Current Risk Assessment Models Research Articles

Do Decision Support Tools Decrease the Prevalence of Hospital-Acquired Venous Thromboembolisms When Compared to Clinical Judgement? A Single-Center Pre-Post Study.

Introduction: Hospital-acquired venous thromboembolisms (HA-VTEs) carry a significant health burden on patients and a financial burden on hospitals due to reimbursement penalties. VTE prophylaxis at our institute was performed through utilizing an order set based on healthcare professionals' perceived level of risk. However, the use of standardized risk assessment models is recommended by multiple professional societies. Furthermore, integrating decision support tools (DST) based on the standardized risk assessment models has been shown to increase the administration of appropriate deep vein thrombosis (DVT) prophylaxis. Nonetheless, such scoring systems are not inherently flawless and their integration into EMR as a mandatory step can come at the risk of healthcare professional fatigue and burnout. We conducted a study to evaluate the incidence of HA-VTE and length of stay pre- and post implementation of a DST. Methods: We conducted a retrospective, pre-post-implementation observational study at a tertiary medical center after implementing a mandatory DST. The DST used Padua scores for medical patients and Caprini scores for surgical patients. Patients were identified through ICD-10 codes and outcomes were collected from electronic charts. Healthcare professionals were surveyed through an anonymous survey and stored securely. Statistical analysis was conducted by using R (version 3.4.3). Results: A total of 343 patients developed HA-VTE during the study period. Of these, 170 patients developed HA-VTE in the 9 months following the implementation of the DST, while 173 patients were identified in the 9 months preceding the implementation. There was no statistically significant difference in mean HA-VTE/1000 discharge/month pre- and post implementation (4.4 (SD 1.6) compared to 4.6 (SD 1.2), confidence interval [CI] -1.6 to 1.2, p = 0.8). The DST was used in 73% of all HA-VTE cases over the first 6 months of implementation. The hospital length of stay (LOS) was 14.2 (SD 1.9) days prior to implementation and 14.1 (SD 1.6) days afterwards. No statistically significant change in readmission rates was noted (8.8% (SD 2.6) prior to implementation and 15.53% (SD 9.6) afterwards, CI -14.27 to 0.74, p = 0.07). Of the 56 healthcare professionals who answered the survey, 84% (n = 47) reported to be dissatisfied or extremely dissatisfied with the DST, while 91% (n = 51) reported that it slowed them down. Conclusions: There were no apparent changes in the prevalence of HA-VTE, length of stay, or readmission rates when VTE prophylaxis was mandated through DST compared to a prior model which used order sets based on perceived risk. Further studies are needed to further evaluate the current risk assessment models and improve healthcare professionals' satisfaction with DST.

Assessing hypercoagulability and VTE risk using thromboelastography and Khorana score in women with cancers receiving chemotherapy.

Venous thromboembolism (VTE) is a common occurrence in cancer and chemotherapy increases thrombosis risk. Current risk assessment models such as the Khorana score (KS) and its modifications have limitations in female cancers. We assessed the coagulation profile of a group of women cancer patients under chemotherapy using thromboelastography (TEG) to determine if this can inform VTE risk assessment. Cancer patients who planned to receive chemotherapy were recruited. Baseline demographics, cancer data, BMI, Khorana Score (KS), and VTE risk factors were recorded and patients were followed for 6 months, for any thrombotic events. A total of 36 patients aged 35-85 (18 breast, 11 endometrial, 7 ovarian cancer) were evaluated. Hypercoagulability was detected in 63% of patients post-chemo cycle 1 and 75% post-cycle 2, with a significant increase in MA (maximum amplitude) and CI (clotting index), reduction in R (reaction time), K (clot kinetics), and LY30 (lysis time after 30 min of MA). KS showed only 7% of patients were high risk, 23% were low, and 70% were intermediate risk. MA and CI significantly increased in patients with intermediate and high-risk KS when compared with the low-risk patients and MA was positively correlated with KS. Five patients developed actual VTE; 100% of the tested ones were hypercoagulable either post-cycle 1 or 2 and 80% were KS intermediate risk. TEG is a hypercoagulability marker and TEG-MA and CI can potentially assess VTE risk. Larger studies are needed to assess the utility of TEG as an adjuvant to KS to better predict VTE in specific female cancers.

