequential for patients with cancer and is associated with worsened survival. Despite substantial improvements in cancer treatment, the risk of VTE has increased in recent years; VTE rates additionally depend on the type of cancer (with pancreas, stomach and primary brain tumours having the highest risk) as well as on individual patient's and cancer treatment factors. Multiple cancer-specific mechanisms of VTE have been identified and can be classified as mechanisms in which the tumour expresses proteins that alter host systems, such as levels of platelets and leukocytes, and in which the tumour expresses procoagulant proteins released into the circulation that directly activate the coagulation cascade or platelets, such as tissue factor and podoplanin, respectively. As signs and symptoms of VTE may be non-specific, diagnosis requires clinical assessment, evaluation of pre-test probability, and objective diagnostic testing with ultrasonography or CT. Risk assessment tools have been validated to identify patients at risk of VTE. Primary prevention of VTE (thromboprophylaxis) has long been recommended in the inpatient and post-surgical settings, and is now an option in the outpatient setting for individuals with high-risk cancer. Anticoagulant therapy is the cornerstone of therapy, with low molecular weight heparin or newer options such as direct oral anticoagulants. Personalized treatment incorporating risk of bleeding and patient preferences is essential, especially as a diagnosis of VTE is often considered by patients even more distressing than their cancer diagnosis, and can severely affect the quality of life. Future research should focus on current knowledge gaps including optimizing risk assessment tools, biomarker discovery, next-generation anticoagulant development and implementation science.

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

Adverse drug events (ADEs) represent one of the most prevalent types of health-care-related harm, and there is substantial room for improvement in the way that they are currently predicted and detected. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged to reduce the frequency of ADEs. We focused on modern machine learning techniques and natural language processing. 78 articles were included in the scoping review. Studies were heterogeneous and applied various AI techniques covering a wide range of medications and ADEs. We identified several key use cases in which AI could contribute to reducing the frequency and consequences of ADEs, through prediction to prevent ADEs and early detection to mitigate the effects. Most studies (73 [94%] of 78) assessed technical algorithm performance, and few studies evaluated the use of AI in clinical settings. Most articles (58 [74%] of 78) were published within the past 5 years, highlighting an emerging area of study. Availability of new types of data, such as genetic information, and access to unstructured clinical notes might further advance the field.

Construction of a Risk Prediction Model for Hospital-Acquired Pulmonary Embolism in Hospitalized Patients

The purpose of this study is to establish a novel pulmonary embolism (PE) risk prediction model based on machine learning (ML) methods and to evaluate the predictive performance of the model and the contribution of variables to the predictive performance. We conducted a retrospective study at the Shanghai Tenth People's Hospital and collected the clinical data of in-patients that received pulmonary computed tomography imaging between January 1, 2014 and December 31, 2018. We trained several ML models, including logistic regression (LR), support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT), compared the models with representative baseline algorithms, and investigated their predictability and feature interpretation. A total of 3619 patients were included in the study. We discovered that the GBDT model demonstrated the best prediction with an area under the curve value of 0.799, whereas those of the RF, LR, and SVM models were 0.791, 0.716, and 0.743, respectively. The sensibilities of the GBDT, LR, RF, and SVM models were 63.9%, 68.1%, 71.5%, and 75%, respectively; the specificities were 81.1%, 66.1, 72.7%, and 65.1%, respectively; and the accuracies were 77.8%, 66.5%, 72.5%, and 67%, respectively. We discovered that the maximum D-dimer level contributed the most to the outcome prediction, followed by the extreme growth rate of the plasma fibrinogen level, in-hospital duration, and extreme growth rate of the D-dimer level. The study demonstrates the superiority of the GBDT model in predicting the risk of PE in hospitalized patients. However, in order to be applied in clinical practice and provide support for clinical decision-making, the predictive performance of the model needs to be prospectively verified.

