Is a COVID-19 prediction model based on symptom tracking through an app applicable in primary care?

Haller, Dagmar M.; Sebo, Paul; Tudrej, B.V.; Maisonneuve, Hubert

doi:10.1093/fampra/cmaa069

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…A recent systematic review concluded that most prediction models for COVID-19 are poorly reported, at high risk of bias, and probably report an over-optimistic performance [ 36 , 37 , 38 ]. Therefore, there are many limitations when transferring the prediction models into the clinical practice, which cast doubt on their true applicability, especially for different outbreak scenarios and/or populations.…”

Section: Discussionmentioning

confidence: 99%

Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach

Callejón-Leblic

Moreno‐Luna

Cuvillo

et al. 2021

JCM

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The COVID-19 outbreak has spread extensively around the world. Loss of smell and taste have emerged as main predictors for COVID-19. The objective of our study is to develop a comprehensive machine learning (ML) modelling framework to assess the predictive value of smell and taste disorders, along with other symptoms, in COVID-19 infection. A multicenter case-control study was performed, in which suspected cases for COVID-19, who were tested by real-time reverse-transcription polymerase chain reaction (RT-PCR), informed about the presence and severity of their symptoms using visual analog scales (VAS). ML algorithms were applied to the collected data to predict a COVID-19 diagnosis using a 50-fold cross-validation scheme by randomly splitting the patients in training (75%) and testing datasets (25%). A total of 777 patients were included. Loss of smell and taste were found to be the symptoms with higher odds ratios of 6.21 and 2.42 for COVID-19 positivity. The ML algorithms applied reached an average accuracy of 80%, a sensitivity of 82%, and a specificity of 78% when using VAS to predict a COVID-19 diagnosis. This study concludes that smell and taste disorders are accurate predictors, with ML algorithms constituting helpful tools for COVID-19 diagnostic prediction.

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Section: Discussionmentioning

confidence: 99%

Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach

Callejón-Leblic

Moreno‐Luna

Cuvillo

et al. 2021

JCM

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“…Performance of predictive scores depends Open access on the population they are applied to and scores developed in inpatient care may be less suitable for primary care. 21 Establishing aetiology is further complicated by the long turnaround times for SARS-CoV-2 tests. 20 There is limited data available on presenting features and the course of disease in children managed as outpatients.…”

Section: Discussionmentioning

confidence: 99%

Paediatric primary care in Germany during the early COVID-19 pandemic: the calm before the storm

et al. 2021

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ObjectivesGlobally, the COVID-19 pandemic has a major impact on healthcare provision. The effects in primary care are understudied. This study aimed to explore changes in consultation numbers and patient management during the COVID-19 pandemic, and to identify challenges for patient care.DesignSurvey of paediatric primary care practices on consultation numbers and patient management changes, and semistructured interviews to identify challenges for patient care. Surveys and interviews were partially linked in an explanatory sequential design to identify patient groups perceived to be at higher risk for worse care during the pandemic.SettingIn and around Düsseldorf, a densely populated area in Western Germany. The primary care facilities are spread over an area with approximately 2 million inhabitants.ParticipantsPrimary care in Germany is provided through practices run by self-employed specialist physicians that are contracted to offer services to patients under public health insurance which is compulsory to the majority of the population. The sample contained 44 paediatric primary care practices in the area, the response rate was 50%.ResultsNumbers of consultations for scheduled developmental examinations remained unchanged compared with the previous year while emergency visits were strongly reduced (mean 87.3 less/week in March–May 2020 compared with 2019, median reduction 55.0%). Children dependent on developmental therapy and with chronic health conditions were identified as patient groups receiving deteriorated care. High patient numbers, including of mildly symptomatic children presenting for health certificates, in combination with increased organisational demands and expected staff outages are priority concerns for the winter.ConclusionsPrimary care paediatricians offered stable service through the early pandemic but expected strained resources for the upcoming winter. Unambiguous guidance on which children should present to primary care and who should be tested would help to allocate resources appropriately, and this guidance needs to consider age group specific issues including high prevalence of respiratory symptoms, dependency on carers and high contact rates.

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“…Many researchers were interested in describing the clinical presentations of COVID-19 patients. Although the majority of studies involved hospital or outpatient clinic populations, some studies were also carried out in the general population or in primary care 7 – 9 , 18 – 20 , 26 . The results obtained are relatively variable.…”

Section: Discussionmentioning

confidence: 99%

“…Yet, most of these studies focused either on the general population 7 – 9 or on inpatients 10 – 13 or patients presenting to the hospital or outpatient clinic 14 – 17 . To our knowledge, only a few studies have so far been conducted in primary care, including findings from our group, focusing on smell and taste disorders 18 – 20 . Several symptoms appear specific to the infection, some of them being relatively frequent (e.g.…”

Section: Introductionmentioning

confidence: 99%

Cross sectional study of the clinical characteristics of French primary care patients with COVID-19

Sebo

Tudrej

Lourdaux³

et al. 2021

Sci Rep

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The early identification of patients suffering from SARS-CoV-2 infection in primary care is of outmost importance in the current pandemic. The objective of this study was to describe the clinical characteristics of primary care patients who tested positive for SARS-CoV-2. We conducted a cross-sectional study between March 24 and May 7, 2020, involving consecutive patients undergoing RT-PCR testing in two community-based laboratories in Lyon (France) for a suspicion of COVID-19. We examined the association between symptoms and a positive test using univariable and multivariable logistic regression, adjusted for clustering within laboratories, and calculated the diagnostic performance of these symptoms. Of the 1561 patients tested, 1543 patients (99%) agreed to participate. Among them, 253 were positive for SARS-CoV-2 (16%). The three most frequently reported ‘ear-nose-throat’ and non-‘ear-nose-throat’ symptoms in patients who tested positive were dry throat (42%), loss of smell (36%) and loss of taste (31%), respectively fever (58%), cough (52%) and headache (45%). In multivariable analyses, loss of taste (OR 3.8 [95% CI 3.3–4.4], p-value < 0.001), loss of smell (OR 3.0 [95% CI 1.9–4.8], p < 0.001), muscle pain (OR 1.6 [95% CI 1.2–2.0], p = 0.001) and dry nose (OR 1.3 [95% CI 1.1–1.6], p = 0.01) were significantly associated with a positive result. In contrast, sore throat (OR 0.6 [95% CI 0.4–0.8], p = 0.003), stuffy nose (OR 0.6 [95% CI 0.6–0.7], p < 0.001), diarrhea (OR 0.6 [95% CI 0.5–0.6], p < 0.001) and dyspnea (OR 0.5 [95% CI 0.3–0.7], p < 0.001) were inversely associated with a positive test. The combination of loss of taste or smell had the highest diagnostic performance (OR 6.7 [95% CI 5.9–7.5], sensitivity 44.7% [95% CI 38.4–51.0], specificity 90.8% [95% CI 89.1–92.3]). No other combination of symptoms had a higher performance. Our data could contribute to the triage and early identification of new clusters of cases.

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Is a COVID-19 prediction model based on symptom tracking through an app applicable in primary care?

Cited by 7 publications

References 6 publications

Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach

Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach

Paediatric primary care in Germany during the early COVID-19 pandemic: the calm before the storm

Cross sectional study of the clinical characteristics of French primary care patients with COVID-19

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