Administrative coding of frailty: Its association with clinical outcomes and resource use in kidney transplantation.

Although not formalized into current risk assessment models,frailtyhas been associated with negative postoperative outcomes in many specialties. However, national analyses of the association between frailty and post-transplant outcomes following kidney transplantation (KT) are lacking. This was a retrospective cohort study of adults undergoing KT from 2016 to 2020 in the Nationwide Readmissions Databases. Frailty was defined using the Johns Hopkins Adjusted Clinical Groups frailty indicator. Of an estimated 95765 patients undergoing KT during the study period, 4918 (5.1%) were frail. After risk adjustment, frail patients were associated with significantly higher odds of in-hospital mortality (AOR 2.17, 95% CI: 1.33-3.57) compared totheir non-frail counterparts. Our findings indicate that frail patients had an average increase in postoperative hospital stay of 1.44 days, a $2300 increase in hospitalization costs, as well as higher odds of developing a major perioperative complication as compared to their non-frail counterparts. Frailty was also associated with greater adjusted risk of non-home discharge. Frailty, as identified by administrative coding, is independently associated with worse surgical outcomes, including increased mortality and resource use, in adults undergoing KT. Given the already limited donor organ pool, novel efforts are needed to ensure adequate optimization and timely post-transplantation care of the growing frail cohort undergoing KT.

1477. Do Federal Risk Assessment Criteria Adequately Classify Patients for Surgical Site Infections Following Colon Surgery?

Abstract Background Infections related to colon surgery are associated with adverse outcomes. Colon SSIs are reportable to National Healthcare Safety Network (NSHN) and Centers for Medicare and Medicaid Services (CMS). Rates of SSIs are factored into federal reimbursement programs; financial penalty may exist for “low-performing” hospitals. CMS employs a risk assessment tool to calculate the number of expected colon SSIs for each center. Methods The NYC H+H consist of hospitals that serve patients of low socioeconomic status. Five of the eleven are designated as level 1 trauma centers. Data regarding patients that underwent colon surgery from 2015-2022, including those that developed SSIs, were obtained from NHSN. Propensity score matching was done to match patients with colon SSIs to those without SSIs. Covariates included the CMS risk factors: gender, diabetes, age, BMI, ASA score, surgical closure type. Results A total of 5217 patients underwent colon surgery from 2015-2022. There were 276 patients with a colon SSI. A propensity matched cohort (n=276), using the CMS risk assessment variables, was matched with the 276 patients with SSIs (Table 1). Other features involving the colon surgery were examined for the two cohorts (Table 2). Compared to the matched controls, there was a disproportionate distribution of wound class groups among the SSI cohort. Significantly more patients had dirty wounds in the SSI group. Surgery was more often emergent, and had a significantly longer duration in the SSI cohort. Of the patients with SSIs, more were trauma cases, and a significantly greater percentage had surgery performed in a trauma center compared to the other hospitals. Conclusion Colon SSI preventive measures are not applicable for emergent and trauma cases. The current risk assessment model used by CMS does not fully adjust for the complexity of various colon surgeries. As such, hospital performance as judged by federal SSI assessments does not correlate with the quality of care provided. Financial penalties imposed by CMS may be devastating, particularly for safety net hospitals serving patients primarily of low socioeconomic status. Until appropriate risk assessment models are developed, the use of colon surgery SSIs as a quality measure should be re-evaluated. Disclosures All Authors: No reported disclosures

Novel field data for exposure of bystanders and residents towards spray drift during application of plant protection products in orchards

