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Development and external validation of a nomogram for predicting postoperative adverse events in elderly patients undergoing lumbar fusion surgery: comparison of three predictive models – Lumbar Fusion

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The article focuses on the development of a predictive tool for adverse events (AEs) in elderly patients undergoing transforaminal lumbar interbody fusion (TLIF) surgery. The study utilized multivariate logistic regression, classification tree, and random forest machine learning algorithms to develop three predictive models. A retrospective review of a prospective Geriatric Lumbar Disease Database was conducted, and the models were validated using testing and external validation datasets. The logistic regression model demonstrated a higher net benefit for clinical intervention compared to the other models. The study concluded that the developed nomogram and online tool could help identify elderly patients at high risk of AEs within 90 days after TLIF surgery

Summarised by Mr Mo Akmal – Lead Spinal Surgeon
The London Spine Unit : innovative sugical centre on Harley Street UK

Published article

: 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.

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J Orthop Surg Res. 2024 Jan 3;19(1):8. doi: 10.1186/s13018-023-04490-1.ABSTRACTBACKGROUND: 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,

J Orthop Surg Res. 2024 Jan 3;19(1):8. doi: 10.1186/s13018-023-04490-1.

ABSTRACT

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).

: 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.

PMID:38166958 | DOI:10.1186/s13018-023-04490-1

The London Spine Unit : innovative sugical centre on Harley Street UK

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Development and external validation of a nomogram for predicting postoperative adverse events in elderly patients undergoing lumbar fusion surgery: comparison of three predictive models

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J Orthop Surg Res. 2024 Jan 3;19(1):8. doi: 10.1186/s13018-023-04490-1.ABSTRACTBACKGROUND: 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

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