Open AccessDevelopment and validation of a predictive model for the prognosis in aneurysmal subarachnoid hemorrhage
Author(s) -
Lai Xiang,
Zhang Wenbo,
Ye Min,
Liu Xiaoping,
Luo Xingda
Publication year - 2020
Publication title -
journal of clinical laboratory analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 50
eISSN - 1098-2825
pISSN - 0887-8013
DOI - 10.1002/jcla.23542
Subject(s) - medicine , receiver operating characteristic , glasgow coma scale , logistic regression , modified rankin scale , subarachnoid hemorrhage , discriminant function analysis , predictive value , surgery , machine learning , ischemic stroke , ischemia , computer science
Abstract Background This study was to conduct a predictive model for the prognosis of aneurysmal subarachnoid hemorrhage (aSAH) and validate the clinical data. Methods A total of 235 aSAH patients were enrolled in this study, dividing into the favorable or poor prognosis groups based on Modified Rankin Scale (mRS) at 3 months postoperatively. Multivariate analysis was assessed using binary Logistic regression and Fisher discriminant analysis. The receiver operating characteristic (ROC) curve was used to determine the cut‐off value. Results Our findings showed that the high Glasgow Coma Scale (GCS) score 24‐hour after surgery reduced the risk of poor prognosis, and the surgical clipping and elevated neutrophil‐lymphocyte ratio (NLR) increased the risk of poor prognosis. The discriminant function was V = 0.881 × GCS score − 0.523 × NLR − 0.422 × therapeutic approach, and V = −0.689 served as a cut‐off value. When V ≥ −0.689, the good prognosis was considered among these patients with aSAH. The correctness for predicting the prognostic outcomes by self‐validation was 85.11%. Conclusion This predictive model established by a discriminant analysis is a useful tool for predicting the prognostic outcomes of aSAH patients, which may help clinicians identify patients at high risk for poor prognosis and optimize treatment after surgery.