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Trevisi G, Caccavella VM, Scerrati A, Signorelli F, Salamone GG, Orsini K, Fasciani C, D'Arrigo S, Auricchio AM, D'Onofrio G, Salomi F, Albanese A, De Bonis P, Mangiola A, Sturiale CL. Machine learning model prediction of 6-month functional outcome in elderly patients with intracerebral hemorrhage. Neurosurg Rev 2022; 45:2857-2867. [PMID: 35522333 PMCID: PMC9349060 DOI: 10.1007/s10143-022-01802-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/22/2022] [Accepted: 04/27/2022] [Indexed: 11/26/2022]
Abstract
Spontaneous intracerebral hemorrhage (ICH) has an increasing incidence and a worse outcome in elderly patients. The ability to predict the functional outcome in these patients can be helpful in supporting treatment decisions and establishing prognostic expectations. We evaluated the performance of a machine learning (ML) model to predict the 6-month functional status in elderly patients with ICH leveraging the predictive value of the clinical characteristics at hospital admission. Data were extracted by a retrospective multicentric database of patients ≥ 70 years of age consecutively admitted for the management of spontaneous ICH between January 1, 2014 and December 31, 2019. Relevant demographic, clinical, and radiological variables were selected by a feature selection algorithm (Boruta) and used to build a ML model. Outcome was determined according to the Glasgow Outcome Scale (GOS) at 6 months from ICH: dead (GOS 1), poor outcome (GOS 2–3: vegetative status/severe disability), and good outcome (GOS 4–5: moderate disability/good recovery). Ten features were selected by Boruta with the following relative importance order in the ML model: Glasgow Coma Scale, Charlson Comorbidity Index, ICH score, ICH volume, pupillary status, brainstem location, age, anticoagulant/antiplatelet agents, intraventricular hemorrhage, and cerebellar location. Random forest prediction model, evaluated on the hold-out test set, achieved an AUC of 0.96 (0.94–0.98), 0.89 (0.86–0.93), and 0.93 (0.90–0.95) for dead, poor, and good outcome classes, respectively, demonstrating high discriminative ability. A random forest classifier was successfully trained and internally validated to stratify elderly patients with spontaneous ICH into prognostic subclasses. The predictive value is enhanced by the ability of ML model to identify synergy among variables.
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Affiliation(s)
- Gianluca Trevisi
- Neurosurgical Unit, Ospedale Spirito Santo, Pescara, Italy.,Department of Neurosciences, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
| | | | - Alba Scerrati
- Department of Neurosurgery, S. Anna University Hospital, Ferrara, Italy.,Department of Morphology, Surgery and Experimental Medicine, University of Ferrara, Ferrara, Italy
| | - Francesco Signorelli
- Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCSS, Rome, Italy
| | | | - Klizia Orsini
- Neurosurgical Unit, Ospedale Spirito Santo, Pescara, Italy
| | | | - Sonia D'Arrigo
- Department of Anesthesiology, Fondazione Policlinico Universitario A. Gemelli IRCSS, Rome, Italy
| | - Anna Maria Auricchio
- Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCSS, Rome, Italy
| | - Ginevra D'Onofrio
- Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCSS, Rome, Italy
| | - Francesco Salomi
- Department of Neurosurgery, S. Anna University Hospital, Ferrara, Italy
| | - Alessio Albanese
- Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCSS, Rome, Italy
| | - Pasquale De Bonis
- Department of Neurosurgery, S. Anna University Hospital, Ferrara, Italy.,Department of Morphology, Surgery and Experimental Medicine, University of Ferrara, Ferrara, Italy
| | - Annunziato Mangiola
- Neurosurgical Unit, Ospedale Spirito Santo, Pescara, Italy.,Department of Neurosciences, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Carmelo Lucio Sturiale
- Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCSS, Rome, Italy. .,Institute of Neurosurgery, Università Cattolica del Sacro Cuore, L.go A. Gemelli 8, 00168, Rome, Italy.
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