Abstract
Accurate prediction of delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) can be critical for planning interventions to prevent poor neurological outcome. This paper presents a model using convolution dictionary learning to extract features from physiological data available from bedside monitors. We develop and validate a prediction model for DCI after SAH, demonstrating improved precision over standard methods alone. 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. Modified Fisher Scale was considered the standard grading scale in clinical use; baseline features also analyzed included age, sex, Hunt-Hess, and Glasgow Coma Scales. An unsupervised approach using convolution dictionary learning was used to extract features from physiological time series (systolic blood pressure and diastolic blood pressure, heart rate, respiratory rate, and oxygen saturation). Classifiers (partial least squares and 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 0.54. Combined demographics and grading scales (baseline features): AUC 0.63. Kernel derived physiologic features: AUC 0.66. Combined baseline and physiologic features with redundant feature reduction: AUC 0.71 on derivation dataset and 0.78 on validation dataset. 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 we could incorporate individual physiologic data to achieve higher classification accuracy.
Highlights
Subarachnoid hemorrhage (SAH) is a major public health burden, affecting 14.5 per 100,000 persons in the United States alone [1, 2]
From May 2006 to December 2014, 562 SAH patients with physiologic data were enrolled. 8 had VSP or delayed cerebral ischemia (DCI) identified before post bleed day (PBD) 3, 66 were missing all candidate features leaving a total of 488 subjects included in the study
A model based on current standard grading scale (MFS) achieved an AUC of 0.56 (SVM-L, 95% confidence intervals (CI), 0.44–0.67)
Summary
Subarachnoid hemorrhage (SAH) is a major public health burden, affecting 14.5 per 100,000 persons in the United States alone [1, 2]. VSP refers to the narrowing of cerebral blood vessels triggered by the unusual presence of blood surrounding the vessel after a ruptured aneurysm, which can result in stroke. It occurs in 30% of SAH patients [8, 9] [54% of SAH patients in coma [10]]. As in other causes of stroke and secondary brain injury in the neurologic intensive care unit (NICU), time is of the essence to detect and intervene. For the higher risk SAH patients, the first 10–14 days are occupied by efforts to detect subtle examination changes that suggest VSP [17], and arrange urgent imaging to confirm VSP. On the converse, discharging patients from the ICU at low risk for DCI can result in significant cost savings [18]
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