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- Publisher Website: 10.1093/neuros/nyz145
- Scopus: eid_2-s2.0-85073459773
- PMID: 31149726
- WOS: WOS:000491255600033
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Article: Machine Learning Algorithm Identifies Patients at High Risk for Early Complications after Intracranial Tumor Surgery: Registry-Based Cohort Study
Title | Machine Learning Algorithm Identifies Patients at High Risk for Early Complications after Intracranial Tumor Surgery: Registry-Based Cohort Study |
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Authors | |
Keywords | Prediction model Neurosurgery Neurocritical care Machine learning algorithm Complication Brain tumor |
Issue Date | 2019 |
Citation | Clinical Neurosurgery, 2019, v. 85, n. 4, p. E756-E764 How to Cite? |
Abstract | Copyright © 2019 by the Congress of Neurological Surgeons. INTRODUCTION: Reliable preoperative identification of patients at high risk for early postoperative complications occurring within 24 h (EPC) of intracranial tumor surgery can improve patient safety and postoperative management. Statistical analysis using machine learning algorithms may generate models that predict EPC better than conventional statistical methods. OBJECTIVE: To train such a model and to assess its predictive ability. METHODS: This cohort study included patients from an ongoing prospective patient registry at a single tertiary care center with an intracranial tumor that underwent elective neurosurgery between June 2015 and May 2017. EPC were categorized based on the Clavien-Dindo classification score. Conventional statistical methods and different machine learning algorithms were used to predict EPC using preoperatively available patient, clinical, and surgery-related variables. The performance of each model was derived from examining classification performance metrics on an out-of-sample test dataset. RESULTS: EPC occurred in 174 (26%) of 668 patients included in the analysis. Gradient boosting machine learning algorithms provided the model best predicting the probability of an EPC. The model scored an accuracy of 0.70 (confidence interval [CI] 0.59-0.79) with an area under the curve (AUC) of 0.73 and a sensitivity and specificity of 0.80 (CI 0.58-0.91) and 0.67 (CI 0.53-0.77) on the test set. The conventional statistical model showed inferior predictive power (test set: accuracy: 0.59 (CI 0.47-0.71); AUC: 0.64; sensitivity: 0.76 (CI 0.64-0.85); specificity: 0.53 (CI 0.41-0.64)). CONCLUSION: Using gradient boosting machine learning algorithms, it was possible to create a prediction model superior to conventional statistical methods. While conventional statistical methods favor patients' characteristics, we found the pathology and surgery-related (histology, anatomical localization, surgical access) variables to be better predictors of EPC. |
Persistent Identifier | http://hdl.handle.net/10722/279367 |
ISSN | 2023 Impact Factor: 3.9 2023 SCImago Journal Rankings: 1.313 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Van Niftrik, Christiaan H.B. | - |
dc.contributor.author | Van Der Wouden, Frank | - |
dc.contributor.author | Staartjes, Victor E. | - |
dc.contributor.author | Fierstra, Jorn | - |
dc.contributor.author | Stienen, Martin N. | - |
dc.contributor.author | Akeret, Kevin | - |
dc.contributor.author | Sebök, Martina | - |
dc.contributor.author | Fedele, Tommaso | - |
dc.contributor.author | Sarnthein, Johannes | - |
dc.contributor.author | Bozinov, Oliver | - |
dc.contributor.author | Krayenbühl, Niklaus | - |
dc.contributor.author | Regli, Luca | - |
dc.contributor.author | Serra, Carlo | - |
dc.date.accessioned | 2019-10-28T03:02:28Z | - |
dc.date.available | 2019-10-28T03:02:28Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Clinical Neurosurgery, 2019, v. 85, n. 4, p. E756-E764 | - |
dc.identifier.issn | 0148-396X | - |
dc.identifier.uri | http://hdl.handle.net/10722/279367 | - |
dc.description.abstract | Copyright © 2019 by the Congress of Neurological Surgeons. INTRODUCTION: Reliable preoperative identification of patients at high risk for early postoperative complications occurring within 24 h (EPC) of intracranial tumor surgery can improve patient safety and postoperative management. Statistical analysis using machine learning algorithms may generate models that predict EPC better than conventional statistical methods. OBJECTIVE: To train such a model and to assess its predictive ability. METHODS: This cohort study included patients from an ongoing prospective patient registry at a single tertiary care center with an intracranial tumor that underwent elective neurosurgery between June 2015 and May 2017. EPC were categorized based on the Clavien-Dindo classification score. Conventional statistical methods and different machine learning algorithms were used to predict EPC using preoperatively available patient, clinical, and surgery-related variables. The performance of each model was derived from examining classification performance metrics on an out-of-sample test dataset. RESULTS: EPC occurred in 174 (26%) of 668 patients included in the analysis. Gradient boosting machine learning algorithms provided the model best predicting the probability of an EPC. The model scored an accuracy of 0.70 (confidence interval [CI] 0.59-0.79) with an area under the curve (AUC) of 0.73 and a sensitivity and specificity of 0.80 (CI 0.58-0.91) and 0.67 (CI 0.53-0.77) on the test set. The conventional statistical model showed inferior predictive power (test set: accuracy: 0.59 (CI 0.47-0.71); AUC: 0.64; sensitivity: 0.76 (CI 0.64-0.85); specificity: 0.53 (CI 0.41-0.64)). CONCLUSION: Using gradient boosting machine learning algorithms, it was possible to create a prediction model superior to conventional statistical methods. While conventional statistical methods favor patients' characteristics, we found the pathology and surgery-related (histology, anatomical localization, surgical access) variables to be better predictors of EPC. | - |
dc.language | eng | - |
dc.relation.ispartof | Clinical Neurosurgery | - |
dc.subject | Prediction model | - |
dc.subject | Neurosurgery | - |
dc.subject | Neurocritical care | - |
dc.subject | Machine learning algorithm | - |
dc.subject | Complication | - |
dc.subject | Brain tumor | - |
dc.title | Machine Learning Algorithm Identifies Patients at High Risk for Early Complications after Intracranial Tumor Surgery: Registry-Based Cohort Study | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1093/neuros/nyz145 | - |
dc.identifier.pmid | 31149726 | - |
dc.identifier.scopus | eid_2-s2.0-85073459773 | - |
dc.identifier.hkuros | 308307 | - |
dc.identifier.volume | 85 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | E756 | - |
dc.identifier.epage | E764 | - |
dc.identifier.eissn | 1524-4040 | - |
dc.identifier.isi | WOS:000491255600033 | - |
dc.identifier.issnl | 0148-396X | - |