First-order optimization algorithms can be considered as a discretization of ordinary differential equations (ODEs). In this perspective, studying the properties of the corresponding trajectories may lead to convergence results which can be transfered to the numerical scheme. In this paper we analyse the following ODE introduced by Attouch et al.: ∀t ⩾ t0, ẍ(t) + α t ẋ(t) + βHF (x(t)) ẋ(t) + ∇F (x(t)) = 0, where α > 0, β > 0 and HF denotes the Hessian of F. This ODE can be derived to build numerical schemes which do not require F to be twice differentiable as shown by Attouch et al. We provide strong convergence results on the error F (x(t)) − F * and integrability properties on ∥∇F (x(t))∥ under some geometry assumptions on F such as quadratic growth around the set of minimizers. In particular, we show that the decay rate of the error for a strongly convex function is O(t −α−ε) for any ε > 0. These results are briefly illustrated at the end of the paper.