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Monitoring of blood biochemical markers for periprosthetic joint infection using ensemble machine learning and UMAP embedding

  • Orthopaedic Surgery
  • Published:
Archives of Orthopaedic and Trauma Surgery Aims and scope Submit manuscript

Abstract

Introduction

Periprosthetic joint infection (PJI) is a serious complication after total joint arthroplasty. It is important to accurately identify PJI and monitor postoperative blood biochemical marker changes for the appropriate treatment strategy. In this study, we aimed to monitor the postoperative blood biochemical characteristics of PJI by contrasting with non-PJI joint replacement cases to understand how the characteristics change postoperatively.

Materials and methods

A total of 144 cases (52 of PJI and 92 of non-PJI) were reviewed retrospectively and split into development and validation cohorts. After exclusion of 11 cases, a total of 133 (PJI: 50, non-PJI: 83) cases were enrolled finally. An RF classifier was developed to discriminate between PJI and non-PJI cases based on 18 preoperative blood biochemical tests. We evaluated the similarity/dissimilarity between cases based on the RF model and embedded the cases in a two-dimensional space by Uniform Manifold Approximation and Projection (UMAP). The RF model developed based on preoperative data was also applied to the same 18 blood biochemical tests at 3, 6, and 12 months after surgery to analyze postoperative pathological changes in PJI and non-PJI. A Markov chain model was applied to calculate the transition probabilities between the two clusters after surgery.

Results

PJI and non-PJI were discriminated with the RF classifier with the area under the receiver operating characteristic curve of 0.778. C-reactive protein, total protein, and blood urea nitrogen were identified as the important factors that discriminates between PJI and non-PJI patients. Two clusters corresponding to the high- and low-risk populations of PJI were identified in the UMAP embedding. The high-risk cluster, which included a high proportion of PJI patients, was characterized by higher CRP and lower hemoglobin. The frequency of postoperative recurrence to the high-risk cluster was higher in PJI than in non-PJI.

Conclusions

Although there was overlap between PJI and non-PJI, we were able to identify subgroups of PJI in the UMAP embedding. The machine-learning-based analytical approach is promising in consecutive monitoring of diseases such as PJI with a low incidence and long-term course.

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Code availability

All R and Python codes used in this study are included the GitHub repository (https://github.com/eiryo-kawakami/PJI_2022_code).

Data availability

The data that support the findings of this study are available from the corresponding author, [N.K.], upon reasonable request.

References

  1. Kapadia BH, Berg RA, Daley JA, Fritz J, Bhave A, Mont MA (2016) Periprosthetic joint infection. Lancet 387:386–394. https://doi.org/10.1016/S0140-6736(14)61798-0

    Article  PubMed  Google Scholar 

  2. Dale H, Hallan G, Hallan G, Espehaug B, Havelin LI, Engesaeter LB (2009) Increasing risk of revision due to deep infection after hip arthroplasty. Acta Orthop 80:639–645. https://doi.org/10.3109/17453670903506658

    Article  PubMed  PubMed Central  Google Scholar 

  3. Parvizi J, Pawasarat IM, Azzam KA, Joshi A, Hansen EN, Bozic KJ (2010) Periprosthetic joint infection: the economic impact of methicillin-resistant infections. J Arthroplasty 25:103–107. https://doi.org/10.1016/j.arth.2010.04.011

    Article  PubMed  Google Scholar 

  4. Everhart JS, Altneu E, Calhoun JH (2013) Medical comorbidities are independent preoperative risk factors for surgical infection after total joint arthroplasty. Clin Orthop Relat Res 471:3112–3119. https://doi.org/10.1007/s11999-013-2923-9

    Article  PubMed  PubMed Central  Google Scholar 

  5. Berbari EF, Osmon DR, Lahr B, Eckel-Passow JE, Tsaras G, Hanssen AD et al (2012) The Mayo prosthetic joint infection risk score: implication for surgical site infection reporting and risk stratification. Infect Control Hosp Epidemiol 33:774–781. https://doi.org/10.1086/666641

