RESUMEN
BACKGROUND: The progression from isolated seizures to status epilepticus (SE) is a critical clinical issue. This study explores the influence of circadian rhythms on this transition and assesses the impact of medication timing on SE conversion rates and patient survival. AIM: To determine the circadian patterns in the transition from isolated seizures to SE and to evaluate the efficacy of medication timing in reducing this conversion and improving survival outcomes. DESIGN AND METHODS: Utilizing the eICU Collaborative Research Database v2.0, a retrospective analysis was performed on patients at risk of SE conversion. The study analyzed the correlation between SE conversion timing and AEDs administration in relation to circadian rhythms, employing a Logit model to evaluate the impact of medication timing on SE conversion and survival. RESULTS: The transition from isolated seizures to SE showed distinct circadian patterns, with a delayed acrophase. Early night-time AEDs administration significantly reduced conversion rates. Medication timing also influenced survival rates, with higher survival during specific periods. CONCLUSION: Circadian rhythms significantly affect the transition from isolated seizures to SE. Timely AEDs administration is crucial for reducing conversions and improving survival. A chronotherapeutic approach aligning AEDs administration with individual circadian vulnerabilities could advance epilepsy management in ICU settings. Future research should focus on personalized medication strategies that utilize circadian rhythms to optimize treatment effects.
RESUMEN
This study aims to explore the relationship between the circadian rhythms of critically ill patients and the incidence of Status Epilepticus (SE), and to develop a predictive model based on circadian rhythm indicators and clinical factors. We conducted a diurnal rhythm analysis of vital sign data from 4413 patients, discovering significant differences in the circadian rhythms of body temperature, blood oxygen saturation, and heart rate between the SE and non-SE groups, which were correlated with the incidence of SE. We also employed various machine learning algorithms to identify the ten most significant variables and developed a predictive model with strong performance and clinical applicability. Our research provides a new perspective and methodology for the study of biological rhythms in critically ill patients, offering new evidence and tools for the prevention and treatment of SE. Our findings are consistent or similar to some in the literature, while differing from or supplementing others. We observed significant differences in the vital signs of epileptic patients at different times of the day across various diagnostic time groups, reflecting the regulatory effects of circadian rhythms. We suggest heightened monitoring and intervention of vital signs in critically ill patients, especially during late night to early morning hours, to reduce the risk of SE and provide more personalized treatment plans.