Advertisement

Surgical process analysis identifies lack of connectivity between sequential fluoroscopic 2D alignment as a critical impediment in femoral intramedullary nailing

  • Hamid Ebrahimi
  • Albert Yee
  • Cari WhyneEmail author
Original Article
  • 398 Downloads

Abstract

Purpose

Identifying key steps and barriers within complex and simple surgical procedures can be accomplished in a structured and rigorous manner using surgical process modeling. For lower extremity long bone fracture stabilization, the current standard of care is closed intramedullary (IM) nailing, which, despite its widespread use, is associated with challenges that greatly impact operative time and lead to the frustration of medical staff. The aim of this study was to identify challenging surgical steps in IM nailing and understand their underlying causation.

Methods

Eight semi-structured interviews with staff orthopedic surgeons and eight detailed surgical observations were conducted to understand the surgical steps, challenges and adapted techniques used in IM nailing. Hierarchical decomposition was then utilized to structure the IM nailing surgical procedure into phases, steps and activities.

Results

In the developed IM nailing surgical process model, the most challenging steps were identified as fracture reduction (75 %) and entry point selection (25 %), both of which were associated with high levels of frustration in the observed surgeries. Both of these steps utilize 2D fluoroscopic imaging to guide 3D alignment. Challenges arise when the alignment in one plane is lost while adjusting the alignment in the perpendicular plane. This leads to unpredictable repetition of activities which can be time-consuming and frustrating.

Conclusion

Identifying the causation of surgical challenges in IM nailing through surgical process modeling forms a knowledge base that can be used to guide future improvements to techniques and surgical instrumentation.

Keywords

Intramedullary nailing Entry point selection Reduction Surgical process modeling Hierarchical decomposition Sequential fluoroscopic 2D alignment 

Notes

Compliance with Ethical Standards

Conflicts of interest

The authors declare that they have no conflict of interest.

Ethical standard

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

The informed consent to participate in this study is attached as a supplementary material.

