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Article: Machine Learning Algorithm Identifies Patients at High Risk for Early Complications after Intracranial Tumor Surgery: Registry-Based Cohort Study

TitleMachine Learning Algorithm Identifies Patients at High Risk for Early Complications after Intracranial Tumor Surgery: Registry-Based Cohort Study
Authors
KeywordsPrediction model
Neurosurgery
Neurocritical care
Machine learning algorithm
Complication
Brain tumor
Issue Date2019
Citation
Clinical Neurosurgery, 2019, v. 85, n. 4, p. E756-E764 How to Cite?
AbstractCopyright © 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 Identifierhttp://hdl.handle.net/10722/279367
ISSN
2017 Impact Factor: 4.475
2015 SCImago Journal Rankings: 1.414

 

DC FieldValueLanguage
dc.contributor.authorVan Niftrik, Christiaan H.B.-
dc.contributor.authorVan Der Wouden, Frank-
dc.contributor.authorStaartjes, Victor E.-
dc.contributor.authorFierstra, Jorn-
dc.contributor.authorStienen, Martin N.-
dc.contributor.authorAkeret, Kevin-
dc.contributor.authorSebök, Martina-
dc.contributor.authorFedele, Tommaso-
dc.contributor.authorSarnthein, Johannes-
dc.contributor.authorBozinov, Oliver-
dc.contributor.authorKrayenbühl, Niklaus-
dc.contributor.authorRegli, Luca-
dc.contributor.authorSerra, Carlo-
dc.date.accessioned2019-10-28T03:02:28Z-
dc.date.available2019-10-28T03:02:28Z-
dc.date.issued2019-
dc.identifier.citationClinical Neurosurgery, 2019, v. 85, n. 4, p. E756-E764-
dc.identifier.issn0148-396X-
dc.identifier.urihttp://hdl.handle.net/10722/279367-
dc.description.abstractCopyright © 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.languageeng-
dc.relation.ispartofClinical Neurosurgery-
dc.subjectPrediction model-
dc.subjectNeurosurgery-
dc.subjectNeurocritical care-
dc.subjectMachine learning algorithm-
dc.subjectComplication-
dc.subjectBrain tumor-
dc.titleMachine Learning Algorithm Identifies Patients at High Risk for Early Complications after Intracranial Tumor Surgery: Registry-Based Cohort Study-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1093/neuros/nyz145-
dc.identifier.pmid31149726-
dc.identifier.scopuseid_2-s2.0-85073459773-
dc.identifier.hkuros308307-
dc.identifier.volume85-
dc.identifier.issue4-
dc.identifier.spageE756-
dc.identifier.epageE764-
dc.identifier.eissn1524-4040-

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