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During the COVID-19 lockdown, a high prevalence of disruption in lifestyle habits was reported, especially concerning sleep habits. The present study aimed to explore the relationship between lifestyles and the COVID-19 lockdown on subjects living in Mexico. A cross-sectional survey was conducted. An online questionnaire was administered to survey participants. A clustering procedure was performed to identify groups of survey respondents with similar characteristics. There were 155 survey respondents. Most of them were women (83%) of young age. The clustering identified two distinct groups of subjects, i.e., Cluster 1 and 2. The subjects in Cluster 2 were younger, more likely to use social media, and spent much more time watching TV/surfing the internet than subjects in Cluster 1. Furthermore, the prevalence of problems with falling and staying asleep during the lockdown was higher in Cluster 2 than in Cluster 1 (14% in Cluster 2 vs. 4% in Cluster 1) even though nobody from Cluster 2 had sleep problems before the lockdown. The study showed that younger respondents were those more affected by lockdown measures. [ABSTRACT FROM AUTHOR]

Simple Summary: This study aimed to predict the Length of hospital Stay (LoS) after laparoscopic right hemicolectomy for colon cancer using machine learning techniques. Accurately forecasting LoS is crucial for improving patient care and hospital resource management. The researchers utilized data from two large Italian studies, CoDIG 1 and CoDIG 2, to train and validate various machine learning models. The Random Forest (RF) algorithm demonstrated the best internal performance, while the Support Vector Machine (SVM) outperformed in external validation. Key factors influencing LoS included the use of fast-track protocols, type of anastomosis, and drainage. These findings could help tailor postoperative care and optimize hospital resources, ultimately enhancing patient outcomes and operational efficiency. The evolution of laparoscopic right hemicolectomy, particularly with complete mesocolic excision (CME) and central vascular ligation (CVL), represents a significant advancement in colon cancer surgery. The CoDIG 1 and CoDIG 2 studies highlighted Italy's progressive approach, providing useful findings for optimizing patient outcomes and procedural efficiency. Within this context, accurately predicting postoperative length of stay (LoS) is crucial for improving resource allocation and patient care, yet its determination through machine learning techniques (MLTs) remains underexplored. This study aimed to harness MLTs to forecast the LoS for patients undergoing right hemicolectomy for colon cancer, using data from the CoDIG 1 (1224 patients) and CoDIG 2 (788 patients) studies. Multiple MLT algorithms, including random forest (RF) and support vector machine (SVM), were trained to predict LoS, with CoDIG 1 data used for internal validation and CoDIG 2 data for external validation. The RF algorithm showed a strong internal validation performance, achieving the best performances and a 0.92 ROC in predicting long-term stays (more than 5 days). External validation using the SVM model demonstrated 75% ROC values. Factors such as fast-track protocols, anastomosis, and drainage emerged as key predictors of LoS. Integrating MLTs into predicting postoperative LOS in colon cancer surgery offers a promising avenue for personalized patient care and improved surgical management. Using intraoperative features in the algorithm enables the profiling of a patient's stay based on the planned intervention. This issue is important for tailoring postoperative care to individual patients and for hospitals to effectively plan and manage long-term stays for more critical procedures. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had direct and indirect effects on daily life. In hospitals, the impact of the pandemic was observed in the diagnostic and therapeutic workflow. In this work, we explored potential changes in activities related to the treatment of foreign body injuries (FBIs) in children and the behavioral habits of physicians during the first wave of the pandemic. An online survey was conducted among physicians of the Susy Safe network. The survey comprised items related to respondent information, reference center characteristics, the treatment of FBIs during the COVID-19 pandemic, and a modified COVID-19 Anxiety Scale (CAS). The survey was distributed among the Susy Safe project international network surveillance registry for FBIs. A total of 58 physicians responded to the survey, including 18 (32%) from Europe and 16 (28%) from South America. The respondents indicated that the estimated number of aspirated foreign bodies during the pandemic was lower than or the same as that before the pandemic (43, 74%), and the same was observed for ingested foreign bodies (43, 74%). In univariable logistic regression, no single predictor was associated with a delay in routine care for children or an increasing tendency of medical personnel to avoid procedures. The workflow of physicians involved in the management of FBIs in children has not changed drastically during the COVID-19 pandemic, especially in emergency departments. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak involved a spread of prediction efforts, especially in the early pandemic phase. A better understanding of the epidemiological implications of the different models seems crucial for tailoring prevention policies. This study aims to explore the concordance and discrepancies in outbreak prediction produced by models implemented and used in the first wave of the epidemic. To evaluate the performance of the model, an analysis was carried out on Italian pandemic data from February 24, 2020. The epidemic models were fitted to data collected at 20, 30, 40, 50, 60, 70, 80, 90, and 98 days (the entire time series). At each time step, we made predictions until May 31, 2020. The Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE) were calculated. The GAM model is the most suitable parameterization for predicting the number of new cases; exponential or Poisson models help predict the cumulative number of cases. When the goal is to predict the epidemic peak, GAM, ARIMA, or Bayesian models are preferable. However, the prediction of the pandemic peak could be made carefully during the early stages of the epidemic because the forecast is affected by high uncertainty and may very likely produce the wrong results. [ABSTRACT FROM AUTHOR]

