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Educational assessment of intrathoracic and extrathoracic surgical stabilization of rib fractures

Open AccessPublished:October 02, 2022DOI:https://doi.org/10.1016/j.injury.2022.09.064

      Highlights

      • Intrathoracic method of surgical stabilization of rib fractures introduced.
      • Learning curves analysis from time per plate and time per fracture.
      • Cumulative sum learning curve analysis.
      • No inflection point on learning curves for intrathoracic or extrathoracic method of SSRF.

      Abstract

      Background

      Surgical stabilization of rib fractures (SSRF) is being done with increased frequency and new advances. Intrathoracic SSRF is a new less invasive approach compared to the traditional extrathoracic plating procedure. Educational assessment can be done through descriptive analysis of learning curves with operation time used as a proxy measurement for learning. The objective of this level 3 observational cohort study is to assess the learning curve of introducing the intrathoracic method of plating at a large academic medical institution.

      Methods

      Intrathoracic surgical stabilization of rib fractures was introduced at a tertiary trauma center in March of 2019. All patients that received SSRF beginning 11/2017 were included. Patients with abbreviated injury scale score of the head, abdomen, extremity, or face greater than three and days from injury to SSRF greater than 4 were excluded. Operation time was determined from time of incision to completion of skin closure. Time per fracture and time per plate were calculated using total operation time. Learning curves and CUSUM graphs for individual surgeons that had completed in more than 3 SSRF cases were generated using and trended for statistical significance.

      Results

      After exclusions, there were 38 patients with extrathoracic SSRF between November 2017–September 2021 and 24 patients with intrathoracic plating between March 2019–Sept. 2021. There were 5 fellows and 6 residents that performed extrathoracic SSRF. Four fellows and 2 residents performed intrathoracic SSRF. Graphs of time per fracture and time per plate over time produced learning curves without an inflection point for extrathoracic or intrathoracic SSRF in any of the following categories: all surgeries (Figs. 1 and 2), academic year (July to June), individual attending surgeons, fellows, or residents.

      Conclusion

      There was no discernible inflection point on the generated learning curves. Time per plate and time per fracture did not decrease as surgeons gained more experience. Introducing intrathoracic SSRF in a large academic hospital may not need to account for a learning curve adjustment period.

