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Developing a prediction model for estimating adrenocorticotropic hormone changes in patients undergoing scheduled open hepatectomy

BACKGROUND: Currently, there is no gold standard for monitoring patients’ intraoperative stress levels under general anesthesia, while excessive stress may affect their postoperative outcomes. This prospective cohort study developed a prediction model using patients’ hemodynamic parameters to predic...

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Detalles Bibliográficos
Autores principales: Guan, Yu, Tang, Jie, Yu, Jiali, Zhu, Yiqi, Li, Ailun, Fang, Fang, Cang, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9279793/
https://www.ncbi.nlm.nih.gov/pubmed/35845526
http://dx.doi.org/10.21037/atm-22-2276
Descripción
Sumario:BACKGROUND: Currently, there is no gold standard for monitoring patients’ intraoperative stress levels under general anesthesia, while excessive stress may affect their postoperative outcomes. This prospective cohort study developed a prediction model using patients’ hemodynamic parameters to predict the change in adrenocorticotropic hormone (ACTH) concentrations, one of the stress hormones, under surgical stimuli to evaluate intraoperative stress levels. METHODS: A total of 205 patients undergoing scheduled open hepatectomy were enrolled in this study to investigate the correlations between ACTH levels and hemodynamic parameters. The ACTH concentration was assessed before surgery (baseline) and 10 minutes after skin incision. Blood pressure (BP) and heart rate (HR) were obtained at baseline and again at 1-minute intervals for 10 minutes after the skin incision. A logistic regression model was built to predict intraoperative stress level based on ACTH fluctuations, using the bootstrapped sampling approach. The model was validated using the internal sample. RESULTS: Three essential variables were used in the prediction model, including two significant variables, namely, baseline ACTH and mean arterial pressure (MAP), and one variable that was close to achieving significance, that is, HR. This model was able to detect 74.9% of patients with predefined unacceptable ACTH changes. The model had an average of area under the curve (AUC) of 0.723 [95% confidence interval (CI): 0.657–0.791]. CONCLUSIONS: The model developed herein may be a potential practical method for predicting intraoperative stress levels. This prediction model may be a preliminary step to building a real-time stress model based on routine monitoring during general anesthesia, needing further validations in an external sample.