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Enzo Grossi

Publications and source records attributed to Enzo Grossi.

At least 19 recordsLinked to original sources
PubMed2006

Development and validation of the short version of the Psychological General Well-Being Index (PGWB-S).

BACKGROUND: The PGWBI is a 22-item health-related Quality of Life (HRQoL) questionnaire developed in US which produces a self-perceived evaluation of psychological well-being expressed by a summary score. The PGWBI has been validated and used in many countries on large samples of the general population and on specific patient groups. Recently a study was carried out in Italy to reduce the number of items of the original questionnaire, yielding the creation of a shorter validated version of the questionnaire (PGWB-S). The purpose of the present paper is to describe the methods adopted and to report and discuss the relevance of results. METHODS: Data for this study were collected from 4 different population samples: two general population samples a student and a patient sample. On the basis of the results of the first (development) sample population, six relevant items were identified statistically from the original questionnaire and grouped to assemble a new summary scale. Following the newly created 6-item questionnaire was administered in three independent population samples. Descriptive statistics, correlation coefficients, univariate and multivariate regression analyses were used to compare the performance of the long and short questionnaire, within and between population samples and across relevant subgroups. A further independent sample extracted by an ongoing cancer clinical trial served as final validation step. RESULTS: Overall, the questionnaires were administered to 1443 subjects. Six items were selected by a step-wise approach to explain 90% of the variance of the summary measure of the original questionnaire. Response rates reached 100%, while missing items were not observed. University students (n = 400) showed the highest mean value of the summary measure (75.3); while the patient sample (n = 28) had the lowest score (71.5). The correlation coefficients between the summary measures and the single items according to the different studies were satisfactory, reaching the highest estimates in the student sample. The internal consistency showed high values of the Cronbach's alpha coefficient (range 0.80-0.92) for all three study samples, coming close to the value of the coefficient established for the original questionnaire (0.94). A cross-validation in an independent sample of 755 cancer patients confirmed the item selection procedure and amount of variance explained by the new shorter questionnaire (ranging from 90. 2 to 95.1 %, across age and sex strata). CONCLUSION: The newly identified PGWB-S showed good acceptability and validity for the use in various settings in Italy. The translation of the PGWB-S into different languages, and its use in other linguistic settings will add evidence about its cross-cultural validity.

Adolescent↗
PubMed2006

How artificial intelligence tools can be used to assess individual patient risk in cardiovascular disease: problems with the current methods.

BACKGROUND: In recent years a number of algorithms for cardiovascular risk assessment has been proposed to the medical community. These algorithms consider a number of variables and express their results as the percentage risk of developing a major fatal or non-fatal cardiovascular event in the following 10 to 20 years DISCUSSION: The author has identified three major pitfalls of these algorithms, linked to the limitation of the classical statistical approach in dealing with this kind of non linear and complex information. The pitfalls are the inability to capture the disease complexity, the inability to capture process dynamics, and the wide confidence interval of individual risk assessment. Artificial Intelligence tools can provide potential advantage in trying to overcome these limitations. The theoretical background and some application examples related to artificial neural networks and fuzzy logic have been reviewed and discussed. SUMMARY: The use of predictive algorithms to assess individual absolute risk of cardiovascular future events is currently hampered by methodological and mathematical flaws. The use of newer approaches, such as fuzzy logic and artificial neural networks, linked to artificial intelligence, seems to better address both the challenge of increasing complexity resulting from a correlation between predisposing factors, data on the occurrence of cardiovascular events, and the prediction of future events on an individual level.

Artificial Intelligence↗
PubMed2006

Use of an Italian version of the telephone interview for cognitive status in Alzheimer's disease.

