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Peirong Huang, Siddharth Narendran, Felipe Pereira, Shinichi Fukuda, Yosuke Nagasaka, Ivana Apicella, Praveen Yerramothu, Kenneth M. Marion, Xiaoyu Cai, Srinivas R. Sadda, Bradley D. Gelfand, Jayakrishna Ambati; The Learning Curve of Murine Subretinal Injection Among Clinically Trained Ophthalmic Surgeons. Trans. Vis. Sci. Tech. 2022;11(3):13. doi: https://doi.org/10.1167/tvst.11.3.13.
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Subretinal injection (SRI) in mice is widely used in retinal research, yet the learning curve (LC) of this surgically challenging technique is unknown.
To evaluate the LC for SRI in a murine model, we analyzed training data from three clinically trained ophthalmic surgeons from 2018 to 2020. Successful SRI was defined as either the absence of retinal pigment epithelium (RPE) degeneration after phosphate buffered saline injection or the presence of RPE degeneration after Alu RNA injection. Multivariable survival-time regression models were used to evaluate the association between surgeon experience and success rate, with adjustment for injection agents, and to calculate an approximate case number to achieve a 95% success rate. Cumulative sum (CUSUM) analyses were performed and plotted individually to monitor each surgeon's simultaneous performance.
Despite prior microsurgery experience, the combined average success rate of the first 50 cases in mice was only 27%. The predicted SRI success rate did not reach a plateau above 95% until approximately 364 prior cases. Using the 364 training cases as a cutoff point, the predicted probability of success for cases 1 to 364 was 65.38%, and for cases 365 to 455 it was 99.32% (P < 0.0001). CUSUM analysis showed an initial upward slope and then remained within the decision intervals with an acceptable success rate set at 95% in the late stage.
This study demonstrates the complexity and substantial LC for successful SRI in mice with high confidence. A systematic training system could improve the reliability and reproducibility of SRI-related experiments and improve the interpretation of experimental results using this technique.
Our prediction model and monitor system allow objective quantification of technical proficiency in the field of subretinal drug delivery and gene therapy for the first time, to the best of our knowledge.
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