scholarly journals Characteristics of Biomarkers on Predictive Ability of Risk Models in Development and Validation Populations

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Suman Kundu
Keyword(s):  
Predictive Ability ◽  
Risk Models ◽  
Development And Validation

scholarly journals Parsimonious Eurolung risk models to predict cardiopulmonary morbidity and mortality following anatomic lung resections: an updated analysis from the European Society of Thoracic Surgeons database

2019 ◽  
Author(s):  
Alessandro Brunelli ◽  
Silvia Cicconi ◽  
Herbert Decaluwe ◽  
Zalan Szanto ◽  
Pierre Emmanuel Falcoz
Keyword(s):  
Risk Model ◽  
Lung Resection ◽  
Predictive Ability ◽  
Risk Models ◽  
Multiple Imputations ◽  
Risk Of Mortality ◽  
Parsimonious Models ◽  
Parsimonious Model ◽  
User Friendly ◽  
Lung Resections

Abstract OBJECTIVES To develop a simplified version of the Eurolung risk model to predict cardiopulmonary morbidity and 30-day mortality after lung resection from the ESTS database. METHODS A total of 82 383 lung resections (63 681 lobectomies, 3617 bilobectomies, 7667 pneumonectomies and 7418 segmentectomies) recorded in the ESTS database (January 2007–December 2018) were analysed. Multiple imputations with chained equations were performed on the predictors included in the original Eurolung models. Stepwise selection was then applied for determining the best logistic model. To develop the parsimonious models, different models were tested eliminating variables one by one starting from the less significant. The models’ prediction power was evaluated estimating area under curve (AUC) with the 10-fold cross-validation technique. RESULTS Cardiopulmonary morbidity model (Eurolung1): the best parsimonious Eurolung1 model contains 5 variables. The logit of the parsimonious Eurolung1 model was as follows: −2.852 + 0.021 × age + 0.472 × male −0.015 × ppoFEV1 + 0.662×thoracotomy + 0.324 × extended resection. Pooled AUC is 0.710 [95% confidence interval (CI) 0.677–0.743]. Mortality model (Eurolung2): the best parsimonious model contains 6 variables. The logit of the parsimonious Eurolung2 model was as follows: −6.350 + 0.047 × age + 0.889 × male −0.055 × BMI −0.010 × ppoFEV1 + 0.892 × thoracotomy + 0.983 × pneumonectomy. Pooled AUC is 0.737 (95% CI 0.702–0.770). An aggregate parsimonious Eurolung2 was also generated by repeating the logistic regression after categorization of the numeric variables. Patients were grouped into 7 risk classes showing incremental risk of mortality (P < 0.0001). CONCLUSIONS We were able to develop simplified and updated versions of the Eurolung risk models retaining the predictive ability of the full original models. They represent a more user-friendly tool designed to inform the multidisciplinary discussion and shared decision-making process of lung resection candidates.

scholarly journals Development and Validation of Risk Models to Select Ever-Smokers for CT Lung Cancer Screening

JAMA ◽  
2016 ◽  
Vol 315 (21) ◽  
pp. 2300 ◽  
Author(s):  
Hormuzd A. Katki ◽  
Stephanie A. Kovalchik ◽  
Christine D. Berg ◽  
Li C. Cheung ◽  
Anil K. Chaturvedi
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Lung Cancer Screening ◽  
Risk Models ◽  
Development And Validation

scholarly journals Development and validation of a novel circular RNA as an independent prognostic factor in acute myeloid leukemia

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jinghan Wang ◽  
Jiajia Pan ◽  
Shujuan Huang ◽  
Fenglin Li ◽  
Jiansong Huang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Propensity Score Analysis ◽  
Predictive Ability ◽  
Circular Rna ◽  
Disease Diagnosis ◽  
Score Analysis ◽  
Development And Validation ◽  
Acute Myeloid

Abstract Background Although there are many clinical and molecular biomarkers in acute myeloid leukemia (AML), the novel and reliable biomarkers are still required to predict the overall survival at the time of disease diagnosis. Methods In order to identify independent predictors, we firstly selected 60 cytogenetically normal AML (CN-AML) patients using the propensity score analysis to balance the confounders and performed circular RNA (circRNA) sequencing. Next, one outcome related to circRNA was selected and validated in the independent cohort of 218 CN-AML patients. We then constructed circRNA-miRNA-mRNA regulated network and performed cellular metabolomic analysis to decipher the underlying biological insights. Results We identified 308 circRNAs as independent candidate predictors of overall survival. Hsa_circ_0075451 expression was validated as an independent predictor with a weak predictive ability for overall survival. The regulated network of this circular RNA indicated 84 hub genes that appear to be regulated by 10 miRNAs sponged by hsa_circ_0075451. The regulatory axis of hsa_circ_0075451 -| miR-330-5p/miR-326 -| PRDM16 was validated by the dual luciferase report assay, fluorescence in situ hybridization, and ShRNA interference assay. Conclusions Our data demonstrates that hsa_circ_0075451 expression may independently contribute to the poor prognosis of AML and present a novel therapeutic target.

scholarly journals Development and Validation of a Robust Apoptosis-related Prognostic Classifier in Patients With Osteosarcoma

2021 ◽  
Author(s):  
Zhifeng Zhang ◽  
Yi Wang ◽  
Fengmei Chen ◽  
Yinquan Zhang ◽  
Zhengmao Guan
Keyword(s):  
Immune Cells ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Predictive Ability ◽  
Clinical Decision ◽  
Individual Treatment ◽  
Biological Processes ◽  
Clinicopathological Factors ◽  
Lasso Regression ◽  
Development And Validation

