scholarly journals Decision Support System to Risk Stratification in the Acute Coronary Syndrome Using Fuzzy Logic

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hui Xiao ◽  
Shah Nazir ◽  
Hanmin Li ◽  
Habib Ullah Khan ◽  
Chengwei Li
Keyword(s):  
Blood Flow ◽  
Acute Coronary Syndrome ◽  
Decision Support ◽  
High Risk ◽  
Decision Support System ◽  
Risk Stratification ◽  
Support System ◽  
Probabilistic Approach ◽  
Coronary Syndrome ◽  
Grace Score

Acute coronary syndrome (ACS) is a set of symptoms and signs which define a range of conditions related with the unexpected reduced blood flow to the heart. In ACS, the heart muscles cannot function properly due to the decrease of blood flow. Myocardial infarction (MI) is a condition which comes under the umbrella of acute coronary syndrome. The aim of risk stratification (RS) in ACS is to recognize patients at high risk of ischemic events. Yet, no investigative study is available to identify the patients at high risk. Therefore, to facilitate this process, it would be ideal to have a reliable and trustworthy method by the help of which the doctors can make early and easy decisions for the patient and for detecting the related disease. This research used the features of GRACE Score to RS in the ACS and presented decision support system (DSS). The concept of probabilistic approach has been used as a tool to model the identified features for decision-making (DM). This technique can be further used for DM purposes to RS in the ACS in healthcare. Furthermore, the result of the proposed method has proved closer and more reliable DM of patient and then eventually can be used for advice of medicine and rest accordingly by the doctors.

scholarly journals Association of Platelet Lymphocyte Ratio (PLR) and Neutrophil Lymphocyte Ratio (NLR) with Global Registry of Acute Coronary Events (GRACE) Score in Acute Coronary Syndrome

2021 ◽  
Vol 8 (5) ◽  
pp. 1-6
Author(s):  
Baginda Yusuf Siregar ◽  
Refli Hasan ◽  
Rahmad Isnanta
Keyword(s):  
Acute Coronary Syndrome ◽  
High Risk ◽  
Risk Stratification ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio ◽  
Coronary Syndrome ◽  
Platelet Lymphocyte Ratio ◽  
Coronary Events ◽  
Global Registry ◽  
Grace Score

Introduction: Acute Coronary Syndrome (ACS) has morbidity and mortality significantly increase, it requires risk stratification for the assessment and selection of initial invasive strategies. The Global Registry of Acute Coronary Events (GRACE) scores recommended as risk stratification of ACS. Some of studies found that the combination of GRACE scores with other clinical and laboratory parameters can increase predictive value of ACS. Platelet Lymphocyte Ratio (PLR) and Neutrophil Lymphocyte Ratio (NLR) act as parameter of systemic inflammation in ACS. Aims of the study to determine the association between PLR and NLR with risk stratification GRACE score. Method: This study is analytical with a cross-sectional retrospective design. This study included 70 patients with a diagnosis of ACS based on medical record data. Data analysis was performed using the Statistical Package for the Social Sciences (SPSS) 22.0. P-value <0.05 was considered statistically significant. Results: This study was found a positive correlation between PLR and NLR with the GRACE score of patients ACS (r=0.485, p<0.001; r=0.570, p<0.001). The PLR and NLR were both found the significantly higher in the high risk GRACE score respectively (188 ± 47, p < 0.001; 7.9± 2.7, p<0.001). The ROC curve analysis, cutt-off PLR of 123 and above (sensitivity of 72.7 %; specificity of 70.3), while cutt-off NLR of 4 and above (sensitivity of 78.8%; specificity of 70.3%) to detect high risk GRACE score. Conclusion: There is a significant association between PLR and NLR with GRACE score Keywords: Platelet Lymphocyte Ratio, Neutrophil Lymphocyte Ratio, GRACE score, Acute Coronary Syndrome.

