Failure mode and effect analysis for linear accelerator‐based paraspinal stereotactic body radiotherapy. Issue 12 (28th October 2021)

• Record Type: Journal Article
• Authors: Lee, Sangkyu; Lovelock, Dale Michael; Kowalski, Alex; Chapman, Kate; Foley, Robert; Gil, Mary; Pastrana, Gerri; Higginson, Daniel S.; Yamada, Yoshiya; Zhang, Lei; Mechalakos, James; Yorke, Ellen
• Is Part Of: Journal of applied clinical medical physics. Volume 22:Issue 12(2021)
• Page Start: 87
• Page End: 96
• Publication Date: 2021-10-28
• Subjects: failure modes and effects analysis -- quality assurance -- root cause analysis -- spinal metastasis -- stereotactic body radiotherapy; Medical physics -- Periodicals; Clinical medicine -- Periodicals; Health Physics; Clinical Medicine; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; 610.153
• DOI: 10.1002/acm2.13455
• Languages: English
• ISSNs: 1526-9914

Abstract

Abstract: Introduction: Paraspinal stereotactic body radiotherapy (SBRT) involves risks of severe complications. We evaluated the safety of the paraspinal SBRT program in a large academic hospital by applying failure modes and effects analysis. Methods: The analysis was conducted by a multidisciplinary committee (two therapists, one dosimetrist, four physicists, and two radiation oncologists). The paraspinal SBRT workflow was segmented into four phases (simulation, treatment planning, delivery, and machine quality assurance (QA)). Each phase was further divided into a sequence of sub‐processes. Potential failure modes (PFM) were identified from each subprocess and scored in terms of the frequency of occurrence, severity and detectability, and a risk priority number (RPN). High‐risk PFMs were identified based on RPN and were studied for root causes using fault tree analysis. Results: Our paraspinal SBRT process was characterized by eight simulations, 11 treatment planning, nine delivery, and two machine QA sub‐processes. There were 18, 29, 19, and eight PFMs identified from simulation, planning, treatment, and machine QA, respectively. The median RPN of the PFMs was 62.9 for simulation, 68.3 for planning, 52.9 for delivery, and 22.0 for machine QA. The three PFMs with the highest RPN were: previous radiotherapy outside the institution is not accurately evaluated (RPN: 293.3), incorrect registration between diagnostic magnetic resonance imaging and simulation computed tomography causing incorrect contours (273.0), and undetected patient movement before ExacTrac baseline (217.8). Remedies to the high RPN failures were implemented, including staff education, standardized magnetic resonance imaging acquisition parameters, and an image fusion process, and additional QA on beam steering. Conclusions: A paraspinal SBRT workflow in a large clinic was evaluated using a multidisciplinary and systematic risk analysis, which led to feasible solutions to key root causes. Treatment planning was a major source of PFMs that systematically affect the safety and quality of treatments. Accurate evaluation of external treatment records remains a challenge.