    Article  PubMed  Google Scholar 

  6. Parvizi J, Tan TL, Goswami K, Higuera C, Della Valle C, Chen AF et al (2018) The 2018 definition of periprosthetic hip and knee infection: an evidence-based and validated criteria. J Arthroplasty 33:1309–14.e2. https://doi.org/10.1016/j.arth.2018.02.078

    Article  PubMed  Google Scholar 

  7. Kunutsor SK, Whitehouse MR, Lenguerrand E, Blom AW, Beswick AD, INFORM Team (2016) Re-infection outcomes following one- and two-stage surgical revision of infected knee prosthesis: a systematic review and meta-analysis. PLoS ONE 11:0151537. https://doi.org/10.1371/journal.pone.0151537

    Article  CAS  Google Scholar 

  8. EPRD (2021) The German Arthroplasty Registry (EPRD) Annual Report 2021, EPRD Deutsche Endoprothesenregister gGmbH. EPRD. https://doi.org/10.36186/reporteprd052022

    Article  Google Scholar 

  9. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–118. https://doi.org/10.1038/nature21056

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D et al (2018) Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med 24:1559–1567. https://doi.org/10.1038/s41591-018-0177-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Khalilia M, Chakraborty S, Popescu M (2011) Predicting disease risks from highly imbalanced data using random forest. BMC Med Inform Decis Mak 11:51. https://doi.org/10.1186/1472-6947-11-51

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kawakami E, Tabata J, Yanaihara N, Ishikawa T, Koseki K, Iida Y et al (2019) Application of artificial intelligence for preoperative diagnostic and prognostic prediction in epithelial ovarian cancer based on blood biomarkers. Clin Cancer Res 25:3006–3015. https://doi.org/10.1158/1078-0432.CCR-18-3378

    Article  CAS  PubMed  Google Scholar 

  13. Kuo F-C, Hu W-H, Hu Y-J (2022) Periprosthetic joint infection prediction via machine learning: comprehensible personalized decision support for diagnosis. J Arthroplasty 37:132–141. https://doi.org/10.1016/j.arth.2021.09.005

    Article  PubMed  Google Scholar 

  14. Parvizi J, Gehrke T (2013) Proceedings of the international consensus meeting on periprosthetic joint infection. Work group 7, diagnosis of periprosthetic joint infection. J Arthroplasty. https://doi.org/10.1016/j.arth.2014.03.009

    Article  PubMed  Google Scholar 

  15. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  16. McInnes L, Healy J, Melville J (2018) UMAP uniform manifold approximation and projection for dimension reduction. J Open Sour Softw. https://doi.org/10.21105/joss.00861

    Article  Google Scholar 

  17. Beck JR, Robert BJ (1988) Markov models of natural history. J Clin Epidemiol 41:619–621. https://doi.org/10.1016/0895-4356(88)90113-8

    Article  CAS  PubMed  Google Scholar 

  18. Sonnenberg FA, Beck JR (1993) Markov models in medical decision making: a practical guide. Med Decis Making 13:322–338. https://doi.org/10.1177/0272989X9301300409

    Article  CAS  PubMed  Google Scholar 

  19. Tada T, Kumada T, Toyoda H, Ohisa M, Akita T, Tanaka J (2018) Long-term natural history of liver disease in patients with chronic hepatitis B virus infection: an analysis using the Markov chain model. J Gastroenterol 53:1196–1205. https://doi.org/10.1007/s00535-018-1467-x

    Article  CAS  PubMed  Google Scholar 

  20. Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B et al (2020) From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2:56–67. https://doi.org/10.1038/s42256-019-0138-9

    Article  PubMed  PubMed Central  Google Scholar 

  21. Jupiter JB, Karchmer AW, Lowell JD, Harris WH (1981) Total hip arthroplasty in the treatment of adult hips with current or quiescent sepsis. J Bone Joint Surg Am 63:194–200

