Stroke 5.0: A Technology Ecosystem to Support Acute Stroke Integrated Clinical Management.

Stroke remains a significant global health burden, with substantial costs and morbidity associated with its occurrence. To address this challenge, STROKE 5.0 proposes a comprehensive approach to stroke care management, integrating advanced digital technologies and clinical expertise. This paper presents the rationale, design, and potential impact of the STROKE 5.0 platform, which aims to optimize stroke care delivery from pre-hospital assessment through acute hospitalization. The platform facilitates early symptom recognition, efficient emergency response, and streamlined hospital management through intelligent decision support systems. By leveraging predictive analytics and personalized care pathways, STROKE 5.0 seeks to enhance clinical outcomes while providing a platform capable of optimizing the efficiency of service delivery. This innovative model represents a proactive shift towards evidence-based, patient-centered stroke care, with implications for healthcare quality improvement and resource allocation in the digital health domain.

Comparing Perioperative Complications of Off-Clamp versus On-Clamp Partial Nephrectomy for Renal Cancer Using a Novel Energy Balancing Weights Method.

Partial nephrectomy (PN) is the primary surgical method for renal tumor treatment, typically involving clamping the renal artery during tumor removal, leading to warm ischemia and potential renal function impairment. Off-clamp approaches have been explored to mitigate organ damage, yet few results have emerged about the possible effects on hemoglobin loss. Most evidence comes from retrospective studies using propensity score matching, known to be sensitive to PS model misspecification. The energy balancing weights (EBW) method offers an alternative method to address bias by focusing on balancing all the characteristics of covariate distribution. We aimed to compare on- vs. off-clamp techniques in PN using EB-weighted retrospective patient data. Out of 333 consecutive PNs (275/58 on/off-clamp ratio), the EBW method achieved balanced variables, notably tumor anatomy and staging. No significant differences were observed in the operative endpoints between on- and off-clamp techniques, although off-clamp PNs showed slight reductions in hemoglobin loss and renal function decline, albeit with slightly higher perioperative blood loss. Our findings support previous evidence, indicating comparable surgical outcomes between standard and off-clamp procedures, with the EBW method proving effective in balancing baseline variables in observational studies comparing interventions.

External Validation and Calibration of the DecaPreT Prediction Model for Decannulation in Patients with Acquired Brain Injury.

We propose a new set of clinical variables for a more accurate early prediction of safe decannulation in patients with severe acquired brain injury (ABI), during a post-acute rehabilitation course. Starting from the already validated DecaPreT scale, we tested the accuracy of new logistic regression models where the coefficients of the original predictors were reestimated. Patients with tracheostomy were retrospectively selected from the database of the neurorehabilitation unit at the S. Anna Institute of Crotone, Italy. New potential predictors of decannulation were screened from variables collected on admission during clinical examination, including (a) age at injury, (b) coma recovery scale-revised (CRS-r) scores, and c) length of ICU period. Of 273 patients with ABI (mean age 53.01 years; 34% female; median DecaPreT = 0.61), 61.5% were safely decannulated before discharge. In the validation phase, the linear logistic prediction model, created with the new multivariable predictors, obtained an area under the receiver operating characteristics curve of 0.901. Our model improves the reliability of simple clinical variables detected at the admission of the post-acute phase in predicting decannulation of ABI patients, thus helping clinicians to plan better rehabilitation.

The Impact of Medical Complications in Predicting the Rehabilitation Outcome of Patients With Disorders of Consciousness After Severe Traumatic Brain Injury.

In this study, we sought to assess the predictors of outcome in patients with disorders of consciousness (DOC) after severe traumatic brain injury (TBI) during neurorehabilitation stay. In total, 96 patients with DOC (vegetative state, minimally conscious state, or emergence from minimally conscious state) were enrolled (69 males; mean age 43.6 ± 20.8 years) and the improvement of the degree of disability, as assessed by the Disability Rating Scale, was considered the main outcome measure. To define the best predictor, a series of demographical and clinical factors were modeled using a twofold approach: (1) logistic regression to evaluate a possible causal effect among variables; and (2) machine learning algorithms (ML), to define the best predictive model. Univariate analysis demonstrated that disability in DOC patients statistically decreased at the discharge with respect to admission. Genitourinary was the most frequent medical complication (MC) emerging during the neurorehabilitation period. The logistic model revealed that the total amount of MCs is a risk factor for lack of functional improvement. ML discloses that the most important prognostic factors are the respiratory and hepatic complications together with the presence of the upper gastrointestinal comorbidities. Our study provides new evidence on the most adverse short-term factors predicting a functional recovery in DOC patients after severe TBI. The occurrence of medical complications during neurorehabilitation stay should be considered to avoid poor outcomes.

