scholarly journals Risk prediction model for lung cancer incorporating metabolic markers: Development and internal validation in a Chinese population

2020 ◽  
Vol 9 (11) ◽  
pp. 3983-3994 ◽  
Author(s):  
Zhangyan Lyu ◽  
Ni Li ◽  
Shuohua Chen ◽  
Gang Wang ◽  
Fengwei Tan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Prediction Model ◽  
Risk Prediction ◽  
Chinese Population ◽  
Risk Prediction Model ◽  
Metabolic Markers ◽  
Internal Validation

An Individual Risk Prediction Model for Lung Cancer Based on a Study in a Chinese Population

2015 ◽  
Vol 101 (1) ◽  
pp. 16-23 ◽  
Author(s):  
Xu Wang ◽  
Kewei Ma ◽  
Jiuwei Cui ◽  
Xiao Chen ◽  
Lina Jin ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Prediction Model ◽  
Risk Prediction ◽  
Chinese Population ◽  
Risk Prediction Model ◽  
Individual Risk

Accuracy of the Vancouver Lung Cancer Risk Prediction Model Compared With That of Radiologists

CHEST Journal ◽  
2019 ◽  
Vol 156 (1) ◽  
pp. 112-119 ◽  
Author(s):  
Heber MacMahon ◽  
Feng Li ◽  
Yulei Jiang ◽  
Samuel G. Armato
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Lung Cancer Risk ◽  
Risk Prediction Model

Development and Validation of a Risk Prediction Model for Patients with COVID-19 Using Linked Population Health Data (Preprint)

2021 ◽  
Author(s):  
Nikolaos Mastellos ◽  
Richard Betteridge ◽  
Prasanth Peddaayyavarla ◽  
Andrew Moran ◽  
Jurgita Kaubryte ◽  
...  
Keyword(s):  
Health Care ◽  
Prediction Model ◽  
Population Health ◽  
Risk Prediction ◽  
Validation Cohort ◽  
Risk Prediction Model ◽  
Adult Patients ◽  
Internal Validation ◽  
Temporal Validation ◽  
Development And Validation

BACKGROUND The impact of the COVID-19 pandemic on health care utilisation and associated costs has been significant, with one in ten patients becoming severely ill and being admitted to hospital with serious complications during the first wave of the pandemic. Risk prediction models can help health care providers identify high-risk patients in their populations and intervene to improve health outcomes and reduce associated costs. OBJECTIVE To develop and validate a hospitalisation risk prediction model for adult patients with laboratory confirmed Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). METHODS The model was developed using pre-linked and standardised data of adult patients with laboratory confirmed SARS-CoV-2 from Cerner’s population health management platform (HealtheIntent®) in the London Borough of Lewisham. A total of 14,203 patients who tested positive for SARS-CoV-2 between 1st March 2020 and 28th February 2021 were included in the development and internal validation cohorts. A second temporal validation cohort covered the period between 1st March 2021 to 30th April 2021. The outcome variable was hospital admission in adult patients with laboratory confirmed SARS-CoV-2. A generalised linear model was used to train the model. The predictive performance of the model was assessed using the area under the receiver operator characteristic curve (ROC-AUC). RESULTS Overall, 14,203 patients were included. Of those, 9,755 (68.7%) were assigned to the development cohort, 2,438 (17.2%) to the internal validation cohort, and 2,010 (14.1%) to the temporal validation cohort. A total of 917 (9.4%) patients were admitted to hospital in the development cohort, 210 (8.6%) in the internal validation cohort, and a further 204 (10.1%) in the temporal validation cohort. The model had a ROC-AUC of 0.85 in both the development and validation cohorts. The most predictive factors were older age, male sex, Asian or Other ethnic minority background, obesity, chronic kidney disease, hypertension and diabetes. CONCLUSIONS The COVID-19 hospitalisation risk prediction model demonstrated very good performance and can be used to stratify risk in the Lewisham population to help providers reduce unnecessary hospital admissions and associated costs, improve patient outcomes, and target those at greatest risk to ensure full vaccination against SARS-CoV-2. Further research may examine the external validity of the model in other populations.

scholarly journals The Cytokinesis-Blocked Micronucleus Assay as a Strong Predictor of Lung Cancer: Extension of a Lung Cancer Risk Prediction Model

2014 ◽  
Vol 23 (11) ◽  
pp. 2462-2470 ◽  
Author(s):  
Randa A. El-Zein ◽  
Mirtha S. Lopez ◽  
Anthony M. D'Amelio ◽  
Mei Liu ◽  
Reginald F. Munden ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Lung Cancer Risk ◽  
Strong Predictor ◽  
Micronucleus Assay ◽  
Risk Prediction Model

scholarly journals Establishment of non–small-cell lung cancer risk prediction model based on prognosis-associated ADME genes

2021 ◽  
Author(s):  
Ke Han ◽  
Jukun Wang ◽  
Kun Qian ◽  
Teng Zhao ◽  
Yi Zhang
Keyword(s):  
Lung Cancer ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Score ◽  
Small Cell ◽  
Risk Prediction Model ◽  
Low Risk ◽  
Regression Analyses ◽  
Small Cell Lung ◽  
Immune Infiltration

Purpose: ADME genes are those involved in the absorption, distribution, metabolism, and excretion (ADME) of drugs. In this study, a non–small-cell lung cancer (NSCLC) risk prediction model was established using prognosis-associated ADME genes, and the predictive performance of this model was evaluated and verified. In addition, multifaceted difference analysis was performed on groups with high and low risk scores. Methods: An NSCLC sample transcriptome and clinical data were obtained from public databases. The prognosis-associated ADME genes were obtained by univariate Cox and lasso regression analyses to build a risk model. Tumor samples were divided into high-risk and low-risk score groups according to the risk score. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of the differentially expressed genes and the differences in the immune infiltration, mutation, and medication reactions in the two groups were studied in detail. Results: A risk prediction model was established with seven prognosis-associated ADME genes. Its good predictive ability was confirmed by studies of the model’s effectiveness. Univariate and multivariate Cox regression analyses showed that the model’s risk score was an independent prognostic factor for patients with NSCLC. The study also showed that the risk score closely correlated with immune infiltration, mutations, and medication reactions. Conclusion: The risk prediction model established with seven ADME genes in this study can predict the prognosis of patients with NSCLC. In addition, significant differences in immune infiltration, mutations, and therapeutic efficacy exist between the high- and low-risk score groups.

scholarly journals Individualized Risk Prediction Model for Lung Cancer in Korean Men

PLoS ONE ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. e54823 ◽  
Author(s):  
Sohee Park ◽  
Byung-Ho Nam ◽  
Hye-Ryung Yang ◽  
Ji An Lee ◽  
Hyunsun Lim ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model
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