Non-purulent skin and soft tissue infections: predictive power of a severity score and the appropriateness of treatment in a prospective cohort

The quality of antimicrobial prescribing in skin and soft tissue management in Australian hospitals: an analysis of the National Antimicrobial Prescribing Survey data

BACKGROUND

Skin and soft tissue infections (SSTIs) are among the most common indications for antimicrobial prescribing in hospitals. Inappropriate antimicrobial use can lead to increased morbidity, unnecessary hospital readmission and increased antimicrobial resistance. This study aims to assess the quality of antimicrobial prescribing practices in SSTI management within Australian hospitals to provide guidance for future practice.

METHODS

A retrospective analysis was conducted with data from the National Antimicrobial Prescribing Survey (NAPS). SSTI prescribing data from Hospital NAPS (2013-2022) and surgical site infection data from Surgical NAPS (2016-2022) datasets were analysed. Variables assessed included guideline compliance, appropriateness as per the structured NAPS algorithm and reasons for inappropriateness.

RESULTS

From the Hospital NAPS dataset, 40,535 antimicrobial prescriptions for SSTIs were analysed. The most common indication was cellulitis (34.1%; n=13,822), and the most prescribed antimicrobial was flucloxacillin (18.8%; n=7,638). SSTI indications had a lower rate of guideline compliance, but a higher rate of appropriateness compared to all other indications for antimicrobial prescriptions (guideline compliance 66.3%, n=21,035 vs 67.4%, n=156,285 appropriateness 75.6%, n=30,639 vs 72.7%, n=209,383). The most common reason for inappropriateness was incorrect dose or frequency (29.3%; n=2,367). From the Surgical NAPS dataset, 5,674 prescriptions for surgical site infections were analysed. 68.2% (n=3,867) were deemed appropriate. The most common reason for inappropriateness was incorrect dose or frequency (27.7%; n=350).

CONCLUSIONS

As SSTIs are a common indication for prescribing an antimicrobial in Australian hospitals, identifying effective antimicrobial stewardship strategies to optimise antimicrobial use for SSTI management is therefore recommended to improve patient outcomes.

Development and external validation of machine learning-based models to predict patients with cellulitis developing sepsis during hospitalisation

OBJECTIVE

Cellulitis is the most common cause of skin-related hospitalisations, and the mortality of patients with sepsis remains high. Some stratification models have been developed, but their performance in external validation has been unsatisfactory. This study was designed to develop and compare different models for predicting patients with cellulitis developing sepsis during hospitalisation.

DESIGN

This is a retrospective cohort study.

SETTING

This study included both the development and the external-validation phases from two independent large cohorts internationally.

PARTICIPANTS AND METHODS

A total of 6695 patients with cellulitis in the Medical Information Mart for Intensive care (MIMIC)-IV database were used to develop models with different machine-learning algorithms. The best models were selected and then externally validated in 2506 patients with cellulitis from the YiduCloud database of our university. The performances and robustness of selected models were further compared in the external-validation group by area under the curve (AUC), diagnostic accuracy, sensitivity, specificity and diagnostic OR.

PRIMARY OUTCOME MEASURES

The primary outcome of interest in this study was the development based on the Sepsis-3.0 criteria during hospitalisation.

RESULTS

Patient characteristics were significantly different between the two groups. In internal validation, XGBoost was the best model, with an AUC of 0.780, and AdaBoost was the worst model, with an AUC of 0.585. In external validation, the AUC of the artificial neural network (ANN) model was the highest, 0.830, while the AUC of the logistic regression (LR) model was the lowest, 0.792. The AUC values changed less in the boosting and ANN models than in the LR model when variables were deleted.

CONCLUSIONS

Boosting and neural network models performed slightly better than the LR model and were more robust in complex clinical situations. The results could provide a tool for clinicians to detect hospitalised patients with cellulitis developing sepsis early.

Current challenges in acute bacterial skin infection management

PURPOSE OF REVIEW

There are aspects of skin and soft tissue infections (SSTIs) that remain unresolved, such as current numbers, classification criteria, how best to define severity and predict the outcome, what diagnostic tests to perform, what new treatment options are available, or what the duration of antibiotic treatment should be. We have reviewed the literature over the last 18 months to clarify these issues and provide our opinion.

RECENT FINDINGS

SSTIs are common and among the top 10 most frequent infections worldwide. They represent a burden on the healthcare system and have a major impact on the quality of life of patients. Regarding classification, the Infectious Diseases Society of America (IDSA) provides a practical guide that distinguishes between uncomplicated and complicated infections, acute and chronic wound infections, and necrotising and nonnecrotizing infections based on skin extension and tissue necrosis. With new microbiological and imaging diagnostic techniques, SSTIs can now be better diagnosed. New PCR techniques are available, and mass spectrometry can be applied to samples collected in liquid transport media. Moreover, new treatment methods such as photodynamic therapy, reactive oxygen, and phages are emerging. SSTI patients can be treated with shorter antibiotic courses if they receive an active drug with good tissue penetration. Antibiotic treatment in necrotizing infections can be shortened to 48 h after the last debridement.

SUMMARY

SSTIs remain a challenge regarding rapid and accurate diagnosis and clinical management.

Assessing Empiric Antimicrobial Therapy With the Modified Dundee Classification for Nonpurulent Skin and Soft Tissue Infections in a Community Hospital System

Background

The modified Dundee classification has recently been validated in various studies for nonpurulent skin and soft tissue infections. This has yet to be applied in the United States and within community hospital settings to optimize antimicrobial stewardship and ultimately patient care.

