Development and validation of a postoperative delirium risk prediction model for non-cardiac surgery in elderly patients
BackgroundPostoperative delirium (POD) is one of the common central nervous system complications in elderly patients after non-cardiac surgery. Therefore, it is necessary to develop and validate a preoperative model for POD risk prediction.MethodsThis study selected 663 elderly patients undergoing n...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-04-01
|
| Series: | Frontiers in Psychiatry |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1414273/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850172995455156224 |
|---|---|
| author | Xu Lin Na Tian Yuanlong Wang Shuhui Hua Jian Kong Shanling Xu Yanan Lin Chuan Li Bin Wang Yanlin Bi |
| author_facet | Xu Lin Na Tian Yuanlong Wang Shuhui Hua Jian Kong Shanling Xu Yanan Lin Chuan Li Bin Wang Yanlin Bi |
| author_sort | Xu Lin |
| collection | DOAJ |
| description | BackgroundPostoperative delirium (POD) is one of the common central nervous system complications in elderly patients after non-cardiac surgery. Therefore, it is necessary to develop and validate a preoperative model for POD risk prediction.MethodsThis study selected 663 elderly patients undergoing non-cardiac elective surgery under general anesthesia for tracheal intubation in general surgery, from September 1st, 2020 to June 1st, 2022. Simple random sampling method was used according to 7: 3. The occurrence of POD within 1 to 7 days after the operation (or before discharge) was followed up by the confusion assessment method (CAM). This study innovatively included the pittsburgh sleep quality index (PSQI) and the numerical pain score (NRS) for clinical work, to explore the relationship between sleep quality and postoperative pain and POD. Univariate and Multivariable Logistic regression analysis was used to analyze stepwise regression to screen independent risk factors for POD. The creation of prediction models involved the integration of outcomes through the implementation of logistic regression analysis. In addition, internal validation is employed to ensure the reproducibility of the model.ResultsA total of 663 elderly patients were enrolled in this study, and 131 (19.76%) patients developed POD. The incidence of POD in each department was not statistically significant. The predictors in the POD column line graph included age, Mini Mental State Examination (MMSE) score, history of diabetes, years of education, sleep quality index, ASA classification, duration of anesthesia and NRS score. The formula Z= 8.293 + 0.102 × age - 1.214 × MMSE + 1.285 × diabetesHistory - 0.304 × yearsOfEducation + 0.602 × PSQI + 1.893 × ASA + 0.027 × anesthesiaTime + 1.297 × NRS. Conducive to the validation group to evaluate the prediction model, the validation group AUC is 0.939 (95% CI 0.894-0.969), the sensitivity is 94.44%, and the specificity is 85.09%. The calibration curves show a good fit between the clinically predicted situation and the actual situation.ConclusionThe clinical prediction model constructed based on these independent risk factors has a good predictive performance, which can provide reference for the early screening and prevention of POD in clinical work.Trial registrationChiCTR2000033639 Retrospectively registered (date of registration: 06/07/2020). |
| format | Article |
| id | doaj-art-92aeae935ff14d0e94767464ef4b7d36 |
| institution | OA Journals |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj-art-92aeae935ff14d0e94767464ef4b7d362025-08-20T02:19:57ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-04-011610.3389/fpsyt.2025.14142731414273Development and validation of a postoperative delirium risk prediction model for non-cardiac surgery in elderly patientsXu Lin0Na Tian1Yuanlong Wang2Shuhui Hua3Jian Kong4Shanling Xu5Yanan Lin6Chuan Li7Bin Wang8Yanlin Bi9Department of Anesthesiology, Qingdao Municipal Hospital, Qingdao, ChinaDepartment of Anesthesiology, The Eighth People’s Hospital of Qingdao, Qingdao, ChinaThe Second School of Clinical Medicine of Binzhou Medical College, Yantai, ChinaThe Second School of Clinical Medicine of Binzhou Medical College, Yantai, ChinaDepartment of Anesthesiology, Weifang Medical College, Weifang, ChinaDepartment of Anesthesiology, Weifang Medical College, Weifang, ChinaDepartment of Anesthesiology, Qingdao Municipal Hospital, Qingdao, ChinaDepartment of Anesthesiology, Qingdao Municipal Hospital, Qingdao, ChinaDepartment of Anesthesiology, Qingdao Municipal Hospital, Qingdao, ChinaDepartment of Anesthesiology, Qingdao Municipal Hospital, Qingdao, ChinaBackgroundPostoperative delirium (POD) is one of the common central nervous system complications in elderly patients after non-cardiac surgery. Therefore, it is necessary to develop and validate a