CoviCare

Nehme, Mayssam; Perone, Sigiriya Aebischer; Guessous, Idris; Lab, Bruno; Grira, Marwne; Beran, David; Innaurato, Giovanni; Carnino, Luisa; Sandoval, Jos Luis; Vogel, Thomas; Socquet, Jennifer; Chappuis, Franois; Alcoba, Gabriel; Braillard, Olivia

doi:10.4414/fms.2020.08550

Cited by 7 publications

(10 citation statements)

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“…In conclusion, we have documented that simple, reasoned treatments for the early-phase symptoms 4 of COVID-19 at home, collected in a recommendation algorithm for family doctors, are beneficial 4 in clinical practice, since they may avoid or limit deterioration of the disease to the point of 4 hospitalization, in addition to having public health implications. Our findings also have important 4 implications for patient quality of life, since adopting the treatment recommendation approach 4 reduced the rate and shortened the duration of symptoms, such as loss of taste or smell, and fatigue, 4 which might otherwise persist for several months (55). Future randomized studies will be required 4 for the consolidation of these observational findings on the potential benefit of the proposed 4 treatment recommendation algorithm.…”

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confidence: 84%

A HOME-TREATMENT ALGORITHM BASED ON ANTI-INFLAMMATORY DRUGS TO PREVENT HOSPITALIZATION OF PATIENTS WITH EARLY COVID-19: A MATCHED-COHORT STUDY (COVER 2)

Consolaro

Suter

Rubis

et al. 2021

Preprint

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Background and Aim: While considerable success has been achieved in the management of patients hospitalized with severe coronavirus disease 2019 (COVID-19), far less progress has been made with early outpatient treatment. We assessed whether the implementation of a home treatment algorithm, designed based upon on a pathophysiologic and pharmacologic rationale, during the initial, mild phase of COVID-19, could effectively reduce hospital admissions. Methods: This fully academic, matched-cohort study evaluated outcomes in 108 consecutive consenting patients with mild COVID-19 managed at home by their family doctors from January 2021 to May 2021, according to the proposed treatment algorithm and in 108 age-, sex-, and comorbidities-matched patients who were given other therapeutic schedules (ClinicalTrials.gov: NCT04854824). The primary outcome was COVID-19-related hospitalization. Analyses were by intention-to-treat. Results: One (0.9%) patient in the recommended cohort and 12 (11.1%) in the control cohort were admitted to hospital (P=0.0136). The proposed algorithm reduced, by 85%, the cumulative length of hospital stays (from 141 to 19 days) and related costs (from Euro 60.316 to Euro 9.058). Only 9.8 patients needed to be treated with the recommended algorithm to prevent one hospitalization event. The rate of resolution of major symptoms was numerically, but not significantly, higher in the recommended compared to the control cohort (97.2% versus 93.5%, respectively; P=0.322). Other symptoms lingered in a lower proportion of patients in the recommended than in the control cohort (20.4% versus 63.9%, respectively; P<0.001), and for a shorter period. Conclusion: The adoption of the proposed outpatient treatment algorithm during the early, mild phase of COVID-19 reduced the incidence of subsequent hospitalization and related costs.

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confidence: 84%

A HOME-TREATMENT ALGORITHM BASED ON ANTI-INFLAMMATORY DRUGS TO PREVENT HOSPITALIZATION OF PATIENTS WITH EARLY COVID-19: A MATCHED-COHORT STUDY (COVER 2)

Consolaro

Suter

Rubis

et al. 2021

Preprint

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“…Most participants enrolled in this trial were symptomatic at baseline, and the distribution of symptoms mirrored other settings. 12,13 Overall, participants in this study were young and had mild disease courses. Enrollment was not restricted based on symptoms, disease severity, or risk of progression to severe disease to evaluate if azithromycin was efficacious very early in COVID-19 before it had progressed.…”

Section: Discussionmentioning

confidence: 99%

Effect of Oral Azithromycin vs Placebo on COVID-19 Symptoms in Outpatients With SARS-CoV-2 Infection

Oldenburg

Pinsky

Brogdon

et al. 2021

JAMA

112

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IMPORTANCE Azithromycin has been hypothesized to have activity against SARS-CoV-2.OBJECTIVE To determine whether oral azithromycin in outpatients with SARS-CoV-2 infection leads to absence of self-reported COVID-19 symptoms at day 14. DESIGN, SETTING, AND PARTICIPANTS Randomized clinical trial of azithromycin vs matching placebo conducted from May 2020 through March 2021. Outpatients from the US were enrolled remotely via internet-based surveys and followed up for 21 days. Eligible participants had a positive SARS-CoV-2 diagnostic test result (nucleic acid amplification or antigen) within 7 days prior to enrollment, were aged 18 years or older, and were not hospitalized at the time of enrollment. Among 604 individuals screened, 297 were ineligible, 44 refused participation, and 263 were enrolled. Participants, investigators, and study staff were masked to treatment randomization.INTERVENTIONS Participants were randomized in a 2:1 fashion to a single oral 1.2-g dose of azithromycin (n = 171) or matching placebo (n = 92). MAIN OUTCOMES AND MEASURESThe primary outcome was absence of self-reported COVID-19 symptoms at day 14. There were 23 secondary clinical end points, including all-cause hospitalization at day 21. RESULTS Among 263 participants who were randomized (median age, 43 years; 174 [66%] women; 57% non-Hispanic White and 29% Latinx/Hispanic), 76% completed the trial. The trial was terminated by the data and safety monitoring committee for futility after the interim analysis. At day 14, there was no significant difference in proportion of participants who were symptom free (azithromycin: 50%; placebo: 50%; prevalence difference, 0%; 95% CI, −14% to 15%; P > .99). Of 23 prespecified secondary clinical end points, 18 showed no significant difference. By day 21, 5 participants in the azithromycin group had been hospitalized compared with 0 in the placebo group (prevalence difference, 4%; 95% CI, −1% to 9%; P = .16).CONCLUSIONS AND RELEVANCE Among outpatients with SARS-CoV-2 infection, treatment with a single dose of azithromycin compared with placebo did not result in greater likelihood of being symptom free at day 14. These findings do not support the routine use of azithromycin for outpatient SARS-CoV-2 infection.