A novel hierarchical machine learning model for hospital-acquired venous thromboembolism risk assessment among multiple-departments

Venous thromboembolism (VTE) is a common vascular disease and potentially fatal complication during hospitalization, and so the early identification of VTE risk is of significant importance. Compared with traditional scale assessments, machine learning methods provide new opportunities for precise early warning of VTE from clinical medical records. This research aimed to propose a two-stage hierarchical machine learning model for VTE risk prediction in patients from multiple departments. First, we built a machine learning prediction model that covered the entire hospital, based on all cohorts and common risk factors. Then, we took the prediction output of the first stage as an initial assessment score and then built specific models for each department. Over the duration of the study, a total of 9213 inpatients, including 1165 VTE-positive samples, were collected from four departments, which were split into developing and test datasets. The proposed model achieved an AUC of 0.879 in the department of oncology, which outperformed the first-stage model (0.730) and the department model (0.787). This was attributed to the fully usage of both the large sample size at the hospital level and variable abundance at the department level. Experimental results show that our model could effectively improve the prediction of hospital-acquired VTE risk before image diagnosis and provide decision support for further nursing and medical intervention.

A review of AI and Data Science support for cancer management

INTRODUCTION

Thanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ AI and Data Science methods to monitor the health status and provide support to cancer patients managed at home.

OBJECTIVE

Our main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt.

METHODS

We designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patient's state from it and deliver coaching/behavior change interventions.

RESULTS

Starting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 109 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching.

CONCLUSION

Development of modern decision support systems for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality-of-life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients.

Burden of venous thromboembolism in patients with pancreatic cancer

Pancreatic cancer (PC) is a devastating malignancy with fewer than 10% of patients being alive at 5 years after diagnosis. Venous thromboembolism (VTE) occurs in approximatively 20% of patients with PC, resulting in increased morbidity, mortality and significant health care costs. The management of VTE is particularly challenging in these frail patients. Adequate selection of the most appropriate anticoagulant for each individual patient according to the current international guidelines is warranted for overcoming treatment challenges. The International Initiative on Thrombosis and Cancer multi-language web-based mobile application (downloadable for free at www.itaccme.com) has been developed to help clinicians in decision making in the most complex situations. In this narrative review, we will discuss the contemporary epidemiology and burden of VTE in PC patients, the performances and limitations of current risk assessment models to predict the risk of VTE, as well as evidence from recent clinical trials for the primary prophylaxis and treatment of cancer-associated VTE that support up-dated clinical practice guidelines.

Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO

Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. To map locations, certain existing methods use the received signal strength indicator (RSSI). Devices need to be configured to allow for dynamic interference patterns when using the RSSI sensors to monitor indoor positions. In this paper, our objective is to explore an alternative method for monitoring a moving user's indoor position using BLE sensors in complex indoor building environments. We developed a Convolutional Neural Network (CNN) based positioning model based on the 2D image composed of the received number of signals indicator from both x and y-axes. In this way, like a pixel, we interact with each 10 × 10 matrix holding the spatial information of coordinates and suggest the possible shift of a sensor, adding a sensor and removing a sensor. To develop CNN we adopted a neuro-evolution approach to optimize and create several layers in the network dynamically, through enhanced Particle Swarm Optimization (PSO). For the optimization of CNN, the global best solution obtained by PSO is directly given to the weights of each layer of CNN. In addition, we employed dynamic inertia weights in the PSO, instead of a constant inertia weight, to maintain the CNN layers' length corresponding to the RSSI signals from BLE sensors. Experiments were conducted in a building environment where thirteen beacon devices had been installed in different locations to record coordinates. For evaluation comparison, we further adopted machine learning and deep learning algorithms for predicting a user's location in an indoor environment. The experimental results indicate that the proposed optimized CNN-based method shows high accuracy (97.92% with 2.8% error) for tracking a moving user's locations in a complex building without complex calibration as compared to other recent methods.

