Hip Prosthesis InfectionSEPTIC-ANNRNCT04119804
Early Discrimination of Periprosthetic Hip Infections Using Neural Networks (SEPTIC-ANNR)
An observational study of Hip Prosthesis Infection, run by Istituto Ortopedico Rizzoli at 1 site in Italy.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Hip Prosthesis Infection
- What is being tested?
- cellular neural networks-genetic algorithm
- How many people
- 36 participants
- Where
- 1 siteItaly
- Study dates
- 3 Oct 2019to 20 Jan 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: A consecutive series of adult patients treated for aseptic and septic loosening of primary hip implants at IRCCS Istituto Ortopedico Rizzoli.
Who may be able to join
- 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)
Who cannot join
- Hip re-revisions
- Incomplete or inadequate radiographic assessment
- Inadequate data to diagnose infection according to 2014 CDC criteria and tissue samples
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Early Discrimination of Periprosthetic Hip Infections Using Neural Networks: a Pilot Study
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).
Read the detailed description
Periprosthetic hip infections are an hot topic in orthopedic surgery, whose incidence is about 1%. The morbidity, mortality and additional costs associated with prolonged hospitalization and further treatments are the main concerns. Periprosthetic infections are generally diagnosed using the CDC (Center for Disease Control and Prevention) criteria (2014). The diagnosis is based on major and minor criteria, including pre-operative and intra-operative parameters. In order to achieve a reliable diagnosis of infection, when fistula are not present, synovial fluid aspiration or tissue samples are required. However, these techniques are expensive and invasive. Moreover, sensitivity is not always so accurate, as shown by some series of revision surgeries performed for presumed aseptic loosening, which turned out to be septic after cultures. Therefore, diagnosis of infection often occurs late and after a long, complex, expensive and not always decisive diagnostic workup, impacting on the timing and success of the treatment.
A practical, rapid, reliable and non-invasive (possibly outpatient) diagnostic procedure for periprosthetic infections would be desirable. It may rely on diagnostic imaging, limiting the collection of liquid or tissues to doubtful cases. Currently, CT and nuclear medicine imaging techniques are not routinely adopted in the diagnosis of infection, due to the modest reliability, costs and exposure to radiant agents.
Recently, neural networks have been introduced: they consist of many simple parallel processors, deeply connected, realizing a computational model. Neural networks mimic brain and its ability to learn. Computational models recognize of visual signals, manage complex situations in real time, classify and manage noise, use associative memory with real-time access to large amounts of data and reconstruct partial or corrupted information. Neural networks have been already used to predict the onset of infections, metastases and treatment failures, integrating clinical and diagnostic imaging data. To date, no studies about neural networks in periprosthetic infection have been conducted. 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).
Specifically, a population of patients, with a complete radiographic history (pre-operative X-rays and a series of other post-operative X-rays), treated for septic or aseptic loosening, is selected.
Both cases are necessary to "instruct" a neural network. The first step consists in identifying a consecutive series of patients with septic or aseptic loosening diagnosis, consulting the hospital database. Thus, patients are categorically divided into septic, or aseptic, loosening. The 2014 CDC criteria are used (as routinely performed in the clinical setting), adding another major and necessary criterion: at least 3 positive intraoperative tissue samples (same micro-organism). In case of aseptic loosening, the case must not meet the CDC 2014 criteria. Thus, the imaging and clinical data of the patients are collected. Having ascertained the diagnosis, the radiographic material is processed (cellular neural networks-genetic algorithm). The proposed procedure processes the radiographic images using the following pipeline and the MatLab software (Mathworks, Natick, US):
- baseline: the first post-implant image is compared to the pre-implant radiographic image;
- progresses are recorded by periodical radiographs using standard and repeatable projections (pelvis X-rays);
- the features are extracted from each image, in the manually segmented area (region of interest - ROI). Three steps take place: 1) image pre-processing, to create uniform frameworks of input data (gray-level images). Color Histogram Equalization; 2) features extracted from neural networks are applied to ROI. Cloning templates using genetic algorithms. The features will be processed by a fully connected layer + SoftMax; 3) features extracted from AutoEconder with fully-connected layer + SoftMax;
- analysis of differential radiographic features (analysis of cellular and convolutive sides) and comparison to the baseline. Post-processing in the following way: 1) fully-connected regression layer and multiclass classifier: will produce the percentage of septic progression risk; 2) fully-connected regression layer and binary classifier: features in the septic / aseptic clusters;
- a final decision tree: fusion of the above-mentioned data, providing a percentage of septic progression risk at the indexed imaging. The aim is to verify whether the neural networks applied to radiographic imaging can accurately, sensitively and specifically recognize a late, chronic periprosthetic hip infection diagnosed according to validated and certain criteria.
