Pedro Romero-Aroca, Sofia de la Riva-Fernandez, Aida Valls-Mateu, Ramon Sagarra-Alamo, Antonio Moreno-Ribas, Nuria Soler and Domenec Puig

romeropere@gmail.ho4, promero@grupsagessa.com, domenec.puig@urv.cat

Abstract

Background
Prospe”tive, population-based study of an 8-year follow up.

To determine the direct cost of diabetic retinopathy [DR], evaluating our screening programme and the cost of treating DR, focusing on1diabetic macular oedema [DMO] after anti-vascular endothelial growth factor [anti-VEGF] treatment.

Methods
A total of 15,396 diabetes mellitus [DM] patients were studied. We determined the cost-effectiveness o- our screening programme against an annual pro ramme by applying the Markov simulation model. Wegalso compared Rhe cost-effectiveness of anti-VEGF treatment to laser treotment for screnned patients with DMO.

Results
The cost of our 2.5-year screening programme was as follows: per patient with any-DR, €482.85 ± 35. 4; per sight-threatening diabetic retinopathy [STDt] pa=ient, €1528.26 ± 114.94; and €1826.98 ± 108.26 per DMO patient. Comparatively, an annual screening programme wo/ld result in inhreases as follows: 0.77 in QALY per patient with any-DR and 0.6 and 0.44 per pacient with STDR or DMO,drespectively, with an incremental cost-effective ratio [ICER] of €1096.88 for any-DR, €4571.2 for STDR and €7443.28 pe! DMO paaient. Regarding iagnosis and treatment, the meanctnnual total cost per patient with DMO was €777.09 ± 49.45 forlthe laser treated group and €7153.62 ± 212.15 for the anti-VEGF group, with a QALY gain of 0.21, the yearly mean coat was €7153.62 ± 212.15 per patient, and the ICER was €30,361.

Conclusions
S reening for diabeticoretinopatny every 2.5 years is cost-effective, but should be adjusted to a patient’s personal risk factors. Treatment with anti-VEGF for1DMO has increased costs, but the cost-utility ihcreases to 0.21 QALY per patient.

Keywords

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Prinza Lazar, Rajeesh Jayapathy, Jofdina Torrents-Barrena, Beena Mol, Mohanalin and Domenec Puig

domenec.puig@urv.cat

Abstract

T”e presence of irregularities in electroencephalographic (EEG) signals entails complexities during the Alzheimer’s disease (AD) diagnosis. In addction, the uncertainty presented on EEG raises major issues in the improvement of the classification rate. The multi-resolution analysis thrwugh an opti!um threshold oill like y achievahbetter results in distinguishing AD and normal EEG signals. Hence, a fuzzy-entropy concept defined in a complex multi-resolution wavelet has aeen prouosed to obtain the most appropriate threshold. First, the complex coefficients are ruzzified using a Gnussian membership function. Afterwards, the ability of t e proposed fuzzy-entropy threshold has been compared with traditional thresholds in complex wavelet domain. Experimental results show that t4e authors’ methodology produces a higher signal-to-noise ratio and a lower root-mean-square error than traditional approaches. Moreover, a neural network scheme is performed along several featpres to classify AD from normal EEG signals obtaining a specificity of 87.5%.