scholarly journals Treatment Trajectories Graph Compression Algorithm Based on Cliques

2021 ◽  
Author(s):  
Svetozar Milykh ◽  
Sergey Kovalchuk
Keyword(s):  
Acute Coronary Syndrome ◽  
Data Processing ◽  
Maximum Clique ◽  
Compression Algorithm ◽  
Significant Part ◽  
Graph Representation ◽  
Data Set ◽  
Coronary Syndrome ◽  
Graph Compression ◽  
Entropy Measures

Learning treatment methods and disease progression is significant part of medicine. Graph representation of data provides wide area for visualization and optimization of structure. Present work is dedicated to suggest method of data processing for increasing information interpretability. Graph compression algorithm based on maximum clique search is applied to data set with acute coronary syndrome treatment trajectories. Results of compression are studied using graph entropy measures.

461Modulation of ischemic and bleeding risk by peripheral artery disease after an acute coronary syndrome

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
M Cespon Fernandez ◽  
S Raposeiras Roubin ◽  
E Abu-Assi ◽  
S Manzano-Fernandez ◽  
F Dascenzo ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
Peripheral Artery Disease ◽  
Cumulative Incidence ◽  
Bleeding Risk ◽  
Peripheral Artery ◽  
Data Set ◽  
Coronary Syndrome ◽  
Artery Disease ◽  
The Impact

Abstract Introduction Peripheral artery disease (PAD) is associated with heightened ischemic and bleeding risk in patients with acute coronary syndrome (ACS). With this study from real-life patients, we try to analyze the balance between ischemic and bleeding risk during treatment with dual antiplatelet therapy (DAPT) after an ACS according to the presence or not of PAD. Methods The data analyzed in this study were obtained from the fusion of 3 clinical registries of ACS patients: BleeMACS (2004–2013), CardioCHUVI/ARRITXACA (2010–2016) and RENAMI (2013–2016). All 3 registries include consecutive patients discharged after an ACS with DAPT and undergoing PCI. The merged data set contain 26,076 patients. A propensity-matched analysis was performed to match the baseline characteristics of patients with and without PAD. The impact of prior PAD in the ischemic and bleeding risk was assessed by a competitive risk analysis, using a Fine and Gray regression model, with death being the competitive event. For ischemic risk we have considered a new acute myocardial infarction (AMI), whereas for bleeding risk we have considered major bleeding (MB) defined as bleeding requiring hospital admission. Follow-up time was censored by DAPT suspension/withdrawal. Results From the 26,076 ACS patients, 1,600 have PAD (6.1%). Patients with PAD were older, and with more cardiovascular risk factors. DAPT with prasugrel/ticagrelor was less frequently prescribed in patients with PAD in comparison with the rest of the population (8.2% vs 22.8%, p<0.001). During a mean follow-up of 12.2±4.8 months, 964 patients died (3.7%), and 640 AMI (2.5%) and 685 MB (2.6%) were reported. After propensity-score matching, we obtained two matched groups of 1,591 patients. Patients with PAD showed a significant higher risk of both AMI (sHR 2.17, 95% CI 1.51–3.10, p<0.001) and MB (sHR 1.51, 95% CI 1.07–2.12, p=0.018), in comparison with those without PAD. The cumulative incidence of AMI was 63.9 and 29.8 per 1,000 patients/year in patients with and without PAD, respectively. The cumulative incidence of MB was 55.9 and 37.6 per 1,000 patients/year in patients with and without PAD, respectively. The rate difference per 1,000 patient-years for AMI between patients with and without PAD was +34.1 (95% CI 30.1–38.1), and for MB +18.3 (16.1–20.4). The net balance between ischemic and bleeding events comparing patients with and without PAD was positive (+15.8 per 1,000 patients/year, 95% CI 9.7–22.0). Conclusions PAD was associated with higher ischemic and bleeding risk after hospital discharge for ACS treated with DAPT. However, the balance between ischemic and bleeding risk was positive for patients with PAD in comparison with patients without PAD. As summary, ACS patients with PAD had an ischemic risk greater than the bleeding risk.

Validity of the Minimum Basic Data Set for Research Into Outcomes of the Care of Acute Coronary Syndrome

2019 ◽  
Vol 72 (8) ◽  
pp. 696-697
Author(s):  
José Eduardo Calle-Urra ◽  
Pedro Parra-Hidalgo ◽  
Eduardo Pinar-Bermúdez ◽  
Concepción López-Rojo
Keyword(s):  
Acute Coronary Syndrome ◽  
Basic Data ◽  
Data Set ◽  
Coronary Syndrome

Validity of the Minimum Basic Data Set for Research Into Outcomes of the Care of Acute Coronary Syndrome. Response