A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients

Deep venous thrombosis (DVT) is associated with significant morbidity, mortality, and increased healthcare costs. Standard scoring systems for DVT risk stratification often provide insufficient stratification of hospitalized patients and are unable to accurately predict which inpatients are most likely to present with DVT. There is a continued need for tools which can predict DVT in hospitalized patients. We performed a retrospective study on a database collected from a large academic hospital, comprised of 99,237 total general ward or ICU patients, 2,378 of whom experienced a DVT during their hospital stay. Gradient boosted machine learning algorithms were developed to predict a patient's risk of developing DVT at 12- and 24-hour windows prior to onset. The primary outcome of interest was diagnosis of in-hospital DVT. The machine learning predictors obtained AUROCs of 0.83 and 0.85 for DVT risk prediction on hospitalized patients at 12- and 24-hour windows, respectively. At both 12 and 24 hours before DVT onset, the most important features for prediction of DVT were cancer history, VTE history, and internal normalized ratio (INR). Improved risk stratification may prevent unnecessary invasive testing in patients for whom DVT cannot be ruled out using existing methods. Improved risk stratification may also allow for more targeted use of prophylactic anticoagulants, as well as earlier diagnosis and treatment, preventing the development of pulmonary emboli and other sequelae of DVT.

The risk of venous thromboembolism in cancer patients receiving chemotherapy: a meta-analysis with systematic review

Background

The Khorana score was developed to predict the risk of venous thromboembolism (VTE) in cancer patients receiving chemotherapy. However, the utility of the Khorana score remains controversial since different studies report varying results. This meta-analysis aims to analyze the incidence of VTE with different risk stratifications using the Khorana score for overall follow-up time, incidence of deep-vein thrombosis (DVT), incidence of pulmonary embolism (PE) and bleeding in cancer patients receiving chemotherapy.

Methods

A systemic search was performed using PubMed, Embase, Cochrane Library and Web of Science for studies describing VTE incidence in cancer patients undergoing chemotherapy. The incidence of VTE was calculated using R computing software.

Results

We included 13 studies in this meta-analysis, with a total of 5,852 cancer patients and 424 VTE cases. Results revealed that overall incidence of low, intermediate and high-risk groups were 2% (95% CI: 1–6%), 11% (95% CI: 6–18%) and 14% (95% CI: 9–20%), respectively. The overall incidence of DVT and PE were 6% (95% CI: 4–10%) and 4% (95% CI: 2–7%), respectively. Lastly, bleeding rate was 4% (95% CI: 2–8%).

Conclusions

According to this meta-analysis, the Khorana score is suitable for cancer patients receiving chemotherapy in a 3–6-month timeframe rather than “forever”. The incidence of PE in this population was significantly greater than what was observed for non-cancer patients. More than half of VTE events occurred within 6 months of commencing chemotherapy.

Development and Validation of a New Clinical Prediction Model of Catheter-Related Thrombosis Based on Vascular Ultrasound Diagnosis in Cancer Patients

Background: Central venous catheters are convenient for drug delivery and improved comfort for cancer patients, but they also cause serious complications. The most common complication is catheter-related thrombosis (CRT). Objectives: This study aimed to evaluate the incidence and risk factors for CRT in cancer patients and develop an effective prediction model for CRT in cancer patients. Methods: The development of our prediction model was based on a retrospective cohort (n = 3,131) from the National Cancer Center. Our prediction model was confirmed in a prospective cohort from the National Cancer Center (n = 685) and a retrospective cohort from the Hunan Cancer Hospital (n = 61). The predictive accuracy and discriminative ability were determined by receiver operating characteristic (ROC) curves and calibration plots. Results: Multivariate analysis demonstrated that sex, cancer type, catheter type, position of the catheter tip, chemotherapy status, and antiplatelet/anticoagulation status at baseline were independent risk factors for CRT. The area under the ROC curve of our prediction model was 0.741 (CI: 0.715-0.766) in the primary cohort and 0.754 (CI: 0.704-0.803) and 0.658 (CI: 0.470-0.845) in validation cohorts 1 and 2, respectively. The model also showed good calibration and clinical impact in the primary and validation cohorts. Conclusions: Our model is a novel prediction tool for CRT risk that accurately assigns cancer patients into high- and low-risk groups. Our model will be valuable for clinicians when making decisions regarding thromboprophylaxis.