As lactoferrin is a nutritional supplement with proven antiviral and immunomodulatory abilities, it may be used to improve the clinical course of COVID-19. The clinical efficacy and safety of bovine lactoferrin were evaluated in the LAC randomized double-blind placebo-controlled trial. A total of 218 hospitalized adult patients with moderate-to-severe COVID-19 were randomized to receive 800 mg/die oral bovine lactoferrin (n = 113) or placebo (n = 105), both given in combination with standard COVID-19 therapy. No differences in lactoferrin vs. placebo were observed in the primary outcomes: the proportion of death or intensive care unit admission (risk ratio of 1.06 (95% CI 0.63–1.79)) or proportion of discharge or National Early Warning Score 2 (NEWS2) ≤ 2 within 14 days from enrollment (RR of 0.85 (95% CI 0.70–1.04)). Lactoferrin showed an excellent safety and tolerability profile. Even though bovine lactoferrin is safe and tolerable, our results do not support its use in hospitalized patients with moderate-to-severe COVID-19. [ABSTRACT FROM AUTHOR]

Lung cancer is still the leading cause of cancer-related death worldwide. Interest is growing towards early detection and advances in liquid biopsy to isolate circulating tumor cells (CTCs). This pilot study aimed to detect epithelial CTCs in the peripheral blood of early-stage non-small cell lung cancer (NSCLC) patients. We used Smart BioSurface® (SBS) slide, a nanoparticle-coated slide able to immobilize viable nucleated cellular fraction without pre-selection and preserve cell integrity. Forty patients undergoing lung resection for NSCLC were included; they were divided into two groups according to CTC value, with a cut-off of three CTCs/mL. All patients were positive for CTCs. The mean CTC value was 4.7(± 5.8 S.D.) per ml/blood. In one patient, next generation sequencing (NGS) analysis of CTCs revealed v-raf murine sarcoma viral oncogene homolog B(BRAF) V600E mutation, which has also been identified in tissue biopsy. CTCs count affected neither overall survival (OS, p = 0.74) nor progression-free survival (p = 0.829). Multivariable analysis confirmed age (p = 0.020) and pNodal-stage (p = 0.028) as negative predictors of OS. Preliminary results of this pilot study suggest the capability of this method in detecting CTCs in all early-stage NSCLC patients. NGS on single cell, identified as CTC by immunofluorescence staining, is a powerful tool for investigating the molecular landscape of cancer, with the aim of personalized therapies. [ABSTRACT FROM AUTHOR]

