PMID- 38166958
OWN - NLM
STAT- MEDLINE
DCOM- 20240105
LR  - 20240116
IS  - 1749-799X (Electronic)
IS  - 1749-799X (Linking)
VI  - 19
IP  - 1
DP  - 2024 Jan 3
TI  - Development and external validation of a nomogram for predicting postoperative 
      adverse events in elderly patients undergoing lumbar fusion surgery: comparison 
      of three predictive models.
PG  - 8
LID - 10.1186/s13018-023-04490-1 [doi]
LID - 8
AB  - BACKGROUND: The burden of lumbar degenerative diseases (LDD) has increased 
      substantially with the unprecedented aging population. Identifying elderly 
      patients with high risk of postoperative adverse events (AEs) and establishing 
      individualized perioperative management is critical to mitigate added costs and 
      optimize cost-effectiveness to the healthcare system. We aimed to develop a 
      predictive tool for AEs in elderly patients with transforaminal lumbar interbody 
      fusion (TLIF), utilizing multivariate logistic regression, single classification 
      and regression tree (hereafter, "classification tree"), and random forest machine 
      learning algorithms. METHODS: This study was a retrospective review of a 
      prospective Geriatric Lumbar Disease Database (age >/= 65). Our outcome measure was 
      postoperative AEs, including prolonged hospital stays, postoperative 
      complications, readmission, and reoperation within 90 days. Patients were grouped 
      as either having at least one adverse event (AEs group) or not (No-AEs group). 
      Three models for predicting postoperative AEs were developed using training 
      dataset and internal validation using testing dataset. Finally, online tool was 
      developed to assess its validity in the clinical setting (external validation). 
      RESULTS: The development set included 1025 patients (mean [SD] age, 72.8 [5.6] 
      years; 632 [61.7%] female), and the external validation set included 175 patients 
      (73.2 [5.9] years; 97 [55.4%] female). The predictive ability of our three models 
      was comparable, with no significant differences in AUC (0.73 vs. 0.72 vs. 0.70, 
      respectively). The logistic regression model had a higher net benefit for 
      clinical intervention than the other models. A nomogram based on logistic 
      regression was developed, and the C-index of external validation for AEs was 0.69 
      (95% CI 0.65-0.76). CONCLUSION: The predictive ability of our three models was 
      comparable. Logistic regression model had a higher net benefit for clinical 
      intervention than the other models. Our nomogram and online tool ( 
      https://xuanwumodel.shinyapps.io/Model_for_AEs/ ) could inform physicians about 
      elderly patients with a high risk of AEs within the 90 days after TLIF surgery.
CI  - (c) 2023. The Author(s).
FAU - Wang, Shuai-Kang
AU  - Wang SK
AD  - Department of Orthopedics, Xuanwu Hospital, Capital Medical University, No.45 
      Changchun Street, Xicheng District, Beijing, China.
AD  - National Clinical Research Center for Geriatric Diseases, Beijing, China.
FAU - Wang, Peng
AU  - Wang P
AD  - Department of Orthopedics, Xuanwu Hospital, Capital Medical University, No.45 
      Changchun Street, Xicheng District, Beijing, China.
AD  - National Clinical Research Center for Geriatric Diseases, Beijing, China.
FAU - Li, Zhong-En
AU  - Li ZE
AD  - Department of Orthopedics, Beijing Friendship Hospital, Capital Medical 
      University, Beijing, China.
FAU - Li, Xiang-Yu
AU  - Li XY
AD  - Department of Orthopedics, Xuanwu Hospital, Capital Medical University, No.45 
      Changchun Street, Xicheng District, Beijing, China.
AD  - National Clinical Research Center for Geriatric Diseases, Beijing, China.
FAU - Kong, Chao
AU  - Kong C
AD  - Department of Orthopedics, Xuanwu Hospital, Capital Medical University, No.45 
      Changchun Street, Xicheng District, Beijing, China.
AD  - National Clinical Research Center for Geriatric Diseases, Beijing, China.
FAU - Lu, Shi-Bao
AU  - Lu SB
AD  - Department of Orthopedics, Xuanwu Hospital, Capital Medical University, No.45 
      Changchun Street, Xicheng District, Beijing, China. shibaolu@xwh.ccmu.edu.cn.
AD  - National Clinical Research Center for Geriatric Diseases, Beijing, China. 
      shibaolu@xwh.ccmu.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20240103
PL  - England
TA  - J Orthop Surg Res
JT  - Journal of orthopaedic surgery and research
JID - 101265112
SB  - IM
MH  - Humans
MH  - Aged
MH  - *Nomograms
MH  - Lumbar Vertebrae/surgery
MH  - Prospective Studies
MH  - *Spinal Fusion/adverse effects
MH  - Postoperative Complications/diagnosis/epidemiology/etiology
MH  - Retrospective Studies
PMC - PMC10763364
OTO - NOTNLM
OT  - Adverse events
OT  - Elderly patients
OT  - Machine learning
OT  - Online tool
OT  - Predictive model
COIS- The authors declare that the article content was composed in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2024/01/04 01:18
MHDA- 2024/01/05 06:42
PMCR- 2024/01/03
CRDT- 2024/01/03 09:24
PHST- 2023/10/11 00:00 [received]
PHST- 2023/12/18 00:00 [accepted]
PHST- 2024/01/05 06:42 [medline]
PHST- 2024/01/04 01:18 [pubmed]
PHST- 2024/01/03 09:24 [entrez]
PHST- 2024/01/03 00:00 [pmc-release]
AID - 10.1186/s13018-023-04490-1 [pii]
AID - 4490 [pii]
AID - 10.1186/s13018-023-04490-1 [doi]
PST - epublish
SO  - J Orthop Surg Res. 2024 Jan 3;19(1):8. doi: 10.1186/s13018-023-04490-1.