      Keywords

      Introduction

      Rib fractures are common traumatic injuries, occurring in up to 20% of patients who experience thoracic trauma [
      • de Moya M.
      • Nirula R.
      • Biffl W.
      Rib fixation: who, What, When?.
      ]. Historical management of traumatic rib fractures focused on supportive care with pain control and ventilatory support, however continuous movement of ribs with respiration can delay bone healing significantly as compared to other bony injuries which can be completely immobilized [
      • Doben A.R.
      • Schubl S.D.
      • Dudaryk R.
      Surgical rib fixation in traumatic rib fractures: is it warranted?.
      ,
      • Tulay C.M.
      • Yaldiz S.
      • Bilge A.
      Do we really know the duration of pain after rib fracture?.
      ]. Modern advances in available hardware systems and thoracic surgical techniques have made surgical stabilization of rib fractures (SSRF) another viable treatment option. Previous studies have demonstrated significant benefits of SSRF over non-operative management of traumatic rib fractures across multiple clinical outcomes [
      • Choi J.
      • Gomez G.I.
      • Kaghazchi A.
      • Borghi J.A.
      • Spain D.A.
      • Forrester J.D.
      Surgical stabilization of Rib fracture to mitigate pulmonary complication and mortality: a systematic review and bayesian meta-analysis.
      ,
      • Green E.A.
      • Guidry C.
      • Harris C.
      • et al.
      Surgical stabilization of traumatic rib fractures is associated with reduced readmissions and increased survival.
      ,
      • Prins J.T.H.
      • Van Lieshout E.M.M.
      • Ali-Osman F.
      • et al.
      Outcome after surgical stabilization of rib fractures versus nonoperative treatment in patients with multiple rib fractures and moderate to severe traumatic brain injury (CWIS-TBI).
      ,
      • Pieracci F.M.
      • Leasia K.
      • Bauman Z.
      • et al.
      A multicenter, prospective, controlled clinical trial of surgical stabilization of rib fractures in patients with severe, nonflail fracture patterns (chest wall injury society NONFLAIL).
      ,
      • Marasco S.F.
      • Balogh Z.J.
      • Wullschleger M.E.
      • et al.
      Rib fixation in non-ventilator dependent chest wall injuries: a prospective randomized trial.
      ]. Compared to non-operative treatment, SSRF has been shown to decrease hospital and intensive care unit (ICU) length of stay, decrease readmissions and pneumonia risk, and improve post-injury pain and quality of life [
      • Choi J.
      • Gomez G.I.
      • Kaghazchi A.
      • Borghi J.A.
      • Spain D.A.
      • Forrester J.D.
      Surgical stabilization of Rib fracture to mitigate pulmonary complication and mortality: a systematic review and bayesian meta-analysis.
      ,
      • Green E.A.
      • Guidry C.
      • Harris C.
      • et al.
      Surgical stabilization of traumatic rib fractures is associated with reduced readmissions and increased survival.
      ,
      • Prins J.T.H.
      • Van Lieshout E.M.M.
      • Ali-Osman F.
      • et al.
      Outcome after surgical stabilization of rib fractures versus nonoperative treatment in patients with multiple rib fractures and moderate to severe traumatic brain injury (CWIS-TBI).
      ,
      • Pieracci F.M.
      • Leasia K.
      • Bauman Z.
      • et al.
      A multicenter, prospective, controlled clinical trial of surgical stabilization of rib fractures in patients with severe, nonflail fracture patterns (chest wall injury society NONFLAIL).
      ,
      • Marasco S.F.
      • Balogh Z.J.
      • Wullschleger M.E.
      • et al.
      Rib fixation in non-ventilator dependent chest wall injuries: a prospective randomized trial.
      ].
      With the continued push for the development of increasingly minimal invasive surgical techniques came the advent of completely thoracoscopic, intrathoracic SSRF. In this video-assisted thoracoscopic surgery (VATS), rib fractures can be visualized and plated from an entirely intrathoracic approach. Potential advantages to this approach include decreased injury to muscles and nerves of the chest wall. In addition, this approach provides improved visualization of sub-scapular and posterior rib fractures compared to an extrathoracic view. Additionally, an intrathoracic approach allows access to the pleural space which can assist with evacuation of hemothorax, intraoperative placement of a chest tube, or repair of concomitant intrathoracic injuries that might otherwise be inaccessible from an extrathoracic approach [
      • Pieracci F.M.
      Completely thoracoscopic surgical stabilization of rib fractures: can it be done and is it worth it?.
      ].
      With the introduction of any new surgical method, there is often a significant learning curve which may be associated with increased operative time and/or adverse outcomes. The length and difficulty of this transition period can be a significant barrier to adoption of new procedural technique. As such, quantifying the learning curve can help objectively evaluate the learning burden of a new procedure and facilitate comparison across related techniques [
      • Valsamis E.M.
      • Chouari T.
      • O'Dowd-Booth C.
      • Rogers B.
      • Ricketts D
      Learning curves in surgery: variables, analysis and applications.
      ]. Learning curves have been used throughout different fields of medicine including thoracic surgery to evaluate the introduction of a number of different procedures [
      • Power A.D.
      • D'Souza D.M.
      • Moffatt-Bruce S.D.
      • Merritt R.E.
      • Kneuertz P.J
      Defining the learning curve of robotic thoracic surgery: what does it take?.
      ,
      • Dimitrovska N.T.
      • Bao F.
      • Yuan P.
      • Hu S.
      • Chu X.
      • Li W.
      Learning curve for two-port video-assisted thoracoscopic surgery lung segmentectomy.
      ,
      • Veronesi G.
      Robotic thoracic surgery: technical considerations and learning curve for pulmonary resection.
      ]. To the best of our knowledge, the learning curve for intrathoracic SSRF has not been previously assessed and thus this observational cohort study aims to evaluate the learning curve of introducing intrathoracic SSRF in a large academic medical center. We hypothesize that the learning curves for intrathoracic and extrathoracic SSRF will demonstrate similar inflection points followed by stabilization when proficiency is reached.

