app

Model Predicts MRSA in Septic Arthritis

MedpageToday

CHICAGO -- A model that included easily obtainable values early in treatment successfully ruled out most cases of methicillin-resistant Staphylococcus aureus (MRSA) in children with septic arthritis, researcher found.

For children who reached cutoff values for fewer than three of these predictors -- C-reactive protein, neutrophil count, platelet count, heart rate, and duration of symptoms -- the negative predictive value for MRSA was 98%, according to Scott B. Rosenfeld of Baylor College of Medicine in Houston and colleagues.

Action Points

  • This study was published as an abstract and presented at a conference. These data and conclusions should be considered to be preliminary until published in a peer-reviewed journal.
  • Investigators retrospectively identified factors predictive of MRSA arthritis in children and developed a model that can be used as a clinical algorithm to reduce the rate of overtreatment for MRSA.

Septic arthritis in children is increasingly being associated with MRSA, and, in fact, in some centers today that includes more than half of cases.

"In an institution like ours, initial treatment will usually be empiric antibiotic therapy with an agent that covers for MRSA like clindamycin or vancomycin, and then a few days later when the culture and sensitivity results are available the regimen is tailored to the organism," Rosenfeld said at the annual meeting of the American Academy of Orthopaedic Surgeons.

"This system is good because it prevents the undertreatment of patients with MRSA, but it's bad because it overtreats all the patients with non-MRSA infections," he said.

Therefore, with the goal of creating an algorithm that could predict the likelihood of MRSA early in the course of treatment, he and his colleagues conducted a retrospective chart review of 138 consecutive children treated for aspiration-confirmed septic arthritis.

After excluding those under a year old and those with incomplete medical data, they identified 16 potential variables in 109 patients that seemed to differ between the 25% of patients with MRSA infections and the remainder who were negative for the resistant infection.

In a multivariate logistic regression analysis, these factors were independently associated with increased odds of MRSA and their optimal cutoff values determined:

  • CRP, OR 10, cutoff 7.9 mg/L
  • Absolute neutrophil count, OR 5.6, cutoff 8.3×106 cells/mL
  • Duration of symptoms, OR 5.2, cutoff 4 days
  • Platelet count, OR 4.2, cutoff 296×106 cells/mL
  • Heart rate, OR 2.6, cutoff 127 beats per minute

"So then we plugged our patients into the model and found that very few patients with fewer than three of the criteria had MRSA, and we felt that was a pretty good threshold for a positive test," he said.

Specifically, among patients with fewer than two positive predictors, there were no cases of MRSA and the model correctly classified the infection in 100% of cases. In those with two predictors, the model correctly identified 97% of cases.

For those at the threshold of three positive predictors, only 48% had MRSA. Of the false positives, 85% were at that threshold of three, he said.

When the number of positive predictive factors rose to four, the model correctly identified 62% of cases, and with five, 100% of cases were correctly identified.

"These results suggest that, in a hospital like ours, where we assume that patients with septic arthritis may have MRSA, this approach could reduce the rate of overtreatment for MRSA from 100% to 34%," he said.

Limitations of the study included its retrospective design, which should be repeated in a prospective study, the researchers noted.

In addition, patients with septic arthritis always need to be closely monitored to evaluate their clinical response to treatment, Rosenfeld concluded.

Disclosures

Rosenfeld reported no disclosures.

Primary Source

American Academy of Orthopaedic Surgeons

Source Reference: Rosenfeld S, et al "Predicting methicillin resistant Staphylococcus aureus septic arthritis in children" AAOS 2013; Abstract 415.