Long-term observation of vitrectomy without subretinal hemorrhage management for massive vitreous hemorrhage secondary to polypoidal choroidal vasculopathy

AIM: To describe the long-term observation of vitrectomy without subretinal hemorrhage(SRH) management for massive vitreous hemorrhage(VH) secondary to polypoidal choroidal vasculopathy(PCV). METHODS: This is a retrospective, consecutive case series. A total of 86 eyes of 86 patients with >14d of massive VH associated with PCV were included. All patients underwent vitrectomy without SRH management, followed by intravitreal ranibizumab injections and/or photodynamic therapy(PDT) as needed. The main outcome measures were best-corrected visual acuity(BCVA), postoperative adverse events and the recurrence of VH. RESULTS: The average follow-up period was 25.5±9.2 mo(range 12-35 mo). Mean BCVA at baseline(2.16±0.39 logM AR)had improved significantly, both 3 mo after surgery(1.42±0.66 log MAR, P<0.001) and by the last visit(1.23±0.74 logM AR, P<0.001). The common postoperative complications included macular subretinal fibrosis in 14 eyes(16.3%) and ciliary body detachment in 4 eyes(4.7%).Nineteen eyes(22.1%) received following treatment with ranibizumab injections without/with PDT, and 15(17.4%)were resolved. Four eyes(4.7%) had recurrent hemorrhage during the follow-up period. In multiple regression analysis,thicker SRH(beta=0.33, P=0.025) in the preoperative B-scan and the presence of foveal subretinal fibrosis(beta=0.28, P=0.018) in the follow up were associated with poor postoperative BCVA. CONCLUSION: Vitrectomy without SRH management for massive VH secondary to PCV improved/stabilized visual function in the long-term observation. Eyes presenting with thicker SRH preoperatively and forming foveal subretinal fibrosis in the follow-up period tended to have worse BCVA.

Optimal Time-consistent Investment and Reinsurance Strategy for Mean-variance Insurers Under the Inside Information

In this paper, we consider the problem of the optimal time-consistent investment and proportional reinsurance strategy under the mean-variance criterion, in which the insurer has some inside information at her disposal concerning the future realizations of her claims process. It is assumed that the surplus of the insurer is governed by a Brownian motion with drift, and the insurer has the possibility to reduce the risk by purchasing proportional reinsurance and investing in financial markets. We first formulate the problem and provide a verification theorem on the extended Hamilton-Jacobi-Bellman equations. Then, the closed-form expression is obtained for the optimal strategy of the optimization problem.

The Optimal Policy for Insurance Company Under Consideration of Internal Competition and the Time Value of Ruin

This paper considers the dividend optimization problem for an insurance company under the consideration of internal competition between different units inside the company. The objective is to find a reinsurance policy and a dividend payment scheme so as to maximize the expected discounted value of the dividend payment, and the expected present value of an amount which the insurer earns until the time of ruin. By solving the corresponding constrained Hamilton-Jacobi-Bellman （HJB） equation, we obtain the value function and the optimal reinsurance policy and dividend payment.

Risk-based Optimal Investment and Proportional Reinsurance of an Insurer with Hidden Regime Switching

In this paper, we study the optimal investment and proportional reinsurance strategy for an insurer in a hidden Markov regime-switching environment. A risk-based approach is considered, where the insurer aims at selecting an optimal strategy with a view to minimizing the risk described by a convex risk measure of its terminal wealth. We solve the problem in two steps. First, we employ the filtering theory to turn the optimization problem with partial observations into one with complete observations. Second, by using BSDEs with jumps, we solve the problem with complete observations.

Convolution Kernels Implementation of Cardinalized Probability Hypothesis Density Filter

The probability hypothesis density （PHD） propagates the posterior intensity in place of the poste- rior probability density of the multi-target state. The cardinalized PHD （CPHD） recursion is a generalization of PHD recursion, which jointly propagates the posterior intensity function and posterior cardinality distribution. A number of sequential Monte Carlo （SMC） implementations of PHD and CPHD filters （also known as SMC- PHD and SMC-CPHD filters, respectively） for general non-linear non-Gaussian models have been proposed. However, these approaches encounter the limitations when the observation variable is analytically unknown or the observation noise is null or too small. In this paper, we propose a convolution kernel approach in the SMC-CPHD filter. The simuIation results show the performance of the proposed filter on several simulated case studies when compared to the SMC-CPHD filter.