Machine learning-based dynamic mortality prediction after traumatic brain injury

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http://hdl.handle.net/10138/309013

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Raj , R , Luostarinen , T , Pursiainen , E , Posti , J P , Takala , R S K , Bendel , S , Konttila , T & Korja , M 2019 , ' Machine learning-based dynamic mortality prediction after traumatic brain injury ' , Scientific Reports , vol. 9 , 17672 . https://doi.org/10.1038/s41598-019-53889-6

Title: Machine learning-based dynamic mortality prediction after traumatic brain injury
Author: Raj, Rahul; Luostarinen, Teemu; Pursiainen, Eetu; Posti, Jussi P.; Takala, Riikka S. K.; Bendel, Stepani; Konttila, Teijo; Korja, Miikka
Contributor: University of Helsinki, Staff Services
University of Helsinki, Helsinki University Hospital Area
University of Helsinki, Helsinki University Hospital Area
University of Helsinki, Helsinki University Hospital Area
University of Helsinki, Clinicum
Date: 2019-11-27
Number of pages: 13
Belongs to series: Scientific Reports
ISSN: 2045-2322
URI: http://hdl.handle.net/10138/309013
Abstract: Our aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensive care after traumatic brain injury. We performed an observational multicenter study including adult TBI patients that were monitored for intracranial pressure (ICP) for at least 24 h in three ICUs. We used machine learning-based logistic regression modeling to create two algorithms (based on ICP, mean arterial pressure [MAP], cerebral perfusion pressure [CPP] and Glasgow Coma Scale [GCS]) to predict 30-day mortality. We used a stratified crossvalidation technique for internal validation. Of 472 included patients, 92 patients (19%) died within 30 days. Following cross-validation, the ICP-MAP-CPP algorithm's area under the receiver operating characteristic curve (AUC) increased from 0.67 (95% confidence interval [CI] 0.60-0.74) on day 1 to 0.81 (95% CI 0.75-0.87) on day 5. The ICP-MAP-CPP-GCS algorithm's AUC increased from 0.72 (95% CI 0.64-0.78) on day 1 to 0.84 (95% CI 0.78-0.90) on day 5. Algorithm misclassification was seen among patients undergoing decompressive craniectomy. In conclusion, we present a new concept of dynamic prognostication for patients with TBI treated in the ICU. Our simple algorithms, based on only three and four main variables, discriminated between survivors and non-survivors with accuracies up to 81% and 84%. These open-sourced simple algorithms can likely be further developed, also in low and middleincome countries.
Subject: INTENSIVE-CARE-UNIT
INTRACRANIAL-PRESSURE
HOSPITAL MORTALITY
SECONDARY INSULTS
HEAD-INJURY
CLASSIFICATION
EPIDEMIOLOGY
MANAGEMENT
COMA
GUIDELINES
3126 Surgery, anesthesiology, intensive care, radiology
3112 Neurosciences
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