CSF rhinorrhoea after endonasal intervention to the skull base (CRANIAL): A multicentre prospective observational study

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doi:10.3389/fonc.2022.1049627

Cited by 4 publications

(11 citation statements)

References 52 publications

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“…It represents the largest dataset of its kind, seeking to audit practice across the UK and Ireland. It revealed a CSFR incidence rate of 3.9% for TSA and 7.1% for EEA, lower than the literature standard, with minimal influence of particular repair regimes on CSFR incidence via traditional statistical analysis ( 25 ). In neurosurgery, machine learning models (MLs), or more specifically neural network models (NNs), have been shown to outperform these traditional statistical methods by leveraging their ability to utilise complex non-linear relationships between the various prediction variables ( 26 – 28 ).…”

Section: Introductionmentioning

confidence: 66%

Section: Methodsmentioning

confidence: 99%

“…A detailed description of the generation of the CRANIAL dataset is described in ( 25 ). In brief, it is a multicentre dataset (30 centres in the UK and Ireland), collected via a prospective observational study in 3 phases encompassing November 2019 – July 2020 ( 22 – 25 ). All TSA (defined as transsphenoidal access to the sella alone) and EEA [defined as acquiring surgical access to an area beyond the sella ( 17 , 19 )] were included.…”

Section: Methodsmentioning

confidence: 99%

“…All TSA (defined as transsphenoidal access to the sella alone) and EEA [defined as acquiring surgical access to an area beyond the sella ( 17 , 19 )] were included. The dataset is composed of baseline characteristic data (e.g., age; sex; tumour diameter), operative data (e.g., intraoperative CSF leak presence; skull base repair method) and postoperative outcomes (e.g., CSFR) ( 22 – 25 ). A taxonomy for skull base repair was adapted from a systematic review of the literature ( 10 , 24 ).…”

Section: Methodsmentioning

confidence: 99%

“…CRANIAL (CSF rhinorrhoea after endonasal intervention to the skull base) was a prospective, multicentre observational study seeking to determine the: (1) scope of the methods of skull base repair; and (2) corresponding rates of CSFR ( 22 – 25 ). It represents the largest dataset of its kind, seeking to audit practice across the UK and Ireland.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning driven prediction of cerebrospinal fluid rhinorrhoea following endonasal skull base surgery: A multicentre prospective observational study

2023

Front. Oncol.

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BackgroundCerebrospinal fluid rhinorrhoea (CSFR) is a common complication following endonasal skull base surgery, a technique that is fundamental to the treatment of pituitary adenomas and many other skull base tumours. The CRANIAL study explored CSFR incidence and related risk factors, particularly skull base repair techniques, via a multicentre prospective observational study. We sought to use machine learning to leverage this complex multicentre dataset for CSFR prediction and risk factor analysis.MethodsA dataset of 865 cases - 725 transsphenoidal approach (TSA) and 140 expanded endonasal approach (EEA) - with cerebrospinal fluid rhinorrhoea as the primary outcome, was used. Relevant variables were extracted from the data, and prediction variables were divided into two categories, preoperative risk factors; and repair techniques, with 6 and 11 variables respectively. Three types of machine learning models were developed in order to predict CSFR: logistic regression (LR); decision tree (DT); and neural network (NN). Models were validated using 5-fold cross-validation, compared via their area under the curve (AUC) evaluation metric, and key prediction variables were identified using their Shapley additive explanations (SHAP) score.ResultsCSFR rates were 3.9% (28/725) for the transsphenoidal approach and 7.1% (10/140) for the expanded endonasal approach. NNs outperformed LR and DT for CSFR prediction, with a mean AUC of 0.80 (0.70-0.90) for TSA and 0.78 (0.60-0.96) for EEA, when all risk factor and intraoperative repair data were integrated into the model. The presence of intraoperative CSF leak was the most prominent risk factor for CSFR. Elevated BMI and revision surgery were also associated with CSFR for the transsphenoidal approach. CSF diversion and gasket sealing appear to be strong predictors of the absence of CSFR for both approaches.ConclusionNeural networks are effective at predicting CSFR and uncovering key CSFR predictors in patients following endonasal skull base surgery, outperforming traditional statistical methods. These models will be improved further with larger and more granular datasets, improved NN architecture, and external validation. In the future, such predictive models could be used to assist surgical decision-making and support more individualised patient counselling.

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Section: Introductionmentioning

confidence: 66%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning driven prediction of cerebrospinal fluid rhinorrhoea following endonasal skull base surgery: A multicentre prospective observational study

2023

Front. Oncol.

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Multi-layered repair of high-flow CSF fistulae following endoscopic skull base surgery without nasal packing or lumbar drains: technical refinements to optimise outcome

et al. 2023

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Aims Post-operative CSF leak remains a significant problem following endoscopic skull base surgery, particularly when there is a high-flow intra-operative CSF leak. Most skull base repair techniques are accompanied by the insertion of a lumbar drain and/or the use of nasal packing which have significant shortcomings. Our aim was to review the results of a large series of endoscopic skull base cases where a high-flow intra-operative CSF leak rate was encountered and repaired to assess if modifications in technique could reduce the post-operative CSF leak rate. Methods A retrospective review of a prospectively maintained database of skull base cases performed by a single surgeon over a 10-year period was performed. Data regarding patient demographics, underlying pathology, skull base repair techniques and post-operative complications were analysed. Results One hundred forty-two cases with high-flow intra-operative CSF leak were included in the study. The most common pathologies were craniopharyngiomas (55/142, 39%), pituitary adenomas (34/142, 24%) and meningiomas (24/142, 17%). The CSF leak rate was 7/36 (19%) when a non-standardised skull base repair technique was used. However, with the adoption of a standardised, multi-layer repair technique, the post-operative CSF leak rate decreased significantly (4/106, 4% vs. 7/36, 19%, p = 0.006). This improvement in the rate of post-operative CSF leak was achieved without nasal packing or lumbar drains. Conclusion With iterative modifications to a multi-layered closure technique for high-flow intra-operative CSF leaks, it is possible to obtain a very low rate of post-operative CSF leak, without lumbar drains or nasal packing.

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Skull Base Chordoma and Chondrosarcoma: Neuroradiologist’s Guide to Diagnosis, Surgical Management, and Proton Beam Therapy

Potter,

Siripurapu,

Herwadkar

et al. 2024

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CSF rhinorrhoea after endonasal intervention to the skull base (CRANIAL): A multicentre prospective observational study

Cited by 4 publications

References 52 publications

Machine learning driven prediction of cerebrospinal fluid rhinorrhoea following endonasal skull base surgery: A multicentre prospective observational study

Machine learning driven prediction of cerebrospinal fluid rhinorrhoea following endonasal skull base surgery: A multicentre prospective observational study

Multi-layered repair of high-flow CSF fistulae following endoscopic skull base surgery without nasal packing or lumbar drains: technical refinements to optimise outcome

Skull Base Chordoma and Chondrosarcoma: Neuroradiologist’s Guide to Diagnosis, Surgical Management, and Proton Beam Therapy

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