Hussam Abou-Al-Shaar1, Mohammed A Azab1, Michael Karsy1, Jian Guan1, William T Couldwell1, Randy L Jensen2. 1. Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA. 2. Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA. Electronic address: neuropub@hsc.utah.edu.
Abstract
OBJECTIVE: Understanding costs of microsurgical or radiosurgical treatment of intracranial meningioma may offer direction in reducing health care costs and establishing cost-effective algorithms. We used the Value Driven Outcomes database, which identifies cost drivers and tracks changes over time, to evaluate cost drivers for management of intracranial meningioma. METHODS: A single-center, retrospective cohort of patients undergoing microsurgery or radiosurgery of intracranial meningiomas from July 2011 to April 2017 was analyzed. Patient and tumor characteristics, subcategory costs, and potential cost drivers were analyzed within each treatment modality. RESULTS: Of 268 intracranial meningiomas, 198 were treated with microsurgery and 70 with stereotactic radiosurgery. Facility costs were the largest contributor to total costs for microsurgery (59.7%), whereas imaging costs were the largest contributor to stereotactic radiosurgery total costs (98.2%). Patients with non-skull base tumors had larger tumors (3.7 ± 1.9 cm vs. 2.7 ± 1.2 cm, P = 0.0001) and were more likely to undergo microsurgery (81.7% vs. 55.2%) than patients with skull base tumors. Univariate analysis suggested that American Society of Anesthesiologists status, length of stay, discharge disposition, and maximal tumor size impacted cost during microsurgery (P = 0.001), but only length of stay (P = 0.0001) and maximal tumor size (P = 0.01) were drivers of total costs on multivariate analysis. For radiosurgery, age significantly affected cost on univariate (P = 0.001) and multivariate (P = 0.003) analysis. CONCLUSIONS: Implementing protocols to reduce facility usage and imaging would mitigate total costs and improve resource utilization while maintaining high-quality patient care. Additional cost-effectiveness studies evaluating patients with true therapeutic equipoise will provide further guidance in these efforts.
OBJECTIVE: Understanding costs of microsurgical or radiosurgical treatment of intracranial meningioma may offer direction in reducing health care costs and establishing cost-effective algorithms. We used the Value Driven Outcomes database, which identifies cost drivers and tracks changes over time, to evaluate cost drivers for management of intracranial meningioma. METHODS: A single-center, retrospective cohort of patients undergoing microsurgery or radiosurgery of intracranial meningiomas from July 2011 to April 2017 was analyzed. Patient and tumor characteristics, subcategory costs, and potential cost drivers were analyzed within each treatment modality. RESULTS: Of 268 intracranial meningiomas, 198 were treated with microsurgery and 70 with stereotactic radiosurgery. Facility costs were the largest contributor to total costs for microsurgery (59.7%), whereas imaging costs were the largest contributor to stereotactic radiosurgery total costs (98.2%). Patients with non-skull base tumors had larger tumors (3.7 ± 1.9 cm vs. 2.7 ± 1.2 cm, P = 0.0001) and were more likely to undergo microsurgery (81.7% vs. 55.2%) than patients with skull base tumors. Univariate analysis suggested that American Society of Anesthesiologists status, length of stay, discharge disposition, and maximal tumor size impacted cost during microsurgery (P = 0.001), but only length of stay (P = 0.0001) and maximal tumor size (P = 0.01) were drivers of total costs on multivariate analysis. For radiosurgery, age significantly affected cost on univariate (P = 0.001) and multivariate (P = 0.003) analysis. CONCLUSIONS: Implementing protocols to reduce facility usage and imaging would mitigate total costs and improve resource utilization while maintaining high-quality patient care. Additional cost-effectiveness studies evaluating patients with true therapeutic equipoise will provide further guidance in these efforts.
Authors: Austin Gamblin; Jason G Garry; Herschel W Wilde; Jared C Reese; Brandon Sherrod; Michael Karsy; Jian Guan; Janel Mortenson; Alexandra Flis; Jeffrey P Rosenbluth; Erica Bisson; Andrew Dailey Journal: Cureus Date: 2019-09-24
Authors: Jai Deep Thakur; Regin Jay Mallari; Alex Corlin; Samantha Yawitz; Amalia Eisenberg; John Rhee; Walavan Sivakumar; Howard Krauss; Neil Martin; Chester Griffiths; Garni Barkhoudarian; Daniel F Kelly Journal: PLoS One Date: 2022-07-28 Impact factor: 3.752