Adult onset epilepsy is defined by phenotypic clusters with unique comorbidities and risks of death

Improving epilepsy diagnosis across the lifespan: approaches and innovations

Epilepsy diagnosis is often delayed or inaccurate, exposing people to ongoing seizures and their substantial consequences until effective treatment is initiated. Important factors contributing to this problem include delayed recognition of seizure symptoms by patients and eyewitnesses; cultural, geographical, and financial barriers to seeking health care; and missed or delayed diagnosis by health-care providers. Epilepsy diagnosis involves several steps. The first step is recognition of epileptic seizures; next is classification of epilepsy type and whether an epilepsy syndrome is present; finally, the underlying epilepsy-associated comorbidities and potential causes must be identified, which differ across the lifespan. Clinical history, elicited from patients and eyewitnesses, is a fundamental component of the diagnostic pathway. Recent technological advances, including smartphone videography and genetic testing, are increasingly used in routine practice. Innovations in technology, such as artificial intelligence, could provide new possibilities for directly and indirectly detecting epilepsy and might make valuable contributions to diagnostic algorithms in the future.

A scoping review of the clinical application of machine learning in data-driven population segmentation analysis

OBJECTIVE

Data-driven population segmentation is commonly used in clinical settings to separate the heterogeneous population into multiple relatively homogenous groups with similar healthcare features. In recent years, machine learning (ML) based segmentation algorithms have garnered interest for their potential to speed up and improve algorithm development across many phenotypes and healthcare situations. This study evaluates ML-based segmentation with respect to (1) the populations applied, (2) the segmentation details, and (3) the outcome evaluations.

MATERIALS AND METHODS

MEDLINE, Embase, Web of Science, and Scopus were used following the PRISMA-ScR criteria. Peer-reviewed studies in the English language that used data-driven population segmentation analysis on structured data from January 2000 to October 2022 were included.

RESULTS

We identified 6077 articles and included 79 for the final analysis. Data-driven population segmentation analysis was employed in various clinical settings. K-means clustering is the most prevalent unsupervised ML paradigm. The most common settings were healthcare institutions. The most common targeted population was the general population.

DISCUSSION

Although all the studies did internal validation, only 11 papers (13.9%) did external validation, and 23 papers (29.1%) conducted methods comparison. The existing papers discussed little validating the robustness of ML modeling.

CONCLUSION

Existing ML applications on population segmentation need more evaluations regarding giving tailored, efficient integrated healthcare solutions compared to traditional segmentation analysis. Future ML applications in the field should emphasize methods' comparisons and external validation and investigate approaches to evaluate individual consistency using different methods.

Multimorbidity in People with Epilepsy

Multimorbidity is an emerging priority in healthcare due to associations with the ageing population, frailty, polypharmacy, health and social care demands. It affects 60-70% of adults and 80% of children with epilepsy. Neurodevelopmental conditions are commonly seen in children with epilepsy, while cancer, cardiovascular and neurodegenerative conditions often afflict older people with epilepsy. Mental health problems are common across the lifespan. Genetic, environmental, social and lifestyle factors contribute to multimorbidity and its consequences. Multimorbid people with epilepsy (PWE) are at higher risk of depression and suicide, premature death, suffer lower health-related quality of life, and require more hospital admissions and health care costs. The best management of multimorbid PWE requires a paradigm shift from the traditional single disease-single comorbidity approach and a refocus on a person-centred approach. Improvements in health care must be informed by assessing the burden of multimorbidity associated with epilepsy, delineating disease clusters, and measuring the effects on health outcomes.

Risk factors associated with new-onset epilepsy in young adults: Population-based study

OBJECTIVE

Risk factors for childhood and late-onset epilepsy are thoroughly described in existing literature, while they are less defined and ambiguous for young adults. We aimed to evaluate the prevalence of various risk factors associated with new-onset epilepsy in adults aged 20-49 years. We included all incident cases in North Macedonia between 2015 and 2018.

METHODS

Study participants were ascertained through the nationwide healthcare platform. We performed a retrospective analysis of their Electronic Health Records and identified associated conditions which are considered putative risk factors for epilepsy.

RESULTS

Of 1691 included patients, 694 (41%) had at least one putative risk factor. The most common were neoplasms and cerebrovascular diseases (8% of cases, respectively), followed by intellectual disability (6%), alcohol/substance abuse (5%), and traumatic brain injury (4%). Infective and inflammatory diseases were infrequent in our cohort (1%). Other very rare risk factors included degenerative and metabolic disorders and malformations of cortical development (2%). Idiopathic (genetic) generalized epilepsies accounted for 7% of incident cases. Chronic alcoholism and illicit drugs abuse (dominantly opioids) were significantly more frequent in males than in females (p < 0.00001).

CONCLUSION

Brain neoplasms and stroke were the most prevalent risk factors associated with new-onset epilepsy in young adults. Furthermore, adult-onset IGE were common in our cohort. The majority of patients did not have any of conventional risk factors and probably specific genetic causes might become increasingly recognized in adult-onset epilepsies in the future.