Courtney Hebert

PPAR-α Ligands Inhibit H2O2-Mediated Activation of Transforming Growth Factor-β1 in Human Mesangial Cells

Transforming growth factor-β1 (TGF-β1) mediates the development of glomerulosclerosis by stimulating mesangial cell production of extracellular matrix (ECM) proteins. TGF-β1 and several ECM genes a...

Electronic Health Record–Based Detection of Risk Factors for Clostridium difficile Infection Relapse

OBJECTIVE A major challenge in treating Clostridium difficile infection (CDI) is relapse. Many new therapies are being developed to help prevent this outcome. We sought to establish risk factors for relapse and determine whether fields available in an electronic health record (EHR) could be used to identify high-risk patients for targeted relapse prevention strategies. DESIGN Retrospective cohort study. SETTING Large clinical data warehouse at a 4-hospital healthcare organization. PARTICIPANTS Data were gathered from January 2006 through October 2010. Subjects were all inpatient episodes of a positive C. difficile test where patients were available for 56 days of follow-up. METHODS Relapse was defined as another positive test between 15 and 56 days after the initial test. Multivariable regression was performed to identify factors independently associated with CDI relapse. RESULTS Eight hundred twenty-nine episodes met eligibility criteria, and 198 resulted in relapse (23.9%). In the final multivariable analysis, risk of relapse was associated with age (odds ratio [OR], 1.02 per year [95% confidence interval (CI), 1.01-1.03]), fluoroquinolone exposure in the 90 days before diagnosis (OR, 1.58 [95% CI, 1.11-2.26]), intensive care unit stay in the 30 days before diagnosis (OR, 0.47 [95% CI, 0.30-0.75]), cephalosporin (OR, 1.80 [95% CI, 1.19-2.71]), proton pump inhibitor (PPI; OR, 1.55 [95% CI, 1.05-2.29]), and metronidazole exposure after diagnosis (OR, 2.74 [95% CI, 1.64-4.60]). A prediction model tuned to ensure a 50% probability of relapse would flag 14.6% of CDI episodes. CONCLUSIONS Data from a comprehensive EHR can be used to identify patients at high risk for CDI relapse. Major risk factors include antibiotic and PPI exposure.

Diagnosis-specific readmission risk prediction using electronic health data: a retrospective cohort study

BackgroundReadmissions after hospital discharge are a common occurrence and are costly for both hospitals and patients. Previous attempts to create universal risk prediction models for readmission have not met with success. In this study we leveraged a comprehensive electronic health record to create readmission-risk models that were institution- and patient- specific in an attempt to improve our ability to predict readmission.MethodsThis is a retrospective cohort study performed at a large midwestern tertiary care medical center. All patients with a primary discharge diagnosis of congestive heart failure, acute myocardial infarction or pneumonia over a two-year time period were included in the analysis.The main outcome was 30-day readmission. Demographic, comorbidity, laboratory, and medication data were collected on all patients from a comprehensive information warehouse. Using multivariable analysis with stepwise removal we created three risk disease-specific risk prediction models and a combined model. These models were then validated on separate cohorts.Results3572 patients were included in the derivation cohort. Overall there was a 16.2% readmission rate. The acute myocardial infarction and pneumonia readmission-risk models performed well on a random sample validation cohort (AUC range 0.73 to 0.76) but less well on a historical validation cohort (AUC 0.66 for both). The congestive heart failure model performed poorly on both validation cohorts (AUC 0.63 and 0.64).ConclusionsThe readmission-risk models for acute myocardial infarction and pneumonia validated well on a contemporary cohort, but not as well on a historical cohort, suggesting that models such as these need to be continuously trained and adjusted to respond to local trends. The poor performance of the congestive heart failure model may suggest that for chronic disease conditions social and behavioral variables are of greater importance and improved documentation of these variables within the electronic health record should be encouraged.

Demonstration of the Weighted-Incidence Syndromic Combination Antibiogram: An Empiric Prescribing Decision Aid

OBJECTIVE Healthcare providers need a better empiric antibiotic prescribing aid than the traditional antibiogram, which supplies no information on the relative frequency of organisms recovered in a given infection and which is uninformative in situations where multiple antimicrobials are used or multiple organisms are anticipated. We aimed to develop and demonstrate a novel empiric prescribing decision aid. DESIGN/SETTING This is a demonstration involving more than 9,000 unique encounters for abdominal-biliary infection (ABI) and urinary tract infection (UTI) to a large healthcare system with a fully integrated electronic health record (EHR). METHODS We developed a novel method of displaying microbiology data called the weighted-incidence syndromic combination antibiogram (WISCA) for 2 clinical syndromes, ABI and UTI. The WISCA combines simple diagnosis and microbiology data from the EHR to (1) classify patients by syndrome and (2) determine, for each patient with a given syndrome, whether a given regimen (1 or more agents) would have covered all the organisms recovered for their infection. This allows data to be presented such that clinicians can see the probability that a particular regimen will cover a particular infection rather than the probability that a single drug will cover a single organism. RESULTS There were 997 encounters for ABI and 8,232 for UTI. A WISCA was created for each syndrome and compared with a traditional antibiogram for the same period. CONCLUSIONS Novel approaches to data compilation and display can overcome limitations to the utility of the traditional antibiogram in helping providers choose empiric antibiotics.

