Exploratory Use of Decision Tree Analysis in Classification of Outcome in Hypoxic-Ischemic Brain Injury

Classification and Regression Tree Predictive Model for Acute Kidney Injury in Traumatic Brain Injury Patients

Background

Acute kidney injury (AKI) is prevalent in hospitalized patients with traumatic brain injury (TBI), and increases the risk of poor outcomes. We designed this study to develop a visual and convenient decision-tree-based model for predicting AKI in TBI patients.

Methods

A total of 376 patients admitted to the emergency department of the West China Hospital for TBI between January 2015 and June 2019 were included. Demographic information, vital signs on admission, laboratory test results, radiological signs, surgical options, and medications were recorded as variables. AKI was confirmed since the second day after admission, based on the Kidney Disease Improving Global Outcomes criteria. We constructed two predictive models for AKI using least absolute shrinkage and selection operator (LASSO) regression and classification and regression tree (CART), respectively. Receiver operating characteristic (ROC) curves of these two predictive models were drawn, and the area under the ROC curve (AUC) was calculated to compare their predictive accuracy.

Results

The incidence of AKI on the second day after admission was 10.4% among patients with TBI. Lasso regression identified five potent predictive factors for AKI: glucose, serum creatinine, cystatin C, serum uric acid, and fresh frozen plasma transfusions. The CART analysis showed that glucose, serum uric acid, and cystatin C ranked among the top three in terms of the feature importance of the decision tree model. The AUC value of the decision-tree predictive model was 0.892, which was higher than the 0.854 of the LASSO regression model, although the difference was not statistically significant.

Conclusion

The decision tree model is valuable for predicting AKI among patients with TBI. This tree-based flowchart is convenient for physicians to identify patients with TBI who are at high risk of AKI and prompts them to develop suitable therapeutic strategies.

Oxfordshire Community Stroke Project Classification: A proposed automated algorithm

Introduction

The Oxfordshire Community Stroke Project (OCSP) proposed a clinical classification for Stroke patients. This classification has proved helpful to predict the risk of neurological complications. However, the OCSP was initially based on findings on the neurological assesment, which can pose difficulties for classifying patients. We aimed to describe the development and the validation step of a computer-based algorithm based on the OCSP classification.

Materials and methods

A flow-chart was created which was reviewed by five board-certified vascular neurologists from which a computer-based algorithm (COMPACT) was developed. Neurology residents from 12 centers were invited to participate in a randomized trial to assess the effect of using COMPACT. They answered a 20-item questionnaire for classifying the vignettes according to the OCSP classification. Each correct answer has been attributed to 1-point for calculating the final score.

Results

Six-two participants agreed to participate and answered the questionnaire. Thirty-two were randomly allocated to use our algorithm, and thirty were allocated to adopt a list of symptoms alone. The group who adopted our algorithm had a median score of correct answers of 16.5[14.5, 17]/20 versus 15[13, 16]/20 points, p = 0.014. The use of our algorithm was associated with the overall rate of correct scores (p = 0.03).

Discussion

Our algorithm seemed a useful tool for any postgraduate year Neurology resident. A computer-based algorithm may save time and improve the accuracy to classify these patients.

Conclusion

An easy-to-use computer-based algorithm improved the accuracy of the OCSP classification, with the possible benefit of further improvement of the prediction of neurological complications and prognostication.