Basic surveillance parameters improve the prediction of delayed cerebral infarction after aneurysmal subarachnoid hemorrhage

Abstract: Background: To establish a practical risk chart for prediction of delayed cerebral infarction (DCI) after aneurysmal subarachnoid hemorrhage (aSAH) by using information that is available until day 5 after ictus.

Methods: We assessed all consecutive patients with aSAH admitted to our service between September 2008 and September 2015 (n = 417). The data set was randomly split into thirds. Two-thirds were used for model development and one-third was used for validation. Characteristics that were present between the bleeding event and day 5 (i.e., prior to >95% of DCI diagnoses) were assessed to predict DCI by using logistic regression models. A simple risk chart was established and validated.

Results: The amount of cisternal and ventricular blood on admission CT (Hijdra sum score), early sonographic vasospasm (i.e., mean flow velocity of either intracranial artery >160 cm/s until day 5), and a simplified binary level of consciousness score until day 5 were the strongest predictors of DCI. A model combining these predictors delivered a high predictive accuracy [the area under the receiver operating characteristic (AUC) curve of 0.82, Nagelkerke's R2 0.34 in the development cohort]. Validation of the model demonstrated a high discriminative capacity with the AUC of 0.82, Nagelkerke's R2 0.30 in the validation cohort.

Conclusion: Adding level of consciousness and sonographic vasospasm between admission and postbleed day 5 to the initial blood amount allows for simple and precise prediction of DCI. The suggested risk chart may prove useful for selection of appropriate candidates for interventions to prevent DCI

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch
• Anmerkungen: Frontiers in neurology. - 13 (2022) , 774720, ISSN: 1664-2295

Beteiligte, Orts- und Zeitangaben

• Ereignis: Veröffentlichung
• (wo): Freiburg
• (wer): Universität
• (wann): 2022
• Urheber: Csók, István ; Grauvogel, Jürgen ; Scheiwe, Christian ; Bardutzky, Jürgen ; Wehrum, Thomas ; Beck, Jürgen ; Reinacher, Peter ; Rölz, Roland

Weitere Informationen

• DOI: 10.3389/fneur.2022.774720
• URN: urn:nbn:de:bsz:25-freidok-2257066
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 25.03.2025, 13:51 MEZ