An assessment of the value of deep neural networks in genetic risk prediction for surgically relevant outcomes

Abstract

AbstractIntroductionPostoperative complications affect up to 15% of surgical patients constituting a major part of the overall disease burden in a modern healthcare system. While several surgical risk calculators have been developed, none have so far been shown to decrease the associated mortality and morbidity. Combining deep neural networks and genomics with the already established clinical predictors may hold promise for improvement.MethodsThe UK Biobank was utilized to build linear and deep learning models for the prediction of surgery relevant outcomes. An initial GWAS for the relevant outcomes was initially conducted to select the Single Nucleotide Polymorphisms for inclusion in the models. Model performance was assessed with Receiver Operator Characteristics of the Area Under the Curve and optimum precision and recall. Feature importance was assessed with SHapley Additive exPlanations.ResultsModels were generated for atrial fibrillation, venous thromboembolism and pneumonia as genetics only, clinical features only and a combined model. For venous thromboembolism, the ROC-AUCs were 59.6% [59.0%-59.7%], 63.4% [63.2%-63.4%] and 66.1% [65.7%-66.1%] for the linear models and 60.0% [57.8%-61.8%], 63.2% [61.2%-65.0%] and 65.4% [63.6%-67.2%] for the deep learning SNP, clinical and combined models, respectively. For atrial fibrillation, the ROC-AUCs were 60.9% [60.6%-61.0%], 78.7% [78.7%-78.7%] and 80.1% [80.0%-80.1%] for the linear models and 59.9% [.6%-61.3%], 78.8% [77.8%-79.8%] and 79.4% [78.8%-80.5%] for the deep learning SNP, clinical and combined models, respectively. For pneumonia, the ROC-AUCs were 57.3% [56.5%-57.4%], 69.2% [69.1%-69.2%] and 70.5% [70.2%-70.6%] for the linear models and 55.5% [54.1%-56.9%], 69.7% [.5%-70.8%] and 69.9% [68.7%-71.0%] for the deep learning SNP, clinical and combined models, respectively.ConclusionIn this report we presented linear and deep learning predictive models for surgery relevant outcomes. Overall, predictability was similar between linear and deep learning models and inclusion of genetics seemed to improve accuracy.