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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.

PURPOSE: To evaluate the performance of a deep learning (DL) algorithm for the detection of COVID-19 on chest radiographs (CXR) MATERIALS AND METHODS: In this retrospective study, a DL model was trained on 112,120 CXR images with 14 labeled classifiers (ChestX-ray14) and fine-tuned using initial CXR on hospital admission of 509 patients, who had undergone COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR) The test set consisted of a CXR on presentation of 248 individuals suspected of COVID-19 pneumonia between February 16 and March 3, 2020 from 4 centers (72 RT-PCR positives and 176 RT-PCR negatives) The CXR were independently reviewed by 3 radiologists and using the DL algorithm Diagnostic performance was compared with radiologists' performance and was assessed by area under the receiver operating characteristics (AUC) RESULTS: The median age of the subjects in the test set was 61 (interquartile range: 39 to 79) years (51% male) The DL algorithm achieved an AUC of 0 81, sensitivity of 0 85, and specificity of 0 72 in detecting COVID-19 using RT-PCR as the reference standard On subgroup analyses, the model achieved an AUC of 0 79, sensitivity of 0 80, and specificity of 0 74 in detecting COVID-19 in patients presented with fever or respiratory systems and an AUC of 0 87, sensitivity of 0 85, and specificity of 0 81 in distinguishing COVID-19 from other forms of pneumonia The algorithm significantly outperforms human readers (P