Sustained usage of an app-based clinical-decision making aid for the management of atherosclerotic cardiovascular disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background Complexity of therapies for atherosclerotic cardiovascular disease (ASCVD) risk reduction represents a challenge for clinicians and may lead to poor uptake of these therapies. Purpose The goal of this project was to design an easy-to-use, point-of-care tool to risk stratify ASCVD patients and provide individualized guidance for clinicians to incorporate these agents. Methods Based on the REACH registry trial and predictive modeling (including 49,689 patients with ASCVD in 44 countries), we designed and implemented an app for secondary risk assessment. Using demographic and comorbidity profiles, this tool was used to calculate an individual's 20-month risk of cardiovascular events and mortality. It also provided graphical comparison to an age-matched control with optimized cardiovascular risk profile to illustrate the modifiable residual risk. The app then utilized the patient's risk profile to provide specific guidance for possible therapeutic interventions SGLT2-inhibitors, GLP1-agonists, PCSK9-inhibitors, Vascular-dose Rivaroxaban, and Icosapent Ethyl. Additionally, it identified individuals who qualified for cardiac rehabilitation or may benefit from smoking cessation interventions, including counselling or pharmacological therapies. We launched a pilot test of the “Residual Cardiovascular Risk: Assessment and Management Guide” app at a regional cardiac center. 240 referring physicians (including family doctors, emergency physicians, internists, and cardiologists) were invited by email or fax to utilize the app. Feedback was solicited from all users three months into the test period. Following this, no further marketing of the app was performed for all users. Usage data was recorded using Google Analytics over a 12-month period and analyzed in 4-month increments. Results From January to December 2021, our app was used to risk stratify 1576 patients. A total of 47 individual users utilized the app over this period. From January to April, the app was used on average 160 times monthly. From May to August, it was used 115 times monthly. From September to December, it was used 118 times monthly. Twenty-four physicians provided feedback; 100% affirmed the functionality, ease of use, and utility of the tool. The app was described as “useful for discussions with patients”, “helpful to optimize patients” and “similar to a mini-cardiology consult”. User suggestions resulted in further improvements to the app, including integration of reports into Electronic Medical Records. Conclusions The early success of this app demonstrates a need for simple, accessible, and individualized guidance for management of ASCVD patients to improve uptake of guideline-based medical therapies. This tool demonstrates sustained usage among clinicians, as well as subjective utility in aiding therapeutic decision making. Future clinical research will focus on the ability of this tool to impact physician prescribing patterns and clinical outcomes. Funding Acknowledgement Type of funding sources: None.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it