Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Ryan L

PROVIDER: S-EPMC7907939 | biostudies-literature | 2021 Jan-Dec

REPOSITORIES: biostudies-literature

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

Ryan Logan L Mataraso Samson S Siefkas Anna A Pellegrini Emily E Barnes Gina G Green-Saxena Abigail A Hoffman Jana J Calvert Jacob J Das Ritankar R

Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis 20210101

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, ...[more]

PMID: 33625875

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Project description:BackgroundAcutely ill medical patients experience deep venous thrombosis (DVT) during the hospitalization, however the time course of DVT is still unclear.ObjectivesTo evaluate risk factors in acutely ill hospitalized medical patients for proximal asymptomatic DVT (ADVT) and symptomatic DVT (SDVT) at admission and discharge.Patients/methodsIn this prospective observational study, consecutive acutely ill medical patients (hospitalized mainly for acute medical disease as infections, neoplasm, anemia, heart failure) underwent compression ultrasonography (CUS) of proximal lower limb veins within 48 h from admission and at discharge to diagnose ADVT and SDVT. Covid-19 patients, anticoagulant therapy, surgical procedures, acute SDVT, and acute pulmonary embolism, were exclusion criteria. Biographical characteristics at hospitalization, D-Dimer (assessed by ELISA)) and DD-improve score.ResultsOf 2,100 patients (1002 females, 998 males, age 71 ± 16 years) 58 (2.7%) had proximal ADVT at admission. Logistic regression analysis showed that age, and active cancer were independently associated with ADVT at admission. The median length of hospitalization was 10 days [interquartile range: 6-15]. During the hospital stay, 6 patients (0.3%) with a negative CUS at admission experienced DVT (2 SDVT and 4 ADVT). In the subgroup of patients (n = 1118), in whom D-dimer was measured at admission, D-Dimer and IMPROVE-DD score were associated with ADVT at admission (n = 37) and with all DVT (n = 42) at discharge. ROC curve defined an IMPROVE-DD score of 2.5 as the optimal cut-off for discriminating patients with and without thrombotic events.ConclusionsWe provide evidence of early development of ADVT in unselected acutely ill medical patients suggesting the need of investigating patients by CUS immediately after hospital admission (within 48 h). Advanced age, active cancer, known thrombophilia and increased IMPROVE-DD score may identify patients at risk. The benefit of anticoagulation needs to be investigated in patients with these specific risk factors and negative CUS at admission.Trial registrationNCT03157843.

Project description:The goal of this study was to categorize patients with abnormal serum phosphate upon hospital admission into distinct clusters utilizing an unsupervised machine learning approach, and to assess the mortality risk associated with these clusters. We utilized the consensus clustering approach on demographic information, comorbidities, principal diagnoses, and laboratory data of hypophosphatemia (serum phosphate ≤ 2.4 mg/dL) and hyperphosphatemia cohorts (serum phosphate ≥ 4.6 mg/dL). The standardized mean difference was applied to determine each cluster's key features. We assessed the association of the clusters with mortality. In the hypophosphatemia cohort (n = 3113), the consensus cluster analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; a higher comorbidity burden, particularly hypertension; diabetes mellitus; coronary artery disease; lower eGFR; and more acute kidney injury (AKI) at admission. Cluster 2 had a comparable hospital mortality (3.7% vs. 2.9%; p = 0.17), but a higher one-year mortality (26.8% vs. 14.0%; p < 0.001), and five-year mortality (20.2% vs. 44.3%; p < 0.001), compared to Cluster 1. In the hyperphosphatemia cohort (n = 7252), the analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; more primary admission for kidney disease; more history of hypertension; more end-stage kidney disease; more AKI at admission; and higher admission potassium, magnesium, and phosphate. Cluster 2 had a higher hospital (8.9% vs. 2.4%; p < 0.001) one-year mortality (32.9% vs. 14.8%; p < 0.001), and five-year mortality (24.5% vs. 51.1%; p < 0.001), compared with Cluster 1. Our cluster analysis classified clinically distinct phenotypes with different mortality risks among hospitalized patients with serum phosphate derangements. Age, comorbidities, and kidney function were the key features that differentiated the phenotypes.

Project description:Machine learning (ML) and deep learning (DL) can successfully predict high prevalence events in very large databases (big data), but the value of this methodology for risk prediction in smaller cohorts with uncommon diseases and infrequent events is uncertain. The clinical course of spontaneous coronary artery dissection (SCAD) is variable, and no reliable methods are available to predict mortality. Based on the hypothesis that machine learning (ML) and deep learning (DL) techniques could enhance the identification of patients at risk, we applied a deep neural network to information available in electronic health records (EHR) to predict in-hospital mortality in patients with SCAD. We extracted patient data from the EHR of an extensive urban health system and applied several ML and DL models using candidate clinical variables potentially associated with mortality. We partitioned the data into training and evaluation sets with cross-validation. We estimated model performance based on the area under the receiver-operator characteristics curve (AUC) and balanced accuracy. As sensitivity analyses, we examined results limited to cases with complete clinical information available. We identified 375 SCAD patients of which mortality during the index hospitalization was 11.5%. The best-performing DL algorithm identified in-hospital mortality with AUC 0.98 (95% CI 0.97-0.99), compared to other ML models (P < 0.0001). For prediction of mortality using ML models in patients with SCAD, the AUC ranged from 0.50 with the random forest method (95% CI 0.41-0.58) to 0.95 with the AdaBoost model (95% CI 0.93-0.96), with intermediate performance using logistic regression, decision tree, support vector machine, K-nearest neighbors, and extreme gradient boosting methods. A deep neural network model was associated with higher predictive accuracy and discriminative power than logistic regression or ML models for identification of patients with ACS due to SCAD prone to early mortality.

Dataset Information

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

Publications

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

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