scholarly journals Prognostic and Therapeutic Implications of Tumor Biology in Colorectal Liver Metastases

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 88
Author(s):  
Carsten Kamphues ◽  
Katharina Beyer ◽  
Georgios Antonios Margonis
Keyword(s):  
Liver Metastases ◽  
Colorectal Liver Metastases ◽  
Clinical Decision Making ◽  
Tumor Biology ◽  
Clinical Decision ◽  
Prognostic Models ◽  
Treatment Benefit ◽  
Colorectal Cancer Liver Metastases ◽  
Therapeutic Implications ◽  
Prognostic Stratification

Prognostic models allow clinicians to predict survival outcomes, facilitate patient–physician discussions, and identify subgroups with potentially distinct prognoses. Although such prognostic stratification cannot directly predict treatment benefit, it can help to inform clinical decision making. This editorial will discuss potential avenues for these topics in the context of colorectal cancer liver metastases (CRLM)[...]

scholarly journals Understanding the usefulness of prognostic models in clinical decision-making

2017 ◽  
Vol 63 (2) ◽  
pp. 121-125 ◽  
Author(s):  
Adrian C Traeger ◽  
Markus Hübscher ◽  
James H McAuley
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Prognostic Models

scholarly journals Validation of parsimonious prognostic models for patients infected with COVID-19

2021 ◽  
Vol 28 (1) ◽  
pp. e100267
Author(s):  
Keerthi Harish ◽  
Ben Zhang ◽  
Peter Stella ◽  
Kevin Hauck ◽  
Marwa M Moussa ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Validation Cohort ◽  
External Validation ◽  
Clinical Decision ◽  
Prognostic Models ◽  
Clinical Prediction ◽  
Inclusion Criteria ◽  
Local Data ◽  
External Data ◽  
Operator Curve

ObjectivesPredictive studies play important roles in the development of models informing care for patients with COVID-19. Our concern is that studies producing ill-performing models may lead to inappropriate clinical decision-making. Thus, our objective is to summarise and characterise performance of prognostic models for COVID-19 on external data.MethodsWe performed a validation of parsimonious prognostic models for patients with COVID-19 from a literature search for published and preprint articles. Ten models meeting inclusion criteria were either (a) externally validated with our data against the model variables and weights or (b) rebuilt using original features if no weights were provided. Nine studies had internally or externally validated models on cohorts of between 18 and 320 inpatients with COVID-19. One model used cross-validation. Our external validation cohort consisted of 4444 patients with COVID-19 hospitalised between 1 March and 27 May 2020.ResultsMost models failed validation when applied to our institution’s data. Included studies reported an average validation area under the receiver–operator curve (AUROC) of 0.828. Models applied with reported features averaged an AUROC of 0.66 when validated on our data. Models rebuilt with the same features averaged an AUROC of 0.755 when validated on our data. In both cases, models did not validate against their studies’ reported AUROC values.DiscussionPublished and preprint prognostic models for patients infected with COVID-19 performed substantially worse when applied to external data. Further inquiry is required to elucidate mechanisms underlying performance deviations.ConclusionsClinicians should employ caution when applying models for clinical prediction without careful validation on local data.

Decoding Tumor Biology of Colorectal Liver Metastases With Radiogenomics

2019 ◽  
Vol 269 (1) ◽  
pp. e3-e4 ◽  
Author(s):  
Gu-Wei Ji ◽  
Ke Wang ◽  
Yong-Xiang Xia ◽  
Xiang-Cheng Li ◽  
Xue-Hao Wang
Keyword(s):  
Liver Metastases ◽  
Colorectal Liver Metastases ◽  
Tumor Biology

Topical Review: Enhancing Understanding of the Clinical Meaningfulness of Outcomes to Assess Treatment Benefit from Psychological Therapies for Children with Chronic Pain

2019 ◽  
Author(s):  
Tonya M Palermo ◽  
Susmita Kashikar-Zuck ◽  
Anne Lynch-Jordan
Keyword(s):  
Chronic Pain ◽  
Clinical Trials ◽  
Clinical Significance ◽  
Clinical Decision Making ◽  
Psychological Treatment ◽  
Clinical Decision ◽  
Health Impact ◽  
Treatment Benefit ◽  
Topical Review ◽  
Pediatric Chronic Pain

Abstract Objective Despite the availability of measures for assessing physical, psychological, and health impact in children with chronic pain, there are not established guidelines for interpretation of children’s pain outcomes following psychological treatment. The purpose of this topical review is to discuss clinical significance as a neglected area of consideration in pediatric chronic pain assessment and to make recommendations on how the field can move toward benchmarking on core outcome domains. Method We review definitions of clinical significance and examples of several methodologies that have been used in other populations or are emerging in pediatric chronic pain including anchor-based methods, distribution-based methods, or multimethod approaches. Results Few measures across pediatric chronic pain outcome domains have established clinical significance of scores to interpret meaningful change following treatment limiting the interpretability of findings from clinical trials. In the context of clinical practice, several efforts to examine clinical significance to improve the translation of evidence-based measurement into standard clinical decision-making exist. Conclusions Recommendations are provided to encourage additional validation efforts of outcome measures in pediatric chronic pain and to encourage authors to report clinical significance in clinical trials of psychological interventions for pediatric chronic pain.

scholarly journals JAK2 (and other genes) be nimble with MPN diagnosis, prognosis, and therapy

Hematology ◽  
2018 ◽  
Vol 2018 (1) ◽  
pp. 110-117 ◽  
Author(s):  
Michele Ciboddo ◽  
Ann Mullally
Keyword(s):  
Clinical Decision Making ◽  
Phenotypic Diversity ◽  
Myeloproliferative Neoplasms ◽  
Clinical Decision ◽  
Prognostic Models ◽  
Driver Mutations ◽  
Therapeutic Approaches ◽  
Ongoing Research ◽  
Substantial Progress ◽  
Made In

Abstract Now that the spectrum of somatic mutations that initiate, propagate, and drive the progression of myeloproliferative neoplasms (MPNs) has largely been defined, recent efforts have focused on integrating this information into clinical decision making. In this regard, the greatest progress has been made in myelofibrosis, in which high-molecular-risk mutations have been identified and incorporated into prognostic models to help guide treatment decisions. In this chapter, we focus on advances in 4 main areas: (1) What are the MPN phenotypic driver mutations? (2) What constitutes high molecular risk in MPN (focusing on ASXL1)? (3) How do we risk-stratify patients with MPN? And (4) What is the significance of molecular genetics for MPN treatment? Although substantial progress has been made, we still have an incomplete understanding of the molecular basis for phenotypic diversity in MPN, and few rationally designed therapeutic approaches to target high-risk mutations are available. Ongoing research efforts in these areas are critical to understanding the biological consequences of genetic heterogeneity in MPN and to improving outcomes for patients.

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