Visceral adiposity and severe COVID-19 disease: application of an artificial intelligence algorithm to improve clinical risk prediction

Goehler, Alexander; Tsu, Harry Tzu-Ming; Seiglie, Jacqueline A; Siedner, Mark J; Lo, Janet; Triant, Virginia; Hsu, John; Foulkes, Andrea; Bassett, Ingrid; Khorasani, Ramin; Wexler, Deborah J; Szolovits, Peter; Meigs, James B; Manne-Goehler, Jennifer

BACKGROUND: Obesity has been linked to severe clinical outcomes among people who are hospitalized with COVID-19. We tested the hypothesis that visceral adipose tissue (VAT) is associated with severe outcomes in patients hospitalized with COVID-19, independent of body mass index (BMI). METHODS: We analyzed data from the Massachusetts General Hospital COVID-19 Data Registry, which included patients admitted with PCR-confirmed SARS-CoV-2 infection from March 11 - May 4, 2020. We used a validated, fully automated artificial intelligence (AI) algorithm to quantify VAT from CT scans during or prior to the hospital admission. VAT quantification took an average 2±0.5 seconds per patient. We dichotomized VAT as high and low at a threshold of ≥100 cm(2) and used Kaplan-Meier curves and Cox proportional hazards regression to assess the relationship between VAT and death or intubation over 28 days, adjusting for age, sex, race, BMI and diabetes status. RESULTS: 378 participants had CT imaging. Kaplan-Meier curves showed that participants with high VAT had a greater risk of the outcome compared to those with low VAT (p<0.005), especially in those with BMI <30 kg/m (2) (p<0.005). In multivariable models, the aHR for high vs. low VAT was unchanged [aHR 1.97 (1.24 – 3.09)], whereas BMI was no longer significant [aHR for obese vs. normal BMI 1.14 (0.71 – 1.82)]. CONCLUSIONS: High VAT is associated with a greater risk of severe disease or death in COVID-19, and can offer more precise information to risk stratify individuals beyond BMI. AI offers a promising approach to routinely ascertain VAT and improve clinical risk prediction in COVID-19.