Methods

A retrospective, descriptive analysis was performed of 120 adult patients admitted to St. Joseph's/Candler Health System for nonpurulent skin and soft tissue infections between January 2020 and September 2021. Patients were classified into their modified Dundee classes, and frequencies of concordance of their empiric antimicrobial regimens with this classification scheme in the emergency department and inpatient settings were compared, along with possible effect modifiers and possible exploratory measures associated with concordance.

Results

Concordance with the modified Dundee classification for the emergency department and inpatient regimens was 10% and 15%, respectively, with broad-spectrum antibiotic use and concordance positively associated with illness severity. Due to substantial broad-spectrum antibiotic use, possible effect modifiers associated with concordance were unable to be validated, and overall no statistically significant differences among exploratory analyses across classification status were observed.

Conclusions

The modified Dundee classification can help identify gaps in antimicrobial stewardship and excessive broad-spectrum antimicrobial usage toward optimizing patient care.

Antimicrobial Management of Skin and Soft Tissue Infections among Surgical Wards in South Africa: Findings and Implications

Skin and soft tissue infections (SSTIs) are one of the most common infectious diseases requiring antibiotics. However, complications of SSTIs may lead to the overprescribing of antibiotics and to subsequent antibiotic resistance. Consequently, monitoring the prescribing alignment with the current recommendations from the South African Standard Treatment Guidelines (STG) is necessary in order to improve future care. This study involved reviewing pertinent patients with SSTIs who were prescribed antimicrobials in the surgical ward of a leading South African tertiary public hospital from April to June 2021 using an adapted data collection tool. Sixty-seven patient files were reviewed. Among the patients with SSTIs, hypertension and chronic osteomyelitis were the most frequent co-morbidities at 22.4% and 13.4%, respectively. The most diagnosed SSTIs were surgical site infections (35.1%), wound site infections (23%), and major abscesses (16.2%). Blood cultures were performed on 40.3% of patients, with Staphylococcus aureus (32.7%) and Enterococcus spp. (21.2%) being the most cultured pathogens. Cefazolin was prescribed empirically for 46.3% of patients for their SSTIs. In addition, SSTIs were treated with gentamycin, ciprofloxacin, and rifampicin at 17.5%, 11.3%, and 8.8%, respectively, with treatment fully complying with STG recommendations in 55.2% of cases. Overall, the most common cause of SSTIs was Staphylococcus aureus, and empiric treatment is recommended as the initial management. Subsequently, culture sensitivities should be performed to enhance adherence to STGs and to improve future care.

Pharmacological Mechanisms of Shangke Huangshui against Skin and Soft Tissue Infection

Background

Skin and soft tissue infections (SSTIs) are a group of common diseases, usually caused by bacteria. Shangke Huangshui (SK) has been widely used to treat various SSTIs diseases for many years, but its mechanism is unclear. Therefore, this study was designed to investigate the anti-infective effect of SK on different skin and soft tissue infection diseases and to explore its underlying mechanism.

Methods

The subcutaneous abscess mouse model, the dermal ulcer rat model, and the infectious soft tissue injury rat model were established by injection of Staphylococcus aureus to evaluate the anti-inflammatory and antibacterial effects of SK. Abscess volume, local appearance score and histological changes, bacterial contents, and hydroxyproline concentration in the skin or soft tissue were analyzed. The levels of NO, TNF-α, IL-1β, and IL-8 in the serum and tissue were determined by ELISA method. The mRNA expression levels of TLR2, MyD88, TAK1, NF-κB, AP-1, and other genes were measured with qRT-PCR method, and the protein expression of TLR2, MyD88, TAK1, NF-κB, and AP-1 was detected by western blot method.

Results

SK had an obvious therapeutic effect on skin and soft tissue infections. It reduced the volume of abscess and promoted the healing of skin ulcer, improved pathological phenomena, such as inflammatory infiltration of skin and soft tissue, reduced the levels of white blood cells and NO in the blood, decreased bacteria contents in the skin and soft tissue. Furthermore, SK decreased the mRNA expression of TLR2, MyD88, TAK1, NF-κB and AP-1, and other related genes and also downregulated the protein expression of TLR2, MyD88, TAK1, NF-κB, and AP-1.

Conclusion

The experiments provide evidence that SK can treat skin and soft tissue infection diseases based on its comprehensive antibacterial and anti-inflammatory effects. The therapeutic mechanism may be associated with the inhibition of TLR2/MyD88/NF-κB signaling pathway.

The impact of the Auckland cellulitis pathway on length of hospital stay, mortality readmission rate, and antibiotic stewardship

BACKGROUND

The Dundee classification of cellulitis severity, previously shown to predict disease outcomes, provides an opportunity to improve the management of patients with cellulitis.

METHODS

We developed and implemented a pathway to guide the management of adults with cellulitis based on their Dundee severity class, and measured its effect on patient outcomes. We compared the outcomes in patients admitted to Auckland City Hospital (ACH) between July 2014 and July 2015 (the baseline cohort) with those in patients admitted between June 2017 and June 2018 (the intervention cohort).

RESULTS

The median length of stay was shorter in the intervention cohort (0.7 days, IQR 0.1 to 3.0 days) than in the baseline cohort (1.8 days, IQR 0.1 to 4.4 days; P<0.001). The 30 day mortality rate declined from 1.8% (19/1092) in the baseline cohort to 0.7% (10/1362; P=0.02) in the intervention cohort. The 30 day cellulitis readmission rate increased from 6% in the baseline cohort to 11% (P<0.001) in the intervention cohort. Adherence to the ACH cellulitis antibiotic guideline improved from 38% to 48% (P<0.01) and was independently associated with reduced length of stay.

CONCLUSIONS

The implementation of the Auckland cellulitis pathway, readily generalizable to other settings, improved the outcomes in patients with cellulitis, and resulted in an annual saving of approximately 1,000 bed days.