Immune checkpoint inhibitors have been successful across several tumor types; however, their efficacy has been uncommon and unpredictable in glioblastomas (GBM), where <10% of patients show long-term responses. To understand the molecular determinants of immunotherapeutic response in GBM, we longitudinally profiled 66 patients, including 17 long-term responders, during standard therapy and after treatment with PD-1 inhibitors (nivolumab or pembrolizumab). Genomic and transcriptomic analysis revealed a significant enrichment of PTEN mutations associated with immunosuppressive expression signatures in non-responders, and an enrichment of MAPK pathway alterations (PTPN11, BRAF) in responders. Responsive tumors were also associated with branched patterns of evolution from the elimination of neoepitopes, as well as differences in T cell clonal diversity and tumor microenvironment profiles. Our study shows that clinical response to anti-PD-1 immunotherapy in GBM is associated with specific molecular alterations, immune expression signatures, and immune infiltration that reflect the tumor’s clonal evolution during treatment.

OBJECTIVEUnplanned 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.METHODSThe 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).RESULTSMean 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.CONCLUSIONSMachine 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.

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OBJECTIVEPress Ganey surveys are common modalities used to assess patient satisfaction scores in an outpatient setting. Despite the existence of data, neurosurgical and orthopedic literature on patient satisfaction following spinal surgery is scarce.METHODSA total of 17,853 patients who underwent spinal procedures at the authors’ institution were analyzed retrospectively for Press Ganey survey participation. Appropriate demographic, surgical, comorbidity, and complication data were collected; 1936 patients had patient satisfaction survey data, and further survey metrics were collected for this subset of patients.RESULTSMale patients, patients with urgent/emergency procedures, and patients with longer length of stay (LOS) were less likely to fill out Press Ganey surveys (OR 0.822, p < 0.001; OR 0.781, p = 0.010; and OR 0.983, p < 0.001, respectively). Posterior approach was negatively associated with Press Ganey participation (OR 0.907, p = 0.055). Patients undergoing fusion procedures were more likely to participate in Press Ganey surveys (OR 1.419, p < 0.001). Of the patients who filled out surveys, there were no positive predictors associated with receiving perfect scores on Press Ganey surveys. High Charlson Comorbidity Index (OR 0.959, p = 0.02), increasing elapsed time since surgery or discharge (OR 0.996, p = 0.03), and increasing LOS (OR 0.965, p = 0.009) were all negatively associated with receiving a perfect score. Patients who underwent a posterior-approach procedure compared with other approaches were less likely to report a low Press Ganey score (OR 0.297, p = 0.046). Patient sex and race did not influence the likelihood of receiving perfect or low Press Ganey scores. Finally, the perceived skill of the surgeon was not a significant predictor for perfect (p > 0.99) or low (p = 0.828) Press Ganey scores.CONCLUSIONSPatient participation in Press Ganey surveys strongly correlated with preoperative factors such as procedure approach and type, as well as postoperative factors such as LOS and complications. No factors were associated with an increased likelihood of receiving a perfect Press Ganey score. Similarly, LOS and time elapsed since surgery to survey completion were significant negative predictors of perfect Press Ganey scores. Skill of surgeon, sex, and race did not correlate with a predictive value for Press Ganey outcomes. In addition, overall comorbid disease burden was found to be a significant negative predictor for high patient satisfaction scores. Further study on predictors of patient satisfaction within spine surgery is needed to better assist physicians in improving the surgical experience for patients.

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