Predicting delayed cerebral ischemia after subarachnoid hemorrhage using physiological time series data

Abstract

To develop and validate a prediction model for delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) using a temporal unsupervised feature engineering approach, demonstrating improved precision over standard features. 488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included. Models were trained on 80%, while 20% were set aside for validation testing. Baseline information and standard grading scales were evaluated: age, sex, Hunt Hess grade, modified Fisher Scale (mFS), and Glasgow Coma Scale (GCS). An unsupervised approach applying random kernels was used to extract features from physiological time series (systolic and diastolic blood pressure, heart rate, respiratory rate, and oxygen saturation). Classifiers (Partial Least Squares, linear and kernel Support Vector Machines) were trained on feature subsets of the derivation dataset. Models were applied to the validation dataset. The performances of the best classifiers on the validation dataset are reported by feature subset. Standard grading scale (mFS): AUC 0.58. Combined demographics and grading scales: AUC 0.60. Random kernel derived physiologic features: AUC 0.74. Combined baseline and physiologic features with redundant feature reduction: AUC 0.77. Current DCI prediction tools rely on admission imaging and are advantageously simple to employ. However, using an agnostic and computationally inexpensive learning approach for high-frequency physiologic time series data, we demonstrated that our models achieve higher classification accuracy.

Improving Prediction of Favourable Outcome After 6 Months in Patients with Severe Traumatic Brain Injury Using Physiological Cerebral Parameters in a Multivariable Logistic Regression Model

“…Meiring et al [ 7 ] showed that common physiological parameters such as HR and MAP and treatment given can predict mortality on the ICU on subsequent days. Other applications are prediction of delayed cerebral ischaemia after subarachnoid haemorrhage [ 9 ], prediction of favourable neurological outcome among children on the ICU with critical illness [ 8 ] or prediction of impending sepsis in neonates [ 28 ]. Although it also has been attempted to use physiological parameters to predict outcome 6 to 12 months after TBI, data used are solely measured before admission or incorporate the whole ICU admission period, hampering (early) clinical assistance [ 4 , 5 , 29 – 31 ].…”

“…However, confounding factors, consequences of the initial trauma (like brain swelling, metabolic crises and inflammation) and the individual response to therapy during ICU admission are not included. Because full supportive care for a certain amount of time from initial presentation is recommended to maximize the potential for recovery from primary and secondary damage [ 6 ], extending prognostic models with early physiological monitoring data might improve the outcome prediction accuracy as has been shown in studies on the ICU for pathologies other than TBI [ 7 – 9 ].…”

Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives

“…predicting outcomes of ruptured aneurysms [33]; predicting ischemia after aneurysmal subarachnoid hemorrhage [34];…”

Journal of Clinical Monitoring and Computing 2019 end of year summary: monitoring tissue oxygenation and perfusion and its autoregulation

“…There is thus a need for better prediction models. In the article published by Park and colleagues in the February 2019 issue [20], the authors set out to develop and validate a new prediction model, using a temporal unsupervised feature engineering approach. For model development, they used data collected retrospectively from 488 patients admitted to their unit/hospital with a SAH.…”