OBJECTIVES: Validation of an Italian version of the Telephone Interview for Cognitive Status (I-TICS). METHODS: Telephone administration of the I-TICS within 6 weeks of face-to-face testing with the Mini Mental State Examination (MMSE), in Probable Alzheimer's disease (AD) patients and healthy controls. Two hundred and seven consecutive outpatients with cognitive impairment were recruited from Dementia Clinic of University Campus BioMedico. Of these, 45 probable AD patients with complete data were analyzed. Other dementias, Mild Cognitive Impairment (MCI), and patients with incomplete data were excluded. The control sample consisted of 64 age- and sex-matched healthy subjects. For diagnosis, an extensive clinical evaluation, laboratory testing, brain imaging, EEG, neuropsychological battery and a depression scale were used. For I-TICS validation, telephone I-TICS and face-to-face MMSE were administered. RESULTS: The I-TICS correlated highly and linearly with the MMSE (Pearson's r=0.904). Conversion equations are provided. Sensitivity and specificity were similar between tests (area under curve=0.894 for the I-TICS; 0.966 for the MMSE). I-TICS sensitivity was 84% and specificity 86% at a cut-off score of 28. No significant difference in accuracy with the MMSE was present. Total agreement between I-TICS and MMSE was 'substantial' at 86% (Cohen's K=0.717). Repeated testing in a subset of patients showed a disease progression related decrease of 4.2 points/year (t=2.664; p=0.018) in I-TICS scores. CONCLUSION: The I-TICS is a valid instrument in clinical and research screening and monitoring of AD. Potential applications in other dementias and MCI are worth further studies.

Aged↗
PubMed2005

Possible contribution of artificial neural networks and linear discriminant analysis in recognition of patients with suspected atrophic body gastritis.

AIM: To investigate whether ANNs and LDA could recognize patients with ABG in a database, containing only clinical and biochemical variables, of a pool of patients with and without ABG, by selecting the most predictive variables and by reducing input data to the minimum. METHODS: Data was collected from 350 consecutive outpatients (263 with ABG, 87 with non-atrophic gastritis and/or celiac disease [controls]). Structured questionnaires with 22 items (anagraphic, anamnestic, clinical, and biochemical data) were filled out for each patient. All patients underwent gastroscopy with biopsies. ANNs and LDA were applied to recognize patients with ABG. Experiment 1: random selection on 37 variables, experiment 2: optimization process on 30 variables, experiment 3: input data reduction on 8 variables, experiment 4: use of only clinical input data on 5 variables, and experiment 5: use of only serological variables. RESULTS: In experiment 1, overall accuracies of ANNs and LDA were 96.6% and 94.6%, respectively, for predicting patients with ABG. In experiment 2, ANNs and LDA reached an overall accuracy of 98.8% and 96.8%, respectively. In experiment 3, overall accuracy of ANNs was 98.4%. In experiment 4, overall accuracies of ANNs and LDA were, respectively, 91.3% and 88.6%. In experiment 5, overall accuracies of ANNs and LDA were, respectively, 97.7% and 94.5%. CONCLUSION: This preliminary study suggests that advanced statistical methods, not only ANNs, but also LDA, may contribute to better address bioptic sampling during gastroscopy in a subset of patients in whom ABG may be suspected on the basis of aspecific gastrointestinal symptoms or non-digestive disorders.

Adult↗
PubMed2005

Medical concepts related to individual risk are better explained with "plausibility" rather than "probability".

BACKGROUND: The concept of risk has pervaded medical literature in the last decades and has become a familiar topic, and the concept of probability, linked to binary logic approach, is commonly applied in epidemiology and clinical medicine. The application of probability theory to groups of individuals is quite straightforward but can pose communication challenges at individual level. Few articles by the way have tried to focus the concept of "risk" at the individual subject level rather than at population level. DISCUSSION: The author has reviewed the conceptual framework which has led to the use of probability theory in the medical field in a time when the principal causes of death were represented by acute disease often of infective origin. In the present scenario, in which chronic degenerative disease dominate and there are smooth transitions between health and disease the use of fuzzy logic rather than binary logic would be more appropriate. The use of fuzzy logic in which more than two possible truth-value assignments are allowed overcomes the trap of probability theory when dealing with uncertain outcomes, thereby making the meaning of a certain prognostic statement easier to understand by the patient. SUMMARY: At individual subject level the recourse to the term plausibility, related to fuzzy logic, would help the physician to communicate to the patient more efficiently in comparison with the term probability, related to binary logic. This would represent an evident advantage for the transfer of medical evidences to individual subjects.

