Extended thromboprophylaxis in heart failure patients; the unmet need.

Heart failure (HF) is considered one of a leading cause of cardiovascular morbidity and mortality worldwide. The association between HF and venous thromboembolism (VTE) has been reported in several studies owing to many physiological and thromboembolic risk factors. Thus, the need for extended thromboprophylaxis during the post-discharge period in HF patients has been evaluated. Most guidelines do not recommend extended thromboprophylaxis because of its uncertain benefits and increased risk of bleeding. However, recent evidence in HF patients revealed no increased risk of bleeding with extended thromboprophylaxis, which highlights the importance of identifying ideal candidates who might benefit from extended thromboprophylaxis. Several risk assessment models (RAMs) have been developed to identify patients at a high risk of VTE who would benefit from in-hospital and post-discharge prophylactic anticoagulation therapy based on the risk-benefit principle. However, their accuracy in predicting VTE is questionable, and none have a standardized approach for evaluating the risk of VTE in HF patients. In this review, we provided an overview of the incidence and pathophysiology of VTE in HF patients, a summary of guideline recommendations for VTE prevention, and a summary of studies evaluating the use of extended thromboprophylaxis, with a focus on subgroup or post-hoc analyses of HF patients. We also discussed the need to design an ideal RAM that can identify candidate patients for extended thromboprophylaxis by stratifying the risk of VTE and identifying the key risk factors for bleeding in medically ill patients, including those with HF.

The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more "personalized" approach to cancer staging.

The American Joint Committee on Cancer (AJCC) staging manual has become the benchmark for classifying patients with cancer, defining prognosis, and determining the best treatment approaches. Many view the primary role of the tumor, lymph node, metastasis (TNM) system as that of a standardized classification system for evaluating cancer at a population level in terms of the extent of disease, both at initial presentation and after surgical treatment, and the overall impact of improvements in cancer treatment. The rapid evolution of knowledge in cancer biology and the discovery and validation of biologic factors that predict cancer outcome and response to treatment with better accuracy have led some cancer experts to question the utility of a TNM-based approach in clinical care at an individualized patient level. In the Eighth Edition of the AJCC Cancer Staging Manual, the goal of including relevant, nonanatomic (including molecular) factors has been foremost, although changes are made only when there is strong evidence for inclusion. The editorial board viewed this iteration as a proactive effort to continue to build the important bridge from a "population-based" to a more "personalized" approach to patient classification, one that forms the conceptual framework and foundation of cancer staging in the era of precision molecular oncology. The AJCC promulgates best staging practices through each new edition in an effort to provide cancer care providers with a powerful, knowledge-based resource for the battle against cancer. In this commentary, the authors highlight the overall organizational and structural changes as well as "what's new" in the Eighth Edition. It is hoped that this information will provide the reader with a better understanding of the rationale behind the aggregate proposed changes and the exciting developments in the upcoming edition. CA Cancer J Clin 2017;67:93-99. © 2017 American Cancer Society.

Comparison of three risk assessment models for thromboembolism in multiple myeloma patients receiving immunomodulators: a Brazilian historical cohort.

Venous thromboembolism (VTE) is among the complications of Multiple Myeloma (MM) and may occur in up to 10% of this patient population. However, medications used in MM therapy such as immunomodulators (IMID) may raise these rates. Thus, risk prediction models have been developed to quantify the risk of VTE in MM patients. The aim of this study is to compare the performance of three risk assessment models for VTE in newly diagnosed MM (NDMM) patients using immunomodulatory agents. A historical cohort study during a 10-year period in a Brazilian metropolis with NDMM treated with IMID. Data were collected from patient's medical charts for the period of one year to calculate the scores using IMPEDE VTE, SAVED, and International Myeloma Working Group (IMWG) guidelines. The area under the curve (AUC) of the Receiver Operating Characteristic curve analysis was calculated to assess the discriminative power of three risk assessment models. We included 131 patients (9 in the VTE group versus 122 in the non VTE group). According to IMPEDE, 19.1, 62.6, and 18.3% of patients were considered low, intermediate, and high risk, respectively. SAVED classified 32.1% as high risk and 64.9% had ≥2 risk factors based on IMWG guidelines. The AUC of the IMPEDE VTE score was 0.80 (95% CI 0.66-0.95, p = 0.002), of the SAVED score was 0.69 (95% CI 0.49-0.89, p = 0.057), and of the IMWG risk score was 0.68 (95% CI 0.48-0.88, p = 0.075). IMPEDE VTE was the most accurate in predicting the development of VTE in Brazilian patients on IMID therapy. The SAVED score and the IMWG guidelines did not show discriminative ability in predicting VTE based on the population involved in this study.

