Timothy J. Yee, Yamaan S. Saadeh, Michael J. Strong, Ayobami L. Ward, Clay M. Elswick, Sudharsan Srinivasan, Paul Park, Mark E. Oppenlander, Daniel E. Spratt, William C. Jackson, and Nicholas J. Szerlip
Decompression with instrumented fusion is commonly employed for spinal metastatic disease. Arthrodesis is typically sought despite limited knowledge of fusion outcomes, high procedural morbidity, and poor prognosis. This study aimed to describe survival, fusion, and hardware failure after decompression and fusion for spinal metastatic disease.
The authors retrospectively examined a prospectively collected, single-institution database of adult patients undergoing decompression and instrumented fusion for spinal metastases. Patients were followed clinically until death or loss to follow-up. Fusion was assessed using CT when performed for oncological surveillance at 6-month intervals through 24 months postoperatively. Estimated cumulative incidences for fusion and hardware failure accounted for the competing risk of death. Potential risk factors were analyzed with univariate Fine and Gray proportional subdistribution hazard models.
One hundred sixty-four patients were identified. The mean age ± SD was 62.2 ± 10.8 years, 61.6% of patients were male, 98.8% received allograft and/or autograft, and 89.6% received postoperative radiotherapy. The Kaplan-Meier estimate of median survival was 11.0 months (IQR 3.5–37.8 months). The estimated cumulative incidences of any fusion and of complete fusion were 28.8% (95% CI 21.3%–36.7%) and 8.2% (95% CI 4.1%–13.9%). Of patients surviving 6 and 12 months, complete fusion was observed in 12.5% and 16.1%, respectively. The estimated cumulative incidence of hardware failure was 4.2% (95% CI 1.5–9.3%). Increasing age predicted hardware failure (HR 1.2, p = 0.003).
Low rates of complete fusion and hardware failure were observed due to the high competing risk of death. Further prospective, case-control studies incorporating nonfusion instrumentation techniques may be warranted.
Noah S. Cutler, Sudharsan Srinivasan, Bryan L. Aaron, Sharath Kumar Anand, Michael S. Kang, David B. Altshuler, Thomas C. Schermerhorn, Todd C. Hollon, Cormac O. Maher, and Siri Sahib S. Khalsa
Normal percentile growth charts for head circumference, length, and weight are well-established tools for clinicians to detect abnormal growth patterns. Currently, no standard exists for evaluating normal size or growth of cerebral ventricular volume. The current standard practice relies on clinical experience for a subjective assessment of cerebral ventricular size to determine whether a patient is outside the normal volume range. An improved definition of normal ventricular volumes would facilitate a more data-driven diagnostic process. The authors sought to develop a growth curve of cerebral ventricular volumes using a large number of normal pediatric brain MR images.
The authors performed a retrospective analysis of patients aged 0 to 18 years, who were evaluated at their institution between 2009 and 2016 with brain MRI performed for headaches, convulsions, or head injury. Patients were excluded for diagnoses of hydrocephalus, congenital brain malformations, intracranial hemorrhage, meningitis, or intracranial mass lesions established at any time during a 3- to 10-year follow-up. The volume of the cerebral ventricles for each T2-weighted MRI sequence was calculated with a custom semiautomated segmentation program written in MATLAB. Normal percentile curves were calculated using the lambda-mu-sigma smoothing method.
Ventricular volume was calculated for 687 normal brain MR images obtained in 617 different patients. A chart with standardized growth curves was developed from this set of normal ventricular volumes representing the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles. The charted data were binned by age at scan date by 3-month intervals for ages 0–1 year, 6-month intervals for ages 1–3 years, and 12-month intervals for ages 3–18 years. Additional percentile values were calculated for boys only and girls only.
The authors developed centile estimation growth charts of normal 3D ventricular volumes measured on brain MRI for pediatric patients. These charts may serve as a quantitative clinical reference to help discern normal variance from pathologic ventriculomegaly.
Badih J. Daou, Siri Sahib S. Khalsa, Sharath Kumar Anand, Craig A. Williamson, Noah S. Cutler, Bryan L. Aaron, Sudharsan Srinivasan, Venkatakrishna Rajajee, Kyle Sheehan, and Aditya S. Pandey
Hydrocephalus and seizures greatly impact outcomes of patients with aneurysmal subarachnoid hemorrhage (aSAH); however, reliable tools to predict these outcomes are lacking. The authors used a volumetric quantitative analysis tool to evaluate the association of total aSAH volume with the outcomes of shunt-dependent hydrocephalus and seizures.
Total hemorrhage volume following aneurysm rupture was retrospectively analyzed on presentation CT imaging using a custom semiautomated computer program developed in MATLAB that employs intensity-based k-means clustering to automatically separate blood voxels from other tissues. Volume data were added to a prospectively maintained aSAH database. The association of hemorrhage volume with shunted hydrocephalus and seizures was evaluated through logistic regression analysis and the diagnostic accuracy through analysis of the area under the receiver operating characteristic curve (AUC).
The study population comprised 288 consecutive patients with aSAH. The mean total hemorrhage volume was 74.9 ml. Thirty-eight patients (13.2%) developed seizures. The mean hemorrhage volume in patients who developed seizures was significantly higher than that in patients with no seizures (mean difference 17.3 ml, p = 0.01). In multivariate analysis, larger hemorrhage volume on initial CT scan and hemorrhage volume > 50 ml (OR 2.81, p = 0.047, 95% CI 1.03–7.80) were predictive of seizures. Forty-eight patients (17%) developed shunt-dependent hydrocephalus. The mean hemorrhage volume in patients who developed shunt-dependent hydrocephalus was significantly higher than that in patients who did not (mean difference 17.2 ml, p = 0.006). Larger hemorrhage volume and hemorrhage volume > 50 ml (OR 2.45, p = 0.03, 95% CI 1.08–5.54) were predictive of shunt-dependent hydrocephalus. Hemorrhage volume had adequate discrimination for the development of seizures (AUC 0.635) and shunted hydrocephalus (AUC 0.629).
Hemorrhage volume is an independent predictor of seizures and shunt-dependent hydrocephalus in patients with aSAH. Further evaluation of aSAH quantitative volumetric analysis may complement existing scales used in clinical practice and assist in patient prognostication and management.