Novel coronavirus disease 2019: predicting prognosis with a computed tomography-based disease severity score and clinical laboratory data.

Introduction: Currently, there are known contributing factors but no comprehensive methods for predicting the mortality risk or intensive care unit (ICU) admission in patients with novel coronavirus disease 2019 (COVID‑19).

Objectives: The aim of this study was to explore risk factors for mortality and ICU admission in patients with COVID‑19, using computed tomography (CT) combined with clinical laboratory data.

Patients And Methods: Patients with polymerase chain reaction-confirmed COVID‑19 (n = 63) from university hospitals in Tehran, Iran, were included. All patients underwent CT examination. Subsequently, a total CT score and the number of involved lung lobes were calculated and compared against collected laboratory and clinical characteristics. Univariable and multivariable proportional hazard analyses were used to determine the association among CT, laboratory and clinical data, ICU admission, and in‑hospital death.

Results: By univariable analysis, in‑hospital mortality was higher in patients with lower oxygen saturation on admission (below 88%), higher CT scores, and a higher number of lung lobes (more than 4) involved with a diffuse parenchymal pattern. By multivariable analysis, in‑hospital mortality was higher in those with oxygen saturation below 88% on admission and a higher number of lung lobes involved with a diffuse parenchymal pattern. The risk of ICU admission was higher in patients with comorbidities (hypertension and ischemic heart disease), arterial oxygen saturation below 88%, and pericardial effusion.

Conclusions: We can identify factors affecting in‑hospital death and ICU admission in COVID-19. This can help clinicians to determine which patients are likely to require ICU admission and to inform strategic healthcare planning in critical conditions such as the COVID‑19 pandemic.