Dynamic Detection of Delayed Cerebral Ischemia: A Study in 3 Centers

Abstract

Background and purpose: Delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage negatively impacts long-term recovery but is often detected too late to prevent damage. We aim to develop hourly risk scores using routinely collected clinical data to detect DCI.

Methods: A DCI classification model was trained using vital sign measurements (heart rate, blood pressure, respiratory rate, and oxygen saturation) and demographics routinely collected for clinical care. Twenty-two time-varying physiological measures were computed including mean, SD, and cross-correlation of heart rate time series with each of the other vitals. Classification was achieved using an ensemble approach with L2-regularized logistic regression, random forest, and support vector machines models. Classifier performance was determined by area under the receiver operating characteristic curves and confusion matrices. Hourly DCI risk scores were generated as the posterior probability at time t using the Ensemble classifier on cohorts recruited at 2 external institutions (n=38 and 40).

Results: Three hundred ten patients were included in the training model (median, 54 years old [interquartile range, 45-65]; 80.2% women, 28.4% Hunt and Hess scale 4-5, 38.7% Modified Fisher Scale 3-4); 101 (33%) developed DCI with a median onset day 6 (interquartile range, 5-8). Classification accuracy before DCI onset was 0.83 (interquartile range, 0.76-0.83) area under the receiver operating characteristic curve. Risk scores applied to external institution datasets correctly predicted 64% and 91% of DCI events as early as 12 hours before clinical detection, with 2.7 and 1.6 true alerts for every false alert.

Conclusions: An hourly risk score for DCI derived from routine vital signs may have the potential to alert clinicians to DCI, which could reduce neurological injury.

Keywords: blood pressure; heart rate; machine learning; respiratory rate; subarachnoid hemorrhage.

Figure 1:

Illustrating the concept of anchoring patients’ data to the DCI onset to capture the temporal dynamics leading to DCI onset. The vertical black line indicates the onset of DCI.

Figure 2:

Overview of the approach.

Figure 3:

Performance of models (M1,…,M14) on Houston dataset over time leading to DCI anchor.

Figure 4:

Performance of models (M1,…,M14) on Aachen dataset over time leading to DCI anchor.

Figure 5:

Classifier performance and risk scores. (A) AU-ROCs for five classifiers (L2-Regularized Logistic Regression, Support Vector Machine – Linear and Kernel, Random Forrest, and Ensemble Classifier) trained on initial demographic and vital sign features. White dotted lines are the median AU-ROCs and the blue box indicates the IQR. Best performing models are highlighted in red and the risk scores were generated using these models. Risk scores generated every 12 hours for Columbia, Houston (using model M14) and Aachen (using model M1). Risk scores generated every 1 hour for Columbia and Houston before Classical DCI and for Aachen before “Perfusion” DCI.