Multivariable Risk Modelling and Survival Analysis with Machine Learning in SARS-CoV-2 Infection

Abstract

Aim: To evaluate the performance of a machine learning model based on demographic variables, blood tests, pre-existing comorbidities, and computed tomography(CT)-based radiomic features to predict critical outcome in patients with acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods: We retrospectively enrolled 694 SARS-CoV-2-positive patients. Clinical and demographic data were extracted from clinical records. Radiomic data were extracted from CT. Patients were randomized to the training (80%, n = 556) or test (20%, n = 138) dataset. The training set was used to define the association between severity of disease and comorbidities, laboratory tests, demographic, and CT-based radiomic variables, and to implement a risk-prediction model. The model was evaluated using the C statistic and Brier scores. The test set was used to assess model prediction performance. Results: Patients who died (n = 157) were predominantly male (66%) over the age of 50 with median (range) C-reactive protein (CRP) = 5 [1, 37] mg/dL, lactate dehydrogenase (LDH) = 494 [141, 3631] U/I, and D-dimer = 6.006 [168, 152.015] ng/mL. Surviving patients (n = 537) had median (range) CRP = 3 [0, 27] mg/dL, LDH = 484 [78, 3.745] U/I, and D-dimer = 1.133 [96, 55.660] ng/mL. The strongest risk factors were D-dimer, age, and cardiovascular disease. The model implemented using the variables identified using the LASSO Cox regression analysis classified 90% of non-survivors as high-risk individuals in the testing dataset. In this sample, the estimated median survival in the high-risk group was 9 days (95% CI; 9–37), while the low-risk group did not reach the median survival of 50% (p < 0.001). Conclusions: A machine learning model based on combined data available on the first days of hospitalization (demographics, CT-radiomics, comorbidities, and blood biomarkers), can identify SARS-CoV-2 patients at risk of serious illness and death.

Does FDG PET-Based Radiomics Have an Added Value for Prediction of Overall Survival in Non-Small Cell Lung Cancer?

“…The median OS in the reference model was 17.0 months in high-risk patients (95%CI, 11-21) and 113 months in the low-risk group (HR 7.47, p < 0.001) (Figure 6A). The median OS in the radiomic model was 16.5 months (95%CI, [11][12][13][14][15][16][17][18][19][20] and 113 months in high-and low-risk groups, respectively (HR 9.64, p < 0.001) (Figure 6B). Figure 6 shows the results of the Kaplan-Meier analysis obtained using the two models.…”

“…Radiomic analysis of lymph nodes and distant metastases would be more cumbersome but, potentially, also more informative. Similarly, unenhanced, low-dose CT radiomic signature could provide useful information to differentiate and identify histology of lung cancer or to predict survival [18,49,50], but the assessment of CT radiomics was beyond the aim of our study and should be considered as a study limitation.…”

“…The model with the best performance was selected using a 10-fold cross-validation and the C-statistic. The detailed description of the method is reported in our previous paper [18].…”