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  • Author or Editor: Kartik Kesavabhotla x
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John A. Boockvar, Apostolos J. Tsiouris, Christoph P. Hofstetter, Ilhami Kovanlikaya, Sherese Fralin, Kartik Kesavabhotla, Stephen M. Seedial, Susan C. Pannullo, Theodore H. Schwartz, Philip Stieg, Robert D. Zimmerman, Jared Knopman, Ronald J. Scheff, Paul Christos, Shankar Vallabhajosula and Howard A. Riina


The authors assessed the safety and maximum tolerated dose of superselective intraarterial cerebral infusion (SIACI) of bevacizumab after osmotic disruption of the blood-brain barrier (BBB) with mannitol in patients with recurrent malignant glioma.


A total of 30 patients with recurrent malignant glioma were included in the current study.


The authors report no dose-limiting toxicity from a single dose of SIACI of bevacizumab up to 15 mg/kg after osmotic BBB disruption with mannitol. Two groups of patients were studied; those without prior bevacizumab exposure (naïve patients; Group I) and those who had received previous intravenous bevacizumab (exposed patients; Group II). Radiographic changes demonstrated on MR imaging were assessed at 1 month postprocedure. In Group I patients, MR imaging at 1 month showed a median reduction in the area of tumor enhancement of 34.7%, a median reduction in the volume of tumor enhancement of 46.9%, a median MR perfusion (MRP) reduction of 32.14%, and a T2-weighted/FLAIR signal decrease in 9 (47.4%) of 19 patients. In Group II patients, MR imaging at 1 month showed a median reduction in the area of tumor enhancement of 15.2%, a median volume reduction of 8.3%, a median MRP reduction of 25.5%, and a T2-weighted FLAIR decrease in 0 (0%) of 11 patients.


The authors conclude that SIACI of mannitol followed by bevacizumab (up to 15 mg/kg) for recurrent malignant glioma is safe and well tolerated. Magnetic resonance imaging shows that SIACI treatment with bevacizumab can lead to reduction in tumor area, volume, perfusion, and T2-weighted/FLAIR signal.

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Jessica S. Haber, Kartik Kesavabhotla, Malte Ottenhausen, Imithri Bodhinayake, Marc J. Dinkin, Alan Z. Segal, Young M. Lee and John A. Boockvar

Cavernous sinus cavernous hemangiomas in pregnancy are extremely rare lesions. The precise management of these lesions remains unknown. The authors present a case of a cavernous hemangioma in pregnancy, centered within the cavernous sinus that underwent postpartum involution without surgical intervention.

A 34-year-old pregnant patient (gravida 1, para 0) presented to an otolaryngologist with persistent headache and left-sided facial pain and numbness in the V1 distribution. While being treated for sinusitis, her symptoms progressed to include a left-sided oculomotor palsy and abducens palsy. Magnetic resonance imaging without contrast revealed an expansile mass within the left cavernous sinus consistent with a cavernous hemangioma. The patient was evaluated by a neurosurgeon who recommended close follow-up and postpartum imaging without surgical intervention. Although the lesion enlarged during pregnancy, the patient was able to undergo an uncomplicated cesarean section at 37 weeks. All facial and ocular symptoms resolved by 9 months postpartum, and MRI showed a decrease in lesion size and reduced mass effect. The authors conclude that nonsurgical management may be a viable approach in patients who have an onset or exacerbation of symptoms associated with cavernous sinus cavernous hemangiomas during pregnancy because postpartum involution may negate the need for surgical intervention.

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Grace Y. Lai, Paul J. Devlin, Kartik Kesavabhotla, Jonathan D. Rich, Duc T. Pham, Matthew B. Potts and Babak S. Jahromi


As the use of left ventricular assist devices (LVADs) has expanded, cerebrovascular complications have become an increasing source of morbidity and mortality in this population. Intracranial hemorrhage (ICH) in particular remains a devastating complication in patients who undergo LVAD placement with no defined management guidelines. The authors therefore reviewed surgical and anticoagulation management and outcomes of patients with LVADs who presented to their institution with ICH.


This retrospective cohort study assessed outcomes of patients who underwent LVAD placement at a single institution between 2007 and 2016 and in whom imaging demonstrated ICH.


During the study period, 281 patients had a HeartMate II or HeartWare LVAD placed. There were 37 episodes of ICH (recurrent in 3 cases). ICHs were categorized as intraparenchymal hemorrhage (IPH; n = 22, 59%), subdural hemorrhage (SDH; n = 6, 16%), and subarachnoid hemorrhage (SAH; n = 9, 24%). Neurosurgical intervention was deemed necessary in 27.3%, 66.7%, and 0% of patients with IPH, SDH, and SAH, respectively; overall survival > 30 days for each type of hemorrhage was 41%, 83%, and 89%, respectively. No patients had LVAD thrombus as a result of reversal of anticoagulation. Combined with prior reports, good outcomes are seen more often following surgery for SDH than for IPH (57% vs 7%, p = 0.004) in patients who underwent VAD placement.


Patients with IPH who undergo LVAD placement have poor outcomes regardless of anticoagulation reversal or neurosurgical intervention, whereas those with SDH may have good outcomes with medical and surgical intervention, and those with SAH appear to do well without anticoagulation reversal or surgery. When needed, anticoagulation reversal was not associated with an increase in LVAD thrombosis in this series.

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Benjamin S. Hopkins, Jonathan T. Yamaguchi, Roxanna Garcia, Kartik Kesavabhotla, Hannah Weiss, Wellington K. Hsu, Zachary A. Smith and Nader S. Dahdaleh


Unplanned preventable hospital readmissions within 30 days are a great burden to patients and the healthcare system. With an estimated $41.3 billion spent yearly, reducing such readmission rates is of the utmost importance. With the widespread adoption of big data and machine learning, clinicians can use these analytical tools to understand these complex relationships and find predictive factors that can be generalized to future patients. The object of this study was to assess the efficacy of a machine learning algorithm in the prediction of 30-day hospital readmission after posterior spinal fusion surgery.


The authors analyzed the distribution of National Surgical Quality Improvement Program (NSQIP) posterior lumbar fusions from 2011 to 2016 by using machine learning techniques to create a model predictive of hospital readmissions. A deep neural network was trained using 177 unique input variables. The model was trained and tested using cross-validation, in which the data were randomly partitioned into training (n = 17,448 [75%]) and testing (n = 5816 [25%]) data sets. In training, the 17,448 training cases were fed through a series of 7 layers, each with varying degrees of forward and backward communicating nodes (neurons).


Mean and median positive predictive values were 78.5% and 78.0%, respectively. Mean and median negative predictive values were both 97%, respectively. Mean and median areas under the curve for the model were 0.812 and 0.810, respectively. The five most heavily weighted inputs were (in order of importance) return to the operating room, septic shock, superficial surgical site infection, sepsis, and being on a ventilator for > 48 hours.


Machine learning and artificial intelligence are powerful tools with the ability to improve understanding of predictive metrics in clinical spine surgery. The authors’ model was able to predict those patients who would not require readmission. Similarly, the majority of predicted readmissions (up to 60%) were predicted by the model while retaining a 0% false-positive rate. Such findings suggest a possible need for reevaluation of the current Hospital Readmissions Reduction Program penalties in spine surgery.