Human health risks associated with urban soils in mining areas.

We studied the chemical composition of As and Pb in total (<2 mm) and fine fractions (<50 µm) of 52 urban soil samples from Minas de Riotinto (mining area) and Aracena (non-exposed area) in SW Spain. In addition to a soil phytotoxicity bioassay using Lactuca Sativa L., we modelled and performed carcinogenic and non-carcinogenic human health risk assessment, later comparing our data with relative cancer mortality rates reported at the municipal level. This study demonstrates that mineralized bedrock and natural soil-forming processes affect the geochemistry of natural (in-situ) urban soils, which in many cases surpass the regulatory levels for As (36 mg/kg) and Pb (275 mg/kg). Fine fractions of in-situ and mixed urban soils -susceptible of inhalation- are significantly enriched in As and Pb with respect to fine fractions of aggregate materials (ex-situ soils of chalky sands and gravel) in Minas de Riotinto. The soils in Minas de Riotinto are significantly enriched in As (total and fine fractions) and Pb (total fraction) with respect to Aracena. Despite elevated bulk concentrations of As and Pb, only one in-situ sample exhibits phytotoxic effects of the soil-water extracts on Lactuca Sativa L. seeds. Health risk assessment of these towns as exposure areas indicates that the soils of Minas de Riotinto are indeed a health risk to the residents, whereas there is no potential risk in Aracena. The reported relative mortality rates in Minas de Riotinto show a greater mortality of carcinogenic tumors potentially related to As and Pb exposure, including lung cancer. Both soil type and use must be considered when administrators or policy-makers evaluate health risks involved in urbanistic decision-making. To minimize exposure risk and adverse health outcomes, we recommend that in-situ soils surpassing regulatory levels for As and Pb in public playgrounds and passing areas should be covered with aggregate materials.

Deep learning approach to predict pain progression in knee osteoarthritis.

OBJECTIVE: To develop and evaluate deep learning (DL) risk assessment models for predicting pain progression in subjects with or at risk of knee osteoarthritis (OA). MATERIALS AND METHODS: The incidence and progression cohorts of the Osteoarthritis Initiative, a multi-center longitudinal study involving 9348 knees in 4674 subjects with or at risk of knee OA that began in 2004 and is ongoing, were used to conduct this retrospective analysis. A subset of knees without and with pain progression (defined as a 9-point or greater increase in pain score between baseline and two or more follow-up time points over the first 48 months) was randomly stratified into training (4200 knees with a mean age of 61.0 years and 60% female) and hold-out testing (500 knees with a mean age of 60.8 years and 60% female) datasets. A DL model was developed to predict pain progression using baseline knee radiographs. An artificial neural network was used to develop a traditional risk assessment model to predict pain progression using demographic, clinical, and radiographic risk factors. A combined model was developed to combine demographic, clinical, and radiographic risk factors with DL analysis of baseline knee radiographs. Area under the curve (AUC) analysis was performed using the hold-out testing dataset to evaluate model performance. RESULTS: The traditional model had an AUC of 0.692 (66.9% sensitivity and 64.1% specificity). The DL model had an AUC of 0.770 (76.7% sensitivity and 70.5% specificity), which was significantly higher (p < 0.001) than the traditional model. The combined model had an AUC of 0.807 (72.3% sensitivity and 80.9% specificity), which was significantly higher (p < 0.05) than the traditional and DL models. CONCLUSIONS: DL models using baseline knee radiographs had higher diagnostic performance for predicting pain progression than traditional models using demographic, clinical, and radiographic risk factors.

Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period.

OBJECTIVE: To develop and evaluate deep learning (DL) risk assessment models for predicting the progression of radiographic medial joint space loss using baseline knee X-rays. METHODS: Knees from the Osteoarthritis Initiative without and with progression of radiographic joint space loss (defined as ≥ 0.7 mm decrease in medial joint space width measurement between baseline and 48-month follow-up X-rays) were randomly stratified into training (1400 knees) and hold-out testing (400 knees) datasets. A DL network was trained to predict the progression of radiographic joint space loss using the baseline knee X-rays. An artificial neural network was used to develop a traditional model for predicting progression utilizing demographic and radiographic risk factors. A combined joint training model was developed using a DL network to extract information from baseline knee X-rays as a feature vector, which was further concatenated with the risk factor data vector. Area under the curve (AUC) analysis was performed using the hold-out test dataset to evaluate model performance. RESULTS: The traditional model had an AUC of 0.660 (61.5% sensitivity and 64.0% specificity) for predicting progression. The DL model had an AUC of 0.799 (78.0% sensitivity and 75.5% specificity), which was significantly higher (P < 0.001) than the traditional model. The combined model had an AUC of 0.863 (80.5% sensitivity and specificity), which was significantly higher than the DL (P = 0.015) and traditional (P < 0.001) models. CONCLUSION: DL models using baseline knee X-rays had higher diagnostic performance for predicting the progression of radiographic joint space loss than the traditional model using demographic and radiographic risk factors.

Do we need prophylactic anticoagulation in ambulatory patients with lung cancer? A review.

Venous thromboembolism is a common complication of malignancy. Lung cancer is considered one of the most thrombogenic cancer types. Primary thromboprophylaxis is not currently recommended for all ambulatory patients with active cancer. In the present narrative review we aim to summarize recent data on the safety and efficacy of primary thromboprophylaxis as well as on venous thromboembolism risk assessment, focusing on ambulatory patients with lung cancer. A potential benefit from prophylactic anticoagulation with low molecular weight heparins in terms of venous thromboembolism risk reduction and increased overall survival in patients with lung cancer, without a significant increase in bleeding risk, has been reported in several studies. Recent studies also reveal promising results of direct oral anticoagulants regarding their efficacy as primary thromboprophylaxis in patients with cancer, including those with lung cancer. However, the use of different study methodologies and the heterogeneity of study populations among the trials limit the extraction of definite results. More randomized, controlled trials, restricted to a well-characterized population of patients with lung cancer, are greatly anticipated. The use of risk assessment tools for stratification of venous thromboembolic risk is warranted. The development of an accurate and practical risk assessment model for patients with lung cancer represents an unmet need.

A systematic review of mobility/immobility in thromboembolism risk assessment models for hospitalized patients.

Hospitalized patients are at risk of venous thromboembolism (VTE) and prophylaxis is often suboptimal due to difficulty in identifying at-risk patients. Simple and validated risk-assessment models (RAMs) are available to assist clinicians in identifying patients who have a high risk for developing VTE. Despite the well-documented association of immobility with increased risk of thrombosis, immobility is not consistently defined in clinical studies. We conducted a systematic review of published VTE RAMs and used objective criteria to determine how the term immobility is defined in RAMs. We identified 17 RAMs with six being externally validated. The concept of immobility is vaguely described in different RAMs, impacting the validity of these models in clinical practice. The wide variability in defining mobility in RAMs precluded its accurate clinical application, further limiting generalization of published RAMs. Externally validated RAMs with clearly defined qualitative or quantitative terms of immobility are needed to assess VTE risk in real-time at the point-of-care.

Building a strategy for soil protection at local and regional scale--the case of agricultural wastes landspreading.

