Search

Abstract Healthcare-acquired infections (HCAI) represent a major health problem worldwide. Stroke and dementia are considered risk factors for HCAI. Preliminary data suggest that use of antipsychotic drugs also increase the risk for HCAI. Here, we performed a retrospective study aimed at investigating the major risk and protective factors for HCAI in a cohort of elderly subjects hospitalized at an Italian tertiary Neurology Clinics. We included all patients with age ≥ 65 years hospitalized at Neurology Clinics of National Institute on Ageing, Ancona, Italy from 1st January 2018 to 31st December 2021. For each patient, the following data were collected: age, sex, use of medical devices, comorbidities, use of antipsychotic medications, development of HCAI. We included 1543 patients (41.4% males; median age 85 years [80–89]). According to multivariable analysis, age, stroke, duration of urinary catheter placement (for all p

Torsion of the hydatid of Morgagni is a rare condition which can cause acute abdominal pain in young female. We present a case report of a 13-year-old girl with acute abdominal pain and treated for torsion of the hydatid of Morgagni. Less than 20 cases of female younger than 18 years old with this condition are been described in the literature. Through our systematic review performed following the PRISMA statement, we want to emphasize the difficulty in diagnosis and the importance of considering this pathology in the differential diagnosis of acute abdomen in females with the aim of obtaining a timely surgical treatment to preserve fertility in these patients.

Objective To estimate the prevalence of overweight at 12 months in an Italian birth cohort and to identify factors related to an increased likelihood of being overweight.Methods The Italian NASCITA birth cohort was analysed. Infants were classified as underweight (

Objective To assess the feasibility of the family paediatrician’s (FP) role in identifying the signs of postpartum depression in parents in time to guarantee child well-being.Design, setting and participants Data for this observational prospective study were collected within the NASCITA (NAscere e creSCere in ITAlia) cohort. During the first visit, paediatricians collected sociodemographic data regarding the parents and information about their health status, the pregnancy and the delivery. Whooley questions were administered during the first and second visits (scheduled 60–90 days after childbirth). Moreover, on the third visit (5–7 months after childbirth) the FP was asked to answer ‘yes’ or ‘no’ to a question on the parental postpartum depression, based on his knowledge and on the acquired information.Results In 2203 couples who completed the assessment, 529 mothers (19.9%), 141 fathers (6.3%) and 110 (5%) couples reported any depressive symptomatology. Of these, 141 mothers (5.3% of the total sample) and 18 fathers (0.8% of the total sample) were classified as ‘likely depressed’. An association was found between maternal postnatal depressive symptoms and having a diagnosed psychiatric disorder during pregnancy (OR 9.49, 95% CI: 3.20 to 28.17), not exclusively breastfeeding at hospital discharge (OR 1.76, 95% CI: 1.19 to 2.61) and the presence of child sleeping disorders at 3 (OR 2.46, 95% CI: 1.41 to 4.28) and 6 months (OR 2.18, 95% CI: 1.37 to 3.47). Another significant predictor of postpartum depression was being primiparous (OR 1.99, 95% CI: 1.31 to 3.02). Concerning the fathers, a significant association was reported only between likely depressed fathers and child sleeping disorders at 3 months (OR 7.64, 95% CI: 2.92 to 19.97). Moreover, having a likely depressed partner was strongly associated with depressive symptoms in fathers (OR 85.53, 95% CI 26.83 to 272.69).Conclusions The findings of this study support the feasibility of an active screening programme for parental postnatal depression during well-child visits as an integral part of postpartum care.Trial registration number NCT03894566; Pre-results.

Laparoscopic Appendectomy (LPSA) is the first choice for appendectomy in pediatric surgery. Trans-Umbilical Laparoscopic Assisted Appendicectomy (TULAA) is another used technique. We compared both these procedures used for the treatment of acute appendicitis. The study was conducted between January 2019 to December 2020. Patients were divided into two groups: LPSA and TULAA groups. The collected data were: operative time, number of conversions, time of canalization and hospital stay. A total of 181 patients were included: 73 were kept in the LPSA and 108 in the TULAA group. Mean operative time was 70.9 minutes (range 45-130 min) for LPS and 56.4 (30-145 min) for TULAA group (p

