A Nomogram for Predicting the Risk of Major Postoperative Complications for Patients with Meningioma

Overview

Journal Neurosurg Rev

Specialty Neurosurgery

Date 2023 Nov 1

PMID 37907646

Authors

Zheng-Qian Guo

Xiao-Yu Xia

Dan Cao

Xu Chen

Yue He

Bao-Feng Wang

Dong-Sheng Guo

Jian Chen

Affiliations

Soon will be listed here.

Abstract

Purpose: To identify risk factors for major postoperative complications in meningioma patients and to construct and validate a nomogram that identify patients at high risk of these complications.

Methods: The medical records of meningioma patients who underwent surgical resection in our hospital from January 2018 to December 2020 were collected. The patients were divided into a training set (815 cases from the main campus in 2018 and 2019) and a validation set (300 cases from two other campuses in 2020). Major postoperative complications were defined as any new neurological deficits and complications classified as Clavien-Dindo Grading (CDG) II or higher. Univariate and multivariate analyses were conducted using the training set to identify independent risk factors. A nomogram was constructed based on these results. And then validated the nomogram through bootstrap re-sampling in both the training and validation sets. The concordance index (C-index) and the area under the curve (AUC) were used to assess the discriminative ability of the nomogram. The Hosmer-Lemeshow test was performed to evaluate the goodness-of-fit. The optimal cutoff point for the nomogram was calculated using Youden's index.

Results: In the training set, 135 cases (16.56%) experienced major postoperative complications. The independent risk factors identified were male sex, recurrent tumors, American Society of Anesthesiologists (ASA) class III-IV, preoperative Karnofsky Performance Scale (KPS) score < 80, preoperative serum albumin < 35 g/L, tumor in the skull base or central sulcus area, subtotal tumor resection (STR), allogeneic blood transfusion, and larger tumor size. A nomogram was constructed based on these risk factors. It demonstrated good predictive performance, with a C-index of 0.919 for the training set and 0.872 for the validation set. The area under the curve (AUC) > 0.7 indicated satisfactory discriminative ability. The Hosmer-Lemeshow test showed no significant deviation from the predicted probabilities. And the cutoff for nomogram total points was about 200 (specificity 0.881 and sensitivity 0.834).

Conclusions: The constructed nomogram demonstrated robust predictive performance for major postoperative complications in meningioma patients. This model can be used by surgeons as a reference in clinical decision-making.

Citing Articles

Machine learning for predicting post-operative outcomes in meningiomas: a systematic review and meta-analysis.

Abualnaja S, Morris J, Rashid H, Cook W, Helmy A Acta Neurochir (Wien). 2024; 166(1):505.

PMID: 39688716 PMC: 11652405. DOI: 10.1007/s00701-024-06344-z.

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