Desai et al., 2017 - Google Patents

Contemporary risk model for inhospital major bleeding for patients with acute myocardial infarction: The acute coronary treatment and intervention outcomes network …

Desai et al., 2017

Document ID
11781109301389971556
Author
Desai N
Kennedy K
Cohen D
Connolly T
Diercks D
Moscucci M
Ramee S
Spertus J
Wang T
McNamara R
Publication year
Publication venue
American heart journal

External Links

Snippet

Background Major bleeding is a frequent complication for patients with acute myocardial infarction (AMI) and is associated with significant morbidity and mortality. Objective To develop a contemporary model for inhospital major bleeding that can both support clinical …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345Medical expert systems, neural networks or other automated diagnosis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/3437Medical simulation or modelling, e.g. simulating the evolution of medical disorders
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10Services
    • G06Q50/22Health care, e.g. hospitals; Social work
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00Detection or diagnosis of diseases
    • G01N2800/32Cardiovascular disorders
    • G01N2800/324Coronary artery diseases, e.g. angina pectoris, myocardial infarction
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00Detection or diagnosis of diseases
    • G01N2800/56Staging of a disease; Further complications associated with the disease
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00Detection or diagnosis of diseases
    • G01N2800/50Determining the risk of developing a disease
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00Detection or diagnosis of diseases
    • G01N2800/52Predicting or monitoring the response to treatment; Prognosis
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00Detection or diagnosis of diseases
    • G01N2800/70Mechanisms involved in disease identification

Similar Documents

Publication Publication Date Title
Desai et al. Contemporary risk model for inhospital major bleeding for patients with acute myocardial infarction: The acute coronary treatment and intervention outcomes network (ACTION) registry®–Get With The Guidelines (GWTG)®
Mathews et al. In-hospital major bleeding during ST-elevation and non–ST-elevation myocardial infarction care: derivation and validation of a model from the ACTION Registry®-GWTG™
Ejaz et al. Risk factors and prediction model for inpatient surgical site infection after major abdominal surgery
Lankeit et al. A strategy combining imaging and laboratory biomarkers in comparison with a simplified clinical score for risk stratification of patients with acute pulmonary embolism
Leening et al. Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician's guide
Sutton et al. The association of left ventricular ejection fraction with clinical outcomes after myocardial infarction: Findings from the Acute Coronary Treatment and Intervention Outcomes Network (ACTION) Registry–Get With the Guidelines (GWTG) Medicare-linked database
Li et al. ST-segment elevation myocardial infarction in China from 2001 to 2011 (the China PEACE-Retrospective Acute Myocardial Infarction Study): a retrospective analysis of hospital data
Cooney et al. Value and limitations of existing scores for the assessment of cardiovascular risk: a review for clinicians
Bawamia et al. Risk scores in acute coronary syndrome and percutaneous coronary intervention: a review
Armstrong et al. Framingham score and LV mass predict events in young adults: CARDIA study
Abu-Assi et al. Comparing the predictive validity of three contemporary bleeding risk scores in acute coronary syndrome
Lobo et al. Prognostic significance of tricuspid annular displacement in normotensive patients with acute symptomatic pulmonary embolism
Palmerini et al. Comparison of clinical and angiographic prognostic risk scores in patients with acute coronary syndromes: Analysis from the Acute Catheterization and Urgent Intervention Triage StrategY (ACUITY) trial
Naccarelli et al. Rates and implications for hospitalization of patients≥ 65 years of age with atrial fibrillation/flutter
Kontos et al. Cardiac arrest and clinical characteristics, treatments and outcomes among patients hospitalized with ST-elevation myocardial infarction in contemporary practice: a report from the National Cardiovascular Data Registry
Park et al. Model for assessing cardiovascular risk in a Korean population
Möhlenkamp et al. Coronary artery calcium score improves cardiovascular risk prediction in persons without indication for statin therapy
Zhang et al. Progression of vascular calcification and clinical outcomes in patients receiving maintenance dialysis
Daugherty et al. Gender differences in the prognostic value of exercise treadmill test characteristics
Abu-Assi et al. Comparison of the reliability and validity of four contemporary risk stratification schemes to predict thromboembolism in non-anticoagulated patients with atrial fibrillation
Gander et al. Peer reviewed: factors related to coronary heart disease risk among men: validation of the Framingham risk score
Thavendiranathan et al. Prediction of 30-day heart failure-specific readmission risk by echocardiographic parameters
Capodanno et al. Incorporating Glomerular filtration rate or creatinine clearance by the modification of diet in renal disease equation or the Cockcroft–Gault equations to improve the Global Accuracy of the Age, Creatinine, Ejection Fraction [ACEF] score in patients undergoing percutaneous coronary intervention
Fine et al. Characteristics and outcomes of patients who achieve high workload (≥ 10 metabolic equivalents) during treadmill exercise echocardiography
Cediel et al. Risk estimation in type 2 myocardial infarction and myocardial injury: the TARRACO risk score