tetano
Editor, Senior Moderator
Cancer Sci
. 2021 Mar 16.
doi: 10.1111/cas.14882. Online ahead of print.
An Individualized Model for Predicting COVID-19 Deterioration in Patients with Cancer: A Multicenter Retrospective Study
Bin Xu[SUP] 1 [/SUP], Ke-Han Song[SUP] 2 [/SUP], Yi Yao[SUP] 1 [/SUP], Xiao-Rong Dong[SUP] 3 [/SUP], Lin-Jun Li[SUP] 4 [/SUP], Qun Wang[SUP] 5 [/SUP], Ji-Yuan Yang[SUP] 6 [/SUP], Wei-Dong Hu[SUP] 7 [/SUP], Zhi-Bin Xie[SUP] 8 [/SUP], Zhi-Guo Luo[SUP] 9 [/SUP], Xiu-Li Luo[SUP] 10 [/SUP], Jing Liu[SUP] 11 [/SUP], Zhi-Guo Rao[SUP] 12 [/SUP], Hui-Bo Zhang[SUP] 1 [/SUP], Jie Wu[SUP] 1 [/SUP], Lan Li[SUP] 1 [/SUP], Hong-Yun Gong[SUP] 1 [/SUP], Qian Chu[SUP] 13 [/SUP], Qi-Bin Song[SUP] 1 [/SUP], Jie Wang[SUP] 14 [/SUP]
Affiliations
Abstract
The 2019 novel coronavirus has spread rapidly around the world. Cancer patients seem to be more susceptible to infection and disease deterioration, but the factors affecting the deterioration remain unclear. We aimed to develop an individualized model for prediction of COVID-19 deterioration in cancer patients. The clinical data of 276 cancer patients diagnosed with COVID-19 in 33 designated hospitals of Hubei, China from December 21, 2019 to March 18, 2020, were collected and randomly divided into a training and a validation cohort by a ratio of 2:1. Cox stepwise regression analysis were conducted to select prognostic factors. The prediction model was developed in the training cohort. The predictive accuracy of the model was quantified by C-index and time-dependent AUC. Internal validation was assessed by the validation cohort. Risk stratification based on the model was performed. Decision curve analysis (DCA) were used to evaluate the clinical usefulness of the model. We found age, cancer type, , CT baseline image features (ground glass opacity and consolidation), laboratory findings (lymphocyte count, serum levels of C-reactive protein, aspartate aminotransferase, direct bilirubin, urea and d-dimer) were significantly associated with symptomatic deterioration. The C-index of the model was 0.755 in the training cohort and 0.779 in the validation cohort. T-AUC values were above 0.7 within 8 weeks both in the training and validation cohorts. Patients were divided into two risk groups based on the nomogram: low-risk (total points? 9.98) and high-risk (total points> 9.98) group. The Kaplan-Meier C-DFS curves presented the significant discrimination between the two risk groups in both training and validation cohort. The model indicated good clinical applicability by DCA curves. This study presents an individualized nomogram model to individually predict the possibility of symptomatic deterioration of COVID-19 in patients with cancer.
Keywords: COVID-19; Cancer; Deterioration; Retrospective Study; Risk model.
. 2021 Mar 16.
doi: 10.1111/cas.14882. Online ahead of print.
An Individualized Model for Predicting COVID-19 Deterioration in Patients with Cancer: A Multicenter Retrospective Study
Bin Xu[SUP] 1 [/SUP], Ke-Han Song[SUP] 2 [/SUP], Yi Yao[SUP] 1 [/SUP], Xiao-Rong Dong[SUP] 3 [/SUP], Lin-Jun Li[SUP] 4 [/SUP], Qun Wang[SUP] 5 [/SUP], Ji-Yuan Yang[SUP] 6 [/SUP], Wei-Dong Hu[SUP] 7 [/SUP], Zhi-Bin Xie[SUP] 8 [/SUP], Zhi-Guo Luo[SUP] 9 [/SUP], Xiu-Li Luo[SUP] 10 [/SUP], Jing Liu[SUP] 11 [/SUP], Zhi-Guo Rao[SUP] 12 [/SUP], Hui-Bo Zhang[SUP] 1 [/SUP], Jie Wu[SUP] 1 [/SUP], Lan Li[SUP] 1 [/SUP], Hong-Yun Gong[SUP] 1 [/SUP], Qian Chu[SUP] 13 [/SUP], Qi-Bin Song[SUP] 1 [/SUP], Jie Wang[SUP] 14 [/SUP]
Affiliations
- PMID: 33728806
- DOI: 10.1111/cas.14882
Abstract
The 2019 novel coronavirus has spread rapidly around the world. Cancer patients seem to be more susceptible to infection and disease deterioration, but the factors affecting the deterioration remain unclear. We aimed to develop an individualized model for prediction of COVID-19 deterioration in cancer patients. The clinical data of 276 cancer patients diagnosed with COVID-19 in 33 designated hospitals of Hubei, China from December 21, 2019 to March 18, 2020, were collected and randomly divided into a training and a validation cohort by a ratio of 2:1. Cox stepwise regression analysis were conducted to select prognostic factors. The prediction model was developed in the training cohort. The predictive accuracy of the model was quantified by C-index and time-dependent AUC. Internal validation was assessed by the validation cohort. Risk stratification based on the model was performed. Decision curve analysis (DCA) were used to evaluate the clinical usefulness of the model. We found age, cancer type, , CT baseline image features (ground glass opacity and consolidation), laboratory findings (lymphocyte count, serum levels of C-reactive protein, aspartate aminotransferase, direct bilirubin, urea and d-dimer) were significantly associated with symptomatic deterioration. The C-index of the model was 0.755 in the training cohort and 0.779 in the validation cohort. T-AUC values were above 0.7 within 8 weeks both in the training and validation cohorts. Patients were divided into two risk groups based on the nomogram: low-risk (total points? 9.98) and high-risk (total points> 9.98) group. The Kaplan-Meier C-DFS curves presented the significant discrimination between the two risk groups in both training and validation cohort. The model indicated good clinical applicability by DCA curves. This study presents an individualized nomogram model to individually predict the possibility of symptomatic deterioration of COVID-19 in patients with cancer.
Keywords: COVID-19; Cancer; Deterioration; Retrospective Study; Risk model.