- Conditions
- Hip Prosthesis Infection
- Being tested
- cellular neural networks-genetic algorithm
- Keywords
- periprosthetic hip infection, cellular neural networks, genetic algorithm, radiography, accuracy, early discrimination
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
cellular neural networks-genetic algorithmDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| 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). Receives: Diagnostic Test: cellular neural networks-genetic algorithm | |
| aseptic loosening | aseptic loosening of primary hip implants not meeting the CDC 2014 criteria Receives: Diagnostic Test: cellular neural networks-genetic algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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.
Measured: 15 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Oct 2019
- Study start3 Oct 2019
- Primary completion20 Jan 2024
- Study completion20 Jan 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (11)Papers the sponsor has linked to this study.
- Other trials for Hip Prosthesis InfectionStudies of the same condition, open and under way.
- More trials run by Istituto Ortopedico RizzoliThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 36 participants (actual)
Sponsor and investigators
- Lead sponsor
- Istituto Ortopedico Rizzoli
- Sponsor type
- University, hospital or other
- Collaborators
- Università degli studi di Messina
- Responsible party
- Sponsor
- Investigators
- Francesco Traina, PhD, Study chair, IRCCS Istituto Ortopedico Rizzoli
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04119804
- Sponsor's protocol ID
- 438/2019/Oss/IOR
- Acronym
- SEPTIC-ANNR
- Status last verified
- Oct 2024
- First submitted
- 7 Oct 2019
- Last updated
- 28 Oct 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 27707850Peel 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.
PubMed 27807152Bargon 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.
PubMed 30348560Abdel 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.
PubMed 30348566Chotanaphuti 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.
PubMed 30348575Amanatullah 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.
PubMed 30348576Ting 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.
PubMed 28343826Heckerling 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.
PubMed 16469531Yamashita 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.
PubMed 29934920Fazal 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.
PubMed 30017288Osmon 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.
PubMed 23223583
Study at a glance
From the registry record, last updated by the sponsor on 28 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Early Discrimination of Periprosthetic Hip Infections Using Neural Networks: a Pilot Study
- ClinicalTrials.gov ID
- NCT04119804
- Acronym / sponsor ID
- SEPTIC-ANNR / 438/2019/Oss/IOR
- Conditions
- Hip Prosthesis Infection
- Keywords
- periprosthetic hip infection, cellular neural networks, genetic algorithm, radiography, accuracy, early discrimination
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 36 participants (actual)
- Lead sponsor
- Istituto Ortopedico RizzoliUniversity, hospital or other
- Collaborators
- Università degli studi di Messina
- Study start
- 3 Oct 2019 (actual)
- Primary completion
- 20 Jan 2024 (actual)
- Study completion
- 20 Jan 2024 (actual)
- First posted
- 8 Oct 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
Detailed description
Periprosthetic hip infections are an hot topic in orthopedic surgery, whose incidence is about 1%. The morbidity, mortality and additional costs associated with prolonged hospitalization and further treatments are the main concerns. Periprosthetic infections are generally diagnosed using the CDC (Center for Disease Control and Prevention) criteria (2014). The diagnosis is based on major and minor criteria, including pre-operative and intra-operative parameters. In order to achieve a reliable diagnosis of infection, when fistula are not present, synovial fluid aspiration or tissue samples are required. However, these techniques are expensive and invasive. Moreover, sensitivity is not always so accurate, as shown by some series of revision surgeries performed for presumed aseptic loosening, which turned out to be septic after cultures. Therefore, diagnosis of infection often occurs late and after a long, complex, expensive and not always decisive diagnostic workup, impacting on the timing and success of the treatment.
A practical, rapid, reliable and non-invasive (possibly outpatient) diagnostic procedure for periprosthetic infections would be desirable. It may rely on diagnostic imaging, limiting the collection of liquid or tissues to doubtful cases. Currently, CT and nuclear medicine imaging techniques are not routinely adopted in the diagnosis of infection, due to the modest reliability, costs and exposure to radiant agents.