Simple Summary: Dairy farmers would benefit from a decision support tool that predicts each cow's probability of survival to future lactations. Based on this output, they might optimize herd breeding decisions by selecting the cows that better cope with the existing housing and management conditions of their own farm. This work explored the accuracy of a novel statistical technique to obtain predictions of cows' probabilities of survival to the second and third lactations, starting from sensor data of daily milk yield, body weight, and rumination time automatically recorded during different stages of the cows' first lactation. Data from six different dairy farms were individually analyzed; in almost all the scenarios, the error associated with the obtained survival predictions was low. The explored decision model applied to the dairy cattle sector revealed good potentialities. Early predictions of cows' probability of survival to different lactations would help farmers in making successful management and breeding decisions. For this purpose, this research explored the adoption of joint models for longitudinal and survival data in the dairy field. An algorithm jointly modelled daily first-lactation sensor data (milk yield, body weight, rumination time) and survival data (i.e., time to culling) from 6 Holstein dairy farms. The algorithm was set to predict survival to the beginning of the second and third lactations (i.e., second and third calving) from sensor observations of the first 60, 150, and 240 days in milk of cows' first lactation. Using 3-time-repeated 3-fold cross-validation, the performance was evaluated in terms of Area Under the Curve and expected error of prediction. Across the different scenarios and farms, the former varied between 45% and 76%, while the latter was between 3.5% and 26%. Significant results were obtained in terms of expected error of prediction, meaning that the method provided survival probabilities in line with the observed events in the datasets (i.e., culling). Furthermore, the performances were stable among farms. These features may justify further research on the use of joint models to predict the survival of dairy cattle. [ABSTRACT FROM AUTHOR]

Stress echocardiography (SE) is based on regional wall motion abnormalities and coronary flow velocity reserve (CFVR). Their independent prognostic capabilities could be better studied with a machine learning (ML) approach. The study aims to assess the SE outcome data by conducting an analysis with an ML approach. We included 6881 prospectively recruited and retrospectively analyzed patients with suspected (n = 4279) or known (n = 2602) coronary artery disease submitted to clinically driven dipyridamole SE. The outcome measure was all-cause death. A random forest survival model was implemented to model the survival function according to the patient's characteristics; 1002 patients recruited by a single, independent center formed the external validation cohort. During a median follow-up of 3.4 years (IQR 1.6–7.5), 814 (12%) patients died. The mortality risk was higher for patients aged >60 years, with a resting ejection fraction < 60%, resting WMSI, positive stress-rest WMSI scores, and CFVR < 3.The C-index performance was 0.79 in the internal and 0.81 in the external validation data set. Survival functions for individual patients were easily obtained with an open access web app. An ML approach can be fruitfully applied to outcome data obtained with SE. Survival showed a constantly increasing relationship with a CFVR < 3.0 and stress-rest wall motion score index > Since processing is largely automated, this approach can be easily scaled to larger and more comprehensive data sets to further refine stratification, guide therapy and be ultimately adopted as an open-source online decision tool. [ABSTRACT FROM AUTHOR]

The aim of the study was to explore the effects of Intentional Rounding, a regular-based proactive patient monitoring, on falls and pressure ulcers in internal medicine units. This is a cluster-randomised controlled study, where units were assigned (1:1) to Intentional Rounding (intervention group) or Standard of Care (control group). The primary outcome was the cumulative incidence of falls and new pressure ulcers. These events were considered separately as secondary endpoints, together with the number of bell calls and the evaluation of patient satisfaction. Primary analyses were carried out on the modified intention-to-treat population (hospitalisation of at least 10 days). Recruitment occurred between October 2019 and March 2020, at which time the study was prematurely closed due to the COVID-19 pandemic. Enrolment totalled 1822 patients at 26 sites; 779 patients were included in the modified intention-to-treat analysis. The intervention group had a lower risk of falls (adjusted incidence rate ratio 0.14; 95% confidence interval, 0.02–0.78; p = 0.03). There were no statistical differences in new pressure ulcers or the cumulative incidence of both adverse events. Mean bell calls for each patient were 15.4 ± 24.1 in the intervention group and 13.7 ± 20.5 in the control group (p = 0.38). Additionally, patient satisfaction in the intervention group was almost at the maximum level. Our study supports the usefulness of Intentional Rounding in a complex and vulnerable population such as that hospitalised in internal medicine units. [ABSTRACT FROM AUTHOR]

