scholarly journals Comparison of Two Blood-Based Genotyping Tests to Investigate the KRAS G12C Mutation in Patients with Non-Small-Cell Lung Cancer at Failure of First-Line Treatments

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2196
Author(s):  
Chiara Nicolazzo ◽  
Alain Gelibter ◽  
Irene Bottillo ◽  
Francesca Belardinilli ◽  
Simona Pisegna ◽  
...  
Keyword(s):  
Real Time ◽  
Progressive Disease ◽  
Clinical Decision Making ◽  
Clonal Evolution ◽  
Clinical Decision ◽  
Neoplastic Tissue ◽  
Liquid Biopsies ◽  
Direct Sampling ◽  
Polymerase Chain ◽  
Tissue Cancer

Although molecular profiling at diagnosis has traditionally relied on direct sampling of neoplastic tissue, cancer clonal evolution represents a critical obstacle to use primary tissue biopsies to guide clinical decision-making at the time of progressive disease. Liquid biopsies might offer enormous advantages over tissue biopsies, tracking in real-time temporal-based tumor dynamics following each line of treatment. Here, we compared two liquid biopsy assays, specifically real-time polymerase chain reaction and next-generation sequencing, to track the KRAS G12C mutation at onset of progression from previous lines of therapy. The KRAS G12C mutation was acquired at the time of progressive disease in 24% of patients. Furthermore, all patients with KRAS G12C mutation-positive tissue became negative in ctDNA at progressive disease. The presence of other somatic mutations in all these samples confirmed the tumor origin of the circulating DNA. This pilot study suggests that in the assessment of the plasma KRAS G12C mutation as a druggable target, real-time PCR assay Idylla might be a suitable approach to better match patients to interventional biomarker-targeted therapies.

scholarly journals Quantitative and Qualitative Analysis of Blood-based Liquid Biopsies to Inform Clinical Decision-making in Prostate Cancer

2021 ◽  
Author(s):  
Irene Casanova-Salas ◽  
Alejandro Athie ◽  
Paul C. Boutros ◽  
Marzia Del Re ◽  
David T. Miyamoto ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Qualitative Analysis ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Liquid Biopsies

scholarly journals Single-copy detection of somatic variants from solid and liquid biopsy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ana-Luisa Silva ◽  
Paulina Klaudyna Powalowska ◽  
Magdalena Stolarek ◽  
Eleanor Ruth Gray ◽  
Rebecca Natalie Palmer ◽  
...  
Keyword(s):  
Real Time ◽  
Single Molecule ◽  
Real Time Pcr ◽  
Clinical Decision Making ◽  
High Sensitivity ◽  
Clinical Decision ◽  
Single Copy ◽  
Turnaround Time ◽  
Copy Detection ◽  
Allele Specific

AbstractAccurate detection of somatic variants, against a background of wild-type molecules, is essential for clinical decision making in oncology. Existing approaches, such as allele-specific real-time PCR, are typically limited to a single target gene and lack sensitivity. Alternatively, next-generation sequencing methods suffer from slow turnaround time, high costs, and are complex to implement, typically limiting them to single-site use. Here, we report a method, which we term Allele-Specific PYrophosphorolysis Reaction (ASPYRE), for high sensitivity detection of panels of somatic variants. ASPYRE has a simple workflow and is compatible with standard molecular biology reagents and real-time PCR instruments. We show that ASPYRE has single molecule sensitivity and is tolerant of DNA extracted from plasma and formalin fixed paraffin embedded (FFPE) samples. We also demonstrate two multiplex panels, including one for detection of 47 EGFR variants. ASPYRE presents an effective and accessible method that simplifies highly sensitive and multiplexed detection of somatic variants.

scholarly journals Accounting for healthcare-seeking behaviours and testing practices in real-time influenza forecasts

2018 ◽  
Author(s):  
Robert Moss ◽  
Alexander E Zarebski ◽  
Sandra J Carlson ◽  
James M McCaw
Keyword(s):  
Real Time ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Surveillance Data ◽  
Surveillance Systems ◽  
Healthcare Seeking ◽  
Public Health Decision ◽  
Predictive Skill ◽  
Health Decision ◽  
Substantial Change

