Faculty Opinions recommendation of Risk assessment of venous thrombosis in families with known hereditary thrombophilia: the MARseilles-NImes prediction model.

Aim: Metabolic syndrome (MS) screening is essential for the early detection of the occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived.Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05–6.85), platelet packed volume (OR: 2.63, CI:2.31–3.79), leukocyte count (OR: 2.01, CI:1.79–2.19), red blood cell count (OR: 1.99, CI:1.80–2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12–1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram.Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.

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Risk assessment for recurrent venous thrombosis

The Lancet ◽

10.1016/s0140-6736(10)60962-2 ◽

2010 ◽

Vol 376 (9757) ◽

pp. 2032-2039 ◽

Cited By ~ 159

Author(s):

Paul Alexander Kyrle ◽

Frits R Rosendaal ◽

Sabine Eichinger

Keyword(s):

Risk Assessment ◽

Venous Thrombosis ◽

Recurrent Venous Thrombosis

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Determinants of Risk for Venous and Arterial Thrombosis in Primary Antiphospholipid Syndrome and in Antiphospholipid Syndrome with Systemic Lupus Erythematosus

The Journal of Rheumatology ◽

10.3899/jrheum.081194 ◽

2009 ◽

Vol 36 (6) ◽

pp. 1195-1199 ◽

Cited By ~ 105

Author(s):

ADRIANA DANOWSKI ◽

MARIO NEWTON LEITÃO de AZEVEDO ◽

JOSE ANGELO de SOUZA PAPI ◽

MICHELLE PETRI

Keyword(s):

Systemic Lupus Erythematosus ◽

Venous Thrombosis ◽

Antiphospholipid Syndrome ◽

Lupus Erythematosus ◽

Antiphospholipid Antibodies ◽

Arterial Thrombosis ◽

Pregnancy Loss ◽

Fold Increase ◽

Systemic Lupus ◽

Hereditary Thrombophilia

Objective.Antiphospholipid syndrome (APS) is characterized by thrombosis (venous and arterial) and pregnancy loss in conjunction with the lupus anticoagulant, IgG or IgM anticardiolipin, or IgG or IgM anti-ß2-glycoprotein I. In most series, only a minority of patients with antiphospholipid antibodies develop a clinical manifestation.Methods.A cross-sectional study of consecutive patients in the Hopkins Lupus Center was performed. Interviews were done and records were reviewed for the following variables: gender, ethnicity, hypertension, triglycerides, cholesterol, smoking, diabetes mellitus, homocysteine, cancer, hepatitis C, hormone replacement therapy/oral contraceptives, hereditary thrombophilia, anticardiolipin antibodies IgG, IgM and IgA, and lupus anticoagulant (LAC). Our aim was to identify risk factors associated with thrombosis and pregnancy loss in patients with antiphospholipid antibodies.Results.A total of 122 patients (84% female, 74% Caucasian) were studied. Patients were divided into 3 groups: primary APS, APS associated with systemic lupus erythematosus, and patients with systemic lupus erythematosus (SLE) with antiphospholipid antibodies but no thrombosis or pregnancy loss. Venous thrombosis was associated with high triglycerides (p = 0.001), hereditary thrombophilia (p = 0.02), anticardiolipin antibodies IgG > 40 (p = 0.04), and LAC (p = 0.012). Hypertriglyceridemia was associated with a 6.4-fold increase, hereditary thrombophilia with a 7.3-fold increase, and anticardiolipin IgG > 40 GPL with a 2.8-fold increase in the risk of venous thrombosis. Arterial thrombosis was associated with hypertension (p = 0.008) and elevated homocysteine (p = 0.044). Hypertension was associated with a 2.4-fold increase in the risk of arterial thrombosis. No correlations were found for pregnancy loss.Conclusion.The frequency of thrombosis and pregnancy loss is greater in APS associated with SLE than in primary APS. Risk factors differ for venous and arterial thrombosis in APS. Treatment of hypertension may be the most important intervention to reduce arterial thrombosis. Elevated triglycerides are a major associate of venous thrombosis, but the benefit of treatment is not known. Hereditary thrombophilia is an associate of venous but not arterial thrombosis, making it cost-effective to investigate only in venous thrombosis.

