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Predictive modeling in pediatric traumatic brain injury using machine learning.

Chong SL, Liu N, Barbier S, Ong ME - BMC Med Res Methodol (2015)

Bottom Line: To predict moderate to severe TBI, we built a machine learning (ML) model and a multivariable logistic regression model and compared their performances by means of Receiver Operating Characteristic (ROC) analysis.The following 4 predictors remained statistically significant after multivariable analysis: Involvement in road traffic accident, a history of loss of consciousness, vomiting and signs of base of skull fracture.At the optimal cutoff scores, the ML method improved upon the logistic regression method with respect to the area under the ROC curve (0.98 vs 0.93), sensitivity (94.9% vs 82.1%), specificity (97.4% vs 92.3%), PPV (90.2% vs 72.7%), and NPV (98.7% vs 95.4%).

View Article: PubMed Central - PubMed

Affiliation: Department of Emergency Medicine, KK Women's and Children's Hospital, Singapore, Singapore. chong.shu-ling@kkh.com.sg.

ABSTRACT

Background: Pediatric traumatic brain injury (TBI) constitutes a significant burden and diagnostic challenge in the emergency department (ED). While large North American research networks have derived clinical prediction rules for the head injured child, these may not be generalizable to practices in countries with traditionally low rates of computed tomography (CT). We aim to study predictors for moderate to severe TBI in our ED population aged < 16 years.

Methods: This was a retrospective case-control study based on data from a prospective surveillance head injury database. Cases were included if patients presented from 2006 to 2014, with moderate to severe TBI. Controls were age-matched head injured children from the registry, obtained in a 4 control: 1 case ratio. These children remained well on diagnosis and follow up. Demographics, history, and physical examination findings were analyzed and patients followed up for the clinical course and outcome measures of death and neurosurgical intervention. To predict moderate to severe TBI, we built a machine learning (ML) model and a multivariable logistic regression model and compared their performances by means of Receiver Operating Characteristic (ROC) analysis.

Results: There were 39 cases and 156 age-matched controls. The following 4 predictors remained statistically significant after multivariable analysis: Involvement in road traffic accident, a history of loss of consciousness, vomiting and signs of base of skull fracture. The logistic regression model was created with these 4 variables while the ML model was built with 3 extra variables, namely the presence of seizure, confusion and clinical signs of skull fracture. At the optimal cutoff scores, the ML method improved upon the logistic regression method with respect to the area under the ROC curve (0.98 vs 0.93), sensitivity (94.9% vs 82.1%), specificity (97.4% vs 92.3%), PPV (90.2% vs 72.7%), and NPV (98.7% vs 95.4%).

Conclusions: In this study, we demonstrated the feasibility of using machine learning as a tool to predict moderate to severe TBI. If validated on a large scale, the ML method has the potential not only to guide discretionary use of CT, but also a more careful selection of head injured children who warrant closer monitoring in the hospital.

No MeSH data available.


Related in: MedlinePlus

Frequency distribution of the logistic regression method and the machine learning method in predicting pediatric TBI.
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Fig3: Frequency distribution of the logistic regression method and the machine learning method in predicting pediatric TBI.

Mentions: Figure 3 illustrates the differences in predicted scores by the logistic regression method and the ML method in terms of frequency distribution. Figure 3(a) shows the results on TBI patients and Figure 3(b) presents the results on non-TBI patients. In non-TBI patients, both methods performed similarly with the ML prediction being slightly more accurate. In TBI patients, the ML method performed better at categorizing most of the TBI patients at high risk for moderate to severe injury. These matched the observations that the ML method achieved higher sensitivity and PPV than the logistic regression method.Figure 3


Predictive modeling in pediatric traumatic brain injury using machine learning.

Chong SL, Liu N, Barbier S, Ong ME - BMC Med Res Methodol (2015)

Frequency distribution of the logistic regression method and the machine learning method in predicting pediatric TBI.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4374377&req=5

Fig3: Frequency distribution of the logistic regression method and the machine learning method in predicting pediatric TBI.
Mentions: Figure 3 illustrates the differences in predicted scores by the logistic regression method and the ML method in terms of frequency distribution. Figure 3(a) shows the results on TBI patients and Figure 3(b) presents the results on non-TBI patients. In non-TBI patients, both methods performed similarly with the ML prediction being slightly more accurate. In TBI patients, the ML method performed better at categorizing most of the TBI patients at high risk for moderate to severe injury. These matched the observations that the ML method achieved higher sensitivity and PPV than the logistic regression method.Figure 3

Bottom Line: To predict moderate to severe TBI, we built a machine learning (ML) model and a multivariable logistic regression model and compared their performances by means of Receiver Operating Characteristic (ROC) analysis.The following 4 predictors remained statistically significant after multivariable analysis: Involvement in road traffic accident, a history of loss of consciousness, vomiting and signs of base of skull fracture.At the optimal cutoff scores, the ML method improved upon the logistic regression method with respect to the area under the ROC curve (0.98 vs 0.93), sensitivity (94.9% vs 82.1%), specificity (97.4% vs 92.3%), PPV (90.2% vs 72.7%), and NPV (98.7% vs 95.4%).

View Article: PubMed Central - PubMed

Affiliation: Department of Emergency Medicine, KK Women's and Children's Hospital, Singapore, Singapore. chong.shu-ling@kkh.com.sg.

ABSTRACT

Background: Pediatric traumatic brain injury (TBI) constitutes a significant burden and diagnostic challenge in the emergency department (ED). While large North American research networks have derived clinical prediction rules for the head injured child, these may not be generalizable to practices in countries with traditionally low rates of computed tomography (CT). We aim to study predictors for moderate to severe TBI in our ED population aged < 16 years.

Methods: This was a retrospective case-control study based on data from a prospective surveillance head injury database. Cases were included if patients presented from 2006 to 2014, with moderate to severe TBI. Controls were age-matched head injured children from the registry, obtained in a 4 control: 1 case ratio. These children remained well on diagnosis and follow up. Demographics, history, and physical examination findings were analyzed and patients followed up for the clinical course and outcome measures of death and neurosurgical intervention. To predict moderate to severe TBI, we built a machine learning (ML) model and a multivariable logistic regression model and compared their performances by means of Receiver Operating Characteristic (ROC) analysis.

Results: There were 39 cases and 156 age-matched controls. The following 4 predictors remained statistically significant after multivariable analysis: Involvement in road traffic accident, a history of loss of consciousness, vomiting and signs of base of skull fracture. The logistic regression model was created with these 4 variables while the ML model was built with 3 extra variables, namely the presence of seizure, confusion and clinical signs of skull fracture. At the optimal cutoff scores, the ML method improved upon the logistic regression method with respect to the area under the ROC curve (0.98 vs 0.93), sensitivity (94.9% vs 82.1%), specificity (97.4% vs 92.3%), PPV (90.2% vs 72.7%), and NPV (98.7% vs 95.4%).

Conclusions: In this study, we demonstrated the feasibility of using machine learning as a tool to predict moderate to severe TBI. If validated on a large scale, the ML method has the potential not only to guide discretionary use of CT, but also a more careful selection of head injured children who warrant closer monitoring in the hospital.

No MeSH data available.


Related in: MedlinePlus