scholarly journals Risk assessment and clinical decision making for colorectal cancer screening

2013 ◽  
Vol 18 (5) ◽  
pp. 1327-1338 ◽  
Author(s):  
Paul C. Schroy ◽  
Sarah E. Caron ◽  
Bonnie J. Sherman ◽  
Timothy C. Heeren ◽  
Tracy A. Battaglia
Keyword(s):  
Colorectal Cancer ◽  
Risk Assessment ◽  
Decision Making ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
Clinical Decision Making ◽  
Clinical Decision

Highly specific detection of KRAS single nucleotide polymorphism by asymmetric PCR/SERS assay

The Analyst ◽  
2021 ◽  
Author(s):  
Nana Lyu ◽  
Vinoth Kumar Rajendran ◽  
Jun Li ◽  
Alexander Engel ◽  
Mark P. Molloy ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Nucleotide Polymorphism ◽  
Asymmetric Pcr ◽  
Specific Detection ◽  
Kras Mutations ◽  
Single Nucleotide ◽  
The Common

The molecular diagnosis of KRAS mutations has become crucial for clinical decision-making in colorectal cancer (CRC) treatments. Currently, the common methods for detecting mutations are based on quantitative PCR, DNA...

scholarly journals Biomechanical Rupture Risk Assessment

Aorta ◽  
2016 ◽  
Vol 04 (02) ◽  
pp. 42-60 ◽  
Author(s):  
T. Christian Gasser
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Validation Studies ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Patient Treatment ◽  
Maximum Diameter ◽  
Individual Risk ◽  
Rupture Risk ◽  
Aneurysm Wall

AbstractAbdominal aortic aneurysm (AAA) rupture is a local event in the aneurysm wall that naturally demands tools to assess the risk for local wall rupture. Consequently, global parameters like the maximum diameter and its expansion over time can only give very rough risk indications; therefore, they frequently fail to predict individual risk for AAA rupture. In contrast, the Biomechanical Rupture Risk Assessment (BRRA) method investigates the wall’s risk for local rupture by quantitatively integrating many known AAA rupture risk factors like female sex, large relative expansion, intraluminal thrombus-related wall weakening, and high blood pressure. The BRRA method is almost 20 years old and has progressed considerably in recent years, it can now potentially enrich the diameter indication for AAA repair. The present paper reviews the current state of the BRRA method by summarizing its key underlying concepts (i.e., geometry modeling, biomechanical simulation, and result interpretation). Specifically, the validity of the underlying model assumptions is critically disused in relation to the intended simulation objective (i.e., a clinical AAA rupture risk assessment). Next, reported clinical BRRA validation studies are summarized, and their clinical relevance is reviewed. The BRRA method is a generic, biomechanics-based approach that provides several interfaces to incorporate information from different research disciplines. As an example, the final section of this review suggests integrating growth aspects to (potentially) further improve BRRA sensitivity and specificity. Despite the fact that no prospective validation studies are reported, a significant and still growing body of validation evidence suggests integrating the BRRA method into the clinical decision-making process (i.e., enriching diameter-based decision-making in AAA patient treatment).

scholarly journals Probability and loss: two sides of the risk assessment coin

2011 ◽  
Vol 35 (11) ◽  
pp. 413-418 ◽  
Author(s):  
Matthew M. Large ◽  
Olav B. Nielssen
Keyword(s):  
Mental Health ◽  
Risk Assessment ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
The Other ◽  
Current Form ◽  
Mental Health Settings ◽  
Future Harm ◽  
Two Sides

SummaryRisk assessment has been widely adopted in mental health settings in the hope of preventing harms such as violence to others and suicide. However, risk assessment in its current form is mainly concerned with the probability of adverse events, and does not address the other component of risk – the extent of the resulting loss. Although assessments of the probability of future harm based on actuarial instruments are generally more accurate than the categorisations made by clinicians, actuarial instruments are of little assistance in clinical decision-making because there is no instrument that can estimate the probability of all the harms associated with mental illness, or estimate the extent of the resulting losses. The inability of instruments to distinguish between the risk of common but less serious harms and comparatively rare catastrophic events is a particular limitation of the value of risk categorisations. We should admit that our ability to assess risk is severely limited, and make clinical decisions in a similar way to those in other areas of medicine – by informed consideration of the potential consequences of treatment and non-treatment.

scholarly journals Data linkage and statistical modelling to provide stratified risk assessment for HAI

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Kim Kavanagh ◽  
Jiafeng Pan ◽  
Chris Robertson ◽  
Marion Bennie ◽  
Charis Marwick ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Gastric Acid ◽  
Clinical Decision Making ◽  
Care Home ◽  
Clinical Decision ◽  
Elevated Risk ◽  
Defined Daily Dose ◽  
Healthcare Associated Infection ◽  
Healthcare Associated

ABSTRACT ObjectivesThe use of “real-time” data to support individual patient management and outcome assessment requires the development of risk assessment models. This could be delivered through a learning health system by the building robust statistical analysis tools onto the existing linked data held by NHS Scotland’s Infection Intelligence Platform (IIP) and developed within the Scottish Healthcare Associated Infection Prevention Institute (SHAIPI). This project will create prediction models for the risk of acquiring a healthcare associated infection (HAI), and particular outcomes, at the point of GP consultation/ hospital admission which could aid clinical decision making. ApproachWe demonstrate the capability using the HAI Clostridium difficile (CDI) from 2010-2013. Using linked national individual level data on community prescribing, hospitalisations, infections and death records we extracted all cases of CDI and by comparing to matched population-based controls, examined the impact of prior hospital admissions, care home residence, comorbidities, exposure to gastric acid suppressive drugs and antibiotic exposure, defined as both cumulative (total defined daily dose (DDD)) and temporal antimicrobial exposure in the previous 6 months, to the risk of CDI acquisition. Antimicrobial exposure was considered for all drugs and the higher risk broad spectrum antibiotics (4Cs). Associations are assessed using conditional logistic regression. Using cross-validation we assess the ability of the model to accurately predict CDI infection. Risk scores for acquisition of CDI are estimated by combining these predictions with age and gender population incidence. ResultsIn the period 2010-2013 there were 1446 cases of CDI with matched 7964 controls. A significant dose-response relationship for exposure to any antimicrobial (1-7 DDDs OR=2.3 rising to OR=4.4 for 29+ DDDs) and, with elevated risk, to the 4C group (1-7 DDDs OR=3.8 rising to OR=17.9 for 29+ DDDs). Exposure elevates CDI risk most in the month after prescription but for 4C antimicrobials the elevated risk remains 6 months later (4C OR=12.4 within 1 month, OR=2.6 4-6 months later). The risk of CDI was also increased with more co-morbidities, previous hospitalisations, care home residency, increased number of prescriptions, and gastric acid suppression. ConclusionDespite limitations to current application in practice,(paucity of patient level in-hospital prescribing data and constraints of the timeliness of the data), when fully developed this system will enable risk classification to identify patients most at risk of HAI and adverse outcomes to aid clinical decision making.

scholarly journals Improving risk assessment of violence among military Veterans: An evidence-based approach for clinical decision-making

2010 ◽  
Vol 30 (6) ◽  
pp. 595-607 ◽  
Author(s):  
Eric B. Elbogen ◽  
Sara Fuller ◽  
Sally C. Johnson ◽  
Stephanie Brooks ◽  
Patricia Kinneer ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Military Veterans ◽  
Clinical Decision ◽  
Evidence Based
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