Abstract Background: Apoptosis plays an important role in the tumorigenesis and the development of osteosarcoma, but the reliable biomarkers for individual treatment and prognosis of osteosarcoma based on apoptosis is lacking.Methods: A total of 1476 apoptosis-related genes were extracted from pathways and biological processes associated with apoptosis downloaded from MSigDB. All of those genes were used to identified the prognosis-related genes by univariate cox regression in the TARGET dataset and the ARS was constructed using the LASSO regression. The performance of the classifier was verified in the training and validation groups. The infiltration of immune cells and the expression levels of the immune checkpoint in different groups were also analyzed. Finally, a nomogram based on ARS and other Clinicopathological factors was constructed to facilitate clinical application.Results: ARS containing 22 apoptosis-related genes were identified, and its predictive ability performed well in both the training and validation groups. Macrophages M1 were highly expressed in the low-score group, and NK cells resting was highly expressed in the high-score group. The samples with low-score had higher expression of CTLA4 and PDL1. A nomogram with excellent predictive effectiveness (AUC= 0.932, 0.984, 0.939, 0.939, 0.948) was constructed to facilitate clinical decision-making.Conclusion: A prognostic classifier based on 22 apoptosis-related genes and a nomogram were constructed to predict the overall survival of patients with osteosarcoma. The classifier also provides a reference for selecting suitable patients for immunotherapy and targeted therapy.

Models for predicting risk of dementia: a systematic review

2018 ◽  
Vol 90 (4) ◽  
pp. 373-379 ◽  
Author(s):  
Xiao-He Hou ◽  
Lei Feng ◽  
Can Zhang ◽  
Xi-Peng Cao ◽  
Lan Tan ◽  
...  
Keyword(s):  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Predictive Ability ◽  
Disease Assessment ◽  
Characteristic Analysis ◽  
Risk Models ◽  
Dementia Risk

BackgroundInformation from well-established dementia risk models can guide targeted intervention to prevent dementia, in addition to the main purpose of quantifying the probability of developing dementia in the future.MethodsWe conducted a systematic review of published studies on existing dementia risk models. The models were assessed by sensitivity, specificity and area under the curve (AUC) from receiver operating characteristic analysis.ResultsOf 8462 studies reviewed, 61 articles describing dementia risk models were identified, with the majority of the articles modelling late life risk (n=39), followed by those modelling prediction of mild cognitive impairment to Alzheimer’s disease (n=15), mid-life risk (n=4) and patients with diabetes (n=3). Age, sex, education, Mini Mental State Examination, the Consortium to Establish a Registry for Alzheimer’s Disease neuropsychological assessment battery, Alzheimer’s Disease Assessment Scale-cognitive subscale, body mass index, alcohol intake and genetic variables are the most common predictors included in the models. Most risk models had moderate-to-high predictive ability (AUC>0.70). The highest AUC value (0.932) was produced from a risk model developed for patients with mild cognitive impairment.ConclusionThe predictive ability of existing dementia risk models is acceptable. Population-specific dementia risk models are necessary for populations and subpopulations with different characteristics.

Statistical Development and Validation of Clinical Prediction Models

2021 ◽  
Author(s):  
Steven J. Staffa ◽  
David Zurakowski
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
External Validation ◽  
Model Development ◽  
Predictive Ability ◽  
Binary Outcome ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Surgery Research ◽  
Development And Validation

Summary Clinical prediction models in anesthesia and surgery research have many clinical applications including preoperative risk stratification with implications for clinical utility in decision-making, resource utilization, and costs. It is imperative that predictive algorithms and multivariable models are validated in a suitable and comprehensive way in order to establish the robustness of the model in terms of accuracy, predictive ability, reliability, and generalizability. The purpose of this article is to educate anesthesia researchers at an introductory level on important statistical concepts involved with development and validation of multivariable prediction models for a binary outcome. Methods covered include assessments of discrimination and calibration through internal and external validation. An anesthesia research publication is examined to illustrate the process and presentation of multivariable prediction model development and validation for a binary outcome. Properly assessing the statistical and clinical validity of a multivariable prediction model is essential for reassuring the generalizability and reproducibility of the published tool.

scholarly journals Basic principles of risk score formulation in medicine

2020 ◽  
Vol 66 (4) ◽  
pp. 516-520
Author(s):  
Rafael Ronsoni ◽  
Bruna Predabon ◽  
Tiago Leiria ◽  
Gustavo de Lima
Keyword(s):  
Missing Values ◽  
Ad Hoc ◽  
Model Development ◽  
Vital Role ◽  
Risk Models ◽  
Basic Principles ◽  
Health Care Performance ◽  
Development And Validation ◽  
Care Performance ◽  
Selection Of

SUMMARY Risk models play a vital role in monitoring health care performance. Despite extensive research and the widespread use of risk models in medicine, there are methodologic problems. We reviewed the methodology used for risk models in medicine. The findings suggest that many risk models are developed in an ad hoc manner. Important aspects such as the selection of risk factors, handling of missing values, and size of the data sample used for model development are not dealt with adequately. Methodologic details presented in publications are often sparse and unclear. Model development and validation processes are not always linked to the clinical aim of the model, which may affect their clinical validity. We make some suggestions in this review for improving methodology and reporting.

Sign in / Sign up

Export Citation Format

Share Document