Exposure to pesticide spray drift during the application of plant protection products (PPP) in orchards poses potential health risks to bystanders and residents. To address this concern, this study aimed to gather novel field data on exposure to spray drift in orchards. The research was conducted by the Julius Kuehn-Institute (JKI), the Federal Office of Consumer Protection and Food Safety (BVL), and the German Federal Institute for Risk Assessment (BfR). From 2018 to 2021, the method for conducting the field trials and analysis was implemented. The data from multiple series of performed field trials conducted between 2021 and 2022 allowed for collecting data on potential dermal exposure (PDE), inhalation exposure, and ground sediment. The trials considered various distances from the treatment area and drift-reducing techniques. Adult and child mannequins were used to simulate bystanders’ exposure, and petri dishes were used to measure ground sediment. The results showed that drift reduction settings (75% drift reduction) considerably reduced exposure levels on the mannequins. Furthermore, a strong correlation between PDE values for adults and children located next to each other at the same distance was observed. This finding suggests a possibility to convert exposure results from adult to child mannequins. The study produced realistic exposure data for bystanders and residents next to a treated area. It contributes to a better understanding of exposure risks from PPP application and includes data for different distances and spray drift reducing technique. If taken into account for further development of the current risk assessment models, new risk management options for the safe application of PPP, including buffer strips or application of drift reducing systems, might be available in near future.

Impact of the new coronavirus infection pandemic on the course of coronary heart disease

ABSTRACT. This study analyzes the impact of the new coronavirus infection pandemic on the course of coronary heart disease. It highlights the role of factors such as endothelial dysfunction, microvascular hypoperfusion, thrombosis, and systemic inflammation in worsening cardiovascular pathology, including coronary heart disease. The paper also explores additional causes for the deterioration of coronary heart disease during the peak of the pandemic. Despite recommendations advocating for percutaneous intervention in all patients with acute coronary syndrome and ST-segment elevation, there has been a notable decrease in the number of percutaneous intervention procedures performed during the peak of the pandemic. This decline is observed in patients with both ST-segment elevation and those without (reductions of 21% and 37%, respectively). Despite the recommendations advocating for percutaneous intervention in all patients with acute coronary syndrome and ST-segment elevation, there has been a notable decrease in the number of percutaneous intervention procedures performed during the peak of pandemic. This decline is observed in patients with both ST-segment elevation and those without (reductions of 21% and 37%, respectively). Furthermore, there has been an increase in the time interval between ambulance contact and the execution of percutaneous procedures, resulting in prolonged myocardial ischemia and subsequently poorer patient prognosis. Moreover, due to limitations in the ability to provide inpatient examination and treatment for individuals with chronic heart failure, there has been a pronounced exacerbation of the condition and an increase in mortality rates. Similarly, the provision of cardiac rehabilitation after myocardial infarction has been significantly restricted. Given the extensive number of patients who have been affected or continue to suffer from new coronavirus infection, it is of utmost importance to develop predictive models that account for the development of post-new coronavirus infection syndrome and the emergence of early and late complications of a new coronavirus infection, including coronary artery disease, and comorbidities. The current risk assessment models, aimed at predicting mortality and guiding the treatment of patients with new coronavirus infection or post-infection, do not comprehensively consider all the necessary parameters nor do they predict the emergence of long-term complications. Therefore, further prospective studies are required to address these limitations.

Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review.

Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess risk. Applications of artificial intelligence methods (AI) such as deep learning (DL) and convolutional neural networks (CNNs) to evaluate individual patient information and imaging showed promise as personalized risk models. We reviewed the current literature for studies related to deep learning and convolutional neural networks with digital mammography for assessing breast cancer risk. We discussed the literature and examined the ongoing and future applications of deep learning techniques in breast cancer risk modeling.

Development of Clinical Risk-prediction Models for Uterine Atony Following Vaginal and Cesarean Delivery

(Int J Obstet Anesth. 2022;51:103550) As 2.5% of births in the United States are impacted by uterine atony, identifying high-risk women can promote timely treatment and subsequently minimize adverse outcomes. However, there is no current risk assessment model that solely focuses on the risk for uterine atony rather than the overall risk for postpartum hemorrhage (PPH). This retrospective study created and tested 2 risk prediction models for antepartum and intrapartum uterine atony.

