risk prediction models
Recently Published Documents


TOTAL DOCUMENTS

612
(FIVE YEARS 297)

H-INDEX

47
(FIVE YEARS 9)

BMC Cancer ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Michele Sassano ◽  
Marco Mariani ◽  
Gianluigi Quaranta ◽  
Roberta Pastorino ◽  
Stefania Boccia

Abstract Background Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors. Methods We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement. Results We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry. Conclusions Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.


JACC: Asia ◽  
2022 ◽  
Author(s):  
Xiaofei Liu ◽  
Peng Shen ◽  
Dudan Zhang ◽  
Yexiang Sun ◽  
Yi Chen ◽  
...  

2021 ◽  
Author(s):  
Ying Gao ◽  
Shu Li ◽  
Yujing Jin ◽  
Lengxiao Zhou ◽  
Shaomei Sun ◽  
...  

BACKGROUND Background: Machine learning algorithms well-suited in cancer research, especially in breast cancer for the investigation and development of riTo assess the performance of available machine learning-based breast cancer risk prediction model. OBJECTIVE Objective: To assess the performance of available machine learning-based breast cancer risk prediction model. METHODS Methods: As of June 9, 2021, articles on breast cancer risk prediction models by machine learning were searched in PubMed, Embase, and Web of Science. Studies describing the development or validation of risk prediction models for predicting future breast cancer risk were included. Pooled area under the curve (AUC) were calculated using the DerSimonian and Laird random-effects model. RESULTS Result: A total of 8 studies with 10 datasets were included. Neural network was the most common machine learning method for the development of risk prediction models. The pooled AUC of machine learning-based optimal risk prediction model reported in each study was 0.73 (95%CI: 0.66-0.80), which was higher than that of traditional risk factor-based risk prediction models (all Pheterogeneity < 0.001). The pooled AUC of neural network-based risk prediction model was higher than that of non-neural network-based optimal risk prediction model (0.71 vs. 0.68). Subgroup analysis showed that incorporation of imaging features risk models had a higher pooled AUC than model of non-incorporation of imaging features (0.73 vs. 0.61; Pheterogeneity =0.001). CONCLUSIONS Conclusions: The pooled machine learning-based breast cancer risk prediction model yield a good prediction performance and promising results.


Cancers ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 6216
Author(s):  
Koldo Garcia-Etxebarria ◽  
Marc Clos-Garcia ◽  
Oiana Telleria ◽  
Beatriz Nafría ◽  
Cristina Alonso ◽  
...  

Background: Colorectal cancer (CRC), a major health concern, is developed depending on environmental, genetic and microbial factors. The microbiome and metabolome have been analyzed to study their role in CRC. However, the interplay of host genetics with those layers in CRC remains unclear. Methods: 120 individuals were sequenced and association analyses were carried out for adenoma and CRC risk, and for selected components of the microbiome and metabolome. The epistasis between genes located in cholesterol pathways was analyzed; modifiable risk factors were studied using Mendelian randomization; and the three omic layers were used to integrate their data and to build risk prediction models. Results: We detected genetic variants that were associated to components of metabolome or microbiome and adenoma or CRC risk (e.g., in LINC01605, PROKR2 and CCSER1 genes). In addition, we found interactions between genes of cholesterol metabolism, and HDL cholesterol levels affected adenoma (p = 0.0448) and CRC (p = 0.0148) risk. The combination of the three omic layers to build risk prediction models reached high AUC values (>0.91). Conclusions: The use of the three omic layers allowed for the finding of biological mechanisms related to the development of adenoma and CRC, and each layer provided complementary information to build risk prediction models.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Maria C. Inacio ◽  
Max Moldovan ◽  
Craig Whitehead ◽  
Janet K. Sluggett ◽  
Maria Crotty ◽  
...  

Abstract Background Entering permanent residential aged care (PRAC) is a vulnerable time for individuals. While falls risk assessment tools exist, these have not leveraged routinely collected and integrated information from the Australian aged and health care sectors. Our study examined individual, system, medication, and health care related factors at PRAC entry that are predictors of fall-related hospitalisations and developed a risk assessment tool using integrated aged and health care data. Methods A retrospective cohort study was conducted on N = 32,316 individuals ≥65 years old who entered a PRAC facility (01/01/2009-31/12/2016). Fall-related hospitalisations within 90 or 365 days were the outcomes of interest. Individual, system, medication, and health care-related factors were examined as predictors. Risk prediction models were developed using elastic nets penalised regression and Fine and Gray models. Area under the receiver operating characteristics curve (AUC) assessed model discrimination. Results 64.2% (N = 20,757) of the cohort were women and the median age was 85 years old (interquartile range 80-89). After PRAC entry, 3.7% (N = 1209) had a fall-related hospitalisation within 90 days and 9.8% (N = 3156) within 365 days. Twenty variables contributed to fall-related hospitalisation prediction within 90 days and the strongest predictors included fracture history (sub-distribution hazard ratio (sHR) = 1.87, 95% confidence interval (CI) 1.63-2.15), falls history (sHR = 1.41, 95%CI 1.21-2.15), and dementia (sHR = 1.39, 95%CI 1.22-1.57). Twenty-seven predictors of fall-related hospitalisation within 365 days were identified, the strongest predictors included dementia (sHR = 1.36, 95%CI 1.24-1.50), history of falls (sHR = 1.30, 95%CI 1.20-1.42) and fractures (sHR = 1.28, 95%CI 1.15-1.41). The risk prediction models had an AUC of 0.71 (95%CI 0.68-0.74) for fall-related hospitalisations within 90 days and 0.64 (95%CI 0.62-0.67) for within 365 days. Conclusion Routinely collected aged and health care data, when integrated at a clear point of action such as entry into PRAC, can identify residents at risk of fall-related hospitalisations, providing an opportunity for better targeting risk mitigation strategies.


Sign in / Sign up

Export Citation Format

Share Document