Detecting and Monitoring Periprosthetic Joint Infection by Using Electrical Bioimpedance Spectroscopy: A Preliminary Case Study.

biofilm infections diagnostic methods diseases diagnostic electrical bioimpedance spectroscopy prediction

Journal

Diagnostics (Basel, Switzerland)
ISSN: 2075-4418
Titre abrégé: Diagnostics (Basel)
Pays: Switzerland
ID NLM: 101658402

Informations de publication

Date de publication:
10 Jul 2022
Historique:
received: 16 05 2022
revised: 30 06 2022
accepted: 08 07 2022
entrez: 27 7 2022
pubmed: 28 7 2022
medline: 28 7 2022
Statut: epublish

Résumé

A method to detect the presence of infection after Total Joint Arthroplasty is presented. The method is based on Electrical Bioimpedance Spectroscopy and guarantees low latency, non-invasiveness, and cheapness with respect to the state of art. Experimental measurements were carried out on a singular patient who had already undergone bilateral Total Knee Arthroplasty. He was affected by a hematogenous Periprosthetic Joint Infections on the left knee. The right knee was adopted as the reference. Measurements were acquired once before the surgical procedure (Diagnosis Phase) and twice in the postoperative phases (Monitoring Phase). The most relevant frequency range, for diagnosis and monitoring phases, was found to be between 10 kHz to 50 kHz. The healing trend predicted by the decrease of impedance magnitude spectrum was reflected in clinical and laboratory results. In addition, one month after the last acquisition (two months after the surgery), the patient fully recovered, confirming the prediction of the Electrical Bioimpedance Spectroscopy technique.

Identifiants

pubmed: 35885583
pii: diagnostics12071680
doi: 10.3390/diagnostics12071680
pmc: PMC9323083
pii:
doi:

Types de publication

Case Reports

Langues

eng

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Auteurs

Marco Balato (M)

Department of Electrical Engineering and Information Technologies (DIETI), University of Naples "Federico II", 80125 Napoli, Italy.

Carlo Petrarca (C)

Department of Electrical Engineering and Information Technologies (DIETI), University of Naples "Federico II", 80125 Napoli, Italy.

Pasquale Arpaia (P)

Department of Electrical Engineering and Information Technologies (DIETI), University of Naples "Federico II", 80125 Napoli, Italy.
Interdepartmental Research Center on Management and Innovation in Healthcare, (CIRMIS), University of Naples "Federico II", 80131 Napoli, Italy.

Nicola Moccaldi (N)

Department of Electrical Engineering and Information Technologies (DIETI), University of Naples "Federico II", 80125 Napoli, Italy.

Francesca Mancino (F)

Department of Electrical Engineering and Information Technologies (DIETI), University of Naples "Federico II", 80125 Napoli, Italy.

Giusy Carleo (G)

Department of Neuroscience and Reproductive and Odontostomatological Sciences, University of Naples "Federico II", 80131 Napoli, Italy.

Simone Minucci (S)

Department of Economics, Engineering, Society and Business Organization (DEIM), University of Tuscia, 01100 Viterbo, Italy.

Massimo Mariconda (M)

Interdepartmental Research Center on Management and Innovation in Healthcare, (CIRMIS), University of Naples "Federico II", 80131 Napoli, Italy.
Department of Public Health, University of Naples "Federico II", 80131 Napoli, Italy.

Giovanni Balato (G)

Department of Public Health, University of Naples "Federico II", 80131 Napoli, Italy.

Classifications MeSH