Validation of the IMPROVE bleeding risk score in Chinese medical patients during hospitalization: Findings from the dissolve-2 study

Background

Venous thromboembolism (VTE) prophylaxis remains suboptimal in China due to the bleeding risk associated with pharmacologic prophylaxis. We used data from the DissolVE-2 study to report the risk factors for bleeding and validated the International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) bleeding risk score (BRS).

Methods

In-hospital major bleeding incidence in medical patients from the DissolVE-2 study were assessed by Kaplan-Meier method. Risk factors associated with clinically relevant bleeding (CRB) were analysed using Cox regression model. Sensitivity, specificity, positive predictive value, negative predictive value and receiver-operating characteristic (ROC) curve was used to compute the diagnostic accuracy of IMPROVE BRS in the study cohort.

Findings

Of the 6623 medical patients, 5076 patients with all relevant clinical details were included for the validation cohort. Overall, 127 CRB events (38 major and 89 clinically relevant non-major bleeding events) occurred in this cohort, with a cumulative incidence rate of 2.6% (95% confidence interval [CI], 2.3–3.4). Application of IMPROVE BRS revealed significantly higher hazards of CRB (hazard ratio [HR]: 7.17, 95% CI, 5.05–10.18) and major bleeding (HR: 13.95, 95% CI, 7.28–26.73) in patients with IMPROVE BRS ≥7. Comparison of predictive parameters revealed higher sensitivity (44.1 vs 35.9) and positive predictive value (10.9 vs 2.6) for CRB in our study than the IMPROVE study, which was substantiated by the area under the curve (0.73, p<0.0001) from the ROC curve analysis.

Interpretation

IMPROVE BRS is a simple model for estimating bleeding risk in Chinese medical patients and could be used in conjunction with VTE risk assessment models to decide prophylactic treatment for VTE.

Funding

This study and the additional data analysis were funded by Sanofi (Beijing) Pharmaceutical Co, Ltd by the Fund of The National Key Research and Development Program of China [Grant 2016YFC0905600] and by CAMS Innovation Fund for Medical Sciences (CIFMS) (No.2018-I2M-1–003)

Inpatient prophylaxis in cancer patients: where is the evidence?

Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Cancer is associated with an increased risk of VTE which is further compounded by acute hospitalization for medical illness. The absolute incidence of VTE of hospitalized cancer patients ranges between 2% and 17% but the rates vary considerably depending on the type of study, method of VTE surveillance and whether pharmacologic thromboprophylaxis is administered. Complicating the interpretation of thromboprophylaxis trials is the paucity of reported data on the relative benefit of thromboprophylaxis relative to an increased risk of hemorrhage inherent to cancer patients. Efforts over the last decade have improved the rates of adherence to in-hospital pharmacologic thromboprophylaxis regimens. Whether these efforts also improve outcomes continues to be debated. In this review, the prevalence of VTE and hemorrhage in hospitalized cancer patients is presented in the context of pharmacologic thromboprophylaxis data along with a discussion of emerging approaches towards VTE risk-adapted prescription of antithrombotics during hospitalization.

Evaluation of the impact of body mass index on venous thromboembolism risk factors

In this paper, we investigate the interaction impacts of body mass index (BMI) on the other important risk factors for venous thromboembolism (VTE), using deep venous thrombosis (DVT) patient data from the International Warfarin Pharmacogenetics Consortium (IWPC). We apply eight machine learning techniques, including naive Bayes classifier (NB), support vector machine (SVM), elastic net regression (ENET), logistic regression (LR), lasso regression (LAR), multivariate adaptive regression splines (MARS), boosted regression tree (BRT) and random forest model (RF). The RF method is selected as the best model for classification. Out of 33 features considered in this study, we identify 12 variables as relatively important risk factors for VTE. Finally, we examine the interaction impacts of BMI on these important VTE risk factors. We conclude that the impacts of risk factors on VTE incidence are varying across different BMI groups, and the variations are different for different risk factors. Therefore the interaction impacts of BMI on the other risk factors have to be taken into account in order to better understand the incidence of VTE.