Clinical Factors Associated to VTE in Multiple Myeloma Patients Treated with Daratumumab

Introduction: Hematologic malignancies, especially multiple myeloma (MM), are associated with a high risk for venous thromboembolism (VTE). In MM, multiple factors increase the risk of VTE including, but not limited to, different treatments. The most recognized therapies that increase VTE risk for MM are immunomodulator drugs (IMIDs), steroids, and carfilzomib. Monoclonal antibodies (mAbs), usually in combination with other treatments, have become the therapy of choice for transplant- and non-transplant candidates in newly diagnosed MM (NDMM) as well as those with relapsed and/or refractory disease (RRMM). Daratumumab is an anti-CD38 monoclonal antibody incorporated in several combination regimens, and its specific effect on VTE risk is not well known. Previous studies have tried to identify clinical and therapy-related factors associated with VTE risk, with limited data on VTE risk and monoclonal antibodies (Wang, J Leuk Lymphoma, 2021). This study aims to identify if an increased risk of VTE exists for patients undergoing treatment with daratumumab-based regimens in efforts to develop a future risk assessment model that incorporates mAbs. Methods: We retrospectively reviewed the medical records of consecutively identified patients who received daratumumab, registered in a data warehouse designed to study the interference of monoclonal antibodies and blood typing procedures, at MD Anderson Cancer Center (MDACC) for the treatment of MM between April 2014 and February 2021. The study was approved by the institutional review board at MDACC. Eligible patients were screened for upper and lower extremity deep venous thrombosis (DVT) and pulmonary embolism (PE) through doppler ultrasound and contrast enhanced radiology reports, respectively, within 365 days after the date of the first dose of daratumumab. A natural language processing algorithm was used to determine the likely VTE positive cases and then was confirmed with manual chart review. Patients were divided into two categories, based on the timing of VTE diagnosis relative to the first dose of daratumumab: VTE post-daratumumab versus non-VTE post-daratumumab. The co-variates of interest were derived from three previous VTE risk stratification systems (SAVED, IMPEDE-VTE, and IMWG) and the differences were compared within the two category-VTE groups. All variables of interest were analyzed using descriptive statistics. Chi-square test or Fisher exact test were used to assess the association between categorical variables, and non-parametric methods were used for continuous numeric variables. Variables with statistically significant differences between VTE-category groups (p≤ 0.05) were analyzed using a binary logistic regression to determine their effect on the VTE occurrence. Results: Out of 258 eligible patients, 32 had evidence of VTE post-daratumumab, for a VTE incidence of 12.4% at 12 months from the initiation of daratumumab. The median time to VTE from the first dose of daratumumab was 3.9 months (IQR 1.6 - 6.1 months). Of the variables tested, we identified that the presence of central venous access (PICC), surgery within the previous 90 days before the initiation of daratumumab, and the history of comorbidities (cardiac disease, CKD, acute infection, or immobilization) were more prevalent in patients with VTE (Table 1). In the logistic regression analysis, only the presence of comorbidities (p = 0.007) was significant to the risk of VTE, but PICC line insertion (p = 0.064) and surgery within the previous 90 days (p = 0.078) were not (Figure 1). The odds of having a new VTE was 3.3 (95% CI, 1.4-7.7). Conclusions: The incidence of VTE during daratumumab therapy appears to be high (>10% in 12 months). The use of concurrent chemotherapy and thalidomide-derivative agents did not appear to increase the risk of VTE in those patients. The presence of comorbidities was the only independent factor associated to the risk of VTE in that cohort. This study suggests that while the incidence of VTE in MM patients is high, the main contributing risk factors for VTE are dynamic and the interplay between those factors changes along the disease course. After validation, these findings could influence and improve current VTE risk assessment models and update primary thromboprophylaxis guidance for improved patient outcomes. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Abstract 15151: Identification of Novel Right Ventricular Function Phenotypes in Pulmonary Arterial Hypertension Using Unbiased K-Means Clustering

