Acute injection-related infections requiring hospitalisation among people who inject drugs: Clinical features, microbiology and management

INTRODUCTION

People who inject drugs are at risk of hospitalisation with injection-related infections (IRI). We audited the clinical features, microbiology and management of IRI at a tertiary service in Melbourne to describe the burden and identify quality improvement opportunities.

METHODS

We performed retrospective review of IRI admissions from January 2017 to April 2019. We extracted admissions where ICD-10 codes or triage text suggested injecting drug use, and the diagnosis suggested IRI. We reviewed these for eligibility and extracted data using a standardised form. We performed mixed-effects logistic regression to determine predictors of unplanned discharge.

RESULTS

From 574 extracted candidate admissions, 226 were eligible, representing 178 patients. Median age was 41 years (interquartile range 36-47), 66% (117/178) male and 49% (111/226) had unstable housing. Over 50% (96/178) had a psychiatric diagnosis and 35% (62/178) were on opioid agonist therapy (OAT) on admission. Skin and soft tissue infection was the most common IRI (119/205, 58%), followed by bacteraemia (36/205, 18%) and endocarditis (26/205, 13%). Management included addictions review (143/226, 63%), blood-borne virus screening (115/226, 51%), surgery (77/226, 34%) and OAT commencement (68/226, 30%). Aggression events (54/226, 15%) and unplanned discharge (69/226, 30%) complicated some admissions. Opioid use without OAT was associated with almost 3-fold increased odds of unplanned discharge compared to no opioid use (odds ratio 2.90, 95% confidence interval 1.23, 6.85, p = 0.015).

DISCUSSION AND CONCLUSION

Comorbidities associated with IRI may be amenable to opportunistic intervention during hospitalisation. Further research is needed to develop optimal models of care for this vulnerable patient group.

Hospitalisation with injection-related infections: Validation of diagnostic codes to monitor admission trends at a tertiary care hospital in Melbourne, Australia

INTRODUCTION

Injection-related infections (IRI) cause morbidity and mortality in people who inject drugs. Hospital administrative datasets can be used to describe hospitalisation trends, but there are no validated algorithms to identify injecting drug use and IRIs. We aimed to validate International Classification of Diseases (ICD) codes to identify admissions with IRIs and use these codes to describe IRIs within our hospital.

METHODS

We developed a candidate set of ICD codes to identify current injecting drug use and IRI and extracted admissions satisfying both criteria. We then used manual chart review data from 1 January 2017 to 30 April 2019 to evaluate the performance of these codes and refine our algorithm by selecting codes with a high-positive predictive value (PPV). We used the refined algorithm to describe trends and outcomes of people who inject drugs with an IRI at Alfred Hospital, Melbourne from 2008 to 2020.

RESULTS

Current injecting drug use was best predicted by opioid-related disorders (F11), 80% (95% confidence interval [CI] 74-85%), and other stimulant-related disorders (F15), 82% (95% CI 70-90%). All PPVs were ≥67% to identify specific IRIs, and ≥84% for identifying any IRI. Using these codes over 12 years, IRIs increased from 138 to 249 per 100 000 admissions, and skin and soft tissues infections (SSTI) were the most common (797/1751, 46%).

DISCUSSION AND CONCLUSION

Validated ICD-based algorithms can inform passive surveillance systems. Strategies to reduce hospitalisation with IRIs should be supported by early intervention and prevention, particularly for SSTIs which may represent delayed access to care.

Hospital Costs of Injection Drug Use in Florida

People who inject drugs (PWID) experience significant injection-related infections (IRIs) at significant healthcare system cost. This study used and validated an algorithm based on the International Classification of Diseases, Tenth Revision, to estimate hospitalized PWID populations, assess the total statewide morbidity for IRIs among PWID, and calculate associated costs of care.