Anshit Goyal, Yagiz U. Yolcu, Aakshit Goyal, Panagiotis Kerezoudis, Desmond A. Brown, Christopher S. Graffeo, Sandy Goncalves, Terence C. Burns, and Ian F. Parney
With the revised WHO 2016 classification of brain tumors, there has been increasing interest in imaging biomarkers to predict molecular status and improve the yield of genetic testing for diffuse low-grade gliomas (LGGs). The T2-FLAIR–mismatch sign has been suggested to be a highly specific radiographic marker of isocitrate dehydrogenase (IDH) gene mutation and 1p/19q codeletion status in diffuse LGGs. The presence of T2-FLAIR mismatch indicates a T2-hyperintense lesion that is hypointense on FLAIR with the exception of a hyperintense rim.
In accordance with PRISMA guidelines, we performed a systematic review of the Ovid Medline, Embase, Scopus, and Cochrane databases for reports of studies evaluating the diagnostic performance of T2-FLAIR mismatch in predicting the IDH and 1p/19q codeletion status in diffuse LGGs. Results were combined into a 2 × 2 format, and the following diagnostic performance parameters were calculated: sensitivity, specificity, positive predictive value, negative predictive value, and positive (LR+) and negative (LR−) likelihood ratios. In addition, we utilized Bayes theorem to calculate posttest probabilities as a function of known pretest probabilities from previous genome-wide association studies and the calculated LRs. Calculations were performed for 1) IDH mutation with 1p/19q codeletion (IDHmut-Codel), 2) IDH mutation without 1p/19q codeletion (IDHmut-Noncodel), 3) IDH mutation overall, and 4) 1p/19q codeletion overall. The QUADAS-2 (revised Quality Assessment of Diagnostic Accuracy Studies) tool was utilized for critical appraisal of included studies.
A total of 4 studies were included, with inclusion of 2 separate cohorts from a study reporting testing and validation (n = 746). From pooled analysis of all cohorts, the following values were obtained for each molecular profile—IDHmut-Codel: sensitivity 30%, specificity 73%, LR+ 1.1, LR− 1.0; IDHmut-Noncodel: sensitivity 33.7%, specificity 98.5%, LR+ 22.5, LR− 0.7; IDH: sensitivity 32%, specificity 100%, LR+ 32.1, LR− 0.7; 1p/19q codeletion: sensitivity 0%, specificity 54%, LR+ 0.01, LR− 1.9. Bayes theorem was used to calculate the following posttest probabilities after a positive and negative result, respectively—IDHmut-Codel: 32.2% and 29.4%; IDHmut-Noncodel: 95% and 40%; IDH: 99.2% and 73.5%; 1p/19q codeletion: 0.4% and 35.1%.
The T2-FLAIR–mismatch sign was an insensitive but highly specific marker of IDH mutation and IDHmut-Noncodel profile, although significant exceptions may exist to this finding. Tumors with a positive sign may still be IDHwt or 1p/19q codeleted. These findings support the utility of T2-FLAIR mismatch as an imaging-based biomarker for positive selection of patients with IDH-mutant gliomas.
Anthony L. Asher, John Knightly, Praveen V. Mummaneni, Mohammed Ali Alvi, Matthew J. McGirt, Yagiz U. Yolcu, Andrew K. Chan, Steven D. Glassman, Kevin T. Foley, Jonathan R. Slotkin, Eric A. Potts, Mark E. Shaffrey, Christopher I. Shaffrey, Regis W. Haid Jr., Kai-Ming Fu, Michael Y. Wang, Paul Park, Erica F. Bisson, Robert E. Harbaugh, and Mohamad Bydon
The Quality Outcomes Database (QOD), formerly known as the National Neurosurgery Quality Outcomes Database (N2QOD), was established by the NeuroPoint Alliance (NPA) in collaboration with relevant national stakeholders and experts. The overarching goal of this project was to develop a centralized, nationally coordinated effort to allow individual surgeons and practice groups to collect, measure, and analyze practice patterns and neurosurgical outcomes. Specific objectives of this registry program were as follows: “1) to establish risk-adjusted national benchmarks for both the safety and effectiveness of neurosurgical procedures, 2) to allow practice groups and hospitals to analyze their individual morbidity and clinical outcomes in real time, 3) to generate both quality and efficiency data to support claims made to public and private payers and objectively demonstrate the value of care to other stakeholders, 4) to demonstrate the comparative effectiveness of neurosurgical and spine procedures, 5) to develop sophisticated ‘risk models’ to determine which subpopulations of patients are most likely to benefit from specific surgical interventions, and 6) to facilitate essential multicenter trials and other cooperative clinical studies.” The NPA has launched several neurosurgical specialty modules in the QOD program in the 7 years since its inception including lumbar spine, cervical spine, and spinal deformity and cerebrovascular and intracranial tumor. The QOD Spine modules, which are the primary subject of this paper, have evolved into the largest North American spine registries yet created and have resulted in unprecedented cooperative activities within our specialty and among affiliated spine care practitioners. Herein, the authors discuss the experience of QOD Spine programs to date, with a brief description of their inception, some of the key achievements and milestones, as well as the recent transition of the spine modules to the American Spine Registry (ASR), a collaboration between the American Association of Neurological Surgeons and the American Academy of Orthopaedic Surgeons (AAOS).