      Methods

      Data collection and inclusion/exclusion criteria

      Institutional review board approval was obtained with a waiver of consent to collect data on all adult (18 years or older) trauma patients who underwent SSRF. In March of 2019, intrathoracic SSRF was introduced into practice at a single, Level-I, academic trauma center. All adult patients that received SSRF from January 2017 to September 2021 were included. Pregnant patients and prisoners were excluded. Additionally, patients with an abbreviated injury scale (AIS) score greater than 3 for the head, abdomen, extremities, or face were excluded. Finally, patients were excluded if the SSRF occurred greater than four days after initial injury. Surgeons, fellows, and residents who performed less than 3 SSRFs were included in aggregate learning curve analysis and excluded from individual learning curve analysis.

      Outcomes

      The primary outcome was time (minutes) per rib plate and time per fracture which served as a measure of operative speed. These variables were calculated by dividing the number of plates (time/plate) and fractures (time/fracture) into the total operation time. Operation time was defined as the number of minutes between initial incision and completion of final skin closure. Secondary outcomes included patient complications as well as mortality. Additional demographic, injury, and hospital stay data were collected. The primary surgeon, operating fellows, and operating residents were recorded for each SSRF procedure. Patient demographic and outcome data for extrathoracic and intrathoracic SSRF cohorts were compared using Student t-test and Chi Square analysis within Excel as appropriate.

      Learning curves

      All statistical analysis was conducted in Python using the package statsmodels. Learning curves are defined as number of cases completed by operating physician on the x-axis and time per plate or time per fracture on the y-axis. Learning curves were generated for extrathoracic and intrathoracic SSRF for the medical center aggregate marking academic year defined as June to following May. Learning for individual surgeons (attendings, fellows, and residents) were created. Cumulative summation (CUSUM) technique was performed for each individual surgeon for both extrathoracic and intrathoracic methods. CUSUM is defined as the running total of the difference of individual data points (x) and the mean (μ): i=1nxiμ. The learning curves were visually inspected for the presence of an inflection point and a subsequent period of stability that would correspond to proficiency within standard models of learning. The learning curves were modeled as a lag one auto-regressive process (AR(1)) with a constant term and linear time dependent trend. For attending surgeons only, the AR(1) model also included an exogenous regression term on the method of plating (extrathoracic vs intrathoracic). This was repeated for fellows but only for extrathoracic given low sample size of intrathoracic. All the above analysis were repeated for the CUSUM curves.

      Results

      Patient demographics

      A total of 38 patients underwent extrathoracic SSRF from January 2017 to September 2021 and 24 patients received intrathoracic SSRF from March 2019 to September 2021 (Table 1). The median injury severity score (ISS) (17 vs. 17, p = 0.98) and hospital length of stay (9 vs 7 days, p = 0.13) were similar between cohorts. Also, there were no difference in the number of plates (4 vs. 4, p = 0.52), fractured ribs (8 vs 7, p = 018), or ribs plated (4 vs. 4, p = 0.27) between the two treatment groups. However, the median time between injury and SSRF was longer in the extrathoracic cohort (3 vs 2 days, p < 0.00; Table 2).
      Table 1Patient demographics.
      Extrathoracic N = 38Intrathoracic N = 24p-value*
      Age- median (range)56 (16–95)54.5(36–90)0.19
      Sex-%male68%63%0.25⁎⁎
      ISS- median (range)17 (9–33)17 (6–24)0.98
      LOS-median (range)9 (5–28)7 (3–25)0.13
      Days to SSRF - median (range)3 (1–4)2 (1–4)<0.001
      ISS = injury severity score, LOS = length of stay in hospital, SSRF = surgical stabilization of rib fracture.
      *t-test two-tail assuming unequal variances.
      ⁎⁎chi-square.
      Table 2Rib injuries.
      Extrathoracic N = 38Intrathoracic N = 24p-value*
      Number of Fractures- median (range)8 (1–17)7 (2–15)0.18
      Number of Ribs Plated- median (range)4 (1–6)4 (2–5)0.27
      Number of Plates- median (range)4 (1–8)4 (1–10)0.52
      *t-test two-tail assuming unequal variances.

