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Chronic subdural hematoma outcome prediction using logistic regression and an artificial neural network

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Abstract

Artificial neural networks (ANN) have not been used in chronic subdural hematoma (CSDH) outcome prediction following surgery. We used two methods, namely logistic regression and ANN, to predict using eight variables CSDH outcome as assessed by the Glasgow outcome score (GOS) at discharge. We had 300 patients (213 men and 87 women) and potential predictors were age, sex, midline shift, intracranial air, hematoma density, hematoma thickness, brain atrophy, and Glasgow coma score (GCS). The dataset was randomly divided to three subsets: (1) training set (150 cases), (2) validation set (75 cases), and (3) test set (75 cases). The training and validation sets were combined for regression analysis. Patients aged 56.5 ± 18.1 years and 228 (76.0%) of them had a favorable outcome. The prevalence of brain atrophy, intracranial air, midline shift, low GCS, thick hematoma, and hyperdense hematoma was 142 (47.3%), 156 (52.0%), 177 (59.0%), 82 (27.3%), 135 (45.0%), and 52 (17.3%), respectively. The regression model did not show an acceptable performance on the test set (area under the curve (AUC) = 0.594; 95% CI, 0.435–0.754; p = 0.250). It had a sensitivity of 69% and a specificity of 46%, and correctly classified 50.7% of cases. A four-layer 8–3–4–1 feedforward backpropagation ANN was then developed and trained. The ANN showed a remarkably superior performance compared to the regression model (AUC = 0.767; 95% CI, 0.652–0.882; p = 0.001). It had a sensitivity of 88% and a specificity of 68%, and correctly classified 218 (72.7%) cases. Considering that GOS strongly correlates with the risk of recurrence, the ANN model can also be used to predict the recurrence of CSDH.

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Authors and Affiliations

  1. Amir-Alam Hospital, Tehran University of Medical Sciences, No.48, Alaei Alley, Ard-e-Iran Street, Shahr-e-Rey, Tehran, Iran

    Mehdi Abouzari, Armin Rashidi & Mehrdad Behzadi

  2. Department of Mathematics, University of Tehran, Tehran, Iran

    Armin Rashidi

  3. Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran

    Mehdi Zandi-Toghani

  4. Loghman Hospital, Shaheed Beheshti University of Medical Sciences, Tehran, Iran

    Marjan Asadollahi

Authors
  1. Mehdi Abouzari
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  2. Armin Rashidi
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  3. Mehdi Zandi-Toghani
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  4. Mehrdad Behzadi
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  5. Marjan Asadollahi
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Correspondence to Mehdi Abouzari.

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Comments

Jack Jallo, Philadelphia, USA

The application of an artificial neural network for outcome prediction of chronic subdural hemorrhage is indeed a novel idea and chronic subdural hemorrhage is one of the commonest types if intracranial injuries. As mentioned by the authors the incidence is expected to rise as the average population age rises, and yet it is one of the less frequently studied pathologies.

This study involves a large number of patients and looks at a large number of common variables for the input. Using a large input layer of eight variables and four-layer model rather than a three layer for the ANN probably contributes to the relatively high sensitivity and specificity of the study as well as the accuracy for prediction of outcome. Also classifying the outcome of patients into two categories, favorable or unfavorable, may provide less information regarding functional outcome, yet may help the ANN model in prediction of recurrence.

Applying the same model on a larger population with different demographics and co-morbidities will increase the validity of the model and possibly allow it to implement it on a larger scale in the future.

J. Humberto Tapia-Pérez, Magdeburg, Germany

Actually, the models on decision support, in medicine, is a very important issue. Many mathematical approaches are available; however, they are not widely understood and easily applicable. The recently increased use of artificial neural networks (ANN), one variant of nonlinear regression, has provided some evidence regarding superiority on decision (prognosis-therapy) support. In neurosurgery, trials or comparisons of these new models are scar yet. The variability and complicated number of interactions in neurosurgical/neurological diseases allow assuming that nonlinear relations are occurring. This problem could be at least theoretically resolved by using ANN.

Until today, there are no available studies on chronic subdural hematoma. This study provides us a direct comparison between two models with logistic and ANN, where a superiority of ANN has been demonstrated. Excluding previously identified factors for poor prognosis allow the developing of new models. The proposed factors for the ANN model facilitate prognostic accuracy in absence of co-morbidities. The calculated accuracy for the ANN model could be increased by introducing known factors.

The correlation between GCS and GOS should be token carefully, it exists the possibility of an internal mathematical structure on this. In other hand as argued by authors, the consideration of GCS more than severity but rather level consciousness is very reasonable. The tomographic factors would be expected, despite actual controversy. The interactions between these imagenologic variables could be adjusted by the internal networks on ANN.

It is needed that the proposed predictors be assessed on bigger populations with inclusion of all the co-morbidities. The more important message on this paper is the possibility to develop better models on decision support by using nonlinear statistics, large comparisons’ series are guaranteed.

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Abouzari, M., Rashidi, A., Zandi-Toghani, M. et al. Chronic subdural hematoma outcome prediction using logistic regression and an artificial neural network. Neurosurg Rev 32, 479–484 (2009). https://doi.org/10.1007/s10143-009-0215-3

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  • DOI: https://doi.org/10.1007/s10143-009-0215-3

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