Early termination of cardiovascular trials as a consequence of poor accrual: analysis of ClinicalTrials.gov 2006-2015

OBJECTIVES: To present a snapshot of experimental cardiovascular research with a focus on geographical and temporal patterns of early termination due to poor accrual. SETTING: The Aggregate Analysis of ClinicalTrials.gov (AACT) database, reflecting ClinicalTrials.gov as of 27 March 2016. DESIGN: The AACT database was searched for all cardiovascular clinical trials that started from January 2006 up to December 2015. RESULTS: Thirteen thousand and seven hundred twenty-nine cardiovascular trials were identified. Of these, 8900 (65%) were classified as closed studies. Globally, 11% of closed trials were terminated. This proportion varied from 9.6% to 14% for trials recruiting from Europe and Americas, respectively, with a slightly decreasing trend (p=0.02) over the study period. The most common reason for trials failing to complete was poor accrual (41%). Intercontinental trials exhibited lower figures of poor accrual as the reason for their early stopping, as compared with trials recruiting in a single continent (28% vs 44%, p=0.002). CONCLUSIONS: Poor accrual significantly challenges the successful completion of cardiovascular clinical trials. Findings are suggestive of a positive effect of globalisation of cardiovascular clinical research on the achievement of enrolment goals within a reasonable time frame

A first estimation of the impact of public health actions against COVID-19 in Veneto (Italy)

Veneto is one of the first Italian regions where the COVID-19 outbreak started spreading. Containment measures were approved soon thereafter. The present study aims at providing a first look at the impact of the containment measures on the outbreak progression in the Veneto region, Italy

Machine learning in clinical and epidemiological research: isn't it time for biostatisticians to work on it?

In recent years, there has been a widespread cross-fertilization between Medical Statistics and Machine Learning (ML) techniques

Association between air pollution and ventricular arrhythmias in high-risk patients (ARIA study): a multicentre longitudinal study

Summary Background Although the effects of air pollution on mortality have been clearly shown in many epidemiological and observational studies, the pro-arrhythmic effects remain unknown. We aimed to assess the short-term effects of air pollution on ventricular arrhythmias in a population of high-risk patients with implantable cardioverter-defibrillators (ICDs) or cardiac resynchronisation therapy defibrillators (ICD-CRT). Methods In this prospective multicentre study, we assessed 281 patients (median age 71 years) across nine centres in the Veneto region of Italy. Episodes of ventricular tachycardia and ventricular fibrillation that were recorded by the diagnostic device were considered in this analysis. Concentrations of particulate matter of less than 10 μm (PM 10 ) and less than 2·5 μm (PM 2·5 ) in aerodynamic diameter, carbon monoxide, nitrogen dioxide, sulphur dioxide, and ozone were obtained daily from monitoring stations, and the 24 h median value was considered. Each patient was associated with exposure data from the monitoring station that was closest to their residence. Patients were followed up for 1 year and then scheduled to have a closing visit, within 1 more year. This study is registered with ClinicalTrials.gov, number NCT01723761. Findings Participants were enrolled from April 1, 2011, to Sept 30, 2012, and follow-ups (completed on April 5, 2014) ranged from 637 to 1177 days (median 652 days). The incidence of episodes of ventricular tachycardia and ventricular fibrillation correlated significantly with PM 2·5 (p 10 . An analysis of ventricular fibrillation episodes alone showed a significant increase in risk of higher PM 2·5 (p=0·002) and PM 10 values (p=0·0057). None of the gaseous pollutants were significantly linked to the occurrence of ventricular tachycardia or ventricular fibrillation. In a subgroup analysis of patients with or without a previous myocardial infarction, only the first showed a significant association between particulate matter and episodes of ventricular tachycardia or ventricular fibrillation. Interpretation Particulate matter has acute pro-arrhythmic effects in a population of high-risk patients, which increase on exposure to fine particles and in patients who have experienced a previous myocardial infarction. The time sequence of the arrhythmic events suggests there is an underlying neurally mediated mechanism. From a clinical point of view, the results of our study should encourage physicians to also consider environmental risk when addressing the prevention of arrhythmic events, particularly in patients with coronary heart disease, advising them to avoid exposure to high levels of fine particulate matter. Funding There was no funding source for this study

Sviluppo e applicazione di tecniche di apprendimento automatico per l'analisi e la classificazione del testo in ambito clinico. Development and Application of Machine Learning Techniques for Text Analyses and Classification in Clinical Research

