Objectives: To utilize artificial neural networks (ANN) for early identification of pathophysiological risk factors of delirium among elders in SICU after non-cardiac surgery. Design: A prospective single-center observational study. Setting: Conducted in a SICU with 19 beds. Patients: Aged 65 and over. Outcome Measures: 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 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 11.940% 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 ANN mainly lies on MIV, which can be used as a relative stable reference for early screening of pathophysiological risk factors of delirium among critically ill elders.
Key words: artificial neural networks, critically ill, elders, delirium, surgical intensive care unit
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