Longevity pattern in Emilia Romagna (Italy) in a dynamic perspective

In this paper, we investigate the pattern of longevity during the last 15 years in Emilia Romagna, a North-Eastern region of Italy, at a municipality level. We consider a specific index of extreme longevity based on people aged 95 and over in two different periods (1995-1999 and 2005-2009). Spatio-temporal modeling is used to tackle at both periods the random variations in the occurrence of people 95+, due to the increasingly rareness of such events, especially in small areas. This method exploits the spatial proximity and the consequent interaction of the geographical areas to smooth the observations, as well as to control for the effects of a set of regressors. As a result, clusters of areas characterized by high and low indexes of longevity are well identified and the temporal evolution of the phenomenon can be depicted. In a parallel analysis, we consider the past levels of mortality on the same cohort of individuals reaching 95 years and over in the second period and when they were aged 80-89 and 90-99. Within this longitudinal framework, the longevity outcome is modeled by a spatial regression. The area-specific structures of mortality are included as regressors, whose effects represent the causal link between the occurrence of people 95+ and the causes of death in the same cohort

The analysis of longitudinal data from life-span carcinogenicity bioassays on Sprague-Dawley rats

Background and aim of the work: Long Term Carcinogenicity Bioassays (LTCB) are among the best instruments to strengthen the evidence on which regulatory agencies base their decision to classify harmful agents as human carcinogens, so they are fundamental to protect public health. The statistical analysis is essential to validate the results from cancer and non-cancer outcomes in carcinogenicity bioassay. This work proposes and applies some methodologies for the analysis of non-cancer outcomes, such as body weights. Methods: We use data from studies already concluded, evaluated and published: 4 bioassays aimed at testing the carcinogenic potential of Coca-Cola on Sprague-Dawley rats of different ages. The analysis of body weights of the second generation of rats was performed using mixed-effects models: linear models were fitted for nonlinear models we considered human non-linear growth functions. Results: Linear models were fitted using the log-transformation of time and polynomial term of third order for time. Sex and treatment influence body weight, age of dams during gestation doesn’t. Growth models: Jenns-Bayley, Count and 1st order Berkey-Reed growth functions were evaluated; the latter best describes the data. Sex and treatment significantly influence all parameters. The direction, magnitude and significance of the effect variable is substantially similar in all models. The analysis of residuals highlights the same issues for all models: the extreme trends in the last part of life heavily affect the models’ performance. Conclusions: Mixed-effects models allowed to account for the structural effect of covariates that act the same way on all individuals, and to add random effects that introduce a correlation among subjects if clustering happens; nonlinear human growth models added information about the whole growth process, therefore these may be useful methods in studies focused on development and sexual maturation

Long-stay in short-stay inpatient facilities: risk factors and barriers to discharge

<p>Abstract</p> <p>Background</p> <p>The aim of the present study was to assess the characteristics of long-stay inpatients in public and private Italian acute inpatient facilities, to identify risk factors and correlates of the long duration of hospital stay in these patients, and to identify possible barriers to alternative placements.</p> <p>Methods</p> <p>All patients in 130 Italian public and private psychiatric inpatient units who had been hospitalized for more than 3 months during a specific index period were assessed with standardized assessment instruments and compared to patients discharged during the same index period, but staying in hospital for less than 3 months (short-stay inpatients). Assessed domains included demographic, clinical, and treatment characteristics, as well as process of care. Logistic regression analysis was used to identify specific variables predicting inpatient long-stay status. Reasons for delaying patient discharge, as reported by treatment teams, were also analyzed.</p> <p>Results</p> <p>No overall differences between long-stay and short-stay patients emerged in terms of symptom severity or diagnostic status. Admission to a private inpatient facility and display of violent behavior during hospital stay were the most powerful predictors of long-stay. Lack of housing and a shortage of community support were the reasons most commonly cited by treatment teams as barriers to discharge.</p> <p>Conclusion</p> <p>Extra-clinical factors are important determinants of prolonged hospitalization in acute inpatient settings.</p

Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis

ObjectiveThis study aims to develop and compare different models to predict the Length of Stay (LoS) and the Prolonged Length of Stay (PLoS) of inpatients admitted through the emergency department (ED) in general patient settings. This aim is not only to promote any specific model but rather to suggest a decision-supporting tool (i.e., a prediction framework).MethodsWe analyzed a dataset of patients admitted through the ED to the “Sant”Orsola Malpighi University Hospital of Bologna, Italy, between January 1 and October 26, 2022. PLoS was defined as any hospitalization with LoS longer than 6 days. We deployed six classification algorithms for predicting PLoS: Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB), AdaBoost, K-Nearest Neighbors (KNN), and logistic regression (LoR). We evaluated the performance of these models with the Brier score, the area under the ROC curve (AUC), accuracy, sensitivity (recall), specificity, precision, and F1-score. We further developed eight regression models for LoS prediction: Linear Regression (LR), including the penalized linear models Least Absolute Shrinkage and Selection Operator (LASSO), Ridge and Elastic-net regression, Support vector regression, RF regression, KNN, and eXtreme Gradient Boosting (XGBoost) regression. The model performances were measured by their mean square error, mean absolute error, and mean relative error. The dataset was randomly split into a training set (70%) and a validation set (30%).ResultsA total of 12,858 eligible patients were included in our study, of whom 60.88% had a PloS. The GB classifier best predicted PloS (accuracy 75%, AUC 75.4%, Brier score 0.181), followed by LoR classifier (accuracy 75%, AUC 75.2%, Brier score 0.182). These models also showed to be adequately calibrated. Ridge and XGBoost regressions best predicted LoS, with the smallest total prediction error. The overall prediction error is between 6 and 7 days, meaning there is a 6–7 day mean difference between actual and predicted LoS.ConclusionOur results demonstrate the potential of machine learning-based methods to predict LoS and provide valuable insights into the risks behind prolonged hospitalizations. In addition to physicians' clinical expertise, the results of these models can be utilized as input to make informed decisions, such as predicting hospitalizations and enhancing the overall performance of a public healthcare system

Spatiotemporal heterogeneity of SARS-CoV-2 diffusion at the city level using geographically weighted Poisson regression model: The case of Bologna, Italy

This paper aimed to analyse the spatio-temporal patterns of the diffusion of SARS-CoV-2, the virus causing coronavirus 2019 (COVID-19, in the city of Bologna, the capital and largest city of the Emilia-Romagna Region in northern Italy. The study took place from February 1st, 2020 to November 20th, 2021 and accounted for space, sociodemographic characteristics and health conditions of the resident population. A second goal was to derive a model for the level of risk of being infected by SARS-CoV-2 and to identify and measure the place-specific factors associated with the disease and its determinants. Spatial heterogeneity was tested by comparing global Poisson regression (GPR) and local geographically weighted Poisson regression (GWPR) models. The key findings were that different city areas were impacted differently during the first three epidemic waves. The area-to-area influence was estimated to exert its effect over an area with 4.7 km radius. Spatio-temporal heterogeneity patterns were found to be independent of the sociodemographic and the clinical characteristics of the resident population. Significant single-individual risk factors for detected SARS-CoV-2 infection cases were old age, hypertension, diabetes and co-morbidities. More specifically, in the global model, the average SARS-CoV-2 infection rate decreased 0.93-fold in the 21–65 years age group compared to the >65 years age group, whereas hypertension, diabetes, and any other co-morbidities (present vs absent), increased 1.28-, 1.39- and 1.15-fold, respectively. The local GWPR model had a better fit better than GPR. Due to the global geographical distribution of the pandemic, local estimates are essential for mitigating or strengthening security measures

Clinical Value of CT-Guided Fine Needle Aspiration and Tissue-Core Biopsy of Thoracic Masses in the Dog and Cat

