JMIR Publications

ABSTRACT

Background:

Postoperative delirium (POD) is a prevalent and serious complication affecting older surgical patients, characterized by cognitive disturbances and fluctuating consciousness. Despite its impact on quality of life and increased mortality, POD’s causes remain inadequately understood and effective diagnostic and preventive measures are lacking. Objektive: The CONFUSED study aims to develop predictive models for POD using an integrative approach combining clinical, proteomic, transcriptomic and epigenetic data.

Objective:

The CONFUSED study aims to develop predictive models for POD using an integrative approach combining clinical, proteomic, transcriptomic and epigenetic data.

Methods:

The CONFUSED study is a prospective observational research conducted at a German university hospital, involving 200 patients undergoing major surgeries under general anesthesia. Key objectives include: 1) Identifying significantly altered genes (transcriptomics) and proteins (proteomics) through comprehensive analyses of blood samples; 2) Applying univariate and multivariate analyses, including AI techniques, to develop predictive models for POD on base of clinical and laboratory data.

Results:

Ethics and dissemination: The study was approved by the Ethics Committee of the Medical Faculty of Ruhr-University Bochum (23-7794) and the Ethics Committee Westfalen-Lippe (2024-082-f-S). Participants are recruited from the anesthesia outpatient clinic and provide informed consent to participate in the study. Blood samples are collected at four critical time points: premedication, immediately postoperatively, and on postoperative days two and five. These samples undergo extensive analysis, including proteomic profiling, RNA microarrays, DNA methylation analysis, and genotyping of potential polymorphisms. Clinical data, such as vital signs, medications, and delirium assessments using CAM and CAM-ICU, are systematically documented. The protocol followed the SPIRIT guidelines.

Conclusions:

The study employs advanced data integration and AI techniques to analyze the collected data. Initial analyses include univariate statistical methods to identify significant proteins and genes and multivariate methods to discover patient subgroups and develop predictive models. AI models, such as random forests and support vector machines, are used to predict the likelihood of POD based on the significant genes and proteins and on clinical data. By integrating these diverse data types and utilizing AI for predictive modeling, the study aims to enhance early diagnosis and personalized management of POD. The results are anticipated to lead to improved preventive strategies and therapeutic approaches, ultimately reducing the incidence and impact of postoperative delirium in surgical patients. Clinical Trial: The study is registered in the German Clinical trials register (DRKS) under the registration number DRKS00033854 on march 18th 2024. This is Version 1.0