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Article ; Online: Prediction of treatment outcome in clinical trials under a personalized medicine perspective

Paola Berchialla / Corrado Lanera / Veronica Sciannameo / Dario Gregori / Ileana Baldi

Scientific Reports, Vol 12, Iss 1, Pp 1-

2022 Volume 8

Abstract: Abstract A central problem in most data-driven personalized medicine scenarios is the estimation of heterogeneous treatment effects to stratify individuals into subpopulations that differ in their susceptibility to a particular disease or response to a ... ...

Abstract	Abstract A central problem in most data-driven personalized medicine scenarios is the estimation of heterogeneous treatment effects to stratify individuals into subpopulations that differ in their susceptibility to a particular disease or response to a specific treatment. In this work, with an illustrative example on type 2 diabetes we showed how the increasing ability to access and analyzed open data from randomized clinical trials (RCTs) allows to build Machine Learning applications in a framework of personalized medicine. An ensemble machine learning predictive model is first developed and then applied to estimate the expected treatment response according to the medication that would be prescribed. Machine learning is quickly becoming indispensable to bridge science and clinical practice, but it is not sufficient on its own. A collaborative effort is requested to clinicians, statisticians, and computer scientists to strengthen tools built on machine learning to take advantage of this evidence flow.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Prior Elicitation for Use in Clinical Trial Design and Analysis

Danila Azzolina / Paola Berchialla / Dario Gregori / Ileana Baldi

International Journal of Environmental Research and Public Health, Vol 18, Iss 1833, p

A Literature Review

2021 Volume 1833

Abstract: Bayesian inference is increasingly popular in clinical trial design and analysis. The subjective knowledge derived from an expert elicitation procedure may be useful to define a prior probability distribution when no or limited data is available. This ... ...

Abstract	Bayesian inference is increasingly popular in clinical trial design and analysis. The subjective knowledge derived from an expert elicitation procedure may be useful to define a prior probability distribution when no or limited data is available. This work aims to investigate the state-of-the-art Bayesian prior elicitation methods with a focus on clinical trial research. A literature search on the Current Index to Statistics (CIS), PubMed, and Web of Science (WOS) databases, considering “prior elicitation” as a search string, was run on 1 November 2020. Summary statistics and trend of publications over time were reported. Finally, a Latent Dirichlet Allocation (LDA) model was developed to recognise latent topics in the pertinent papers retrieved. A total of 460 documents pertinent to the Bayesian prior elicitation were identified. Of these, 213 (45.4%) were published in the “Probability and Statistics” area. A total of 42 articles pertain to clinical trial and the majority of them (81%) reports parametric techniques as elicitation method. The last decade has seen an increased interest in prior elicitation and the gap between theory and application getting narrower and narrower. Given the promising flexibility of non-parametric approaches to the experts’ elicitation, more efforts are needed to ensure their diffusion also in applied settings.
Keywords	prior elicitation ; latent dirichlet allocation ; clinical trial ; Medicine ; R
Subject code	310
Language	English
Publishing date	2021-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A SuperLearner-enforced approach for the estimation of treatment effect in pediatric trials

Danila Azzolina / Rosanna Comoretto / Liviana Da Dalt / Silvia Bressan / Dario Gregori

Digital Health, Vol

2023 Volume 9

Abstract: 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 ... ...

Abstract	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.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	310
Language	English
Publishing date	2023-08-01T00:00:00Z
Publisher	SAGE Publishing
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Non-communicable chronic diseases

Vicente Paulo Alves / Regina Célia de Oliveira / Dario Gregori

Health Policy Open, Vol 2, Iss , Pp 100041- (2021)

Mortality of older adult citizens in Brazil and Italy before the Covid-19 pandemic

2021

Abstract: The purpose of this study was to outline the main non-communicable chronic diseases that led older people to death in Brazil and Italy before the SARS-CoV-2 pandemic according to age gaps and region of residence. This study has identified that the ... ...

Abstract	The purpose of this study was to outline the main non-communicable chronic diseases that led older people to death in Brazil and Italy before the SARS-CoV-2 pandemic according to age gaps and region of residence. This study has identified that the highest mortality rate among Brazilian and Italian women took place in lower-income areas, potentially due to insufficient public policies to increase income and improve health, which would in turn reduce the risk of chronic diseases and increase life expectancy. Men showed higher mortality rates in different regions, and tended to die earlier. Our results highlight socioeconomic differences in the areas with the highest death rates due to non-communicable chronic diseases, emphasizing the relevance of public policies to meet the needs of the overall population.
Keywords	Mortality ; Older adult ; Sex ; Economic differences ; Public policies ; Public aspects of medicine ; RA1-1270
Subject code	300
Language	English
Publishing date	2021-12-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Does the Integration of Pre-Coded Information with Narratives Improve in-Hospital Falls’ Surveillance?

