Literature DB >> 30869562

Prediction of Outcome After Aneurysmal Subarachnoid Hemorrhage.

Carlina E van Donkelaar1, Nicolaas A Bakker1, Jaqueline Birks2, Nic J G M Veeger3, Jan D M Metzemaekers1, Andrew J Molyneux4, Rob J M Groen1, J Marc C van Dijk1.   

Abstract

Background and Purpose- Early prediction of clinical outcome after aneurysmal subarachnoid hemorrhage (aSAH) is still lacking accuracy. In this observational cohort study, we aimed to develop and validate an accurate bedside prediction model for clinical outcome after aSAH, to aid decision-making at an early stage. Methods- For the development of the prediction model, a prospectively kept single-center cohort of 1215 aSAH patients, admitted between 1998 and 2014, was used. For temporal validation, a prospective cohort of 224 consecutive aSAH patients from the same center, admitted between 2015 and 2017, was used. External validation was performed using the ISAT (International Subarachnoid Aneurysm Trial) database (2143 patients). Primary outcome measure was poor functional outcome 2 months after aSAH, defined as modified Rankin Scale score 4-6. The model was constructed using multivariate regression analyses. Performance of the model was examined in terms of discrimination and calibration. Results- The final model included 4 predictors independently associated with poor outcome after 2 months: age, World Federation of Neurosurgical Societies grade after resuscitation, aneurysm size, and Fisher grade. Temporal validation showed high discrimination (area under the receiver operating characteristic curve, 0.90; 95% CI, 0.85-0.94), external validation showed fair to good discrimination (area under the receiver operating characteristic curve, 0.73; 95% CI, 0.70-0.76). The model showed satisfactory calibration in both validation cohorts. The SAFIRE grading scale was derived from the final model: size of the aneurysm, age, Fisher grade, world federation of neurosurgical societies after resuscitation. Conclusions- The SAFIRE grading scale is an accurate, generalizable, and easily applicable model for early prediction of clinical outcome after aSAH.

Entities:  

Keywords:  aneurysm; calibration; regression analysis; resuscitation; subarachnoid hemorrhage

Mesh:

Year:  2019        PMID: 30869562     DOI: 10.1161/STROKEAHA.118.023902

Source DB:  PubMed          Journal:  Stroke        ISSN: 0039-2499            Impact factor:   7.914


  15 in total

1.  Prediction of Outcome Using Quantified Blood Volume in Aneurysmal SAH.

Authors:  W E van der Steen; H A Marquering; L A Ramos; R van den Berg; B A Coert; A M M Boers; M D I Vergouwen; G J E Rinkel; B K Velthuis; Y B W E M Roos; C B L M Majoie; W P Vandertop; D Verbaan
Journal:  AJNR Am J Neuroradiol       Date:  2020-05-14       Impact factor: 3.825

2.  Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction.

Authors:  Vittorio Stumpo; Victor E Staartjes; Giuseppe Esposito; Carlo Serra; Luca Regli; Alessandro Olivi; Carmelo Lucio Sturiale
Journal:  Acta Neurochir Suppl       Date:  2022

3.  Severe cognitive impairment in aneurysmal subarachnoid hemorrhage: Predictors and relationship to functional outcome.

Authors:  Joseph R Geraghty; Melissa N Lara-Angulo; Milen Spegar; Jenna Reeh; Fernando D Testai
Journal:  J Stroke Cerebrovasc Dis       Date:  2020-06-20       Impact factor: 2.136

4.  Predictors of Prolonged Mechanical Ventilation Among Patients with Aneurysmal Subarachnoid Hemorrhage After Microsurgical Clipping.

Authors:  Ching-Hua Huang; Shih-Ying Ni; Hsueh-Yi Lu; Abel Po-Hao Huang; Lu-Ting Kuo
Journal:  Neurol Ther       Date:  2022-02-20

5.  Easily created prediction model using deep learning software (Prediction One, Sony Network Communications Inc.) for subarachnoid hemorrhage outcomes from small dataset at admission.

Authors:  Masahito Katsuki; Yukinari Kakizawa; Akihiro Nishikawa; Yasunaga Yamamoto; Toshiya Uchiyama
Journal:  Surg Neurol Int       Date:  2020-11-06

6.  Skeletal muscle atrophy and myosteatosis are not related to long-term aneurysmal subarachnoid hemorrhage outcome.

Authors:  Yuanyuan Shen; Stef Levolger; Abdallah H A Zaid Al-Kaylani; Maarten Uyttenboogaart; Carlina E van Donkelaar; J Marc C Van Dijk; Alain R Viddeleer; Reinoud P H Bokkers
Journal:  PLoS One       Date:  2022-03-04       Impact factor: 3.240

7.  Easily Created Prediction Model Using Automated Artificial Intelligence Framework (Prediction One, Sony Network Communications Inc., Tokyo, Japan) for Subarachnoid Hemorrhage Outcomes Treated by Coiling and Delayed Cerebral Ischemia.

Authors:  Masahito Katsuki; Shin Kawamura; Akihito Koh
Journal:  Cureus       Date:  2021-06-16

8.  Initial pupil status is a strong predictor for in-hospital mortality after aneurysmal subarachnoid hemorrhage.

Authors:  Marius M Mader; Andras Piffko; Nora F Dengler; Franz L Ricklefs; Lasse Dührsen; Nils O Schmidt; Jan Regelsberger; Manfred Westphal; Stefan Wolf; Patrick Czorlich
Journal:  Sci Rep       Date:  2020-03-16       Impact factor: 4.379

9.  Body mass index and leptin levels in serum and cerebrospinal fluid in relation to delayed cerebral ischemia and outcome after aneurysmal subarachnoid hemorrhage.

Authors:  Michael Veldeman; Miriam Weiss; Tim Philipp Simon; Anke Hoellig; Hans Clusmann; Walid Albanna
Journal:  Neurosurg Rev       Date:  2021-04-17       Impact factor: 3.042

10.  Outcome prediction in aneurysmal subarachnoid hemorrhage: a comparison of machine learning methods and established clinico-radiological scores.

Authors:  Nora Franziska Dengler; Vince Istvan Madai; Meike Unteroberdörster; Esra Zihni; Sophie Charlotte Brune; Adam Hilbert; Michelle Livne; Stefan Wolf; Peter Vajkoczy; Dietmar Frey
Journal:  Neurosurg Rev       Date:  2021-01-20       Impact factor: 3.042

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