Dataset Information

Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models.

ABSTRACT:

Background

The purpose of this study was to build a model of machine learning (ML) for the prediction of mortality in patients with isolated moderate and severe traumatic brain injury (TBI).

Methods

Hospitalized adult patients registered in the Trauma Registry System between January 2009 and December 2015 were enrolled in this study. Only patients with an Abbreviated Injury Scale (AIS) score ? 3 points related to head injuries were included in this study. A total of 1734 (1564 survival and 170 non-survival) and 325 (293 survival and 32 non-survival) patients were included in the training and test sets, respectively.

Results

Using demographics and injury characteristics, as well as patient laboratory data, predictive tools (e.g., logistic regression [LR], support vector machine [SVM], decision tree [DT], naive Bayes [NB], and artificial neural networks [ANN]) were used to determine the mortality of individual patients. The predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operator characteristic curves. In the training set, all five ML models had a specificity of more than 90% and all ML models (except the NB) achieved an accuracy of more than 90%. Among them, the ANN had the highest sensitivity (80.59%) in mortality prediction. Regarding performance, the ANN had the highest AUC (0.968), followed by the LR (0.942), SVM (0.935), NB (0.908), and DT (0.872). In the test set, the ANN had the highest sensitivity (84.38%) in mortality prediction, followed by the SVM (65.63%), LR (59.38%), NB (59.38%), and DT (43.75%).

Conclusions

The ANN model provided the best prediction of mortality for patients with isolated moderate and severe TBI.

SUBMITTER: Rau CS

PROVIDER: S-EPMC6226171 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models.

Rau Cheng-Shyuan CS Kuo Pao-Jen PJ Chien Peng-Chen PC Huang Chun-Ying CY Hsieh Hsiao-Yun HY Hsieh Ching-Hua CH

PloS one 20181109 11

<h4>Background</h4>The purpose of this study was to build a model of machine learning (ML) for the prediction of mortality in patients with isolated moderate and severe traumatic brain injury (TBI).<h4>Methods</h4>Hospitalized adult patients registered in the Trauma Registry System between January 2009 and December 2015 were enrolled in this study. Only patients with an Abbreviated Injury Scale (AIS) score ≥ 3 points related to head injuries were included in this study. A total of 1734 (1564 sur ...[more]

PMID: 30412613

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Project description:ImportanceBiological therapies have revolutionized inflammatory bowel disease management, but many patients do not respond to biological monotherapy. Identification of likely responders could reduce costs and delays in remission.ObjectiveTo identify patients with Crohn disease likely to be durable responders to ustekinumab before committing to long-term treatment.Design, setting, and participantsThis cohort study analyzed data from 3 phase 3 randomized clinical trials (UNITI-1, UNITI-2, and IM-UNITI) conducted from 2011 to 2015. Participants (n = 401) were individuals with active (C-reactive protein [CRP] measurement of ≥5 mg/L at enrollment) Crohn disease who received ustekinumab therapy. Data analysis was performed from November 1, 2017, to June 1, 2018.ExposuresAll included patients were exposed to 1 or more dose of ustekinumab for 8 weeks or more.Main outcomes and measuresRandom forest methods were used in building 2 models for predicting Crohn disease remission, with a CRP level lower than 5 mg/dL as a proxy for biological remission, beyond week 42 of ustekinumab treatment. The first model used only baseline data, and the second used data through week 8.ResultsIn total, 401 participants, with a mean (SD) age of 36.3 (12.6) years and 170 male (42.4%), were included. The week-8 model had a mean area under the receiver operating characteristic curve (AUROC) of 0.78 (95% CI, 0.69-0.87). In the testing data set, 27 of 55 participants (49.1%) classified as likely to have treatment success achieved success with a CRP level lower than 5 mg/L after week 42, and 7 of 65 participants (10.8%) classified as likely to have treatment failure achieved this outcome. In the full cohort, 87 patients (21.7%) attained remission after week 42. A prediction model using the week-6 albumin to CRP ratio had an AUROC of 0.76 (95% CI, 0.71-0.82). Baseline ustekinumab serum levels did not improve the model's prediction performance.Conclusions and relevanceIn patients with active Crohn disease, demographic and laboratory data before week 8 of treatment appeared to allow the prompt identification of likely nonresponders to ustekinumab without the need for costly drug-level monitoring.

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Project description:ObjectivesAfter traumatic brain injury, continuous electroencephalography is widely used to detect electrographic seizures. With the development of standardized continuous electroencephalography terminology, we aimed to describe the prevalence and burden of ictal-interictal patterns, including electrographic seizures after moderate-to-severe traumatic brain injury and to correlate continuous electroencephalography features with functional outcome.DesignPost hoc analysis of the prospective, randomized controlled phase 2 multicenter INTREPID study (ClinicalTrials.gov: NCT00805818). Continuous electroencephalography was initiated upon admission to the ICU. The primary outcome was the 3-month Glasgow Outcome Scale-Extended. Consensus electroencephalography reviews were performed by raters certified in standardized continuous electroencephalography terminology blinded to clinical data. Rhythmic, periodic, or ictal patterns were referred to as "ictal-interictal continuum"; severe ictal-interictal continuum was defined as greater than or equal to 1.5 Hz lateralized rhythmic delta activity or generalized periodic discharges and any lateralized periodic discharges or electrographic seizures.SettingTwenty U.S. level I trauma centers.PatientsPatients with nonpenetrating traumatic brain injury and postresuscitation Glasgow Coma Scale score of 4-12 were included.InterventionsNone.Measurements and main resultsAmong 152 patients with continuous electroencephalography (age 34 ± 14 yr; 88% male), 22 (14%) had severe ictal-interictal continuum including electrographic seizures in four (2.6%). Severe ictal-interictal continuum burden correlated with initial prognostic scores, including the International Mission for Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (r = 0.51; p = 0.01) and Injury Severity Score (r = 0.49; p = 0.01), but not with functional outcome. After controlling clinical covariates, unfavorable outcome was independently associated with absence of posterior dominant rhythm (common odds ratio, 3.38; 95% CI, 1.30-9.09), absence of N2 sleep transients (3.69; 1.69-8.20), predominant delta activity (2.82; 1.32-6.10), and discontinuous background (5.33; 2.28-12.96) within the first 72 hours of monitoring.ConclusionsSevere ictal-interictal continuum patterns, including electrographic seizures, were associated with clinical markers of injury severity but not functional outcome in this prospective cohort of patients with moderate-to-severe traumatic brain injury. Importantly, continuous electroencephalography background features were independently associated with functional outcome and improved the area under the curve of existing, validated predictive models.

Dataset Information

Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models.

Background

Methods

Results

Conclusions

Publications

Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models.

Similar Datasets

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