(1) Objectives: The aim of this study was to investigate the impact of the prolonged use of continuous-flow left ventricular assist devices (LVADs) on heart transplant (HTx) candidates. (2) Methods: Between January 2012 and December 2019, we included all consecutive patients diagnosed with end-stage heart failure considered for HTx at our institution, who were also eligible for LVAD therapy as a bridge to transplant (BTT). Patients were divided into two groups: those who received an LVAD as BTT (LVAD group) and those who were listed without durable support (No-LVAD group). (3) Results: A total of 250 patients were analyzed. Of these, 70 patients (28%) were directly implanted with an LVAD as BTT, 11 (4.4%) received delayed LVAD implantation, and 169 (67%) were never assisted with an implantable device. The mean follow-up time was 36 ± 29 months. In the multivariate analysis of survival before HTx, LVAD implantation showed a protective effect: LVAD vs. No-LVAD HR 0.01 (p < 0.01) and LVAD vs. LVAD delayed HR 0.13 (p = 0.02). Mortality and adverse events after HTx were similar between LVAD and No-LVAD (p = 0.65 and p = 0.39, respectively). The multi-state survival analysis showed a significantly higher probability of death for No-LVAD vs. LVAD patients with (p = 0.03) or without (p = 0.04) HTx. (4) Conclusions: The use of LVAD as a bridge to transplant was associated with an overall survival benefit, compared to patients listed without LVAD support. [ABSTRACT FROM AUTHOR]

The pandemic outbreak of COVID-19 has posed several questions about public health emergency risk communication. Due to the effort required for the population to adopt appropriate behaviors in response to the emergency, it is essential to inform the public of the epidemic situation with transparent data sources. The COVID-19ita project aimed to develop a public open-source tool to provide timely, updated information on the pandemic's evolution in Italy. It is a web-based application, the front end for the eponymously named R package freely available on GitHub, deployed both in English and Italian. The web application pulls the data from the official repository of the Italian COVID-19 outbreak at the national, regional, and provincial levels. The app allows the user to select information to visualize data in an interactive environment and compare epidemic situations over time and across different Italian regions. At the same time, it provides insights about the outbreak that are explained and commented upon to yield reasoned, focused, timely, and updated information about the outbreak evolution. [ABSTRACT FROM AUTHOR]

This paper presents the epidemiology of foreign body injuries in the Pediatric Emergency Department (PED) of Padova (Italy) along with its trends over an eleven-year period based on administrative data. Annual incidence rates (IRs) of PED presentations for foreign body (FB) injuries per 1000 person-years were calculated. Univariable and multivariable generalized linear (GLM) Poisson models were estimated to evaluate the relationship between FB injury incidence and year, triage priority, nationality, injury site, and FB type. During the study period, there were 217,900 presentations of pediatric residents in the province of Padova; of these, 3084 (1.5%) reported FB injuries involving the ears, nose, throat, gastrointestinal tract or eyes. The annual IR of FB injury episodes increased from 10.45 for 1000 residents in 2007 (95% CI, 9.24, 11.77) to 12.66 for 1000 residents in 2018 (95% CI, 11.35, 14.08). Nonfood items were the FBs that were most frequently reported. The intermediate urgent triage code was the most represented for FB injuries, with IRs ranging from 5.44 (95% CI: 4.59, 6.40) in 2008 to 8.56 in 2018 (95% CI: 7.50, 9.74). A total of 170 patients who presented for FB injuries were hospitalized (5.5%). The annual FB-related injury IR has increased over time, although most episodes are not life threatening. Educational and prevention programs on FB-related injuries should be promoted and dedicated to childcare providers. [ABSTRACT FROM AUTHOR]

