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Article: Development and Validation of Risk Prediction Models for COVID-19 Positivity in a Hospital Setting

TitleDevelopment and Validation of Risk Prediction Models for COVID-19 Positivity in a Hospital Setting
Authors
KeywordsCOVID-19
Prediction model
Nomogram
White cell count
Chest x-ray
Issue Date2020
PublisherElsevier - Open Access. The Journal's web site is located at http://www.elsevier.com/locate/ijid
Citation
International Journal of Infectious Diseases, 2020, v. 101, p. 74-82 How to Cite?
AbstractObjectives: To develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation. Methods: Patients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer–Lemeshow (H–L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4 and 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Results: A total of 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. The first prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880−0.941]). The second model developed has the same variables except contact history (AUC = 0.880 [CI = 0.844−0.916]). Both were externally validated on the H–L test (p = 0.781 and 0.155, respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV. Conclusion: Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation.
Persistent Identifierhttp://hdl.handle.net/10722/287273
ISSN
2019 Impact Factor: 3.202
2015 SCImago Journal Rankings: 1.148
PubMed Central ID

 

DC FieldValueLanguage
dc.contributor.authorNg, MY-
dc.contributor.authorWan, EYF-
dc.contributor.authorWong, HYF-
dc.contributor.authorLeung, ST-
dc.contributor.authorLee, JCY-
dc.contributor.authorChin, TWY-
dc.contributor.authorLo, CSY-
dc.contributor.authorLui, MMS-
dc.contributor.authorChan, EHT-
dc.contributor.authorFong, AHT-
dc.contributor.authorFung, SY-
dc.contributor.authorChing, OH-
dc.contributor.authorChiu, KWH-
dc.contributor.authorChung, TWH-
dc.contributor.authorVardhanabhuti, V-
dc.contributor.authorLam, HYS-
dc.contributor.authorTo, KKW-
dc.contributor.authorChiu, JLF-
dc.contributor.authorLam, TPW-
dc.contributor.authorKhong, PL-
dc.contributor.authorLiu, RWT-
dc.contributor.authorChan, JWM-
dc.contributor.authorWu, KLA-
dc.contributor.authorLung, KC-
dc.contributor.authorHung, IFN-
dc.contributor.authorLau, CS-
dc.contributor.authorKuo, MD-
dc.contributor.authorIp, MSM-
dc.date.accessioned2020-09-22T02:58:28Z-
dc.date.available2020-09-22T02:58:28Z-
dc.date.issued2020-
dc.identifier.citationInternational Journal of Infectious Diseases, 2020, v. 101, p. 74-82-
dc.identifier.issn1201-9712-
dc.identifier.urihttp://hdl.handle.net/10722/287273-
dc.description.abstractObjectives: To develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation. Methods: Patients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer–Lemeshow (H–L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4 and 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Results: A total of 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. The first prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880−0.941]). The second model developed has the same variables except contact history (AUC = 0.880 [CI = 0.844−0.916]). Both were externally validated on the H–L test (p = 0.781 and 0.155, respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV. Conclusion: Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation.-
dc.languageeng-
dc.publisherElsevier - Open Access. The Journal's web site is located at http://www.elsevier.com/locate/ijid-
dc.relation.ispartofInternational Journal of Infectious Diseases-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectCOVID-19-
dc.subjectPrediction model-
dc.subjectNomogram-
dc.subjectWhite cell count-
dc.subjectChest x-ray-
dc.titleDevelopment and Validation of Risk Prediction Models for COVID-19 Positivity in a Hospital Setting-
dc.typeArticle-
dc.identifier.emailNg, MY: myng2@hku.hk-
dc.identifier.emailWan, EYF: yfwan@hku.hk-
dc.identifier.emailFong, AHT: ahtfong@hku.hk-
dc.identifier.emailChiu, KWH: kwhchiu@hku.hk-
dc.identifier.emailVardhanabhuti, V: varv@hku.hk-
dc.identifier.emailTo, KKW: kelvinto@hku.hk-
dc.identifier.emailKhong, PL: plkhong@hku.hk-
dc.identifier.emailHung, IFN: ivanhung@hkucc.hku.hk-
dc.identifier.emailLau, CS: cslau@hku.hk-
dc.identifier.emailIp, MSM: msmip@hku.hk-
dc.identifier.authorityNg, MY=rp01976-
dc.identifier.authorityWan, EYF=rp02518-
dc.identifier.authorityChiu, KWH=rp02074-
dc.identifier.authorityVardhanabhuti, V=rp01900-
dc.identifier.authorityTo, KKW=rp01384-
dc.identifier.authorityKhong, PL=rp00467-
dc.identifier.authorityHung, IFN=rp00508-
dc.identifier.authorityLau, CS=rp01348-
dc.identifier.authorityIp, MSM=rp00347-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1016/j.ijid.2020.09.022-
dc.identifier.pmid32947055-
dc.identifier.pmcidPMC7491462-
dc.identifier.scopuseid_2-s2.0-85093667923-
dc.identifier.hkuros314561-
dc.identifier.volume101-
dc.identifier.spage74-
dc.identifier.epage82-
dc.publisher.placeUnited Kingdom-

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