Table 2.
Summary of risk prediction models incorporating genetic variants.
First Author, Year | Country | Outcome a | Genetic Factors | Non-Genetic Risk Factors | Study Type | Study-Setting | Tripod Level ᵇ | Reported Performance Measures | Overall Risk of Bias | |
---|---|---|---|---|---|---|---|---|---|---|
Development | Validation | |||||||||
Bao, 2020b [35] | China | OCC | 7 SNP ᶜ-constructed GRS | Selenium level | CC | Hospital-based | 1a | AIC, OR | High | Not applicable |
Chung, 2017 [44] | Taiwan | OCC | 4 SNPs ᵈ | Age and betel quid chewing | CC | Hospital-based | 2b | AUROC, Sens, Spec, OR | High | High |
Chung, 2019 [45] | Taiwan | OCC | 2 SNPs ᵉ | Age, betel quid chewing and alcohol consumption | CC | Hospital-based | 1a | AUROC | High | High |
Fritsche, 2020a [46] | United Kingdom (UK Biobank) |
OCC | 1,119,238 SNPs | EHR-derived phenotypes | CC | General population | 1b | AUROC, R2, Brier score, OR | High | High |
Fritsche, 2020b [46] | Finland (FinnGen) | OCC ᶠ | 931,954 SNPs | EHR-derived phenotypes | CC | General population | 1b | AUROC, R2, Brier score, OR | High | High |
Miao, 2016 [47] | China | OCC | 3 SNPs | Age | CC | Hospital-based | 1a | Balance accuracy | High | Not applicable |
Abbreviations: AIC, Akaike Information Criterion; AUROC, area under the receiver operating characteristic curve; CC, case-control; EHR, electronic health record; GRS, genetic risk score; OCC, oral cavity cancer; OR, odds ratio; Sens, sensitivity; SNP, single nucleotide polymorphism; Spec, specificity. a Each prediction model is for either a single- or combined-outcome. b Classification of prediction model according to the TRIPOD guidelines [30,31]: 1a, development only; 1b, development and validation using resampling; 2b, nonrandom split-sample development and validation; c cIncluded SNPs: rs1800668, rs3746165, rs7310505, rs4964287, rs9605030, rs3788317, rs13054371. d Included SNPs: rs2070833, rs550675, rs139994842, rs2822641. e Included SNPs: rs550675, rs28647489. f Tongue cancer.