NCOG-06. A NOVEL ONLINE CALCULATOR PREDICTING SHORT-TERM POSTOPERATIVE OUTCOMES IN METASTATIC BRAIN CANCER PATIENTS. (9th November 2020)

• Record Type: Journal Article
• Authors: Khalafallah, Adham; Jimenez, Adrian; Patel, Palak; Huq, Sakibul; Azmeh, Omar; Mukherjee, Debraj
• Is Part Of: Neuro-oncology. Volume 22(2020)Supplement 2
• Page Start: ii130
• Page End: ii130
• Publication Date: 2020-11-09
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noaa215.545
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: BACKGROUND: Establishing predictors of hospital length of stay (LOS), discharge deposition, and total hospital charges is essential to providing high-quality, value-based care. Though previous research has investigated these outcomes for patients with metastatic brain tumors, there are currently no tools that synthesize such research findings and allow for prediction of these outcomes on a patient-by-patient basis. OBJECTIVE: We sought to develop a prediction calculator that uses patient demographic and clinical information to predict extended hospital length of stay, nonroutine discharge disposition, and high total hospital charges for patients with metastatic brain tumors. METHODS: Patients undergoing surgery for metastatic brain tumors at a single academic institution were analyzed (2017-2019). Multivariate logistic regression was used to identify independent predictors of extended LOS (> 8 days), nonroutine discharge, and high total hospital charges (> $45, 660.00). p < 0.05 was considered statistically significant. C-statistics and the Hosmer-Lemeshow test were used to assess model discrimination and calibration, respectively. RESULTS: A total of 222 patients were included in our analysis, with a mean age of 62.2 years. The majority of patients were male (52.7%) and Caucasian (76.6%). Our models predicting extended LOS, nonroutine discharge, and high hospital charges had optimism-corrected c-statistics > 0.7, and all three models demonstrated adequate calibration (p > 0.05). The final models are available as an online calculator (https://neurooncsurgery.shinyapps.io/mets_brain_cancer_calculator/ ). CONCLUSIONS: Our models predicting postoperative outcomes allow for individualized risk-estimation for patients following surgery for metastatic brain cancer. Our results may be useful in helping clinicians to provide high-value care and to ensure optimal patient outcomes