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

A SuperLearner-enforced approach for the estimation of treatment effect in pediatric trials

Background: Randomized Clinical Trials (RCT) represent the gold standard among scientific evidence. RCTs are tailored to control selection bias and the confounding effect of baseline characteristics on the effect of treatment. However, trial conduction and enrolment procedures could be challenging, especially for rare diseases and paediatric research. In these research frameworks, the treatment effect estimation could be compromised. A potential countermeasure is to develop predictive models on the probability of the baseline disease based on previously collected observational data. Machine learning (ML) algorithms have recently become attractive in clinical research because of their flexibility and improved performance compared to standard statistical methods in developing predictive models. Objective: This manuscript proposes an ML-enforced treatment effect estimation procedure based on an ensemble SuperLearner (SL) approach, trained on historical observational data, to control the confounding effect. Methods: The REnal SCarring Urinary infEction trial served as a motivating example. Historical observational study data have been simulated through 10,000 Monte Carlo (MC) runs. Hypothetical RCTs have been also simulated, for each MC run, assuming different treatment effects of antibiotics combined with steroids. For each MC simulation, the SL tool has been applied to the simulated observational data. Furthermore, the average treatment effect (ATE), has been estimated on the trial data and adjusted for the SL predicted probability of renal scar. Results: The simulation results revealed an increased power in ATE estimation for the SL-enforced estimation compared to the unadjusted estimates for all the algorithms composing the ensemble SL

A Bayesian Sample Size Estimation Procedure Based on a B-Splines Semiparametric Elicitation Method

Sample size estimation is a fundamental element of a clinical trial, and a binomial experiment is the most common situation faced in clinical trial design. A Bayesian method to determine sample size is an alternative solution to a frequentist design, especially for studies conducted on small sample sizes. The Bayesian approach uses the available knowledge, which is translated into a prior distribution, instead of a point estimate, to perform the final inference. This procedure takes the uncertainty in data prediction entirely into account. When objective data, historical information, and literature data are not available, it may be indispensable to use expert opinion to derive the prior distribution by performing an elicitation process. Expert elicitation is the process of translating expert opinion into a prior probability distribution. We investigated the estimation of a binomial sample size providing a generalized version of the average length, coverage criteria, and worst outcome criterion. The original method was proposed by Joseph and is defined in a parametric framework based on a Beta-Binomial model. We propose a more flexible approach for binary data sample size estimation in this theoretical setting by considering parametric approaches (Beta priors) and semiparametric priors based on B-splines

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

No abstract availabl

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

Mixed-case Format and Lexical Decision Performance: Initial Uppercase Is Special

Previous research has shown that there are phenomena that may require a route to word identification by means other than through letters. For example, in a lexical decision task, in which an experimental participant is asked to determine if a string of letters is a word or not, responses to items in a MIXed caSE format are slower than to items in PURE UPPERCASE or pure lowercase formats. In this experiment, we investigated the effect of different mixed-case formats on lexical decision performance, focusing on the type and location of the case transition. Twenty-four students participated in a lexical decision making experiment, consisting of twelve blocks of sixty-four six-letter items. Each block contained an equal number of words and pseudowords (nonwords that conform to rules of English orthography) presented in eight different case formats (e.g., travel, TRAVEL, Travel, tRAVEL, traveL, TRAVEl, traVEL and TRAvel). We found that mean response times to Initial Uppercase, PURE UPPERCASE and pure lowercase formats were all faster than the mean response times to all other MIXEd casE formats, suggesting that perception of initial uppercase items—a standard orthographic form in English—is different from that of other mixed-case formats.https://engagedscholarship.csuohio.edu/u_poster_2014/1015/thumbnail.jp

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

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

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