The emerging technique of computer-assisted jaw reconstruction (CAJR), which facilitates preoperative surgery simulation and transfers the virtual plan to a real operation, significantly impacted conventional surgical approaches (1, 2). Studies have reported the benefits of CAJR compared to conventional freehand jaw reconstruction, including reductions of ischemia time, operation time, and related costs, and improvement of reconstruction accuracy (3–10).

With the increasing popularization of CAJR surgery, the needs of standardizing surgical training and optimizing patient outcomes are urgent. The cumulative sum (CUSUM) control chart is a sequential analysis technique in statistical quality control, typically used for monitoring change detection (11). Now, the concept of CUSUM analysis has been used by surgeons to assess the learning curve in complex surgeries, such as robot-assisted surgery and endoscopic surgery (12–14). It allows surgeons to precisely detect potential imperfections and then improve surgical outcomes. However, very sparse data are available on learning curves of free flap jaw reconstruction (15–18). To our best knowledge, there is no study reporting the learning curve in the practice of CAJR.

Our team started performing CAJR using 3D-printed patient-specific surgical plates (PSSP) in December 2016. We reported our first experience in 2018 and indicated that CAJR with PSSP is feasible, safe, and precise (19). This study aimed to analyze the surgical outcomes and learning curve for the first 58 consecutive CAJR cases using 3D-printed PSSP performed by a single surgical team in a single institution.

This study was approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (No. UW 16-315), registered in ClinicalTrials.gov with a No. of NCT03057223 and in The University of Hong Kong Clinical Trials Centre with a study identifier of HKUCTR-2113 (www.HKUCTR.com).

A total of 58 consecutive patients who underwent CAJR using PSSP were included. Baseline characteristics of total cases are shown in Table 2 .

The concept of learning curve in medicine was commonly defined as the time taken and/or the number of procedures an average surgeon needs to be able to perform a procedure independently with a reasonable outcome (30). As modern surgical training is always beset by problems like increased working hours, inadequate training facilities, lack of resources, and medicolegal issues (31), the understanding of learning curves on surgical procedures can make the training more efficient and standard. However, a study reviewed assessments of learning curve in health technologies and indicated that learning curves were rarely evaluated formally with a proper study design and statistical method (32). As a new tool, the CUSUM technique for learning curve has been introduced and proposed as a useful instrument in the field of surgical training, which allows quantitative monitoring of individual performance during the learning process (28, 33).

Nowadays, computer-assisted surgery is increasingly utilized in reconstructive surgery, while no literature has reported the learning curve for this surgical procedure. In the present study, we performed CUSUM analysis on the learning curve of CAJR using 3D-printed PSSP by a single surgical team. Although operation time is the most common determinant for the learning curve on surgery, the assessment on the basis of a single parameter might be simplistic (12, 34). We tried to analyze trends in multidimensional variables in the present study.

For the primary outcome, our study revealed a three-stage learning pattern of CAJR with the use of PSSP, including initial learning, plateau, and overlearning. The first stage is initial learning. The learning curve regarding surgical success stabilized after 11 cases, and first achieved proficiency after 23 cases. The second stage is plateau. When surgeons felt competent in this surgical procedure, the significantly increased proportions of tumor staging and concomitant surgery suggested that the complexity of surgery after 23 cases was higher than the earlier cases. The concomitant surgery, simultaneous dental implant placement with the aid of a “three-in-one” patient-specific surgical guide (35), was also a technically challenging procedure. Accordingly, a fluctuation of proficiency was observed at this stage. The final stage is overlearning. A steady proficiency was completely achieved after 35 cases. Overall, the surgical failures in our cohort were infrequent (5.2%), and no performance of the surgeon below the unacceptable boundary line was observed, which suggests that application of 3D-printed PSSP in CAJR with free flap is safe and feasible.

For the secondary outcomes, we found that although the four graphs of CUSUM analysis had different curve shapes, a similar ascending trend occurred from 23–31 cases to 43–46 cases. The increased complexity after 23 cases could also reasonably explain the increased total operation time and intraoperative blood loss. As a result, we actually observed two phenomena of “learning curve” in the CUSUM analysis graphs of the total operation time, length of hospital stay, and bone graft deviation. In the first learning curve (from 1st to 28th–31st cases), the inflexion points occurred between 8 and 17 cases, from which operation time, length of hospital stay, and bone graft deviation started to diminish. In the second learning curve (from 28th–31st to 58th cases), stabilizations of the operation time, length of hospital stay, and bone graft deviation occurred between 43 and 46 cases.

The main novelty of this study was using the CUSUM technique to analyze the learning curve of CAJR with PSSP. We utilized a chosen level of surgical failure rate as strict as two times the currently acceptable level (11.1%) to determine proficiency, which was one strength of our study (28). The cohort of patients from a prospective clinical trial without dropout was also the strength. Since no study described the learning curve of CAJR surgery, our work can provide the first quantitative assessment on this topic to the literature. It should be noted that our surgical team had previous experience in conventional free flap jaw reconstruction surgery and also CAJR without the use of PSSP (36), which could help shorten the learning process. Learning curve varies from different surgeons, but its stage pattern may be similar. Among three stages, the occurrence and persistence of a plateau depend on many reasons, such as the interference by previous experience, the nature of the task, and the motivation (37). Finding out the correct cause and getting over this period will be an important portion in surgical training.

There are certain limitations of the present study that need to be addressed. First, the CUSUM analysis included different diseases (malignant and benign tumors), surgeries (maxillary and mandibular reconstructions), and free flaps (fibular and iliac flaps), which influenced the homogeneity of enrolled cases. A multi-center clinical trial, with a big enough sample size of the same disease and surgery, might be needed to overcome this limitation. However, it will lead to other limitations of different expertise of multiple surgical teams and hospital setting. Second, post-operative oral function and quality of life are important outcomes for jaw reconstruction. However, there are a lot of confounding factors influencing these outcomes, which will need a well-designed prospective randomized control trial for further investigation. Thus, we did not include them in the present analysis. Last, the learning curve of time spent on preoperative preparation, such as virtual surgical planning and PSSP design, was not reported in this study. Previously, we reported that the time spent on virtual surgery and plate design was 18.8 ± 13.2 h, and the time taken for 3D printing, post-processing, and product delivery was 162.9 ± 55.2 h (38). The consecutive data of the time spent on preoperative preparation with a large sample size would provide a better understanding of the whole learning process of CAJR using PSSP.

A three-stage learning pattern of CAJR with the use of PSSP was revealed, including initial learning, plateau, and overlearning, which may guide the clinical teaching and training of CAJR using PSSP. Based on CUSUM analysis, surgical proficiency was obtained after 23 cases. Stabilization of total operation time, length of hospital stay, and bone graft deviation occurred after 8–17 cases. Our study provided evidence to guide the training of this new surgical procedure to ensure patient safety and clinical outcomes.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster. The patients/participants provided their written informed consent to participate in this study.

W-yZ and Y-xS: study design. Y-xS: study supervision. W-yZ, WC, JP and W-fY: data collection. W-yZ and MW: manuscript preparation, statistical analysis, and data interpretation. All authors contributed to the article and approved the submitted version.

The study was funded by the Health and Medical Research Fund (No. 08192096), Hong Kong SAR, Guangdong Science and Technology Department (No. 2019A050516001), University Research Committee Platform Technology Funding 2021, and HKU Seed Fund for Translational and Applied Research (202010160032).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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