Abstract
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 NLR hi IgG hi phenotype are most likely to have severe disease, especially compared to those with the NLR lo IgG lo 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 NLR hi IgG hi phenotype was significantly worse than the NLR lo IgG hi 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.
【저자키워드】 COVID-19, IgG, nomogram, prediction, Neutrophil-to-lymphocyte ratio, 【초록키워드】 coronavirus disease, public health, Monocytes, immune response, Prognosis, disease severity, risk, procalcitonin, predictors, progression, outcome, lactate dehydrogenase, immune, Clinical outcome, Disease progression, Characteristics, severity of COVID-19, Patient, albumin, Therapeutic strategies, Platelet, phenotype, age, Severe patient, prognostic, Intensive, predict, NLR, supportive care, Analysis, severe disease, COVID-19 patient, high risk, Blood urea nitrogen, Logistic, Factor, severe patients, follow-up time, effective, identify, significantly, indicated, median, 【제목키워드】 outcome, immune, Characteristics, patients with COVID-19,