References

  1. 1.
    Hazan EJ, Joskowicz L (2003) Computer-assisted image-guided intramedullary nailing of femoral shaft fractures. Tech Orthop 18(2):191–200. doi: 10.1097/00013611-200306000-00008 CrossRefGoogle Scholar
  2. 2.
    Crookshank MC, Edwards MR, Sellan M, Whyne CM, Schemitsch EH (2014) Can fluoroscopy-based computer navigation improve entry point selection for intramedullary nailing of femur fractures? Clin Orthop Relat Res 472:2720–2727. doi: 10.1007/s11999-013-2878-x PubMedCentralCrossRefPubMedGoogle Scholar
  3. 3.
    Liebmann P, Bohn S, Neumuth T (2011) Design and validation of a robust surgical workflow management system. M2Cai2011Google Scholar
  4. 4.
    Lalys F, Jannin P (2014) Surgical process modelling: a review. Int J Comput Assist Radiol Surg 9:495–511. doi: 10.1007/s11548-013-0940-5 CrossRefPubMedGoogle Scholar
  5. 5.
    Ahmadi S-A, Sielhorst T, Stauder R, Horn M, Feussner H, Navab N (2006) Recovery of surgical workflow without explicit models. Med Image Comput Comput Assist Interv 9:420–428. doi: 10.1007/11866565_52
  6. 6.
    Raimbault M, Jannin P, Morandi X, Riffaud L, Gibaud B (2003) Models of surgical procedures for multimodal image-guided neurosurgery. Stud Health Technol Inform 95(October 2001):50–55. doi: 10.3233/978-1-60750-939-4-50 PubMedGoogle Scholar
  7. 7.
    Neumuth D, Loebe F, Herre H, Neumuth T (2011) Modeling surgical processes: a four-level translational approach. Artif Intell Med 51(3):147–161. doi: 10.1016/j.artmed.2010.12.003 CrossRefPubMedGoogle Scholar
  8. 8.
    Neumuth T, Jannin P, Schlomberg J, Meixensberger J, Wiedemann P, Burgert O (2011) Analysis of surgical intervention populations using generic surgical process models. Int J Comput Assist Radiol Surg 6:59–71. doi: 10.1007/s11548-010-0475-y PubMedCentralCrossRefPubMedGoogle Scholar
  9. 9.
    Wang L, Landes J, Weidert S, Blum T, Heide A, Euler E, Navab N (2010) First animal cadaver study for interlocking of intramedullary nails under camera augmented mobile C-arm: a surgical workflow based preclinical evaluation. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinform). LNCS 6135:56–66. doi: 10.1007/978-3-642-13711-2_6
  10. 10.
    Riffaud L, Neumuth T, Morandi X, Trantakis C, Meixensberger J, Burgert O, Trelhu B, Jannin P (2010) Recording of surgical processes: a study comparing senior and junior neurosurgeons during lumbar disc herniation surgery. Neurosurgery 67. doi: 10.1227/NEU.0b013e3181f741d7
  11. 11.
    Forestier G, Lalys F, Riffaud L, Trelhu B, Jannin P (2012) Classification of surgical processes using dynamic time warping. J Biomed Inform 45(2):255–264. doi: 10.1016/j.jbi.2011.11.002 CrossRefPubMedGoogle Scholar
  12. 12.
    Casaletto JA, Rajaratnam V (2004) Surgical process re-engineering: carpal tunnel decompression—a model. Hand Surg 09(1):19–27. doi: 10.1142/S0218810404002066 CrossRefGoogle Scholar
  13. 13.
    Sandelowski M (1995) Focus on qualitative methods sample size in qualitative. Res Nurs Heal 18:179–183. doi: 10.1002/nur.4770180211 CrossRefGoogle Scholar
  14. 14.
    Coyne IT (1997) Sampling in qualitative research. Purposeful and theoretical sampling; merging or clear boundaries? J Adv Nurs 26:623–630. doi: 10.1046/j.1365-2648.1997.t01-25-00999.x
  15. 15.
    Miller G, Galanter E, Pribram K (1960) Plans and the structure of behavior. Henry Holt, New YorkGoogle Scholar
  16. 16.
    Muller ME, Allgower M, Schneider R, Willenegger H (1991) Manual of internal fixation: techniques recommended by the AO-ASIF Group, 3rd edn. Springer, BerlinGoogle Scholar
  17. 17.
    Roberts JW, Libet LA, Wolinsky PR (2012) Who is in danger? Impingement and penetration of the anterior cortex of the distal femur during intramedullary nailing of proximal femur fractures. J Trauma Acute Care Surg 73:249–254. doi: 10.1097/TA.0b013e318256a0b6 CrossRefPubMedGoogle Scholar
  18. 18.
    Kanawati AJ, Jang B, McGee R, Sungaran J (2014) The influence of entry point and radius of curvature on femoral intramedullary nail position in the distal femur. J Orthop 11:68–71. doi: 10.1016/j.jor.2014.04.010 PubMedCentralCrossRefPubMedGoogle Scholar
  19. 19.
  20. 20.
    Weil YA, Liebergall M, Mosheiff R, Helfet DL, Pearle AD (2007) Long bone fracture reduction using a fluoroscopy-based navigation system? A feasibility and accuracy study. Comput Aided Surg 12:295–302. doi: 10.1080/10929080701657974
  21. 21.
    Diotte B, Fallavollita P, Wang L, Weidert S, Euler E, Thaller P (2015) Multi-modal intra-operative navigation during distal locking of intramedullary nails. IEEE Trans Med Imaging 34(2):487–495CrossRefPubMedGoogle Scholar
  22. 22.
    Westphal R, Winkelbach S, Wahl F, Gosling T, Oszwald M, Hufner T, Krettek C (2009) Robot-assisted long bone fracture reduction. Int J Robot Res 28(10):1259–1278. doi: 10.1177/0278364909101189 CrossRefGoogle Scholar

Copyright information

© CARS 2015

Authors and Affiliations

  1. 1.Holland Musculoskeletal ProgramSunnybrook Research InstituteTorontoCanada
  2. 2.Institute of Biomaterials and Biomedical EngineeringUniversity of TorontoTorontoCanada
  3. 3.Division of Orthopaedic Surgery, Department of SurgeryUniversity of TorontoTorontoCanada

Personalised recommendations