Existing and emerging methods of diagnosis and monitoring of pediatric musculoskeletal infection

Michalowitz, Andrew; Yang, Jenny; Castañeda, Pablo; Litrenta, Jody

doi:10.1016/j.injury.2020.06.020

Cited by 14 publications

(16 citation statements)

References 52 publications

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“…CRP and ESR have been shown to be the most effective tests in evaluating acute hematogenous osteomyelitis [21]. ESR is still conventionally collected, but alone, it does not have a high positive predictive value, and it is also slower to return to its normal levels, making it less useful in monitoring infections [22]. Another study reported that cases with ESR less than 25 mm/h had an 86% probability of not having septic arthritis [20].…”

Section: Discussionmentioning

confidence: 99%

D-dimer may aid in the diagnosis of pediatric musculoskeletal infections: a prospective study

Topak¹,

Nazik²,

Seyithanoğlu

et al. 2023

Journal of Pediatric Orthopaedics B

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Musculoskeletal infections, including septic arthritis, osteomyelitis, and soft tissue infections, are critical morbidity factors for children and adolescents. This study investigated the role of D-dimer levels for diagnosing childhood musculoskeletal infections. This single-center prospective study was initiated in April 2020 following approval from the local ethics committee. The study included 54 children, divided into the infection group (n = 21), comprising patients who underwent surgical treatment for childhood musculoskeletal infections and had macroscopically visible purulent discharge during surgery, and the control group (n = 33), comprising healthy children. In the infection group, the mean values of erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), plasma D-dimer, and white blood cell (WBC) were 39.42 ± 27.00 mm/h, 101.50 ± 76.90 mg/l, 2.34 ± 2.59 mg/l, and 15.55 ± 6.86 × 10 9 /l, respectively. On comparison, the infection group showed higher levels of WBC, CRP, ESR, D-dimer, and neutrophil-to-lymphocyte ratio than the control group. When the D-dimer cutoff value of 0.43 mg/l was taken, it was observed that it had 95.2% sensitivity and 81.8% specificity. The area under curve (AUC) of the above-mentioned parameters calculated via receiver operating characteristic curves showed CRP levels as the optimum predictor of childhood musculoskeletal infections, followed by the ESR, plasma D-dimer, and WBC levels in descending order (AUC: 0.999, 0.997, 0.986, and 0.935, respectively). D-dimer is another test, which in combination with other conventional established tests (CRP and ESR) can be helpful in diagnosis of pediatric infection. We recommend the addition of D-Dimer to ESR, CRP, and WBC as a first-line investigation in cases with suspected pediatric musculoskeletal infections.

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Section: Discussionmentioning

confidence: 99%

D-dimer may aid in the diagnosis of pediatric musculoskeletal infections: a prospective study

Topak¹,

Nazik²,

Seyithanoğlu

et al. 2023

Journal of Pediatric Orthopaedics B

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“…Michalowitz et al 11 showed that the identification of a pathogen in musculoskeletal infection affecting pediatric patients was difficult. The rate of culture-negative musculoskeletal infections was approximately 50%, and in some cases it was as high as 70%.…”

Section: Application Of Mngs For the Detection Of Bacteriamentioning

confidence: 99%

Progress in the application of metagenomic next-generation sequencing in pediatric infectious diseases

Wang

2022

Pediatrics & Neonatology

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“…With the increasing throughput and decreasing cost of DNA sequencing, whole genome sequencing (WGS) may be an alternative for rapidly identification of pathogens of all types of phyla based on a small sample of DNA (19). Genome-wide studies of DNA variation related to antibiotic resistance phenotypes are one of the most concerning and have garnered high public interest, especially since several multidrug-resistant strains have emerged worldwide (20).…”

Section: Introductionmentioning

confidence: 99%

“…With the increasing throughput and decreasing cost of DNA sequencing, large numbers of bacterial genomes have been submitted to public databases (23, 24). Genome-wide studies of DNA variation related to antibiotic resistance phenotypes have garnered high public interest, especially since several multidrug-resistant strains have emerged worldwide (25).…”

Section: Introductionmentioning

confidence: 99%

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Identification of a specific biomarker ofAcinetobacter baumanniiGlobal Clone 1 by machine learning and PCR related to metabolic fitness of ESKAPE pathogens

Álvarez

Quiroga

Centrón

2021

Preprint

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Since the emergence of clones with epidemic behavior, or “high-risk clones”, uninterrupted investigations have been undertaken to understand the genetic and biological basis that led their widespread in nosocomial settings at a global scale. However, its complex genetic and ecological nature only allowed the identification of partial biomarkers with low specificity. The implication of our study is that we successfully applied machine learning algorithms to recognize genetic biomarkers for the identification of Acinetobacter baumannii Global Clone 1, one of the most widespread high-risk clones. Support Vector Machine model predicted U1 sequence with 367 nucleotides length as a specific biomarker for A. baumannii GC1 strains. U1 sequence matched AYE genome in ABAYE1552 locus between 558 and 924 coordinates that corresponds to a fragment of the moaCB gene which encodes a bifunctional protein that includes the molybdenum cofactor biosynthesis protein C and protein B. This region of the gene moaCB allows to differentiate between the A. baumannii GC1 and non-GC1 strains, becoming a suitable biomarker capable of being translated into clinical settings as a molecular typing method such as PCR. Our findings highlight that machine learning can be fruitfully useful even in complex gaps of knowledge of epidemic clones and signify a noteworthy contribution to the literature that could be used to identify challenging nosocomial biomarkers for other multidrug resistant pandemic clones.IMPORTANCEHigh-risk clones play a major role in the spread of resistance. The factors driving the emergence and the dissemination capacity of these clones are not well understood. The dissemination of A. baumannii strains belonging to high-risk GC1 is a global concern. Molecular methods have been used to type GC1 clones such as a particular deletion in intI1 that identify among islands in comM with a Tn6019 backbone. However, since some GC1 strains do not harbor the genomic island AbaR, and intI1 is very common in clinical samples, a more specific biomarker is necessary. The significance of our research is that we successfully applied machine learning methods to predict A. baumannii GC1. Our findings highlight that machine learning can be successfully applied not only to find high-risk clones putative biomarkers but also in disentangling genetic traits that could be used as therapeutic targets to reduce dissemination of lineages with epidemic behavior.

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Existing and emerging methods of diagnosis and monitoring of pediatric musculoskeletal infection

Cited by 14 publications

References 52 publications

D-dimer may aid in the diagnosis of pediatric musculoskeletal infections: a prospective study

D-dimer may aid in the diagnosis of pediatric musculoskeletal infections: a prospective study

Progress in the application of metagenomic next-generation sequencing in pediatric infectious diseases

Identification of a specific biomarker ofAcinetobacter baumanniiGlobal Clone 1 by machine learning and PCR related to metabolic fitness of ESKAPE pathogens

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