Patients with a reported penicillin allergy had a 50% increased odds of SSI, attributable to the receipt of second-line perioperative antibiotics. Clarification of penicillin allergies as part of routine preoperative care may decrease SSI risk.

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OBJECTIVE An estimated 293,300 healthcare-associated cases of Clostridium difficile infection (CDI) occur annually in the United States. To date, research has focused on developing risk prediction models for CDI that work well across institutions. However, this one-size-fits-all approach ignores important hospital-specific factors. We focus on a generalizable method for building facility-specific models. We demonstrate the applicability of the approach using electronic health records (EHR) from the University of Michigan Hospitals (UM) and the Massachusetts General Hospital (MGH). METHODS We utilized EHR data from 191,014 adult admissions to UM and 65,718 adult admissions to MGH. We extracted patient demographics, admission details, patient history, and daily hospitalization details, resulting in 4,836 features from patients at UM and 1,837 from patients at MGH. We used L2 regularized logistic regression to learn the models, and we measured the discriminative performance of the models on held-out data from each hospital. RESULTS Using the UM and MGH test data, the models achieved area under the receiver operating characteristic curve (AUROC) values of 0.82 (95% confidence interval [CI], 0.80-0.84) and 0.75 ( 95% CI, 0.73-0.78), respectively. Some predictive factors were shared between the 2 models, but many of the top predictive factors differed between facilities. CONCLUSION A data-driven approach to building models for estimating daily patient risk for CDI was used to build institution-specific models at 2 large hospitals with different patient populations and EHR systems. In contrast to traditional approaches that focus on developing models that apply across hospitals, our generalizable approach yields risk-stratification models tailored to an institution. These hospital-specific models allow for earlier and more accurate identification of high-risk patients and better targeting of infection prevention strategies. Infect Control Hosp Epidemiol 2018;39:425-433.

We identified transmission of mcr-1 in a United States acute care hospital that likely occurred via duodenoscope despite no identifiable breaches in reprocessing or infection control practices. Duodenoscope design flaws leading to transmission of multidrug-resistant organsisms persist despite recent initiatives to improve device safety. Reliable detection of colistin resistance is currently challenging for clinical laboratories, particularly given the absence of an FDA-cleared test; improved clinical laboratory capacity for colistin susceptibility testing is needed to prevent the spread of mcr-carrying bacteria in healthcare settings.

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Objective To determine the impact of MRSA/VRE designations, or flags, on selected hospital operational outcomes. Design Retrospective cohort study of inpatients admitted to the Massachusetts General Hospital during 2010–2011. Methods Operational outcomes were time to bed arrival, acuity-unrelated within-hospital transfers, and length of stay. Demographic and clinical characteristics – including age, gender, severity of illness on admission, admit day of week, residence prior to admission, hospitalization within the prior 30 days, clinical service, and discharge destination – were used as covariates. Results A total of 81,288 admissions were included. After adjusting for covariates, patients with a MRSA/VRE flag at the time of admission experienced a mean delay in time to bed arrival of 1.03 (9.63 [95% CI 9.39–9.88] hours vs. 8.60 [95% CI 8.47–8.73] hours); had 1.19 times the odds [95% CI, 1.13–1.26] of experiencing an acuity-unrelated within-hospital transfer, and experienced a mean length of stay 1.76 days longer (7.03 [95% CI 6.82–7.24] days vs. 5.27 [95% CI 5.15–5.38] days) compared to patients with no MRSA/VRE flag. Conclusions MRSA/VRE designation was associated with delays in time to bed arrival, increased likelihood of acuity-unrelated within-hospital transfers, and extended length of stay. Efforts to identify patients who have cleared MRSA/VRE colonization are critically important to mitigate inefficient use of resources and improve inpatient flow.

OBJECTIVETo validate a system to detect ventilator associated events (VAEs) autonomously and in real time.DESIGNRetrospective review of ventilated patients using a secure informatics platform to identify VAEs (ie, automated surveillance) compared to surveillance by infection control (IC) staff (ie, manual surveillance), including development and validation cohorts.SETTINGThe Massachusetts General Hospital, a tertiary-care academic health center, during January-March 2015 (development cohort) and January-March 2016 (validation cohort).PATIENTSVentilated patients in 4 intensive care units.METHODSThe automated process included (1) analysis of physiologic data to detect increases in positive end-expiratory pressure (PEEP) and fraction of inspired oxygen (FiO2); (2) querying the electronic health record (EHR) for leukopenia or leukocytosis and antibiotic initiation data; and (3) retrieval and interpretation of microbiology reports. The cohorts were evaluated as follows: (1) manual surveillance by IC staff with independent chart review; (2) automated surveillance detection of ventilator-associated condition (VAC), infection-related ventilator-associated complication (IVAC), and possible VAP (PVAP); (3) senior IC staff adjudicated manual surveillance-automated surveillance discordance. Outcomes included sensitivity, specificity, positive predictive value (PPV), and manual surveillance detection errors. Errors detected during the development cohort resulted in algorithm updates applied to the validation cohort.RESULTSIn the development cohort, there were 1,325 admissions, 479 ventilated patients, 2,539 ventilator days, and 47 VAEs. In the validation cohort, there were 1,234 admissions, 431 ventilated patients, 2,604 ventilator days, and 56 VAEs. With manual surveillance, in the development cohort, sensitivity was 40%, specificity was 98%, and PPV was 70%. In the validation cohort, sensitivity was 71%, specificity was 98%, and PPV was 87%. With automated surveillance, in the development cohort, sensitivity was 100%, specificity was 100%, and PPV was 100%. In the validation cohort, sensitivity was 85%, specificity was 99%, and PPV was 100%. Manual surveillance detection errors included missed detections, misclassifications, and false detections.CONCLUSIONSManual surveillance is vulnerable to human error. Automated surveillance is more accurate and more efficient for VAE surveillance.Infect Control Hosp Epidemiol 2018;826-833.

We developed and validated a DES model of patient flow incorporating MRSA/VRE flags. The model allowed for quantification of the substantial impact of MRSA/VRE flags on hospital efficiency and potentially avoidable nosocomial transmission.