    Article  CAS  PubMed  Google Scholar 

  22. Peersman G, Laskin R, Davis J, Peterson M (2001) Infection in total knee replacement: a retrospective review of 6489 total knee replacements. Clin Orthop Relat Res 392:15–23

    Article  Google Scholar 

  23. Marchant MH Jr, Viens NA, Cook C, Vail TP, Bolognesi MP (2009) The impact of glycemic control and diabetes mellitus on perioperative outcomes after total joint arthroplasty. J Bone Joint Surg Am 91:1621–1629. https://doi.org/10.2106/JBJS.H.00116

    Article  PubMed  Google Scholar 

  24. Jaberi FM, Parvizi J, Haytmanek CT, Joshi A, Purtill J (2008) Procrastination of wound drainage and malnutrition affect the outcome of joint arthroplasty. Clin Orthop Relat Res 466:1368–1371. https://doi.org/10.1007/s11999-008-0214-7

    Article  PubMed  PubMed Central  Google Scholar 

  25. Mills E, Eyawo O, Lockhart I, Kelly S, Wu P, Ebbert JO (2011) Smoking cessation reduces postoperative complications: a systematic review and meta-analysis. Am J Med 124:144–54.e8. https://doi.org/10.1016/j.amjmed.2010.09.013

    Article  PubMed  Google Scholar 

  26. Lieberman JR, Fuchs MD, Haas SB, Garvin KL, Goldstock L, Gupta R et al (1995) Hip arthroplasty in patients with chronic renal failure. J Arthroplasty 10:191–195. https://doi.org/10.1016/s0883-5403(05)80126-3

    Article  CAS  PubMed  Google Scholar 

  27. Pour AE, Matar WY, Mehdi Jafari S, Purtill JJ, Austin MS, Parvizi J (2011) Total joint arthroplasty in patients with hepatitis C. J Bone Joint Surg 93:1448–1454. https://doi.org/10.2106/jbjs.j.00219

    Article  PubMed  Google Scholar 

  28. Shohat N, Goswami K, Tan TL, Yayac M, Soriano A, Sousa R et al (2020) 2020 Frank Stinchfield award: identifying who will fail following irrigation and debridement for prosthetic joint infection. Bone Joint J. 102-B:11–19. https://doi.org/10.1302/0301-620X.102B7.BJJ-2019-1628.R1

    Article  PubMed  Google Scholar 

  29. Rouzrokh P, Ramazanian T, Wyles CC, Philbrick KA, Cai JC, Taunton MJ et al (2021) Deep learning artificial intelligence model for assessment of hip dislocation risk following primary total hip arthroplasty from postoperative radiographs. J Arthroplasty 36:2197–203.e3. https://doi.org/10.1016/j.arth.2021.02.028

    Article  PubMed  PubMed Central  Google Scholar 

  30. Ye Y, Chen W, Gu M, Xian G, Pan B, Zheng L et al (2020) Serum globulin and albumin to globulin ratio as potential diagnostic biomarkers for periprosthetic joint infection: a retrospective review. J Orthop Surg Res 15:459. https://doi.org/10.1186/s13018-020-01959-1

    Article  PubMed  PubMed Central  Google Scholar 

  31. Greenky M, Gandhi K, Pulido L, Restrepo C, Parvizi J (2012) Preoperative anemia in total joint arthroplasty: is it associated with periprosthetic joint infection? Clin Orthop Relat Res 470:2695–2701. https://doi.org/10.1007/s11999-012-2435-z

    Article  PubMed  PubMed Central  Google Scholar 

  32. Sodhi N, Anis HK, Vakharia RM, Acuña AJ, Gold PA, Garbarino LJ et al (2020) What are risk factors for infection after primary or revision total joint arthroplasty in patients older than 80 years? Clin Orthop Relat Res 478:1741–1751. https://doi.org/10.1097/corr.0000000000001389

    Article  PubMed  PubMed Central  Google Scholar 

  33. Qin L, Li F, Gong X, Wang J, Huang W, Hu N (2020) Combined measurement of D-Dimer and C-reactive protein levels: highly accurate for diagnosing chronic periprosthetic joint infection. J Arthroplasty 35:229–234. https://doi.org/10.1016/j.arth.2019.08.012