Introduction: Survival in pulmonary arterial hypertension (PAH) is associated with right ventricular (RV) function rather than pulmonary vascular resistance (PVR). At present, RV function is not incorporated in current risk assessment models and studies evaluating RV function are generally limited to single-center studies. The aim was to identify novel RV function phenotypes using unbiased k-means clustering in patients with PAH. Methods: Baseline clinical and hemodynamic data in patients with PAH were harmonized data from 5 PAH clinical trials (COMPASS3: n=98, EARLY: n=176, GRIPHON: n=720, MAESTRO: n=44, and SERAPHIN: n=728). Pearson correlation analysis was used to select variables that reduce the co-linearity (|r| < 0.7) between hemodynamic variables used in the models. K-means clustering methods were applied to advanced RV hemodynamics including end-systolic pressure (ESP), arterial elastance (Ea), single-beat end-diastolic elastance (Eed), Pulse pressure (PP), stroke volume (SV) and RV stroke work (RVSW). Survival analysis was used to compare all-cause mortality among each cluster group. Results: Participants in the harmonized dataset (n=1766) had a mean PAP of 53 ± 17 mmHg, a cardiac output of 4.3±1.4 L/min, a wedge pressure of 9±4 mmHg and a PVR of 11.8±7.4 WU. K-means clustering identified four distinct RV cluster phenotypes that range from mild to severe RV dysfunction (Fig. A). Participants in RV cluster 1 (RV C1) have severe RV dysfunction with increased pressure/afterload, decreased cardiac function, and increased diastolic stiffness compared to RV cluster 4 (RV C4). (Fig. A) RV C1 had decreased six-minute walk distance compared to other clusters. RVC1 and RVC2 had increased risk of mortality compared to RV C4 (Fig. B). Conclusion: Applying K-means clustering to harmonized hemodynamic data from PAH clinical trial data identified 4 unique RV function phenotypes with different clinical outcomes.

Data-driven spatiotemporal assessment of the event-size distribution of the Groningen extraction-induced seismicity catalogue

For induced seismicity, the non-stationary, heterogeneous character of subsurface stress perturbations can be a source of spatiotemporal variations in the scaling of event sizes; one of the critical parameters controlling seismic hazard and risk. We demonstrate and test a systematic, statistical, penalized-likelihood approach to analysing both spatial and temporal variations in event size distributions. The methodology used is transferable to the risk analysis of any subsurface operation, especially for small earthquake catalogues. We explore the whole solution space and circumvent conventional, arbitrary choices that require a priori knowledge of these variations. We assess the effect of possible bias in the derivation, e.g., due to tapering of the earthquake-size distribution, correlation between the b-value and the magnitude of completeness and correlation between the b-value and the largest magnitude observed. We analyse the spatiotemporal variations in the earthquake-size distribution of the Groningen induced seismicity catalogue (December 1991–November 16, 2021). We find statistically significant spatial variations without any compelling, statistical evidence of a temporal variation. Furthermore, we find that the largest magnitudes observed are inconsistent with the sampling statistics of an unconstrained earthquake-size distribution. Current risk assessment models likely overestimate the probability of larger magnitude events (M ≥ 3.0) and thus the risk posed.

Cervical Cancer Metastasis and Recurrence Risk Prediction Based on Deep Convolutional Neural Network

Background: Evaluating the risk of metastasis and recurrence of a cervical cancer patient is critical for appropriate adjuvant therapy. However, current risk assessment models usually involve the testing of tens to thousands of genes from patients’ tissue samples, which is expensive and timeconsuming. Therefore, computer-aided diagnosis and prognosis prediction based on Hematoxylin and Eosin (H&E) pathological images have received much attention recently. Objective: The prognosis of whether patients will have metastasis and recurrence can support accurate treatment for patients in advance and help reduce patient loss. It is also important for guiding treatment after surgery to be able to quickly and accurately predict the risk of metastasis and recurrence of a cervical cancer patient. Method: To address this problem, we propose a hybrid method. Transfer learning is used to extract features, and it is combined with traditional machine learning in order to analyze and determine whether patients have the risks of metastasis and recurrence. First, the proposed model retrieved relevant patches using a color-based method from H&E pathological images, which were then subjected to image preprocessing steps such as image normalization and color homogenization. Based on the labeled patched images, the Xception model with good classification performance was selected, and deep features of patched pathological images were automatically extracted with transfer learning. After that, the extracted features were combined to train a random forest model to predict the label of a new patched image. Finally, a majority voting method was developed to predict the metastasis and recurrence risk of a patient based on the predictions of patched images from the whole-slide H&E image. Results: In our experiment, the proposed model yielded an area under the receiver operating characteristic curve of 0.82 for the whole-slide image. The experimental results showed that the high-level features extracted by the deep convolutional neural network from the whole-slide image can be used to predict the risk of recurrence and metastasis after surgical resection and help identify patients who might receive additional benefit from adjuvant therapy. Conclusion: This paper explored the feasibility of predicting the risk of metastasis and recurrence from cervical cancer whole slide H&E images through deep learning and random forest methods.