scholarly journals DECISION SUPPORT SYSTEM OF A TAB MANUFACTURING PLANT

1970 ◽  
Vol 41 (1) ◽  
pp. 71-79 ◽  
Author(s):  
Deepika Garg ◽  
Kuldeep Kumar ◽  
Jai Sigh
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Differential Equations ◽  
Support System ◽  
Probabilistic Approach ◽  
Death Process ◽  
Manufacturing Plant ◽  
Transition Diagram ◽  
In Series ◽  
Grinding Machine

This paper discusses a decision support system for a Tab manufacturing plant. Tab manufacturing mainly consists of six subsystems working in series .Two subsystems namely Grinding machine, Electroplating machine are supported by stand-by units with perfect switch over devices and the remaining four subsystem are prone to failure. The mathematical model of Tab manufacturing plant has been developed using Markov birth – death Process. The differential equations has been developed on the basis of probabilistic approach using transition diagram which are further solved for steady state availability in order to develop the decision matrices which provide the various availability levels for different combinations of failure and repair rates of each subsystem.Key words: Availability; Differential Equations; Markov process; Steady StateDOI: 10.3329/jme.v41i1.5365Journal of Mechanical Engineering, Vol. ME 41, No. 1, June 2010 71-79

Importance of a Risk Stratification Strategy to Identify High-risk Patients Presenting With Cocaine-associated Acute Coronary Syndrome

2018 ◽  
Vol 17 (3) ◽  
pp. 147-150 ◽  
Author(s):  
Samineh Sehatbakhsh ◽  
Alexander Kushnir ◽  
Stefanie Furlan ◽  
Elie Donath ◽  
Waqas Ghumman ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
High Risk ◽  
Risk Stratification ◽  
High Risk Patients ◽  
Coronary Syndrome ◽  
Risk Patients

The role of GRACE score in the prediction of high-risk coronary anatomy in patients with non-ST elevation acute coronary syndrome

2015 ◽  
pp. 592-597 ◽  
Author(s):  
Burcak Kilickiran Avci ◽  
Baris Ikitimur ◽  
Ozge Ozden Tok ◽  
Murat Cimci ◽  
Emre Erturk ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
High Risk ◽  
Coronary Anatomy ◽  
Coronary Syndrome ◽  
St Elevation ◽  
Elevation Acute Coronary Syndrome ◽  
Grace Score

scholarly journals SUMMARY REQUIRING REGARDLESS OF CREATION DECISION SUPPORT SYSTEM FOR THE DISTRIBUTION CAPABILITIES TO PROVIDE SUFFICIENT EFFICIENCY OF BORDER REGION OF SYSTEMS FOR OPTO-ELECTRONIC SURVEILLANCE

2020 ◽  
Vol 1 (1) ◽  
pp. 81-90
Author(s):  
O. BOROVYK ◽  
◽  
D. BOROVYK ◽  
D. CIMBRIQUE ◽  
◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Surveillance System ◽  
Probabilistic Approach ◽  
Border Region ◽  
Time Periods ◽  
Weather Changes ◽  
The Impact ◽  
Laws Of Distribution

The article is devoted to the substantiation of the choice of the basic methodology for assessing the effectiveness of the optoelectronic surveillance system, which could be taken as the basic model of this system for the distribution of forces and means to ensure a sufficient level of border protection efficiency. taking into account in the specified basic methodology for assessing the effectiveness of the system. As a result of the study it was found that as a basic method of assessing the effectiveness of the optoelectronic surveillance system, it is advisable to adopt a method based on a probabilistic approach to describe the processes that accompany the functioning of the optoelectronic surveillance system. It is also substantiated that the main provisions that need to be taken into account when creating a decision support system for the distribution of forces and means to ensure a sufficient level of border protection in the field of optoelectronic surveillance are: homogeneous in terms of features of technical means of border protection time periods; adequate integrated laws of distribution of time periods, which determine the probability of non-detection of the target to its approach to a given distance by certain technical means of border protection, which are part of the studied system of optoelectronic surveillance; points of "stitching" of various integral laws of distribution at the ends of the specified periods; the impact of weather changes over a period of time, which characterizes the period of the service organization, on the functioning of the system; "Dead" zones, which are dynamically variable depending on the natural and man-made conditions that arise in the area of responsibility.