Severe influenza in 33 US hospitals, 2013–2014: Complications and risk factors for death in 507 patients

BACKGROUND Influenza A (H1N1) pdm09 became the predominant circulating strain in the United States during the 2013-2014 influenza season. Little is known about the epidemiology of severe influenza during this season. METHODS A retrospective cohort study of severely ill patients with influenza infection in intensive care units in 33 US hospitals from September 1, 2013, through April 1, 2014, was conducted to determine risk factors for mortality present on intensive care unit admission and to describe patient characteristics, spectrum of disease, management, and outcomes. RESULTS A total of 444 adults and 63 children were admitted to an intensive care unit in a study hospital; 93 adults (20.9%) and 4 children (6.3%) died. By logistic regression analysis, the following factors were significantly associated with mortality among adult patients: older age (>65 years, odds ratio, 3.1 [95% CI, 1.4-6.9], P=.006 and 50-64 years, 2.5 [1.3-4.9], P=.007; reference age 18-49 years), male sex (1.9 [1.1-3.3], P=.031), history of malignant tumor with chemotherapy administered within the prior 6 months (12.1 [3.9-37.0], P<.001), and a higher Sequential Organ Failure Assessment score (for each increase by 1 in score, 1.3 [1.2-1.4], P<.001). CONCLUSION Risk factors for death among US patients with severe influenza during the 2013-2014 season, when influenza A (H1N1) pdm09 was the predominant circulating strain type, shifted in the first postpandemic season in which it predominated toward those of a more typical epidemic influenza season.

Clinical Significance of Methicillin-Resistant Staphylococcus aureus Colonization on Hospital Admission: One-Year Infection Risk

Background Methicillin-resistant Staphylococcus aureus (MRSA) nasal colonization among inpatients is a well-established risk factor for MRSA infection during the same hospitalization, but the long-term risk of MRSA infection is uncertain. We performed a retrospective cohort study to determine the one-year risk of MRSA infection among inpatients with MRSA-positive nasal polymerase chain reaction (PCR) tests confirmed by positive nasal culture (Group 1), patients with positive nasal PCR but negative nasal culture (Group 2), and patients with negative nasal PCR (Group 3). Methodology/Principal Findings Subjects were adults admitted to a four-hospital system between November 1, 2006 and March 31, 2011, comprising 195,255 admissions. Patients underwent nasal swab for MRSA PCR upon admission; if positive, nasal culture for MRSA was performed; if recovered, MRSA was tested for Panton-Valentine Leukocidin (PVL). Outcomes included MRSA-positive clinical culture and skin and soft tissue infection (SSTI). Group 1 patients had a one-year risk of MRSA-positive clinical culture of 8.0% compared with 3.0% for Group 2 patients, and 0.6% for Group 3 patients (p<0.001). In a multivariable model, the hazard ratios for future MRSA-positive clinical culture were 6.52 (95% CI, 5.57 to 7.64) for Group 1 and 3.40 (95% CI, 2.70 to 4.27) for Group 2, compared with Group 3 (p<0.0001). History of MRSA and concurrent MRSA-positive clinical culture were significant risk factors for future MRSA-positive clinical culture. Group 1 patients colonized with PVL-positive MRSA had a one-year risk of MRSA-positive clinical culture of 10.1%, and a one-year risk of MRSA-positive clinical culture or SSTI diagnosis of 21.7%, compared with risks of 7.1% and 12.5%, respectively, for patients colonized with PVL-negative MRSA (p = 0.04, p = 0.005, respectively). Conclusions/Significance MRSA nasal colonization is a significant risk factor for future MRSA infection; more so if detected by culture than PCR. Colonization with PVL-positive MRSA is associated with greater risk than PVL-negative MRSA.

Bacterial and viral co-infections complicating severe influenza: Incidence and impact among 507 U.S. patients, 2013–14

