Diagnostic accuracy of symptoms as a diagnostic tool for SARS-CoV 2 infection: a cross-sectional study in a cohort of 2,173 patients

Abstract

Background The SARS-CoV-2 pandemic continues to be a priority health problem; According to the World Health Organization data from October 13, 2020, 37,704,153 confirmed COVID-19 cases have been reported, including 1,079,029 deaths, since the outbreak. The identification of potential symptoms has been reported to be a useful tool for clinical decision-making in emergency departments to avoid overload and improve the quality of care. The aim of this study was to evaluate the performances of symptoms as a diagnostic tool for SARS -CoV-2 infection. Methods An observational, cross-sectional, prospective and analytical study was carried out, during the period of time from April 14 to July 21, 2020. Data (demographic variables, medical history, respiratory and non-respiratory symptoms) were collected by emergency physicians. The diagnosis of COVID-19 was made using SARS-CoV-2 RT-PCR. The diagnostic accuracy of these characteristics for COVID-19 was evaluated by calculating the positive and negative likelihood ratios. A Mantel-Haenszel and multivariate logistic regression analysis was performed to assess the association of symptoms with COVID-19. Results A prevalence of 53.72% of SARS-CoV-2 infection was observed. The symptom with the highest sensitivity was cough 71%, and a specificity of 52.68%. The symptomatological scale, constructed from 6 symptoms, obtained a sensitivity of 83.45% and a specificity of 32.86%, taking ≥2 symptoms as a cut-off point. The symptoms with the greatest association with SARS-CoV-2 were: anosmia odds ratio (OR) 3.2 (95% CI; 2.52–4.17), fever OR 2.98 (95% CI; 2.47–3.58), dyspnea OR 2.9 (95% CI; 2.39–3.51]) and cough OR 2.73 (95% CI: 2.27–3.28). Conclusion The combination of ≥2 symptoms / signs (fever, cough, anosmia, dyspnea and oxygen saturation < 93%, and headache) results in a highly sensitivity model for a quick and accurate diagnosis of COVID-19, and should be used in the absence of ancillary diagnostic studies. Symptomatology, alone and in combination, may be an appropriate strategy to use in the emergency department to guide the behaviors to respond to the disease. Trial registration Institutional registration R-2020-3601-145, Federal Commission for the Protection against Sanitary Risks 17 CI-09-015-034, National Bioethics Commission: 09 CEI-023-2017082.

Differentiating COVID-19 and dengue from other febrile illnesses in co-epidemics: Development and internal validation of COVIDENGUE scores

“…Conversely, at higher risk thresholds (>0.5), when it came to predict an event, the dengue score displayed a better sensitivity (in other words, higher TP rate, or a lower cost of FN versus FP cost). Taken together with respect to the SARS-CoV-2 strategy of testing, isolating, and tracing, our findings encourage evaluating the addition of clinical or biological discriminative variables [ 41 , 42 ] in the COVID-19 score to improve its sensitivity across the risk thresholds while in regard to the dengue strategy of testing, isolating, and targeted vector control, they encourage the fitting with more specific epidemiological variables highly predictive of an infective bite by an Aedes mosquito.…”

“…The COVIDENGUE scores were developed using MLR [ 9 ] which is the gold standard method for assessing non-ordered polytomous categorical outcomes [ 27 , 28 ]. Except for active smoking whose protective effect against SARS-CoV-2 infection remains a matter of debate among researchers [ 29 , 30 ], all predictors retained to build the scores had been previously identified as relevant indicators of COVID-19 or dengue or associated with OFIs (especially respiratory infections) [ [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] ] This ensures both the validity of content of this scoring system and the possibility of contrasted predictions. For example, international travel had been identified as a source of COVID-19 during the first pandemic wave [ [36] , [37] , [38] ].…”

Differentiating COVID-19 and dengue from other febrile illnesses in co-epidemics: Development and internal validation of COVIDENGUE scores

“…Conversely, at higher risk thresholds (>0.5), when it came to predict an event, the dengue score displayed a better sensitivity (in other words, higher TP rate, or a lower cost of FN versus FP cost). Taken together with respect to the SARS-CoV-2 strategy of testing, isolating, and tracing, our findings encourage evaluating the addition of clinical or biological discriminative variables [38,39] in the COVID-19 score to improve its sensitivity across the risk thresholds while in regard to the dengue strategy of testing, isolating, and targeted vector control, they encourage the fitting with more specific epidemiological variables highly predictive of an infective bite by an Aedes mosquito.…”

“…For example, international travel had been identified as a source of COVID-19 during the first pandemic wave [34,35]. In a recent Colombian study [38], dengue proved more symptomatic than COVID-19 and dengue patients came to the hospital in greater numbers than COVID-19 patients. In Brazil's Amazonian basin, prior to a dengue infection, as diagnosed by positive IgG antibodies was associated with twice the risk of clinically apparent COVID-19 [36].…”

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

“…Assaf et al [46]; Chou et al [55] Neural network, random forest, classification and regression decision tree (CRT) Van Singer et al [96]; Möckel et al [79] Logistic regression and CRT Diep et al [56] Logistic regression, Mann-Whitey, chi-cuadrado Saegerman et al [88] Binary logistic regression and bootstrapped quantile regression, classification and regression tree analysis. Romero-Gameros et al [87] Logistic regression, Mantel-Haenszel Bolourani et al [50] Artificial intelligence, logistic regression, XGBoost combines a recursive gradient-boosting method called Newton boosting, with a decision-tree model, decision making Goodacre et al [65]; Feng et al [46] Multivariable regression with least absolute shrinkage and selection operator (LASSO) Gavelli et al [64] logistic regression and cox regression…”