Time-dependent Estimates of Recurrence and Survival in Colon Cancer: Clinical Decision Support System Tool Development for Adjuvant Therapy and Oncological Outcome Assessment

2014 ◽  
Vol 80 (5) ◽  
pp. 441-453 ◽  
Author(s):  
Scott R. Steele ◽  
Anton Bilchik ◽  
Eric K. Johnson ◽  
Aviram Nissan ◽  
George E. Peoples ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Decision Support ◽  
High Risk ◽  
Adjuvant Therapy ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Predictive Values

Unanswered questions remain in determining which high-risk node-negative colon cancer (CC) cohorts benefit from adjuvant therapy and how it may differ in an equal access population. Machine-learned Bayesian Belief Networks (ml-BBNs) accurately estimate outcomes in CC, providing clinicians with Clinical Decision Support System (CDSS) tools to facilitate treatment planning. We evaluated ml-BBNs ability to estimate survival and recurrence in CC. We performed a retrospective analysis of registry data of patients with CC to train–test–crossvalidate ml-BBNs using the Department of Defense Automated Central Tumor Registry (January 1993 to December 2004). Cases with events or follow-up that passed quality control were stratified into 1-, 2-, 3-, and 5-year survival cohorts. ml-BBNs were trained using machine-learning algorithms and k-fold crossvalidation and receiver operating characteristic curve analysis used for validation. BBNs were comprised of 5301 patients and areas under the curve ranged from 0.85 to 0.90. Positive predictive values for recurrence and mortality ranged from 78 to 84 per cent and negative predictive values from 74 to 90 per cent by survival cohort. In the 12-month model alone, 1,132,462,080 unique rule sets allow physicians to predict individual recurrence/mortality estimates. Patients with Stage II (N0M0) CC benefit from chemotherapy at different rates. At one year, all patients older than 73 years of age with T2–4 tumors and abnormal carcinoembryonic antigen levels benefited, whereas at five years, all had relative reduction in mortality with the largest benefit amongst elderly, highest T-stage patients. ml-BBN can readily predict which high-risk patients benefit from adjuvant therapy. CDSS tools yield individualized, clinically relevant estimates of outcomes to assist clinicians in treatment planning.

scholarly journals Development of a decision support system for tsunami evacuation: application to the Jiyang District of Sanya city in China

2017 ◽  
Vol 17 (3) ◽  
pp. 335-343 ◽  
Author(s):  
Jingming Hou ◽  
Ye Yuan ◽  
Peitao Wang ◽  
Zhiyuan Ren ◽  
Xiaojuan Li
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
High Risk ◽  
Decision Support System ◽  
Travel Time ◽  
Support System ◽  
Spatial Data ◽  
Population Distribution ◽  
Numerical Models ◽  
Tsunami Inundation

Abstract. Major tsunami disasters often cause great damage in the first few hours following an earthquake. The possible severity of such events requires preparations to prevent tsunami disasters or mitigate them. This paper is an attempt to develop a decision support system for rapid tsunami evacuation for local decision makers. Based on the numerical results database of tsunami disasters, this system can quickly obtain the tsunami inundation and travel time. Because numerical models are calculated in advance, this system can reduce decision-making time. Population distribution, as a vulnerability factor, was analyzed to identify areas of high risk for tsunami disasters. Combined with spatial data, this system can comprehensively analyze the dynamic and static evacuation process and identify problems that negatively impact evacuation, thus supporting the decision-making for tsunami evacuation in high-risk areas. When an earthquake and tsunami occur, this system can rapidly obtain the tsunami inundation and travel time and provide information to assist with tsunami evacuation operations.

