AB1393 RHEUMABUDDY4.0 LEADING THE PATH TO A PATIENT-DRIVEN ELECTRONIC SUPPORT AND MONITORING TOOL

Background

The use of health apps has become more popular in recent years, but it is still a small and rather unregulated market. Few apps have been designed in collaboration with patients and these mostly address patient reported symptoms. Some clinical registries have already developed patient apps to complete patient-reported outcome measures (PROMs) on smartphones, which normally would have been collected during an outpatient visit and have shown interchangeability. The next step would be providing a patient app offering possibilities not only of individual disease tracking, but to provide automated peer support, health information and behavioural advice.

Objectives

We aimed to further develop and validate RheumaBuddy, a health app for patients with rheumatoid arthritis (RA), from a standard monitoring app to an intelligent health app tailored to the needs of the user that provides transparency to all important stakeholders in Rheumatology care.

Methods

This is an international interdisciplinary project between Austrian and Danish partners funded by the EUREKA program. Rheumatologists, health scientists, digital data experts and patients with RA joined forces in a 4 phase-program, running from 2020 to 2023. Phase 1 continues to develop the app in a co-creation approach in several iterations with patients. Phase 2 concerns developing an automated learning algorithm based on user data to identify patient strata and connect these with helpful non-pharmacological interventions. Phase 3 connects healthcare system data on diagnosis, medication prescription, healthcare facility usage with a large clinical RA database. By that we develop patient pathways that correlate high granularity data with system resources to retrieve results on socioeconomic impact. In phase 4 a randomised clinical trial will evaluate the effect of the developed RB4.0 on clinical disease activity and quality of life.

Results

Currently, RB is regularly being used by more than 3100 patients in 35 countries and 8 languages throughout Europe. The current RheumaBuddy version offers logging of symptoms using Likert scale questions, a joint mannequin to mark painful body parts and a peer-support forum. Additionally, the user can anytime display his/her entries over time in a graphical report and also share data with the healthcare provider. This version is extended with tracking of sleep, working hours and other behaviours. A consultation compass function helps the patient to reflect on goals and issues before the rheumatologist visit.

Within this project, we already established a first version of a Recommender System (RS), which computes correlations between user entries (e.g. between a user’s mood and pain), thus providing individual feedback. Through integration of information obtained from the app with claims data and clinical data from a RA registry, patterns can be identified and translated into different case models that concern the impact of common RA symptoms. By mapping these scenarios with evidence based behaviour and lifestyle advice, the “virtual coach” (advanced RS) will be developed and integrated into the RB4.0 system. During continuous data collection on app users, similarities in user behaviour can be identified, and similar entry patterns can be grouped. This will allow users to exchange and learn from each other regarding certain difficult situations (ex. “life-hack”) etc.

We are creating a comprehensive system in providing feedback to both clinical and psychosocial aspects of coping and disease management, as well as everyday practicalities for living with a chronic disease. Figure 1 displays these aspects, contributing the empowerment of patients.

Figure 1.

RB4.0 shall support people living with RA in dealing with disease impact

Conclusion

RheumaBuddy4.0 will provide RA patients the means to improve their quality of life on an individual level, better understand their needs and therapy which could support overcoming barriers of successful shared decision making to achieve better outcomes.

Disclosure of Interests

Paul Studenic: None declared, Tanja Stamm Speakers bureau: AbbVie, Novartis, Roche, Sanofi, and Takeda, Consultant of: AbbVie and Sanofi Genzyme, Grant/research support from: AbbVie and Roche, Yuki Seidler: None declared, Andreas Dam Speakers bureau: Gilead, Galapagos, BMS, Roche, Takeda, Merck, Consultant of: Gilead, Galapagos, BMS, Roche, Takeda, Merck, Nadine Weibrecht: None declared, Günther Zauner: None declared, Thomas H Jakobsen: None declared, Rebekka L. Hansen: None declared, Nikolas Popper Speakers bureau: Roche, Consultant of: as CSO of dwh GmbH, Tanita-Christina Wilhelmer: None declared, Helga Radner Speakers bureau: Gilead, Merck Sharp, Pfizer, Abbvie, Consultant of: Gilead, Merck Sharp, Pfizer, Abbvie, Romualdo Ramos: None declared, James Rickmann: None declared, Christoph Urach: None declared, Lars Erik Kristensen Speakers bureau: AbbVie, Pfizer Janssen, Novartis, Galapagos, UCB, Biogen and Eli Lilly, Consultant of: AbbVie, Pfizer, Janssen, Novartis, Galapagos, UCB, Biogen and Eli Lilly, Grant/research support from: IIT grants from UCB, Biogen, Eli Lilly, Novartis, Tanja Schjødt Jørgensen Speakers bureau: AbbVie, Pfizer, Roche, Novartis, UCB, Biogen and Eli Lilly, Consultant of: AbbVie, Pfizer, Roche, Novartis, UCB, Biogen and Eli Lilly

