A Systematic Review of End-of-Life Visual Impairment in Open-Angle Glaucoma: An Epidemiological Autopsy

PURPOSE: Glaucoma patients are still at risk of becoming blind. It is of clinical significance to determine the risk of blindness and its causes to prevent its occurrence. This systematic review estimates the number of treated glaucoma patients with end-of-life visual impairment (VI) and blindness and the factors that are associated with this.

METHODS: A systematic literature search in relevant databases was conducted in August 2014 on end-of-life VI. A total of 2574 articles were identified, of which 5 on end-of-life VI. Several data items were extracted from the reports and presented in tables.

RESULTS: All studies had a retrospective design. A considerable number of glaucoma patients were found to be blind at the end of their life; with up to 24% unilateral and 10% bilateral blindness. The following factors were associated with blindness: (1) baseline severity of visual field loss: advanced stage of glaucoma or substantial visual field loss at the initial visit; (2) factors influencing progression: fluctuation of intraocular pressure (IOP) during treatment, presence of pseudoexfoliation, poor patient compliance, higher IOP; (3) longer time period: longer duration of disease and older age at death because of a longer life expectancy; and (4) coexistence of other ocular pathology.

CONCLUSIONS: Further prevention of blindness in glaucoma patients is needed. To reach this goal, it is important to address the risk factors for blindness identified in this review, especially those that can be modified, such as advanced disease at diagnosis, high and fluctuating IOP, and poor compliance.

Fault Diagnosis in Internal Combustion Engines Using Nonlinear Multivariate Statistics

This paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce nonlinear relationships between the recorded engine variables, the paper proposes the use of nonlinear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new nonlinear variable reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady-state operating conditions. More precisely, nonlinear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause of abnormal engine behaviour. The paper shows that this can lead to (i) an enhanced identification of potential root causes of abnormal events and (ii) the masking of faulty sensor readings. The effectiveness of the enhanced NLPCA based monitoring scheme is illustrated by its application to a sensor fault and a process fault. The sensor fault relates to a drift in the fuel flow reading, whilst the process fault relates to a partial blockage of the intercooler. These faults are introduced to a Volkswagen TDI 1.9 Litre diesel engine mounted on an experimental engine test bench facility.