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Risk Assessment in Chinese Hospitalized Patients Comparing the Padua and Caprini Scoring Algorithms

Clinical and Applied Thrombosis/Hemostasis ◽

10.1177/1076029618797465 ◽

2018 ◽

Vol 24 (9_suppl) ◽

pp. 127S-135S ◽

Cited By ~ 4

Author(s):

Xiaolan Chen ◽

Lei Pan ◽

Hui Deng ◽

Jingyuan Zhang ◽

Xinjie Tong ◽

...

Keyword(s):

Risk Assessment ◽

Venous Thrombosis ◽

Deep Venous Thrombosis ◽

Blood Platelets ◽

Predictive Ability ◽

Hospitalized Patients ◽

Surgical Patients ◽

Receiver Operating Curve ◽

Lower Specificity ◽

Higher Sensitivity

The current venous thromboembolism (VTE) guidelines recommend all patients to be assessed for the risk of VTE using risk assessment models (RAMs). The study was to evaluate the performance of the Caprini and Padua RAMs among Chinese hospitalized patients. We reviewed data from 189 patients with deep venous thrombosis (DVT) and 201 non-DVT patients. Deep venous thrombosis risk factors were obtained from all patients. The sensitivity and specificity of the Caprini and Padua scores for all patients were calculated. The receiver operating curve (ROC) and the area under the ROC curve (AUC) were used to evaluate the performance of each score. We documented that age, acute infection, prothrombin time (PT), D-dimer, erythrocyte sedimentation rate, blood platelets, and anticoagulation were significantly associated with the occurrence of DVT ( P < .05). These results were true for all medical and surgical patients group (G1), as well as the analysis of medical versus surgical patients (G2). Finally, analysis of the scores in patients with and without cancer was also done (G3). The Caprini has a higher sensitivity but a lower specificity than the Padua ( P < .05). Caprini has a better predictive ability for the first 2 groups ( P < .05). We found Caprini and Padua scores have a similar predictive value for patients with cancer ( P > .05), while Caprini has a higher predictive ability for no cancer patients in G3 than Padua ( P < .05). For Chinese hospitalized patients, Caprini has a higher sensitivity but a lower specificity than Padua. Overall, Caprini RAM has a better predictive ability than Padua RAM.

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Improving compliance of risk assessment and nonpharmacological interventions for deep venous thrombosis prevention in a respiratory ICU: a best practice implementation project

JBI Evidence Implementation ◽

10.1097/xeb.0000000000000260 ◽

2020 ◽

Vol 19 (3) ◽

pp. 268-278

Author(s):

Jinyan Wu ◽

Shunfang Zhu ◽

Zachary Munn ◽

Chunlan Zhou

Keyword(s):

Risk Assessment ◽

Venous Thrombosis ◽

Deep Venous Thrombosis ◽

Best Practice ◽

Implementation Project ◽

Practice Implementation

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Fire Risk Assessment Models Using Statistical Machine Learning and Optimized Risk Indexing

Applied Sciences ◽

10.3390/app10124199 ◽

2020 ◽

Vol 10 (12) ◽

pp. 4199

Author(s):

Myoung-Young Choi ◽

Sunghae Jun

Keyword(s):

Machine Learning ◽

Risk Assessment ◽

Logistic Regression ◽

Prediction Model ◽

Fire Risk ◽

Fire Occurrence ◽

Statistical Machine Learning ◽

Fire Insurance ◽

Comparison Results ◽

Fire Risk Assessment

It is very difficult for us to accurately predict occurrence of a fire. But, this is very important to protect human life and property. So, we study fire hazard prediction and evaluation methods to cope with fire risks. In this paper, we propose three models based on statistical machine learning and optimized risk indexing for fire risk assessment. We build logistic regression, deep neural networks (DNN) and fire risk indexing models, and verify performances between proposed and traditional models using real investigated data related to fire occurrence in Korea. In general, fire prediction models currently in use do not provide satisfactory levels of accuracy. The reason for this result is that the factors affecting fire occurrence are very diverse and frequency of fire occurrence is very sparse. To improve accuracy of fire occurrence, we first build logistic regression and DNN models. In addition, we construct a fire risk indexing model for a more improved model of fire prediction. To illustrate comparison results between our research models and current fire prediction model, we use real fire data investigated in Korea between 2011 to 2017. From the experimental results of this paper, we can confirm that accuracy of prediction by the proposed method is superior to the existing fire occurrence prediction model. Therefore, we expect the proposed model to contribute to evaluating the possibility of fire risk in buildings and factories in the field of fire insurance and to calculate the fire insurance premium.

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