Introduction About 60% of bipolar disorder (BD) cases are initially misdiagnosed as major depressive disorder (MDD), preventing BD patients from receiving appropriate treatment. An urgency exists to identify reliable biomarkers for improving differential diagnosis (DD). Machine learning methods may help translate current knowledge on biomarkers of mood disorders into clinical practice by providing individual-level classification. No study so far has combined biological data with clinical data to provide a multifactorial predictive model for DD. Objectives Define a predictive algorithm for BD and MDD by integrating structural neuroimaging and inflammatory data with neuropsychological measures (NM). Two different algorithms were compared: multiple kernel learning (MKL) and elastic net regularized logistic regression (EN). Methods In a sample of 141 subjects (70 MDD; 71 BD), two different models were implemented for each algorithm: 1) structural neuroimaging measures only (i.e. voxel-based morphometry (VBM), white matter fractional anisotropy (FA), and mean diffusivity (MD)); 2) VBM, FA, and MD combined with NM. In a subsample of 71 subjects (36 BD; 38 MDD), two similar models were implemented: 1) VBM, FA, and, MD combined with only NM; 2) VBM, FA, and MD combined with NM and peripheral inflammatory markers. Finally, the best model was selected for comparison with healthy controls (HC). Results Overall, the EN model based on all the modalities achieved the highest accuracy (AUC = 90.2%), outperforming MKL (AUC=85%). EN correctly classified BD and MDD with a diagnostic accuracy of 78.3%, sensitivity of 75%, and specificity of 81.6%. The most significant predictors of BD (variable inclusion probability (VIP) > 80%) were the parahippocampal cingulate, interleukin 9, chemokine CCL5, posterior thalamic radiation, and internal capsule, whereas MDD was best predicted by chemokine CCL23, the anterior cerebellum, and the sagittal stratum. In contrast, NM did not help to differentiate between MDD and BD. However, they help to distinguish patients from HC. Psychomotor coordination and speed of information processing discriminated between MDD and HC (VIP>90%), whereas fluency, working memory, and executive functions differentiated between BD and HC (VIP>80%). Conclusions In summary, BD was predicted by a strong proinflammatory profile, whereas MDD was identified by structural neuroimaging data. A multimodal approach offers additional instruments to improve personalized diagnosis in clinical practice and enhance the ability to make DD. Disclosure of Interest None Declared

Introduction Depression is the predominant mood alteration in bipolar disorder (BD), leading to overlapping symptomatology with major depressive disorder (MDD). Consequently, in clinical assessment, almost 60% of BD patients are misdiagnosed as affected by MDD. This calls for the creation of a framework for the differentiation of BD and MDD patients based on reliable biomarkers. Since machine learning (ML) enables to make predictions at the single-subject level, it appears to be particularly suitable for this task. Objectives We implemented a ML pipeline for the differentiation between depressed BD and MDD patients based on structural neuroimaging features. Methods Diffusion tensor imaging (DTI) and T1-weighted magnetic resonance imaging (MRI) data were acquired for 282 depressed BD (n=180) and MDD (n=102) patients. Axial (AD), radial (RD), mean (MD) diffusivity, and fractional anisotropy (FA) maps were extracted from DTI images, and voxel-based morphometry (VBM) measures were obtained from T1-weighted images. Each feature was entered separately into a 5-fold nested cross-validated ML pipeline differentiating between BD and MDD patients, comprising: confound regression for nuisance variables removal (i.e., age and sex), feature standardization, principal component analysis, and an elastic-net penalized regression. The models underwent 5000 random permutations as a test for significance, and the McNemar’s test was used to assess whether there was any significant difference between the models (significance threshold was set to p

