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dc.creatorNg, Ming-Yen
dc.creatorWan, Eric Yuk Fai
dc.creatorWong, Ho Yuen Frank
dc.creatorLeung, Siu Ting
dc.creatorLee, Jonan Chun Yin
dc.creatorChin, Thomas Wing-Yan
dc.creatorLo, Christine Shing Yen
dc.creatorLui, Macy Mei-Sze
dc.creatorChan, Edward Hung Tat
dc.creatorFong, Ambrose Ho-Tung
dc.creatorYung, Fung Sau
dc.creatorChing, On Hang
dc.creatorChiu, Keith Wan-Hang
dc.creatorChung, Tom Wai Hin
dc.creatorVardhanbhuti, Varut
dc.creatorLam, Hiu Yin Sonia
dc.creatorTo, Kelvin Kai Wang
dc.creatorChiu, Jeffrey Long Fung
dc.creatorLam, Tina Poy Wing
dc.creatorKhong, Pek Lan
dc.creatorLiu, Raymond Wai To
dc.creatorChan, Johnny Wai Man
dc.creatorWu, Ka Lun Alan
dc.creatorLung, Kwok-Cheung
dc.creatorHung, Ivan Fan Ngai
dc.creatorLau, Chak Sing
dc.creatorKuo, Michael D.
dc.creatorIp, Mary Sau-Man
dc.description.abstractObjectives: To develop: (1) two validated risk prediction models for 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. Database was randomly split 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, 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Results: 1330 patients (mean age 58.2±24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. 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]). Second model developed has same variables except contact history (AUC=0.880 [CI=0.844- 0.916]). Both were externally validated on 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
dc.format.extent28 páginasspa
dc.publisherInternational Journal of Infectious Diseasesspa
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectPrediction Modelspa
dc.subjectWhite cell countspa
dc.subjectChest x-rayspa
dc.titleDevelopment and validation of risk prediction models for COVID-19 positivity in a hospital settingspa
dc.subject.lembSíndrome respiratorio agudo gravespa
dc.rights.localAbierto (Texto Completo)spa

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