Predicting inpatient complications from cerebral aneurysm clipping: the Nationwide Inpatient Sample 2005–2009

Clinical article

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  • 2 Appledore Neurosurgery Group, Portsmouth Hospital, Portsmouth;
  • | 4 Geisel School of Medicine at Dartmouth, Hanover;
  • | 1 Section of Neurosurgery, and
  • | 3 Departments of Medicine and
  • | 6 Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire; and
  • | 5 Section of Vascular Surgery, SUNY Stony Brook, New York
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Object

Precise delineation of individualized risks of morbidity and mortality is crucial in decision making in cerebrovascular neurosurgery. The authors attempted to create a predictive model of complications in patients undergoing cerebral aneurysm clipping (CAC).

Methods

The authors performed a retrospective cohort study of patients who had undergone CAC in the period from 2005 to 2009 and were registered in the Nationwide Inpatient Sample (NIS) database. A model for outcome prediction based on preoperative individual patient characteristics was developed.

Results

Of the 7651 patients in the NIS who underwent CAC, 3682 (48.1%) had presented with unruptured aneurysms and 3969 (51.9%) with subarachnoid hemorrhage. The respective inpatient postoperative risks for death, unfavorable discharge, stroke, treated hydrocephalus, cardiac complications, deep vein thrombosis, pulmonary embolism, and acute renal failure were 0.7%, 15.3%, 5.3%, 1.5%, 1.3%, 0.6%, 2.0%, and 0.1% for those with unruptured aneurysms and 11.5%, 52.8%, 5.5%, 39.2%, 1.7%, 2.8%, 2.7%, and 0.8% for those with ruptured aneurysms. 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 using the area under the receiver operating characteristic curve, and it was found to have good discrimination.

Conclusions

The featured model can provide individualized estimates of the risks of postoperative complications based on preoperative conditions and can potentially be used as an adjunct in decision making in cerebrovascular neurosurgery.

Abbreviations used in this paper:

ARF = acute renal failure; AVM = arteriovenous malformation; CAC = cerebral aneurysm clipping; CAD = coronary artery disease; CHF = congestive heart failure; COPD = chronic obstructive pulmonary disease; CRF = chronic renal failure; DVT = deep vein thrombosis; NIS = Nationwide Inpatient Sample; PE = pulmonary embolism; PVD = peripheral vascular disease; SAH = subarachnoid hemorrhage; SE = standard error.

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