Random Forest–Based Prediction of Outcome and Mortality in Patients with Traumatic Brain Injury Undergoing Primary Decompressive Craniectomy
| Autor: | René Opšenák, Martin Hanko, Jakub Soršák, Martin Benčo, Pavol Snopko, Marian Grendar, Juraj Šutovský, Branislav Kolarovszki, Kamil Zeleňák |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Adult
Male Decompressive Craniectomy medicine.medical_specialty Traumatic brain injury medicine.medical_treatment Glasgow Outcome Scale Outcome (game theory) Random Allocation 03 medical and health sciences 0302 clinical medicine Predictive Value of Tests Brain Injuries Traumatic medicine Humans In patient Prospective Studies Prospective cohort study Prognostic models Aged Postoperative Care Models Statistical business.industry Middle Aged Prognosis medicine.disease Surgery Random forest Treatment Outcome Area Under Curve 030220 oncology & carcinogenesis Female Decompressive craniectomy Neurology (clinical) business Algorithms 030217 neurology & neurosurgery |
| Zdroj: | World Neurosurgery. 148:e450-e458 |
| ISSN: | 1878-8750 |
| DOI: | 10.1016/j.wneu.2021.01.002 |
| Popis: | Background Various prognostic models are used to predict mortality and functional outcome in patients after traumatic brain injury with a trend to incorporate machine learning protocols. None of these models is focused exactly on the subgroup of patients indicated for decompressive craniectomy. Evidence regarding efficiency of this surgery is still incomplete, especially in patients undergoing primary decompressive craniectomy with evacuation of traumatic mass lesions. Methods In a prospective study with a 6-month follow-up period, we assessed postoperative outcome and mortality of 40 patients who underwent primary decompressive craniectomy for traumatic brain injuries during 2018–2019. The results were analyzed in relation to a wide spectrum of preoperatively available demographic, clinical, radiographic, and laboratory data. Random forest algorithms were trained for prediction of both mortality and unfavorable outcome, with their accuracy quantified by area under the receiver operating curves (AUCs) for out-of-bag samples. Results At the end of the follow-up period, we observed mortality of 57.5%. Favorable outcome (Glasgow Outcome Scale [GOS] score 4–5) was achieved by 30% of our patients. Random forest–based prediction models constructed for 6-month mortality and outcome reached a moderate predictive ability, with AUC = 0.811 and AUC = 0.873, respectively. Random forest models trained on handpicked variables showed slightly decreased AUC = 0.787 for 6-month mortality and AUC = 0.846 for 6-month outcome and increased out-of-bag error rates. Conclusions Random forest algorithms show promising results in prediction of postoperative outcome and mortality in patients undergoing primary decompressive craniectomy. The best performance was achieved by Classification Random forest for 6-month outcome. |
| Databáze: | OpenAIRE |
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