Giulia Lorenzoni / Roberta Rampazzo / Alessia Buratin / Paola Berchialla / Dario Gregori

Applied Sciences, Vol 11, Iss 4406, p

2021 Volume 4406

Abstract: To evaluate the value added by information reported in narratives (extracted through text mining techniques) in enhancing the characterization of falls patterns. Data on falls notified to the Risk Management Service of a Local Health Authority in Italy ... ...

Abstract	To evaluate the value added by information reported in narratives (extracted through text mining techniques) in enhancing the characterization of falls patterns. Data on falls notified to the Risk Management Service of a Local Health Authority in Italy were considered in the analysis. Each record reported detailed pre-coded information about patient and fall’s characteristics, together with a narrative description of the fall. At first, multiple correspondence analysis (MCA) was performed on pre-coded information only. Then, it was re-run on the pre-coded data augmented with a variable representing the output analysis of the narrative records. This second analysis required a pre-processing of the narratives followed by text mining. Finally, a Hierarchical Clustering on the two MCA was carried out to identify distinct fall patterns. The dataset included 202 falls’ records. Three clusters corresponding to three distinct profiles of falls were identified through the Hierarchical Clustering performed using only pre-coded information. Hierarchical Clustering with the topic variable provided overlapping results. The present findings showed that the cluster analysis is effective in characterizing fall patterns; however, they do not sustain the hypothesis that the analysis of free-text information improves our understanding of such phenomenon.
Keywords	falls ; narratives ; text mining ; cluster analysis ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2021-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Positioning of Vascular Access in Pediatric Patients

Chiara Moreal / Rosanna I. Comoretto / Sara Buchini / Dario Gregori

Journal of Clinical Medicine, Vol 10, Iss 2590, p

An Observational Study Focusing on Adherence to Current Guidelines

2021 Volume 2590

Abstract: Venous access devices (VADs) play an important role in different clinical contexts. In pediatric subjects, VAD placement is more complicated than in adults due to children’s poor cooperativity and reduced vascular access. Adherence to guidelines for the ... ...

Abstract	Venous access devices (VADs) play an important role in different clinical contexts. In pediatric subjects, VAD placement is more complicated than in adults due to children’s poor cooperativity and reduced vascular access. Adherence to guidelines for the placement of VADs could prevent the occurrence of complications, but data in the literature are general and not exhaustive, especially with regard to the pediatric population. The objective of this study was to assess adherence to guidelines for the placement of VADs in a pediatric setting. A retrospective observational study was conducted in the general ward of a pediatric hospital in the northern region of Italy. Data related to consecutive admissions in the period from 1 January to 31 December 2019 were collected according to the availability of clinical documentation. A cohort of 251 subjects was considered, yielding a total of 367 VADs. Device permanence in situ and the effective administration of intravenous therapy were associated with an increased risk of complications, while adherence to guidelines was an important protective factor. Adherence to guidelines for the placement of VADs is an independent and positive predictive factor for the prevention of complications due to the presence of a vascular device.
Keywords	vascular access ; nursing ; pediatric patients ; adherence to guidelines ; complications ; Medicine ; R
Subject code	610
Language	English
Publishing date	2021-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: COVID-19 hospitalizations and patients' age at admission

Danila Azzolina / Rosanna Comoretto / Corrado Lanera / Paola Berchialla / Ileana Baldi / Dario Gregori

Frontiers in Public Health, Vol

The neglected importance of data variability for containment policies

2022 Volume 10

Abstract: IntroductionAn excess in the daily fluctuation of COVID-19 in hospital admissions could cause uncertainty and delays in the implementation of care interventions. This study aims to characterize a possible source of extravariability in the number of ... ...

