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Neurocrit Care. 2018 Apr;28(2):184-193. doi: 10.1007/s12028-017-0466-8.

Electronic Health Data Predict Outcomes After Aneurysmal Subarachnoid Hemorrhage.

Author information

1
Department of Neurology, Lunder 6 Neurosciences Intensive Care Unit, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA. sfzafar@mgh.harvard.edu.
2
Department of Neurology, Lunder 6 Neurosciences Intensive Care Unit, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
3
Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
4
Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA.

Abstract

BACKGROUD:

Using electronic health data, we sought to identify clinical and physiological parameters that in combination predict neurologic outcomes after aneurysmal subarachnoid hemorrhage (aSAH).

METHODS:

We conducted a single-center retrospective cohort study of patients admitted with aSAH between 2011 and 2016. A set of 473 predictor variables was evaluated. Our outcome measure was discharge Glasgow Outcome Scale (GOS). For laboratory and physiological data, we computed the minimum, maximum, median, and variance for the first three admission days. We created a penalized logistic regression model to determine predictors of outcome and a multivariate multilevel prediction model to predict poor (GOS 1-2), intermediate (GOS 3), or good (GOS 4-5) outcomes.

RESULTS:

One hundred and fifty-three patients met inclusion criteria; most were discharged with a GOS of 3. Multivariate analysis predictors of mortality (AUC 0.9198) included APACHE II score, Glasgow Come Scale (GCS), white blood cell (WBC) count, mean arterial pressure, variance of serum glucose, intracranial pressure (ICP), and serum sodium. Predictors of death/dependence versus independence (GOS 4-5)(AUC 0.9456) were levetiracetam, mechanical ventilation, WBC count, heart rate, ICP variance, GCS, APACHE II, and epileptiform discharges. The multiclass prediction model selected GCS, admission APACHE II, periodic discharges, lacosamide, and rebleeding as significant predictors; model performance exceeded 80% accuracy in predicting poor or good outcome and exceeded 70% accuracy for predicting intermediate outcome.

CONCLUSIONS:

Variance in early physiologic data can impact patient outcomes and may serve as targets for early goal-directed therapy. Electronically retrievable features such as ICP, glucose levels, and electroencephalography patterns should be considered in disease severity and risk stratification scores.

KEYWORDS:

EEG; Machine learning; Neurologic outcomes; Predictive analytics; Subarachnoid hemorrhage

PMID:
28983801
PMCID:
PMC5886829
[Available on 2019-04-01]
DOI:
10.1007/s12028-017-0466-8

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