Agricultural wastes (AW) are produced in huge quantities worldwide and may cause detrimental effects on environmental quality, affecting soil, water, and air quality. Given the growing soil degradation worldwide, the need for more food of good quality and therefore the intensified agriculture, it is important to develop recycling plans even for those types of treated AW (e.g., composts) that are not considered hazardous. Two strategic approaches for safe and sustainable landspreading of organic wastes are proposed, depending on wastes properties and hazard potential, i.e., an approach appropriate for traditionally used wastes (manures and composts) and another approach for wastes that are potentially hazardous or hazardous and should only be reused under specific restrictions. Both approaches foresee concrete steps, require close cooperation between farmers and local/regional authorities, and are appropriate to ensure environmental sustainability at AW recycling or disposal areas. Desktop and web application tools are also presented that are anticipated to assist authorities in implementing their monitoring strategies.

Predictors for the prescription of pharmacological prophylaxis for venous thromboembolism during hospitalization in Internal Medicine: a sub-analysis of the FADOI-NoTEVole study.

Patients hospitalized in Internal Medicine Units (IMUs) may frequently experience both an increased risk for thrombosis and bleeding. The use of risk assessment models (RAMs) could aid their management. We present a post-hoc analysis of the FADOI-NoTEVole study, an observational, retrospective, multi-center study conducted in 38 Italian IMUs. The primary aim of the study was to evaluate the predictors associated with the prescription of thromboprophylaxis during hospitalization. The secondary objective was to evaluate RAMs adherence. Univariate analyses were conducted as preliminary evaluations of the variables associated with prescribing pharmacological thromboprophylaxis during hospital stay. The final multivariable logistic model was obtained by a stepwise selection method, using 0.05 as the significance level for entering an effect into the model. Thromboprophylaxis was then correlated with the RAMs and the number of predictors found in the multivariate analysis. Thromboprophylaxis was prescribed to 927 out of 1387 (66.8%) patients with a Padua Prediction score (PPS) ≥ 4. Remarkably, 397 in 1230 (32.3%) patients with both PPS ≥ 4 and an IMPROVE bleeding risk score (IBS) < 7 did not receive it. The prescription of thromboprophylaxis mostly correlated with reduced mobility (OR 2.31; 95% CI 1.90-2.81), ischemic stroke (OR 2.38; 95% CI 1.34-2.91), history of previous thrombosis (OR 2.46; 95% CI 1.49-4.07), and the presence of a central venous catheter (OR 3.00; 95% CI 1.99-4.54). The bleeding risk assessment using the IBS did not appear to impact physicians' decisions. Our analysis provides insight into how indications for thromboprophylaxis were determined, highlighting the difficulties faced by physicians with patients admitted to IMUs.

Predictive Model for Pulmonary Embolism in Pregnant and Postpartum Women: A 10-Year Retrospective Study.

Background: Pulmonary embolism (PE) in pregnant and postpartum women is fatal, and risk assessment is crucial for effective and safe management, the aim of this retrospective study was to establish a nomogram for predicting the risk of PE in pregnant and postpartum women. Methods: Totally 343 subjects suspected of PE at the Obstetrics Department of Affiliated Dongyang Hospital of Wenzhou Medical University from January 2012 to December 2021 were retrospective analyzed in our study. Pregnant women suspected of PE and who underwent computed tomographic pulmonary angiography examination were included in the study. The least absolute shrinkage and selection operator regression technique was used to select the best prediction features, and multivariate logistic regression is used to build the prediction model. Bootstrap resampling 1000 times was used to validate the model visualized by nomogram. Evaluate the performance of the model from three aspects: identification, calibration and clinical utility. Results: Our predictive model indicated that chest tightness, anhelation, lactate, and D-dimer were associated with PE. The area under the receiver operating characteristic curve of the model was 0.836 (95% CI: [0.770-0.902]), indicating that our model had a good differential diagnostic performance. Good consistency between prediction and real observation was presented as the calibration curve. Decision curve analysis indicated that our model had a good net clinical benefit. Conclusions: We developed a novel numerical model for selecting risk factors for PE in pregnant and postpartum women. Our results may help obstetricians and gynaecologists to develop individualized treatment plans and PE prevention strategies.

A nomogram for predicting the risk of pulmonary embolism in neurology department suspected PE patients: A 10-year retrospective analysis.

