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Erica F. Bisson, Praveen V. Mummaneni, John Knightly, Mohammed Ali Alvi, Anshit Goyal, Andrew K. Chan, Jian Guan, Michael Biase, Andrea Strauss, Steven Glassman, Kevin Foley, Jonathan R. Slotkin, Eric Potts, Mark Shaffrey, Christopher I. Shaffrey, Regis W. Haid Jr., Kai-Ming Fu, Michael Y. Wang, Paul Park, Anthony L. Asher, and Mohamad Bydon

patients lost to follow-up at 2 years with those who successfully underwent follow-up. Methods Cohort For this study, the Quality Outcomes Database (QOD), formerly known as the National Neurosurgical Quality and Outcomes Database (N 2 QOD), was queried for patients undergoing surgery for Meyerding grade I degenerative lumbar spondylolisthesis between July 1, 2014, and June 30, 2016. The QOD is a prospective multiinstitutional registry, established in 2012 with the objective of assessing risk-adjusted expected morbidity and 30-day and 12-month patient-reported outcomes

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Oren Berkowitz, Douglas Kondziolka, David Bissonette, Ajay Niranjan, Hideyuki Kano, and L. Dade Lunsford

patient outcomes. We created a new computer database designed to track patients, record procedure parameters, and monitor outcomes. Rather than allow patient information to be housed in different places, we sequestered all medical records and imaging studies within the physical space of the Center for Image-Guided Neurosurgery at the UPMC. This center also provided working space for chart reviews and computer data entry. A prime requirement at the outset was that no chart could leave this space unless signed out to an individual and specific location. Methods The

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Shyam J. Kurian, Yagiz Ugur Yolcu, Jad Zreik, Mohammed Ali Alvi, Brett A. Freedman, and Mohamad Bydon

might be underpowered compared to large database studies, and they may be missing additional factors that would impact the outcomes. Stemming from this idea, there have also been extensive studies investigating predictors of increased readmission rates by using various databases. 7 , 8 However, this brings us back to the question of applicability, raising concerns regarding the optimal way to do the assessment. Ideally, tools using predictive models would be needed to comfortably apply big data analyses to institutions. Still, the differences in using big data versus

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Yimo Lin, I-Wen Pan, Rory R. Mayer, and Sandi Lam

R esearch conducted using large administrative data sets has been increasing over recent decades. 25 These data sources have the advantages of large sample sizes and wide geographic ranges, but they also have weaknesses including limited granularity of clinical detail and challenges with confounders. The Kids’ Inpatient Database (KID; ) contains information on inpatient hospitalization discharges. Clinical and resource use information are available from discharge abstracts created by hospitals for billing

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Kunal Varshneya, Rayyan T. Jokhai, Parastou Fatemi, Martin N. Stienen, Zachary A. Medress, Allen L. Ho, John K. Ratliff, and Anand Veeravagu

procedures, particularly in bone-correction surgeries, 18 but none of the studies specifically evaluated ASD surgery. As such, the objective of this study was to identify risk factors associated with increased odds of reoperation within 2 years of primary ASD surgery in Medicare patients. We hypothesized that high comorbidity burden, obesity, tobacco use, and osteoporosis may increase the odds of reoperation in patients with ASD who receive Medicare. Methods Data Source This study used MarketScan Medicare Supplemental database (Truven Health Analytics) records from

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Janet M. Legare, Chengxin Liu, Richard M. Pauli, Adekemi Yewande Alade, S. Shahrukh Hashmi, Jeffrey W. Campbell, Cory J. Smid, Peggy Modaff, Mary Ellen Little, David F. Rodriguez-Buritica, Maria Elena Serna, Jaqueline T. Hecht, Julie E. Hoover-Fong, and Michael B. Bober

institutions were entered into a Research Electronic Data Capture (REDCap) database. 17 The total achondroplasia cohort is referred to as the Primary Achondroplasia Cohort (PAC). The analyses presented herein focus on CMD, the date and patient age CMD was performed, indications, and outcomes. For these analyses, CMD was defined as surgical enlargement of the foramen magnum alone or enlargement of the foramen magnum plus C1 or C1 and C2 laminectomy. Surgical indications were categorized as follows: acute life-threatening event, central apnea by polysomnography (PSG

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John R. W. Kestle

I n this issue, Parker et al. 4 report their analysis of data from a large multi-institutional source called the Premier Perspective Database. This database includes billing and discharge information from inpatient stays and outpatient visits at more than 600 US hospitals. More than 10,000 adults and 1770 children undergoing shunt insertion are included, and antibiotic-impregnated catheters (AICs) were used in about 10% (8.9% of adults, 12.9% of children). The authors found reduced infection rates with (vs without) AICs (adults 2.2% vs 3.6%; children 2

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Yakov Gologorsky, John J. Knightly, Yi Lu, John H. Chi, and Michael W. Groff

transferred to medical records. The processing of information in medical records, which is then entered into administrative databases for later analysis, follows a typical sequence in most hospitals. Trained medical coders abstract the clinical information in the medical record and the discharge summary. Numerical codes for diagnoses, procedures, and complications are assigned according to the ICD-9-CM. In our hospital, 1 primary and as many as 19 secondary codes are assigned for each hospitalization. These codes are then collated into a discharge abstract, which is

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William C. Newman, Paul S. Kubilis, and Brian L. Hoh

C omorbidities have a significant effect on patient outcomes. Accounting for this effect is especially important in retrospective reviews of large databases; overpowered studies are at risk for finding significant results because of inaccurate patient risk stratification. We previously created and validated a neurovascular comorbidities index (NCI) for risk stratification of patients with an unruptured intracranial aneurysm and demonstrated that our model’s ability to predict patient outcomes was statistically significantly improved over that of the routinely

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Dominic A. Harris, Danielle E. Sorte, Sandi K. Lam, and Andrew P. Carlson

,659 pediatric patients (0.03%) with blunt trauma. 18 In other studies, the incidence has ranged from 0.3% to 0.9%. 1 , 8 , 16 It is unclear if the lower incidence in pediatric trauma is related to underdiagnosis due to lower incidence of screening in the pediatric population or actual decreased incidence due to protective physiological characteristics in children. 16 , 18 Given the rare instances of cerebrovascular injuries, a large national database can provide a larger sample size to better delineate the incidence and risk factors for BCVI in the pediatric population. The