Introduction Every year at least one million people die by suicide, with major depressive disorder (MDD) being one of the major causes of suicide deaths. Current suicide risk assessments rely on subjective information, are time consuming, low predictive, and poorly reliable. Thus, finding objective biomarkers of suicidality is crucial to move clinical practice towards a precision psychiatry framework, enhancing suicide risk detection and prevention for MDD. Objectives The present study aimed at applying machine learning (ML) algorithms on both grey matter and white-matter voxel-wise data to discriminate MDD suicide attempters (SA) from non-attempters (nSA). Methods 91 currently depressed MDD patients (24 SA, 67 nSA) underwent a structural MRI session. T1-weighted images and diffusion tensor imaging scans were respectively pre-processed using Computational Atlas Toolbox 12 (CAT12) and spatial tract-based statistics (TBSS) on FSL, to obtain both voxel-based morphometry (VBM) and fractional anisotropy (FA) measures. Three classification models were built, entering whole-brain VBM and FA maps alone into a Support Vector Machine (SVM) and combining both modalities into a Multiple Kernel Learning (MKL) algorithm. All models were trained through a 5-fold nested cross-validation with subsampling to calculate reliable estimates of balanced accuracy, specificity, sensitivity, and area under the receiver operator curve (AUC). Results Models’ performances are summarized in Table 1.Table 1. Models’ performances. Input features Algorithm Specificity Sensitivity Balanced accuracy AUC VBM SVM 55.00% 50.00% 52.50% 0.55 FA SVM 72.00% 54.00% 63.00% 0.62 VBM and FA MKL 68.00% 54.00% 61.00% 0.58 Abbreviations: AUC, area under the receiver operator curve; FA, fractional anisotropy; VBM, voxel-based morphometry. Conclusions Overall, although overcoming the random classification accuracy (i.e., 50%), performances of all models classifying SA and nSA MDD patients were moderate, possibly due to the imbalanced numerosity of classes, with SVM on FA reaching the highest accuracy. Thus, future studies may enlarge the sample and add different features (e.g., functional neuroimaging data) to develop an objective and reliable predictive model to assess and hence prevent suicide risk among MDD patients. Disclosure of Interest None Declared

Introduction One of the main obstacles in providing effective treatments for major depressive disorder (MDD) is clinical heterogeneity, whose neurobiological correlates are not clearly defined. A biologically meaningful stratification of depressed patients is needed to promote tailored diagnostic procedures. Objectives Using structural data, we performed an unsupervised clustering to define clinically meaningful clusters of depressed patients. Methods T1-weighted and diffusion tensor images were obtained from 102 MDD patients. In 64 patients, clinical symptoms, number of stressful life events, severity and exposure to adverse childhood experiences were evaluated using the Beck Depression Inventory (BDI), Schedule of Recent Experiences (SRE), Risky Family Questionnaire (RFQ), and Childhood Trauma Questionnaire (CTQ). Clustering analyses were performed with extracted tract-based fractional anisotropy (TBSS, FSL), cortical thickness, surface area, and regional measures of grey matter volumes (CAT12). Gaussian mixture model was implemented for clustering, considering Support Vector Machine (SVM) as classifier. A 10x2 repeated cross-validation with grid search was performed for hyperparameters tuning and clusters’ stability. The optimal number of clusters was determined by normalized stability, Akaike and Bayesian information criterion. Analyses were adjusted for total intracranial volume, age, and sex. The clinical relevance of the identified clusters was assessed through MANOVA, considering domains of clinical scales as dependent variables and clusters’ labels as fixed factors. Discriminant analysis was subsequently performed to assess the discriminative power of these variables. Results Cross-validated clustering approach identified 2 highly stable clusters (normalized stability=0.316, AIC=-80292.48, BIC=351329.16). MANOVA showed a significant between-clusters difference in clinical scales scores (p=0.038). Discriminant analysis distinguished the two clusters with an accuracy of 78.1%, with BDI behavioural and CTQ minimisation/denial domains showing the highest discriminant values (0.325 and 0.313). Conclusions Our results defined two biologically informed clusters of MDD patients associated with childhood trauma and specific clinical profiles, which may assist in targeting effective interventions and treatments. Disclosure of Interest None Declared

