{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T09:00:42Z","timestamp":1768554042432,"version":"3.49.0"},"reference-count":45,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T00:00:00Z","timestamp":1647302400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Center of Excellence of Neurovascular Intervention and Surgery and North-Eastern Stroke Research Group, Khon Kaen University"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Objective<\/jats:title>\n<jats:p>A 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.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>Data on patients with aSAH hospitalized in a university\u2019s 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\u2019s criteria, which maximized the summation between sensitivity or true positive rate and the specificity or true negative rate.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>Patients (n, 121) with poor outcomes (modified Rankin Scale, mRS score, 4\u20136) 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\u20133). Six variables, including age &gt;70 years, diabetes mellitus, World Federation of Neurosurgical Societies (WFNS) scaling of IV-V, modified Fisher grading of 3\u20134, 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\u201310, the cut point at score 3 yielded the maximum Youden\u2019s index (0.527), which resulted in a sensitivity of 77.7% and specificity of 75.0%.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>A simple 0\u201310 scoring system on six risk factors for poor outcomes was validated for aSAH and should be advocated for use in limited resource settings.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0264844","type":"journal-article","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T17:34:56Z","timestamp":1647365696000},"page":"e0264844","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Preoperative predictors of poor outcomes\u00a0in Thai patients with aneurysmal subarachnoid 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