One of the most relevant public health issues among pediatric injuries concerns foreign body (FB) aspiration. The risk perception of choking hazards (CH) and risk perception, in general, are complex multifactorial problems that play a significant role in defining protective behavior. Risk prevention policies should take this aspect into account. A lack of scientific knowledge of FB injury risk perception may be evidenced in Brazil and other newly developed countries. This study aims to characterize the differences and peculiarities in risk perception of CH between Italian and Brazilian populations. The risk perception among adults in Italy and Brazil between September and October 2017 was investigated in a survey. A Multiple Correspondence Analysis was carried out to identify the latent components characterizing the risk perception in Italian and Brazilian population samples. The most relevant dimension characterizing risk perception is the “Professional–educational status and the related perception of Risk” (13% of factorial inertia). The Italians identify batteries and magnets as the most dangerous choking risks (20% of responses). On the other hand, Brazilian people, mainly manual laborers (22%) with secondary or primary education (94%), perceive coins as the most dangerous items (30% of responses, p < 0.001). Socio-economic issues characterize the subjective risk perception of Italian and Brazilian survey respondents. In this framework, data-driven prevention strategies could be helpful to tailor intervention strategies to the cultural context to which they are addressed. [ABSTRACT FROM AUTHOR]

Testing for the SARS-CoV-2 infection is critical for tracking the spread of the virus and controlling the transmission dynamics. In the early phase of the pandemic in Italy, the decentralized healthcare system allowed regions to adopt different testing strategies. The objective of this paper is to assess the impact of the extensive testing of symptomatic individuals and their contacts on the number of hospitalizations against a more stringent testing strategy limited to suspected cases with severe respiratory illness and an epidemiological link to a COVID-19 case. A Poisson regression modelling approach was adopted. In the first model developed, the cumulative daily number of positive cases and a temporal trend were considered as explanatory variables. In the second, the cumulative daily number of swabs was further added. The explanatory variable, given by the number of swabs over time, explained most of the observed differences in the number of hospitalizations between the two strategies. The percentage of the expected error dropped from 70% of the first, simpler model to 15%. Increasing testing to detect and isolate infected individuals in the early phase of an outbreak improves the capability to reduce the spread of serious infections, lessening the burden of hospitals. [ABSTRACT FROM AUTHOR]

The present study aimed to assess the feasibility and reliability of an a3utomatic food intake measurement device in estimating energy intake from energy-dense foods. Eighteen volunteers aged 20–36 years were recruited from the University of Padova. The device used in the present study was the Bite Counter (Bite Technologies, Pendleton, USA). The rationale of the device is that the wrist movements occurring in the act of bringing food to the mouth present unique patterns that are recognized and recorded by the Bite Counter. Subjects were asked to wear the Bite Counter on the wrist of the dominant hand, to turn the device on before the first bite and to turn it off once he or she finished his or her meal. The accuracy of caloric intake was significantly different among the methods used. In addition, the device’s accuracy in estimating energy intake varied according to the type and amount of macronutrients present, and the difference was independent of the number of bites recorded. Further research is needed to overcome the current limitations of wearable devices in estimating caloric intake, which is not independent of the food being eaten. [ABSTRACT FROM AUTHOR]

Several European countries suspended or changed recommendations for the use of Vaxzevria (AstraZeneca) for suspected adverse effects due to atypical blood-clotting. This research aims to identify a reference point towards the number of thrombotic events expected in the Italian population over 50 years of age who received Vaxzevria from 22 January to 12 April 2021. The venous thromboembolism (VT) and immune thrombocytopenia (ITP) event rates were estimated from a population-based cohort. The overall VT rate was 1.15 (95% CI 0.93–1.42) per 1000 person-years, and the ITP rate was 2.7 (95% CI 0.7–11) per 100,000 person-years. These figures translate into 83 and two expected events of VT and ITP, respectively, in the 15 days following the first administration of Vaxzevria. The number of thrombotic events reported from the Italian Medicines Agency does not appear to have increased beyond that expected in individuals over 50 years of age. [ABSTRACT FROM AUTHOR]

The present work aims to identify the predictors of hemodynamic failure (HF) developed during pediatric intensive care unit (PICU) stay testing a set of machine learning techniques (MLTs), comparing their ability to predict the outcome of interest. The study involved patients admitted to PICUs between 2010 and 2020. Data were extracted from the Italian Network of Pediatric Intensive Care Units (TIPNet) registry. The algorithms considered were generalized linear model (GLM), recursive partition tree (RPART), random forest (RF), neural networks models, and extreme gradient boosting (XGB). Since the outcome is rare, upsampling and downsampling algorithms have been applied for imbalance control. For each approach, the main performance measures were reported. Among an overall sample of 29,494 subjects, only 399 developed HF during the PICU stay. The median age was about two years, and the male gender was the most prevalent. The XGB algorithm outperformed other MLTs in predicting HF development, with a median ROC measure of 0.780 (IQR 0.770–0.793). PIM 3, age, and base excess were found to be the strongest predictors of outcome. The present work provides insights for the prediction of HF development during PICU stay using machine-learning algorithms. [ABSTRACT FROM AUTHOR]

