Intraoperative scrub nurse turnover in orthopaedic surgery procedures: An opportunity for improved operating room efficiency.

BACKGROUND: Scrub nurses play a crucial role in facilitating orthopaedic surgeries, and thus intraoperative scrub nurse turnover may disrupt the workflow of the surgical team and prolong duration of surgery (DOS). The purpose of this study was to quantify the impact of intraoperative scrub nurse turnover on operative time of orthopaedic surgeries lasting less than 3h in duration. METHODS: Prospectively collected databases from two institutions were retrospectively queried to identify all orthopaedic procedures of maximum mean duration of 180min from March 4th, 2018 to August 31st, 2022. Cases were divided into two groups, those with scrub nurse turnover and those without. Propensity score matching was conducted to match groups by surgeon, hospital, patient age, gender, and ASA classification. Unpaired t-tests were used to compare mean DOS for each surgical procedure. Average treatment effect on treated (ATET) with 95% confidence intervals (CIs) were calculated. RESULTS: Scrub nurse turnover significantly prolonged DOS for both bone forearm facture open reduction and internal fixation (ORIF) (ATET=21.08, p=0.001), ankle ORIF (ATET=21.26, p<0.001), clavicle ORIF (ATET=16.16, p=0.028), femur intramedullary nail (ATET=11.52, p=0.003), rotator cuff repair (ATET=16.88, p<0.001), partial discectomy (ATET=10.52, p=0.001), total knee arthroplasty (TKA) (ATET=5.69, p<0.001), anterior total hip arthroplasty (THA) (ATET=8.80, p<0.001), lateral THA (ATET=7.02, p<0.001), and uncemented hip hemiarthroplasty (ATET=16.79, p=0.049). CONCLUSION: Intraoperative scrub nurse turnover significantly prolongs surgical times in orthopaedic surgeries lasting up to 3h in duration. This highlights the importance of developing strategies to prevent intraoperative scrub nurse turnover to improve OR efficiency and decrease healthcare costs.

Thickness and design features of clinical cranial implants-what should automated methods strive to replicate?

PURPOSE: New deep learning and statistical shape modelling approaches aim to automate the design process for patient-specific cranial implants, as highlighted by the MICCAI AutoImplant Challenges. To ensure applicability, it is important to determine if the training data used in developing these algorithms represent the geometry of implants designed for clinical use. METHODS: Calavera Surgical Design provided a dataset of 206 post-craniectomy skull geometries and their clinically used implants. The MUG500+ dataset includes 29 post-craniectomy skull geometries and implants designed for automating design. For both implant and skull shapes, the inner and outer cortical surfaces were segmented, and the thickness between them was measured. For the implants, a 'rim' was defined that transitions from the repaired defect to the surrounding skull. For unilateral defect cases, skull implants were mirrored to the contra-lateral side and thickness differences were quantified. RESULTS: The average thickness of the clinically used implants was 6.0 ± 0.5 mm, which approximates the thickness on the contra-lateral side of the skull (relative difference of -0.3 ± 1.4 mm). The average thickness of the MUG500+ implants was 2.9 ± 1.0 mm, significantly thinner than the intact skull thickness (relative difference of 2.9 ± 1.2 mm). Rim transitions in the clinical implants (average width of 8.3 ± 3.4 mm) were used to cap and create a smooth boundary with the skull. CONCLUSIONS: For implant modelers or manufacturers, this shape analysis quantified differences of cranial implants (thickness, rim width, surface area, and volume) to help guide future automated design algorithms. After skull completion, a thicker implant can be more versatile for cases involving muscle hollowing or thin skulls, and wider rims can smooth over the defect margins to provide more stability. For clinicians, the differing measurements and implant designs can help inform the options available for their patient specific treatment.