Machine-learning model for the prediction of preeclampsia – a step toward personalized risk assessment

The identification of women at risk for preeclampsia based on current risk assessment models yields disappointing results. The use of machine-learning methods may assist in improving our prediction model and generate personalized risk assessment for the individual risk of a specific patient in the first trimester to develop preeclampsia (PET).

Multiclassification Method of Landslide Risk Assessment in Consideration of Disaster Levels: A Case Study of Xianyang City, Shaanxi Province

Geological disaster risk assessment can quantitatively assess the risk of disasters to hazard-bearing bodies. Visualizing the risk of geological disasters can provide scientific references for regional engineering construction, urban planning, and disaster prevention and mitigation. There are some problems in the current binary classification landslide risk assessment model, such as a single sample type, slow multiclass classification speed, large differences in the number of positive and negative samples, and large errors in classification results. This paper introduces multilevel landslide hazard scale samples, selects multiple types of samples according to the divided multilevel landslide hazard scale grade, and proposes a landslide hazard assessment model based on a multiclass support vector machine (SVM). Due to the objective limitations of the single weighting method, the combined weights are used to determine the vulnerability of the landslide hazard-bearing body, and the analytic hierarchy process (AHP) and entropy method are combined to construct a landslide vulnerability assessment model that considers subjective and objective weights. This paper takes landslide disasters in Xianyang City, Shaanxi Province, as the research object. Based on the landslide hazard assessment model and the landslide vulnerability assessment model, a landslide risk assessment experiment is carried out. It generates the landslide risk assessment zoning map and summarizes the risk characteristics of landslides in various towns. The experimental results verify the feasibility and effectiveness of the proposed model and provide important decision support for decision makers in Xianyang City.

Prognostic Value of Red Blood Cell Distribution Width in Transcatheter Aortic Valve Replacement Patients.

Elevated red blood cell distribution width (RDW) level has been shown to be associated with poor outcomes in patients with cardiovascular disease. Limited data are available regarding the prognostic value of RDW in transcatheter aortic valve replacement (TAVR) patients. Therefore, we aimed to investigate the impact of RDW variation on outcomes of TAVR patients. From March 20, 2012, to February 20, 2020, the pre-TAVR RDW levels of 1,163 consecutive TAVR patients were examined. Receiver operating curves were set to define the most accurate cut-point, which was subsequently validated in our validation set. Associations of RDW levels with early and long-term outcomes were investigated. A total of 988 patients were eligible for the analysis. Patients with 30-day, 1-year, and 7-year mortality had significantly higher pre-TAVR RDW levels (15.8% [12.9-19.1] vs 14.7% [11.6-26.3], P = 0.01; 16% [12.3-26.3] vs 14.7% [11.6-24.3], P < 0.001; 15.6% [12.3-26.3] vs 14.6% [11.6-24.3], P < 0.001, respectively). A RDW of 14.5% was found as the most sensitive and specific cut-point for mortality at 1 and 7 years (HR = 2.6, 95% CI: 1.6-4.2, P < 0.001; HR = 1.8, 95% CI: 1.3-2.4, P < 0.001), with mortality of 22% versus 10% at 1 year (P < 0.001) and 37% versus 27% at 7 years (P < 0.001) in patients with RDW ≥14.5% versus those with RDW <14.5%. RDW is an important prognostic factor in TAVR patients. A RDW level higher than 14.5% is significantly associated with post-TAVR early and late mortality. RDW levels should be incorporated into current risk assessment models as an additional variable to predict post-TAVR outcomes.

