Introduction: The coronavirus disease 2019 (COVID-19) has quickly become a global threat to public health, and it is difficult to predict severe patients and their prognosis. Here, we intended developing effective models for the late identification of patients at disease progression and outcome.
Methods: A total of 197 patients were included with a 20-day median follow-up time. We first developed a nomogram for disease severity discrimination, then created a prognostic nomogram for severe patients.
Results: In total, 40.6% of patients were severe and 59.4% were non-severe. The multivariate logistic analysis indicated that IgG, neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase, platelet, albumin, and blood urea nitrogen were significant factors associated with the severity of COVID-19. Using immune response phenotyping based on NLR and IgG level, the logistic model showed patients with the NLRhi IgGhi phenotype are most likely to have severe disease, especially compared to those with the NLRlo IgGlo phenotype. The C-indices of the two discriminative nomograms were 0.86 and 0.87, respectively, which indicated sufficient discriminative power. As for predicting clinical outcomes for severe patients, IgG, NLR, age, lactate dehydrogenase, platelet, monocytes, and procalcitonin were significant predictors. The prognosis of severe patients with the NLRhi IgGhi phenotype was significantly worse than the NLRlo IgGhi group. The two prognostic nomograms also showed good performance in estimating the risk of progression.
Conclusions: The present nomogram models are useful to identify COVID-19 patients with disease progression based on individual characteristics and immune response-related indicators. Patients at high risk for severe illness and poor outcomes from COVID-19 should be managed with intensive supportive care and appropriate therapeutic strategies.
Keywords: COVID-19; IgG; neutrophil-to-lymphocyte ratio; nomogram; prediction.
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