      Surgeon information

      There were a total of fourteen individual surgeons that performed greater than three SSRF (Table 3). The practicing fellows were members of a one-year surgical critical care fellowship. Included residents were participating in their chief year. Surgeons did not have prior minimally invasive thoracic training prior to the introduction of the intrathoracic SSRF method.
      Table 3Surgeon demographics.
      ExtrathoracicIntrathoracic
      AttendingFellowResidentAttendingFellowResident
      Performed SSRF981591012
      Performed >3 SSRF354321

      Outcomes

      There was no difference in mortality between groups (n = 1 vs 1, p = 0.69; Table 4). The extrathoracic cohort had ten total complications including cardiac arrest, emphysema, respiratory failure, and iatrogenic injury and three patients underwent additional thoracotomies. The intrathoracic cohort had three total complications including respiratory failure and ischemic stroke with no patients undergoing additional thoracotomies (p = 0.19). Intrathoracic SSRF procedures trended toward longer total operative time (196 vs 162 min, p = 0.069), time per fracture (31 vs 24 min, p = 0.099), and time per plate (50 vs 48 min, p = 0.178) as compared to extrathoracic SSRF.
      Table 4Complications and mortality.
      Extrathoracic N = 38Intrathoracic N = 24P-value
      Mortality110.699⁎⁎
      Complications1030.193*
      Additional thoracotomy300.148*
      Operation Time -mean (minutes)1621960.069⁎⁎
      Time/Fracture -mean (minutes)24310.099⁎⁎
      Time/Plate -mean (minutes)48590.178⁎⁎
      Complications: Respiratory failure, unplanned intubation, subacute ischemic stroke, cardiac arrest, traumatic sub-q emphysema, iatrogenic injury, ileus, pulmonary embolism.
      *Chi-square.
      ⁎⁎t-test two-tail assuming unequal variances.

