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Selection of antiseizure medications (ASMs) for newly diagnosed epilepsy remains largely a trial-and-error process. We have developed a machine learning (ML) model using retrospective data collected from five international cohorts that predicts response to different ASMs as the initial treatment for individual adults with new-onset epilepsy. This study aims to prospectively evaluate this model in Australia using a randomised controlled trial design.
At least 234 adult patients with newly diagnosed epilepsy will be recruited from 14 centres in Australia. Patients will be randomised 1:1 to the ML group or usual care group. The ML group will receive the ASM recommended by the model unless it is considered contraindicated by the neurologist. The usual care group will receive the ASM selected by the neurologist alone. Both the patient and neurologists conducting the follow-up will be blinded to the group assignment. Both groups will be followed up for 52 weeks to assess treatment outcomes. Additional information on adverse events, quality of life, mood and use of healthcare services and productivity will be collected using validated questionnaires. Acceptability of the model will also be assessed.
The primary outcome will be the proportion of participants who achieve seizure-freedom (defined as no seizures during the 12-month follow-up period) while taking the initially prescribed ASM. Secondary outcomes include time to treatment failure, time to first seizure after randomisation, changes in mood assessment score and quality of life score, direct healthcare costs, and loss of productivity during the treatment period.
This trial will provide class I evidence for the effectiveness of a ML model as a decision support tool for neurologists to select the first ASM for adults with newly diagnosed epilepsy.
This study is approved by the Alfred Health Human Research Ethics Committee (Project 130/23). Findings will be presented in academic conferences and submitted to peer-reviewed journals for publication.
ACTRN12623000209695.
To investigate the self-reported levels of social support from friends and family and from nurses as mediators of the relationship between self-rated physical and psychological condition in hospitalised patients.
Cross-sectional study of adult inpatients at a large tertiary-care hospital in the northeast United States.
Multiple mediation analysis of survey data.
In surveys received from 324 inpatients, one fourth of the variation in patients' self-rated psychological condition was explained by self-rated physical condition. Social support from family and friends mediated a significant proportion (11.0%) of the relationship between self-rated physical and psychological condition, however social support from nurses did not.
Social support from family and friends can positively influence the psychological health of inpatients, but nurses are not an adequate replacement for the social support provided by family and friends.
Although nurses cannot replace the social support provided by family and friends, the assessment of social isolation and care planning of interventions to support patients is a fundamental nursing role. Technology to connect patients with friends and family should be used to mitigate isolation for hospitalised patients unable to receive in-person visits from loved ones.
The influence of social support from family and friends and nurses was addressed. The study found social support from family and friends, but not nurses, to influence the relationship between physical and psychological ratings. This finding has implications for the role of nurses in the hospital setting.
Strengthening the Reporting of Observational Studies in Epidemiology guidelines were followed.
FreshRSS 