Abstract:Objectives: We investigated the prevalence of anosmia and ageusia in adult patients with a laboratoryconfirmed diagnosis of infection with severe acute respiratory distress syndrome coronavirus-2 (SARS-CoV-2). Methods: This was a retrospective observational analysis of patients infected with SARS-CoV-2 admitted to hospital or managed in the community and their household contacts across a London population during the period March 1st to April 1st, 2020. Symptomatology and duration were extracted from routinely … Show more
“…A total of 11074 COVID-19 patients (mean age 46.7±10.4 years and males 46.9%) were included in the final analysis ( table 1 ). 2 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 The overall prevalence of “loss of smell” in COVID-19 patients was 52.0% (CI: 42.5%-61.6%, I 2 =99.4%) ( Figure 2 ). A total of 21 studies compared these symptoms in COVID-19 patients (n=2196) and controls (n=3425).…”
Section: Resultsmentioning
confidence: 99%
“…Among COVID-19 patients, the odds of patients with severe disease and “loss of smell” were significantly low when compared to patients with severe disease and without “loss of smell” (OR: 0.36, CI 0.27–0.48, p <0.01, I 2 =27.4% ( Figure 4 ). 2 , 21 , 32 , 37 , 52 , 54 , 57 Figure 2 Forest plot demonstrating overall prevalence of “loss of smell” in COVID-19 patients. Figure 3 Forest plot comparing prevalence in COVID-19 vs control group for “loss of smell”.…”
Background
The presence of olfactory dysfunction or “loss of smell” has been reported as an atypical symptom in patients with coronavirus disease 2019 (COVID-19). We performed a systematic review and meta-analysis of the available literature to evaluate the prevalence of “loss of smell” in COVID-19 as well as its utility for prognosticating the disease severity.
Methods
An exhaustive search of the PubMed/Medline, Embase, Web of Science, Cochrane Library, LitCovid NIH, and WHO COVID-19 database was conducted through August 6
th
, 2020. All studies reporting the prevalence of “loss of smell” (anosmia and/or hyposmia/microsmia) in laboratory-confirmed COVID-19 patients were included. Pooled prevalence for cases (positive COVID-19 through reverse transcriptase (RT-PCR) and/or serology IgG/IgM) and controls (negative RT-PCR and/or serology) was compared, and the odds ratio (OR), 95% confidence interval (CI) and the p-value were calculated. A p-value of <0.05 was considered statistically significant.
Results
A total of 51 studies with 11074 confirmed COVID-19 patients were included. Of these, 21 studies used a control group with 3425 patients. The symptom of “loss of smell” (OR: 14.7, CI: 8.9–24.3) was significantly higher in the COVID-19 group when compared to the control group. Seven studies comparing severe COVID-19 patients with- and without “loss of smell” demonstrated favorable prognosis for patients with “loss of smell” (OR: 0.36, CI 0.27–0.48).
Conclusion
Olfactory dysfunction or “loss of smell” is a prevalent symptom in COVID-19 patients. Moreover, COVID-19 patients with “loss of smell” appear to have a milder course of the disease.
“…A total of 11074 COVID-19 patients (mean age 46.7±10.4 years and males 46.9%) were included in the final analysis ( table 1 ). 2 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 The overall prevalence of “loss of smell” in COVID-19 patients was 52.0% (CI: 42.5%-61.6%, I 2 =99.4%) ( Figure 2 ). A total of 21 studies compared these symptoms in COVID-19 patients (n=2196) and controls (n=3425).…”
Section: Resultsmentioning
confidence: 99%
“…Among COVID-19 patients, the odds of patients with severe disease and “loss of smell” were significantly low when compared to patients with severe disease and without “loss of smell” (OR: 0.36, CI 0.27–0.48, p <0.01, I 2 =27.4% ( Figure 4 ). 2 , 21 , 32 , 37 , 52 , 54 , 57 Figure 2 Forest plot demonstrating overall prevalence of “loss of smell” in COVID-19 patients. Figure 3 Forest plot comparing prevalence in COVID-19 vs control group for “loss of smell”.…”
Background
The presence of olfactory dysfunction or “loss of smell” has been reported as an atypical symptom in patients with coronavirus disease 2019 (COVID-19). We performed a systematic review and meta-analysis of the available literature to evaluate the prevalence of “loss of smell” in COVID-19 as well as its utility for prognosticating the disease severity.
