Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis.

Hostettler, Isabel Charlotte; Muroi, Carl; Richter, Johannes Konstantin; Schmid, Josef; Neidert, Marian Christoph; Seule, Martin; Boss, Oliver; Pangalu, Athina; Germans, Menno Robbert; Keller, Emanuela (2018). Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis. Journal of neurosurgery, 129(6), pp. 1499-1510. American Association of Neurological Surgeons 10.3171/2017.7.JNS17677

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OBJECTIVE The aim of this study was to create prediction models for outcome parameters by decision tree analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH). METHODS The database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7. RESULTS The overall mortality was 18.4%. The accuracy of the decision tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of < 5%. Prediction accuracy for survival on day 1 was 75.2%. The most important differentiating factor was the interleukin-6 (IL-6) level on day 1. Favorable functional outcome, defined as Glasgow Outcome Scale scores of 4 and 5, was observed in 68.6% of patients. Favorable functional outcome at all time points had a prediction accuracy of 71.1% in the training data set, with procalcitonin on day 1 being the most important differentiating factor at all time points. A total of 148 patients (27%) developed VP shunt dependency. The most important differentiating factor was hyperglycemia on admission. CONCLUSIONS The multiple variable analysis capability of decision trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The decision tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for prognostication.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology

UniBE Contributor:

Richter, Johannes Konstantin

Subjects:

600 Technology > 610 Medicine & health

ISSN:

0022-3085

Publisher:

American Association of Neurological Surgeons

Language:

English

Submitter:

Nicole Rösch

Date Deposited:

23 Apr 2018 09:05

Last Modified:

28 Oct 2019 11:14

Publisher DOI:

10.3171/2017.7.JNS17677

PubMed ID:

29350603

Uncontrolled Keywords:

BNI = Barrow Neurological Institute CRP = C-reactive protein DCI = delayed cerebral ischemia GOS = Glasgow Outcome Scale IL-6 = interleukin-6 PCT = procalcitonin VP = ventriculoperitoneal WFNS = World Federation of Neurosurgical Societies aSAH = aneurysmal subarachnoid hemorrhage clinical outcome death decision tree analysis delayed cerebral infarction shunt dependency subarachnoid hemorrhage vascular disorders

BORIS DOI:

10.7892/boris.113289

URI:

https://boris.unibe.ch/id/eprint/113289

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