Developmental and epileptic encephalopathies (DEEs) are a group of diseases characterized by developmental impairment and phases of plateauing/regression induced by epileptic activity. Treatment aims to reduce seizures and establish appropriate rehabilitation interventions. With the rising emerging potential for precision medicine, early diagnosis and acknowledgement of the natural history are crucial to put in place targeted treatments and promptly detect/prevent specific comorbidities. Dravet syndrome (DS) is a well-known type of DEE. Symptom onset is in the first year of life with convulsive seizures in otherwise typically developing infants. During childhood, drug-resistant epilepsy occurs together with developmental slowing, leading to cognitive impairment. Even if physician awareness of DS has improved in last decades, time to diagnosis is still over 2 year. Diagnosis is often delayed as it is difficult to differentiate at onset from Febrile Seizures (FS). These two conditions present substantial clinical differences, but might be overlapping at onset. The first description of DS is relatively recent. A few studies reporting clinical features of adults are emerging only the recent past. For these reasons, natural history and long-term outcome of DS are only partially known to date. In this research project we explored novel strategies for early diagnosing and understanding the natural history of DS, through different approaches. i) By using Natural Language Processing (NLP), a technology consisting in processing written information, in unstructured digital medical records. Data were collected from a document-based hospital data warehouse. Using Unified Medical Language System Meta-thesaurus, phenotype concepts could be recognized in medical reports. In a first study, we selected individuals with DS and individuals with Febrile Seizures (FS). A phenome-wide analysis was performed evaluating the statistical associations between the phenotypes of DS and FS, based on concepts found in the reports produced before 2 years. We found significative higher representation of concepts related to seizures’ phenotypes distinguishing DS from FS in the first phases. In a second study, we used the same technique to retrace the known natural history of DS, by comparing all reports of individual of DS Cohort with two control groups. This study showed that clinical concepts from reports of children with DS and their appearance during the clinical course (median age of appearance), follows the same outline as what is known in DS. We conclude that narrative medical reports of individuals with DS, contain specific clinical concept which can be automatically detected by software exploiting NLP, allowing an early diagnosis and the retracing of the natural history. ii) By addressing surveys to caregivers, to explore the domains that really matters for patients and their family at different ages. We found that items related to daily life had the highest compared to items about seizures. Increase of individuals’ age was associated with a less important impact of seizure duration and of the need of hospital referral. iii) By outlining the adaptive-behavior profile (ABP) of adolescents and adults with DS and exploring correlations with clinical history. We found two subpopulations: one with a ‘lower’ ABP and one with a ‘higher’ ABP, corresponding respectively to individuals in whom myoclonic seizures or generalized spike-and-wave activity were present or absent on EEG. These studies further delineate the natural history of DS and can help tailor patient-centered outcome measures in future clinical trials. These approaches could be transposed to other less known DEEs, to decrease time of diagnosis and outline the natural history.
Le encefalopatie dello sviluppo ed epilettiche (ESE) sono condizioni caratterizzate da ritardo dello sviluppo e fasi di plateau/regressione cognitiva indotte dall’attività epilettica. Il trattamento mira al controllo delle crisi ed ad una presa in carico riabilitativa appropriata. La diagnosi precoce e la definizione della storia naturale rappresentano fattori cruciali per mettere in atto trattamenti mirati ed individuare prontamente/prevenire le specifiche comorbidità. La Sindrome di Dravet (SD) è tra le più note ESE. L’esordio è nel primo anno di vita con crisi convulsive, cui seguono la comparsa di una epilessia farmacoresistente ed un deficit cognitivo. La diagnosi viene mediamente posta dopo i 2 anni. Questo ritardo è spesso dovuto alla difficoltà nel differenziare all’esordio la SD dalle convulsioni febbrili (CF), condizioni simili nella loro presentazione iniziale. La SD è una condizione di relativa recente descrizione. Solo negli ultimi anni stanno venendo alla luce le caratteristiche cliniche degli adulti affetti da Sindrome di Dravet, delle comorbidità associate, ed una miglior definizione dell’outcome a lungo termine. In questo progetto di ricerca, abbiamo esplorato l’utilizzo di nuove strategie per diagnosticare e delineare la storia naturale della SD, attraverso diversi approcci: i) Applicando il Natural Language Processing (NLP), una tecnologie che consiste nella processazione di informazioni scritte, ai referti medici digitali. I dati sono stati raccolti da un database ospedaliero. Attraverso l’Unified Medical Language System Meta-thesaurus, i concetti clinici sono stati riconosciuti e decodificati nei referti medici. Abbiamo selezionato due coorti: pazienti con SD e pazienti con CF. Attraverso una phenome-wide analisi, abbiamo analizzato la frequenza dei concetti presenti nei referti prodotti prima dei 2 anni, nelle due popolazioni. Abbiamo riscontrato una maggior rappresentazione di concetti legati alle caratteristiche tipiche della SD nei referti dei pazienti con SD, rispetto a quella con CF. In un secondo studio abbiamo applicato la stessa tecnica con l’obiettivo di ricostruire la storia