Early Discrimination of Periprosthetic Hip Infections Using Neural Networks (SEPTIC-ANNR)

NCT04119804 | Completed DMC

• 1 other identifier: 438/2019/Oss/IOR
• Study Type: observational
• Target: 36
• Locations: 1 country
• Sites: 1

Brief Summary

The study is about the role of cellular neural networks-genetic algorithm in the diagnosis of periprosthetic hip infections. A retrospective case series of septic and aseptic loosening of primary hip arthroplasties is selected. The diagnosis of septic loosening is made according to well-established criteria (CDC 2014 and culture samples). The serial radiographs of the selected patients are processed using cellular neural networks-genetic algorithm. The purpose of this study is to evaluate whether neural networks (cellular neural networks-genetic algorithm), applied to conventional radiographies, are accurate, sensitive and specific for the early-discrimination of a periprosthetic hip infection, already diagnosed with well-recognized methods (CDC 2014).

Conditions

Hip Prosthesis Infection

Keywords

periprosthetic hip infection; cellular neural networks; genetic algorithm; radiography; accuracy; early discrimination

Outcome Measures

Primary Outcomes (3)

• Accuracy

  Definition: ability of the cellular neural network to discriminate between septic and aseptic loosening. Technique: the diagnostic accuracy will be measured as a receiver operating characteristic (ROC) curve, according to the maximum likelihood method (binomial approximation). Metric: percentage. Minimum-maximum values: 0-100.

  15 years

• Sensitivity

  Definition: the probability of being septic in septic hips with ascertained CDC criteria. Technique: true positive / (true positive + false negative). Metric: percentage. Minimum-maximum values: 0-100.

  15 years

• Specificity

  Definition: proportion of aseptic loosening in total of aseptic loosening ascertained using CDC criteria Technique: True negative / (true negative + false positive) Metric: percentage. Minimum-maximum values: 0-100.

  15 years

Study Arms (2)

septic loosening

Septic loosening of primary hip implants according to the 2014 CDC criteria (as routinely performed in the clinical setting), adding another major and necessary criterion: at least 3 positive intraoperative tissue samples (same micro-organism).

Diagnostic Test: cellular neural networks-genetic algorithm

aseptic loosening

aseptic loosening of primary hip implants not meeting the CDC 2014 criteria

Diagnostic Test: cellular neural networks-genetic algorithm

Interventions

cellular neural networks-genetic algorithm DIAGNOSTIC_TEST

Cellular neural networks-genetic algorithm applied to conventional radiographs of hip implants with a well-established diagnosis of loosening. The study is not intended to use a software without a CE mark as a medical device, or to use the software as a tool to diagnose or prevent human disease, according to Directive 93/42 / European Economic Community. The study will evaluate if the software, properly calibrated, is able to recognize with adequate accuracy infections already diagnosed with validated methods.

aseptic loosening; septic loosening

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

A consecutive series of adult patients treated for aseptic and septic loosening of primary hip implants at IRCCS Istituto Ortopedico Rizzoli.

You may qualify if:

• Revisions of primary total hip arthroplasty due to septic and aseptic loosening
• In case of septic loosening, diagnosis of late chronic periprosthetic hip infection
• Complete clinical data
• Complete lab data (pre-revision erythrocyte sedimentation rate and C-reactive protein, at least 5 intraoperative tissue samples).
• Complete radiographic assessment (pre-implant X-ray, a series of post-operative X-rays, pre-revision X-ray)

You may not qualify if:

• Hip re-revisions
• Incomplete or inadequate radiographic assessment
• Inadequate data to diagnose infection according to 2014 CDC criteria and tissue samples

Sponsors & Collaborators

• Istituto Ortopedico Rizzoli: lead
• Università degli studi di Messina: collaborator

Study Sites (1)

IRCCS Istituto Ortopedico Rizzoli

Bologna, 40136, Italy

Location

Related Publications (11)

• Verberne SJ, Raijmakers PG, Temmerman OP. The Accuracy of Imaging Techniques in the Assessment of Periprosthetic Hip Infection: A Systematic Review and Meta-Analysis. J Bone Joint Surg Am. 2016 Oct 5;98(19):1638-1645. doi: 10.2106/JBJS.15.00898.

  PMID: 27707850 RESULT

• Peel TN, Spelman T, Dylla BL, Hughes JG, Greenwood-Quaintance KE, Cheng AC, Mandrekar JN, Patel R. Optimal Periprosthetic Tissue Specimen Number for Diagnosis of Prosthetic Joint Infection. J Clin Microbiol. 2016 Dec 28;55(1):234-243. doi: 10.1128/JCM.01914-16. Print 2017 Jan.

