Risk of recurrence after nephrectomy: Comparison of predictive ability of validated risk models

2022 ◽  
Author(s):  
Carlotta Palumbo ◽  
Davide Perri ◽  
Monica Zacchero ◽  
Gianmarco Bondonno ◽  
Jessica Di Martino ◽  
...  
Keyword(s):  
Predictive Ability ◽  
Risk Models ◽  
Risk Of Recurrence

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.

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.

Prediction of Late Recurrence and Distant Metastasis in Early-stage Breast Cancer: Overview of Current and Emerging Biomarkers

2020 ◽  
Vol 21 (10) ◽  
pp. 1008-1025
Author(s):  
A. Gouri ◽  
B. Benarba ◽  
A. Dekaken ◽  
H. Aoures ◽  
S. Benharkat
Keyword(s):  
Breast Cancer ◽  
Distant Metastasis ◽  
Predictive Accuracy ◽  
Early Stage ◽  
Predictive Ability ◽  
Late Recurrence ◽  
Lymph Node Status ◽  
Risk Of Recurrence ◽  
Increased Risk ◽  
Hormonal Receptor Status

Recently, a significant number of breast cancer (BC) patients have been diagnosed at an early stage. It is therefore critical to accurately predict the risk of recurrence and distant metastasis for better management of BC in this setting. Clinicopathologic patterns, particularly lymph node status, tumor size, and hormonal receptor status are routinely used to identify women at increased risk of recurrence. However, these factors have limitations regarding their predictive ability for late metastasis risk in patients with early BC. Emerging molecular signatures using gene expression-based approaches have improved the prognostic and predictive accuracy for this indication. However, the use of their based-scores for risk assessment has provided contradictory findings. Therefore, developing and using newly emerged alternative predictive and prognostic biomarkers for identifying patients at high- and low-risk is of great importance. The present review discusses some serum biomarkers and multigene profiling scores for predicting late recurrence and distant metastasis in early-stage BC based on recently published studies and clinical trials.

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