Development and external validation of a nomogram for predicting postoperative adverse events in elderly patients undergoing lumbar fusion surgery: comparison of three predictive models.
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 c...
| Publicado en: | Journal of Orthopaedic Surgery & Research Vol. 19; no. 1; pp. 1 - 12 |
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| Autores principales: | , , , , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
BioMed Central
1/3/2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=174559436&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174559436 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1749799X 38N9 jtl: Journal of Orthopaedic Surgery & Research issn: 1749799X maglogo: N pubinfo: dt: 1/3/2024 vid: 19 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 174559436 174559436 174559436 10.1186/s13018-023-04490-1 174559436 ppf: 1 ppct: 11 formats: tig: atl: Development and external validation of a nomogram for predicting postoperative adverse events in elderly patients undergoing lumbar fusion surgery: comparison of three predictive models. aug: au: Wang, Shuai-Kang Wang, Peng Li, Zhong-En Li, Xiang-Yu Kong, Chao Lu, Shi-Bao affil: https://ror.org/013xs5b60 Department of Orthopedics, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, China sug: subj: Adverse Drug Event Risk Factors Postoperative Complications Risk Factors Spinal Fusion Methods Lumbar Vertebrae Surgery Surgical Patients Prediction Models Risk Assessment Models, Statistical Sensitivity and Specificity Human Multiple Logistic Regression Decision Trees Random Forest Machine Learning Algorithms Retrospective Design Aged Male Female ROC Curve Confidence Intervals Descriptive Statistics Comparative Studies Length of Stay Readmission Reoperation Funding Source Aged: 65+ years Male Female 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%5ffor%5fAEs/) could inform physicians about elderly patients with a high risk of AEs within the 90 days after TLIF surgery. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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