  PMID: 27807152 RESULT

• Bargon R, Bruenke J, Carli A, Fabritius M, Goel R, Goswami K, Graf P, Groff H, Grupp T, Malchau H, Mohaddes M, Novaes de Santana C, Phillips KS, Rohde H, Rolfson O, Rondon A, Schaer T, Sculco P, Svensson K. General Assembly, Research Caveats: Proceedings of International Consensus on Orthopedic Infections. J Arthroplasty. 2019 Feb;34(2S):S245-S253.e1. doi: 10.1016/j.arth.2018.09.076. Epub 2018 Oct 19. No abstract available.

  PMID: 30348560 RESULT

• Abdel Karim M, Andrawis J, Bengoa F, Bracho C, Compagnoni R, Cross M, Danoff J, Della Valle CJ, Foguet P, Fraguas T, Gehrke T, Goswami K, Guerra E, Ha YC, Klaber I, Komnos G, Lachiewicz P, Lausmann C, Levine B, Leyton-Mange A, McArthur BA, Mihalic R, Neyt J, Nunez J, Nunziato C, Parvizi J, Perka C, Reisener MJ, Rocha CH, Schweitzer D, Shivji F, Shohat N, Sierra RJ, Suleiman L, Tan TL, Vasquez J, Ward D, Wolf M, Zahar A. Hip and Knee Section, Diagnosis, Algorithm: Proceedings of International Consensus on Orthopedic Infections. J Arthroplasty. 2019 Feb;34(2S):S339-S350. doi: 10.1016/j.arth.2018.09.018. Epub 2018 Oct 19. No abstract available.

  PMID: 30348566 RESULT

• Chotanaphuti T, Courtney PM, Fram B, In den Kleef NJ, Kim TK, Kuo FC, Lustig S, Moojen DJ, Nijhof M, Oliashirazi A, Poolman R, Purtill JJ, Rapisarda A, Rivero-Boschert S, Veltman ES. Hip and Knee Section, Treatment, Algorithm: Proceedings of International Consensus on Orthopedic Infections. J Arthroplasty. 2019 Feb;34(2S):S393-S397. doi: 10.1016/j.arth.2018.09.024. Epub 2018 Oct 19. No abstract available.

  PMID: 30348575 RESULT

• Amanatullah D, Dennis D, Oltra EG, Marcelino Gomes LS, Goodman SB, Hamlin B, Hansen E, Hashemi-Nejad A, Holst DC, Komnos G, Koutalos A, Malizos K, Martinez Pastor JC, McPherson E, Meermans G, Mooney JA, Mortazavi J, Parsa A, Pecora JR, Pereira GA, Martos MS, Shohat N, Shope AJ, Zullo SS. Hip and Knee Section, Diagnosis, Definitions: Proceedings of International Consensus on Orthopedic Infections. J Arthroplasty. 2019 Feb;34(2S):S329-S337. doi: 10.1016/j.arth.2018.09.044. Epub 2018 Oct 19. No abstract available.

  PMID: 30348576 RESULT

• Ting NT, Della Valle CJ. Diagnosis of Periprosthetic Joint Infection-An Algorithm-Based Approach. J Arthroplasty. 2017 Jul;32(7):2047-2050. doi: 10.1016/j.arth.2017.02.070. Epub 2017 Mar 2.

  PMID: 28343826 RESULT

• Heckerling PS, Canaris GJ, Flach SD, Tape TG, Wigton RS, Gerber BS. Predictors of urinary tract infection based on artificial neural networks and genetic algorithms. Int J Med Inform. 2007 Apr;76(4):289-96. doi: 10.1016/j.ijmedinf.2006.01.005. Epub 2006 Feb 15.

  PMID: 16469531 RESULT

• Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging. 2018 Aug;9(4):611-629. doi: 10.1007/s13244-018-0639-9. Epub 2018 Jun 22.

  PMID: 29934920 RESULT

• Fazal MI, Patel ME, Tye J, Gupta Y. The past, present and future role of artificial intelligence in imaging. Eur J Radiol. 2018 Aug;105:246-250. doi: 10.1016/j.ejrad.2018.06.020. Epub 2018 Jun 22.

  PMID: 30017288 RESULT

• Osmon DR, Berbari EF, Berendt AR, Lew D, Zimmerli W, Steckelberg JM, Rao N, Hanssen A, Wilson WR; Infectious Diseases Society of America. Diagnosis and management of prosthetic joint infection: clinical practice guidelines by the Infectious Diseases Society of America. Clin Infect Dis. 2013 Jan;56(1):e1-e25. doi: 10.1093/cid/cis803. Epub 2012 Dec 6.

  PMID: 23223583 RESULT

Related Links

• Reference

Study Officials

• Francesco Traina, PhD

  IRCCS Istituto Ortopedico Rizzoli

  STUDY CHAIR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 7, 2019
• First Posted: October 8, 2019
• Study Start: October 3, 2019
• Primary Completion: January 20, 2024
• Study Completion: January 20, 2024
• Last Updated: October 28, 2024

Record last verified: 2024-10

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)