Increasing the reliability of fully automated surveillance for central line–associated bloodstream infections

OBJECTIVETo increase reliability of the algorithm used in our fully automated electronic surveillance system by adding rules to better identify bloodstream infections secondary to other hospital-acquired infections.METHODSIntensive care unit (ICU) patients with positive blood cultures were reviewed. Central line–associated bloodstream infection (CLABSI) determinations were based on 2 sources: routine surveillance by infection preventionists, and fully automated surveillance. Discrepancies between the 2 sources were evaluated to determine root causes. Secondary infection sites were identified in most discrepant cases. New rules to identify secondary sites were added to the algorithm and applied to this ICU population and a non-ICU population. Sensitivity, specificity, predictive values, and kappa were calculated for the new models.RESULTSOf 643 positive ICU blood cultures reviewed, 68 (10.6%) were identified as central line–associated bloodstream infections by fully automated electronic surveillance, whereas 38 (5.9%) were confirmed by routine surveillance. New rules were tested to identify organisms as central line–associated bloodstream infections if they did not meet one, or a combination of, the following: (I) matching organisms (by genus and species) cultured from any other site; (II) any organisms cultured from sterile site; (III) any organisms cultured from skin/wound; (IV) any organisms cultured from respiratory tract. The best-fit model included new rules I and II when applied to positive blood cultures in an ICU population. However, they didn’t improve performance of the algorithm when applied to positive blood cultures in a non-ICU population.CONCLUSIONElectronic surveillance system algorithms may need adjustment for specific populations.Infect. Control Hosp. Epidemiol. 2015;36(12):1396–1400</jats:sec

Which comorbid conditions should we be analyzing as risk factors for healthcare-associated infections?

OBJECTIVETo determine which comorbid conditions are considered causally related to central-line associated bloodstream infection (CLABSI) and surgical-site infection (SSI) based on expert consensus.DESIGNUsing the Delphi method, we administered an iterative, 2-round survey to 9 infectious disease and infection control experts from the United States.METHODSBased on our selection of components from the Charlson and Elixhauser comorbidity indices, 35 different comorbid conditions were rated from 1 (not at all related) to 5 (strongly related) by each expert separately for CLABSI and SSI, based on perceived relatedness to the outcome. To assign expert consensus on causal relatedness for each comorbid condition, all 3 of the following criteria had to be met at the end of the second round: (1) a majority (&gt;50%) of experts rating the condition at 3 (somewhat related) or higher, (2) interquartile range (IQR)≤1, and (3) standard deviation (SD)≤1.RESULTSFrom round 1 to round 2, the IQR and SD, respectively, decreased for ratings of 21 of 35 (60%) and 33 of 35 (94%) comorbid conditions for CLABSI, and for 17 of 35 (49%) and 32 of 35 (91%) comorbid conditions for SSI, suggesting improvement in consensus among this group of experts. At the end of round 2, 13 of 35 (37%) and 17 of 35 (49%) comorbid conditions were perceived as causally related to CLABSI and SSI, respectively.CONCLUSIONSOur results have produced a list of comorbid conditions that should be analyzed as risk factors for and further explored for risk adjustment of CLABSI and SSI.Infect Control Hosp Epidemiol 2017;38:449–454</jats:sec

Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort

Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of electronic health records (EHRs) offers a unique opportunity to build machine learning algorithms to identify drug-drug interactions that drive adverse events. In this study, we investigated hospitalizations\u27 data to study drug interactions with non-steroidal anti-inflammatory drugs (NSAIDS) that result in drug-induced liver injury (DILI). We propose a logistic regression based machine learning algorithm that unearths several known interactions from an EHR dataset of about 400,000 hospitalization. Our proposed modeling framework is successful in detecting 87.5% of the positive controls, which are defined by drugs known to interact with diclofenac causing an increased risk of DILI, and correctly ranks aggregate risk of DILI for eight commonly prescribed NSAIDs. We found that our modeling framework is particularly successful in inferring associations of drug-drug interactions from relatively small EHR datasets. Furthermore, we have identified a novel and potentially hepatotoxic interaction that might occur during concomitant use of meloxicam and esomeprazole, which are commonly prescribed together to allay NSAID-induced gastrointestinal (GI) bleeding. Empirically, we validate our approach against prior methods for signal detection on EHR datasets, in which our proposed approach outperforms all the compared methods across most metrics, such as area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC)

Influenza vaccination among healthcare workers: Ten-year experience of a large healthcare organization

OBJECTIVE: To describe the results of different measures implemented to improve compliance with the healthcare worker (HCW) influenza immunization program at BJC HealthCare between 1997 and 2007. DESIGN: Descriptive retrospective study. SETTING: BJC HealthCare, a 13-hospital nonprofit healthcare organization in the Midwest. METHODS: Review and analysis of HCW influenza vaccination data from all BJC HealthCare Occupational Health Services and hospitals between 1997 and 2007. Occupational health staff, infection prevention personnel and key influenza vaccine campaign leaders were also interviewed regarding implementation measures during the study years. RESULTS: At the end of 2007, BJC HealthCare had approximately 26,000 employees. Using multiple progressive interventions, influenza vaccination rates among BJC employees increased from 45% in 1997 to 71.9% in 2007 (p<0.001). The influenza vaccination rate in 2007 was significantly higher than in 2006, 71.9% versus 54.2% (p<0.001). Five hospitals had influenza vaccination rates over the target goal of 80% in 2007. The most successful interventions were adding influenza vaccination rates to the incented quality scorecard and declination statements, both implemented in 2007. The most important barriers identified in the interviews related to HCWs’ misconceptions about influenza vaccination and a perceived lack of leadership support. CONCLUSIONS: Influenza vaccination rates in HCWs significantly improved with multiple interventions over the years. However, the BJC HealthCare influenza vaccination target of 80% was not attained at all hospitals with these measures. More aggressive interventions such as implementing mandatory influenza vaccination policies are needed to achieve higher vaccination rates