Potentially Harmful Ionizing Radiation Exposure from Diagnostic Tests and Medical Procedures in Patients with Aneurysmal Subarachnoid Hemorrhage.
Aneurysmal subarachnoid hemorrhage
Hemorrhagic stroke
Radiation exposure
Subarachnoid hemorrhage
Journal
World neurosurgery
ISSN: 1878-8769
Titre abrégé: World Neurosurg
Pays: United States
ID NLM: 101528275
Informations de publication
Date de publication:
08 2020
08 2020
Historique:
received:
25
02
2020
revised:
26
04
2020
accepted:
27
04
2020
pubmed:
11
5
2020
medline:
18
12
2020
entrez:
11
5
2020
Statut:
ppublish
Résumé
Patients with aneurysmal subarachnoid hemorrhage (aSAH) may have significant potentially harmful ionizing radiation exposure (PHIRE) from diagnostic tests and medical procedures (DTMP) during their initial hospitalization. In this single-center, retrospective, observational study, we evaluated the incidence of PHIRE using all patients with radiographically proven aSAH who survived hospitalization over a 6-year period. Patient data were then used to fit a full logistic regression model, a reduced-variable logistic regression model with least absolute shrinkage and selection operator penalty, and a nonparametric tree-based model. Testing data were then used to calculate each predictive model's accuracy. Of 192 patients included in this study, 69 (35.9%) met criteria for PHIRE. Patients with PHIRE were more likely to have a poor Hunt-Hess Score (40.6% vs. 12.2%, P < 0.0001), a poor modified Fischer Grading Scale score (30.4% vs. 16.3%, P = 0.03), ventriculostomy (91.3% vs. 47.2%, P < 0.0001), vasospasm (81.2% vs. 34.1%, P < 0.0001), and ventriculoperitoneal shunt (31.9% vs. 10.6%, P < 0.001). Parametric PHIRE prediction modeling with a full logistic regression model and reduced-logistic regression modeling with least absolute shrinkage and selection operator penalty demonstrated PHIRE prediction accuracy of 67% and 78% accuracy, respectively. Nonparametric tree-based PHIRE modeling demonstrated a prediction accuracy of 58%. On the basis of our data, PHIRE occurs in approximately 35% of aSAH patients. The reduced-variable logistic regression model had the greatest predictive accuracy for PHIRE. Future studies should validate our findings and predictive models and, if our conclusions hold, further clarification of the risks of PHIRE and methods to reduce PHIRE should be investigated.
Sections du résumé
BACKGROUND
Patients with aneurysmal subarachnoid hemorrhage (aSAH) may have significant potentially harmful ionizing radiation exposure (PHIRE) from diagnostic tests and medical procedures (DTMP) during their initial hospitalization.
METHODS
In this single-center, retrospective, observational study, we evaluated the incidence of PHIRE using all patients with radiographically proven aSAH who survived hospitalization over a 6-year period. Patient data were then used to fit a full logistic regression model, a reduced-variable logistic regression model with least absolute shrinkage and selection operator penalty, and a nonparametric tree-based model. Testing data were then used to calculate each predictive model's accuracy.
RESULTS
Of 192 patients included in this study, 69 (35.9%) met criteria for PHIRE. Patients with PHIRE were more likely to have a poor Hunt-Hess Score (40.6% vs. 12.2%, P < 0.0001), a poor modified Fischer Grading Scale score (30.4% vs. 16.3%, P = 0.03), ventriculostomy (91.3% vs. 47.2%, P < 0.0001), vasospasm (81.2% vs. 34.1%, P < 0.0001), and ventriculoperitoneal shunt (31.9% vs. 10.6%, P < 0.001). Parametric PHIRE prediction modeling with a full logistic regression model and reduced-logistic regression modeling with least absolute shrinkage and selection operator penalty demonstrated PHIRE prediction accuracy of 67% and 78% accuracy, respectively. Nonparametric tree-based PHIRE modeling demonstrated a prediction accuracy of 58%.
CONCLUSIONS
On the basis of our data, PHIRE occurs in approximately 35% of aSAH patients. The reduced-variable logistic regression model had the greatest predictive accuracy for PHIRE. Future studies should validate our findings and predictive models and, if our conclusions hold, further clarification of the risks of PHIRE and methods to reduce PHIRE should be investigated.
Identifiants
pubmed: 32387402
pii: S1878-8750(20)30913-X
doi: 10.1016/j.wneu.2020.04.203
pii:
doi:
Types de publication
Journal Article
Observational Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
e153-e160Informations de copyright
Copyright © 2020 Elsevier Inc. All rights reserved.