The influence of biological rhythms on the initial onset of status epilepticus in critically ill inpatients and the study of its predictive Model

ABSTRACT

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.

KEYWORDS:

• Circadian rhythms
• status epilepticus
• diurnal rhythm analysis
• machine learning
• predictive modeling

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Due to the license of eICU-CRD database, we cannot directly supply the data file.

Author contribution

Nan Cheng: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data; Analysis or interpretation of data.

Ruoxue Bai: Drafting/revision of the manuscript for content, including medical writing for content; Analysis or interpretation of data.

Lan Li: Drafting/revision of the manuscript for content, including medical writing for content; Study concept or design.

Xu Zhang: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data.

Xiaoru Kan: Drafting/revision of the manuscript for content, including medical writing for content; Analysis or interpretation of data.

Jinghan Liu: Drafting/revision of the manuscript for content, including medical writing for content; Study concept or design.

Yujie Qi: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data.

Shaowei Li: Drafting/revision of the manuscript for content, including medical writing for content; Analysis or interpretation of data.

Zhenliang Hui: Drafting/revision of the manuscript for content, including medical writing for content; Study concept or design.

Jun Chen: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data; Study concept or design.

Consent for publication

All authors read and approved the manuscript.

Ethics approval

Since this was a retrospective study, no consent was required from patients.

Supplementary data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/07420528.2024.2351490.

Additional information

Funding

This manuscript has been funded by Natural Science Foundation of Shaanxi Province [2023-JC-YB-656], and Shaanxi University of Chinese Medicine Open projects of Key Laboratory of Encephalopathy [KF2218].