About: A predictive nomogram for predicting improved clinical outcome probability in patients with COVID-19 in zhejiang province, china

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	A predictive nomogram for predicting improved clinical outcome probability in patients with COVID-19 in zhejiang province, china
Creator	Li, Lanjuan Sheng, Jifang Wang, Qing Xu, Kaijin Wu, Jingjing Shi, Ding Li, Y Li, L Wang, K Wang, Q Wu, J Xie, J Hu, X Shi, D Bao, M Fang, Daiqiong Li, Yating Wang, Kaicen Wu, W Wu, Wenrui Xie, Jiaojiao Xu, K B#, Bao Bao, Mingyang Fang, D Hu, Xiaoyi Sheng, J
source	Elsevier; Medline; PMC
abstract	The aim of this research was to develop a quantitative method for clinicians to predict the probability of improved prognosis in patients with coronavirus disease 2019 (COVID-19). Data on 104 patients admitted to hospital with laboratory-confirmed COVID-19 infection from 10 January 2020 to 26 February 2020 were collected. Clinical information and laboratory findings were collected and compared between the outcomes of improved patients and non-improved patients. The least absolute shrinkage and selection operator (LASSO) logistics regression model and two-way stepwise strategy in the multivariate logistics regression model were used to select prognostic factors for predicting clinical outcomes in COVID-19 patients. The concordance index (C-index) was used to assess the discrimination of the model, and internal validation was performed through bootstrap resampling. A novel predictive nomogram was constructed by incorporating these features. Of the 104 patients included in the study (median age 55 years), 75 (72.1%) had improved short-term outcomes, while 29 (27.9%) showed no signs of improvement. There were numerous differences in clinical characteristics and laboratory findings between patients with improved outcomes and patients without improved outcomes. After a multi-step screening process, prognostic factors were selected and incorporated into the nomogram construction, including immunoglobulin A (IgA), C-reactive protein (CRP), creatine kinase (CK), Acute Physiology and Chronic Health Evaluation II (APACHE II), and interaction between CK and APACHE II. The C-index of our model was 0.962 (95% confidence interval (CI), 0.931–0.993) and still reached a high value of 0.948 through bootstrapping validation. A predictive nomogram we further established showed close performance compared with the ideal model on the calibration plot and was clinically practical according to the decision curve and clinical impact curve. The nomogram we constructed is useful for clinicians to predict improved clinical outcome probability for each COVID-19 patient, which may facilitate personalized counselling and treatment.
has issue date	2020-06-06(xsd:dateTime)
bibo:doi	10.1016/j.eng.2020.05.014
bibo:pmid	32837744
has license	no-cc
sha1sum (hex)	5e6cb7d9007e7fcf1cdab8909759534ac79bb58e
schema:url	https://doi.org/10.1016/j.eng.2020.05.014
resource representing a document's title	A predictive nomogram for predicting improved clinical outcome probability in patients with COVID-19 in zhejiang province, china
has PubMed Central identifier	PMC7274972
has PubMed identifier	32837744
schema:publication	Engineering (Beijing)
resource representing a document's body	covid:5e6cb7d9007e7fcf1cdab8909759534ac79bb58e#body_text
is schema:about of	named entity 'stepwise' named entity 'high' named entity 'predict' named entity 'model' named entity 'logistics' named entity 'close' named entity 'Nomogram' named entity 'Patients' named entity 'zhejiang province' named entity 'hospital' named entity 'features' named entity 'The' named entity 'strategy' named entity 'The' named entity 'patients' named entity 'patients' named entity 'infection' named entity 'resampling' named entity 'probability' named entity 'laboratory' named entity 'prognosis' named entity 'improved' named entity 'improvement' named entity 'improved' named entity 'creatine kinase' named entity 'The' named entity 'absolute' named entity 'probability' named entity 'probability' named entity 'COVID-19' named entity 'Physiology' named entity 'bootstrap resampling' named entity 'immunoglobulin' named entity 'nomogram' named entity 'clinical outcomes' named entity 'bootstrapping' named entity 'creatine kinase' named entity 'zhejiang province' named entity 'non-parametric' named entity 'COVID-19' named entity 'Sputum' named entity 'IgA' named entity 'mucus' named entity 'critical illness' named entity '95% CI' named entity 'activated partial thromboplastin time' named entity 'regression model' named entity 'COVID' named entity 'IgA' named entity 'COVID' named entity 'white blood cell counts' named entity 'IgG' named entity 'ICU' named entity 'physiology' named entity 'procalcitonin'

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