Methods
An exhaustive search of the PubMed/Medline, Embase, Web of Science, Cochrane Library, LitCovid NIH, and WHO COVID-19 database was conducted through August 6
th
, 2020. All studies reporting the prevalence of “loss of smell” (anosmia and/or hyposmia/microsmia) in laboratory-confirmed COVID-19 patients were included. Pooled prevalence for cases (positive COVID-19 through reverse transcriptase (RT-PCR) and/or serology IgG/IgM) and controls (negative RT-PCR and/or serology) was compared, and the odds ratio (OR), 95% confidence interval (CI) and the p-value were calculated. A p-value of <0.05 was considered statistically significant.
Results
A total of 51 studies with 11074 confirmed COVID-19 patients were included. Of these, 21 studies used a control group with 3425 patients. The symptom of “loss of smell” (OR: 14.7, CI: 8.9–24.3) was significantly higher in the COVID-19 group when compared to the control group. Seven studies comparing severe COVID-19 patients with- and without “loss of smell” demonstrated favorable prognosis for patients with “loss of smell” (OR: 0.36, CI 0.27–0.48).
Conclusion
Olfactory dysfunction or “loss of smell” is a prevalent symptom in COVID-19 patients. Moreover, COVID-19 patients with “loss of smell” appear to have a milder course of the disease.
“…Predictors were chosen in concordance with previously published literature 9,13,14 , and included demographic details (age and sex), comorbidities (chronic respiratory disease, obesity, hypertension, diabetes, ischaemic heart disease, cardiac failure, chronic liver disease, chronic kidney disease, and history of a cerebrovascular event), symptomatology (fever, cough, dyspnoea, myalgia, abdominal pain, diarrhoea and vomiting, confusion, collapse, and olfactory change), and the number of days of symptoms prior to admission. Length of hospital stay to discharge, or death, was recorded for all patients to allow for survival analysis in the Cox regression model.…”
Background Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2.MethodBetween March 1 - April 24, 2020, 398 patients were identified with laboratory confirmed SARS-CoV-2 in a London teaching hospital. Data from electronic health records were extracted and used to create two predictive models using: 1) a Cox regression model and 2) an artificial neural network (ANN). Model performance profiles were assessed by validation, discrimination, and calibration.Results Both the Cox regression and ANN models achieved high accuracy (83.8%, 95% confidence interval (CI): 73.8 - 91.1 and 90.0%, 95% CI: 81.2 - 95.6, respectively). The area under the receiver operator curve (AUROC) for the ANN (92.6%, 95% CI: 91.1 - 94.1) was significantly greater than that of the Cox regression model (86.9%, 95% CI: 85.7 - 88.2), p=0.0136. Both models achieved acceptable calibration with Brier scores of 0.13 and 0.11 for the Cox model and ANN, respectively. ConclusionWe demonstrate an ANN which is non-inferior to a Cox regression model but with potential for further development such that it can learn as new data becomes available. Deep learning techniques are particularly suited to complex datasets with non-linear solutions, which make them appropriate for use in conditions with a paucity of prior knowledge. Accurate prognostic models for SARS-CoV-2 can provide benefits at the patient, departmental and organisational level.
“…Predictors were chosen in concordance with previously published literature 10,14,15 , and included demographic details (age and sex), comorbidities (chronic respiratory disease, obesity, hypertension, diabetes, ischaemic heart disease, cardiac failure, chronic liver disease, chronic kidney disease, and history of a cerebrovascular event), symptomatology (fever, cough, dyspnoea, myalgia, abdominal pain, diarrhoea and vomiting, confusion, collapse, and olfactory change), and the number of days of symptoms prior to admission. Length of hospital stay to discharge, or death, was recorded for all patients to allow for survival analysis in the Cox regression model.…”
Background Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2.MethodBetween March 1 - April 24, 2020, 398 patients were identified with laboratory confirmed SARS-CoV-2 in a London teaching hospital. Data from electronic health records were extracted and used to create two predictive models using: 1) a Cox regression model and 2) an artificial neural network (ANN). Model performance profiles were assessed by validation, discrimination, and calibration.Results Both the Cox regression and ANN models achieved high accuracy (83.8%, 95% confidence interval (CI): 73.8 - 91.1 and 90.0%, 95% CI: 81.2 - 95.6, respectively). The area under the receiver operator curve (AUROC) for the ANN (92.6%, 95% CI: 91.1 - 94.1) was significantly greater than that of the Cox regression model (86.9%, 95% CI: 85.7 - 88.2), p=0.0136. Both models achieved acceptable calibration with Brier scores of 0.13 and 0.11 for the Cox model and ANN, respectively. ConclusionWe demonstrate an ANN which is non-inferior to a Cox regression model but with potential for further development such that it can learn as new data becomes available. Deep learning techniques are particularly suited to complex datasets with non-linear solutions, which make them appropriate for use in conditions with a paucity of prior knowledge. Accurate prognostic models for SARS-CoV-2 can provide benefits at the patient, departmental and organisational level.
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