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Simple nomogram based on initial laboratory data for predicting the probability of ICU transfer of COVID-19 patients: Multicenter retrospective study.
J Med Virol. 2020 Jun 30 [Online ahead of print]JM

Abstract

This retrospective, multicenter study investigated risk factors associated with intensive care unit (ICU) admission and transfer in 461 adult patients with confirmed coronavirus disease 2019 (COVID-19) hospitalized from January 22 to March 14, 2020 in Hunan Province, China. Outcomes of ICU and non-ICU patients were compared, and a simple nomogram for predicting the probability of ICU transfer after hospital admission was developed based on initial laboratory data using a Cox proportional hazards regression model. Differences in laboratory indices were observed between patients admitted to the ICU and those who were not admitted. Several independent predictors of ICU transfer in COVID-19 patients were identified including older age (≥65 years) (hazard ratio [HR]=4.02), hypertension (HR=2.65), neutrophil count (HR=1.11), procalcitonin level (HR=3.67), prothrombin time (HR=1.28), and d-dimer level (HR=1.25). Lymphocyte count and albumin level were negatively associated with mortality (HR=0.08 and 0.86, respectively). The developed model provides a means for identifying, at hospital admission, the subset of patients with COVID-19 who are at high risk of progression and would require transfer to the ICU within 3 and 7 days after hospitalization. This method of early patient triage allows more effective allocation of limited medical resources. This article is protected by copyright. All rights reserved.

Authors+Show Affiliations

Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China. Research Unit of Respiratory Disease, Central South University, Changsha, Hunan, 410011, China. Diagnosis and Treatment Center of Respiratory Disease, Central South University, Changsha, Hunan, 410011, China.Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China. Research Unit of Respiratory Disease, Central South University, Changsha, Hunan, 410011, China. Diagnosis and Treatment Center of Respiratory Disease, Central South University, Changsha, Hunan, 410011, China.Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China. Research Unit of Respiratory Disease, Central South University, Changsha, Hunan, 410011, China. Diagnosis and Treatment Center of Respiratory Disease, Central South University, Changsha, Hunan, 410011, China.Department of Respiratory Medicine, Zhuzhou Central Hospital, Zhuzhou, Hunan, 412000, China.Department of Respiratory Medicine, Yueyang Second People's Hospital, Designated Hospital of Junshan District, Yueyang, Hunan, 414005, China.Department of Respiratory and Critical Medicine, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, China.Department of Emergency Medicine, Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.Department of Respiratory Medicine, First Attached Hospital of Shaoyang University, Shaoyang, Hunan, 422001, China.Department of Respiratory Medicine, Affiliated Shaoyang Central Hospital of University of South China, Shaoyang, Hunan, 422001, China.Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China. Research Unit of Respiratory Disease, Central South University, Changsha, Hunan, 410011, China. Diagnosis and Treatment Center of Respiratory Disease, Central South University, Changsha, Hunan, 410011, China.Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China. Research Unit of Respiratory Disease, Central South University, Changsha, Hunan, 410011, China. Diagnosis and Treatment Center of Respiratory Disease, Central South University, Changsha, Hunan, 410011, China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32603535

Citation

Zeng, Zihang, et al. "Simple Nomogram Based On Initial Laboratory Data for Predicting the Probability of ICU Transfer of COVID-19 Patients: Multicenter Retrospective Study." Journal of Medical Virology, 2020.
Zeng Z, Ma Y, Zeng H, et al. Simple nomogram based on initial laboratory data for predicting the probability of ICU transfer of COVID-19 patients: Multicenter retrospective study. J Med Virol. 2020.
Zeng, Z., Ma, Y., Zeng, H., Huang, P., Liu, W., Jiang, M., Xiang, X., Deng, D., Liao, X., Chen, P., & Chen, Y. (2020). Simple nomogram based on initial laboratory data for predicting the probability of ICU transfer of COVID-19 patients: Multicenter retrospective study. Journal of Medical Virology. https://doi.org/10.1002/jmv.26244
Zeng Z, et al. Simple Nomogram Based On Initial Laboratory Data for Predicting the Probability of ICU Transfer of COVID-19 Patients: Multicenter Retrospective Study. J Med Virol. 2020 Jun 30; PubMed PMID: 32603535.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Simple nomogram based on initial laboratory data for predicting the probability of ICU transfer of COVID-19 patients: Multicenter retrospective study. AU - Zeng,Zihang, AU - Ma,Yiming, AU - Zeng,Huihui, AU - Huang,Peng, AU - Liu,Wenlong, AU - Jiang,Mingyan, AU - Xiang,Xudong, AU - Deng,Dingding, AU - Liao,Xin, AU - Chen,Ping, AU - Chen,Yan, Y1 - 2020/06/30/ PY - 2020/7/1/entrez PY - 2020/7/1/pubmed PY - 2020/7/1/medline KW - COVID-19 KW - ICU transfer KW - Laboratory examination KW - Nomogram JF - Journal of medical virology JO - J. Med. Virol. N2 - This retrospective, multicenter study investigated risk factors associated with intensive care unit (ICU) admission and transfer in 461 adult patients with confirmed coronavirus disease 2019 (COVID-19) hospitalized from January 22 to March 14, 2020 in Hunan Province, China. Outcomes of ICU and non-ICU patients were compared, and a simple nomogram for predicting the probability of ICU transfer after hospital admission was developed based on initial laboratory data using a Cox proportional hazards regression model. Differences in laboratory indices were observed between patients admitted to the ICU and those who were not admitted. Several independent predictors of ICU transfer in COVID-19 patients were identified including older age (≥65 years) (hazard ratio [HR]=4.02), hypertension (HR=2.65), neutrophil count (HR=1.11), procalcitonin level (HR=3.67), prothrombin time (HR=1.28), and d-dimer level (HR=1.25). Lymphocyte count and albumin level were negatively associated with mortality (HR=0.08 and 0.86, respectively). The developed model provides a means for identifying, at hospital admission, the subset of patients with COVID-19 who are at high risk of progression and would require transfer to the ICU within 3 and 7 days after hospitalization. This method of early patient triage allows more effective allocation of limited medical resources. This article is protected by copyright. All rights reserved. SN - 1096-9071 UR - https://www.unboundmedicine.com/medline/citation/32603535/Simple_nomogram_based_on_initial_laboratory_data_for_predicting_the_probability_of_ICU_transfer_of_COVID-19_patients:_Multicenter_retrospective_study L2 - https://doi.org/10.1002/jmv.26244 DB - PRIME DP - Unbound Medicine ER -
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