Development and validation of a nomogram for predicting critical respiratory events during early anesthesia recovery in elderly patients.
Critical respiratory events
Elderly patients
Nomogram
Postanesthesia care unit
Prediction model
Journal
BMC medical informatics and decision making
ISSN: 1472-6947
Titre abrégé: BMC Med Inform Decis Mak
Pays: England
ID NLM: 101088682
Informations de publication
Date de publication:
16 Sep 2024
16 Sep 2024
Historique:
received:
26
12
2023
accepted:
06
09
2024
medline:
17
9
2024
pubmed:
17
9
2024
entrez:
16
9
2024
Statut:
epublish
Résumé
Elderly patients undergoing recovery from general anesthesia face a heightened risk of critical respiratory events (CREs). Despite this, there is a notable absence of effective predictive tools tailored to this specific demographic. This study aims to develop and validate a predictive model (nomogram) to address this gap. CREs pose significant risks to elderly patients during the recovery phase from general anesthesia, making it an important issue in perioperative care. With the increasing aging population and the complexity of surgical procedures, it is crucial to develop effective predictive tools to improve patient outcomes and ensure patient safety during post-anesthesia care unit (PACU) recovery. A total of 324 elderly patients who underwent elective general anesthesia in a grade A tertiary hospital from January 2023 to June 2023 were enrolled. Risk factors were identified using least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was constructed and represented as a nomogram. Internal validation of the model was performed using Bootstrapping. This study followed the TRIPOD checklist for reporting. The indicators included in the nomogram were frailty, snoring, patient-controlled intravenous analgesia (PCIA), emergency delirium and cough intensity at extubation. The diagnostic performance of the nomogram model was satisfactory, with AUC values of 0.990 and 0.981 for the training set and internal validation set, respectively. The optimal cutoff value was determined to be 0.22, based on a Youden index of 0.911. The F1-score was 0.927, and the MCC was 0.896. The calibration curve, Brier score (0.046), and HL test demonstrated acceptable consistency between the predicted and actual results. DCA revealed high net benefits of the nomogram prediction across all threshold probabilities. This study developed and validated a nomogram to identify elderly patients in the PACU who are at higher risk of CREs. The identified predictive factors included frailty condition, snoring syndrome, PCIA, emergency delirium, and cough intensity at extubation. By identifying patients at higher risk of CREs early on, medical professionals can implement targeted strategies to mitigate the occurrence of complications and provide better postoperative care for elderly patients recovering from general anesthesia.
Sections du résumé
BACKGROUND
BACKGROUND
Elderly patients undergoing recovery from general anesthesia face a heightened risk of critical respiratory events (CREs). Despite this, there is a notable absence of effective predictive tools tailored to this specific demographic. This study aims to develop and validate a predictive model (nomogram) to address this gap. CREs pose significant risks to elderly patients during the recovery phase from general anesthesia, making it an important issue in perioperative care. With the increasing aging population and the complexity of surgical procedures, it is crucial to develop effective predictive tools to improve patient outcomes and ensure patient safety during post-anesthesia care unit (PACU) recovery.
METHODS
METHODS
A total of 324 elderly patients who underwent elective general anesthesia in a grade A tertiary hospital from January 2023 to June 2023 were enrolled. Risk factors were identified using least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was constructed and represented as a nomogram. Internal validation of the model was performed using Bootstrapping. This study followed the TRIPOD checklist for reporting.
RESULTS
RESULTS
The indicators included in the nomogram were frailty, snoring, patient-controlled intravenous analgesia (PCIA), emergency delirium and cough intensity at extubation. The diagnostic performance of the nomogram model was satisfactory, with AUC values of 0.990 and 0.981 for the training set and internal validation set, respectively. The optimal cutoff value was determined to be 0.22, based on a Youden index of 0.911. The F1-score was 0.927, and the MCC was 0.896. The calibration curve, Brier score (0.046), and HL test demonstrated acceptable consistency between the predicted and actual results. DCA revealed high net benefits of the nomogram prediction across all threshold probabilities.
CONCLUSIONS
CONCLUSIONS
This study developed and validated a nomogram to identify elderly patients in the PACU who are at higher risk of CREs. The identified predictive factors included frailty condition, snoring syndrome, PCIA, emergency delirium, and cough intensity at extubation. By identifying patients at higher risk of CREs early on, medical professionals can implement targeted strategies to mitigate the occurrence of complications and provide better postoperative care for elderly patients recovering from general anesthesia.
Identifiants
pubmed: 39285407
doi: 10.1186/s12911-024-02671-4
pii: 10.1186/s12911-024-02671-4
doi:
Types de publication
Journal Article
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
257Subventions
Organisme : Scientific research project of Education Department of Zhejiang Province
ID : Y202351310
Organisme : Department of Zhejiang Health Science and Technology Project Science and Technology
ID : 2024KY101
Informations de copyright
© 2024. The Author(s).
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