AbstractFor diseases such as influenza, where the majority of infected persons experience mild (if any) symptoms, surveillance systems are sensitive to changes in healthcare-seeking and clinical decision-making behaviours. This presents a challenge when trying to interpret surveillance data in near-real-time (e.g., in order to provide public health decision-support). Australia experienced a particularly large and severe influenza season in 2017, perhaps in part due to (a) mild cases being more likely to seek healthcare; and (b) clinicians being more likely to collect specimens for RT-PCR influenza tests. In this study we used weekly Flutracking surveillance data to estimate the probability that a person with influenza-like illness (ILI) would seek healthcare and have a specimen collected. We then used this estimated probability to calibrate near-real-time seasonal influenza forecasts at each week of the 2017 season, to see whether predictive skill could be improved. While the number of self-reported influenza tests in the weekly surveys are typically very low, we were able to detect a substantial change in healthcare seeking behaviour and clinician testing behaviour prior to the high epidemic peak. Adjusting for these changes in behaviour in the forecasting framework improved predictive skill. Our analysis demonstrates a unique value of community-level surveillance systems, such as Flutracking, when interpreting traditional surveillance data.

scholarly journals Early Identification of Patients with Acute Gastrointestinal Bleeding using Electronic Health Record Phenotyping

2020 ◽  
Author(s):  
Dennis Shung ◽  
Cynthia Tsay ◽  
Loren Laine ◽  
Prem Thomas ◽  
Caitlin Partridge ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Gastrointestinal Bleeding ◽  
Real Time ◽  
Decision Rule ◽  
Language Processing ◽  
Clinical Decision Making ◽  
External Validation ◽  
Clinical Decision ◽  
Internal Validation ◽  
Electronic Health

Background and AimGuidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time within electronic health records (EHRs) would overcome a major impediment. This requires an automated mechanism to accurately identify (“phenotype”) patients with GIB at the time of presentation. The goal is to identify patients with acute GIB by developing and evaluating EHR-based phenotyping algorithms for emergency department (ED) patients.MethodsWe specified criteria using structured data elements to create rules for identifying patients, and also developed a natural-language-processing (NLP)-based algorithm for automated phenotyping of patients, tested them with tenfold cross-validation (n=7144) and external validation (n=2988), and compared them with the standard method for encoding patient conditions in the EHR, Systematized Nomenclature of Medicine (SNOMED). The gold standard for GIB diagnosis was independent dual manual review of medical records. The primary outcome was positive predictive value (PPV).ResultsA decision rule using GIB-specific terms from ED triage and from ED review-of-systems assessment performed better than SNOMED on internal validation (PPV=91% [90%-93%] vs. 74% [71%-76%], P<0.001) and external validation (PPV=85% [84%-87%] vs. 69% [67%-71%], P<0.001). The NLP algorithm (external validation PPV=80% [79-82%]) was not superior to the structured-datafields decision rule.ConclusionsAn automated decision rule employing GIB-specific triage and review-of-systems terms can be used to trigger EHR-based deployment of risk stratification models to guide clinical decision-making in real time for patients with acute GIB presenting to the ED.

scholarly journals Real-time utilisation of administrative data in the ED to identify older patients at risk: development and validation of the Dynamic Silver Code

BMJ Open ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. e033374 ◽  
Author(s):  
Daniela Balzi ◽  
Giulia Carreras ◽  
Francesco Tonarelli ◽  
Luca Degli Esposti ◽  
Paola Michelozzi ◽  
...  
Keyword(s):  
At Risk ◽  
Cohort Study ◽  
Real Time ◽  
Administrative Data ◽  
Older Patients ◽  
Clinical Decision Making ◽  
External Validation ◽  
Clinical Decision ◽  
Patients At Risk ◽  
Ed Visits