Outcome prediction in disorders of consciousness: the role of coma recovery scale revised.

BACKGROUND: To evaluate the utility of the revised coma remission scale (CRS-r), together with other clinical variables, in predicting emergence from disorders of consciousness (DoC) during intensive rehabilitation care. METHODS: Data were retrospectively extracted from the medical records of patients enrolled in a specialized intensive rehabilitation unit. 123 patients in a vegetative state (VS) and 57 in a minimally conscious state (MCS) were included and followed for a period of 8 weeks. Demographical and clinical factors were used as outcome measures. Univariate and multivariate Cox regression models were employed for examining potential predictors for clinical outcome along the time. RESULTS: VS and MCS groups were matched for demographical and clinical variables (i.e., age, aetiology, tracheostomy and route of feeding). Within 2 months after admission in intensive neurorehabilitation unit, 3.9% were dead, 35.5% had a full recovery of consciousness and 66.7% remained in VS or MCS. Multivariate analysis demonstrated that the best predictor of functional improvement was the CRS-r scores. In particular, patients with values greater than 12 at admission were those with a favourable likelihood of emergence from DoC. CONCLUSIONS: Our study highlights the role of the CRS-r scores for predicting a short-term favorable outcome.

Personality biomarkers of pathological gambling: A machine learning study.

BACKGROUND: The application of artificial intelligence to extract predictors of Gambling disorder (GD) is a new field of study. A plethora of studies have suggested that maladaptive personality dispositions may serve as risk factors for GD. NEW METHOD: Here, we used Classification and Regression Trees algorithm to identify multivariate predictive patterns of personality profiles that could identify GD patients from healthy controls at an individual level. Forty psychiatric patients, recruited from specialized gambling clinics, without any additional comorbidity and 160 matched healthy controls completed the Five-Factor model of personality as measured by the NEO-PI-R, which were used to build the classification model. RESULTS: Classification algorithm was able to discriminate individuals with GD from controls with an AUC of 77.3% (95% CI 0.65-0.88, p<0.0001). A multidimensional construct of traits including sub-facets of openness, neuroticism and conscientiousness was employed by algorithm for classification detection. COMPARISON WITH EXISTING METHOD(S): To the best of our knowledge, this is the first study that combines behavioral data with machine learning approach useful to extract multidimensional features characterizing GD realm. CONCLUSION: Our study provides a proof-of-concept demonstrating the potential of the proposed approach for GD diagnosis. The multivariate combination of personality facets characterizing individuals with GD can potentially be used to assess subjects' vulnerability in clinical setting.

Clinical and radiological evaluation of sacroiliac joints compared with ultrasound examination in early spondyloarthritis.

OBJECTIVE: To compare clinical and X-ray examinations with US findings of SI joints (SIJ) in early SpA patients. METHODS: Twenty-three early SpA patients, diagnosed according to Assessment of SpondyloArthritis international Society criteria, were investigated clinically [sacral sulcus tenderness, BASMI, BASFI, BASDAI, pain and fatigue visual analogue scale (VAS), morning stiffness and sleep disturbance], with SIJ X-rays (New York score) and with My Lab70 US 7-10 MHz US (Esaote, Genoa, Italy), evaluating the width of the SIJ capsule and posterior sacroiliac (PSL) and sacrotuberosus (STL) ligament thickness and comparing the results with 23 healthy controls. RESULTS: SIJ width [right 2.2 (0.6) and left 2.3 (0.7) in SpA vs 1.6 (0.1) and 1.7 (0.2) in healthy controls, respectively, expressed as mean (s.d.)] and STL thickness [right 3.9 (1.3) and left 3.4 (1.0) vs 1.8 (0.1) and 1.8 (0.1), respectively, expressed as mean (s.d.)] were higher in SpA patients than in controls (P < 0.001 and P < 0.05, respectively). PSL thickness was similar in patients and controls. Only STL thickness was higher when SIJ was tender at clinical examination (P < 0.01) and correlated with pain VAS (P < 0.001) and BASFI (P < 0.05). Furthermore, SIJ US results were unrelated to X-ray findings (similar when X-ray sacroiliitis was present and not). CONCLUSION: Our exploratory study suggested that in early SpA patients US might be a promising method, complementary to other imaging techniques, to study articular and soft tissue periarticular involvement of SIJ, independent of clinical and X-ray examination.

