Development and validation of delirium prediction models for noncardiac surgery patients
Copyright © 2023 Elsevier Inc. All rights reserved..
STUDY OBJECTIVE: Postoperative delirium is associated with morbidity and mortality, and its incidence varies widely. Using known predisposing and precipitating factors, we sought to develop postoperative delirium prediction models for noncardiac surgical patients.
DESIGN: Retrospective prediction model study.
SETTING: Major quaternary medical center.
PATIENTS: Our January 2016 to June 2020 training dataset included 51,677 patients of whom 2795 patients had delirium. Our July 2020 to January 2022 validation dataset included 14,438 patients of whom 912 patients had delirium.
INTERVENTIONS: None.
MEASUREMENTS: We trained and validated two static prediction models and one dynamic delirium prediction model. For the static models, we used random survival forests and traditional Cox proportional hazard models to predict postoperative delirium from preoperative variables, or from a combination of preoperative and intraoperative variables. We also used landmark modeling to dynamically predict postoperative delirium using preoperative, intraoperative, and postoperative variables before onset of delirium.
MAIN RESULTS: In the validation analyses, the static random forest model had a c-statistic of 0.81 (95% CI: 0.79, 0.82) and a Brier score of 0.04 with preoperative variables only, and a c-statistic of 0.86 (95% CI: 0.84, 0.87) and a Brier score of 0.04 when preoperative and intraoperative variables were combined. The corresponding Cox models had similar discrimination metrics with slightly better calibration. The dynamic model - using all available data, i.e., preoperative, intraoperative and postoperative data - had an overall c-index of 0.84 (95% CI: 0.83, 0.85).
CONCLUSIONS: Using preoperative and intraoperative variables, simple static models performed as well as a dynamic delirium prediction model that also included postoperative variables. Baseline predisposing factors thus appear to contribute far more to delirium after noncardiac surgery than intraoperative or postoperative variables. Improved postoperative data capture may help improve delirium prediction and should be evaluated in future studies.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:93 |
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| Enthalten in: |
Journal of clinical anesthesia - 93(2024) vom: 20. März, Seite 111319 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Rössler, Julian [VerfasserIn] |
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| Links: |
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| Themen: |
Anesthesia |
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| Anmerkungen: |
Date Completed 15.01.2024 Date Revised 12.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jclinane.2023.111319 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM364755725 |
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| 100 | 1 | |a Rössler, Julian |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Development and validation of delirium prediction models for noncardiac surgery patients |
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| 500 | |a Date Completed 15.01.2024 | ||
| 500 | |a Date Revised 12.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 Elsevier Inc. All rights reserved. | ||
| 520 | |a STUDY OBJECTIVE: Postoperative delirium is associated with morbidity and mortality, and its incidence varies widely. Using known predisposing and precipitating factors, we sought to develop postoperative delirium prediction models for noncardiac surgical patients | ||
| 520 | |a DESIGN: Retrospective prediction model study | ||
| 520 | |a SETTING: Major quaternary medical center | ||
| 520 | |a PATIENTS: Our January 2016 to June 2020 training dataset included 51,677 patients of whom 2795 patients had delirium. Our July 2020 to January 2022 validation dataset included 14,438 patients of whom 912 patients had delirium | ||
| 520 | |a INTERVENTIONS: None | ||
| 520 | |a MEASUREMENTS: We trained and validated two static prediction models and one dynamic delirium prediction model. For the static models, we used random survival forests and traditional Cox proportional hazard models to predict postoperative delirium from preoperative variables, or from a combination of preoperative and intraoperative variables. We also used landmark modeling to dynamically predict postoperative delirium using preoperative, intraoperative, and postoperative variables before onset of delirium | ||
| 520 | |a MAIN RESULTS: In the validation analyses, the static random forest model had a c-statistic of 0.81 (95% CI: 0.79, 0.82) and a Brier score of 0.04 with preoperative variables only, and a c-statistic of 0.86 (95% CI: 0.84, 0.87) and a Brier score of 0.04 when preoperative and intraoperative variables were combined. The corresponding Cox models had similar discrimination metrics with slightly better calibration. The dynamic model - using all available data, i.e., preoperative, intraoperative and postoperative data - had an overall c-index of 0.84 (95% CI: 0.83, 0.85) | ||
| 520 | |a CONCLUSIONS: Using preoperative and intraoperative variables, simple static models performed as well as a dynamic delirium prediction model that also included postoperative variables. Baseline predisposing factors thus appear to contribute far more to delirium after noncardiac surgery than intraoperative or postoperative variables. Improved postoperative data capture may help improve delirium prediction and should be evaluated in future studies | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Anesthesia | |
| 650 | 4 | |a Delirium | |
| 650 | 4 | |a Dynamic modeling | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Noncardiac surgery | |
| 650 | 4 | |a Postoperative | |
| 650 | 4 | |a Prediction | |
| 700 | 1 | |a Shah, Karan |e verfasserin |4 aut | |
| 700 | 1 | |a Medellin, Sara |e verfasserin |4 aut | |
| 700 | 1 | |a Turan, Alparslan |e verfasserin |4 aut | |
| 700 | 1 | |a Ruetzler, Kurt |e verfasserin |4 aut | |
| 700 | 1 | |a Singh, Mriganka |e verfasserin |4 aut | |
| 700 | 1 | |a Sessler, Daniel I |e verfasserin |4 aut | |
| 700 | 1 | |a Maheshwari, Kamal |e verfasserin |4 aut | |
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