Objective: The purpose of this retrospective study was to establish a numerical model for predicting the risk of pulmonary embolism (PE) in neurology department patients. Methods: A total of 1,578 subjects with suspected PE at the neurology department from January 2012 to December 2021 were considered for enrollment in our retrospective study. The patients were randomly divided into the training cohort and the validation cohort in the ratio of 7:3. The least absolute shrinkage and selection operator regression were used to select the optimal predictive features. Multivariate logistic regression was used to establish the numerical model, and this model was visualized by a nomogram. The model performance was assessed and validated by discrimination, calibration, and clinical utility. Results: Our predictive model indicated that eight variables, namely, age, pulse, systolic pressure, hemoglobin, neutrophil count, low-density lipoprotein, D-dimer, and partial pressure of oxygen, were associated with PE. The area under the receiver operating characteristic curve of the model was 0.750 [95% confidence interval (CI): 0.721-0.783] in the training cohort and 0.742 (95% CI: 0.689-0.787) in the validation cohort, indicating that the model showed a good differential performance. A good consistency between the prediction and the real observation was presented in the training and validation cohorts. The decision curve analysis in the training and validation cohorts showed that the numerical model had a good net clinical benefit. Conclusion: We established a novel numerical model to predict the risk factors for PE in neurology department suspected PE patients. Our findings may help doctors to develop individualized treatment plans and PE prevention strategies.

Predictive Accuracy Comparison of Prognostic Scoring Systems for Survival in Patients Undergoing TIPS Placement: A Systematic Review and Meta-analysis.

RATIONALE AND OBJECTIVES: This meta-analysis aimed to evaluate the performance of different risk assessment models (RAMs) for survival after Transjugular Intrahepatic Portosystemic Shunt (TIPS) in patients with cirrhotic portal hypertension. MATERIALS AND METHODS: A systematic search of PubMed, WOS, Embase, Cochrane, and CNKI from inception to February 2023 was conducted. We comprehensively reviewed and aggregated data from numerous studies covering prevalent RAMs such as Child-Turcotte-Pugh, the Model for End-Stage Liver Disease (MELD), MELD-Sodium (MELD-Na), the Freiburg Index of Post-TIPS Survival (FIPS), Bilirubin-platelet, Chronic Liver Failure Consortium Acute Decompensation score, and Albumin-Bilirubin grade across different timeframes. For this study, short-term is defined as outcomes within a year while long-term refers to outcomes beyond one year. The area under the receiver operating characteristic (AUC) curve or Concordance Statistics was chosen as the metric to assess predictive capacity for mortality outcomes across six predetermined time intervals. Mean effect sizes at various time points were determined using robust variance estimation. RESULTS: MELD consistently stood out as a primary short-term survival predictor, particularly for 1 month (± 2 weeks) (AUC: 0.72) and 3 months of (± 1 month) survival (AUC: 0.72). MELD-Na showed the best long-term predictive ability, with an AUC of 0.70 at 3.5 years (± 1.5 years). FIPS performed well for 6 months of (± 2 months) survival (AUC: 0.68) and overall transplant-free survival (AUC: 0.75). Efficacy nuances were observed in RAMs when applied to particular subgroups. Meta-regression emphasized the potential predictor overlaps in models like MELD and FIPS. CONCLUSION: This meta-analysis underscores the MELD score as the premier predictor for short-term survival following TIPS. Meanwhile, the FIPS score and MELD-Na model exhibit potential in forecasting long-term outcomes. The study accentuates the significance of RAM selection for enhancing patient outcomes and advocates for additional research to corroborate these findings and fine-tune risk assessment in TIPS.

Rural micro-credit model design and credit risk assessment via improved LSTM algorithm.

Rural microcredit plays an important role in promoting rural economic development and increasing farmers' income. However, traditional credit risk assessment models may have insufficient adaptability in rural areas. This study is based on the improved Long Short Term Memory (LSTM) algorithm using self organizing method, aiming to design an optimized evaluation model for rural microcredit risk. The improved LSTM algorithm can better capture the long-term dependence between the borrower's historical behavior and risk factors with its advantages in sequential data modeling. The experimental results show that the rural microcredit risk assessment model based on the self organizing LSTM algorithm has higher accuracy and stability compared to traditional models, and can effectively control credit default risk, providing more comprehensive risk management support for financial institutions. In addition, the model also has real-time monitoring and warning functions, which helps financial institutions adjust their decisions in a timely manner and reduce credit losses. The practical application of this study is expected to promote the stable development of rural economy and the advancement of financial technology. However, future work needs to further validate the practical application effectiveness and interpretability of the model, taking into account the special circumstances of different rural areas, in order to achieve sustainable application of the model in the rural microcredit market.