An optimal decision making model for supporting week hospital management.

Week Hospital is an innovative inpatient health care organization and management, by which hospital stay services are planned in advance and delivered on week-time basis to elective patients. In this context, a strategic decision is the optimal clinical management of patients, and, in particular, devising efficient and effective admission and scheduling procedures, by tackling different requirements such as beds' availability, diagnostic resources, and treatment capabilities. The main aim is to maximize the patient flow, by ensuring the delivery of all clinical services during the week. In this paper, the optimal management of Week Hospital patients is considered. We have developed and validated an innovative integer programming model, based on clinical resources allocation and beds utilization. In particular, the model aims at scheduling Week Hospital patients' admission/discharge, possibly reducing the length of stay on the basis of an available timetable of clinical services. The performance of the model has been evaluated, in terms of efficiency and robustness, by considering real data coming from a Week Hospital Rheumatology Division. The experimental results have been satisfactory and demonstrate the effectiveness of the proposed approach.

A novel, population-specific approach to define frailty.

The description of frailty, a syndrome of the elderly due to the decline of homeostatic capacities, has opened new opportunities in the study of the biological basis of human aging. However, the noticeable heterogeneity for this trait in different geographic areas makes it difficult to use standardized methods for measuring the quality of aging in different populations. Consequently, the necessity to carry out population-specific surveys to define tools which are able to highlight groups of subjects with homogeneous aging phenotype within each population has emerged. We carried out an extensive monitoring of the status of the elderly population in Calabria, southern Italy, performing a geriatric multidimensional evaluation of 680 subjects (age range 65-108 years). Then, in order to classify the subjects, we applied a cluster analysis which considered physical, cognitive, and psychological parameters such as classification variables. We identified groups of subjects homogeneous for the aging phenotypes. The diagnostic and predictive soundness of our classification was confirmed by a 3-year longitudinal study. In fact, both Kaplan-Meier estimates of the survival functions and Cox proportional hazard models indicate higher survival chance for subjects characterized by lower frailty. The availability of operative frailty phenotypes allows a reappraisal of the biological basis of healthy aging as it regards both biomarkers correlated with the frail phenotype and the genetic variability associated with the phenotypes identified. Indeed, we found that the frailty phenotype is strongly correlated with clinical parameters associated with the nutritional status.

Digital ulcers in scleroderma: staging, characteristics and sub-setting through observation of 1614 digital lesions.

OBJECTIVE: To evaluate in SSc, the frequency of digital lesions and the morphology, characteristics, natural course and time to healing of 1614 digital ulcers (DUs). METHODS: One hundred SSc patients were followed up for 4 years. In the first step, the digital lesions were observed and classified at the time of presentation [digital pitting scar (DPS); DU; calcinosis; gangrene]. In the second step, DUs were divided into subsets according to their origin and main features. In the third step, the time to healing was recorded for each DU and the influence of DU main characteristics on time to healing was also evaluated. RESULTS: In the first step, 1614 digital lesions were observed: DPS, 712 (44.1%) lesions; DU, 785 (48.6%); calcinosis, 110 (6.8%); and gangrene, 7 (0.8%). In the second step, DUs were subsetted as follows: DU developed on DPS (8.8%), pure DU; DU developed on calcinosis (60%); DU derived from gangrene. In the third step, the mean time to healing was 25.6 (15.6) days in DPS, 76.2 (64) days in pure DU, 93.6 (59.2) days in calcinosis ulcers and 281.1 (263.3) in gangrene. CONCLUSIONS: In SSc, digital lesions are represented by DPS, DU, calcinosis and gangrene, and provide an evidence-based DU subsetting according to their origin and main characteristics. Subsetting may be helpful for a precise DU evaluation and staging, and in randomized controlled trials for a precise identification of those DUs that are to be included in therapeutic studies.

