Unknown

Dataset Information

0

Development and validation of risk prediction models for COVID-19 positivity in a hospital setting.


ABSTRACT:

Objectives

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.

SUBMITTER: Ng MY 

PROVIDER: S-EPMC7491462 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

altmetric image

Publications

Development and validation of risk prediction models for COVID-19 positivity in a hospital setting.

Ng Ming-Yen MY   Wan Eric Yuk Fai EYF   Wong Ho Yuen Frank HYF   Leung Siu Ting ST   Lee Jonan Chun Yin JCY   Chin Thomas Wing-Yan TW   Lo Christine Shing Yen CSY   Lui Macy Mei-Sze MM   Chan Edward Hung Tat EHT   Fong Ambrose Ho-Tung AH   Fung Sau Yung SY   Ching On Hang OH   Chiu Keith Wan-Hang KW   Chung Tom Wai Hin TWH   Vardhanbhuti Varut V   Lam Hiu Yin Sonia HYS   To Kelvin Kai Wang KKW   Chiu Jeffrey Long Fung JLF   Lam Tina Poy Wing TPW   Khong Pek Lan PL   Liu Raymond Wai To RWT   Chan Johnny Wai Man JWM   Wu Alan Ka Lun AKL   Lung Kwok-Cheung KC   Hung Ivan Fan Ngai IFN   Lau Chak Sing CS   Kuo Michael D MD   Ip Mary Sau-Man MS  

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases 20200915


<h4>Objectives</h4>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.<h4>Methods</h4>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  ...[more]

Similar Datasets

| S-EPMC8153031 | biostudies-literature
| S-EPMC7769558 | biostudies-literature
| S-EPMC8141270 | biostudies-literature
| S-EPMC7836127 | biostudies-literature
| S-EPMC9129120 | biostudies-literature
| S-EPMC8593277 | biostudies-literature
| S-EPMC7249246 | biostudies-literature
| S-EPMC10254410 | biostudies-literature
| S-EPMC7989331 | biostudies-literature
| S-EPMC10069944 | biostudies-literature