Development and external validation of a diagnostic multivariable prediction model for a prompt identification of cases at high risk for SARS-COV-2 infection among patients admitted to the emergency department

BackgroundAn urgent need exists for an early detection of cases with a high-risk of SARS-CoV-2 infection, particularly in high-flow and -risk settings, such as emergency departments (EDs). The aim of this work is to develop and validate a predictive model for the evaluation of SARS-CoV-2 infection risk, with the rationale of using this tool to manage ED patients. MethodsA retrospective study was performed by cross-sectionally reviewing the electronical case records of patients admitted to Niguarda Hospital or referred to its ED in the period 15 March to 24 April 2020. Derivation sample was composed of non-random inpatients hospitalized on 24 April and admitted before 22 April 2020. Validation sample was composed of consecutive patients who visited the ED between 15 and 25 March 2020. The association between the dichotomic outcome and each predictor was explored by univariate analysis with logistic regression models. ResultsA total of 113 patients in the derivation sample and 419 in the validation sample were analyzed. History of fever, elder age and low oxygen saturation showed to be significant predictors of SARS-CoV-2 infection. The neutrophil count improves the discriminative ability of the model, even if its calibration and usefulness in terms of diagnosis is unclear. ConclusionThe discriminatory ability of the identified models makes the overall performance suboptimal; their implementation to calculate the individual risk of infection should not be used without additional investigations. However, they could be useful to evaluate the spatial allocation of patients while awaiting the result of the nasopharyngeal swab. Key Messages boxO_ST_ABSWhat is already known on this topicC_ST_ABS1 year after the onset of the coronavirus disease 2019 (COVID-19) pandemic, the trend of its spread has not shown a substantial global reduction. An urgent need exists for efficient early detection of cases with a high risk of SARS-CoV-2 infection and a number of diagnostic prediction models have been developed, but a few models were externally validated in high-flow and -risk settings, such as emergency departments (EDs). What this study addsThis study develops and validate predictive models for the evaluation of SARS-CoV-2 infection risk, with the rationale of using these tools to promptly manage patients who are afferent to the ED, allocating them accordingly to the risk of infection while awaiting swab result. History of fever, older age and low oxygen saturation showed to be significant predictors of the presence of SARS-CoV-2 infection. The use of laboratory findings, such as neutrophil count, showed to improve the discriminative ability of the model, even if its calibration and usefulness in terms of diagnosis is unclear.