Evaluation of a targeted COVID-19 vaccination strategy for Austria–a decision-analytic modeling study

Background

The supply of COVID-19 vaccine was limited when introduced. We aimed to inform decision makers at an early stage about targeted COVID vaccination strategies, accounting for limited capacities and adherence to support vaccination prioritization in Austria.

Methods

We applied a dynamic agent-based population model to compare different vaccination prioritization strategies targeting the elderly (65 ≥ years), middle aged (45-64 years), younger (15-44 years), vulnerable (risk of severe disease due to comorbidities), and healthcare workers (HCW), to minimize COVID-19-related hospitalizations and deaths. First, outcomes were optimized for an initially available vaccine batch for 200,000 individuals. Second, stepwise optimization was performed, deriving a prioritization sequence for 2.5 million people. We considered sterilizing and non-sterilizing immunity, with different assumptions of effectiveness, over a 6-month period. The project team was advised by a Standing Policy and Expert Panel, consisting of Austrian decision makers, clinical and ethical experts as well as international modeling specialists.

Results

Maximum reduction of hospitalizations and deaths was achieved by starting vaccinations with the elderly and vulnerable, followed by middle-aged, HCW, and younger individuals. Optimizations for vaccinating 2.5 million individuals yielded the same prioritization and avoided about one third of deaths and hospitalizations. Starting vaccination with HCWs leads to slightly smaller reductions. The negative effects of COVID-19-related HCW absenteeism were not yet considered in our model.

Conclusions

Our decision-analytic study shows that the elderly and vulnerable should be prioritized for vaccination until further vaccines are available to minimize COVID-19-related hospitalizations and deaths. An important ethical aspect complementing our modeling results is the protection of HCW, maximizing their occupational safety and ensuring risk-compensatory justice.

Key messages

To minimize COVID‐19‐related hospitalizations and death the elderly and vulnerable should be prioritized for vaccination until further vaccines are available.

Prioritizing health care workers for COVID-19 vaccination is slightly less effective in the simulation but they may be considered for occupational safety and to ensure risk-compensatory justice.

Evaluation of undetected cases during the COVID-19 epidemic in Austria

BACKGROUND

Knowing the number of undetected cases of COVID-19 is important for a better understanding of the spread of the disease. This study analyses the temporal dynamic of detected vs. undetected cases to provide guidance for the interpretation of prevalence studies performed with PCR or antibody tests to estimate the detection rate.

METHODS

We used an agent-based model to evaluate assumptions on the detection probability ranging from 0.1 to 0.9. For each general detection probability, we derived age-dependent detection probabilities and calibrated the model to reproduce the epidemic wave of COVID-19 in Austria from March 2020 to June 2020. We categorized infected individuals into presymptomatic, symptomatic unconfirmed, confirmed and never detected to observe the simulated dynamic of the detected and undetected cases.

RESULTS

The calculation of the age-dependent detection probability ruled values lower than 0.4 as most likely. Furthermore, the proportion of undetected cases depends strongly on the dynamic of the epidemic wave: during the initial upswing, the undetected cases account for a major part of all infected individuals, whereas their share decreases around the peak of the confirmed cases.

CONCLUSIONS

The results of prevalence studies performed to determine the detection rate of COVID-19 patients should always be interpreted with regard to the current dynamic of the epidemic wave. Applying the method proposed in our analysis, the prevalence study performed in Austria in April 2020 could indicate a detection rate of 0.13, instead of the prevalent ratio of 0.29 between detected and estimated undetected cases at that time.