tetano
Editor, Senior Moderator
J Med Virol
. 2021 Aug 17.
doi: 10.1002/jmv.27275. Online ahead of print.
A nomogram prediction of outcome in patients with COVID-19 based on individual characteristics incorporating immune response-related indicators
Fang Tang[SUP] 1 2 [/SUP], Xiaoshuai Zhang[SUP] 3 [/SUP], Bicheng Zhang[SUP] 4 [/SUP], Bo Zhu[SUP] 5 [/SUP], Jun Wang[SUP] 6 7 8 [/SUP]
Affiliations
Abstract
Purpose: The coronavirus disease 2019 (COVID-19) has quickly became a global threat to public health, and it is difficult to predict severe patients and their prognosis. Here we intend to develop effective models for late identification of patients at disease progression and outcome.
Methods: A total of 197 patients were included with 20-day median follow-up time. We first developed a nomogram for disease severity discrimination, then created a prognostic nomogram for severe patients.
Results: 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 NLRhiIgGhi phenotype are most likely to have severe disease, especially compared to those with NLRloIgGlo phenotype. The C-indices of the two discriminative nomogram was 0.86 and 0.87 respectively, which indicated sufficient discriminative power. As for predicting clinical outcome for severe patients, IgG, NLR, age, lactate dehydrogenase, platelet, monocytes, and procalcitonin were significant predictors. The prognosis of severe patients with NLRhiIgGhi phenotype were significantly worse than NLRloIgGhi group. The two prognostic nomograms also showed good performance in estimating the risk of progression.
Conclusion: The present nomogram models are useful to identify COVID-19 patients with progression based on individual characteristics and immune response-related indicators. Patients at high-risk for severe illness and poor outcome from COVID-19 should be managed with intensive supportive care and appropriate therapeutic strategies. This article is protected by copyright. All rights reserved.
Keywords: COVID-19; IgG; neutrophil-to-lymphocyte ratio; nomogram; prediction.
. 2021 Aug 17.
doi: 10.1002/jmv.27275. Online ahead of print.
A nomogram prediction of outcome in patients with COVID-19 based on individual characteristics incorporating immune response-related indicators
Fang Tang[SUP] 1 2 [/SUP], Xiaoshuai Zhang[SUP] 3 [/SUP], Bicheng Zhang[SUP] 4 [/SUP], Bo Zhu[SUP] 5 [/SUP], Jun Wang[SUP] 6 7 8 [/SUP]
Affiliations
- PMID: 34403145
- DOI: 10.1002/jmv.27275
Abstract
Purpose: The coronavirus disease 2019 (COVID-19) has quickly became a global threat to public health, and it is difficult to predict severe patients and their prognosis. Here we intend to develop effective models for late identification of patients at disease progression and outcome.
Methods: A total of 197 patients were included with 20-day median follow-up time. We first developed a nomogram for disease severity discrimination, then created a prognostic nomogram for severe patients.
Results: 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 NLRhiIgGhi phenotype are most likely to have severe disease, especially compared to those with NLRloIgGlo phenotype. The C-indices of the two discriminative nomogram was 0.86 and 0.87 respectively, which indicated sufficient discriminative power. As for predicting clinical outcome for severe patients, IgG, NLR, age, lactate dehydrogenase, platelet, monocytes, and procalcitonin were significant predictors. The prognosis of severe patients with NLRhiIgGhi phenotype were significantly worse than NLRloIgGhi group. The two prognostic nomograms also showed good performance in estimating the risk of progression.
Conclusion: The present nomogram models are useful to identify COVID-19 patients with progression based on individual characteristics and immune response-related indicators. Patients at high-risk for severe illness and poor outcome from COVID-19 should be managed with intensive supportive care and appropriate therapeutic strategies. This article is protected by copyright. All rights reserved.
Keywords: COVID-19; IgG; neutrophil-to-lymphocyte ratio; nomogram; prediction.