A novel risk analysis of clinical reference dosimetry based on failure modes and effects analysis. (February 2019)

• Record Type: Journal Article
• Authors: Ochi, Yusuke; Saito, Akito; Kawahara, Daisuke; Suzuki, Tatsuhiko; Tsuneda, Masato; Tanaka, Sodai; Nishio, Teiji; Ozawa, Shuichi; Murakami, Yuji; Nagata, Yasushi
• Is Part Of: Physica medica. Volume 58(2019)
• Page Start: 59
• Page End: 65
• Publication Date: 2019-02
• Subjects: Clinical reference dosimetry -- Failure modes and effects analysis (FMEA) -- Output drift -- Patient safety; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2019.01.014
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: A linac output drift and number of patients were important parameters to evaluate a risk. Fewer fractions and less measurement frequency resulted in higher RPN. Relative risk was different by approximately twofold between two linacs. Novel risk analysis is able to evaluate a risk of each facility and machines in common. Abstract: Purpose: The output of a linear accelerator (linac) is one of the most important quality assurance (QA) factors in radiotherapy. However, there is no quantitative rationale for frequency and tolerance. The purpose of this study is to develop a novel risk analysis of clinical reference dosimetry based on failure modes and effects analysis (FMEA). Methods: Clinical reference dosimetry data and the daily output data of two linacs (Clinac iX and Clinac 6EX) at Hiroshima University Hospital were analyzed. The analysis involved the number of patients per year for five types of fractionations. Risk priority number (RPN) is defined as the product of occurrence ( O ), severity ( S ), and detectability ( D ) in standard FMEA. In addition, we introduced "severity due to output drifting" (mean output change per day) ( S ′) and the number of patients per year for five types of fractionations ( W ). We calculated the RPN = O × S × D × S ′ × W and quantitatively evaluated the risk for clinical reference dosimetry. Results: Fewer fractions and less output calibration frequency resulted in higher RPN. Since clinical reference dosimetry data has a drift effect, which is missing in human processes, it was essential to use S ′ in addition to standard FMEA. Moreover, the parameter W was important in evaluating interinstitutional QA for clinical reference dosimetry. The relative risk of Clinac 6EX to Clinac iX was different approximately by twofold. Conclusions: We developed a novel index that can quantitatively evaluate risk for clinical reference dosimetry of each facility and machines in common on the basis of FMEA.