Introduction Bipolar patients (BP) frequently have cognitive deficits, that impact on prognosis and quality of life. Finding biomarkers for this condition is essential to improve patients’ healthcare. Given the association between cognitive dysfunctions and structural brain abnormalities, we used a machine learning approach to identify patients with cognitive deficits. Objectives The aim of this study was to assess if structural neuroimaging data could identify patients with cognitive impairments in several domains using a machine learning framework. Methods Diffusion tensor imaging and T1-weighted images of 150 BP were acquired and both grey matter voxel-based morphometry (VBM) and tract-based white matter fractional anisotropy (FA) measures were extracted. Support vector machine (SVM) models were trained through a 10-fold nested cross-validation with subsampling. VBM and FA maps were entered separately and in combination as input features to discriminate BP with and without deficits in six cognitive domains, assessed through the Brief Assessment of Cognition in Schizophrenia. Results The best classification performance for each cognitive domain is illustrated in Table 1. FA was the most relevant neuroimaging modality for the prediction of verbal memory, verbal fluency, and executive functions deficits, whereas VBM was more predictive for working memory and motor speed domains.Table 1. Performance of best classification models. Input feature Balance Accuracy (%) Specificity (%) Sensitivity (%) Verbal Memory FA 60.17 51.31 43 Verbal Fluency FA 57.67 62 53.33 Executive functions FA 60 63.33 56.67 Working Memory VBM 56.50 56 57 Motor speed VBM 53.50 47.67 59.33 Attention and processing speed VBM + FA 58.33 49.17 67.5 Conclusions Overall, the tested SVM models showed a good predictive performance. Although only partially, our results suggest that different structural neuroimaging data can predict cognitive deficits in BP with accuracy higher than chance level. Unexpectedly, only for the attention and processing speed domain the best model was obtained combining the structural features. Future research may promote data fusion methods to develop better predictive models. Disclosure of Interest None Declared

Abstract Larger thalamic volume has been found in children with obsessive-compulsive disorder (OCD) and children with clinical-level symptoms within the general population. Particular thalamic subregions may drive these differences. The ENIGMA-OCD working group conducted mega- and meta-analyses to study thalamic subregional volume in OCD across the lifespan. Structural T1-weighted brain magnetic resonance imaging (MRI) scans from 2649 OCD patients and 2774 healthy controls across 29 sites (50 datasets) were processed using the FreeSurfer built-in ThalamicNuclei pipeline to extract five thalamic subregions. Volume measures were harmonized for site effects using ComBat before running separate multiple linear regression models for children, adolescents, and adults to estimate volumetric group differences. All analyses were pre-registered ( https://osf.io/73dvy ) and adjusted for age, sex and intracranial volume. Unmedicated pediatric OCD patients (

Bipolar disorder (BD) and major depressive disorder (MDD) are severe psychiatric illnesses that share among their environmental risk factors the exposure to adverse childhood experiences (ACE). Exposure to ACE has been associated with long-term changes in brain structure and the immune response. In the lasts decades, brain abnormalities including alterations of white matter (WM) microstructure and higher levels of peripheral immune/inflammatory markers have been reported in BD and MDD and an association between inflammation and WM microstructure has been shown. However, differences in these measures have been reported by comparing the two diagnostic groups. The aim of the present study was to investigate the interplay between ACE, inflammation, and WM in BD and MDD. We hypothesize that inflammation will mediate the association between ACE and WM and that this will be different in the two groups. A sample of 200 patients (100 BD, 100 MDD) underwent 3T MRI scan and ACE assessment through Childhood Trauma Questionnaire. A subgroup of 130 patients (75 MDD and 55 BD) underwent blood sampling for the assessment of immune/inflammatory markers. We observed that ACE associated with higher peripheral levels of IL-2, IL-17, bFGF, IFN-γ, TNF-α, CCL3, CCL4, CCL5, and PDGF-BB only in the BD group. Further, higher levels of CCL3 and IL-2 associated with lower FA in BD. ACE were found to differently affect WM microstructure in the two diagnostic groups and to be negatively associated with FA and AD in BD patients. Mediation analyses showed a significant indirect effect of ACE on WM microstructure mediated by IL-2. Our findings suggest that inflammation may mediate the detrimental effect of early experiences on brain structure and different mechanisms underlying brain alterations in BD and MDD.

Purpose The NASCITA Study, a national-level, population-based, prospective cohort study, was set up to better understand the early health status of Italian children, comprising their physical, cognitive and psychological development, and how it is affected by social and health determinants, including nurturing care. NASCITA will also assess geographical differences and disparities in healthcare.Participants Participating family paediatricians from throughout Italy enrolled infants born during the enrolment period (April 2019–July 2020). The 5054 newborns seen by the 139 paediatricians for at least two visits, including the first well-child visit, and for whom parental consent was given, make up the baseline population.Findings to date Mothers had a mean age at delivery of 33.0 years and tended to have a high or medium level of education (42.5% university and 41.7% high school degrees) and to be employed (69.7%). One-third (36.1%) took folic acid supplementation appropriately, and 6.5% smoked or consumed alcohol (10.0%) during pregnancy. One-third (31.7%) of deliveries were caesarean deliveries. Concerning the newborns, 5.8% had a low birth weight and 6.2% were born prematurely. The majority (87.7%) slept in the supine position, and 63.6% were exclusively breast fed at 1 month, with a decreasing north to south prevalence (χ2t 52; p

