New research was presented at IDWeek 2016, the combined annual meeting of the Infectious Diseases Society of America, Society for Healthcare Epidemiology of America, HIV Medicine Association, and Pediatric Infectious Diseases Society, from October 26-30 in New Orleans. The features below highlight some of the studies presented at the conference.
Patient- and Environment-to-Nurse Pathogen Transmission
Data are lacking on the transmission of pathogens to healthcare professionals from patients and patients’ environments. For a study, researchers obtained cultures from 40 nurses’ scrubs, 167 patients, and the patients’ rooms during three separate, 12-hour ICU shifts. All nurses used new scrubs for each shift. During these shifts, 22 confirmed transmissions were observed, among which six were from patient to nurse, six were from environment to nurse, and 10 were from patient to environment. No nurse-to-patient or nurse-to-environment transmissions were observed.
Outcomes With Rapid Microarray Testing
An available gram-negative blood culture test is able to detect four gram-negative genera, four species, and six resistance genes within 2 hours of blood culture positivity. The impact of pairing this test with an antimicrobial stewardship intervention on antimicrobial and clinical outcomes has not been well defined. Study investigators compared patients admitted with a blood culture positive for a gram-negative organism during the 6 months before (pre group) and after (post group) use of this microarray test as part of an antimicrobial stewardship intervention. The number and type of antimicrobial switches were similar between the groups, as were in-hospital mortality rates. However, median time from gram stain to antimicrobial switch was significantly decreased in the post group. Median time to active therapy among patients on inactive antimicrobial was also shorter for the post group. The post group also had a shorter median hospital length of stay.
Antibiotic Prescribing Upon Hospital Discharge
Few studies have characterized and determined the rate of inappropriate antimicrobial prescribing at hospital discharge. For a study, antimicrobial appropriateness was examined among randomly selected patients prescribed an antimicrobial at discharge. For patients discharged on antibiotics, 7- and 30-day readmission rates were 6.4% and 19.4%, respectively, compared with hospital-wide rates of 3.7% and 13.8%, respectively. Of patients discharged on antibiotics, 22% had no clinical indication of infection, 13% had an antibiotic with an inappropriate spectrum of activity, 17% received the incorrect dose, 55% received an antibiotic course that was too long, and 7% received a course that was too short.
CKD & Hepatitis C
Data are lacking on the effects of chronic kidney disease (CKD) on the incidence of hepatic and extra-hepatic outcomes in this patient population. Researchers compared rates of anemia, hyperbilirubinemia, end-stage liver disease, cryoglobulinemia, hepatocellular carcinoma (HCC), and mortality between patients with hepatitis C and CKD or hepatitis C alone. Patients with both conditions had a 61% higher mortality rate, an 85% higher rate of liver transplant recommendations, and more than two-fold higher rates of anemia and cryoglobulinemia. CKD was not found to be associated with increased risks for hyperbilirubinemia or HCC.
Identifying PrEP Candidates
A lack of sexual health and risk evaluations conducted as part of routine care has been identified as a barrier to initiating pre-exposure prophylaxis (PrEP) in more patients at high-risk for HIV infection. Researchers developed an automated algorithm to identify people at higher risk for acquiring HIVusing routinely collected information in electronic medical records. The study team looked at more than 100 variables—including demographics, diagnoses, drug prescriptions, laboratory tests, and procedures—among 138 patients with HIV and 13,800 matched controls. Using logistic regression modeling and machine learningto predict incident HIV infections among cases versus controls, the authors found that their machine learning algorithm was able to identify those who were likely to at higher risk for HIV infection and therefore candidates for PrEP.
NEWS FROM IDWEEK 2016
MORE FROM IDWEEK 2016