Abstract	IntroductionAn excess in the daily fluctuation of COVID-19 in hospital admissions could cause uncertainty and delays in the implementation of care interventions. This study aims to characterize a possible source of extravariability in the number of hospitalizations for COVID-19 by considering age at admission as a potential explanatory factor. Age at hospitalization provides a clear idea of the epidemiological impact of the disease, as the elderly population is more at risk of severe COVID-19 outcomes. Administrative data for the Veneto region, Northern Italy from February 1, 2020, to November 20, 2021, were considered.MethodsAn inferential approach based on quasi-likelihood estimates through the generalized estimation equation (GEE) Poisson link function was used to quantify the overdispersion. The daily variation in the number of hospitalizations in the Veneto region that lagged at 3, 7, 10, and 15 days was associated with the number of news items retrieved from Global Database of Events, Language, and Tone (GDELT) regarding containment interventions to determine whether the magnitude of the past variation in daily hospitalizations could impact the number of preventive policies.ResultsThis study demonstrated a significant increase in the pattern of hospitalizations for COVID-19 in Veneto beginning in December 2020. Age at admission affected the excess variability in the number of admissions. This effect increased as age increased. Specifically, the dispersion was significantly lower in people under 30 years of age. From an epidemiological point of view, controlling the overdispersion of hospitalizations and the variables characterizing this phenomenon is crucial. In this context, the policies should prevent the spread of the virus in particular in the elderly, as the uncontrolled diffusion in this age group would result in an extra variability in daily hospitalizations.DiscussionThis study demonstrated that the overdispersion, together with the increase in hospitalizations, results in a lagged inflation of the ...
Keywords	COVID-19 ; overdispersion ; prevention policies ; hospitalizations ; GDELT data ; Public aspects of medicine ; RA1-1270
Subject code	333
Language	English
Publishing date	2022-11-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Bayesian Sample Size Estimation Procedure Based on a B-Splines Semiparametric Elicitation Method

Danila Azzolina / Paola Berchialla / Silvia Bressan / Liviana Da Dalt / Dario Gregori / Ileana Baldi

International Journal of Environmental Research and Public Health, Vol 19, Iss 14245, p

2022 Volume 14245

Abstract: 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, ... ...

Abstract	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.
Keywords	Bayesian trial ; semiparametric ; elicitation ; sample size ; phase II ; Medicine ; R
Subject code	310
Language	English
Publishing date	2022-10-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Web-Based Application to Monitor and Inform about the COVID-19 Outbreak in Italy

Corrado Lanera / Danila Azzolina / Francesco Pirotti / Ilaria Prosepe / Giulia Lorenzoni / Paola Berchialla / Dario Gregori

Healthcare, Vol 10, Iss 473, p

The {COVID-19ita} Initiative

2022 Volume 473

Abstract: 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 ... ...

Abstract	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.
Keywords	COVID-19 ; web application ; shiny app ; monitoring tool ; Medicine ; R
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Adherence to Mediterranean diet in Italy (ARIANNA) cross-sectional survey

Marco Silano / Dario Gregori / Giulia Lorenzoni / Francesca Iacoponi / Silvia Gallipoli / Marco Ghidina / Federica Zobec / Erica Cardamone / Rita Di Benedetto

BMJ Open, Vol 13, Iss

study protocol

2023 Volume 3

Abstract: Introduction There is evidence, although limited, that the Italian population has been no longer following a Mediterranean dietary pattern. The ARIANNA (Adherence to the Mediterranean Diet in Italy) project consists of a survey-based cross-sectional ... ...

Abstract	Introduction There is evidence, although limited, that the Italian population has been no longer following a Mediterranean dietary pattern. The ARIANNA (Adherence to the Mediterranean Diet in Italy) project consists of a survey-based cross-sectional study with the objective of gaining a greater knowledge of adherence to the Mediterranean Diet and its main determinants in different age groups of the Italian population.Methods/analysis The ARIANNA study will involve males and females aged ≥7 years, born and resident in Italy, and proficient in Italian. The voluntary enrolment will be in the period between March 2023 and May 2023. The data, which will include sociodemographic factors and dietary habits, will be collected through a web-based questionnaire. Adherence to the Mediterranean Diet will be assessed through the use of two validated score systems: the Mediterranean Diet Quality Index in children and adolescents for participants aged ≤16 years and the Mediterranean Diet Serving Score for participants aged ≥17 years. A review of the scientific literature will be carried out to collect historical data on adherence to the Mediterranean dietary pattern in the Italian population, which will be compared with those collected within this project.Ethics and dissemination The ARIANNA study has been approved by the Ethics Committee of Istituto Superiore di Sanità. The results will be disseminated through peer-reviewed papers, leaflets and documents for the general public. A report will be presented to the national policy makers, to give them the tools to implement appropriate intervention to improve, in necessary, the adherence to Mediterranean dietary pattern in Italy.
Keywords	Medicine ; R
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	BMJ Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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