An algorithm for identifying causes of reoperations after orthopedic fracture surgery in health administrative data: a diagnostic accuracy study using the Danish National Patient Register
DOI:
https://doi.org/10.2340/17453674.2024.42633Keywords:
Fractures, Infection, Nonunion, ValidationAbstract
Background and purpose: Disease- or procedure-specific registers offer valuable information but are costly and often inaccurate regarding outcome measures. Alternatively, automatically collected data from administrative systems could be a solution, given their high completeness. Our primary aim was to validate a method for identifying secondary surgical procedures (reoperations) in the Danish National Patient Register (DNPR) within the first year following primary fracture surgery. The secondary aim was to evaluate the accuracy of the diagnosis and procedure codes used to determine the causes of these reoperations. Finally, we developed algorithms to enhance precision in identifying the reasons for reoperations.
Methods: In a national cohort of 11,551 patients with primary fracture surgery, reoperations were identified through subsequent surgical procedure codes in the DNPR. Each patient record was reviewed to confirm the reoperations and causes. To improve accuracy, a stepwise algorithm was developed for each cause.
Results: We identified 2,347 possible reoperations; 2,212 were validated as true reoperations by review of patient record, i.e., a 94% positive predictive value (PPV). However, the coding for the causes of these reoperations was inaccurate. Our algorithm identified major reoperations with a sensitivity/PPV of 89/77%, minor reoperations 99%/89%, infections 77/85%, nonunion 82/56%, early re-osteosynthesis 90/75%, and secondary arthroplasties 95/87%.
Conclusion: While the overall reported reoperations in the DNPR had a high PPV, the predefined diagnosis and procedure codes alone were not sufficient to accurately determine the causes of these reoperations. An algorithm was developed for this purpose, yielding acceptable results for all causes except nonunion.
Downloads
References
Lerner R K, Esterhai J L Jr, Polomano R C, Cheatle M D, Heppenstall R B. Quality of life assessment of patients with posttraumatic fracture nonunion, chronic refractory osteomyelitis, and lower-extremity amputation. Clin Orthop Relat Res 1993; (295): 28-36. DOI: https://doi.org/10.1097/00003086-199310000-00006
Obremskey W T, Pappas N, Attallah-Wasif E, Tornetta P 3rd, Bhandari M. Level of evidence in orthopaedic journals. J Bone Joint Surg Am 2005; 87(12): 2632-8. doi: 10.2106/jbjs.E.00370. DOI: https://doi.org/10.2106/JBJS.E.00370
Oliveira P R, Leonhardt M C, Carvalho V C, Kojima K E, Silva J S, Rossi F, et al. Incidence and risk factors associated with infection after intramedullary nailing of femoral and tibial diaphyseal fractures: prospective study. Injury 2018; 49(10): 1905-11. doi: 10.1016/j.injury.2018.07.024. DOI: https://doi.org/10.1016/j.injury.2018.07.024
Mills L A, Aitken S A, Simpson A. The risk of non-union per fracture: current myths and revised figures from a population of over 4 million adults. Acta Orthop 2017; 88(4): 434-9. doi: 10.1080/17453674.2017.1321351. DOI: https://doi.org/10.1080/17453674.2017.1321351
Ingraham A M, Richards K E, Hall B L, Ko C Y. Quality improvement in surgery: the American College of Surgeons National Surgical Quality Improvement Program approach. Adv Surg 2010; 44: 251-67. doi: 10.1016/j.yasu.2010.05.003. DOI: https://doi.org/10.1016/j.yasu.2010.05.003
Möller M, Wolf O, Bergdahl C, Mukka S, Rydberg E M, Hailer N P, et al. The Swedish Fracture Register: ten years of experience and 600,000 fractures collected in a National Quality Register. BMC Musculoskelet Disord 2022; 23(1): 141. doi: 10.1186/s12891-022-05062-w. DOI: https://doi.org/10.1186/s12891-022-05062-w
Murdoch T B, Detsky A S. The inevitable application of big data to health care. JAMA 2013; 309(13): 1351-2. doi: 10.1001/jama.2013.393. DOI: https://doi.org/10.1001/jama.2013.393
Sund R. Quality of the Finnish Hospital Discharge Register: a systematic review. Scand J Public Health 2012; 40(6): 505-15. doi: 10.1177/1403494812456637. DOI: https://doi.org/10.1177/1403494812456637
Bossuyt P M, Reitsma J B, Bruns D E, Gatsonis C A, Glasziou P P, Irwig L, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ 2015; 351:h5527. doi: 10.1136/bmj.h5527. DOI: https://doi.org/10.1136/bmj.h5527
Benchimol E I, Manuel D G, To T, Griffiths A M, Rabeneck L, Guttmann A. Development and use of reporting guidelines for assessing the quality of validation studies of health administrative data. J Clin Epidemiol 2011; 64(8): 821-9. doi: 10.1016/j.jclinepi.2010.10.006. DOI: https://doi.org/10.1016/j.jclinepi.2010.10.006
Danmarks Statistik. Available from: https://www.statistikbanken.dk/statbank5a/default.asp?w=1440.
