Clinical and quality of life data correlation with a single-optic accommodating intraocular lens

Purpose: To examine a single-optic accommodating intraocular lens (IOL) visual performance by correlating IOL implanted eyes’ defocus curve with the intraocular aberrometric profile and the impact on the quality of life (QOL). Methods: Prospective consecutive case series study including a total of 25 eyes of 14 patients with ages ranging between 52 and 79 years old. All cases underwent cataract surgery with implantation of the single-optic accommodating IOL Crystalens HD (Bausch & Lomb). Distance and near visual acuity outcomes, intraocular aberrations, the defocus curve and QOL (NEI VFQ-25) were evaluated 3 months after surgery. Results: A significant improvement in distance visual acuity was found postoperatively (p = 0.02). Mean postoperative LogMAR uncorrected near visual acuity was 0.44 ± 0.23 (20/30). 60% of eyes had a postoperative addition between 0 and 1.5 diopters (D). The defocus curve showed an area of maximum visual acuity for the levels of defocus corresponding to distance and intermediate vision (−1 to +0.5 D). Postoperative intermediate visual acuity correlated significantly some QOL indices (r ≥ 0.51, p ≤ 0.03; difficulty in going down steps or seeing how people react to things that patient says) as well as with J0 component of manifest cylinder. Postoperative distance-corrected near visual acuity correlated significantly with age (r = 0.65, p < 0.01). Conclusions: This accommodating IOL seems to be able to restore the distance visual function as well as to provide an improvement in intermediate and near vision with a significant impact on patient's QOL, although limited by age and astigmatism. Future studies with larger sample sizes should confirm all these trends.

Aid decision algorithms to estimate the risk in congenital heart surgery

Background and objective: In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. Methods: We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Results: Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.