Faculty Bibliography

INTRODUCTION/BACKGROUND:It is common among microbiology laboratories to blind the Clostridioides difficile (C. difficile) BioFire FilmArray GI Panel result in fear of overdiagnosis. METHODS:We examined the rate of missed community-onset C. difficile infection (CDI) diagnosis and associated outcomes. Adult patients with FilmArray GI Panel positive for C. difficile on hospital admission who lacked dedicated C. difficile testing were included. RESULTS:Among 144 adults with a FilmArray Panel positive for C. difficile, 18 did not have concurrent dedicated C. difficile testing. Eight patients were categorized as possible, 5 as probable and 4 as definite cases of missed CDI diagnosis. We observed associated delays in initiation of appropriate therapy, intensive care unit admissions, hospital readmissions, colorectal surgery and death/discharge to hospice. Five out of 17 lacked risk factors for CDI. CONCLUSION/CONCLUSIONS:The practice of concealing C. difficile FilmArray GI Panel results needs to be reconsidered in patients presenting with community-onset colitis.

OBJECTIVE:The data regarding the effectiveness of chemical prophylaxis against recurrent C. difficile infection (CDI) remain conflicting. DESIGN/METHODS:Retrospective cohort study on the effectiveness of oral vancomycin for prevention of recurrent CDI. SETTING/METHODS:Two academic centers in New York. METHODS:Two participating hospitals implemented an automated alert recommending oral vancomycin 125 mg twice daily in patients with CDI history scheduled to receive systemic antimicrobials. Measured outcomes included breakthrough and recurrent CDI rates, defined as CDI during and 1 month after initiation of prophylaxis, respectively. A self-controlled, before-and-after study design was employed to examine the effect of vancomycin prophylaxis on the prevalence of vancomycin-resistant Enterococcus spp (VRE) colonization and infection. RESULTS:We included 264 patients in the analysis. Breakthrough CDI was identified in 17 patients (6.4%; 95% confidence interval [CI], 3.8%-10.1%) and recurrent in 22 patients (8.3%; 95% CI, 5.3%-12.3%). Among the 102 patients with a history of CDI within the 3 months preceding prophylaxis, 4 patients (3.9%; 95% CIs, 1.1%-9.7%) had breakthrough CDI and 9 had recurrent disease (8.8%; 95% CIs, 4.1%-16.1%). In the 3-month period following vancomycin prophylaxis, we detected a statistically significant increase in both the absolute number of VRE (χ2, 0.003) and the ratio of VRE to VSE isolates (χ2, 0.003) compared to the combined period of 1.5 months preceding and the 3-4.5 months following prophylaxis. This effect persisted 6 months following prophylaxis. CONCLUSIONS:Prophylactic vancomycin is an effective strategy to prevent CDI recurrence, but it increases the risk of VRE colonization. Thus, a careful selection of patients with high benefit-to-risk ratio is needed for the implementation of this preventive policy.

Widespread use of antibiotics has resulted in an increase in antimicrobial-resistant microorganisms. Although not all bacterial contact results in infection, patients can become asymptomatically colonized, increasing the risk of infection and pathogen transmission. Consequently, many institutions have begun active surveillance, but in non-research settings, the resulting data are often incomplete and may include non-random testing, making conventional epidemiological analysis problematic. We describe a mathematical model and inference method for in-hospital bacterial colonization and transmission of carbapenem-resistant Enterobacteriaceae that is tailored for analysis of active surveillance data with incomplete observations. The model and inference method make use of the full detailed state of the hospital unit, which takes into account the colonization status of each individual in the unit and not only the number of colonized patients at any given time. The inference method computes the exact likelihood of all possible histories consistent with partial observations (despite the exponential increase in possible states that can make likelihood calculation intractable for large hospital units), includes techniques to improve computational efficiency, is tested by computer simulation, and is applied to active surveillance data from a 13-bed rehabilitation unit in New York City. The inference method for exact likelihood calculation is applicable to other Markov models incorporating incomplete observations. The parameters that we identify are the patient-patient transmission rate, pre-existing colonization probability, and prior-to-new-patient transmission probability. Besides identifying the parameters, we predict the effects on the total prevalence (0.07 of the total colonized patient-days) of changing the parameters and estimate the increase in total prevalence attributable to patient-patient transmission (0.02) above the baseline pre-existing colonization (0.05). Simulations with a colonized versus uncolonized long-stay patient had 44% higher total prevalence, suggesting that the long-stay patient may have been a reservoir of transmission. High-priority interventions may include isolation of incoming colonized patients and repeated screening of long-stay patients.

To determine whether deep surgical site infection (dSSI) rate exhibits temporal variability, dSSI rates following 98,068 cases were analyzed. The overall dSSI rate decreased significantly between 2009 and 2018. Summer had a significantly greater rate of dSSI than winter. There was no difference in dSSI rate in July versus other months.

Background. Pneumonia (PNU) is the second most common nosocomial infection in the United States and is associated with substantial morbidity and mortality. While definitions from CDC were developed to increase the reliability of surveillance data, reduce the burden of surveillance in healthcare facilities, and enhance the utility of surveillance data for improving patient safety - the algorithm is still laborious. We propose an implementation of a refined algorithm script which combines two CDC definitions with the use of natural language processing (NLP), a tool which relies on pattern matching to determine whether a condition of interest is reported as present or absent in a report, to automate PNU surveillance. Methods. Using SAS v9.4 to write a query, we used a combination of National Healthcare Safety Network's (NHSN) PNU and ventilator-associated event (VAE) definitions that use discrete fields found in electronic medical records (EMR) and trained an NLP tool to determine whether chest x-ray report was indicative of PNU (Fig1). To validate, we assessed sensitivity/specificity of NLP tool results compared with clinicians' interpretations. Results. The NLP tool was highly accurate in classifying the presence of PNU in chest x-rays. After training the NLP tool, there were only 4% discrepancies between NLP tool and clinicians interpretations of 223 x-ray reports - sensitivity 92.2% (81.1-97.8), specificity 97.1% (93.4-99.1), PPV 90.4% (79.0-96.8), NPV 97.7% (94.1- 99.4). Combining the automated use of discrete EMR fields with NLP tool significantly reduces the time spent manually reviewing EMRs. A manual review for PNU without automation requires approximately 10 minutes each day per admission. With a monthly average of 2,350 adult admissions at our hospital and 16,170 patient-days for admissions with at least 2 days, the algorithm saves approximately 2,695 review hours. Conclusion. The use of discrete EMR fields with an NLP tool proves to be a timelier, cost-effective yet accurate alternative to manual PNU surveillance review. By allowing an automated algorithm to review PNU, timely reports can be sent to units about individual cases. Compared with traditional CDC surveillance definitions, an automated tool allows real-time critical review for infection and prevention activities