J NeuroIntervent Surg 6:342-348 doi:10.1136/neurintsurg-2013-010815
  • Hemorrhagic stroke
  • Original research

A predictive model of outcomes during cerebral aneurysm coiling

Editor's Choice
  1. Clifford Eskey1,4,6
  1. 1Section of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
  2. 2Appledore Neurosurgery Group, Portsmouth Hospital, Portsmouth, New Hampshire, USA
  3. 3Department of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
  4. 4Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
  5. 5Section of Vascular Surgery, SUNY Stony Brook, Stony Brook, New York, USA
  6. 6Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
  1. Correspondence to Dr K Bekelis, Section of Neurosurgery, Dartmouth-Hitchcock Medical Center, One Medical Center Dr, Lebanon, NH 03756, USA; kbekelis{at}
  • Received 9 May 2013
  • Revised 30 May 2013
  • Accepted 31 May 2013
  • Published Online First 4 July 2013


Background Benchmarking of complications is necessary in the context of the developing path to accountable care. We attempted to create a predictive model of negative outcomes in patients undergoing cerebral aneurysm coiling (CACo).

Methods We performed a retrospective cohort study involving patients who underwent CACo from 2005 to 2009 and who were registered in the Nationwide Inpatient Sample database. A model for outcome prediction based on individual patient characteristics was developed.

Results Of the 10 607 patients undergoing CACo, 6056 presented with unruptured aneurysms (57.1%) and 4551 with subarachnoid hemorrhage (42.9%). The respective inpatient postoperative risks were 0.3%, 5.7%, 1.8%, 0.8%, 0.5%, 0.2%, 1.9%, and 0.1% for unruptured aneurysms, and 13.8%, 52.8%, 4.9%, 36.7%, 1%, 2.9%, 2.3%, and 0.8% for ruptured aneurysms for death, unfavorable discharge, stroke, treated hydrocephalus, cardiac complications, deep vein thrombosis, pulmonary embolism, and acute renal failure. Multivariate analysis identified risk factors independently associated with the above outcomes. A validated model for outcome prediction based on individual patient characteristics was developed. The accuracy of the model was estimated by the area under the receiver operating characteristic curve, and it was found to have good discrimination.

Conclusions The presented model can aid in the prediction of the incidence of postoperative complications, and can be used as an adjunct in tailoring the treatment of cerebral aneurysms.



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