Comparison and Validation of Different Risk Assessment Models in Patients with Venous Thromboembolism During Pregnancy and Postpartum: A Retrospective Study in China.

Introduction: Venous thromboembolism (VTE) causes maternal mortality and morbidity. This study compares the predictive performance of three risk assessment models (Caprini model, Wells Score, and RCOG model) for VTE risk in Chinese patients during pregnancy and postpartum. Materials and Methods: We conducted a retrospective study in Nanfang Hospital of Southern Medical University and the Third Affiliated Hospital of Guangzhou Medical University from January 2003 to January 2022. Patients were risk-classified using the three risk assessment models, and the models were evaluated for accuracy. Results: A total of 302 patients with confirmed VTE during pregnancy and postpartum were identified. Of these, 142 (47.0%) were diagnosed during pregnancy and 160 (53.0%) postpartum. A total of 276 (91.4%) patients had DVT only, 14 (4.6%) had PE only, and 12 (4.0%) were diagnosed with both. Of the 142 antepartum patients, 140 were categorized at moderate or above-risk levels by the Caprini model, 134 in the Wells Score model, and 112 in the RCOG model, respectively. The number of postpartum patients at moderate or above-risk levels identified by the Caprini, Wells Score, and RCOG models were 160, 152, and 118. The Caprini model significantly stratified VTE patients into a moderate or high-risk level (p < 0.05, Friedman Test). The Caprini model was also more effective at assessing the risk of VTE among postpartum patients than antepartum patients (p < 0.05, Friedman Test). Conclusion: We have demonstrated that the Caprini model is an effective prediction tool for the maternal population during peripartum, especially in the postpartum period. Results from the Caprini model will help obstetricians or physicians stratify potential risk patients and guide prophylaxis decisions. The RCOG model was not particularly useful in Chinese VTE patients during pregnancy and postpartum. Multicenter prospective validations of the RCOG model in Chinese maternal populations are needed.

Improved utilisation of venous thromboembolism prophylaxis in renal-impaired patients following a clinical pharmacist intervention.

OBJECTIVES: To evaluate the role of the clinical pharmacist in improving venous thromboembolism (VTE) prophylaxis prescription in patients with renal impairment (RI). METHODS: This was an interventional cross-sectional study conducted in a nephrology ward. Patients' risk scores for VTE and bleeding during hospitalisation (evaluated by the Caprini Risk Assessment Model (RAM), Padua Prediction Score and IMPROVE Bleeding Risk Score, respectively), and the rate of VTE prophylaxis administration to patients, were evaluated before and after a clinical pharmacist's intervention. RESULTS: In the pre-intervention phase, 34.8% of high-VTE-risk patients, of whom 12.5% were also at high risk of bleeding, received pharmacological prophylaxis. Moreover, 22.2% of low-VTE-risk patients received prophylaxis. In the intervention phase, prophylaxis was administered to all high-risk patients (mechanical prophylaxis in 7% of patients with a high risk of both VTE and bleeding, and heparin in the remainder) and to 3.3% of those at low risk of VTE. CONCLUSIONS: The clinical pharmacist's intervention using RAMs can improve the rate of thrombosis prophylaxis prescription in patients with RI who have a high risk of VTE.

Identifying women with increased risk of breast cancer and implementing risk-reducing strategies and supplemental imaging.