Indoor Localization System Based on Bluetooth Low Energy for Museum Applications

In the last few years, indoor localization has attracted researchers and commercial developers. Indeed, the availability of systems, techniques and algorithms for localization allows the improvement of existing communication applications and services by adding position information. Some examples can be found in the managing of people and/or robots for internal logistics in very large warehouses (e.g., Amazon warehouses, etc.). In this paper, we study and develop a system allowing the accurate indoor localization of people visiting a museum or any other cultural institution. We assume visitors are equipped with a Bluetooth Low Energy (BLE) device (commonly found in modern smartphones or in a small chipset), periodically transmitting packets, which are received by geolocalized BLE receivers inside the museum area. Collected packets are provided to the locator server to estimate the positions of the visitors inside the museum. The position estimation is based on a feed-forward neural network trained by a measurement campaign in the considered environment and on a non-linear least square algorithm. We also provide a strategy for deploying the BLE receivers in a given area. The performance results obtained from measurements show an achievable position estimate accuracy below 1 m.

Machine learning approach to predict medication overuse in migraine patients

Machine learning (ML) is largely used to develop automatic predictors in migraine classification but automatic predictors for medication overuse (MO) in migraine are still in their infancy. Thus, to understand the benefits of ML in MO prediction, we explored an automated predictor to estimate MO risk in migraine. To achieve this objective, a study was designed to analyze the performance of a customized ML-based decision support system that combines support vector machines and Random Optimization (RO-MO). We used RO-MO to extract prognostic information from demographic, clinical and biochemical data. Using a dataset of 777 consecutive migraine patients we derived a set of predictors with discriminatory power for MO higher than that observed for baseline SVM. The best four were incorporated into the final RO-MO decision support system and risk evaluation on a five-level stratification was performed. ROC analysis resulted in a c-statistic of 0.83 with a sensitivity and specificity of 0.69 and 0.87, respectively, and an accuracy of 0.87 when MO was predicted by at least three RO-MO models. Logistic regression analysis confirmed that the derived RO-MO system could effectively predict MO with ORs of 5.7 and 21.0 for patients classified as probably (3 predictors positive), or definitely at risk of MO (4 predictors positive), respectively. In conclusion, a combination of ML and RO - taking into consideration clinical/biochemical features, drug exposure and lifestyle - might represent a valuable approach to MO prediction in migraine and holds the potential for improving model precision through weighting the relative importance of attributes.

Thromboprophylaxis in the End-of-Life Cancer Care: The Update

Cancer patients are at increased risk for venous thromboembolism (VTE), which further increases with advanced stages of malignancy, prolonged immobilization, or prior history of thrombosis. To reduce VTE-related mortality, many official guidelines encourage the use of thromboprophylaxis (TPX) in cancer patients in certain situations, e.g., during chemotherapy or in the perioperative period. TPX in the end-of-life care, however, remains controversial. Most recommendations on VTE prophylaxis in cancer patients are based on the outcomes of clinical trials that excluded patients under palliative or hospice care. This translates to the paucity of official guidelines on TPX dedicated to this group of patients. The problem should not be underestimated as VTE is known to be associated with symptoms adversely impacting the quality of life (QoL), i.e., limb or chest pain, dyspnea, hemoptysis. In end-of-life care, where the assurance of the best possible QoL should be the highest priority, VTE prophylaxis may eliminate the symptom burden related to thrombosis. However, large randomized studies determining the benefits and risks profiles of TPX in patients nearing the end of life are lacking. This review summarized available data on TPX in this population, analyzed potential tools for VTE risk prediction in the view of this group of patients, and summarized the most current recommendations on TPX pertaining to terminal care.

Primary Thromboprophylaxis in Ambulatory Cancer Patients: Where Do We Stand?

