Rank-based risk target data analysis using digital twin on oil pipeline network based on manifold learning

Oil and Gas Pipeline (OGP) projects face a wide scope of wellbeing and security Risk Factors (RFs) all around the world, especially in the oil and gas delivering nations having influencing climate and unsampled data. Lacking data about the reasons for pipeline risk predictor and unstructured data about the security of the OGP prevent endeavors of moderating such dangers. This paper, subsequently, means to foster a risk analyzing framework in view of a comprehensive methodology of recognizing, dissecting and positioning the related RFs, and assessing the conceivable pipeline characteristics. Hazard Mitigation Methods (HMMs), which are the initial steps of this approach. A new methodology has been created to direct disappointment investigation of pinhole erosion in pipelines utilizing the typical pipeline risk strategy and erosion climate reenactments during a full life pattern of the pipeline. Hence in the proposed work, manifold learning with rank based clustering algorithm is incorporated with the cloud server for improved data analysis. The probability risk rate is identified from the burst pressure by clustering the normal and leak category to improve the accuracy of the prediction system experimented on the lab-scale oil pipeline system. The numerical results like auto-correlation, periodogram, Laplace transformed P-P Plot are utilized to estimate the datasets restructured by the manifold learning approach. The obtained experimental results shows that the cloud server datasets are clustered with rank prioritization to make proactive decision in faster manner by distinguishing labelled and unlabeled pressure attributes.

Radiomics Analysis Derived From LGE-MRI Predict Sudden Cardiac Death in Participants With Hypertrophic Cardiomyopathy

Objectives: To identify significant radiomics features derived from late gadolinium enhancement (LGE) images in participants with hypertrophic cardiomyopathy (HCM) and assess their prognostic value in predicting sudden cardiac death (SCD) endpoint.Method: The 157 radiomic features of 379 sequential participants with HCM who underwent cardiovascular magnetic resonance imaging (MRI) were extracted. CoxNet (Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net) and Random Forest models were applied to optimize feature selection for the SCD risk prediction and cross-validation was performed.Results: During a median follow-up of 29 months (interquartile range, 20–42 months), 27 participants with HCM experienced SCD events. Cox analysis revealed that two selected features, local binary patterns (LBP) (19) (hazard ratio (HR), 1.028, 95% CI: 1.032–1.134; P = 0.001) and Moment (1) (HR, 1.212, 95%CI: 1.032–1.423; P = 0.02) provided significant prognostic value to predict the SCD endpoints after adjustment for the clinical risk predictors and late gadolinium enhancement. Furthermore, the univariately significant risk predictor was improved by the addition of the selected radiomics features, LBP (19) and Moment (1), to predict SCD events (P < 0.05).Conclusion: The radiomics features of LBP (19) and Moment (1) extracted from LGE images, reflecting scar heterogeneity, have independent prognostic value in identifying high SCD risk patients with HCM.

Does Prior Stroke Predict Long-Term Recurrent Stroke After Percutaneous Coronary Intervention? Five-Year Results From a Large Cohort Study

Background: We found a positive correlation between the prior stroke history and recurrent stroke in patients who underwent percutaneous coronary intervention (PCI) in our previous study, which indicated the close interaction of stroke and cardiovascular diseases. However, it is unclear whether prior stroke is still associated with worse prognosis at a longer follow-up period.Methods: A total of 10,724 coronary heart disease (CHD) patients who received PCI from January to December 2013 were prospectively enrolled and were subsequently divided into the prior stroke (n = 1,150) and non-prior stroke (n = 9,574) groups according to their history. Baseline characteristics and 5-year outcomes were recorded.Results: Patients with prior stroke had more clinical risk factors, as well as more extensive coronary artery lesions. Although in-hospital outcomes were similar between patients from the two groups, the 5-year follow-up result revealed that patients with prior stroke experienced higher incidence of stroke, major adverse cardiac and cerebrovascular events (MACCEs), all-cause death, and cardiac death (7.0 vs. 3.0%, p < 0.001; 25.9 vs. 20.3%, p < 0.001; 5.3 vs. 3.5%, p = 0.002; 3.1 vs. 2.1%, p = 0.032, respectively). After the propensity score matching, the 5-year stroke rate was still higher in the prior stroke group (6.8 vs. 3.4%, p = 0.001). The multivariable regression analysis also identified the prior stroke as a risk predictor of the 5-year stroke (HR = 2.011, 95% CI: 1.322–3.059, p = 0.001).Conclusions: Coronary heart disease patients with prior stroke who received PCI had a higher incidence of 5-year long-term adverse cardiovascular and cerebrovascular events, especially recurrent stroke. Prior stroke was a strong risk predictor of future stroke events.