Abstract Background Influenza acts synergistically with bacterial co-pathogens. Few studies have described co-infection in a large cohort with severe influenza infection. Objectives To describe the spectrum and clinical impact of co-infections. Study design Retrospective cohort study of patients with severe influenza infection from September 2013 through April 2014 in intensive care units at 33 U.S. hospitals comparing characteristics of cases with and without co-infection in bivariable and multivariable analysis. Results Of 507 adult and pediatric patients, 114 (22.5%) developed bacterial co-infection and 23 (4.5%) developed viral co-infection. Staphylococcus aureus was the most common cause of co-infection, isolated in 47 (9.3%) patients. Characteristics independently associated with the development of bacterial co-infection of adult patients in a logistic regression model included the absence of cardiovascular disease (OR 0.41 [0.23–0.73], p=0.003), leukocytosis (>11K/μl, OR 3.7 [2.2–6.2], p<0.001; reference: normal WBC 3.5–11K/μl) at ICU admission and a higher ICU admission SOFA score (for each increase by 1 in SOFA score, OR 1.1 [1.0–1.2], p=0.001). Bacterial co-infections (OR 2.2 [1.4–3.6], p=0.001) and viral co-infections (OR 3.1 [1.3–7.4], p=0.010) were both associated with death in bivariable analysis. Patients with a bacterial co-infection had a longer hospital stay, a longer ICU stay and were likely to have had a greater delay in the initiation of antiviral administration than patients without co-infection (p<0.05) in bivariable analysis. Conclusions Bacterial co-infections were common, resulted in delay of antiviral therapy and were associated with increased resource allocation and higher mortality.

Common Approaches to the Control of Multidrug-resistant Organisms Other Than Methicillin-resistant Staphylococcus aureus (MRSA)

Curbing antibiotic resistance is a challenge in health care today. Infections caused by multidrug-resistant organisms are estimated to cause 12,000 deaths and cost 3.5 billion dollars in excess health care costs in the United States annually. This article focuses on relevant infection control measures for vancomycin-resistant enterococci, multidrug-resistant gram-negative infections, and Clostridium difficile. Common control strategies targeting these pathogens are reviewed and opportunities for research and more effective deployment of existing tools are highlighted. When there is less extensive evidence available from the published literature, the experience with methicillin-resistant Staphylococcus aureus is discussed as it might apply to other pathogens.

The Influence of Context on Antimicrobial Prescribing for Febrile Respiratory Illness: A Cohort Study

BACKGROUND Little is known about the influence of contextual factors on a physicians likelihood to prescribe antimicrobials for febrile respiratory illness (FRI). Context includes epidemiologic context (for example, a pandemic period) and personal context (for example, recent exposure to other patients with FRI). OBJECTIVE To examine the association between contextual factors and antimicrobial prescribing for FRI. DESIGN 5.5-year retrospective cohort study. SETTING A network of Midwestern primary care providers. PATIENTS All patients presenting with FRI during influenza seasons between 2006 and 2011. MEASUREMENTS Antimicrobial prescribing for FRI during pandemic and seasonal influenza periods. RESULTS 28 301 unique patient encounters for FRI with 69 physicians in 26 practices were included. An antibiotic was prescribed in 12 795 (45.2%) cases. The range of prescribing among physicians was 17.9% to 83.7%. Antibiotics were prescribed in 47.5% of encounters during the seasonal period and 39.2% during the pandemic period (P < 0.001). After multivariable adjustment for patient and physician characteristics, antibiotic prescribing was lower in the pandemic period (odds ratio [OR], 0.72 [95% CI, 0.68 to 0.77]) than in the seasonal period. The likelihood of prescribing an antibiotic decreased as the number of FRI cases that a physician had seen in the previous week increased (OR, 0.93 [CI, 0.86 to 1.01] for 2 to 3 patients with FRI seen in the previous week; OR, 0.84 [CI, 0.77 to 0.91] for 4 to 6 patients; OR, 0.71 [CI, 0.64 to 0.78] for 7 to 11 patients; and OR, 0.57 [CI, 0.51 to 0.63] for ≥12 patients compared with the reference range of 0 to 1 patients). Pandemic season and recent personal context were also associated with antiviral prescribing. LIMITATION Retrospective study in a single geographic area. CONCLUSION Epidemiologic context and the number of cases of FRI that a physician had recently seen were associated with his or her likelihood to prescribe antimicrobials for FRI. Interventions that enhance a physicians contextual awareness may improve antimicrobial use. PRIMARY FUNDING SOURCE NorthShore University HealthSystem.

Knowledge Management and Informatics Considerations for Comparative Effectiveness Research: A Case-driven Exploration

Background: As clinical data are increasingly collected and stored electronically, their potential use for comparative effectiveness research (CER) grows. Despite this promise, challenges face those wishing to leverage such data. In this paper we aim to enumerate some of the knowledge management and informatics issues common to such data reuse. Design: After reviewing the current state of knowledge regarding biomedical informatics challenges and best practices related to CER, we then present 2 research projects at our institution. We analyze these and highlight several common themes and challenges related to the conduct of CER studies. Finally, we represent these emergent themes. Results: The informatics challenges commonly encountered by those conducting CER studies include issues related to data information and knowledge management (eg, data reuse, data preparation) as well as those related to people and organizational issues (eg, sociotechnical factors and organizational factors). Examples of these are described in further detail and a formal framework for describing these findings is presented. Conclusions: Significant challenges face researchers attempting to use often diverse and heterogeneous datasets for CER. These challenges must be understood in order to be dealt with successfully and can often be overcome with the appropriate use of informatics best practices. Many research and policy questions remain to be answered in order to realize the full potential of the increasingly electronic clinical data available for such research.