Poor recognition of delirium among hospitalized elderlies is a typical challenge for health care professionals. Considering methodological insufficiency for assessing time-varying diseases, a continuous-time Markov multi-state transition model (CTMMTM) was used to investigate delirium evolution in elderly patients. This is a longitudinal observational study performed in September 2016 in an Italian hospital. Change of delirium states was modeled according to the 4AT score. A Cox model (CM) and a CTMMTM were used for identifying factors affecting delirium onset both with a two-state and three-state model. In this study, 78 patients were enrolled and evaluated for 5 days. Both the CM and the CTMMTM show that urine catheter (UC), aging, drugs, and invasive devices (ID) are risk factors for delirium onset. The CTMMTM model shows that transition from no-delirium/cognitive impairment to delirium was associated with aging (HR = 1.14; 95%CI, 1.05, 1.23) and neuroleptics (HR = 4.3; 1.57, 11.77), dopaminergic drugs (HR = 3.89; 1.2, 12.6), UC (HR = 2.92; 1.09, 7.79) and ID (HR = 1.67; 103, 2.71). These results are confirmed by the multivariable model. Aging, ID, antibiotics, drugs affecting the central nervous system, and absence of moving ability are identified as the significant predictors of delirium. Additionally, it seems that modeling with CTMMTM may show associations that are not directly detectable with the traditional CM. [ABSTRACT FROM AUTHOR]

The present work aims to identify the predictors of COVID-19 in-hospital mortality testing a set of Machine Learning Techniques (MLTs), comparing their ability to predict the outcome of interest. The model with the best performance will be used to identify in-hospital mortality predictors and to build an in-hospital mortality prediction tool. The study involved patients with COVID-19, proved by PCR test, admitted to the "Ospedali Riuniti Padova Sud" COVID-19 referral center in the Veneto region, Italy. The algorithms considered were the Recursive Partition Tree (RPART), the Support Vector Machine (SVM), the Gradient Boosting Machine (GBM), and Random Forest. The resampled performances were reported for each MLT, considering the sensitivity, specificity, and the Receiving Operative Characteristic (ROC) curve measures. The study enrolled 341 patients. The median age was 74 years, and the male gender was the most prevalent. The Random Forest algorithm outperformed the other MLTs in predicting in-hospital mortality, with a ROC of 0.84 (95% C.I. 0.78–0.9). Age, together with vital signs (oxygen saturation and the quick SOFA) and lab parameters (creatinine, AST, lymphocytes, platelets, and hemoglobin), were found to be the strongest predictors of in-hospital mortality. The present work provides insights for the prediction of in-hospital mortality of COVID-19 patients using a machine-learning algorithm. [ABSTRACT FROM AUTHOR]

Elderly patients are at risk of malnutrition and need an appropriate assessment of energy requirements. Predictive equations are widely used to estimate resting energy expenditure (REE). In the study, we conducted a systematic review of REE predictive equations in the elderly population and compared them in an experimental population. Studies involving subjects older than 65 years of age that evaluated the performance of a predictive equation vs. a gold standard were included. The retrieved equations were then tested on a sample of 88 elderly subjects enrolled in an Italian nursing home to evaluate the agreement among the estimated REEs. The agreement was assessed using the intraclass correlation coefficient (ICC). A web application, equationer, was developed to calculate all the estimated REEs according to the available variables. The review identified 68 studies (210 different equations). The agreement among the equations in our sample was higher for equations with fewer parameters, especially those that included body weight, ICC = 0.75 (95% CI = 0.69–0.81). There is great heterogeneity among REE estimates. Such differences should be considered and evaluated when estimates are applied to particularly fragile populations since the results have the potential to impact the patient's overall clinical outcome. [ABSTRACT FROM AUTHOR]