299. D-dimer as an ICU Admission Risk Predictor in COVID-19 Patients, A Prospective Study

Background Since the onset of the 2019 coronavirus disease 2019 (COVID-19) pandemic, the rapid increase in community-acquired pneumonia (CAP) cases has led to an excessive rate of intensive care units (ICU) admissions, a rate varying between 5-18%, depending on the country. Consequently, the study of serum biomarkers, such as D-dimer, have been utilized to identify patient with severe disease. However, further data is needed to confirm the association between this serum concentration of D-dimer and the risk of ICU admission. Thus, the aim of this study was to determine if serum concentration of D-dimer predict the risk of ICU admission in patients with COVID-19 and CAP. Methods A prospective observational study was carried out at the Clinica Universidad de La Sabana, Colombia. Patients older than 18 years old, hospitalized for COVID-19 or CAP were included. Then, patients were stratified into ICU and non-ICU patients. Plasma samples were collected within the first 24 hours of hospital admission to quantify D-dimer using the PATHFAST system. Concentrations were compared among groups and to assess the biomarker capacity to predict ICU admission risk, ROC curves were used. Finally, a DeLong test was applied to compare their differences. Results A total of 240 patients diagnosed with lower respiratory tract infection were included in the study. 88 patients were COVID-19 negative (CAP) and 152 were positive. Plasma concentrations of D-dimer (µg/ml) were significantly higher in COVID-19 patients admitted to the ICU when compared with non-ICU COVID-19 admitted patients (Median [IQR]; 1.54 [0.9-3.25] Vs. 1.13 [0.69-1.69], p=0.005). The area under curve (AUC) ROC to predict ICU admission was 0.62 among COVID-19 patients. DeLong’s test p value was 0.24. Serum D-dimer an ICU admission Conclusion D-dimer seems to be a promising tool to identify COVID-19 patients with disease. However, this predicting capacity was not observed in CAP patients. Further studies are needed to identify the mechanisms underling the elevation of D-dimer in COVID-19 patients. Disclosures All Authors: No reported disclosures

Incorporating Ensemble approach into Automated Machine Learning in an attempt to predict Cerebral Infarction in patients presenting with Subarachnoid Hemorrhage : Secondary analysis of a novel exploration

Automated machine learning is explored to develop a sensitive risk predictor for cerebral infarction in patients presenting with subarachnoid haemorrhage.

Incorporating Ensemble approach into Automated Machine Learning in an attempt to predict the Risk of Postoperative Delirium After Deep Brain Stimulation Surgery in Parkinson's Disease Patients : Secondary analysis of a retrospective cohort study

Automated machine learning is explored to develop a sensitive risk predictor for postoperative delirium in elderly Parkinson's Disease patients who have received deep brain stimulation surgical intervention.

The ceramide- and phosphatidylcholine- based Coronary Event Risk Test2 (CERT2) and cardiovascular mortality in men and women with type 2 diabetes

The recently introduced Coronary Event Risk Test version 2 (CERT2) is a validated cardiovascular risk predictor score that uses circulating ceramide and phosphatidylcholine concentrations. The purpose of this study was to investigate the power of CERT2 to predict cardiovascular mortality in 280 male and 121 female patients with type 2 diabetes (T2DM). Prospectively, we recorded 55 cardiovascular deaths in men and 19 in women during a mean follow-up time of 7.6±3.6 and 8.1±3.4 years respectively. Overall, cardiovascular survival decreased with increasing CERT2 risk categories (figure 1). In Cox regression models, CERT2 significantly predicted the incidence of cardiovascular mortality in male patients with T2DM (unadj. HR 1.82 [1.39–2.37] per standard deviation; p<0.001), the unadj. HR in women was 1.36 [0.83–2.22]; p=0.228). After adjustment for age, BMI, current smoking, LDL cholesterol, HDL cholesterol, hypertension, and statin use the HR in men was 1.73 [1.31–2.29]; p<0.001) and 1.40 [083–2.36]; p=0.210 in women. Interaction terms CERT2 x gender were non-significant both in univariate analysis (p=0.354) and after multivariate adjustment (p=0.359). We conclude that sex does not significantly impact the association of CERT2 with cardiovascular mortality in patients with T2DM. FUNDunding Acknowledgement Type of funding sources: None. Figure 1