The most severe parkinsonian patients are most improved with duodenal levodopa infusion

Objectives:To find variables correlated to improvement with intraduodenal levodopa/carbidopa infusion (Duodopa) in order to identify potential candidates for this treatment. Two clinical studies comparing Duodopa with oral treatments in patients with advanced Parkinson’s disease have shown significant improvement in percent on-time on a global treatment response scale (TRS) based on hourly and half-hourly clinical ratings and in median UPDRS scores.Methods:Data from study 1 comparing infusion with Sinemet CR (12 patients, Nyholm et al, Clin Neuropharmacol 2003; 26(3): 156-163) and study 2 comparing infusion with individually optimised conventional combination therapies (18 patients, Nyholm et al, Neurology, in press) were used. Measures of severity were defined as total UPDRS score and scores for sections II and III, percent functional on-time and mean squared error of ratings on the TRS and as mean of diary questions about mobility and satisfaction (only study 2). Absolute improvement was defined as difference in severity, and relative improvement was defined as percent absolute improvement/severity on oral treatment. Pearson correlation coefficients between measures of improvement and other variables were calculated.Results:Correlations (r2>0.28, p<0.05) between severity during oral treatment and absolute improvement on infusion were found for: Total UPDRS, UPDRS III and TRS ratings (studies 1 and 2) and for diary question 1 (mobility) and UPDRS II (study 2). Correlation to relative improvement was found for total UPDRS (study 2, r2=0.47). Figure 1 illustrates absolute improvement in total UPDRS vs. total UPDRS during oral treatment (study 2).Conclusion:Correlating different measures of severity and improvement revealed that patients with more severe symptoms were most improved and that the relation between severity and improvement was linear within the studied groups. The result, which was reproducible between two clinical studies, could be useful when deciding candidates for the treatment.

Analysis of response to enteral infusion of levodopa in patients with Parkinson's disease

Objective: We present a new evaluation of levodopa plasma concentrations and clinical effects during duodenal infusion of a levodopa/carbidopa gel (Duodopa ) in 12 patients with advanced Parkinson s disease (PD), from a study reported previously (Nyholm et al, Clin Neuropharmacol 2003; 26(3): 156-163). One objective was to investigate in what state of PD we can see the greatest benefits with infusion compared with corresponding oral treatment (Sinemet CR). Another objective was to identify fluctuating response to levodopa and correlate to variables related to disease progression. Methods: We have computed mean absolute error (MAE) and mean squared error (MSE) for the clinical rating from -3 (severe parkinsonism) to +3 (severe dyskinesia) as measures of the clinical state over the treatment periods of the study. Standard deviation (SD) of the rating was used as a measure of response fluctuations. Linear regression and visual inspection of graphs were used to estimate relationships between these measures and variables related to disease progression such as years on levodopa (YLD) or unified PD rating scale part II (UPDRS II).Results: We found that MAE for infusion had a strong linear correlation to YLD (r2=0.80) while the corresponding relation for oral treatment looked more sigmoid, particularly for the more advanced patients (YLD>18).

Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.