Physical function is a patient-oriented indicator and can be considered a proxy for the assignment of healthcare personnel. The study aims to create an algorithm that classifies patients into homogeneous groups according to physical function. A two-step machine-learning algorithm was applied to administrative data recorded between 2015 and 2018 at the University Hospital of Padova. A clustering-large-applications (CLARA) algorithm was used to partition patients into homogeneous groups. Then, machine learning technique (MLT) classifiers were used to categorize the doubtful records. Based on the results of the CLARA algorithm, records were divided into three groups according to the Barthel index: <45, >65, ≥45 and ≤65. The support vector machine was the MLT showing the best performance among doubtful records, reaching an accuracy of 66%. The two-step algorithm, since it splits patients into low and high resource consumption, could be a useful tool for organizing healthcare personnel allocation according to the patients' assistance needs. [ABSTRACT FROM AUTHOR]

We aimed to assess the perceived impact of the lockdown, imposed to control the spreading of COVID-19, on the adherence of Italian celiac disease (CD) subjects to the gluten-free diet by a web-based survey. A total of 1983 responses were analyzed, 1614 (81.4%) by CD adults and 369 (18.6%) by parents/caregivers of CD children/adolescents. The compliance with the GFD was unchanged for 69% of the adults and 70% of the children, and improved for 29% of both. The factors increasing the probability to report stricter compliance were the presence of CD symptoms in the last year before the lockdown (odds ratio (OR) 1.98, 95% confidence interval (CI) 1.46–2.26), a partial usual adherence to gluten-free diet (GFD) (OR 1.91, 95% CI 1.2–3.06), and having tried recipes with naturally gluten-free ingredients more than usual (OR 1.58, 95% CI 1.28–1.96) for adults; the presence of CD symptoms in the last year (OR 2.05, 95% CI 1.21–3.47), still positive CD antibodies (OR 1.89, 95% CI 1.14–3.13), and other family members with CD (OR 2.24, 95% CI 1.3–3.85) for children/adolescents. Therefore, the lockdown led to a reported improved adherence to the GFD in one-third of the respondents, in particular in those with previous worse disease control, offering the opportunity to avoid sources of contamination/transgression and increase the use of naturally gluten-free products. [ABSTRACT FROM AUTHOR]

Nutrition is emerging as a key factor in promoting healthy lifestyles in the growing elderly population across Europe. In this study, we examined the non-animal-derived food source consumption among the elderly European population to evaluate the actual contributions of these foods to the diet of the elders. We gathered 21 studies conducted in 17 European countries to evaluate the fruit, vegetable, and legume (along with their derived products) consumption among the elderly (>65 years) population. Foods' nutritional values were calculated and compared to the recommended intakes. A Bayesian multilevel hierarchical analysis was conducted to estimate the caloric intake of food categories and to compare the elderly and general adult populations. Although the lowest consumption was generally associated with the lowest nutrient and fiber intake, the reverse was not always the case. Concerning the general adult population, no differences in the related caloric intake of elders were noticed. Differences were instead evident when foods were divided into subclasses. Elderly populations consume fruit and fruit products, but they drink less fruit and vegetable juices and nectars. In conclusion, elderlies' fruit and vegetable consumption showed a peculiar pattern with respect to the general adult population, whose recognition could be helpful to address tailored policies. Constantly updated studies, including all the lifespan ages, are warranted to design tailored effective public health interventions. [ABSTRACT FROM AUTHOR]

