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J Knee Surg 2023; 36(06): 637-643
DOI: 10.1055/s-0041-1741396
Original Article

The Use of Artificial Neural Networks for the Prediction of Surgical Site Infection Following TKA

Ingwon Yeo
1   Department of Orthopedic Surgery, Bioengineering Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
,
Christian Klemt
1   Department of Orthopedic Surgery, Bioengineering Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
,
Matthew Gerald Robinson
1   Department of Orthopedic Surgery, Bioengineering Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
,
John G. Esposito
1   Department of Orthopedic Surgery, Bioengineering Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
,
Akachimere Cosmas Uzosike
1   Department of Orthopedic Surgery, Bioengineering Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
,
Young-Min Kwon
1   Department of Orthopedic Surgery, Bioengineering Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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Abstract

This is a retrospective study. Surgical site infection (SSI) is associated with adverse postoperative outcomes following total knee arthroplasty (TKA). However, accurately predicting SSI remains a clinical challenge due to the multitude of patient and surgical factors associated with SSI. This study aimed to develop and validate machine learning models for the prediction of SSI following primary TKA. This is a retrospective study for patients who underwent primary TKA. Chart review was performed to identify patients with superficial or deep SSIs, defined in concordance with the criteria of the Musculoskeletal Infection Society. All patients had a minimum follow-up of 2 years (range: 2.1–4.7 years). Five machine learning algorithms were developed to predict this outcome, and model assessment was performed by discrimination, calibration, and decision curve analysis. A total of 10,021 consecutive primary TKA patients was included in this study. At an average follow-up of 2.8 ± 1.1 years, SSIs were reported in 404 (4.0%) TKA patients, including 223 superficial SSIs and 181 deep SSIs. The neural network model achieved the best performance across discrimination (area under the receiver operating characteristic curve = 0.84), calibration, and decision curve analysis. The strongest predictors of the occurrence of SSI following primary TKA, in order, were Charlson comorbidity index, obesity (BMI >30 kg/m2), and smoking. The neural network model presented in this study represents an accurate method to predict patient-specific superficial and deep SSIs following primary TKA, which may be employed to assist in clinical decision-making to optimize outcomes in at-risk patients.

Keywords

total knee arthroplasty - surgical site infection - machine learning - artificial intelligence


Publication History

Received: 17 August 2021

Accepted: 16 November 2021

Article published online:
11 January 2022

© 2022. Thieme. All rights reserved.

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333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

 

