About: Background: Clinical diagnosis of COVID-19 poses an enormous challenge to early detection and prevention of COVID-19, which is of crucial importance for pandemic containment. Cases of COVID-19 may be hard to distinguish clinically from other acute viral diseases, resulting in an overwhelming load of laboratory screening. Sudden onset of taste and smell loss emerge as hallmark of COVID-19. The optimal ways for including these symptoms in the screening of suspected COVID-19 patients should now be established. Methods: We performed a case-control study on patients that were PCR-tested for COVID-19 (112 positive and 112 negative participants), recruited during the first wave (March 2020 - May 2020) of COVID-19 pandemic in Israel. Patients were interviewed by phone regarding their symptoms and medical history and were asked to rate their olfactory and gustatory ability before and during their illness on a 1-10 scale. Prevalence and degrees of symptoms were calculated, and odds ratios were estimated. Symptoms-based logistic-regression classifiers were constructed and evaluated on a hold-out set. Results: Changes in smell and taste occurred in 68% (95% CI 60%-76%) and 72% (64%-80%), of positive patients, with 24 (11-53 range) and 12 (6-23) respective odds ratios. The ability to smell was decreased by 0.5 {+/-} 1.5 in negatives, and by 4.5 {+/-} 3.6 in positives, and to taste by 0.4 {+/-} 1.5 and 4.9 {+/-} 3.8, respectively (mean {+/-} SD). A penalized logistic regression classifier based on 5 symptoms (degree of smell change, muscle ache, lack of appetite, fever, and a negatively contributing sore throat), has 66% sensitivity, 97% specificity and an area under the ROC curve of 0.83 (AUC) on a hold-out set. A classifier based on degree of smell change only is almost as good, with 66% sensitivity, 97% specificity and 0.81 AUC. Under the assumption of 8% positives among those tested, the predictive positive value (PPV) of this classifier is 0.68 and negative predictive value (NPV) is 0.97. Conclusions: Self-reported quantitative olfactory changes, either alone or combined with other symptoms, provide a specific and powerful tool for clinical diagnosis of COVID-19. The applicability of this tool for prioritizing COVID-19 laboratory testing is facilitated by a simple calculator presented here.   Goto Sponge  NotDistinct  Permalink

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  • Background: Clinical diagnosis of COVID-19 poses an enormous challenge to early detection and prevention of COVID-19, which is of crucial importance for pandemic containment. Cases of COVID-19 may be hard to distinguish clinically from other acute viral diseases, resulting in an overwhelming load of laboratory screening. Sudden onset of taste and smell loss emerge as hallmark of COVID-19. The optimal ways for including these symptoms in the screening of suspected COVID-19 patients should now be established. Methods: We performed a case-control study on patients that were PCR-tested for COVID-19 (112 positive and 112 negative participants), recruited during the first wave (March 2020 - May 2020) of COVID-19 pandemic in Israel. Patients were interviewed by phone regarding their symptoms and medical history and were asked to rate their olfactory and gustatory ability before and during their illness on a 1-10 scale. Prevalence and degrees of symptoms were calculated, and odds ratios were estimated. Symptoms-based logistic-regression classifiers were constructed and evaluated on a hold-out set. Results: Changes in smell and taste occurred in 68% (95% CI 60%-76%) and 72% (64%-80%), of positive patients, with 24 (11-53 range) and 12 (6-23) respective odds ratios. The ability to smell was decreased by 0.5 {+/-} 1.5 in negatives, and by 4.5 {+/-} 3.6 in positives, and to taste by 0.4 {+/-} 1.5 and 4.9 {+/-} 3.8, respectively (mean {+/-} SD). A penalized logistic regression classifier based on 5 symptoms (degree of smell change, muscle ache, lack of appetite, fever, and a negatively contributing sore throat), has 66% sensitivity, 97% specificity and an area under the ROC curve of 0.83 (AUC) on a hold-out set. A classifier based on degree of smell change only is almost as good, with 66% sensitivity, 97% specificity and 0.81 AUC. Under the assumption of 8% positives among those tested, the predictive positive value (PPV) of this classifier is 0.68 and negative predictive value (NPV) is 0.97. Conclusions: Self-reported quantitative olfactory changes, either alone or combined with other symptoms, provide a specific and powerful tool for clinical diagnosis of COVID-19. The applicability of this tool for prioritizing COVID-19 laboratory testing is facilitated by a simple calculator presented here.
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