ObjectiveIdentification of older patients at risk, among those accessing the emergency department (ED), may support clinical decision-making. To this purpose, we developed and validated the Dynamic Silver Code (DSC), a score based on real-time linkage of administrative data.Design and settingThe ‘Silver Code National Project (SCNP)’, a non-concurrent cohort study, was used for retrospective development and internal validation of the DSC. External validation was obtained in the ‘Anziani in DEA (AIDEA)’ concurrent cohort study, where the DSC was generated by the software routinely used in the ED.ParticipantsThe SCNP contained 281 321 records of 180 079 residents aged 75+ years from Tuscany and Lazio, Italy, admitted via the ED to Internal Medicine or Geriatrics units. The AIDEA study enrolled 4425 subjects aged 75+ years (5217 records) accessing two EDs in the area of Florence, Italy.InterventionsNone.Outcome measuresPrimary outcome: 1-year mortality. Secondary outcomes: 7 and 30-day mortality and 1-year recurrent ED visits.ResultsAdvancing age, male gender, previous hospital admission, discharge diagnosis, time from discharge and polypharmacy predicted 1-year mortality and contributed to the DSC in the development subsample of the SCNP cohort. Based on score quartiles, participants were classified into low, medium, high and very high-risk classes. In the SCNP validation sample, mortality increased progressively from 144 to 367 per 1000 person-years, across DSC classes, with HR (95% CI) of 1.92 (1.85 to 1.99), 2.71 (2.61 to 2.81) and 5.40 (5.21 to 5.59) in class II, III and IV, respectively versus class I (p<0.001). Findings were similar in AIDEA, where the DSC predicted also recurrent ED visits in 1 year. In both databases, the DSC predicted 7 and 30-day mortality.ConclusionsThe DSC, based on administrative data available in real time, predicts prognosis of older patients and might improve their management in the ED.

scholarly journals Utility and Acceptability of Influenza Surveillance amongst Emergency Providers

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Andrea F. Dugas ◽  
Howard Burkom ◽  
Anna L. DuVal ◽  
Richard Rothman
Keyword(s):  
Decision Making ◽  
Emergency Department ◽  
Real Time ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Surveillance Data ◽  
Influenza Surveillance ◽  
Additional Information ◽  
Provider Surveys

We provided emergency department providers with a real-time laboratory-based influenza surveillance tool, and evaluated the utility and acceptability of the surveillance information using provider surveys. The majority of emergency department providers found the surveillance data useful and indicated the additional information impacted their clinical decision making regarding influenza testing and treatment.

scholarly journals Precision cancer therapy by molecular profiling – A Theoretical Study

2021 ◽  
Author(s):  
V. Sah
Keyword(s):  
Large Scale ◽  
Clinical Decision Making ◽  
Single Cell Analysis ◽  
Clinical Decision ◽  
Molecular Profiling ◽  
Outcome Analysis ◽  
Precision Oncology ◽  
Liquid Biopsies ◽  
Next Generation Sequencing Technology ◽  
Genomic Studies

The amount of druggable tumor-specific molecular aberrations has increased significantly over the last decade, with biomarker-matched therapies demonstrating a major survival advantage in many cancer forms. Therefore, molecular pathology has been critical not just for tumor detection and prognosis, but also for clinical decision-making in everyday practice. The advent of next-generation sequencing technology and the proliferation of large-scale tumor molecular profiling services through universities worldwide have transformed the area of precision oncology. When systematic genomic studies become more accessible in clinical and laboratory environments, healthcare professionals face the difficult challenge of outcome analysis and translation. This study summarizes existing and future methods to implementing precision cancer medicine, outlining the obstacles and possible strategies for facilitating the understanding and maximization of molecular profiling findings. Beyond tumor DNA sequencing, we discuss innovative molecular characterization techniques such as transcriptomics, immunophenotyping, epigenetic profiling, and single-cell analysis. Additionally, we discuss present and future uses of liquid biopsies for evaluating blood-based biomarkers such as circulating tumor cells and nucleic acids. Finally, the shortcomings of genotype-based treatments give insight into opportunities to extend personalized medicine beyond genomics.

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