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“…Post–COVID-19 groups began to emerge worldwide, with information varying in its accuracy at times. In Geneva, to better assist health care professionals in their learning approach and management of patients with SARS-CoV-2 infection, the CoviCare program created algorithms and guidelines for the management of acute and long-term complications of COVID-19 [ 11 - 13 ] and shared them online with all health care professionals on local and international levels. In November 2020, a post–COVID-19 interdisciplinary adult consultation was established in the Division of Primary Care Medicine at the Geneva University Hospitals Geneva, Switzerland [ 14 ], followed by an interdisciplinary pediatric consultation in the Division of General Pediatrics in May 2021 [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

The Development of a Chatbot Technology to Disseminate Post–COVID-19 Information: Descriptive Implementation Study

Nehme¹,

Schneider²,

Perrin³

et al. 2023

J Med Internet Res

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Background Post–COVID-19, or long COVID, has now affected millions of individuals, resulting in fatigue, neurocognitive symptoms, and an impact on daily life. The uncertainty of knowledge around this condition, including its overall prevalence, pathophysiology, and management, along with the growing numbers of affected individuals, has created an essential need for information and disease management. This has become even more critical in a time of abundant online misinformation and potential misleading of patients and health care professionals. Objective The RAFAEL platform is an ecosystem created to address the information about and management of post–COVID-19, integrating online information, webinars, and chatbot technology to answer a large number of individuals in a time- and resource-limited setting. This paper describes the development and deployment of the RAFAEL platform and chatbot in addressing post–COVID-19 in children and adults. Methods The RAFAEL study took place in Geneva, Switzerland. The RAFAEL platform and chatbot were made available online, and all users were considered participants of this study. The development phase started in December 2020 and included developing the concept, the backend, and the frontend, as well as beta testing. The specific strategy behind the RAFAEL chatbot balanced an accessible interactive approach with medical safety, aiming to relay correct and verified information for the management of post–COVID-19. Development was followed by deployment with the establishment of partnerships and communication strategies in the French-speaking world. The use of the chatbot and the answers provided were continuously monitored by community moderators and health care professionals, creating a safe fallback for users. Results To date, the RAFAEL chatbot has had 30,488 interactions, with an 79.6% (6417/8061) matching rate and a 73.2% (n=1795) positive feedback rate out of the 2451 users who provided feedback. Overall, 5807 unique users interacted with the chatbot, with 5.1 interactions per user, on average, and 8061 stories triggered. The use of the RAFAEL chatbot and platform was additionally driven by the monthly thematic webinars as well as communication campaigns, with an average of 250 participants at each webinar. User queries included questions about post–COVID-19 symptoms (n=5612, 69.2%), of which fatigue was the most predominant query (n=1255, 22.4%) in symptoms-related stories. Additional queries included questions about consultations (n=598, 7.4%), treatment (n=527, 6.5%), and general information (n=510, 6.3%). Conclusions The RAFAEL chatbot is, to the best of our knowledge, the first chatbot developed to address post–COVID-19 in children and adults. Its innovation lies in the use of a scalable tool to disseminate verified information in a time- and resource-limited environment. Additionally, the use of machine learning could help professionals gain knowledge about a new condition, while concomitantly addressing patients’ concerns. Lessons learned from the RAFAEL chatbot will further encourage a participative approach to learning and could potentially be applied to other chronic conditions.

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CoviCare

Cited by 7 publications

References 0 publications

A HOME-TREATMENT ALGORITHM BASED ON ANTI-INFLAMMATORY DRUGS TO PREVENT HOSPITALIZATION OF PATIENTS WITH EARLY COVID-19: A MATCHED-COHORT STUDY (COVER 2)

A HOME-TREATMENT ALGORITHM BASED ON ANTI-INFLAMMATORY DRUGS TO PREVENT HOSPITALIZATION OF PATIENTS WITH EARLY COVID-19: A MATCHED-COHORT STUDY (COVER 2)

Effect of Oral Azithromycin vs Placebo on COVID-19 Symptoms in Outpatients With SARS-CoV-2 Infection

The Development of a Chatbot Technology to Disseminate Post–COVID-19 Information: Descriptive Implementation Study

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