Chronic Disease↗
PubMed2005

Artificial neural networks allow the use of simultaneous measurements of Alzheimer disease markers for early detection of the disease.

BACKGROUND: Previous studies have shown that in platelets of mild Alzheimer Disease (AD) patients there are alterations of specific APP forms, paralleled by alteration in expression level of both ADAM 10 and BACE when compared to control subjects. Due to the poor linear relation among each key-element of beta-amyloid cascade and the target diagnosis, the use of systems able to afford non linear tasks, like artificial neural networks (ANNs), should allow a better discriminating capacity in comparison with classical statistics. OBJECTIVE: To evaluate the accuracy of ANNs in AD diagnosis. METHODS: 37 mild-AD patients and 25 control subjects were enrolled, and APP, ADM10 and BACE measures were performed. Fifteen different models of feed-forward and complex-recurrent ANNs (provided by Semeion Research Centre), based on different learning laws (back propagation, sine-net, bi-modal) were compared with the linear discriminant analysis (LDA). RESULTS: The best ANN model correctly identified mild AD patients in the 94% of cases and the control subjects in the 92%. The corresponding diagnostic performance obtained with LDA was 90% and 73%. CONCLUSION: This preliminary study suggests that the processing of biochemical tests related to beta-amyloid cascade with ANNs allows a very good discrimination of AD in early stages, higher than that obtainable with classical statistics methods.

Journal Article↗
PubMed2005

The REALAB project: a new method for the formulation of reference intervals based on current data.

BACKGROUND: In a primary healthcare center concerned more with maintaining wellness than with diagnosing and monitoring illness, it is particularly important to compare patients' results with reference intervals derived from a matched population by use of defined statistical methods. METHODS: Laboratory results over a 3-year period (approximately 15,000,000 records; 197,350 individuals) were retrieved from our laboratory information system. An inclusion/exclusion procedure for individual patients was applied based on (a) presence of at least 1 of 23 previously defined "basic tests"; (b) only 1 measurement per test by the laboratory over the 3-year period; (c) for each test, absence of any abnormality in the correlated tests. Before the third step, correlations among quantities were assessed by a Spearman correlation matrix, comparing each of the 23 basic tests with all remaining tests by use of a novel multivariate algorithm. RESULTS: The initial sample group (n = 197,350) was reduced stepwise by the selection criteria outlined above to 166,027, then to 93,649, and finally to 61 246 individuals constituting our reference sample group. Results from the last 2 groups were used to calculate sex-specific, and in some cases age-related, reference limits for the 23 basic tests and for 13 additional quantities. Reference limits were calculated throughout this study by nonparametric estimation of percentiles. CONCLUSION: Reference values derived by retrospective analysis of large samples of data obtained at a given institution are particularly suitable for the evaluation of results for the presenting patient population at that institution.

Adolescent↗
PubMed2005

The traveling salesman problem as a new screening test in early Alzheimer's disease: an exploratory study. Visual problem-solving in AD.

BACKGROUND AND AIMS: In the clinical setting, brief general mental status tests tend to detect early-stage Alzheimer's disease (AD) less well than more specific cognitive tests. Some preliminary information was collected on the diagnostic accuracy of the Traveling Salesman Problem (TSP) compared with the Mini-Mental State Examination (MMSE) in recognizing early AD from normal aging. METHODS: Fifteen AD outpatients (mean +/- SD MMSE: 24.45 +/- 2.61) and 30 age- and education-matched controls were submitted in a single blind protocol to a paper-and-pencil visually-presented version of the TSP, containing a random array of 30 points (TSP30). The task consisted of drawing the shortest continuous path, passing through each point once and only once, and returning to the starting point. Path lengths for subjects' solutions were computed and compared with the optimal solution given by a specific evolutionary algorithm called GenD. RESULTS: TP30 discriminated significantly better between AD subjects and controls (ROC curve AUC = 0.976; 95% CI 0.94-1.01) compared with the MMSE corrected for age and education (ROC curve AUC = 0.877; 95% CI 0.74-1.005). A path length of 478.2354, taken as "cut-off point", classified correctly subjects with a sensitivity of 93.3% and a specificity of 99.3%, whereas a score corrected for age and education of 25.85 on the MMSE had a sensitivity of 73.3% and a specificity of 96.7%. CONCLUSIONS: The TSP seems to be particularly sensitive to early AD and independent of patient's age and educational level. The high diagnostic ability, simplicity, and independence of age and education make the TSP promising as a screening test for early AD.

