Automatically Generated CT Severity Scores for COVID-19 Predict Death or Intubation at 1-Month Follow-Up

L. Boulogne and B. van Ginneken

Annual Meeting of the Radiological Society of North America 2022.

PURPOSE: To evaluate the ability of CORADS-AI, an automatic system that was originally developed for scoring the presence and current extent of a COVID19 infection from chest Computed Tomography (CT), to predict death or intubation after one month for positive COVID-19 patients. METHODS AND MATERIALS: CORADS-AI was developed in a previous study using CT scans from COVID-19 patients in the Netherlands. From a CT scan, CORADS-AI automatically segments each pulmonary lobe and produces a CT Severity Score (CTSS) at the lobe and patient level. This score is based on the percentage of affected tissue and has been used in routine clinical practice. We applied CORADS-AI to all 1205 patients that were involved in the STOIC study, had a positive COVID-19 RT-PCR result, and for which a CT scan was publicly available. 301 of them had died or had to be intubated at 1-month follow-up. We applied logistic regression with 5-fold cross validation on patient sex, age, and the patient level CTSS output of CORADS-AI to predict death or intubation after one month. We compared our method with the logistic regression model from the original STOIC study. This model received age, sex, several clinical variables, and manual CT annotations for lung disease extent as input. It was developed with all 4238 patients from the STOIC study that were positive for COVID-19 both according to RT-PCR testing and CT reading. RESULTS: Our model obtained an AUC of 0.72+-0.02 (mean+-std. dev.) for predicting death or intubation after one month. When using solely the patient level CTSS output, we obtained an AUC of 0.68+-0.03. In comparison, the original STOIC study reported an AUC of their model of 0.69 (95% CI: 0.67, 0.71). CONCLUSIONS: We showed that CORADS-AI can predict death or intubation after one month for positive COVID-19 patients. Adding age and sex information to the model improved its results. The performance was comparable to that of the model developed in the original STOIC study, which used additional clinical variables and manual CT annotations. This comparison should be interpreted carefully, since that model was evaluated on a different subset of the STOIC cohort. CLINICAL RELEVANCE/APPLICATION: We showed the potential of CORADS-AI, of which the output format is already used in clinical practice, for aiding radiologists in predicting the course of a COVID-19 infection.