A Hyper-Solution Framework for SVM Classification: Application for Predicting Destabilizations in Chronic Heart Failure Patients.

Support Vector Machines (SVMs) represent a powerful learning paradigm able to provide accurate and reliable decision functions in several application fields. In particular, they are really attractive for application in medical domain, where often a lack of knowledge exists. Kernel trick, on which SVMs are based, allows to map non-linearly separable data into potentially linearly separable one, according to the kernel function and its internal parameters value. During recent years non-parametric approaches have also been proposed for learning the most appropriate kernel, such as linear combination of basic kernels. Thus, SVMs classifiers may have several parameters to be tuned and their optimal values are usually difficult to be identified a-priori. Furthermore, combining different classifiers may reduce risk to perform errors on new unseen data. For such reasons, we present an hyper-solution framework for SVM classification, based on meta-heuristics, that searches for the most reliable hyper-classifier (SVM with a basic kernel, SVM with a combination of kernel, and ensemble of SVMs), and for its optimal configuration. We have applied the proposed framework on a critical and quite complex issue for the management of Chronic Heart Failure patient: the early detection of decompensation conditions. In fact, predicting new destabilizations in advance may reduce the burden of heart failure on the healthcare systems while improving quality of life of affected patients. Promising reliability has been obtained on 10-fold cross validation, proving our approach to be efficient and effective for an high-level analysis of clinical data.

High frequency ultrasound measurement of digital dermal thickness in systemic sclerosis.

BACKGROUND: Currently, assessment of dermal thickness in systemic sclerosis (SSc) is performed by palpation and assessment using the modified Rodnan skin score (mRSS). OBJECTIVE: To verify whether high frequency ultrasound (US) may be a reliable and a reproducible method to measure digital dermal thickness. METHODS: In 70 patients with SSc, skin thickness was evaluated with US by 2 observers at 2 different sites on the second digit of the dominant limb to determine the interobserver variability. Patients and controls were examined twice by the first observer for intraobserver variability. Patients were divided into three subgroups according to the phase of the disease (oedematous, fibrotic or atrophic). RESULTS: At both examined areas, US showed a significant dermal thickening (p<0.001) in the whole group of patients with SSc. A low intraobserver and interobserver variability was found. A highly significant correlation between the global mRSS and the local dermal thickness at the two examined sites (p=0.032, p=0.021) was detected. Skin thickness was significantly higher in the oedematous than in the fibrotic group (p<0.001) and significantly higher in the fibrotic and the oedematous group (p<0.001) than in the atrophic group (p<0.002). CONCLUSIONS: US is a reliable tool giving reproducible results, and is able to detect digital dermal thickening in SSc.

A novel similarity-measure for the analysis of genetic data in complex phenotypes.

BACKGROUND: Recent technological advances in DNA sequencing and genotyping have led to the accumulation of a remarkable quantity of data on genetic polymorphisms. However, the development of new statistical and computational tools for effective processing of these data has not been equally as fast. In particular, Machine Learning literature is limited to relatively few papers which are focused on the development and application of data mining methods for the analysis of genetic variability. On the other hand, these papers apply to genetic data procedures which had been developed for a different kind of analysis and do not take into account the peculiarities of population genetics. The aim of our study was to define a new similarity measure, specifically conceived for measuring the similarity between the genetic profiles of two groups of subjects (i.e., cases and controls) taking into account that genetic profiles are usually distributed in a population group according to the Hardy Weinberg equilibrium. RESULTS: We set up a new kernel function consisting of a similarity measure between groups of subjects genotyped for numerous genetic loci. This measure weighs different genetic profiles according to the estimates of gene frequencies at Hardy-Weinberg equilibrium in the population. We named this function the "Hardy-Weinberg kernel". The effectiveness of the Hardy-Weinberg kernel was compared to the performance of the well established linear kernel. We found that the Hardy-Weinberg kernel significantly outperformed the linear kernel in a number of experiments where we used either simulated data or real data. CONCLUSION: The "Hardy-Weinberg kernel" reported here represents one of the first attempts at incorporating genetic knowledge into the definition of a kernel function designed for the analysis of genetic data. We show that the best performance of the "Hardy-Weinberg kernel" is observed when rare genotypes have different frequencies in cases and controls. The ability to capture the effect of rare genotypes on phenotypic traits might be a very important and useful feature, as most of the current statistical tools loose most of their statistical power when rare genotypes are involved in the susceptibility to the trait under study.