Aged↗
PubMed2005

Two different Alzheimer diseases in men and women: clues from advanced neural networks and artificial intelligence.

BACKGROUND: Studies of the gender-related differences in the clinical presentation of Alzheimer's disease (AD) have focused on specific aspects of the disease (eg, circulating metabolites, cognitive capacity, or epidemiologic trends). OBJECTIVE: This study accounts for several descriptors of the disease simultaneously, providing a multidimensional analysis of a cohort of patients with AD. METHODS: Our analysis was conducted using self-organizing maps (SOMs). The high number (60) of independent variables (clinical, demographic, biochemical, and neuropsychological) observed in the study patients defines a complex and high-dimensional input space that can be processed by SOMs. Without supervision, SOMs examine nonlinear relations among the variables and cluster observations so that topologic relationships between variables correspond to the similarity of their distribution. Through such nonlinear autoclustering, subsets of observations (ie, clusters of subjects) can be identified in which essential information is concentrated. Each subject is identified by particular values of the variables (the record), and a specific set of variable values (the codebook) defines a distinct class. RESULTS: The study sample included 211 patients with mild to moderate AD (143 women, 68 men; mean [SD] age, 71.9 [7.2] years). All patients were assigned to 3 macroclasses-called A, B, and C-on the basis of matrix codebook neighborhoods. In terms of vectorial distance between codebooks, class A and B were quite similar, whereas the separation between class C and classes A and B was evident. The SOM distribution of values of variables across the output matrix did not show any specific pattern for most of the considered characteristics. However, we found only male patients in class C. This class distinction was not substantially changed when sex was removed from the database. Male and female patients were comparable with respect to dementia severity, demographic characteristics, psychiatric and behavioral symptoms, indicators of physical disability, and general health status. CONCLUSIONS: SOMs indicate nonlinear multifactorial interactions among the descriptors of the features of AD that seem to be linked to sex and would have been missed by traditional statistical analysis. This finding may offer a novel epidemiologic rationale for research into different pathogenic mechanisms in men and women with AD.

Aged↗
PubMed2005

Artificial neural networks are able to recognize gastro-oesophageal reflux disease patients solely on the basis of clinical data.

BACKGROUND: Artificial neural networks (ANN) are modelling mechanisms that are highly flexible and adaptive to solve the non-linearity inherent in the relationship between symptoms and underlying pathology. OBJECTIVES: To assess the efficacy of ANN in achieving a diagnosis of gastro-oesophageal reflux disease (GORD) using oesophagoscopy or pH-metry as a diagnostic gold standard and discriminant analysis as a statistical comparator technique in a group of patients with typical GORD symptoms and with or without GORD objective findings (e.g. a positive oesophagoscopy or a pathological oesophageal pH-metry). METHODS: The sample of 159 cases (88 men, 71 women) presenting with typical symptoms of GORD, were subdivided on the basis of endoscopy and pH-metry results into two groups: GORD patients with or without oesophagitis, group 1 (N=103), and pH and endoscopy-negative patients in whom both examinations were negative, group 2 (N=56). A total of 101 different independent variables were collected: demographic information, medical history, generic health state and lifestyle, intensity and frequency of typical and atypical symptoms based on the Italian version of the Gastroesophageal Reflux Questionnaire (Mayo Clinic). The diagnosis was used as a dependent variable. Different ANN models were assessed. RESULTS: Specific evolutionary algorithms selected 45 independent variables, concerning clinical and demographic features, as predictors of the diagnosis. The highest predictive performance was achieved by a 'back propagation' ANN, which was consistently 100% accurate in identifying the correct diagnosis compared with 78% obtained by traditional discriminant analysis. CONCLUSION: On the basis of this preliminary work, the use of ANN seems to be a promising approach for predicting diagnosis without the need for invasive diagnostic methods in patients suffering from GORD symptoms.