2228Circulating serum extracellular matrix degradation enzyme cathepsin S predicts mortality and improves risk stratification over the GRACE score in patients with non-ST elevation acute coronary syndrom

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
K Sopova ◽  
G Georgiopoulos ◽  
M Mueller-Hennessen ◽  
M Sachse ◽  
N Vlachogiannis ◽  
...  
Keyword(s):  
Extracellular Matrix ◽  
Acute Coronary Syndrome ◽  
Risk Stratification ◽  
Cathepsin S ◽  
Matrix Degradation ◽  
Extracellular Matrix Degradation ◽  
Coronary Syndrome ◽  
Increased Risk ◽  
All Cause Mortality ◽  
Grace Score

Abstract Background Blood-based biomarkers may be useful in the identification of residual risk for death or acute myocardial infarction (AMI) in patients with a previous acute coronary syndrome. Cathepsin S (CTSS) is a lysosomal cysteine protease with potent elastolytic and collagenolytic activity, which plays an important role in cardiovascular disease through extracellular matrix degradation, vasa vasorum development and atherosclerotic plaque rupture. The aim of the present study was to determine the prognostic and reclassification value of baseline circulating levels of CTSS after adjustment for the Global Registry of Acute Coronary Events (GRACE) score, which is widely recommended for risk stratification in non-ST-segment elevation acute coronary syndrome (NSTE-ACS). Methods CTSS was measured in blood samples collected from 1,129 consecutive patients with adjudicated NSTE-ACS presenting at an acute chest pain unit for evaluation of a possible acute coronary syndrome. Cardiovascular (CV) death and a composite of all-cause mortality and AMI were evaluated as the primary and secondary endpoints of the study, respectively. The additive prognostic value of CTSS over the GRACE score was estimated by the Net Reclassification Index (NRI) that examines the net upward and downward reclassification into correct pre-defined risk categories. Results After a median follow-up of 21 months, 101 (8.95%) deaths were reported, of which 63 (5.6%) were of cardiac origin. The combined endpoint occurred in 176 (15.6%) patients. Patients with CTSS in the highest tertile presented the greatest risk for all-cause mortality (HR=1.84 for highest versus lowest tertile of CTSS distribution, 95% CI 1.1–3.08, P=0.02) and CV death (HR=2.5 for highest versus lowest tertile of CTSS distribution, 95% CI 1.24–5.05, P=0.011) after adjustment for age, gender, diabetes mellitus, hs-cTnT, hsCRP, revascularization and index diagnosis. Similarly, CTSS was associated with increased risk of cardiovascular death after adjusting for the GRACE Score (adjusted HR for highest versus lowest tertile of CTSS distribution=2.34, 95% CI 1.18–4.64, P=0.015). Further, CTSS predicted the combined endpoint of all-cause death or non-fatal MI independently of the GRACE Score (adjusted HR for highest versus lowest tertile of CTSS distribution=1.67, 95% CI 1.15–2.42, P=0.007). When CTSS was added over the GRACE Score, it conferred significant reclassification value for CV death (NRI=21.4%, P=0.008). Similarly, CTSS correctly reclassified risk for all-cause death or non-fatal MI (P=0.006) in 15.9% of the population. Conclusions Circulating CTSS predicts mortality and improves risk stratification of patients with NSTE-ACS over the GRACE score recommended by clinical guidelines. The clinical application of CTSS as a novel biomarker in NSTE-ACS should be further explored and validated.

scholarly journals Impact of Patient Reminders on Papanicolaou Test Completion for High-Risk Patients Identified by a Clinical Decision Support System

2018 ◽  
Vol 27 (5) ◽  
pp. 569-574 ◽  
Author(s):  
Kathy L. MacLaughlin ◽  
Maya E. Kessler ◽  
Ravikumar Komandur Elayavilli ◽  
Branden C. Hickey ◽  
Marianne R. Scheitel ◽  
...  
Keyword(s):  
Decision Support ◽  
High Risk ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Papanicolaou Test ◽  
Risk Patients ◽  
Test Completion
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