Abstract
ObjectiveA scoring system for aneurysmal subarachnoid hemorrhage (aSAH) is useful for guiding treatment decisions, especially in urgent-care limited settings. This study developed a simple algorithm of clinical conditions and grading to predict outcomes in patients treated by clipping or coiling.MethodsData on patients with aSAH hospitalized in a university’s neurovascular center in Thailand from 2013 to 2018 were obtained for chart review. Factors associated with poor outcomes evaluated at one year were identified using a stepwise logistic regression model. For each patient, the rounded regression coefficients of independent risk factors were linearly combined into a total score, which was assessed for its performance in predicting outcomes using receiver operating characteristic analysis. An appropriate cutoff point of the scores for poor outcomes was based on Youden’s criteria, which maximized the summation between sensitivity or true positive rate and the specificity or true negative rate.ResultsPatients (n, 121) with poor outcomes (modified Rankin Scale, mRS score, 4–6) had a significantly higher proportion of old age, underlying hypertension, diabetes and chronic kidney disease, high clinical severity grading, preoperative rebleeding, and hydrocephalus than those (n, 336) with good outcomes (mRS score, 0–3). Six variables, including age >70 years, diabetes mellitus, World Federation of Neurosurgical Societies (WFNS) scaling of IV-V, modified Fisher grading of 3–4, rebleeding, and hydrocephalus, were identified as independent risk factors and were assigned a score weight of 2, 1, 2, 1, 3 and 1, respectively. Among the total possible scores ranging from 0–10, the cut point at score 3 yielded the maximum Youden’s index (0.527), which resulted in a sensitivity of 77.7% and specificity of 75.0%.ConclusionA simple 0–10 scoring system on six risk factors for poor outcomes was validated for aSAH and should be advocated for use in limited resource settings.
Highlights
Aneurysmal subarachnoid hemorrhage accounts for 5–10% of hemorrhagic strokes and is associated with a high mortality rate [1–3]
The rounded regression coefficients of independent risk factors were linearly combined into a total score, which was assessed for its performance in predicting outcomes using receiver operating characteristic analysis
Among the total possible scores ranging from 0–10, the cut point at score 3 yielded the maximum Youden’s index (0.527), which resulted in a sensitivity of 77.7% and specificity of 75.0%
Summary
Aneurysmal subarachnoid hemorrhage (aSAH) accounts for 5–10% of hemorrhagic strokes and is associated with a high mortality rate [1–3]. Ten percent of these cases may die before reaching a hospital [4], and a third will remain disabled despite treatment [5]. Several studies have identified factors affecting functional dependency or mortality in patients with aSAH [9–12]. Predicting the outcomes of the disease contributes insightful guidance for clinicians during treatment decision-making. It can provide invaluable information for patients and relatives in navigating the treatment or no-treatment dilemma. The WFNS scale, which is measured after adequate neurological resuscitation, is a good predictor of treatment outcomes [14, 15]. Other factors associated with poor outcomes include old age, hyperglycemia, hydrocephalus, and high-grade Fisher scores [1, 2, 4, 9, 18]
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