Adult↗
PubMed2004

The metabolic syndrome in treatment-seeking obese persons.

Obesity is a major risk factor for several metabolic diseases, frequently clustering to form the metabolic syndrome, carrying a high risk of cardiovascular mortality. We aimed to assess the prevalence of the metabolic syndrome in treatment-seeking obese subjects and the potential protective effect of physical activity. A cross-sectional analysis of data from a large Italian database of treatment-seeking obese subjects was performed. The metabolic syndrome was defined according to the criteria provisionally set by the National Cholesterol Education Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults, based on waist circumference, fasting glucose, triglyceride (TG) and high-density lipoprotein-cholesterol (HDL-C) levels, and arterial pressure. Data were available in 1,889 Caucasian subjects, 78% females, from 25 obesity centers. Minimum criteria for the metabolic syndrome were fulfilled in 53% of cases. The prevalence increased with age and obesity class and was negatively associated with participation in a structured program of physical activity (odds ratio, 0.76; 0.58 to 0.99; P =.041), after correction for age, sex, and body mass. The prevalence of cardiovascular disease was higher in subjects with the metabolic syndrome. A subset of 12.8% of cases had no metabolic abnormalities. They had a lower prevalence of abdominal obesity and cardiovascular disease. Isolated obesity was significantly associated with physical activity (odds ratio, 1.86; 1.33 to 2.60; P =.0003). Multiple metabolic disorders are present in most obese patients, and their prevalence is lower in physically active subjects. It is time to move towards a more integrated approach and to reconsider resource allocation to improve lifestyle changes for large-scale control of obesity.

Adult↗
PubMed2004

Recognition of patients with cardiovascular disease by artificial neural networks.

BACKGROUND: Artificial neural networks (ANNs) are computer algorithms inspired by the highly interactive processing of the human brain. When exposed to complex data sets, ANNs can learn the mechanisms that correlate different variables and perform complex classification tasks. AIMS: A database, of 949 patients and 54 variables, was analysed to evaluate the capacity of ANNs to recognise patients with (VE+, n = 196) or without (VE-, n = 753) a history of vascular events on the basis of vascular risk factors (VRFs), carotid ultrasound variables (UVs) or both. METHOD: The performance of ANN was assessed by calculating the percentage of correct identifications of VE+ and VE- patients (sensitivity and specificity, respectively) and the prediction accuracy (weighted mean between sensitivity and specificity). RESULTS: The results showed that ANNs can be trained to identify VE+ and VE- subjects more accurately than discriminant analyses. When VRFs and UVs were used as input variables, the prediction accuracies of the ANN providing the best results were 80.8% and 79.2%, respectively. The addition of gender, age, weight, height and body mass index to UVs increased accuracy of prediction to 83.0%. When the ANNs were allowed to choose the relevant input data automatically (I.S. system-Semeion), 37 variables were selected among 54, five of which were UVs. Using this set of variables as input data, the performance of the ANNs in the classification task reached a prediction accuracy of 85.0%. with the 92.0% correct classification of VE+ patients. CONCLUSIONS: Artificial neural network technology is highly promising in the development of accurate diagnostic tools designed to recognize patients at high risk of cardiovascular diseases.

Adult↗
PubMed2004

Artificial neural networks and artificial organisms can predict Alzheimer pathology in individual patients only on the basis of cognitive and functional status.

