Tag: mortality rate

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Patterns of C-2 fracture in the elderly: comparison of etiology, treatment, and mortality among speci c fracture types

J Neurosurg Spine 27:494–500, 2017

Previous studies have focused on Type II odontoid fractures and have failed to report on the effect of other C-2 fracture types on treatment and outcome. The purpose of this study was to compare patient characteristics, cause of injury, predisposing factors to fracture, treatments, and mortality rates among C-2 fracture types in a cohort of elderly patients 70 years of age and older.

METHODS A retrospective cohort study design was used. Patients who sustained a C-2 fracture between 2002 and 2011 and who were admitted to the authors’ Level 1 trauma center were identified using the Discharge Abstract Database and the International Statistical Classification of Diseases and Related Health Problems (ICD-10) code S12.1. Fractures were classified as odontoid Type I, II, or III; hangman’s; C-2 complex (hangman’s appearance on sagittal images, Type III odontoid on coronal cuts); and other (miscellaneous). Age, sex, predisposing factors to falls, cause of injury, treatment, presence of autofusion in the subaxial cervical spine, and mortality rates were compared between fracture patterns.

RESULTS One hundred forty-one patients were included; their mean age was 82 years. Fractures included Type II odontoid (57%), complex (19%), Type III odontoid (11%), hangman’s (8%), and other (5%). Falls from a standing height accounted for 47% of injuries, and 65% of patients had ≥ 3 risk factors for falls. Subaxial autofusion was more common in odontoid fractures (p = 0.002). Treatment was mainly nonoperative (p < 0.0001). The 1-year mortality rate was 27%. Four patients died of spinal cord injury.

CONCLUSIONS Although not as common as Type II odontoid fractures, other C-2 fractures including hangman’s, complex, and Type III odontoid fractures accounted for close to half of the injuries in the study cohort. There were few differences between the fracture types with respect to cause of injury, predisposing factors, or mortality rate. However, surgical treatment was more common for Type II odontoid fractures.

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Complications following cranioplasty: incidence and predictors in 348 cases

cranioplasty

J Neurosurg 123:182–188, 2015

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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