naturale, confrontando i concetti presenti in tutti i referti dei pazienti con SD, con quelli di due popolazioni di controllo. I concetti risultati più presenti nei pazienti con SD, permettevano, attraverso l’analisi dell’età della loro comparsa, di ricostruire le caratteristiche fenotipiche longitudinali della SD. In conclusione, questi due studi dimostrano che i referti clinici di soggetti con SD contengono termini clinici che possono essere estrapolati “automaticamente” attraverso il NLP al fine di permettere una diagnosi precoce e la profilazione della storia naturale. ii) Attraverso la somministrazione di questionari ai caregiver, con il fine di esplorare quali aspetti relativi al benessere del proprio figlio, impattassero maggiormente sul pazienti, nelle diverse età. Gli ambiti relativi alla vita quotidiana hanno ottenuto punteggi più alti rispetto a quelli legati alle crisi epilettiche. La durata delle crisi e la necessità di un ricovero ospedaliero, assumono un impatto meno significativo con l’aumentare dell’età. iii) Attraverso l’analisi del profilo adattivo (PA) di adolescenti ed adulti con DS e l’esplorazione di eventuali correlazioni con la storia clinica. Abbiamo individuato due sottopopolazioni: una con PA 'peggiore' e una ‘migliore’, corrispondenti rispettivamente a individui che avevano presentato o meno crisi miocloniche o attività di tipo generalizzato all’ EEG. Questi studi delineano ulteriormente la storia naturale della DS. Gli approcci utilizzati potrebbero essere trasposti ad altre ESE, meno conosciute clinicamente, per accelerare il tempo della diagnosi e delinearne la storia naturale.
Nuove strategie per diagnosticare e delineare la storia naturale nelle encefalopatie dello sviluppo ed epilettiche: modelli applicati alla Sindrome di Dravet / Tommaso Lo Barco , 2023 May 19. 35. ciclo, Anno Accademico 2021/2022.
Nuove strategie per diagnosticare e delineare la storia naturale nelle encefalopatie dello sviluppo ed epilettiche: modelli applicati alla Sindrome di Dravet.
LO BARCO, TOMMASO
2023
Abstract
Developmental and epileptic encephalopathies (DEEs) are a group of diseases characterized by developmental impairment and phases of plateauing/regression induced by epileptic activity. Treatment aims to reduce seizures and establish appropriate rehabilitation interventions. With the rising emerging potential for precision medicine, early diagnosis and acknowledgement of the natural history are crucial to put in place targeted treatments and promptly detect/prevent specific comorbidities. Dravet syndrome (DS) is a well-known type of DEE. Symptom onset is in the first year of life with convulsive seizures in otherwise typically developing infants. During childhood, drug-resistant epilepsy occurs together with developmental slowing, leading to cognitive impairment. Even if physician awareness of DS has improved in last decades, time to diagnosis is still over 2 year. Diagnosis is often delayed as it is difficult to differentiate at onset from Febrile Seizures (FS). These two conditions present substantial clinical differences, but might be overlapping at onset. The first description of DS is relatively recent. A few studies reporting clinical features of adults are emerging only the recent past. For these reasons, natural history and long-term outcome of DS are only partially known to date. In this research project we explored novel strategies for early diagnosing and understanding the natural history of DS, through different approaches. i) By using Natural Language Processing (NLP), a technology consisting in processing written information, in unstructured digital medical records. Data were collected from a document-based hospital data warehouse. Using Unified Medical Language System Meta-thesaurus, phenotype concepts could be recognized in medical reports. In a first study, we selected individuals with DS and individuals with Febrile Seizures (FS). A phenome-wide analysis was performed evaluating the statistical associations between the phenotypes of DS and FS, based on concepts found in the reports produced before 2 years. We found significative higher representation of concepts related to seizures’ phenotypes distinguishing DS from FS in the first phases. In a second study, we used the same technique to retrace the known natural history of DS, by comparing all reports of individual of DS Cohort with two control groups. This study showed that clinical concepts from reports of children with DS and their appearance during the clinical course (median age of appearance), follows the same outline as what is known in DS. We conclude that narrative medical reports of individuals with DS, contain specific clinical concept which can be automatically detected by software exploiting NLP, allowing an early diagnosis and the retracing of the natural history. ii) By addressing surveys to caregivers, to explore the domains that really matters for patients and their family at different ages. We found that items related to daily life had the highest compared to items about seizures. Increase of individuals’ age was associated with a less important impact of seizure duration and of the need of hospital referral. iii) By outlining the adaptive-behavior profile (ABP) of adolescents and adults with DS and exploring correlations with clinical history. We found two subpopulations: one with a ‘lower’ ABP and one with a ‘higher’ ABP, corresponding respectively to individuals in whom myoclonic seizures or generalized spike-and-wave activity were present or absent on EEG. These studies further delineate the natural history of DS and can help tailor patient-centered outcome measures in future clinical trials. These approaches could be transposed to other less known DEEs, to decrease time of diagnosis and outline the natural history.File | Dimensione | Formato | |
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