Electronic Health Data Predict Outcomes After Aneurysmal Subarachnoid Hemorrhage
| Autor: | Jennifer A. Kim, Kathryn L. O’Connor, Eva N. Postma, Sahar F. Zafar, Lucas M. Fleuren, Sophia Bechek, Siddharth Biswal, Durga Jonnalagadda, M. Brandon Westover, Apeksha Shenoy, Emily J. Boyle, Aman B. Patel, Mouhsin S. Shafi, Eric Rosenthal |
|---|---|
| Přispěvatelé: | Graduate School, Neurosurgery |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Adult
Male medicine.medical_specialty Subarachnoid hemorrhage Multivariate analysis medicine.medical_treatment Glasgow Outcome Scale Critical Care and Intensive Care Medicine Logistic regression Article Machine Learning 03 medical and health sciences 0302 clinical medicine Internal medicine Outcome Assessment Health Care medicine Electronic Health Records Humans Intracranial pressure Aged Retrospective Studies Mechanical ventilation Models Statistical APACHE II business.industry 030208 emergency & critical care medicine Retrospective cohort study Electroencephalography Intracranial Aneurysm Middle Aged Subarachnoid Hemorrhage medicine.disease Prognosis Patient Discharge Anesthesia Female Neurology (clinical) business 030217 neurology & neurosurgery |
| Zdroj: | Neurocritical care, 28. Humana Press |
| ISSN: | 1541-6933 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink