The aim of this study was to evaluate the long-term efficacy and safety of the Artiflex® lens implant and to follow the evolution of the number of corneal endothelial cells over time. Design It was a retrospective study of an observational case series of patients who underwent surgery at "The INVISION Ophthalmic Hospital" (Almería, Spain) in 2007 and who were followed for 10 years. Methods Setting: Clinical practice. Study population included 53 eyes of 30 patients who underwent an Artiflex® lens implant for the correction of myopia from −4 to −14 D. Each patient included in this study had stable myopia for at least 2 years and a contraindication for corneal refractive surgery. The efficacy index was defined as the quotient between uncorrected distance visual acuity postoperative and best-corrected distance visual acuity (BCDVA) preoperative. The safety index was calculated as the quotient between BCDVA postop and BCDVA preop. Results The average efficacy and safety indices of the lenses implanted were 1.1 (SD 0.30) and 1.06 (SD 0.2) at 10 years of follow-up. In this period of time there has been a loss of 12% of the corneal endothelial cells. The postoperative complications were pigment dispersion in four eyes (7%) of four patients and decentration of phakic intraocular lens in two eyes (4%) of another two patients. Conclusions The Artiflex® foldable phakic lens could be a safe and effective long-term alternative for myopic patients in whom laser surgery was contraindicated.

(1) Background: Keratoconus is a non-inflammatory corneal disease characterized by gradual thinning of the stroma, resulting in irreversible visual quality and quantity decline. Early detection of keratoconus and subsequent prevention of possible risks are crucial factors in its progression. Random forest is a machine learning technique for classification based on the construction of thousands of decision trees. The aim of this study was to use the random forest technique in the classification and prediction of subclinical keratoconus, considering the metrics proposed by Pentacam and Corvis. (2) Methods: The design was a retrospective cross-sectional study. A total of 81 eyes of 81 patients were enrolled: sixty-one eyes with healthy corneas and twenty patients with subclinical keratoconus (SCKC): This initial stage includes patients with the following conditions: (1) minor topographic signs of keratoconus and suspicious topographic findings (mild asymmetric bow tie, with or without deviation; (2) average K (mean corneal curvature) <46, 5 D; (3) minimum corneal thickness (ECM) > 490 μm; (4) no slit lamp found; and (5) contralateral clinical keratoconus of the eye. Pentacam topographic and Corvis biomechanical variables were collected. Decision tree and random forest were used as machine learning techniques for classifications. Random forest performed a ranking of the most critical variables in classification. (3) Results: The essential variable was SP A1 (stiffness parameter A1), followed by A2 time, posterior coma 0º, A2 velocity and peak distance. The model efficiently predicted all patients with subclinical keratoconus (Sp = 93%) and was also a good model for classifying healthy cases (Sen = 86%). The overall accuracy rate of the model was 89%. (4) Conclusions: The random forest model was a good model for classifying subclinical keratoconus. The SP A1 variable was the most critical determinant in classifying and identifying subclinical keratoconus, followed by A2 time.

The pandemic reminded us that the pathogen evolution still has a serious effect on human societies. States, however, can prepare themselves for the emergence of a novel pathogen with unknown characteristics by analysing potential scenarios. Game theory offers such an appropriate tool. In our game-theoretical framework, the state is playing against a pathogen by introducing non-pharmaceutical interventions to fulfil its socio-political goals, such as guaranteeing hospital care to all needed patients, keeping the country functioning, while the applied social restrictions should be as soft as possible. With the inclusion of activity and economic sector dependent transmission rate, optimal control of lockdowns and health care capacity management is calculated. We identify the presence and length of a pre-symptomatic infectious stage of the disease to have the greatest effect on the probability to cause a pandemic. Here we show that contrary to intuition, the state should not strive for the great expansion of its health care capacities even if its goal is to provide care for all requiring it and minimize the cost of lockdowns.