Breast cancer (BC) is the second most common cancer in women, affecting 1 in 8 women in the United States (12.5%) in their lifetime. However, some women have a higher lifetime risk of BC because of genetic and lifestyle factors, mammographic breast density, and reproductive and hormonal factors. Because BC risk is variable, screening and prevention strategies should be individualized after considering patient-specific risk factors. Thus, health care professionals need to be able to assess risk profiles, identify high-risk women, and individualize screening and prevention strategies through a shared decision-making process. In this article, we review the risk factors for BC, risk-assessment models that identify high-risk patients, and preventive medications and lifestyle modifications that may decrease risk. We also discuss the benefits and limitations of various supplemental screening methods.

Breast Cancer Risk Assessment and Management of the High-Risk Patient.

Breast cancer is the most commonly diagnosed nonskin cancer in women. To decrease the breast cancer burden, conserve resources, and decrease unnecessary treatments, guidelines suggest interventions be reserved for those women at greatest risk for disease. Risk assessment incorporating breast cancer risk factors and risk assessment models is of paramount importance in identifying women who have the greatest benefit from risk reduction strategies. Principles of shared decision-making should guide practitioners to incorporate patients' values, goals, and objectives in decisions around genetic testing, pharmacologic intervention, enhanced surveillance, and other risk reduction strategies.

Association between risk of venous thromboembolism and mortality in patients with COVID-19.

OBJECTIVES: To investigate the association of risk of venous thromboembolism with 30-day mortality in COVID-19 patients. METHODS: A total of 1030 COVID-19 patients were retrospectively collected, with baseline data on demographics, sequential organ failure assessment (SOFA) score, and VTE risk assessment models (RAMs), including Padua prediction score (PPS), International Medical Prevention Registry (IMPROVE), and Caprini. RESULTS: Thirty-day mortality increased progressively from 2% in patients at low VTE risk to 63% in those at high risk defined by PPS. Similar findings were observed in IMPROVE and Caprini scores. Progressive increases in VTE risk were also associated with higher SOFA score. High risk of VTE was independently associated with mortality regardless of adjusted gender, smoking status and some comorbidities, with hazard ratios of 29.19, 37.37 and 20.60 for PPS, IMPROVE and Caprini RAM, respectively (P < 0.001 for all comparisons). The predictive accuracy of PPS (area under curve (AUC) 0.900), IMPROVE (AUC 0.917), or Caprini (AUC 0.861) RAM for risk of hospitalized mortality was unexpectedly strong. CONCLUSIONS: We established that the presence of a high risk of VTE identifies a group of COVID-19 patients at higher risk for mortality. Furthermore, there is a high accuracy of VTE RAMs to predict mortality in these patients.

Performance of Risk Assessment Models for Prevalent or Undiagnosed Type 2 Diabetes Mellitus in a Multi-Ethnic Population-The Helius Study.

Background: Most risk assessment models for type 2 diabetes (T2DM) have been developed in Caucasians and Asians; little is known about their performance in other ethnic groups. Objectives: We aimed to identify existing models for the risk of prevalent or undiagnosed T2DM and externally validate them in a multi-ethnic population currently living in the Netherlands. Methods: A literature search to identify risk assessment models for prevalent or undiagnosed T2DM was performed in PubMed until December 2017. We validated these models in 4,547 Dutch, 3,035 South Asian Surinamese, 4,119 African Surinamese, 2,326 Ghanaian, 3,598 Turkish, and 3,894 Moroccan origin participants from the HELIUS (Healthy LIfe in an Urban Setting) cohort study performed in Amsterdam. Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test). We identified 25 studies containing 29 models for prevalent or undiagnosed T2DM. C-statistics varied between 0.77-0.92 in Dutch, 0.66-0.83 in South Asian Surinamese, 0.70-0.82 in African Surinamese, 0.61-0.81 in Ghanaian, 0.69-0.86 in Turkish, and 0.69-0.87 in the Moroccan populations. The C-statistics were generally lower among the South Asian Surinamese, African Surinamese, and Ghanaian populations and highest among the Dutch. Calibration was poor (Hosmer-Lemeshow p < 0.05) for all models except one. Conclusions: Generally, risk models for prevalent or undiagnosed T2DM show moderate to good discriminatory ability in different ethnic populations living in the Netherlands, but poor calibration. Therefore, these models should be recalibrated before use in clinical practice and should be adapted to the situation of the population they are intended to be used in.