Data from several studies have pointed out the existence of a strong correlation between Alzheimer's disease (AD) neuropathology and cognitive state. However, because of their highly complex and nonlinear relationship, it has been difficult to develop a predictive model for individual patient classification through traditional statistical approaches. When exposed to complex data sets, artificial neural networks (ANNs) can recognize patterns, learn the relationship of different variables, and address classification tasks. To predict the results of postmortem brain examinations, we applied ANNs to the Nun Study data set, a longitudinal epidemiological study, which includes annual cognitive and functional evaluation. One hundred seventeen subjects from the study participated in this analysis. We determined how demographic data and the cognitive and functional variables of each subject during the last year of her life could predict the presence of brain pathology expressed as Braak stages, neurofibrillary tangles (NFTs) and neuritic plaques (NPs) count in the neocortex and hippocampus, and brain atrophy. The result of this analysis was then compared with traditional statistical models. ANNs proved to be better predictors than Linear Discriminant Analysis in all experimentations (+ approximately 10% in overall accuracy), especially when assembled in Artificial Organisms (+ approximately 20% in overall accuracy). Demographic, cognitive, and clinical variables were better predictors of tangles count in the neocortex and in the hippocampus when compared to NPs count. These findings strengthen the hypothesis that neurofibrillary pathology may represent the major anatomic substrate of the cognitive impairment found in AD.

Alzheimer Disease↗
PubMed2003

Assessment of a new synbiotic preparation in healthy volunteers: survival, persistence of probiotic strains and its effect on the indigenous flora.

BACKGROUND: Use of synbiotic preparations as dietary supplement is believed to be a valid approach to restore and maintain colonic microflora. However, only few papers have been published on the assessment of these food supplements and none of them have used molecular biology techniques to evaluate the effects of the probiotic components. METHODS: Twelve healthy volunteers were recruited. Faecal samples were taken before and at various time points during the administration period and at day 3 in the post-treatment period. Stool culture were performed and amplified ribosomal DNA restriction analysis was used to detect L. paracasei, the major bacterial component of the synbiotic products. RESULTS: An increase of at least 1 log of L. paracasei-like bacteria was observed in all subjects. An increase of as much as 3 log was seen in subjects who had a low number of L. paracasei-like lactobacilli at the baseline. The counts of L. paracasei-like lactobacilli were found to persist for at least 3 days after discontinuation of intake in healthy volunteers in 7 subjects. Genetic analysis showed that the maiority of vancomicin insensitive lactobacilli were real L. paracasei, as the strains administered with the tested product. CONCLUSION: This study has shown that the strains of L paracasei administered with a synbiotic dietary supplement are able to survive through the gastrointestinal tract and to persist for at least a few days. It was also shown the efficacy of a synbiotic preparation to positively affect the microflora of healthy volunteers.

Journal Article↗
PubMed2002

Use of artificial networks in clinical trials: a pilot study to predict responsiveness to donepezil in Alzheimer's disease.

OBJECTIVES: To evaluate the accuracy of artificial neural networks compared with discriminant analysis in classifying positive and negative response to the cholinesterase inhibitor donepezil in a group of Alzheimer's disease (AD) patients. DESIGN: Convenience sample. SETTING: Patients with mild to moderate AD consecutively admitted to a geriatric day hospital and treated with donepezil 5 mg/day. PARTICIPANTS: Sixty-one older patients of both sexes with AD. MEASUREMENTS: Accuracy in detecting subjects sensitive (responders) or not (nonresponders) to 3-month therapy with ANNs. The criterion standard for evaluation of efficacy was the scores of Alzheimer's Disease Assessment Scale-Cognitive portion and Clinician's Interview Based Impression of Change-plus scales. RESULTS: ANNs were more effective in discriminating between responders and nonresponders than other advanced statistical methods, particularly linear discriminant analysis. The total accuracy in predicting the outcome was 92.59%. CONCLUSIONS: ANNs appear to be a useful tool in detecting patient responsiveness to pharmacological treatment in AD.

Aged↗