Recently, neural networks have been introduced: they consist of many simple parallel processors, deeply connected, realizing a computational model. Neural networks mimic brain and its ability to learn. Computational models recognize of visual signals, manage complex situations in real time, classify and manage noise, use associative memory with real-time access to large amounts of data and reconstruct partial or corrupted information. Neural networks have been already used to predict the onset of infections, metastases and treatment failures, integrating clinical and diagnostic imaging data. To date, no studies about neural networks in periprosthetic infection have been conducted. 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).
Specifically, a population of patients, with a complete radiographic history (pre-operative X-rays and a series of other post-operative X-rays), treated for septic or aseptic loosening, is selected.
Both cases are necessary to "instruct" a neural network. The first step consists in identifying a consecutive series of patients with septic or aseptic loosening diagnosis, consulting the hospital database. Thus, patients are categorically divided into septic, or aseptic, loosening. The 2014 CDC criteria are used (as routinely performed in the clinical setting), adding another major and necessary criterion: at least 3 positive intraoperative tissue samples (same micro-organism). In case of aseptic loosening, the case must not meet the CDC 2014 criteria. Thus, the imaging and clinical data of the patients are collected. Having ascertained the diagnosis, the radiographic material is processed (cellular neural networks-genetic algorithm). The proposed procedure processes the radiographic images using the following pipeline and the MatLab software (Mathworks, Natick, US):
- baseline: the first post-implant image is compared to the pre-implant radiographic image;
- progresses are recorded by periodical radiographs using standard and repeatable projections (pelvis X-rays);
- the features are extracted from each image, in the manually segmented area (region of interest - ROI). Three steps take place: 1) image pre-processing, to create uniform frameworks of input data (gray-level images). Color Histogram Equalization; 2) features extracted from neural networks are applied to ROI. Cloning templates using genetic algorithms. The features will be processed by a fully connected layer + SoftMax; 3) features extracted from AutoEconder with fully-connected layer + SoftMax;
- analysis of differential radiographic features (analysis of cellular and convolutive sides) and comparison to the baseline. Post-processing in the following way: 1) fully-connected regression layer and multiclass classifier: will produce the percentage of septic progression risk; 2) fully-connected regression layer and binary classifier: features in the septic / aseptic clusters;
- a final decision tree: fusion of the above-mentioned data, providing a percentage of septic progression risk at the indexed imaging. The aim is to verify whether the neural networks applied to radiographic imaging can accurately, sensitively and specifically recognize a late, chronic periprosthetic hip infection diagnosed according to validated and certain criteria.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| septic loosening | – | Diagnostic Test: cellular neural networks-genetic algorithmSeptic 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). |
| aseptic loosening | – | Diagnostic Test: cellular neural networks-genetic algorithmaseptic loosening of primary hip implants not meeting the CDC 2014 criteria |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- A consecutive series of adult patients treated for aseptic and septic loosening of primary hip implants at IRCCS Istituto Ortopedico Rizzoli.
Key inclusion criteria (5 listed)
- 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)
Key exclusion criteria (3 listed)
- Hip re-revisions
- Incomplete or inadequate radiographic assessment
- Inadequate data to diagnose infection according to 2014 CDC criteria and tissue samples
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| cellular neural networks-genetic algorithmCellular 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. | Diagnostic test | aseptic loosening, septic loosening |
Primary outcome
Accuracy
Time frame: 15 years
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.
Sensitivity
Time frame: 15 years
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.
Specificity
Time frame: 15 years
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Oct 2024.
- Results publication: PubMed 27707850Verberne 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.
- Results publication: PubMed 27807152Peel 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.
- Results publication: PubMed 30348560Bargon 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.
- Results publication: PubMed 30348566Abdel 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.
- Results publication: PubMed 30348575Chotanaphuti 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.
- Results publication: PubMed 30348576Amanatullah 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.
- Results publication: PubMed 28343826Ting 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.
- Results publication: PubMed 16469531Heckerling 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.
- Results publication: PubMed 29934920Yamashita 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.
- Results publication: PubMed 30017288Fazal 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.
- Results publication: PubMed 23223583Osmon 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.
- ReferenceLink provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Francesco Traina, PhD study chair
IRCCS Istituto Ortopedico Rizzoli
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Hip Prosthesis Infection, run by Istituto Ortopedico Rizzoli at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 28 Oct 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy (time frame: 15 years). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04119804, which also lists 11 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Oct 2024.