    Article  PubMed  Google Scholar 

  34. Li C, Ojeda Thies C, Xu C, Trampuz A (2020) Is combining serum interleukin-6 and C-reactive protein a reliable diagnostic tool in periprosthetic joint infections? J Orthop Surg Res 15:450. https://doi.org/10.1186/s13018-020-01864-7

    Article  PubMed  PubMed Central  Google Scholar 

  35. Maier SP, Klemt C, Tirumala V, Oganesyan R, van den Kieboom J, Kwon Y-M (2020) Elevated ESR/CRP ratio is associated with reinfection after debridement, antibiotics, and implant retention in chronic periprosthetic joint infections. J Arthroplasty 35:3254–3260. https://doi.org/10.1016/j.arth.2020.06.007

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank Shiho Saito for invaluable assistance with data management.

Funding

We thank Life Innovation Platform YOKOHAMA (LIP. Yokohama) for financial support. This work was, in part, supported by SECOM Science and Technology Foundation (to E.K.), Japan Science and Technology Agency (JST) Moonshot R&D Grants (JPMJMS2025, to E.K.), a JST CREST Grant (JPMJCR20H4, to E.K.), and Japan Agency for Medical Research and Development (AMED) Grants (JP21wm0325007, JP20fk0108412, JP20fk0108413, JP21gm5010003, all to E.K.).

Author information

Author notes

  1. Eiryo Kawakami and Naomi Kobayashi equally contributed to this work.

Authors and Affiliations

  1. Medical Sciences Innovation Hub Program (MIH), RIKEN, 1-7-22 Suehiro-Cho, Tsurumi-Ku, Yokohama, Kanagawa, 230-0045, Japan

    Eiryo Kawakami, Yuichiro Ichihara & Tetsuo Ishikawa

  2. Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, 1-7-22 Suehiro-Cho, Tsurumi-Ku, Yokohama, Kanagawa, 230-0045, Japan

    Eiryo Kawakami & Tetsuo Ishikawa

  3. Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chiba City, Chiba, 260-8670, Japan

    Eiryo Kawakami

  4. Center for Artificial Intelligence in Therapeutics (CAIST), Chiba University, 1-8-1 Inohana, Chiba City, Chiba, 260-8670, Japan

    Eiryo Kawakami

  5. Institute for Advanced Academic Research (IAAR), Chiba University, 1-33 Yayoi-Cho, Inage-Ku, Chiba City, Chiba, 263-8522, Japan

    Eiryo Kawakami

  6. Department of Orthopaedics Surgery, Yokohama City University Medical Center, 4-57, Urafune-Cho, Minami-Ku, Yokohama, Kanagawa, 232-0024, Japan

    Naomi Kobayashi

  7. Department of Orthopaedics Surgery, Yokohama City University, 3-9 Fukuura, Kanazawa-Ku, Yokohama, Kanagawa, 236-0004, Japan

    Yuichiro Ichihara, Hyonmin Choe & Yutaka Inaba

  8. Department of Extended Intelligence for Medicine, the Ishii-Ishibashi Laboratory, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan

    Tetsuo Ishikawa

  9. Department of Clinical Laboratory Center, Yokohama City University, 3-9 Fukuura, Kanazawa-Ku, Yokohama, Kanagawa, 236-0004, Japan

    Akito Tomoyama

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  1. Eiryo Kawakami
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Correspondence to Naomi Kobayashi.

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This retrospective observational study was approved by our institutional research ethics committee (UMIN ID: UMIN000030180).

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Kawakami, E., Kobayashi, N., Ichihara, Y. et al. Monitoring of blood biochemical markers for periprosthetic joint infection using ensemble machine learning and UMAP embedding. Arch Orthop Trauma Surg 143, 6057–6067 (2023). https://doi.org/10.1007/s00402-023-04898-8

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