Diagnosis of thoracic lesions on the basis of history and physical examination is often challenging. Diagnostic imaging is therefore of paramount importance in this field. Radiology has traditionally been considered the diagnostic procedure of choice for these diseases. Nevertheless, it is often not possible to differentiate inflammatory/infectious lesions from neoplastic diseases. A correct cytological and histopathologic diagnosis is therefore needed for an accurate diagnosis and subsequent prognostic and therapeutic approach. In human medicine, Computed Tomography (CT) and CT-guided biopsy are used in the presence of lesions which are not adequately diagnosed with other procedures. In the present study, thoracic lesions from 52 dogs and 10 cats of different sex, breed and size underwent both CT-guided fine-needle aspiration (FNAB) and tissue-core biopsy (TCB). Clinical examination, hematobiochemical analysis and chest radiography were performed on all animals. In this study, 59 of 62 histopathological samples were diagnostic (95.2%). Cytology was diagnostic in 43 of 62 samples (69.4%). General sensitivity, accuracy and PPV for FNAB and TCB were 67.7%, 67.7% and 100% and 96.7%, 95.2% and 98.3%, respectively. Combining the two techniques, the overall mean accuracy for diagnosis was 98.4%. Nineteen of 62 cases showed complications (30.6%). Mild pneumothorax was seen in 16 cases, whereas mild hemorrhage occurred in three cases. No major complications were encountered. CT-guided FNAB cytology can be considered a useful and reliable technique, especially for small lesions or lesions located close to vital organs and therefore dangerous to biopsy in other way

aberrant inos signaling is under genetic control in rodent liver cancer and potentially prognostic for the human disease

Mounting evidence underlines the role of inducible nitric oxidesynthase (iNOS) in hepatocellular carcinoma (HCC) develop-ment, but its functional interactions with pathways involved inHCC progression remain uninvestigated. Here, we analyzed inpreneoplastic and neoplastic livers from Fisher 344 and BrownNorway rats, possessing different genetic predisposition to HCC,in transforming growth factor-a (TGF-a) and c-Myc–TGF-atransgenic mice, characterized by different susceptibility toHCC, and in human HCC: (i) iNOS function and interactionswith nuclear factor-kB (NF-kB) and Ha-RAS/extracellularsignal-regulated kinase (ERK) during hepatocarcinogenesis;(ii) influence of genetic predisposition to liver cancer on thesepathways and role of these cascades in determining a susceptibleor resistant phenotype and (iii) iNOS prognostic value in humanHCC. We found progressive iNos induction in rat and mouse liverlesions, always at higher levels in the most aggressive models rep-resented by HCC of rats genetically susceptible to hepatocarcino-genesis and c-Myc–TGF-a transgenic mice. iNOS, inhibitor of kBkinase/NF-kB and RAS/ERK upregulation was significantly higherin HCC with poorer prognosis (as defined by patients' survivallength) and positively correlated with tumor proliferation, genomicinstability and microvascularization and negatively with apoptosis.Suppression of iNOS signaling by aminoguanidine led to decreasedHCC growth and NF-kB and RAS/ERK expression and increasedapoptosis both in vivo and in vitro. Conversely, block of NF-kBsignaling by sulfasalazine or short interfering RNA (siRNA) orERK signaling by UO126 caused iNOS downregulation in HCCcell lines. These findings indicate that iNOS cross talk with NF-kB and Ha-RAS/ERK cascades influences HCC growth and prog-nosis, suggesting that key component of iNOS signaling could rep-resent important therapeutic targets for human HCC.IntroductionHepatocellular carcinoma (HCC) is one of the most frequent anddeadliest human cancers worldwide. Current therapies do not improvesignificantly the prognosis of patients with unresectable HCC (1,2).This emphasizes the need to investigate the molecular mechanismsresponsible for HCC development to identify new targets for earlydiagnosis, chemoprevention and treatment.Numerous genes regulating susceptibility to HCC and controllinggrowth, progression and redifferentiation of preneoplastic and neo-plastic lesions have been mapped in rodents (3). Decrease in growthability and/or marked redifferentiation of preneoplastic lesion char-acterizes rodent strains resistant to hepatocarcinogenesis (3,4). Con-sequently, studies on the mechanisms underlying the acquisition ofa phenotype susceptible/resistantto hepatocarcinogenesis in rodentstrains, carrying preneoplastic lesions differently prone to progressto HCC, may lead to the discovery of prognostic markers and ther-apeutic targets for the human disease. Dysplastic nodules and HCCinduced in susceptible Fisher 344 (F344) rats show upregulation ofc-Myc, Cyclin D1, E and A and E2f1 genes, increased cyclinD1–Cdk4, cyclin E–Cdk2 and E2f1–Dp1 complexes and retinoblas-toma protein (pRb) hyperphosphorylation (4–6). These changes areabsent or less pronounced in liver lesions from resistant Brown Norway(BN) rats, where a block of

