Abstract P02: Predicting mortality in SARS-CoV-2 (COVID-19) positive patients in the inpatient setting using a Novel Deep Neural Network

Abstract

Abstract Background The second wave of the COVID-19 pandemic is anticipated to be worse than the initial one and will strain the healthcare systems even more during the winter months. Our aim was to develop a machine learning-based model to predict mortality using the deep learning Neo-V framework. We hypothesized this novel machine learning approach could be applied to COVID-19 patients to predict mortality successfully with high accuracy. Methods The current Deep-Neo-V model is built on our previously statistically rigorous machine learning framework [Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework] that evaluated statistically significant risk factors, generated new combined variables, and then supply these risk factors to the deep neural network to predict mortality in RT-PCR positive COVID-19 patients in the inpatient setting. We analyzed adult patients (≥18 years) admitted to the Aga Khan University Hospital, Pakistan with a working diagnosis of COVID-19 infection (n=1228). We excluded patients that were negative on COVID19 on RT-PCR, had incomplete or missing health records. The first phase selection of the risk factors was done using Cox-regression univariate and multivariate analyses. In the second phase, we generated new variables and tested those statistically significant for mortality and in the third and final phase, we applied deep neural networks and other traditional machine learning models like Decision Tree Model, k-nearest neighbor models, and others. Results A total of 1228 cases were diagnosed as COVID-19 infection, we excluded 14 patients after the exclusion criteria, and (n=)1214 patients were analyzed. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being septic shock (hazard ratio [HR], 4.30; 95% confidence interval [CI], 2.91-6.37), supportive treatment (HR, 3.51; 95% CI, 2.01-6.14), abnormal international normalized ratio (INR) (HR, 3.24; 95% CI, 2.28-4.63), admission to the intensive care unit (ICU) (HR, 3.24; 95% CI, 2.22-4.74), treatment with invasive ventilation (HR, 3.21; 95% CI, 2.15-4.79) and laboratory lymphocytic derangement (HR, 2.79; 95% CI, 1.6-4.86). Machine learning results showed our DNN (Neo-V) model outperformed all conventional machine learning models with test set accuracy of 99.53%, sensitivity of 89.87%, and specificity of 95.63%; positive predictive value, 50.00%; negative predictive value, 91.05%; and area under the curve of the receiver-operator curve of 88.5. Conclusion Our novel Deep-Neo-V model outperformed all other machine learning models. The model is easy to implement, user friendly, and with high accuracy. Citation Format: Fahad S. Ahmed, Maleeha Naseem, Hajra Arshad, Syeda Amrah Hashmi, Furqan Irfan. Predicting mortality in SARS-CoV-2 (COVID-19) positive patients in the inpatient setting using a Novel Deep Neural Network [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2021 Feb 3-5. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(6_Suppl):Abstract nr P02.