Development and validation of risk prediction models for COVID-19 positivity in a hospital setting
| Autor: | On Hang Ching, Tom Wai-Hin Chung, Mary S.M. Ip, Jonan Chun Yin Lee, Siu Ting Leung, Johnny Wai Man Chan, Ming-Yen Ng, Varut Vardhanbhuti, Michael D. Kuo, Christine Shing Yen Lo, Keith Wan-Hang Chiu, Thomas Wing Yan Chin, Kelvin K. W. To, Alan Ka Lun Wu, Kwok Cheung Lung, Pek-Lan Khong, Ambrose Ho Tung Fong, Jeffrey Long Fung Chiu, Edward Hung Tat Chan, Ho Yuen Frank Wong, Chak Sing Lau, Hiu Yin Sonia Lam, Ivan Hung, Sau Yung Fung, Tina Poy Wing Lam, Macy Mei Sze Lui, Eric Yuk Fai Wan, Raymond W. Liu |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Male Chest x-ray GGO Ground glass opacity Hospital setting White cell count Logistic regression Risk prediction models Nomogram 0302 clinical medicine CXR Chest x-rays Statistics 80 and over PPV Positive predictive value Medicine 030212 general & internal medicine Aged 80 and over screening and diagnosis PEff Pleural effusion WCC Total white blood cell count General Medicine Middle Aged Hospitals Detection Infectious Diseases Medical Microbiology COVID-19 Coronavirus Disease 2019 Area Under Curve Public Health and Health Services CT Computed tomography Female 4.2 Evaluation of markers and technologies Microbiology (medical) Adult Coronavirus disease 2019 (COVID-19) 030106 microbiology Total white blood cell count Microbiology Article lcsh:Infectious and parasitic diseases 03 medical and health sciences Prediction model NPV Negative predictive value Humans Model development lcsh:RC109-216 General hospital Aged Probability SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 business.industry SARS-CoV-2 COVID-19 RT-PCR Reverse transcription polymerase chain reaction H-L Hosmer-Lemeshow test AUC Area under the curve Nomograms Good Health and Well Being Logistic Models business OR Odds ratio |
| Zdroj: | International Journal of Infectious Diseases, Vol 101, Iss, Pp 74-82 (2020) International Journal of Infectious Diseases |
| ISSN: | 1201-9712 |
| Popis: | Highlights • Developed two simple-to use nomograms for identifying COVID-19 positive patients. • Probabilities are provided to allow healthcare leaders to decide suitable cut-offs. • Variables are age, white cell count, chest x-ray appearances and contact history. • Model variables are easily available in the general hospital setting. Objectives 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 utilisation. |
| Databáze: | OpenAIRE |
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