Comparative Effectiveness of Fidaxomicin vs Vancomycin in Populations With Immunocompromising Conditions for the Treatment of Clostridioides difficile Infection: A Single-Center Study

Background

Clostridioides difficile infection (CDI) is a leading cause of morbidity in immunocompromised hosts with increased risk of complications and recurrences. In this study, we examined the clinical effectiveness of fidaxomicin vs vancomycin in treating CDI in this patient population.

Methods

This single-center retrospective study evaluated patients with CDI between 2011 and 2021. The primary outcome was a composite of clinical failure, relapse at 30 days, or CDI-related death. A multivariable cause-specific Cox proportional hazards model was used to test the relationship between treatment and the composite outcome, adjusting for confounders and treating death from other causes as a competing risk.

Results

This study analyzed 238 patients who were immunocompromised and treated for CDI with oral fidaxomicin (n = 38) or vancomycin (n = 200). There were 42 composite outcomes: 4 (10.5%) in the fidaxomicin arm and 38 (19.0%) in the vancomycin arm. After adjustment for sex, number of antecedent antibiotics, CDI severity and type of immunosuppression, fidaxomicin use significantly decreased the risk of the composite outcome as compared with vancomycin (10.5% vs 19.0%; hazard ratio, 0.28; 95% CI, .08-.93). Furthermore, fidaxomicin was associated with 70% reduction in the combined risk of 30- and 90-day relapse following adjustment (hazard ratio, 0.27; 95% CI, .08-.91).

Conclusions

The findings of this study suggest that the use of fidaxomicin for treatment of CDI reduces poor outcomes in patients who are immunocompromised.

Maintaining the Utility of Coronavirus Disease 2019 Pandemic Severity Surveillance: Evaluation of Trends in Attributable Deaths and Development and Validation of a Measurement Tool

BACKGROUND

Death within a specified time window following a positive SARS-CoV-2 test is used by some agencies for attributing death to COVID-19. With Omicron variants, widespread immunity, and asymptomatic screening, there is cause to re-evaluate COVID-19 death attribution methods and develop tools to improve case ascertainment.

METHODS

All patients who died following microbiologically confirmed SARS-CoV-2 in the Veterans Health Administration (VA) and at Tufts Medical Center (TMC) were identified. Records of selected vaccinated VA patients with positive tests in 2022, and of all TMC patients with positive tests in 2021-2022, were manually reviewed to classify deaths as COVID-19-related (either directly caused by or contributed to), focused on deaths within 30 days. Logistic regression was used to develop and validate a surveillance model for identifying deaths in which COVID-19 was causal or contributory.

RESULTS

Among vaccinated VA patients who died ≤30 days after a positive test in January-February 2022, death was COVID-19-related in 103/150 cases (69%) (55% causal, 14% contributory). In June-August 2022, death was COVID-19-related in 70/150 cases (47%) (22% causal, 25% contributory). Similar results were seen among the 71 patients who died at TMC. A model including hypoxemia, remdesivir, and anti-inflammatory drugs had positive and negative predictive values of 0.82-0.95 and 0.64-0.83, respectively.

CONCLUSIONS

By mid-2022, "death within 30 days" did not provide an accurate estimate of COVID-19-related death in 2 US healthcare systems with routine admission screening. Hypoxemia and use of antiviral and anti-inflammatory drugs-variables feasible for reporting to public health agencies-would improve classification of death as COVID-19-related.

512. The relationship of Clostridioides difficile in-vitro antibiotic resistance, prior exposure to antibiotics and ribotype: A single center study

Background

Antibiotic exposure is the most important risk factor for Clostridioides difficile associated diarrhea (CDAD). Little information has been reported on the relationship between prior antibiotic exposure, antibiotic resistance of C. difficile, and isolate ribotype.

Methods

C. difficile isolates were collected from the stools of patients with CDAD. Isolates were ribotyped by PCR-based fragment analysis. Susceptibility testing was performed using Clinical & Laboratory Standards Institute (CLSI) recommended methods. MIC results were interpreted using European Committee on Antimicrobial Susceptibility Testing (EUCAST) epidemiologic cut off values or the CLSI breakpoints. Demographic data and antibiotic exposure in the 30 days period prior to C. difficile isolation were collected via retrospective chart review.

Results

Of a total of 490 C. difficile isolates, 335 (67.3%) isolates were resistant to one or more antibiotics (Table 1). Patients who had higher Charlson Comorbidity Score (CCI) and who had hospital associated CDAD were more likely to receive antibiotics (Table 2). Moxifloxacin resistance was significantly associated with prior fluoroquinolone exposure (OR 2.5, CI 95% 1.5-4.1). Fluoroquinolone use was associated with vancomycin resistance (OR 2.7, CI 95% 1.3-5.6) and rifampin resistance (OR 1.9, CI 95% 1.2-3). Infection with a clindamycin resistant isolate was more likely to be hospital acquired (p = 0.006). We identified 54 different ribotypes (Figure 1). Prior treatment with aminopenicillins increased the odds of acquiring ribotype 053-163 (OR 2.9, 95% CI 1.0-8.2) and ribotype 002 (OR 3.2; CI 95% 1.0-10) (Table 3). .Prior treatment with cephalosporins had an increased risk of 014-020 (OR 2.0, CI 95% 1.2-3.3); prior treatment with carbapenems was associated with ribotype 106 (OR 2.3, CI 95% 1.2-4.4). Isolation of ribotype 027 following prior treatment to fluroquinolones was marginally significant (p = 0.054).

Conclusion

Specific antibiotic exposure may select for infection by specific C. difficile ribotypes, which can often lead to infection with resistant strains. Prior fluoroquinolone treatment, in particular, was associated with an increased risk of isolating multi-drug resistant strains.

Disclosures

Cheleste M. Thorpe, MD, Actelion: Grant/Research Support|Deinove: Advisor/Consultant|General Mills: Grant/Research Support|Merck: Grant/Research Support|Pfizer: Grant/Research Support|Summit: Advisor/Consultant|Summit: Grant/Research Support David R. Snydman, MD, Merck: Advisor/Consultant|Merck and Company: Grant/Research Support|Prolacta: Advisor/Consultant|Prolacta: Grant/Research Support|Seres: Advisor/Consultant|Seres Health: Grant/Research Support|Summit Plc: Grant/Research Support|Takeda: Advisor/Consultant|Takeda: Grant/Research Support|Visterra: Advisor/Consultant.