Uremic Neuropathy: Epiemiological Study in Hemodialysis Patients

Background/Aims. Uremic Neuropathy (UN) highly limits the individual self-sufficiency causing near-continuous pain. An estimation of the actual UN prevalence among hemodialysis patients was the aim of the present work. Methods. We studied 225 prevalent dialysis patients from two Italian Centres. The Michigan Neuropathy Score Instrument (MNSI), already validated in diabetic neuropathy, was used for the diagnosis of UN. It consisted of a questionnaire (MNSI_Q) and a physical-clinical evaluation (MNSI_P). Patients without any disease possibly inducing secondary neuropathy and with MNSI score  3 have been diagnosed as affected by UN. Electroneurographic (ENG) lower limbs examination was performed in these patients to compare sensory conduction velocities (SCV) and sensory nerve action potentials (SNAP) with the MNSI results. Results. Thirtyseven patients (16.4%) were identified as being affected by UN, while 9 (4%) presented a score <3 in spite of neuropathic symptoms. In the 37 UN patients a significant correlation was found between MNSI_P and SCV (r2 = 0.1959; p<0.034) as well as SNAP (r2 = 0.3454; p=0.027) both measured by ENG. Conclusions. UN is an underestimated disease among the dialysis population even though it represents a huge problem in terms of pain and quality of life. MNSI could represent a valid and simple clinical-instrumental screening test for the early diagnosis of UN in view of an early therapeutic approach.

Multidimensional item response theory models with general and specific latent traits for ordinal data

The aim of the thesis is to propose a Bayesian estimation through Markov chain Monte Carlo of multidimensional item response theory models for graded responses with complex structures and correlated traits. In particular, this work focuses on the multiunidimensional and the additive underlying latent structures, considering that the first one is widely used and represents a classical approach in multidimensional item response analysis, while the second one is able to reflect the complexity of real interactions between items and respondents. A simulation study is conducted to evaluate the parameter recovery for the proposed models under different conditions (sample size, test and subtest length, number of response categories, and correlation structure). The results show that the parameter recovery is particularly sensitive to the sample size, due to the model complexity and the high number of parameters to be estimated. For a sufficiently large sample size the parameters of the multiunidimensional and additive graded response models are well reproduced. The results are also affected by the trade-off between the number of items constituting the test and the number of item categories. An application of the proposed models on response data collected to investigate Romagna and San Marino residents' perceptions and attitudes towards the tourism industry is also presented.