Development of predictive nomograms for clinical use to quantify the risk of isolating resistance prone organisms in patients with infected foot ulcers

Abstract

Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus (MRSA) have been considered prevalent pathogens in foot infections. However, whether empiric therapy directed against these organisms is necessary, and in whom to consider treatment, is rather unclear. The aim of this study was to develop predictive algorithms for forecasting the probability of isolating these organisms in the infected wounds of patients in a population where the prevalence of resistant pathogens is low. This was a retrospective study of regression model-based risk factor analysis that included 140 patients who presented with infected, culture positive foot ulcers to two urban hospitals. A total of 307 bacteria were identified, most frequently MRSA (11.1%). P. aeruginosa prevalence was 6.5%. In the multivariable analysis, amputation (odds ratio (OR) 5.75, 95% confidence interval (CI) 1.48–27.63), renal disease (OR 5.46, 95% CI 1.43–25.16) and gangrene (OR 2.78, 95% CI 0.82–9.59) were identified as risk factors associated with higher while diabetes (OR 0.07, 95% CI 0.01–0.34) and Infectious Diseases Society of America infection severity >3 (OR 0.18, 95% CI 0.03–0.65) were associated with lower odds of P. aeruginosa isolation (C statistic 0.81). Similar analysis for MRSA showed that amputation was associated with significantly lower (OR 0.29, 95% CI 0.09–0.79) risk, while history of MRSA infection (OR 5.63, 95% CI 1.56–20.63) and osteomyelitis (OR 2.523, 95% CI 1.00–6.79) was associated with higher odds of isolation (C statistic 0.69). We developed two predictive nomograms with reasonable to strong ability to discriminate between patients who were likely of being infected with P. aeruginosa or MRSA and those who were not. These analyses confirm the association of some, but also question the significance of other frequently described risk factors in predicting the isolation of these organisms.

Diabetic Foot Infections: Local Prevalence of and Case–Control Study of Risk Factors for Methicillin-Resistant Staphylococcus aureus and Pseudomonas aeruginosa

“…A history of MRSA was the only risk factor significantly associated with MRSA DFIs, though MRSA nasal colonization approached significance ( P = .07); both risk factors have been reported previously [ 3 , 5 ]. Notably, MRSA nasal colonization has been characterized as an insensitive but specific predictor of MRSA in DFIs [ 8 , 9 ].…”

“…In this survey of DFI cultures, MRSA was less prevalent than literature values (10%–20%) [ 3 , 4 ], whereas PsA was more prevalent (5%–10%) [ 5–7 ]. In the case–control study, a history of MRSA was associated with MRSA DFIs, and the presence of gangrene was associated with PsA DFIs.…”

“…The case–control study only identified gangrene as a risk factor for PsA DFI, as has been previously described [ 5 ]. PsA is classically associated with other gangrenous infections such as ecthyma gangrenosum, and it is intuitive that the presence of gangrene—a surrogate for the presence of PsA—would have the strongest association with PsA DFIs.…”

“…The IDSA recommends empirically covering PsA in cases of high local prevalence (undefined in the guidance), warm climate, and frequent exposure of the feet to water [ 1 ]. Other reported risk factors include a history of amputation, wound dressings, and chronic kidney disease [ 5 , 10 ].…”

A Web-Based Dynamic Nomogram to Predict the Risk of Methicillin-Resistant Staphylococcal Infection in Patients with Pneumonia

“…Another retrospective study showed that a history of MRSA infection (OR 5.6, 95% CI 1.56–20.63) and osteomyelitis (OR 2.5, 95% CI 1.00–6.79) was linked to a higher chance of MRSA isolation (C statistic of 0.7). Previous work also introduced a predictive nomogram with moderate to good discrimination for MRSA infection in patients with infected foot ulcers [ 23 ]. In this study, we developed a model and used the bootstrap method to validate the nomogram with an AUC value of 0.70 and a sensitivity of 92.39% at a threshold of 0.3 in the dataset ( Figure 2 and Figure 3 A,B and Table 3 ).…”