Venous thromboembolism (VTE), comprising deep-vein thrombosis and pulmonary embolism, is a frequent complication in ambulatory cancer patients. Despite the high risk, routine thromboprophylaxis is not recommended because of the high number needed to treat and the risk of bleeding. Two recent trials demonstrated that the number needed to treat can be reduced by selecting cancer patients at high risk for VTE with prediction scores, leading the latest guidelines to suggest such an approach in clinical practice. Yet, the interpretation of these trial results and the translation of the guideline recommendations to clinical practice may be less straightforward. In this clinically-oriented review, some of the controversies are addressed by focusing on the burden of VTE in cancer patients, discussing the performance of available risk assessment scores, and summarizing the findings of recent trials. This overview can help oncologists, hematologists, and vascular medicine specialists decide about thromboprophylaxis in ambulatory cancer patients.

Comparing different venous thromboembolism risk assessment machine learning models in Chinese patients

OBJECTIVE

Venous thromboembolism (VTE) is a fatal complication and the most common preventable cause of death in hospitals. The risk-to-benefit ratio of thromboprophylaxis depends on the performance of the risk assessment model. A linear model, the Padua model, is recommended for medical inpatients in the United States but is not suitable for Chinese inpatients due to differences in race and disease spectrum. Currently, machine learning (ML) methods show advantages in modeling complex data patterns and have been applied to clinical data analysis. This study aimed to build VTE risk assessment ML models among Chinese inpatients and compare the predictive validity of the ML models with that of the Padua model.

METHODS

We used 376 patients, including 188 patients with VTE, to build a model and then evaluate the predictive validity of the model in a consecutive clinical dataset from Peking Union Medical College Hospital. Nine widely used ML methods were trained on the model derivation set and then compared with the Padua model.

RESULTS

Among the nine ML methods, random forest (RF), boosting-based methods, and logistic regression achieved a higher specificity, Youden index, positive predictive value, and area under the receiver operating characteristic curve than the Padua model on both the test and clinical validation sets. However, their sensitivities were inferior to that of the Padua model. Combined with the receiver operating characteristic curve, RF, as the best performing model, maintained high specificity with relatively better sensitivity and captured VTE patients' patterns more precisely.

CONCLUSIONS

Advances in ML technology provide powerful tools for medical data analysis, and choosing models conforming to the disease pattern would achieve good performance. Popular ML models do not surpass the Padua model on all indicators of validity, and the drawback of low sensitivity should be improved upon in the future.

Cancer-Related Stroke: An Emerging Subtype of Ischemic Stroke with Unique Pathomechanisms

Systemic cancer and ischemic stroke are common conditions and two of the most frequent causes of death among the elderly. The association between cancer and stroke has been reported worldwide. Stroke causes severe disability for cancer patients, while cancer increases the risk of stroke. Moreover, cancer-related stroke is expected to increase due to advances in cancer treatment and an aging population worldwide. Because cancer and stroke share risk factors (such as smoking and obesity) and treatment of cancer can increase the risk of stroke (e.g., accelerated atherosclerosis after radiation therapy), cancer may accelerate conventional stroke mechanisms (i.e., atherosclerosis, small vessel disease, and cardiac thrombus). In addition, active cancer and chemotherapy may enhance thrombin generation causing stroke related to coagulopathy. Patients with stroke due to cancer-related coagulopathy showed the characteristics findings of etiologic work ups, D-dimer levels, and infarct patterns. In this review, we summarized the frequency of cancer-related stroke among patients with ischemic stroke, mechanisms of stroke with in cancer patients, and evaluation and treatment of cancer-related stroke. We discussed the possibility of cancer-related stroke as a stroke subtype, and presented the most recent discoveries in the pathomechanisms and treatment of stroke due to cancer-related coagulopathy.