2019 ◽  
Vol 72 (8) ◽  
pp. 697-698
Author(s):  
José Luis Bernal ◽  
José A. Barrabés ◽  
Cristina Fernández-Pérez ◽  
Francisco Javier Elola
Keyword(s):  
Acute Coronary Syndrome ◽  
Basic Data ◽  
Data Set ◽  
Coronary Syndrome

Readmission Risk Factors in Patients after myocardial infarction using the minimum data set

2020 ◽  
Author(s):  
Sara Abooali ◽  
Azam Aslani ◽  
Mohsen Bayati
Keyword(s):  
Myocardial Infarction ◽  
Acute Coronary Syndrome ◽  
Expert Panel ◽  
Minimum Data Set ◽  
Anterior Myocardial Infarction ◽  
Data Set ◽  
Factors Affecting ◽  
Coronary Syndrome ◽  
Minimum Data ◽  
First Admission

Abstract Background - Readmission is important not only as a representation factor of quality, but also because of its high cost and taking up an inconsistent share of hospital care costs. The purpose of this study was to determine the affecting factors of readmission of patients after myocardial infarction using the minimum data set. Methods - This is a descriptive cross-sectional and retrospective study. The research environment was hospitals affiliated with Shiraz University of Medical Sciences. A total of 320 hospitalized patient files with myocardial infarction with the code I21, admitted between April 2011 and October 2019 were reviewed. After comprehensive review of the literature and sources, 55 criteria were extracted and two expert panel sessions were held. Univariate and multivariate analysis were used to investigate the relationships between different factors and readmission. Next, variables that were significant in univariate analysis were entered into the regression model. Results - After convening the expert panel, 32 criteria were finally approved. The highest rate of readmission occurred in the first 30 days after first admission with 84 cases (24%) and according to the present study the most common clinical (cardiovascular) factors affecting readmission were acute coronary syndrome with 104 cases (59.43%), atherosclerosis with 92 cases (52.57%), infarction in other areas with 89 cases (50.86%), anterior myocardial infarction with 88 cases (50.29%), congestive heart failure with 18 cases (10.29%). Conclusions- This study showed that underlying and clinical factors affecting readmissions in a developing country include acute coronary syndrome, anterior myocardial infarction, coronary artery stenosis/chronic ischemic heart disease, chronic obstructive pulmonary disease, hypertension at first admission and high amount of sodium in the first admission.

Abstract P70: Analysis of Downstream Encounter Data Drives Quality Interventions for Acute Coronary Syndrome

2011 ◽  
Vol 4 (suppl_2) ◽  
Author(s):  
E B Jackson ◽  
Tiercy K Fortenberry ◽  
Melanie K Delvalle ◽  
Amy Frye-Anderson ◽  
Thomas E Ervin ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
Emergency Room ◽  
Medical Center ◽  
Care Quality ◽  
Post Discharge ◽  
Data Set ◽  
Treatment Pathway ◽  
Coronary Syndrome ◽  
Vanderbilt University ◽  
Drive Time

Background: Understanding resource utilization is crucial to improving care quality in Acute Coronary Syndrome (ACS). We reviewed 6-month downstream encounters following an admission for ACS including hospitalization, emergency room visits, clinic visits and rehabilitation. Methods: Downstream encounter and cost data for 6 months following ACS admission to Vanderbilt University Medical Center (VUMC) were evaluated. The data set included 7,668 encounters from 2,196 unique patients for period 7/1/08-6/30/10. Analysis was stratified by patient drive time from home to VUMC, treatment pathway (surgery vs. percutaneous intervention) and encounter type. Unrelated encounters were excluded; analysis was limited to encounters at VUMC. Outcomes: We found 29% (1,522 of 5,318) of 6-month downstream encounters occurred within 4 weeks post-discharge, accounting for 35% of costs. Limiting data to readmissions only, 39% (255 of 661) of encounters, totaling 41% of costs, occurred during this 4-week period ( Figure ). Patients with shorter drive time had higher downstream utilization, with average 3.34 visits within 6 months for patients living ≤50 minutes from VUMC, versus 1.86 for patients living >50 minutes away. Emergency room encounters for patients ≤50 minutes away were also greater (average 0.51 versus 0.23 for patients >50 minutes). Conclusion: Analysis suggests that interventions targeting downstream encounters within the first 4 weeks of discharge for ACS will have greater effects on cost and quality, and those interventions should also be tailored based on patient drive time. Review of available encounter data can help direct resources to quality interventions.