Schmidt M, Pedersen L, Sørensen H T. The Danish Civil Registration System as a tool in epidemiology. Eur J Epidemiol 2014; 29(8): 541-9. doi: 10.1007/s10654-014-9930-3. DOI: https://doi.org/10.1007/s10654-014-9930-3
Lynge E, Sandegaard J L, Rebolj M. The Danish National Patient Register. Scand J Public Health 2011; 39(7 Suppl): 30-3. doi: 10.1177/1403494811401482. DOI: https://doi.org/10.1177/1403494811401482
Harris P A, Taylor R, Minor B L, Elliott V, Fernandez M, O’Neal L, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inform 2019; 95:103208. doi: https://doi.org/10.1016/j.jbi.2019.103208. DOI: https://doi.org/10.1016/j.jbi.2019.103208
Harris P A, Taylor R, Thielke R, Payne J, Gonzalez N, Conde J G. Research electronic data capture (REDCap): a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009; 42(2): 377-81. doi: https://doi.org/10.1016/j.jbi.2008.08.010. DOI: https://doi.org/10.1016/j.jbi.2008.08.010
Roennegaard A B, Gundtoft P H, Tengberg P T, Viberg B. Completeness and validity of the Danish fracture database. Injury 2023; 54(10): 110769. doi: 10.1016/j.injury.2023.05.001. DOI: https://doi.org/10.1016/j.injury.2023.05.001
Schmidt M, Schmidt S A, Sandegaard J L, Ehrenstein V, Pedersen L, Sørensen H T. The Danish National Patient Registry: a review of content, data quality, and research potential. Clin Epidemiol 2015; 7:449-90. doi: 10.2147/clep.S91125. DOI: https://doi.org/10.2147/CLEP.S91125
Committee NM-S. NOMESCO Classification of Surgical Procedures, version 1.11. Copenhagen: NOMESCO; 2006.
Ehrenstein V, Hellfritzsch M, Kahlert J, Langan S M, Urushihara H, Marinac-Dabic D, et al. Validation of algorithms in studies based on routinely collected health data: general principles. Am J Epidemiol 2024; 193(11): 1612-24. doi: 10.1093/aje/kwae071. DOI: https://doi.org/10.1093/aje/kwae071
Hermansen L L, Viberg B, Overgaard S. Development of a diagnostic algorithm identifying cases of dislocation after primary total hip arthroplasty-based on 31,762 patients from the Danish Hip Arthroplasty Register. Acta Orthop 2021; 92(2): 137-42. doi: 10.1080/17453674.2020.1868708. DOI: https://doi.org/10.1080/17453674.2020.1868708
Koutalos A A, Baltas C, Akrivos V, Arnaoutoglou C, Malizos K N. Mortality, functional outcomes and quality of life after hip fractures complicated by infection: a case control study. J Bone Jt Infect 2021; 6(8): 347-54. doi: 10.5194/jbji-6-347-2021. DOI: https://doi.org/10.5194/jbji-6-347-2021
Metsemakers W J, Morgenstern M, McNally M A, Moriarty T F, McFadyen I, Scarborough M, et al. Fracture-related infection: a consensus on definition from an international expert group. Injury 2018; 49(3): 505-10. doi: 10.1016/j.injury.2017.08.040. DOI: https://doi.org/10.1016/j.injury.2017.08.040
Varnum C, Pedersen A B, Gundtoft P H, Overgaard S. The what, when and how of orthopaedic registers: an introduction into register-based research. EFORT Open Rev 2019; 4(6): 337-43. doi: 10.1302/2058-5241.4.180097. DOI: https://doi.org/10.1302/2058-5241.4.180097
Kristensen P K, Röck N D, Christensen H C, Pedersen A B. The Danish Multidisciplinary Hip Fracture Registry: 13-year results from a population-based cohort of hip fracture patients. Clin Epidemiol 2020; 12: 9-21. doi: 10.2147/clep.S231578. DOI: https://doi.org/10.2147/CLEP.S231578
Lian T, Brandrud A, Mariero L, Nordsletten L, Figved W. 60% reduction of reoperations and complications for elderly patients with hip fracture through the implementation of a six-item improvement programme. BMJ Open Qual 2022; 11(3): e001848. doi: 10.1136/bmjoq-2022-001848. DOI: https://doi.org/10.1136/bmjoq-2022-001848
Rogmark C, Nåtman J, Jobory A, Hailer N P, Cnudde P. The association of surgical approach and bearing size and type with dislocation in total hip arthroplasty for acute hip fracture. Bone Joint J 2022; 104-b(7): 844-51. doi: 10.1302/0301-620x.104b7.Bjj-2021-1772.R1. DOI: https://doi.org/10.1302/0301-620X.104B7.BJJ-2021-1772.R1
Additional Files
Published
How to Cite
License
Copyright (c) 2025 Signe S Jensen, Anders B Rønnegaard, Per H Gundtoft, Søren Kold, Bjarke Viberg

This work is licensed under a Creative Commons Attribution 4.0 International License.
PlumX (by Elsevier) is an altmetrics platform that tracks and visualizes the online attention, usage, captures, citations, and social media engagement.