      Learning curves

      For extrathoracic SSRFs, there were no identifiable inflection points along the overall learning curves or when stratified by academic year (Fig. 1). Notably, the 32nd case performed may have elevated time/plate and time/fracture as the patient received only one plate. Similarly for intrathoracic SSRFs, there were no identifiable inflection points along the overall learning curves or when stratified by academic year (Fig. 2). For intrathoracic SSRF, case 19 and 20 each received 2 plates, which was the cohort minimum. There were also no discernable inflection points on visual inspection on the learning curves for each individual attending surgeon for either extrathoracic or intrathoracic SSRF for both time/plate (Fig. 3) or time/fracture (Fig. 4). Using the AR(1) model for the attending surgeon learning curves, we could not reject the null hypothesis of the trend term being different from zero. In other words, there were no linear trends across experience. For fellows, the null hypothesis could not be rejected for time per plate but the null hypothesis could be rejected for time per fracture, indicating a possible time dependent trend. Fellow surgeons D and G showed significant negative time dependent linear trends while F showed a positive trend in time per fracture learning curves (Table 5). There was insufficient data to adequately analyzes the learning curves of residents.
      Fig 1
      Fig. 1Aggregate learning curves of all extrathoracic surgical stabilization of rib fractures.
      Fig 2
      Fig. 2Aggregate learning curves of all intrathoracic surgical stabilization of rib fractures.
      Fig 3
      Fig. 3Individual learning curves for extrathoracic and intrathoracic SSRF by time per plate.
      Fig 4
      Fig. 4Individual learning curves for extrathoracic and intrathoracic SSRF by time per fracture.
      Table 5Learning curve trends.
      Time/PlateTime/Fracture
      SurgeonP-valueCoefficient95% CIP-valueCoefficient95% CI
      A0.31.451−1.292, 4.1940.35−0.59621.847, 0.655
      B0.4861.3442.436, 5.124−2.24990.127−5.137, 0.638
      C0.451−1.597, 3.5970.338−0.841−2.563, 0.881
      D0.24.295−2.275, 10.8650.025−5.317−9.96, −0.674
      F0.818−0.1888−1.796, 1.41905.25493.217, 7.293
      G0.840.4812−4.193, 5.1550−7.059311.025, −3.094
      CUSUM analysis for each individual (Figs. 5 and 6) did not visually indicate fit of the expected learning curve model of a period of change followed by a plateau. For attendings, we could not reject the null hypothesis of no trend. For fellows, similarly the null hypothesis could not be rejected except for surgeon F and G. The CUSUM curve for time per fracture of surgeon F showed positive trend consistent with the positive trend of the learning curve (Table 6). On the other hand, the CUSUM curve for time per plate for surgeon G also had a positive trend but the corresponding learning curve did not.
      Fig 5
      Fig. 5Cumulative sum graphs for individual extrathoracic and intrathoracic SSRF by time per plate.
      Fig 6
      Fig. 6Cumulative sum graphs for individual extrathoracic and intrathoracic SSRF by time per fracture.
      Table 6CUSUM trends.
      Time/PlateTime/Fracture
      SurgeonP-valueCoefficient95% CIP-valueCoefficient95% CI
      A0.972−0.0482−2.697, 2.6010.265−0.595−1.641, 0.451
      B0.1372.4886−0.794, 5.7710.871−0.20672.706, 2.293
      C0.1461.66470.582, 3.9120.847−0.16671.858, 1.525
      D0.6471.57445.171, 8.320.4−2.01736.719, 2.685
      F0.516−0.3535−1.419, 0.7130.0027.83032.777, 12.884
      G08.61194.747, 12.4770.716−1.578610.083, 6.925