  • References

  • 1 Sloan M, Premkumar A, Sheth NP. Projected volume of primary total joint arthroplasty in the U.S., 2014 to 2030. J Bone Joint Surg Am 2018; 100 (17) 1455-1460
    CrossrefPubMedGoogle Scholar
  • 2 Edwards JR, Peterson KD, Mu Y. et al. National Healthcare Safety Network (NHSN) report: data summary for 2006 through 2008, issued December 2009. Am J Infect Control 2009; 37 (10) 783-805
    CrossrefPubMedGoogle Scholar
  • 3 National Nosocomial Infections Surveillance System. National Nosocomial Infections Surveillance (NNIS) System Report, data summary from January 1992 through June 2004, issued October 2004. Am J Infect Control 2004; 32 (08) 470-485
    CrossrefPubMedGoogle Scholar
  • 4 Kurtz S, Ong K, Lau E, Mowat F, Halpern M. Projections of primary and revision hip and knee arthroplasty in the United States from 2005 to 2030. J Bone Joint Surg Am 2007; 89 (04) 780-785
    CrossrefPubMedGoogle Scholar
  • 5 Shohat N, Bauer T, Buttaro M. et al. Hip and knee section, what is the definition of a periprosthetic joint infection (PJI) of the knee and the hip? Can the same criteria be used for both joints?: Proceedings of international consensus on orthopedic infections. J Arthroplasty 2019; 34 (2S): S325-S327
    CrossrefPubMedGoogle Scholar
  • 6 Berríos-Torres SI, Umscheid CA, Bratzler DW. et al; Healthcare Infection Control Practices Advisory Committee. Centers for disease control and prevention guideline for the prevention of surgical site infection, 2017. JAMA Surg 2017; 152 (08) 784-791
    CrossrefPubMedGoogle Scholar
  • 7 Bozic KJ, Lau E, Kurtz S. et al. Patient-related risk factors for periprosthetic joint infection and postoperative mortality following total hip arthroplasty in Medicare patients. J Bone Joint Surg Am 2012; 94 (09) 794-800
    CrossrefPubMedGoogle Scholar
  • 8 Klemt C, Tirumala V, Smith EJ, Padmanabha A, Kwon Y-M. Development of a preoperative risk calculator for re-infection following revision surgery for periprosthetic joint infection. J Arthroplasty 2021; 36 (02) 693-699
    CrossrefPubMedGoogle Scholar
  • 9 Hopkins BS, Mazmudar A, Driscoll C. et al. Using artificial intelligence (AI) to predict postoperative surgical site infection: a retrospective cohort of 4046 posterior spinal fusions. Clin Neurol Neurosurg 2020; 192 (January): 105718
    CrossrefPubMedGoogle Scholar
  • 10 Parvizi J, Zmistowski B, Berbari EF. et al. New definition for periprosthetic joint infection: from the Workgroup of the Musculoskeletal Infection Society. Clin Orthop Relat Res 2011; 469 (11) 2992-2994
    CrossrefPubMedGoogle Scholar
  • 11 Ferro CAT. Comparing probabilistic forecasting systems with the Brier score. Weather Forecast 2007; 22 (05) 1076-1088
    CrossrefPubMedGoogle Scholar
  • 12 Bozic KJ, Ong K, Lau E. et al. Estimating risk in Medicare patients with THA: an electronic risk calculator for periprosthetic joint infection and mortality. Clin Orthop Relat Res 2013; 471 (02) 574-583
    CrossrefPubMedGoogle Scholar
  • 13 Inacio MCS, Pratt NL, Roughead EE, Graves SE. Predicting infections after total joint arthroplasty using a prescription based comorbidity measure. J Arthroplasty 2015; 30 (10) 1692-1698
    CrossrefPubMedGoogle Scholar
  • 14 Shah AA, Devana SK, Lee C, Kianian R, van der Schaar M, SooHoo NF. Development of a novel, potentially universal machine learning algorithm for prediction of complications after total hip arthroplasty. J Arthroplasty 2021; 36 (05) 1655-1662
    CrossrefPubMedGoogle Scholar
  • 15 Tunthanathip T, Sae-Heng S, Oearsakul T, Sakarunchai I, Kaewborisutsakul A, Taweesomboonyat C. Machine learning applications for the prediction of surgical site infection in neurological operations. Neurosurg Focus 2019; 47 (02) E7
    CrossrefPubMedGoogle Scholar
  • 16 Antonelli B, Chen AF. Reducing the risk of infection after total joint arthroplasty: preoperative optimization. Arthroplasty 2019; 1 (04) 1-13
    PubMedGoogle Scholar
  • 17 Werner BC, Higgins MD, Pehlivan HC, Carothers JT, Browne JA. Super obesity is an independent risk factor for complications after primary total hip arthroplasty. J Arthroplasty 2017; 32 (02) 402-406
    CrossrefPubMedGoogle Scholar
  • 18 Singh JA, Jensen MR, Harmsen WS, Gabriel SE, Lewallen DG. Cardiac and thromboembolic complications and mortality in patients undergoing total hip and total knee arthroplasty. Ann Rheum Dis 2011; 70 (12) 2082-2088
    CrossrefPubMedGoogle Scholar