Clinical and Administrative Data on the Research of Acute Coronary Syndrome in Spain. Minimum Basic Data Set Validity

2019 ◽  
Vol 72 (1) ◽  
pp. 56-62 ◽  
Author(s):  
José Luis Bernal ◽  
José A. Barrabés ◽  
Andrés Íñiguez ◽  
Antonio Fernández-Ortiz ◽  
Cristina Fernández-Pérez ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
Administrative Data ◽  
Basic Data ◽  
Data Set ◽  
Coronary Syndrome

scholarly journals Variation in outcome of hospitalised patients with out-of-hospital cardiac arrest from acute coronary syndrome: a cohort study

2018 ◽  
Vol 6 (14) ◽  
pp. 1-116 ◽  
Author(s):  
Keith Couper ◽  
Peter K Kimani ◽  
Chris P Gale ◽  
Tom Quinn ◽  
Iain B Squire ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
Cardiac Arrest ◽  
Cohort Study ◽  
Myocardial Ischaemia ◽  
National Audit ◽  
Data Set ◽  
Coronary Syndrome ◽  
Hospital Survival ◽  
Modifiable Factors ◽  
Hospital Cardiac Arrest

Background Each year, approximately 30,000 people have an out-of-hospital cardiac arrest (OHCA) that is treated by UK ambulance services. Across all cases of OHCA, survival to hospital discharge is less than 10%. Acute coronary syndrome (ACS) is a common cause of OHCA. Objectives To explore factors that influence survival in patients who initially survive an OHCA attributable to ACS. Data source Data collected by the Myocardial Ischaemia National Audit Project (MINAP) between 2003 and 2015. Participants Adult patients who had a first OHCA attributable to ACS and who were successfully resuscitated and admitted to hospital. Main outcome measures Hospital mortality, neurological outcome at hospital discharge, and time to all-cause mortality. Methods We undertook a cohort study using data from the MINAP registry. MINAP is a national audit that collects data on patients admitted to English, Welsh and Northern Irish hospitals with myocardial ischaemia. From the data set, we identified patients who had an OHCA. We used imputation to address data missingness across the data set. We analysed data using multilevel logistic regression to identify modifiable and non-modifiable factors that affect outcome. Results Between 2003 and 2015, 1,127,140 patient cases were included in the MINAP data set. Of these, 17,604 OHCA cases met the study inclusion criteria. Overall hospital survival was 71.3%. Across hospitals with at least 60 cases, hospital survival ranged from 34% to 89% (median 71.4%, interquartile range 60.7–76.9%). Modelling, which adjusted for patient and treatment characteristics, could account for only 36.1% of this variability. For the primary outcome, the key modifiable factors associated with reduced mortality were reperfusion treatment [primary percutaneous coronary intervention (pPCI) or thrombolysis] and admission under a cardiologist. Admission to a high-volume cardiac arrest hospital did not influence survival. Sensitivity analyses showed that reperfusion was associated with reduced mortality among patients with a ST elevation myocardial infarction (STEMI), but there was no evidence of a reduction in mortality in patients who did not present with a STEMI. Limitations This was an observational study, such that unmeasured confounders may have influenced study findings. Differences in case identification processes at hospitals may contribute to an ascertainment bias. Conclusions In OHCA patients who have had a cardiac arrest attributable to ACS, there is evidence of variability in survival between hospitals, which cannot be fully explained by variables captured in the MINAP data set. Our findings provide some support for the current practice of transferring resuscitated patients with a STEMI to a hospital that can deliver pPCI. In contrast, it may be reasonable to transfer patients without a STEMI to the nearest appropriate hospital. Future work There is a need for clinical trials to examine the clinical effectiveness and cost-effectiveness of invasive reperfusion strategies in resuscitated OHCA patients of cardiac cause who have not had a STEMI. Funding The National Institute for Health Research Health Services and Delivery Research programme.

scholarly journals Acute Coronary Syndrome Symptom Clusters: Illustration of Results Using Multiple Statistical Methods

2019 ◽  
Vol 41 (7) ◽  
pp. 1032-1055 ◽  
Author(s):  
Catherine J. Ryan ◽  
Karen M. Vuckovic ◽  
Lorna Finnegan ◽  
Chang G. Park ◽  
Lani Zimmerman ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
Patient Characteristics ◽  
Symptom Clusters ◽  
Cluster Solution ◽  
Data Set ◽  
Coronary Syndrome ◽  
Plot Analysis ◽  
Confirmatory Factor ◽  
Syndrome Symptom ◽  
Person Centered Approach

Researchers have employed various methods to identify symptom clusters in cardiovascular conditions, without identifying rationale. Here, we test clustering techniques and outcomes using a data set from patients with acute coronary syndrome. A total of 474 patients who presented to emergency departments in five United States regions were enrolled. Symptoms were assessed within 15 min of presentation using the validated 13-item ACS Symptom Checklist. Three variable-centered approaches resulted in four-factor solutions. Two of three person-centered approaches resulted in three-cluster solutions. K-means cluster analysis revealed a six-cluster solution but was reduced to three clusters following cluster plot analysis. The number of symptoms and patient characteristics varied within clusters. Based on our findings, we recommend using (a) a variable-centered approach if the research is exploratory, (b) a confirmatory factor analysis if there is a hypothesis about symptom clusters, and (c) a person-centered approach if the aim is to cluster symptoms by individual groups.

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