      Discussion

      SSRF has been increasingly favored over non-operative management of rib fractures [
      • Choi J.
      • Gomez G.I.
      • Kaghazchi A.
      • Borghi J.A.
      • Spain D.A.
      • Forrester J.D.
      Surgical stabilization of Rib fracture to mitigate pulmonary complication and mortality: a systematic review and bayesian meta-analysis.
      ,
      • Prins J.T.H.
      • Van Lieshout E.M.M.
      • Ali-Osman F.
      • et al.
      Outcome after surgical stabilization of rib fractures versus nonoperative treatment in patients with multiple rib fractures and moderate to severe traumatic brain injury (CWIS-TBI).
      ,
      • Pieracci F.M.
      • Leasia K.
      • Bauman Z.
      • et al.
      A multicenter, prospective, controlled clinical trial of surgical stabilization of rib fractures in patients with severe, nonflail fracture patterns (chest wall injury society NONFLAIL).
      ]. With the continued adoption of this procedure, new minimally invasive techniques such as the intrathoracic approach have emerged. Learning curves are a tool which can be used to assess the implementation of new operative or procedural techniques. By identifying the number of cases required to obtain proficiency, the burden of learning can be addressed through comparative analysis between novel and traditional methods. The aim of this study was to assess the learning curve for the introduction of intrathoracic SSRF at a tertiary institution with previous experience using extrathoracic SSRF. We hypothesized there would be a detectable trend in the learning curve for individual surgeons using time per plate and time per fracture as a proxy of learning. No inflection points were identified within the descriptive learning curves or using CUSUM analysis for either extrathoracic or intrathoracic SSRFs on either the institutional or individual level. Contrary to what we expected, the time per plate and time per fracture did not differ as attending surgeons gained more experience according to both learning curves and CUSUM curves. Fellows’ time per fracture learning curves did show inconsistent trends with two surgeons gain of competency and another showing loss. The CUSUM modeling for fellows failed to adequately corroborate this findings.
      A typical learning curve includes an initial value, a slope as the learning proxy changes with experience followed by a stage where the learning proxy ceases to decrease with further cases implying proficiency [
      • Feldman Liane S.
      • Cao Jiguo
      • Andalib Amin
      • Fraser Shannon
      • Fried G.M.
      A method to characterize the learning curve for performance of a fundamental laparoscopic simulator task: defining “learning plateau” and “learning rate”.
      ]. Data can be split into a pre-mastery and post-mastery group if an inflection point is defined. The learning curves generated for both extrathoracic and intrathoracic SSRF did not demonstrate an inflection point on either the institutional or individual scale.
      The CUSUM has been proposed as another method of analyzing learning. CUSUM analysis can display whether a process is within a boundary or “out of control” [
      • Bolsin S.
      • Colson M.
      The use of the cusum technique in the assessment of trainee competence in new procedures.
      ,
      • Biau D.J.
      • Resche-Rigon M.
      • Godiris-Petit G.
      • Nizard R.S.
      • Porcher R.
      Quality control of surgical and interventional procedures: a review of the CUSUM.
      ]. A learning curve can be observed using CUSUM when points of change stabilize to zero, indicating that a steady state (at the mean value of the proxy) has been reached. The CUSUM graphs for individual surgeons performing both extrathoracic and intrathoracic SSRF did not show deviations from a steady state that indicating no change in operative time throughout experiences. On inspection of surgeon C's learning curve and CUSUM curve, one can see a parabolic trend. The test this hypothesis, we fitted a AR(1) including a second order polynomial trend which did show the existence of a significant trend component (p < 0.001). Interpretation of this as a true indication of learning is likely overfitting based on limited sample size, and the trend is likely reflective of a temporary excursion from the mean.
      Previous studies of learning curves for in vivo introduction of new surgical techniques displayed inflection points occurring between 10 and 80 cases [
      • Vieira E.
      • Guimarães T.C.
      • Pontes E.C.A.
      • et al.
      Initial experience in the microsurgical treatment of ruptured brain aneurysms in the endovascular era: characteristics and safety of the learning curve in the first 300 consecutively treated patients.
      ,
      • Liu J.
      • Tan L.
      • Thigpen B.
      • et al.
      Evaluation of the learning curve and safety outcomes in robotic assisted vaginal natural orifice transluminal endoscopic hysterectomy: a case series of 84 patients.
      ,
      • Boone B.A.
      • Zenati M.
      • Hogg M.E.
      • et al.
      Assessment of quality outcomes for robotic pancreaticoduodenectomy: identification of the learning curve.
      ,
      • Lu Y.
      • Zhang R.
      Analysis of the learning curve for artificial pneumothorax during an endoscopic McKeown-type resection of oesophageal carcinoma.
      ,
      • Wang T.
      • Ma M.Y.
      • Wu B.
      • Zhao Y.
      • Ye X.F.
      • Li T.
      Learning curve associated with thoraco-laparoscopic esophagectomy for esophageal cancer patients in the prone position.
      ,
      • Chang C.C.
      • Yen Y.T.
      • Lin C.Y.
      • Chen Y.Y.
      • Huang W.L.
      • Tseng Y.L.
      Single-port video-assisted thoracoscopic surgery subsegmentectomy: the learning curve and initial outcome.
      ] while our study included a total of 23 cases and a maximum of nine cases for individual analysis. Hence, there is a possibility that an inflection point in the learning curve could be observed by assessing more cases. Unfortunately, our institution has an annual change of residents and fellows which limits the total number of cases that can be observed for each individual over time. However, we can infer from the SSRF cases that were collected over a period of 3 years that there was not a clinically significant increase in operation time after introducing the intrathoracic SSRF method. Additionally, when comparing extrathoracic and intrathoracic learning curves, there are no observable differences. This implies that introducing intrathoracic SSRF at an institution that performs extrathoracic SSRF will likely not impact operative time. As our study was unable to identify the number of cases needed to reach proficiency for either operative method, learning may not need to be considered as a confounding factor when comparing intrathoracic against extrathoracic SSRF.
      A recent single center study of SSRF over 10 years found that total operation time and time per plate increased with study year [
      • Prins J.T.H.
      • Leasia K.
      • Sauaia A.
      • et al.
      A decade of surgical stabilization of rib fractures: the effect of study year on patient selection, operative characteristics, and in-hospital outcome.
      ]. Our study did not find such an increase in time by study year for either method of SSRF but we also did not identify a decrease in operation time as might be expected. The prior study suggested onboarding of new surgeons as an underlying cause for the lack of learning curve. Analysis of learning curves for the aforementioned variables sub-grouped by academic year did not show learning curves coinciding with the addition of new fellows, residents, or faculty hires.
      While the majority of learning curve studies utilize total operation time as a proxy for learning [
      • Pernar L.I.M.
      • Robertson F.C.
      • Tavakkoli A.
      • Sheu E.G.
      • Brooks D.C.
      • Smink D.S.
      An appraisal of the learning curve in robotic general surgery.
      ], time is only indirectly associated with patient outcomes and may not precisely correlate with proficiency [
      • Ramsay C.R.
      • Grant A.M.
      • Wallace S.A.
      • Garthwaite P.H.
      • Monk A.F.
      • Russell I.T.
      Statistical assessment of the learning curves of health technologies.
      ]. We chose to use time per plate and time per fracture in order to minimize the effect of other concurrent procedures that took place during the same operative trip. However, this was a calculated variable rather than an observational measurement. Direct patient outcomes were collected as qualitative measurements of learning. Mortality was extremely rare in both the extrathoracic and intrathoracic cohorts while complications were experienced less in patients that underwent intrathoracic plating. More pre-operative injuries such as hemothorax could be identified in the intrathoracic group due to the visualization by video-assisted thoracoscopic surgery, which is necessary for this approach. Certain complications cannot be directly associated with the specific method of plating given the traumatic setting of the patient cohort. Therefore, patient outcomes of mortality and complications could not be used as a feasible proxy for learning in our study. Identification of an adequate proxy for learning that directly affects patient outcomes in the trauma setting would be valuable for future learning curve analysis in the field of trauma surgery.
      Limitations of this study include the low number of procedures included, operative time as an indirect proxy of learning, low number of case experiences for individual analysis, and lack of precise record of which proportion of total operation time was needed for the SSRF as compared to other operative intervention. Additionally, our institution is one of the first centers to adopt the intrathoracic SSRF method and benefited from courses taught by a single faculty member which may have facilitated an optimal learning environment. This data may not be universally applicable to centers that do not have access to such an instructor.

