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Timely identification of multidrug-resistant gram-negative infections remains an epidemiological challenge. Statistical models for predicting drug resistance can offer utility where rapid diagnostics are unavailable or resource-impractical. Logistic regression–derived risk scores are common in the healthcare epidemiology literature. Machine learning–derived decision trees are an alternative approach for developing decision support tools. Our group previously reported on a decision tree for predicting ESBL bloodstream infections. Our objective in the current study was to develop a risk score from the same ESBL dataset to compare these 2 methods and to offer general guiding principles for using each approach.
Using a dataset of 1,288 patients with Escherichia coli or Klebsiella spp bacteremia, we generated a risk score to predict the likelihood that a bacteremic patient was infected with an ESBL-producer. We evaluated discrimination (original and cross-validated models) using receiver operating characteristic curves and C statistics. We compared risk score and decision tree performance, and we reviewed their practical and methodological attributes.
In total, 194 patients (15%) were infected with ESBL-producing bacteremia. The clinical risk score included 14 variables, compared to the 5 decision-tree variables. The positive and negative predictive values of the risk score and decision tree were similar (>90%), but the C statistic of the risk score (0.87) was 10% higher.
A decision tree and risk score performed similarly for predicting ESBL infection. The decision tree was more user-friendly, with fewer variables for the end user, whereas the risk score offered higher discrimination and greater flexibility for adjusting sensitivity and specificity.
To characterize the risk of infection after MRSA decolonization with intranasal mupirocin.
Multicenter, retrospective cohort study.
Tertiary care neonatal intensive care units (NICUs) from 3 urban hospitals in the United States ranging in size from 45 to 100 beds.
MRSA-colonized neonates were identified from NICU admissions occurring from January 2007 to December 2014, during which a targeted decolonization strategy was used for MRSA control. In 2 time-to-event analyses, MRSA-colonized neonates were observed from the date of the first MRSA-positive surveillance screen until (1) the first occurrence of novel gram-positive cocci in sterile culture or discharge or (2) the first occurrence of novel gram-negative bacilli in sterile culture or discharge. Mupirocin exposure was treated as time varying.
A total of 522 MRSA-colonized neonates were identified from 16,144 neonates admitted to site NICUs. Of the MRSA-colonized neonates, 384 (74%) received mupirocin. Average time from positive culture to mupirocin treatment was 3.5 days (standard deviation, 7.2 days). The adjusted hazard of gram-positive cocci infection was 64% lower among mupirocin-exposed versus mupirocin-unexposed neonates (hazard ratio, 0.36; 95% confidence interval [CI], 0.17–0.76), whereas the adjusted hazard ratio of gram-negative bacilli infection comparing mupirocin-exposed and -unexposed neonates was 1.05 (95% CI, 0.42–2.62).
In this multicentered cohort of MRSA-colonized neonates, mupirocin-based decolonization treatment appeared to decrease the risk of infection with select gram-positive organisms as intended, and the treatment was not significantly associated with risk of subsequent infections with organisms not covered by mupirocin’s spectrum of activity.
Show how detailed incubation period estimates can be used to identify and investigate potential healthcare-associated infections and dangerous diseases.
We used the incubation period of 9 respiratory viruses to derive decision rules for distinguishing between community- and hospital-acquired infection. We developed a method, implemented in a simple spreadsheet, that can be used to investigate the exposure history of an individual patient and more specifically to identify the probable time and location of infection. Illustrative examples are used to explain and evaluate this technique.
If the risks of hospital and community infection are equal, 95% of patients who develop symptoms of adenovirus infection within 5 days of hospital admission will have been infected in the community, as will 95% of patients who develop symptoms within 3 days for human-coronavirus infection, 2.5 days for severe acute respiratory syndrome, 1 day for influenza A, 0.5 day for influenza B, 12 days for measles, 2 days for parainfluenza, 4 days for respiratory syncytial virus infection, and 1.5 days for rhinovirus infection. Sources of infection suggested by analysis of the symptom onset times of individual patients are consistent with those from detailed investigations.
This work shows how a detailed understanding of the incubation period can be an effective tool for identifying the source of infection, ultimately ensuring patient safety.
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