Abstract
OBJECT
The factors that contribute to periprocedural complications following cranioplasty, including patient-specific and surgery-specific factors, need to be thoroughly assessed. The aim of this study was to evaluate risk factors that predispose patients to an increased risk of cranioplasty complications and death.
METHODS
The authors conducted a retrospective review of all patients at their institution who underwent cranioplasty following craniectomy for stroke, subarachnoid hemorrhage, epidural hematoma, subdural hematoma, and trauma between January 2000 and December 2011. The following predictors were tested: age, sex, race, diabetic status, hypertensive status, tobacco use, reason for craniectomy, urgency status of the craniectomy, graft material, and location of cranioplasty. The cranioplasty complications included reoperation for hematoma, hydrocephalus postcranioplasty, postcranioplasty seizures, and cranioplasty graft infection. A multivariate logistic regression analysis was performed. Confidence intervals were calculated as the 95% CI.
RESULTS
Three hundred forty-eight patients were included in the study. The overall complication rate was 31.32% (109 of 348). The mortality rate was 3.16%. Predictors of overall complications in multivariate analysis were hypertension (OR 1.92, CI 1.22-3.02), increasing age (OR 1.02, CI 1.00-1.04), and hemorrhagic stroke (OR 3.84, CI 1.93-7.63). Predictors of mortality in multivariate analysis were diabetes mellitus (OR 7.56, CI 1.56-36.58), seizures (OR 7.25, CI 1.238-42.79), bifrontal cranioplasty (OR 5.40, CI 1.20-24.27), and repeated surgery for hematoma evacuation (OR 13.00, CI 1.51-112.02). Multivariate analysis was also applied to identify the variables that affect the development of seizures, the need for reoperation for hematoma evacuation, the development of hydrocephalus, and the development of infections.
CONCLUSIONS
The authors' goal was to provide the neurosurgeon with predictors of morbidity and mortality that could be incorporated in the clinical decision-making algorithm. Control of a patient's risk factors and early recognition of complications may help practitioners avoid the exhaustive list of complications.
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