INTRODUCTION:
CVD risk prediction scores (e.g. Framingham score(1), SCORE-2(2)) and corresponding tool modalities (e.g., risk charts, mobile apps) aim to target preventive interventions by estimating an individual’s probability of an incident CVD event over a specified time horizon, usually 10 years(3). These scores are recommended to guide primary prevention and clinical decision-making, yet their use in routine care across sub-Saharan Africa (SSA) remains uneven, with variability in workflow integration and clinician uptake(4–6).

METHODS:
This scoping review maps and synthesises the available literature on the implementation of CVD risk prediction scores in SSA. An Implementation Research Logic Model was applied to organise implementation determinants, strategies, and outcomes, alongside relevant implementation frameworks(7). The review was conducted in accordance with JBI guidance for scoping reviews(8).

RESULTS:
Of 4930 records from six databases that were screened, 42 publications met the inclusion criteria, representing ten countries and ten risk scores. Four primary use cases for CVD risk scores in SSA were identified: policy and economic decision-making; screening and referral of individuals; treatment of those at high CVD risk; and risk communication. Implementation of CVD risk tools was generally reported as cost-effective. The most frequently identified barriers related to health system challenges and limited local resources, and the most common strategies implemented were revising professional roles through task shifting and training of healthcare staff. While feasibility and acceptability outcomes were widely studied, evidence on long-term sustainability was limited and suggested sub-optimal sustained use.

CONCLUSIONS:
Implementation efforts can build on the facilitators and strategies identified in the existing literature. However, improving the sustained use of CVD risk prediction tools in SSA will require addressing health system and service constraints. Future research and interventions should prioritise health system integration to support equitable and sustainable implementation of CVD risk prediction tools in SSA.