Praveen V. Mummaneni, Mohamad Bydon, John J. Knightly, Mohammed Ali Alvi, Yagiz U. Yolcu, Andrew K. Chan, Kevin T. Foley, Jonathan R. Slotkin, Eric A. Potts, Mark E. Shaffrey, Christopher I. Shaffrey, Kai-Ming Fu, Michael Y. Wang, Paul Park, Cheerag D. Upadhyaya, Anthony L. Asher, Luis Tumialan, and Erica F. Bisson
Optimizing patient discharge after surgery has been shown to impact patient recovery and hospital/physician workflow and to reduce healthcare costs. In the current study, the authors sought to identify risk factors for nonroutine discharge after surgery for cervical myelopathy by using a national spine registry.
The Quality Outcomes Database cervical module was queried for patients who had undergone surgery for cervical myelopathy between 2016 and 2018. Nonroutine discharge was defined as discharge to postacute care (rehabilitation), nonacute care, or another acute care hospital. A multivariable logistic regression predictive model was created using an array of demographic, clinical, operative, and patient-reported outcome characteristics.
Of the 1114 patients identified, 11.2% (n = 125) had a nonroutine discharge. On univariate analysis, patients with a nonroutine discharge were more likely to be older (age ≥ 65 years, 70.4% vs 35.8%, p < 0.001), African American (24.8% vs 13.9%, p = 0.007), and on Medicare (75.2% vs 35.1%, p < 0.001). Among the patients younger than 65 years of age, those who had a nonroutine discharge were more likely to be unemployed (70.3% vs 36.9%, p < 0.001). Overall, patients with a nonroutine discharge were more likely to present with a motor deficit (73.6% vs 58.7%, p = 0.001) and more likely to have nonindependent ambulation (50.4% vs 14.0%, p < 0.001) at presentation. On multivariable logistic regression, factors associated with higher odds of a nonroutine discharge included African American race (vs White, OR 2.76, 95% CI 1.38–5.51, p = 0.004), Medicare coverage (vs private insurance, OR 2.14, 95% CI 1.00–4.65, p = 0.04), nonindependent ambulation at presentation (OR 2.17, 95% CI 1.17–4.02, p = 0.01), baseline modified Japanese Orthopaedic Association severe myelopathy score (0–11 vs moderate 12–14, OR 2, 95% CI 1.07–3.73, p = 0.01), and posterior surgical approach (OR 11.6, 95% CI 2.12–48, p = 0.004). Factors associated with lower odds of a nonroutine discharge included fewer operated levels (1 vs 2–3 levels, OR 0.3, 95% CI 0.1–0.96, p = 0.009) and a higher quality of life at baseline (EQ-5D score, OR 0.43, 95% CI 0.25–0.73, p = 0.001). On predictor importance analysis, baseline quality of life (EQ-5D score) was identified as the most important predictor (Wald χ2 = 9.8, p = 0.001) of a nonroutine discharge; however, after grouping variables into distinct categories, socioeconomic and demographic characteristics (age, race, gender, insurance status, employment status) were identified as the most significant drivers of nonroutine discharge (28.4% of total predictor importance).
The study results indicate that socioeconomic and demographic characteristics including age, race, gender, insurance, and employment may be the most significant drivers of a nonroutine discharge after surgery for cervical myelopathy.