Data-driven operational failure likelihood model for microbiologically influenced corrosion

Corrosion is a threat to asset integrity, with engineering challenges and economic burdens. Since the last decade, microbiologically influenced corrosion (MIC) began to be recognized among corrosion professionals as a severe corrosion form. It is challenging to detect and predict MIC due to the complex behaviour of microorganisms. The current MIC risk assessment models define the dependencies of parameters with their synergic interactions. A data-driven approach is needed to utilize available operational and microbiological data and learn as the data changes. The model proposed in this study is used to strengthen the variables' correlation and their features to assess MIC likelihood. It can integrate available field and laboratory data into a Learning-based Bayesian network (LBN) model. The model minimizes current research gap and has the advantage of adapting to changes in process operation. It is based on an advanced Bayesian learning algorithm, which develops topology of the Bayesian network (BN) from the input data and its parameters.This paper focuses on the development of the LBN model that utilizes available MIC data for likelihood estimation. The model is benchmarked and validated using data reported in the public domain. The application of the model is demonstrated on the processing facility on a Floating, Production, Storage and Offloading (FPSO). The topology and parameter estimation will update as data changes/improve to capture the system behaviour to assess MIC likelihood, which helps in decision-making to control and mitigate MIC threats.

Attack path prediction based on Bayesian game model

The current network risk assessment model often ignores the impact of attack cost and intrusion intention on network security. In order to better solve the problem of information security defense strategy selection and accurately assess the target network risk, this paper proposes an attack path prediction method based on game model.The atomic attack probability is calculated by vulnerability value, attack cost and attack benefit. The static risk assessment model is established combined with Bayesian belief network quantitative attack graph. And the dynamic update model of intrusion intention is used to realize the effective prediction of attack action under rational assumption, which provides the basis for dynamic defense measures of attack surface. The experimental results verify the feasibility and effectiveness of the model and method.

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.

The Pragmatic Impact of Frailty on Outcomes of Coronary Artery Bypass Grafting

Although not formalized into current risk assessment models, frailty has been associated with negative postoperative outcomes in many specialties. Using administrative coding, we evaluated the impact of frailty on in-hospital death, complications, and resource use in a nationally representative cohort of patients undergoing isolated coronary artery bypass grafting (CABG). Patients aged 18 years and older who underwent isolated CABG across the United States were identified using the 2005 to 2016 National Inpatient Sample. Frailty was defined using a derivative of the validated Johns Hopkins Adjusted Clinical Groups frailty-defining diagnosis indicator. Mortality, length of stay, inflation-adjusted costs, and postoperative complications were evaluated using multilevel multivariable regression. Of an estimated 2,137,618 patients undergoing isolated CABG, 85,879 (4.0%) were considered frail. The proportion of frail patients increased over the study period (nonparametric test for trend P= .002), while annual mortality rates declined (nonparametric test for trend P <.001). Frail patients were older (68.9 ± 10.7 years vs 65.0 ± 10.6 years, P < .001), and more commonly female (32.8% vs 26.2%, P < .001). After adjustment, frailty was associated with increased odds of in-hospital death (adjusted odds ratio [AOR], 2.49; 95% confidence interval [CI], 2.30-2.70; P < .001), major complications (AOR, 2.55; 95% CI, 2.39-2.71; P < .001), increased length of stay (AOR, 1.40; 95% CI, 1.09-2.11; P < .001), and costs (AOR, 1.03; 95% CI, 1.02-1.07; P < .001). Frailty, as identified by administrative coding, serves as a strong independent predictor of death and complications after CABG. Incorporation of frailty into risk models may aid in counseling patients about operative risk and benchmarking outcomes.

The use of cultural risk assessment within the 1994 Tribal land-grant colleges and universities

In 1994, the United States Congress established 35 Colleges or Universities on Reservation Lands of the Native Americans throughout the Midwest and Western United States. These new institutions were provided annual funds from the United States Department of Agriculture for education, research and extension, components of the Land-Grant system. Today, issues related to risk assessment and risk management confront tribal decision-makers as they cope with risks, both real and perceived, that include the transportation of hazardous materials through the reservation, the clean-up of contaminated sites within the reservation, the environmental restoration of Federal facilities, the siting of waste treatment, storage, and disposal facilities, the development of tribal mineral and other natural resources, and the construction and operation of industrial and commercial facilities within the reservation. Tribal decision-makers lack Indian-specific epidemiologic, genetic, and cultural information that impact current risk assessment models needed to incorporate tribal cultural issues. There is a need to enhance the science skills of tribal college faculty in assisting tribal councils and tribal colleges in the long-term planning and stewardship of natural resources on their reservations.