Background: Fibroepithelial polyps are benign tumors located in the upper ureter and ureteropelvic junction. Given their location in rare cases they can cause hydronephrosis. Clinically and radiologically, it is difficult to distinguish them from ureteropelvic junction obstruction. Case report: A 6-years boy was admitted with intermittent left flank pain. An ultrasound revealed a III degree hydronephrosis, confirmed by an MR examination which highlighted an obstruction at the level of the middle tract of the ureter. We performed a robotic intervention. During surgery the presence of ureteral polyp was evident as the cause of the obstruction. An end-to-end uretero-ureteral anastomosis was performed. Conclusion: Ureteral polyps were mainly reported in adults, but recently several series revealed higher incidence among pediatric patients. Diagnosis of ureteral polyps is challenging, but even without a definite diagnosis, the best strategy is that of a robotic approach similar to that of the pathologies of the ureteropelvic junction.

Abstract Microstructural alterations in cortico-subcortical connections are thought to be present in obsessive–compulsive disorder (OCD). However, prior studies have yielded inconsistent findings, perhaps because small sample sizes provided insufficient power to detect subtle abnormalities. Here we investigated microstructural white matter alterations and their relation to clinical features in the largest dataset of adult and pediatric OCD to date. We analyzed diffusion tensor imaging metrics from 700 adult patients and 645 adult controls, as well as 174 pediatric patients and 144 pediatric controls across 19 sites participating in the ENIGMA OCD Working Group, in a cross-sectional case-control magnetic resonance study. We extracted measures of fractional anisotropy (FA) as main outcome, and mean diffusivity, radial diffusivity, and axial diffusivity as secondary outcomes for 25 white matter regions. We meta-analyzed patient-control group differences (Cohen’s d) across sites, after adjusting for age and sex, and investigated associations with clinical characteristics. Adult OCD patients showed significant FA reduction in the sagittal stratum (d = −0.21, z = −3.21, p = 0.001) and posterior thalamic radiation (d = −0.26, z = −4.57, p

Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses.Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods.Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models.Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.

Laparoscopic Appendectomy (LPSA) is the first choice for appendectomy in pediatric surgery. Trans-Umbilical Laparoscopic Assisted Appendicectomy (TULAA) is another used technique. We compared both these procedures used for the treatment of acute appendicitis. The study was conducted between January 2019 to December 2020. Patients were divided into two groups: LPSA and TULAA groups. The collected data were: operative time, number of conversions, time of canalization and hospital stay. A total of 181 patients were included: 73 were kept in the LPSA and 108 in the TULAA group. Mean operative time was 70.9 minutes (range 45-130 min) for LPS and 56.4 (30-145 min) for TULAA group (p <0.0001). Complications rate showed no statistically significant difference between both the two groups. However, conversions showed a statistically significant difference (p=0.04). Both techniques showed similar results. TULAA technique takes a significantly shorter operating time. The selection between LPSA and TULAA techniques depends on the experience of the surgeon's work and the personal laparoscopic learning curve. In our experience LPSA was a useful technique to improve the laparoscopic skill of the pediatric surgery residents. [ABSTRACT FROM AUTHOR]

Fibroepithelial polyps are benign tumors located in the upper ureter and ureteropelvic junction. Given their location in rare cases they can cause hydronephrosis. Clinically and radiologically, it is difficult to distinguish them from ureteropelvic junction obstruction. A 6-years boy was admitted with intermittent left flank pain. An ultrasound revealed a III degree hydronephrosis, confirmed by an MR examination which highlighted an obstruction at the level of the middle tract of the ureter. We performed a robotic intervention. During surgery the presence of ureteral polyp was evident as the cause of the obstruction. An end-to-end uretero-ureteral anastomosis was performed. Ureteral polyps were mainly reported in adults, but recently several series revealed higher incidence among pediatric patients. Diagnosis of ureteral polyps is challenging, but even without a definite diagnosis, the best strategy is that of a robotic approach similar to that of the pathologies of the ureteropelvic junction. [ABSTRACT FROM AUTHOR]