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A Patient Risk Model to Determine the Optimal Frequency of Quality Control for Radiotherapy Machines

M Ma*, J Dai, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Beijing,100021,China

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: quality control (QC) frequencies for radiotherapy machines are fixed such as daily, weekly or monthly, which is suggested in technical reports by experienced experts. In this study, a patient risk model was built to determine the optimal frequencies of QC activities for a specific machine.

Methods: risk models have been successfully implemented in clinical chemistry. For machine QC activities, the model had five steps: 1) the QC rules and the number of subgroups (n) were selected through the power function graph, and error types and measurement modes of machines were analyzed to choose the proper risk assessment formula; 2) the average run length (ARL) was obtained through computer simulation; 3) the increase in the probability of producing an unacceptable patient results (?P?) was computed; 4) the expected increase in the number of unacceptable patient results (E(Nu )) was calculated with the risk assessment formula; 5) the optimal QC frequency was determined as the minimum integer value of the expected number of patient samples tested between QC tests (E(NB )), which satisfied E(Nu )<1. The model was implemented on a Tomotherapy machine to predict the QC frequency of the output constancy. The individual control chart(I-Chart) was used to evaluate the effectiveness of the proposed model.

Results: to the power function graphs, n was set to 5, and the 1_3s control rule was selected. As the system error increased, ARL decreased whereas ?P? increased. The optimal frequency was every 21 patients. The I-Chart showed that this frequency could detect the machine failure earlier compared with the conventional daily frequency. The model could monitor whether the Tomotherapy machine was under good condition and predict the time to adjust the machine.

Conclusion: patient risk model is a quantitative method to determine the optimal QC frequency and improve the cost-efficiency of QC activities.

Download ePoster [PDF]

Funding Support, Disclosures, and Conflict of Interest: This work was supported by the National Natural Science Foundation of China (11875320).

Keywords

Quality Control, Quality Assurance, Optimization

Taxonomy

TH- External Beam- Photons: Development (new technology and techniques)

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