tetano
Editor, Senior Moderator
Clin Chim Acta
. 2020 Dec 3;S0009-8981(20)30552-0.
doi: 10.1016/j.cca.2020.11.019. Online ahead of print.
A Nomogram to Early Predict Isolation Length for Non-severe COVID-19 Patients Based on Laboratory Investigation: A Multicenter Retrospective Study in Zhejiang Province, China
Yan Xia[SUP] 1 [/SUP], Yan Zhang[SUP] 2 [/SUP], Shijin Yuan[SUP] 1 [/SUP], Jiangnan Chen[SUP] 3 [/SUP], Wei Zheng[SUP] 4 [/SUP], Xiaoping Xu[SUP] 5 [/SUP], Xinyou Xie[SUP] 6 [/SUP], Jun Zhang[SUP] 7 [/SUP]
Affiliations
Abstract
Background: Majority coronavirus disease 2019 (COVID-19) patients are classified as mild and moderate (non-severe) diseases. We aim to develop a model to predict isolation length for non-severe patients.
Methods: Among 188 non-severe patients, 96 patients were enrolled as training cohort to identify factors associated with isolation length via Cox regression model and develop a nomogram. Other 92 patients formed as validation cohort to validate nomogram. Concordance index (C-index), area under the curve (AUC) and calibration curves were used to evaluated nomogram.
Results: Increasing absolute eosinophil count (AEC) after admission was correlated with shorter isolation length (P=0.02). Baseline activated partial thromboplastin time (APTT) > 30s was correlated with longer isolation length (P=0.03). A nomogram to predict isolation probability at 11-, 16- and 21-day was developed and validated. The C-indices of training and validation cohort were 0.604 and 0.682 respectively. Both cohorts showed a good discriminative ability (AUC, 11-day: 0.646 vs 0.730; 16-day: 0.663 vs 0.750; 21-day: 0.711 vs 0.783; respectively) and calibration power.
Conclusions: Baseline APTT and dynamic change of AEC were two significant factors associated with isolation length of non-severe patients. Nomogram could predict isolation probability for each patient to estimate appropriate quarantine length.
Keywords: Absolute eosinophil count; Activated partial thromboplastin time; COVID-19; Isolation length; Nomogram.
. 2020 Dec 3;S0009-8981(20)30552-0.
doi: 10.1016/j.cca.2020.11.019. Online ahead of print.
A Nomogram to Early Predict Isolation Length for Non-severe COVID-19 Patients Based on Laboratory Investigation: A Multicenter Retrospective Study in Zhejiang Province, China
Yan Xia[SUP] 1 [/SUP], Yan Zhang[SUP] 2 [/SUP], Shijin Yuan[SUP] 1 [/SUP], Jiangnan Chen[SUP] 3 [/SUP], Wei Zheng[SUP] 4 [/SUP], Xiaoping Xu[SUP] 5 [/SUP], Xinyou Xie[SUP] 6 [/SUP], Jun Zhang[SUP] 7 [/SUP]
Affiliations
- PMID: 33279501
- DOI: 10.1016/j.cca.2020.11.019
Abstract
Background: Majority coronavirus disease 2019 (COVID-19) patients are classified as mild and moderate (non-severe) diseases. We aim to develop a model to predict isolation length for non-severe patients.
Methods: Among 188 non-severe patients, 96 patients were enrolled as training cohort to identify factors associated with isolation length via Cox regression model and develop a nomogram. Other 92 patients formed as validation cohort to validate nomogram. Concordance index (C-index), area under the curve (AUC) and calibration curves were used to evaluated nomogram.
Results: Increasing absolute eosinophil count (AEC) after admission was correlated with shorter isolation length (P=0.02). Baseline activated partial thromboplastin time (APTT) > 30s was correlated with longer isolation length (P=0.03). A nomogram to predict isolation probability at 11-, 16- and 21-day was developed and validated. The C-indices of training and validation cohort were 0.604 and 0.682 respectively. Both cohorts showed a good discriminative ability (AUC, 11-day: 0.646 vs 0.730; 16-day: 0.663 vs 0.750; 21-day: 0.711 vs 0.783; respectively) and calibration power.
Conclusions: Baseline APTT and dynamic change of AEC were two significant factors associated with isolation length of non-severe patients. Nomogram could predict isolation probability for each patient to estimate appropriate quarantine length.
Keywords: Absolute eosinophil count; Activated partial thromboplastin time; COVID-19; Isolation length; Nomogram.