MicroRNAs and Neutrophil Activation Markers Predict Venous Thrombosis in Pancreatic Ductal Adenocarcinoma and Distal Extrahepatic Cholangiocarcinoma

Cancer-associated venous thrombosis (VTE) increases mortality and morbidity. However, limited tools are available to identify high risk patients. Upon activation, neutrophils release their content through different mechanisms, thereby prompting thrombosis. We explored plasma microRNAs (miRNAs) and neutrophil activation markers to predict VTE in pancreatic ductal adenocarcinoma (PDAC) and distal extrahepatic cholangiocarcinoma (DECC). Twenty-six PDAC and 6 DECC patients recruited at cancer diagnosis, were examined for deep vein thrombosis and pulmonary embolisms, and were then followed-up with clinical examinations, blood collections, and biCUS. Ten patients developed VTE and were compared with 22 age- and sex-matched controls. miRNA expression levels were measured at diagnosis and right before VTE, and neutrophil activation markers (cell-free DNA, nucleosomes, calprotectin, and myeloperoxidase) were measured in every sample obtained during follow-up. We obtained a profile of 7 miRNAs able to estimate the risk of future VTE at diagnosis (AUC = 0.95; 95% Confidence Interval (CI) (0.987, 1)) with targets involved in the pancreatic cancer and complement and coagulation cascades pathways. Seven miRNAs were up- or down-regulated before VTE compared with diagnosis. We obtained a predictive model of VTE with calprotectin as predictor (AUC = 0.77; 95% CI (0.57, 0.95)). This is the first study that addresses the ability of plasma miRNAs and neutrophil activation markers to predict VTE in PDAC and DECC.

Predicting VTE in Cancer Patients: Candidate Biomarkers and Risk Assessment Models

Risk prediction of chemotherapy-associated venous thromboembolism (VTE) is a compelling challenge in contemporary oncology, as VTE may result in treatment delays, impaired quality of life, and increased mortality. Current guidelines do not recommend thromboprophylaxis for primary prevention, but assessment of the patient's individual risk of VTE prior to chemotherapy is generally advocated. In recent years, efforts have been devoted to building accurate predictive tools for VTE risk assessment in cancer patients. This review focuses on candidate biomarkers and prediction models currently under investigation, considering their advantages and disadvantages, and discussing their diagnostic performance and potential pitfalls.

Real-world features associated with cancer-related venous thromboembolic events

Background

The incidence of venous thromboembolism (VTE) is 1–2/1000 individuals. Patients with cancer, especially during chemotherapy, are at enhanced risk, but real-world data on factors associated with VTE events are still scarce.

Aim

The aim of this retrospective study was to survey the incidence of VTE based on a large hospital database, and to identify comorbidities and features associated with VTE events. We focused on cancer-related VTE events and on factors indicating increased VTE risk during chemotherapy.

Methods

The cohort included patients treated at Turku University Hospital during years 2005–2013. Health information was derived and analysed from multiple electronic databases. The diagnoses of VTE and all comorbidities, including type of cancer, were based on International Classification of Diseases 10th Revision coding. For further analysis, we focused on 16 common types of cancers treated with chemotherapy. Age, gender, surgery, radiotherapy, distant metastasis, available laboratory values and platinum-based chemotherapy were evaluated for VTE group, and associations were estimated by Cox regression analyses.

Results

The entire database contained information from 495 089 patients, of whom 5452 (1.1%) had a VTE diagnosis. Among individuals with VTE, 1437 (26.4%) had diagnosis of coronary heart disease and 1467 (26.9%) had cancer diagnosis. Among 7778 patients with cancer treated with chemotherapy, 282 (3.6%) had a VTE, platinum-based chemotherapy being a major risk factor (HR 1.77, 95% CI 1.40 to 2.24, p<0.001). In multivariate analysis, elevated blood neutrophil counts (>3.25×10⁹ cells/L, HR 1.96, 95% CI 1.33 to 2.89, p<0.001) and plasma creatinine (>62.5 μmol/L; HR 1.60, 95% CI 1.21 to 2.13, p=0.001) values were independent indicators of increased VTE risk during chemotherapy.

Conclusions

Longitudinal electronic health record analysis provides a powerful tool to gather meaningful real-world information to study clinical associations, like comorbidities, and to identify markers associated with VTE. The combination of various clinical and laboratory variables could be used for VTE risk evaluation and targeted prevention.