ArticleNeurosurgical review2023
A nomogram for predicting the risk of major postoperative complications for patients with meningioma.
Article in Neurosurgical review, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.
- Machine learning for predicting post-operative outcomes in meningiomas: a systematic review and meta-analysis.Acta neurochirurgica · 2024Pooled it
- Adverse events in meningioma surgery classified using the therapy-disability-neurology (TDN) grading system.Journal of neuro-oncology · 2025Article
- International survey-based assessment of the reliability, validity, and interpretability of the TDN grade for neurosurgical adverse events.Scientific reports · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
purposeTo 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.
methodsThe 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.
resultsIn 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).
conclusionsThe 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.
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