The content of Electronic Health Records (EHRs) is hugely heterogeneous, depending on the overall health system structure. Possibly, the most present and underused unstructured type of data included in the EHRs is the free-text. Nowadays, with Machine Learning (ML), we can take advantage of automatic models to encode narratives showing performance comparable to the human ones. In this dissertation, the focus is on the investigation of ML Techniques (MLT) to get insights from free-text in clinical settings. We considered two main groups of free-text involved in clinical research. The first is composed of extensive documents like research papers or study protocols. For this group, we considered 14 Systematic Reviews (SRs), including 7,494 studies from PubMed and a whole snapshot of 233,609 trials from ClinicalTrials.gov. Pediatric EHRs compose the second group, for which we considered two sources of data: one of 6,903,035 visits from the Italian Pedianet database, and the second of 2,723 Spanish discharging notes from pediatric Emergency Departments (EDs) of nine hospitals in Nicaragua. The first contribution reported is an automatic system trained to replicate a search from specialized search engines to clinical registries. The model purposed showed very high classification performances (AUC from 93.4% to 99.9% among the 14 SRs), with the added value of a reduced amount of non-relevant studies extracted (mean of 472 and maximum of 2119 additional records compared to 572 and 2680 of the original manual extraction respectively). A comparative study to explore the effect of changing different MLT or methods to manage class imbalance is reported. A whole investigation on pediatric ED visits collected from nine hospitals in Nicaragua was reported, showing a mean accuracy in the classification of discharge diagnoses of 78.31% showing promising performance of an ML for the automatic classification of ED free-text discharge diagnoses in the Spanish language. A further contribution aimed to improve the accuracy of infectious disease detection at the population level. That is a crucial public health issue that can provide the background information necessary for the implementation of effective control strategies, such as advertising and monitoring the effectiveness of vaccination campaigns. Among the two studies reported of classify cases of Varicella-Zoster Virus and types of otitis, both the primary ML paradigms of shallow and deep models were explored. In both cases the results were highly promising; in the latter, reaching performances comparable to the human ones (Accuracy 96.59% compared with 95.91% achieved by human annotators, and balanced F1 score of 95.47% compared with 93.47%). A further relevant side goal achieved rely on the languages investigated. The international research on the use of MLTs to classify EHRs is focused on English-based datasets mainly. Hence, results on non-English databases, like the Italian Pedianet or the Spanish of ED visits considered in the dissertation are essential to assess general applicability of MLTs at a general linguistic level. Showing performances comparable to the human ones, the dissertation highlights the real possibility to start to incorporate ML systems on daily clinical practice to produce a concrete improvement in the health care processes when free-text comes into account.Il contenuto delle cartelle cliniche elettroniche (EHR) è estremamente eterogeneo, dipendendo della struttura generale del sistema sanitario. Al loro interno, il testo libero èprobabilmente la tipologia di dati non strutturato più presente e contemporaneamente sottoutilizzato. Al giorno d'oggi, grazie alle tecniche di Machine Learning (MLT), possiamo sfruttare modelli automatici per codificarne il contenuto testuale con prestazioni comparabili a quelle umane. In questa tesi, l'attenzione si concentra sull'investigazione delle MLT per l'ottenimento di informazioni utili non triviali dal testo libero in contesti clinici. Abbiamo considerato due tipi principali di testo libero coinvolti nella ricerca clinica. Il primo è composto da documenti estesi come articoli scientifici o protocolli di studio. Per questo gruppo, abbiamo preso in considerazione 14 revisioni sistematiche (SR), tra cui 7.494 studi di PubMed e un'intera istantanea composta da 233.609 studi clinici da ClinicalTrials.gov. Le cartelle cliniche elettroniche pediatriche compongono il secondo gruppo, per il quale abbiamo considerato due fonti di dati: una di 6.903.035 visite dal database italiano Pedianet e la seconda da 2.723 note di dimissione ospedaliera scritte in spagnolo e provenienti dai dipartimenti di emergenza (DE) pediatrica di nove ospedali in Nicaragua. Il primo contributo riportato è un sistema automatico addestrato per replicare una ricerca dai motori di ricerca specializzati ai registri clinici. Il modello proposto ha mostrato prestazioni di classificazione molto elevate (AUC dal 93,4% al 99,9% tra i 14 SR), con il valore aggiunto di una quantità ridotta di studi non rilevanti estratti (media di 472 e massimo di 2119 record aggiuntivi rispetto a 572 e 2680 dell'estrazione manuale originale rispettivamente). Viene riportato anche uno studio comparativo per esplorare l'effetto dell'utilizzo di differenti MLT e di metodi diversi per gestire gli effetti dello squilibro di numerosità nelle classi. Nella tesi è riportata inoltre un'intera indagine sulle visite pediatriche presso i DE raccolte presso i nove ospedali del Nicaragua. In tale indagine emerge un'accuratezza media nella classificazione delle diagnosi di dimissione coi modelli proposti del 78,31%, mostrando promettenti prestazioni per un sistema ML per la classificazione automatica delle diagnosi di dimissione da testo libero in lingua spagnola. Un ulteriore contributo riportato ha mirato a migliorare l'accuratezza del rilevamento delle malattie infettive a livello di popolazione. Questo è un problema cruciale per la salute pubblica che può fornire le informazioni di base necessarie per l'implementazione di strategie di controllo efficaci, come la notifica e il monitoraggio di efficacia di campagne di vaccinazione. Tra i due studi riportati, sono stati esplorati entrambi i paradigmi primari di ML classici e profondi. In entrambi i casi i risultati sono stati molto promettenti; nel secondo, raggiungendo prestazioni paragonabili a quelle umane (precisione del 96,59% rispetto al 95,91% raggiunta dagli annotatori umani e livello F1 bilanciato del 95,47% rispetto al 93,47%). Un ulteriore obiettivo secondario ma rilevante raggiunto riguarda le lingue indagate. La ricerca internazionale sull'uso delle MLT per classificare gli EHR si concentra principalmente su set di dati testuali in lingua inglese. Pertanto, i risultati su database non inglesi, come il Pedianet italiano o quello spagnolo delle visite ED considerate nella tesi, risultano contributi chiave per valutare l'applicabilità generale delle MLT a livello linguistico generale. Mostrando prestazioni paragonabili a quelle umane, la tesi evidenzia la reale possibilità di iniziare a incorporare i sistemi ML nella pratica clinica quotidiana per produrre un miglioramento concreto nei processi sanitari quando si tiene conto del testo libero