Forkhead box M1B is a determinant of rat susceptibility to hepatocarcinogenesis and sustains ERK activity in human HCC

Background and aim: Previous studies indicate unrestrained cell cycle progression in liver lesions from hepatocarcinogenesis-susceptible Fisher 344 (F344) rats and a block of G1–S transition in corresponding lesions from resistant Brown Norway (BN) rats. Here, the role of the Forkhead box M1B (FOXM1) gene during hepatocarcinogenesis in both rat models and human hepatocellular carcinoma (HCC) was assessed. Methods and results: Levels of FOXM1 and its targets were determined by immunoprecipitation and real-time PCR analyses in rat and human samples. FOXM1 function was investigated by either FOXM1 silencing or overexpression in human HCC cell lines. Activation of FOXM1 and its targets (Aurora Kinose A, Cdc2, cyclin B1, Nek2) occurred earlier and was most pronounced in liver lesions from F344 than BN rats, leading to the highest number of Cdc2–cyclin B1 complexes (implying the highest G2–M transition) in F344 rats. In human HCC, the level of FOXM1 progressively increased from surrounding non-tumorous livers to HCC, reaching the highest levels in tumours with poorer prognosis (as defined by patients’ length of survival). Furthermore, expression levels of FOXM1 directly correlated with the proliferation index, genomic instability rate and microvessel density, and inversely with apoptosis. FOXM1 upregulation was due to extracellular signal-regulated kinase (ERK) and glioblastoma-associated oncogene 1 (GLI1) combined activity, and its overexpression resulted in increased proliferation and angiogenesis and reduced apoptosis in human HCC cell lines. Conversely, FOXM1 suppression led to decreased ERK activity, reduced proliferation and angiogenesis, and massive apoptosis of human HCC cell lines. Conclusions: FOXM1 upregulation is associated with the acquisition of a susceptible phenotype in rats and influences human HCC development and prognosis

Beta-Blocker Use in Older Hospitalized Patients Affected by Heart Failure and Chronic Obstructive Pulmonary Disease: An Italian Survey From the REPOSI Register

Beta (β)-blockers (BB) are useful in reducing morbidity and mortality in patients with heart failure (HF) and concomitant chronic obstructive pulmonary disease (COPD). Nevertheless, the use of BBs could induce bronchoconstriction due to β2-blockade. For this reason, both the ESC and GOLD guidelines strongly suggest the use of selective β1-BB in patients with HF and COPD. However, low adherence to guidelines was observed in multiple clinical settings. The aim of the study was to investigate the BBs use in older patients affected by HF and COPD, recorded in the REPOSI register. Of 942 patients affected by HF, 47.1% were treated with BBs. The use of BBs was significantly lower in patients with HF and COPD than in patients affected by HF alone, both at admission and at discharge (admission, 36.9% vs. 51.3%; discharge, 38.0% vs. 51.7%). In addition, no further BB users were found at discharge. The probability to being treated with a BB was significantly lower in patients with HF also affected by COPD (adj. OR, 95% CI: 0.50, 0.37-0.67), while the diagnosis of COPD was not associated with the choice of selective β1-BB (adj. OR, 95% CI: 1.33, 0.76-2.34). Despite clear recommendations by clinical guidelines, a significant underuse of BBs was also observed after hospital discharge. In COPD affected patients, physicians unreasonably reject BBs use, rather than choosing a β1-BB. The expected improvement of the BB prescriptions after hospitalization was not observed. A multidisciplinary approach among hospital physicians, general practitioners, and pharmacologists should be carried out for better drug management and adherence to guideline recommendations