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A Retrospective Study Establishing a Nomogram Predictive Model for Postoperative High-Activity Delirium After
Yingying Tu1, Wenwen Du2, Yuanyuan Pan2
1Nursing Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, People's Republic of China.
Background:
Postoperative high-activity delirium (PDHA) manifests as a high alertness, restlessness, hallucinations, and delusions. Occurrence of PDHA represents an increased risk of poor prognosis for patients.
Objective:
To establish and validate a nomogram prediction model for high-activity delirium after non-cardiac surgery in a post-anesthesia care unit (PACU).
Methods:
This study retrospectively enrolled adult patients who underwent non-cardiac surgery and were observed in the PACU as training data. Patients were divided into PDHA (199 patients) and non-PDHA (396 patients) groups. Patients' general data, preoperative indicators, intraoperative conditions, and postoperative PACU conditions were collected. The risk factors for PDHA were identified using univariate and multivariate logistic regression analyses. A predictive column chart was created using R language. Adult patients who underwent non-cardiac surgery and entered the PACU for observation were randomly selected as the validation set data (198 cases) for model performance validation.
Results:
The incidence rate of adult PDHA in the PACU was 0.275%. Sex, age, smoking history, low preoperative albumin level, Society of Anesthesiologists (ASA) classification, anesthesia duration, and postoperative PACU pain score were independent risk factors for hyperactive delirium in PACU adults. In this study, an adult PACU PDHA nomogram prediction model was developed. The training dataset verified that the ROC curve (area under the curve) and 95% confidence interval (95% CI) were 0.936 (0.917-0.955). The ROC curve of the validation data row showed that the area under the curve and 95% CI were 0.926 (0.885-0.967).
Conclusion:
The nomogram predictive model for PACU adult high-activity delirium constructed in this study showed good predictive performance. This model could enable the visualization and graphical prediction of adult high-activity delirium occurrence after PACU, which has clinical value.
