Adherence in Rheumatoid Arthritis patients assessed with a validated Italian version of the 5-item compliance questionnaire for rheumatology

OBJECTIVES: The 5-item Compliance Questionnaire for Rheumatology (CQR5) proved reliability and validity in respect of identification of patients likely to be high adherers (HAs) to anti-rheumatic treatment, or low adherers (LAs), i.e. taking<80% of their medications correctly. The objective of the study was to validate an Italian version of CQR5 (I-CQR5) in rheumatoid arthritis (RA) patients and to investigate factors associated with high adherence. METHODS: RA patients, undergoing treatment with ≥1 self-administered conventional synthetic disease-modifying anti-rheumatic drug (csDMARD) or biological DMARD (bDMARD), were enrolled. The cross-cultural adaptation and validation of I-CQR5 followed standardised guidelines. I-CQR5 was completed by patients on one occasion. Data were subjected to factor analysis and Partial Credit model Parametrisation (PCM) to assess construct validity of I-CQR5. Analysis of factors associated with high adherence included demographic, social, clinical and treatment information. Factors achieving a p<0.10 in univariate analysis were included in multivariable analysis. RESULTS: Among 604 RA patients, 274 patients were included in the validation and 328 in the analysis of factors associated with adherence. Factor analysis and PCM confirmed the construct validity and consistency of I-CQR5. HAs were found to be 109 (35.2%) of the patients. bDMARD treatment and employment were found to be independently associated with high adherence: OR 2.88 (1.36-6.1), p=0.006 and OR 2.36 (1.21-4.62), p=0.012, respectively. CONCLUSIONS: Only one-third of RA patients were HAs according to I-CQR5. bDMARDs and employment status increased by almost 3-fold the likelihood of being highly adherent to the anti-rheumatic treatment.Peer reviewe

Completion Optimization in the Bakken Petroleum System Using Data Mining

Therefore, the primary goal of this study was to identify optimal completion practices using publicly available well completion and production information and applying data-mining techniques that could accommodate nonlinear relationships.https://commons.und.edu/eerc-publications/1018/thumbnail.jp

Potential uses of olive oil secoiridoids for the prevention and treatment of cancer: A narrative review of preclinical studies

The Mediterranean diet (MD) is a combination of foods mainly rich in antioxidants and anti-inflammatory nutrients that have been shown to have many health-enhancing effects. Extra-virgin olive oil (EVOO) is an important component of the MD. The importance of EVOO can be attributed to phenolic compounds, represented by phenolic alcohols, hydroxytyrosol, and tyrosol, and to secoiridoids, which include oleocanthal, oleacein, oleuropein, and ligstroside (along with the aglycone and glycosidic derivatives of the latter two). Each secoiridoid has been studied and char-acterized, and their effects on human health have been documented by several studies. Secoiridoids have antioxidant, anti-inflammatory, and anti-proliferative properties and, therefore, exhibit anti-cancer activity. This review summarizes the most recent findings regarding the pharmacological properties, molecular targets, and action mechanisms of secoiridoids, focusing attention on their preventive and anti-cancer activities. It provides a critical analysis of preclinical, in vitro and in vivo, studies of these natural bioactive compounds used as agents against various human cancers. The prospects for their possible use in human cancer prevention and treatment is also discussed

Monitoring public perception of health risks in brazil and italy: Cross-cultural research on the risk perception of choking in children

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

Fitting Early Phases of the COVID-19 Outbreak: A Comparison of the Performances of Used Models

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

Automatic Forecast of Intensive Care Unit Admissions: The Experience During the COVID-19 Pandemic in Italy

The experience of the COVID-19 pandemic showed the importance of timely monitoring of admissions to the ICU admissions. The ability to promptly forecast the epidemic impact on the occupancy of beds in the ICU is a key issue for adequate management of the health care system. Despite this, most of the literature on predictive COVID-19 models in Italy has focused on predicting the number of infections, leaving trends in ordinary hospitalizations and ICU occupancies in the background. This work aims to present an ETS approach (Exponential Smoothing Time Series) time series forecasting tool for admissions to the ICU admissions based on ETS models. The results of the forecasting model are presented for the regions most affected by the epidemic, such as Veneto, Lombardy, Emilia-Romagna, and Piedmont. The mean absolute percentage errors (MAPE) between observed and predicted admissions to the ICU admissions remain lower than 11% for all considered geographical areas. In this epidemiological context, the proposed ETS forecasting model could be suitable to monitor, in a timely manner, the impact of COVID-19 disease on the health care system, not only during the early stages of the pandemic but also during the vaccination campaign, to quickly adapt possible preventive interventions

Nanostructured Lipid Carriers-Containing Anticancer Compounds: Preparation, Characterization, and Cytotoxicity Studies

This article describes the development of nanostructured lipid carriers (NLC) as colloidal carriers for two antitumor compounds that possess a remarkable antineoplastic activity. But their limited stability and low solubility in water could give a very low parenteral bioavailability. Results revealed an enhancement of the cytotoxicity effect of drug-loaded NLC on human prostate cancer (PC-3) and human hepatocellular carcinoma (HuH-6, HuH-7) cell lines with respect to that of both free drugs. Results of characterization studies strongly support the potential application of these drugs-loaded NLC as prolonged delivery systems for lipophilic drugs by several administration routes, in particular for intravenous administration

Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning-Based Text-Mining Approach

Background: Unintentional injury is the leading cause of death in young children. Emergency department (ED) diagnoses are a useful source of information for injury epidemiological surveillance purposes. However, ED data collection systems often use free-text fields to report patient diagnoses. Machine learning techniques (MLTs) are powerful tools for automatic text classification. The MLT system is useful to improve injury surveillance by speeding up the manual free-text coding tasks of ED diagnoses. Objective: This research aims to develop a tool for automatic free-text classification of ED diagnoses to automatically identify injury cases. The automatic classification system also serves for epidemiological purposes to identify the burden of pediatric injuries in Padua, a large province in the Veneto region in the Northeast Italy. Methods: The study includes 283, 468 pediatric admissions between 2007 and 2018 to the Padova University Hospital ED, a large referral center in Northern Italy. Each record reports a diagnosis by free text. The records are standard tools for reporting patient diagnoses. An expert pediatrician manually classified a randomly extracted sample of approximately 40, 000 diagnoses. This study sample served as the gold standard to train an MLT classifier. After preprocessing, a document-term matrix was created. The machine learning classifiers, including decision tree, random forest, gradient boosting method (GBM), and support vector machine (SVM), were tuned by 4-fold cross-validation. The injury diagnoses were classified into 3 hierarchical classification tasks, as follows: injury versus noninjury (task A), intentional versus unintentional injury (task B), and type of unintentional injury (task C), according to the World Health Organization classification of injuries. Results: The SVM classifier achieved the highest performance accuracy (94.14%) in classifying injury versus noninjury cases (task A). The GBM method produced the best results (92% accuracy) for the unintentional and intentional injury classification task (task B). The highest accuracy for the unintentional injury subclassification (task C) was achieved by the SVM classifier. The SVM, random forest, and GBM algorithms performed similarly against the gold standard across different tasks. Conclusions: This study shows that MLTs are promising techniques for improving epidemiological surveillance, allowing for the automatic classification of pediatric ED free-text diagnoses. The MLTs revealed a suitable classification performance, especially for general injuries and intentional injury classification. This automatic classification could facilitate the epidemiological surveillance of pediatric injuries by also reducing the health professionals' efforts in manually classifying diagnoses for research purposes

Is a more aggressive COVID-19 case detection approach mitigating the burden on ICUs? Some reflections from Italy

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