Our final abstract of the day will be presented by Arca Malala, who is a medical student. The title of the abstract, Glioblastoma Readmission Risk Score, Estimating 30-Day Readmission Risk After Glioblastoma Resection. The author, Block, Prateek Argawal, Nicholas Goel, Joseph Durgin, Mohit Jayrami, Eileen Maloney-Wilensky, Donald O'Rourke, Michael Sean Grady, Khalil Abdullah, and Steven Bram. So good afternoon, everyone. Thanks for everyone sticking around for the last presentation. So today I'm going to talk about the glioblastoma readmission risk score. It's basically a simple additive tool to estimate the risk of 30-day readmission after glioblastoma resection. So without further ado, we have no conflicts of interest to report. So basically, 30-day readmission is being used as an increasing quality metric and is tied to reimbursement for many common conditions, and it's now being used in many neurosurgical situations. What's interesting is that these readmissions may or may not be preventable, depending on where you look in the literature. So really understanding the pre-operative and post-operative risk factors and patient selection is a crucial part of selecting the right patient to do surgery on and then choosing which patients to follow up on closely to ensure that they aren't readmitted and that this metric isn't affected in overall patient quality of care. So in this current study, basically we had a cohort of 666 unique resections from our institution from 467 different patients, and we wanted to determine which factors were predictive of 30-day readmission, and then second, use that information to develop an easy-to-use risk score calculator. So basically we took our entire patient cohort, collected a variety of information, demographics, medical history, and sort of things about the operation itself and post-operative complications. We first performed univariate logistic regression just to see which factors in univariate analysis were relevant to readmission. And then in order to develop an additive score, we basically used a genetic learning algorithm to select which factors to include, did a multivariate regression, and then converted that to a score. So in univariate logistic regression, the risk factors were recurrent resection, post-operative surgical site infection as sort of would be expected as a seven-fold increase in readmission, venous pneumonic events, VP shunts, and interestingly, discharge to a rehab facility when compared to discharge to home. Obviously a lot of factors go into that decision to discharge the patient. Other sort of in that variable, other factors were discharge to SNF. We were probably underpowered to detect a difference in discharge to SNF just given the patient population we're using, but that's another possibility. And then in terms of protective factors, expectedly, having a higher KPS is protective against readmission and interestingly, MGMT, methylation, and also if post-op chemoradiation is indicated. In terms of our demographic factors, BMI and hypertension and smoking actually in univariate analysis were not significant. They had sort of, you can call it a trend toward significance, but did not meet that criteria. And the location of the tumor, IDH status, EGFR, variety of other tumor markers, extended resection, and the days in the ICU or the hospital were not associated with increased 30-day readmission. So then we developed this readmission risk score and basically the genetic learning algorithm sort of suggested that these factors would be the most predictive in 30-day readmission. So as we saw in univariate analysis, MGMT, methylation, and post-op radiation were both protective. More consistent with what we would think, smoking status was a great risk factor for causing readmission, as was increased BMI and then lower post-op KPS. And then post-operative events like SSIs, pulmonary embolism, or EP shunts would also cause readmission. And what's particularly interesting is that you can make a lot of, you can predict a significant portion of readmission risk based off preoperative risk factors alone. So if someone has a BMI of greater than 30, is a smoker, and has a KPS less than 50, per our data, they have a greater than 90% chance of being readmitted versus even a more typical readmission of like a BMI of 25 to 30, a KPS of 50 to 70 has about a 48% risk of readmission. So with regard to the post-operative risk factors, a lot of these, especially the PE and the surgical site infection, aren't things that necessarily can be controlled in all situations but they do identify patients that may merit further follow-up prior to discharge. So basically with the score, you know, first of all it's this, it's a routine simple score that you can kind of use in the office or in the inpatient setting. Preoperatively it's a great way to screen and counsel patients and then post-operatively is to follow up with these patients. The risk factors that we identified were smoking, BMI, and KPS post-op, various post-op events and then the MGMT methylation, the chemoradiation were interesting that they were protective. To our knowledge, this is the largest series in this particular area to date and the first time this has been converted to a score so we are looking to publish this soon and hopefully have this in routine practice. And thank you everyone for sticking around and thank you to all the advanced practice providers out there for all the amazing work that you do. Thank you. Any questions?

Glioblastoma Readmission Risk Score

30-day readmission risk

Glioblastoma resection

Predictive factors

Risk score calculator