      Conclusion

      Time per plate and time per fracture did not differ as surgeons gained more experience. Introducing intrathoracic SSRF in a large academic hospital that already performs extrathoracic SSRF may not need to account for a learning curve adjustment period. A direct proxy of learning in the setting of SSRF is needed.

      CRediT authorship contribution statement

      Madelyn Frank: Investigation, Data curation, Formal analysis, Conceptualization, Methodology, Project administration, Writing – original draft, Writing – review & editing. Brynn Sargent: Data curation, Writing – original draft, Writing – review & editing. Erika Tay-Lasso: Investigation, Data curation, Conceptualization, Project administration, Supervision, Validation. Gabrielle Hovis: Data curation. Colin Kincaid: Data curation. William Grant: Data curation. Leonardo Alaniz: Data curation. Justin Yi: Methodology, Formal analysis, Validation. Theresa L Chin: Writing – review & editing. Cristobal Barrios: Writing – review & editing. Jeffry Nahmias: Writing – review & editing. Fredric Pieracci: Writing – review & editing. Sebastian Schubl: Conceptualization, Writing – review & editing, Methodology.

      Declaration of Competing Interest

      This research did not receive any grant funding. Dr. Schubl is an educational consultant for Zimmer Biomet. For remaining authors, no conflicts were declared.

      Memberships

      Sebastian Schubl is a member of EAST and AAST.

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