Moderated Poster

Poster, Podium & Video Sessions

MP86-03: A Predictive Risk Stratification Model for Delirium After Major Urologic Cancer Surgeries

Monday, May 15
3:30 PM - 5:30 PM
Location: BCEC: Room 156

Presentation Authors: Albert Ha*, Ross Krasnow, Tammy Hsieh, Adam Kibel, Boston, MA, James Rudolph, Providence, RI, Benjamin Chung, Stanford, CA, Steven Chang, Boston, MA

Introduction: Post-operative delirium is a common complication in the elderly and contributes to increased healthcare costs, mortality, cognitive decline, and hospital length of stay. No definitive pre-operative risk prediction model for patients undergoing major urologic cancer surgeries is currently available.

Methods: Using the Premier Hospital Database, we retrospectively identified patients who had undergone radical prostatectomy (RP), radical nephrectomy (RN), partial nephrectomy (PN), and radical cystectomy (RC) from 2003 to 2013. Post-operative delirium was defined using International Classification of Disease, Ninth Revision (ICD-9) codes, as well as post-operative use of antipsychotics, sitters, and restraints. Potential pre-operative risk factors of delirium were extrapolated from patient, hospital, and surgical characteristics. A pre-operative delirium risk prediction score was developed from our multivariate model. Its performance was quantified using Receiver Operating Characteristic (ROC) analysis. All analyses were survey-weighted and clustered by hospitals to achieve estimates generalizable to the US population.

Results: We identified 165,387 patients representing a weighted total of 1,097,355 patients from 490 hospitals who had undergone RP, RN, PN, or RC. Our model revealed a wide range of clinical and demographic factors that significantly contribute to the risk for post-operative delirium (Figure A). Our delirium risk score was associated with the development of post-operative delirium (Odds Ratio: 1.31, 95% CI 1.29-1.33, p <0.001, Figure B), and it demonstrated good discrimination in the prediction of delirium (Receiver Operator Characteristic [ROC] area = 0.76, 95% CI, 0.76-0.77, Figure C). The ability of the risk score to predict delirium was consistent across surgical subgroups, and the risk score was also predictive of the duration of delirium (Incidence Rate Ratio = 1.07, 95% CI 1.04-1.11, p<0.001).

Conclusions: The preliminary results of our pre-operative delirium risk prediction tool are promising given its consistency with published delirium risk factors and ease of use. Further validation of this model will shed insight about its clinical utility to identify patients at high-risk of post-operative delirium who may benefit from early therapeutic intervention.

Source Of Funding: None

Albert Ha

Harvard Medical School

Albert Ha is currently a fourth year medical student at Harvard Medical School and will begin his Urology residency at New York Presbyterian Columbia University Medical Center in June 2017. Albert grew up in sunny Los Angeles, CA and attended college at Duke University, where he graduated magna cum laude, Phi Beta Kappa, and with department distinction as a Biology/Biochemistry major. Following graduation, Albert worked as a high school Biology teacher in Los Angeles under the Teach for America program. In his spare time, Albert enjoys practicing Tae Kwon Do, road cycling, swimming, and collecting dessert wine/craft beer.


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MP86-03: A Predictive Risk Stratification Model for Delirium After Major Urologic Cancer Surgeries

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