In stroke survivors, rectus femoris (RF) spasticity is often implicated in gait pattern alterations such as stiff knee gait (SKG). Botulinum toxin type A (BoNT-A) is considered the gold standard for focal spasticity treatment. However—even if the accuracy of injection is crucial for BoNT-A efficacy—instrumented guidance for BoNT-A injection is not routinely applied in clinical settings. In order to investigate the possible implications of an inadequate BoNT-A injection on patients' clinical outcome, we evaluated the ultrasound-derived RF characteristics (muscle depth, muscle thickness, cross-sectional area and mean echo intensity) in 47 stroke survivors. In our sample, we observed wide variability of RF depth in both hemiparetic and unaffected side of included patients (0.44 and 3.54 cm and between 0.25 and 3.16 cm, respectively). Moreover, our analysis did not show significant differences between treated and non-treated RF in stroke survivors. These results suggest that considering the inter-individual variability in RF muscle depth and thickness, injection guidance should be considered for BoNT-A treatment in order to optimize the clinical outcome of treated patients. In particular, ultrasound guidance may help the clinicians in the long-term follow-up of muscle quality. [ABSTRACT FROM AUTHOR]

Assessment of heart rate variability (HRV) and cardiac ectopic beats is a clinically relevant topic. The present exploratory observational study aimed to inspect the relationships of lifestyle, dietary patterns, and anthropometrics with HRV, premature ventricular complexes (PVCs), and supraventricular premature complexes (SVPCs). A cross-sectional study enrolling subjects undergoing Holter monitoring was performed. Sociodemographic and clinical characteristics, body composition (full-body bio-impedentiometry), dietary patterns (validated food frequency questionnaire and 24 h dietary recall), and quality of life were assessed. Generalized additive models were estimated to evaluate the relationships between outcomes of interest and variables collected. The study enrolled 121 consecutive patients undergoing 24 h Holter monitoring. Upon univariable analysis, HRV was found to have an inverse association with mass of body fat (MBF) (p-value 0.015), while doing physical activity was associated with a significantly higher HRV (p-value 0.036). Upon multivariable analysis, fruit consumption in the 24 h dietary recall was found to be directly associated with HRV (p-value 0.044). The present findings might be useful for improving the management of patients attending cardiac rhythm labs, and to tailor ad hoc prevention strategies (modification of lifestyle and eating habits) based on Holter parameters. [ABSTRACT FROM AUTHOR]

(1) Background: The high heterogeneity of inflammatory bowel disease (IBD) makes the study of this condition challenging. In subjects affected by Crohn's disease (CD), extra-intestinal manifestations (EIMs) have a remarkable potential impact on health status. Increasing numbers of patient characteristics and the small size of analyzed samples make EIMs prediction very difficult. Under such constraints, Bayesian machine learning techniques (BMLTs) have been proposed as a robust alternative to classical models for outcome prediction. This study aims to determine whether BMLT could improve EIM prediction and statistical support for the decision-making process of clinicians. (2) Methods: Three of the most popular BMLTs were employed in this study: Naϊve Bayes (NB), Bayesian Network (BN) and Bayesian Additive Regression Trees (BART). They were applied to a retrospective observational Italian study of IBD genetics. (3) Results: The performance of the model is strongly affected by the features of the dataset, and BMLTs poorly classify EIM appearance. (4) Conclusions: This study shows that BMLTs perform worse than expected in classifying the presence of EIMs compared to classical statistical tools in a context where mixed genetic and clinical data are available but relevant data are also missing, as often occurs in clinical practice. [ABSTRACT FROM AUTHOR]

Nitrogen balance (NB) is considered a good marker of adequate protein intake and it has been suggested to be a good predictor of patients' health outcomes. However, in literature, there is a lack of large randomized trials examining NB-guided protein intake in patients in intensive care units (ICUs). A randomized controlled trial enrolling patients admitted to ICU was done to compare changes in NB. Participants were randomized to a standard or protein-fortified diet (protein intake of 1.8 g/kg/day according to the guidelines of the Society of Critical Care Medicine and the American Society for Parenteral and Enteral Nutrition). The primary endpoint was represented by the NB on Day 1, 3, and study exit. Forty patients were enrolled in the study (19 in the protein-fortified group). The longitudinal analysis showed that, on Day 3, patients randomized to the protein-fortified diet were more likely (p < 0.001) to present better NB (at 3 days, patients in the protein-fortified diet were estimated to have a nitrate value of 5.22 g more than patients in the standard diet, 95% CI 3.86–6.58). The protein-fortified diet was found to be significantly and directly associated with changes in NB in critically ill patients admitted to ICU. [ABSTRACT FROM AUTHOR]