Impiego degli Inibitori della Pompa protonica (IPP) in Piemonte: indagine sulle abitudini prescrittive dei Medici di Medicina Generale

Proton Pump Inhibitors (PPIs) (Omeprazole, Lansoprazole, Pantoprazole, Rabeprazole and Esomeprazole), one of the most commonly prescribed classes of medications in the primary care setting, are considered a major advance in the treatment of acid-peptic diseases. In Italy PPIs are reimbursed by National Health Service on the basis of CUF (Commissione Unica del Farmaco) 1 and 48 Notes. In 2002 and 2003 a significant increase in PPIs consumption and expenditure have been documented, showing differences between regions. The aim of this study is to investigate and monitor, at regional level, type and entity of PPIs use through a drug utilization study, evaluating prescribing behaviour and compliance of PPIs treatments with CUF Notes indications. The study has been carried out on a sample of 436 General Practitioners belonging to 22 Piemonte's ASL (Aziende Sanitarie Locali). The data analysis shows that acid-related pathologies are significantly more common in patients with at least 50 years of age and the most frequent condition is represented by gastroesophageal reflux disease. Despite the general conditions of PPIs use by General Practitioners in terms of duration and dosage of therapy result in most cases (from 49% to 80% for duration and from 54% to 97% for dosage) compliant with what proposed by CUF Notes, in some cases the same CUF Notes indications seem to be not observed. Consequently the Piemonte Region has decided to plan a guideline on PPIs rational use. Such guideline, expected to be introduced in the regional area, may also be considered as an instrument able to lead to a more appropriate expenditure for this drug class. Moreover, in order to control PPIs expenditure, pharmacoeconomic methodologies can be applied allowing to identify the most cost - effective active substance and therapeutic scheme, overcoming CUF Notes which consider all PPIs use under the same reimbursement conditions

Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression.

To employ Artificial Intelligence to model, predict and simulate the amyotrophic lateral sclerosis (ALS) progression over time in terms of variable interactions, functional impairments, and survival. We employed demographic and clinical variables, including functional scores and the utilisation of support interventions, of 3940 ALS patients from four Italian and two Israeli registers to develop a new approach based on Dynamic Bayesian Networks (DBNs) that models the ALS evolution over time, in two distinct scenarios of variable availability. The method allows to simulate patients' disease trajectories and predict the probability of functional impairment and survival at different time points. DBNs explicitly represent the relationships between the variables and the pathways along which they influence the disease progression. Several notable inter-dependencies were identified and validated by comparison with literature. Moreover, the implemented tool allows the assessment of the effect of different markers on the disease course, reproducing the probabilistically expected clinical progressions. The tool shows high concordance in terms of predicted and real prognosis, assessed as time to functional impairments and survival (integral of the AU-ROC in the first 36 months between 0.80-0.93 and 0.84-0.89 for the two scenarios, respectively). Provided only with measurements commonly collected during the first visit, our models can predict time to the loss of independence in walking, breathing, swallowing, communicating, and survival and it can be used to generate in silico patient cohorts with specific characteristics. Our tool provides a comprehensive framework to support physicians in treatment planning and clinical decision-making. [Abstract copyright: © 2022. The Author(s).

Testing the diagnostic accuracy of [18F]FDG-PET in discriminating spinal- and bulbar-onset amyotrophic lateral sclerosis.

peer reviewedPURPOSE: The role for [18F]FDG-PET in supporting amyotrophic lateral sclerosis (ALS) diagnosis is not fully established. In this study, we aim at evaluating [18F]FDG-PET hypo- and hyper-metabolism patterns in spinal- and bulbar-onset ALS cases, at the single-subject level, testing the diagnostic value in discriminating the two conditions, and the correlations with core clinical symptoms severity. METHODS: We included 95 probable-ALS patients with [18F]FDG-PET scan and clinical follow-up. [18F]FDG-PET images were analyzed with an optimized voxel-based-SPM method. The resulting single-subject SPM-t maps were used to: (a) assess brain regional hypo- and hyper-metabolism; (b) evaluate the accuracy of regional hypo- and hyper metabolism in discriminating spinal vs. bulbar-onset ALS; (c) perform correlation analysis with motor symptoms severity, as measured by ALS-FRS-R. RESULTS: Primary motor cortex showed the most frequent hypo-metabolism in both spinal-onset (∼57%) and bulbar-onset (∼64%) ALS; hyper-metabolism was prevalent in the cerebellum in both spinal-onset (∼56.5%) and bulbar-onset (∼55.7%) ALS, and in the occipital cortex in bulbar-onset (∼62.5%) ALS. Regional hypo- and hyper-metabolism yielded a very low accuracy (AUC < 0.63) in discriminating spinal- vs. bulbar-onset ALS, as obtained from single-subject SPM-t-maps. Severity of motor symptoms correlated with hypo-metabolism in sensorimotor cortex in spinal-onset ALS, and with cerebellar hyper-metabolism in bulbar-onset ALS. CONCLUSIONS: The high variability in regional hypo- and hyper-metabolism patterns, likely reflecting the heterogeneous pathology and clinical phenotypes, limits the diagnostic potential of [18F]FDG-PET in discriminating spinal and bulbar onset patients

Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression

Objective: To employ Artificial Intelligence to model, predict and simulate the amyotrophic lateral sclerosis (ALS) progression over time in terms of variable interactions, functional impairments, and survival. Methods: We employed demographic and clinical variables, including functional scores and the utilisation of support interventions, of 3940 ALS patients from four Italian and two Israeli registers to develop a new approach based on Dynamic Bayesian Networks (DBNs) that models the ALS evolution over time, in two distinct scenarios of variable availability. The method allows to simulate patients' disease trajectories and predict the probability of functional impairment and survival at different time points. Results: DBNs explicitly represent the relationships between the variables and the pathways along which they influence the disease progression. Several notable inter-dependencies were identified and validated by comparison with literature. Moreover, the implemented tool allows the assessment of the effect of different markers on the disease course, reproducing the probabilistically expected clinical progressions. The tool shows high concordance in terms of predicted and real prognosis, assessed as time to functional impairments and survival (integral of the AU-ROC in the first 36&nbsp;months between 0.80-0.93 and 0.84-0.89 for the two scenarios, respectively). Conclusions: Provided only with measurements commonly collected during the first visit, our models can predict time to the loss of independence in walking, breathing, swallowing, communicating, and survival and it can be used to generate in silico patient cohorts with specific characteristics. Our tool provides a comprehensive framework to support physicians in treatment planning and clinical decision-making