Literature DB >> 32860455

Development and validation of a predictive model for the prognosis in aneurysmal subarachnoid hemorrhage.

Xiang Lai1, Wenbo Zhang1, Min Ye1, Xiaoping Liu1, Xingda Luo1.   

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.
© 2020 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC.

Entities:  

Keywords:  aneurysmal subarachnoid hemorrhage; discriminant analysis; prognosis; validation

Mesh:

Year:  2020        PMID: 32860455      PMCID: PMC7755773          DOI: 10.1002/jcla.23542

Source DB:  PubMed          Journal:  J Clin Lab Anal        ISSN: 0887-8013            Impact factor:   3.124


  44 in total

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4.  Development and validation of a predictive model for the prognosis in aneurysmal subarachnoid hemorrhage.

Authors:  Xiang Lai; Wenbo Zhang; Min Ye; Xiaoping Liu; Xingda Luo
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  4 in total

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