  • 19 Rozell JC, Courtney PM, Dattilo JR, Wu CH, Lee GC. Should all patients be included in alternative payment models for primary total hip arthroplasty and total knee arthroplasty?. J Arthroplasty 2016; 31 (9 Suppl) 45-49
    CrossrefPubMedGoogle Scholar
  • 20 Johnsen SP, Sørensen HT, Pedersen AB, Lucht U, Søballe K, Overgaard S. Patient-related predictors of implant failure after primary total hip replacement in the initial, short- and long-term: a nationwide Danish follow-up study including 36 984 patients. J Bone Jt Surg - Ser B 2006; 88 (10) 1303-1308
    CrossrefPubMedGoogle Scholar
  • 21 Schwarzkopf R, Ho J, Snir N, Mukamel DD. Factors influencing discharge destination after total hip arthroplasty: a California State Database Analysis. Geriatr Orthop Surg Rehabil 2015; 6 (03) 215-219
    CrossrefPubMedGoogle Scholar
  • 22 Shah AN, Vail TP, Taylor D, Pietrobon R. Comorbid illness affects hospital costs related to hip arthroplasty: quantification of health status and implications for fair reimbursement and surgeon comparisons. J Arthroplasty 2004; 19 (06) 700-705
    CrossrefPubMedGoogle Scholar
  • 23 Caballero B. Humans against obesity: who will win?. Adv Nutr 2019; 10 (Suppl. 01) S4-S9
    CrossrefPubMedGoogle Scholar
  • 24 Kunutsor SK, Whitehouse MR, Blom AW, Beswick AD. INFORM Team. Patient-related risk factors for periprosthetic joint infection after total joint arthroplasty: a systematic review and meta-analysis. PLoS One 2016; 11 (03) e0150866
    CrossrefPubMedGoogle Scholar
  • 25 Alvi HM, Mednick RE, Krishnan V, Kwasny MJ, Beal MD, Manning DW. The effect of BMI on 30 day outcomes following total joint arthroplasty. J Arthroplasty 2015; 30 (07) 1113-1117
    CrossrefPubMedGoogle Scholar
  • 26 Duchman KR, Pugely AJ, Martin CT, Gao Y, Bedard NA, Callaghan JJ. Operative time affects short-term complications in total joint arthroplasty. J Arthroplasty 2017; 32 (04) 1285-1291
    CrossrefPubMedGoogle Scholar
  • 27 Patel VP, Walsh M, Sehgal B, Preston C, DeWal H, Di Cesare PE. Factors associated with prolonged wound drainage after primary total hip and knee arthroplasty. J Bone Joint Surg Am 2007; 89 (01) 33-38
    CrossrefPubMedGoogle Scholar
  • 28 Maradit Kremers H, Kremers WK, Berry DJ, Lewallen DG. Social and behavioral factors in total knee and hip arthroplasty. J Arthroplasty 2015; 30 (10) 1852-1854
    CrossrefPubMedGoogle Scholar
  • 29 Singh JA, Schleck C, Harmsen WS, Jacob AK, Warner DO, Lewallen DG. Current tobacco use is associated with higher rates of implant revision and deep infection after total hip or knee arthroplasty: a prospective cohort study. BMC Med 2015; 13: 283
    CrossrefPubMedGoogle Scholar
  • 30 Springer BD. Modifying risk factors for total joint arthroplasty: strategies that work nicotine. J Arthroplasty 2016; 31 (08) 1628-1630
    CrossrefPubMedGoogle Scholar
  • 31 Kapadia BH, Issa K, Pivec R, Bonutti PM, Mont MA. Tobacco use may be associated with increased revision and complication rates following total hip arthroplasty. J Arthroplasty 2014; 29 (04) 777-780
    CrossrefPubMedGoogle Scholar
  • 32 Tan TL, Maltenfort MG, Chen AF. et al. Development and evaluation of a preoperative risk calculator for periprosthetic joint infection following total joint arthroplasty. J Bone Joint Surg Am 2018; 100 (09) 777-785
    CrossrefPubMedGoogle Scholar
  • 33 Bini SA. Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care?. J Arthroplasty 2018; 33 (08) 2358-2361
    CrossrefPubMedGoogle Scholar
  • 34 Klemt C, Smith EJ, Tirumala V, Bounajem G, van den Kieboom J, Kwon Y-M. Outcomes and risk factors associated with 2-stage reimplantation requiring an interim spacer exchange for periprosthetic joint infection. J Arthroplasty 2021; 36 (03) 1094-1100
    CrossrefPubMedGoogle Scholar
  • 35 Klemt C, Tirumala V, Oganesyan R, Xiong L, van den Kieboom J, Kwon Y-M. Single-stage revision of the infected total knee arthroplasty is associated with improved functional outcomes: a propensity score-matched cohort study. J Arthroplasty 2021; 36 (01) 298-304
    CrossrefPubMedGoogle Scholar
  • 36 Shohat N, Goswami K, Tan TL. et al; ESCMID Study Group of Implant Associated Infections (ESGIAI) and the Northern Infection Network of Joint Arthroplasty (NINJA). 2020 Frank Stinchfield Award: identifying who will fail following irrigation and debridement for prosthetic joint infection. Bone Joint J 2020; 102-B (7_Supple_B): 11-19
    CrossrefPubMedGoogle Scholar