preoperative model for POD risk prediction.MethodsThis study selected 663 elderly patients undergoing non-cardiac elective surgery under general anesthesia for tracheal intubation in general surgery, from September 1st, 2020 to June 1st, 2022. Simple random sampling method was used according to 7: 3. The occurrence of POD within 1 to 7 days after the operation (or before discharge) was followed up by the confusion assessment method (CAM). This study innovatively included the pittsburgh sleep quality index (PSQI) and the numerical pain score (NRS) for clinical work, to explore the relationship between sleep quality and postoperative pain and POD. Univariate and Multivariable Logistic regression analysis was used to analyze stepwise regression to screen independent risk factors for POD. The creation of prediction models involved the integration of outcomes through the implementation of logistic regression analysis. In addition, internal validation is employed to ensure the reproducibility of the model.ResultsA total of 663 elderly patients were enrolled in this study, and 131 (19.76%) patients developed POD. The incidence of POD in each department was not statistically significant. The predictors in the POD column line graph included age, Mini Mental State Examination (MMSE) score, history of diabetes, years of education, sleep quality index, ASA classification, duration of anesthesia and NRS score. The formula Z= 8.293 + 0.102 × age - 1.214 × MMSE + 1.285 × diabetesHistory - 0.304 × yearsOfEducation + 0.602 × PSQI + 1.893 × ASA + 0.027 × anesthesiaTime + 1.297 × NRS. Conducive to the validation group to evaluate the prediction model, the validation group AUC is 0.939 (95% CI 0.894-0.969), the sensitivity is 94.44%, and the specificity is 85.09%. The calibration curves show a good fit between the clinically predicted situation and the actual situation.ConclusionThe clinical prediction model constructed based on these independent risk factors has a good predictive performance, which can provide reference for the early screening and prevention of POD in clinical work.Trial registrationChiCTR2000033639 Retrospectively registered (date of registration: 06/07/2020).https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1414273/fullnon-cardiac surgeryelderlydeliriumrisk factorsprediction model |
| spellingShingle | Xu Lin Na Tian Yuanlong Wang Shuhui Hua Jian Kong Shanling Xu Yanan Lin Chuan Li Bin Wang Yanlin Bi Development and validation of a postoperative delirium risk prediction model for non-cardiac surgery in elderly patients Frontiers in Psychiatry non-cardiac surgery elderly delirium risk factors prediction model |
| title | Development and validation of a postoperative delirium risk prediction model for non-cardiac surgery in elderly patients |
| title_full | Development and validation of a postoperative delirium risk prediction model for non-cardiac surgery in elderly patients |
| title_fullStr | Development and validation of a postoperative delirium risk prediction model for non-cardiac surgery in elderly patients |
| title_full_unstemmed | Development and validation of a postoperative delirium risk prediction model for non-cardiac surgery in elderly patients |
| title_short | Development and validation of a postoperative delirium risk prediction model for non-cardiac surgery in elderly patients |
| title_sort | development and validation of a postoperative delirium risk prediction model for non cardiac surgery in elderly patients |
| topic | non-cardiac surgery elderly delirium risk factors prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1414273/full |
| work_keys_str_mv | AT xulin developmentandvalidationofapostoperativedeliriumriskpredictionmodelfornoncardiacsurgeryinelderlypatients AT natian developmentandvalidationofapostoperativedeliriumriskpredictionmodelfornoncardiacsurgeryinelderlypatients AT yuanlongwang developmentandvalidationofapostoperativedeliriumriskpredictionmodelfornoncardiacsurgeryinelderlypatients AT shuhuihua developmentandvalidationofapostoperativedeliriumriskpredictionmodelfornoncardiacsurgeryinelderlypatients AT jiankong developmentandvalidationofapostoperativedeliriumriskpredictionmodelfornoncardiacsurgeryinelderlypatients AT shanlingxu developmentandvalidationofapostoperativedeliriumriskpredictionmodelfornoncardiacsurgeryinelderlypatients AT yananlin developmentandvalidationofapostoperativedeliriumriskpredictionmodelfornoncardiacsurgeryinelderlypatients AT chuanli developmentandvalidationofapostoperativedeliriumriskpredictionmodelfornoncardiacsurgeryinelderlypatients AT binwang developmentandvalidationofapostoperativedeliriumriskpredictionmodelfornoncardiacsurgeryinelderlypatients AT yanlinbi developmentandvalidationofapostoperativedeliriumriskpredictionmodelfornoncardiacsurgeryinelderlypatients |