Pathophysiological factors of delirium among critically ill elders after non-cardiac surgery based on artificial neural networks: a pilot study

Abstract

Objectives: To utilize artificial neural networks (ANN) for early identification of pathophysiological risk factors of delirium among elders in surgical intensive care unit (SICU) after non-cardiac surgery.
Methodology: This prospective, single-center observational study was conducted at an SICU with 19 beds. Patients aged 65 and over were included. Delirium was screened by the Richmond Agitation-Sedation Scale and Confusion Assessment Method for the ICU. Factors analyzed were age, gender, disease, symptoms, sedatives and analgesics used, and biochemical parameters. Mean impact value (MIV) of each variable was calculated by MATLAB. Then, ANN was established based on the SPSS 20.0.
Results: Data from 134 patients were analyzed. The mean age was 77.045 ± 7.375 years (65-94), 50.7% were male and 11.94% had delirium. There were 13 important risk factors based on MIV, which were included to build ANN. The important pathophysiological risk factors were age, the use of sedatives, dose of propofol, dose of remifentanil, acidosis, fever, hyponatremia, hyperkalemia, albumin level, pre-albumin level and Child-Pugh score, and anemia. The area under ROC curve was 0.893.
Conclusion: Application and significance of artificial neural networks mainly lies on mean impact value, which can be used as a relative stable reference for early screening of pathophysiological risk factors of delirium among critically ill elders.
Citation: Ji M, Xing S, Yang Y. Pathophysiological factors of delirium among critically ill elders after non-cardiac surgery based on artificial neural networks: a pilot study. Anaesth Pain & Intensive Care 2018;22(4):­­424-430

Clinical Trial registration: Chinese Clinical Trial Register No. ChiCTR-OOC-16008154.
Received: 28 Sep 2018, Reviewed: 16 Oct, 30 Oct 2018, Corrected: 01 Nov 2018, Accepted: 02 Nov 2018