Beneficial effects of iodized salt prophylaxis on thyroid volume in an iodine deficient area of southern Italy.

OBJECTIVE AND SUBJECTS: Goitre prevalence in school-age children is an indicator of the severity of iodine deficiency disorders (IDD) in an endemic area. The aims of the present study were (i) to provide ultrasound thyroid volume (TV) reference values in a healthy population of school-children aged 11-14 year living in iodine-sufficient areas of Calabria region (ii) to assess both goitre prevalence and urinary iodine (UI) concentration in all children aged 11-14 year from four mildly iodine-deficient areas in which we have carried out a program of salt iodization and (iii) to evaluate the efficacy of the iodoprophylaxis in an adult population living in a small village of the same endemic area. DESIGN: Cross-sectional and prospective studies. METHODS: TV was assessed by ultrasonography and iodine intake was estimated by measuring iodine excretion in spot urine samples. Results We provided the ultrasound normal reference values as a function of age and body surface area, which displayed significant differences from those recommended by the World Health Organization. By adopting local criteria, the prevalence of goitre in children ranged from 23.4% to 27.7% normalized for age and body surface area, respectively, while the UI excretion was < 100 microg/l in 38% of subjects studied. In an adult population living in the same endemic area, goitre prevalence was lowest in the 18-27-year-old age group, and increased progressively with age. CONCLUSION: We propose for the first time local reference ultrasound values for TV in a population of 11-14-year-old school-children that should be used for monitoring IDDs and have demonstrated the beneficial effects of iodoprophylaxis in consistent with reduced goitre prevalence in children and in the young adult population studied.

HEARTFAID: A knowledge based platform of services for supporting medical-clinical management of heart failure within elderly population.

HEARTFAID is a research and development project aimed at devising, developing and validating an innovative knowledge based platform of services, able to improve early diagnosis and to make more effective the medical-clinical management of heart diseases within elderly population. Chronic Heart Failure is one of the most remarkable health problems for prevalence and morbidity, especially in the developed western countries, with a strong impact in terms of social and economic effects. All these aspects are typically emphasized within the elderly population, with very frequent hospital admissions and a significant increase of medical costs. Recent studies and experiences have demonstrated that accurate heart failure management programs, based on a suitable integration of inpatient and outpatient clinical procedures, might prevent and reduce hospital admissions, improving clinical status and reducing costs. HEARTFAID aims at defining efficient and effective health care delivery organization and management models for the "optimal" management of the care in the field of cardiovascular diseases. The HEARTFAID innovative computerized system will improve the processes of diagnosis, prognosis and therapy provision, providing the following services: * electronic health record for easy and ubiquitous access to heterogeneous patients data;* integrated services for healthcare professionals, including patient telemonitoring, signal and image processing, alert and alarm system;* clinical decision support in the heart failure domain, based on pattern recognition in historical data, knowledge discovery analysis and inferences on patients' clinical data.The formalization of the pre-existing clinical knowledge and the discovery of new elicited knowledge represent the core of the HEARTFAID platform.

A new organ transplantation location-allocation policy: a case study of Italy.

In this paper, we propose a location model for the optimal organization of transplant system. Instead of simulation approach, which is typical when facing many health care applications, our approach is distinctively based on a mathematical programming formulation of the relevant problem. In particular, we focus on the critical role of time in transplantation process as well as on a spatial distribution of transplant centers. The allocation of transplantable organs across regions with the objective of attaining regional equity in health care, is the aim of this paper. Our model differs from previous modeling approaches in that it considers the nationwide reorganization of the transplant system, identifying system barriers that may impair equity and efficiency. The demolition of these barriers may leads on a reduction of waiting lists and of wasted organs. We provide the basic structure and the properties of the model, and validate it on a real case study. The experimental validation of the model demonstrates the effectiveness and robustness of our proposal.