Development and validation of a predictive model for PACU hypotension in elderly patients undergoing sedated gastrointestinal endoscopy.


Journal

Aging clinical and experimental research
ISSN: 1720-8319
Titre abrégé: Aging Clin Exp Res
Pays: Germany
ID NLM: 101132995

Informations de publication

Date de publication:
18 Jul 2024
Historique:
received: 21 03 2024
accepted: 05 07 2024
medline: 18 7 2024
pubmed: 18 7 2024
entrez: 18 7 2024
Statut: epublish

Résumé

Hypotension, characterized by abnormally low blood pressure, is a frequently observed adverse event in sedated gastrointestinal endoscopy procedures. Although the examination time is typically short, hypotension during and after gastroscopy procedures is frequently overlooked or remains undetected. This study aimed to construct a risk nomogram for post-anesthesia care unit (PACU) hypotension in elderly patients undergoing sedated gastrointestinal endoscopy. This study involved 2919 elderly patients who underwent sedated gastrointestinal endoscopy. A preoperative questionnaire was used to collect data on patient characteristics; intraoperative medication use and adverse events were also recorded. The primary objective of the study was to evaluate the risk of PACU hypotension in these patients. To achieve this, the least absolute shrinkage and selection operator (LASSO) regression analysis method was used to optimize variable selection, involving cyclic coordinate descent with tenfold cross-validation. Subsequently, multivariable logistic regression analysis was applied to build a predictive model using the selected predictors from the LASSO regression. A nomogram was visually developed based on these variables. To validate the model, a calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used. Additionally, external validation was conducted to further assess the model's performance. The LASSO regression analysis identified predictors associated with an increased risk of adverse events during surgery: age, duration of preoperative water abstinence, intraoperative mean arterial pressure (MAP) <65 mmHg, decreased systolic blood pressure (SBP), and use of norepinephrine (NE). The constructed model based on these predictors demonstrated moderate predictive ability, with an area under the ROC curve of 0.710 in the training set and 0.778 in the validation set. The DCA indicated that the nomogram had clinical applicability when the risk threshold ranged between 20 and 82%, which was subsequently confirmed in the external validation with a range of 18-92%. Incorporating factors such as age, duration of preoperative water abstinence, intraoperative MAP <65 mmHg, decreased SBP, and use of NE in the risk nomogram increased its usefulness for predicting PACU hypotension risk in elderly patient undergoing sedated gastrointestinal endoscopy.

Sections du résumé

BACKGROUND BACKGROUND
Hypotension, characterized by abnormally low blood pressure, is a frequently observed adverse event in sedated gastrointestinal endoscopy procedures. Although the examination time is typically short, hypotension during and after gastroscopy procedures is frequently overlooked or remains undetected. This study aimed to construct a risk nomogram for post-anesthesia care unit (PACU) hypotension in elderly patients undergoing sedated gastrointestinal endoscopy.
METHODS METHODS
This study involved 2919 elderly patients who underwent sedated gastrointestinal endoscopy. A preoperative questionnaire was used to collect data on patient characteristics; intraoperative medication use and adverse events were also recorded. The primary objective of the study was to evaluate the risk of PACU hypotension in these patients. To achieve this, the least absolute shrinkage and selection operator (LASSO) regression analysis method was used to optimize variable selection, involving cyclic coordinate descent with tenfold cross-validation. Subsequently, multivariable logistic regression analysis was applied to build a predictive model using the selected predictors from the LASSO regression. A nomogram was visually developed based on these variables. To validate the model, a calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used. Additionally, external validation was conducted to further assess the model's performance.
RESULTS RESULTS
The LASSO regression analysis identified predictors associated with an increased risk of adverse events during surgery: age, duration of preoperative water abstinence, intraoperative mean arterial pressure (MAP) <65 mmHg, decreased systolic blood pressure (SBP), and use of norepinephrine (NE). The constructed model based on these predictors demonstrated moderate predictive ability, with an area under the ROC curve of 0.710 in the training set and 0.778 in the validation set. The DCA indicated that the nomogram had clinical applicability when the risk threshold ranged between 20 and 82%, which was subsequently confirmed in the external validation with a range of 18-92%.
CONCLUSION CONCLUSIONS
Incorporating factors such as age, duration of preoperative water abstinence, intraoperative MAP <65 mmHg, decreased SBP, and use of NE in the risk nomogram increased its usefulness for predicting PACU hypotension risk in elderly patient undergoing sedated gastrointestinal endoscopy.

Identifiants

pubmed: 39023685
doi: 10.1007/s40520-024-02807-6
pii: 10.1007/s40520-024-02807-6
doi:

Types de publication

Journal Article Validation Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

149

Subventions

Organisme : National Natural Science Foundation of China
ID : 82172190
Organisme : General Project of Medical Scientific Research Project of Jiangsu Provincial Health Commission
ID : M2021105
Organisme : Special Fund for Yangzhou Key Laboratory Cultivation
ID : YZ20211148

Informations de copyright

© 2024. The Author(s).

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Auteurs

Zi Wang (Z)

Department of Anesthesiology, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Jiangsu, Yangzhou, 225001, China.
Yangzhou University, Jiangsu, Yangzhou, 225001, China.

Juan Ma (J)

Department of Anesthesiology, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Jiangsu, Yangzhou, 225001, China.
Yangzhou University, Jiangsu, Yangzhou, 225001, China.

Xin Liu (X)

Department of Anesthesiology, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Jiangsu, Yangzhou, 225001, China.

Ju Gao (J)

Department of Anesthesiology, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Jiangsu, Yangzhou, 225001, China. gaoju_003@163.com.

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