Randomized clinical trial of a customized electronic alert requiring an affirmative response compared to a control group receiving a commercial passive CPOE alert: NSAID�Cwarfarin co-prescribing as a test case

Background

Studies that have looked at the effectiveness of computerized decision support systems to prevent drug–drug interactions have reported modest results because of low response by the providers to the automated alerts.

Objective

To evaluate, within an inpatient computerized physician order entry (CPOE) system, the incremental effectiveness of an alert that required a response from the provider, intended as a stronger intervention to prevent concurrent orders of warfarin and non-steroidal anti-inflammatory drugs (NSAIDs).

Design

Randomized clinical trial of 1963 clinicians assigned to either an intervention group receiving a customized electronic alert requiring affirmative response or a control group receiving a commercially available passive alert as part of the CPOE. The study duration was 2 August 2006 to 15 December 2007.

Measurements

Alert adherence was compared between study groups.

Results

The proportion of desired ordering responses (ie, not reordering the alert-triggering drug after firing) was lower in the intervention group (114/464 (25%) customized alerts issued) than in the control group (154/560 (28%) passive alerts firing). The adjusted OR of inappropriate ordering was 1.22 (95% CI 0.69 to 2.16).

Conclusion

A customized CPOE alert that required a provider response had no effect in reducing concomitant prescribing of NSAIDs and warfarin beyond that of the commercially available passive alert received by the control group. New CPOE alerts cannot be assumed to be effective in improving prescribing, and need evaluation.     

Making electronic prescribing alerts more effective: scenario-based experimental study in junior doctors

Objective

Expert authorities recommend clinical decision support systems to reduce prescribing error rates, yet large numbers of insignificant on-screen alerts presented in modal dialog boxes persistently interrupt clinicians, limiting the effectiveness of these systems. This study compared the impact of modal and non-modal electronic (e-) prescribing alerts on prescribing error rates, to help inform the design of clinical decision support systems.

Design

A randomized study of 24 junior doctors each performing 30 simulated prescribing tasks in random order with a prototype e-prescribing system. Using a within-participant design, doctors were randomized to be shown one of three types of e-prescribing alert (modal, non-modal, no alert) during each prescribing task.

Measurements

The main outcome measure was prescribing error rate. Structured interviews were performed to elicit participants'' preferences for the prescribing alerts and their views on clinical decision support systems.

Results

Participants exposed to modal alerts were 11.6 times less likely to make a prescribing error than those not shown an alert (OR 11.56, 95% CI 6.00 to 22.26). Those shown a non-modal alert were 3.2 times less likely to make a prescribing error (OR 3.18, 95% CI 1.91 to 5.30) than those not shown an alert. The error rate with non-modal alerts was 3.6 times higher than with modal alerts (95% CI 1.88 to 7.04).

Conclusions

Both kinds of e-prescribing alerts significantly reduced prescribing error rates, but modal alerts were over three times more effective than non-modal alerts. This study provides new evidence about the relative effects of modal and non-modal alerts on prescribing outcomes.     

Provider management strategies of abnormal test result alerts: a cognitive task analysis

Objective

Electronic medical records (EMRs) facilitate abnormal test result communication through “alert” notifications. The aim was to evaluate how primary care providers (PCPs) manage alerts related to critical diagnostic test results on their EMR screens, and compare alert-management strategies of providers with high versus low rates of timely follow-up of results.

Design

28 PCPs from a large, tertiary care Veterans Affairs Medical Center (VAMC) were purposively sampled according to their rates of timely follow-up of alerts, determined in a previous study. Using techniques from cognitive task analysis, participants were interviewed about how and when they manage alerts, focusing on four alert-management features to filter, sort and reduce unnecessary alerts on their EMR screens.

Results

Provider knowledge of alert-management features ranged between 4% and 75%. Almost half (46%) of providers did not use any of these features, and none used more than two. Providers with higher versus lower rates of timely follow-up used the four features similarly, except one (customizing alert notifications). Providers with low rates of timely follow-up tended to manually scan the alert list and process alerts heuristically using their clinical judgment. Additionally, 46% of providers used at least one workaround strategy to manage alerts.

Conclusion

Considerable heterogeneity exists in provider use of alert-management strategies; specific strategies may be associated with lower rates of timely follow-up. Standardization of alert-management strategies including improving provider knowledge of appropriate tools in the EMR to manage alerts could reduce the lack of timely follow-up of abnormal diagnostic test results.     

Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial

Background

Accurate clinical problem lists are critical for patient care, clinical decision support, population reporting, quality improvement, and research. However, problem lists are often incomplete or out of date.

Objective

To determine whether a clinical alerting system, which uses inference rules to notify providers of undocumented problems, improves problem list documentation.

Study Design and Methods

Inference rules for 17 conditions were constructed and an electronic health record-based intervention was evaluated to improve problem documentation. A cluster randomized trial was conducted of 11 participating clinics affiliated with a large academic medical center, totaling 28 primary care clinical areas, with 14 receiving the intervention and 14 as controls. The intervention was a clinical alert directed to the provider that suggested adding a problem to the electronic problem list based on inference rules. The primary outcome measure was acceptance of the alert. The number of study problems added in each arm as a pre-specified secondary outcome was also assessed. Data were collected during 6-month pre-intervention (11/2009–5/2010) and intervention (5/2010–11/2010) periods.

Results

17 043 alerts were presented, of which 41.1% were accepted. In the intervention arm, providers documented significantly more study problems (adjusted OR=3.4, p<0.001), with an absolute difference of 6277 additional problems. In the intervention group, 70.4% of all study problems were added via the problem list alerts. Significant increases in problem notation were observed for 13 of 17 conditions.

Conclusion

Problem inference alerts significantly increase notation of important patient problems in primary care, which in turn has the potential to facilitate quality improvement.

Trial Registration

ClinicalTrials.gov: {"type":"clinical-trial","attrs":{"text":"NCT01105923","term_id":"NCT01105923"}}NCT01105923.     

How is the electronic health record being used? Use of EHR data to assess physician-level variability in technology use

Background

Studies of the effects of electronic health records (EHRs) have had mixed findings, which may be attributable to unmeasured confounders such as individual variability in use of EHR features.

Objective

To capture physician-level variations in use of EHR features, associations with other predictors, and usage intensity over time.

Methods

Retrospective cohort study of primary care providers eligible for meaningful use at a network of federally qualified health centers, using commercial EHR data from January 2010 through June 2013, a period during which the organization was preparing for and in the early stages of meaningful use.

Results

Data were analyzed for 112 physicians and nurse practitioners, consisting of 430 803 encounters with 99 649 patients. EHR usage metrics were developed to capture how providers accessed and added to patient data (eg, problem list updates), used clinical decision support (eg, responses to alerts), communicated (eg, printing after-visit summaries), and used panel management options (eg, viewed panel reports). Provider-level variability was high: for example, the annual average proportion of encounters with problem lists updated ranged from 5% to 60% per provider. Some metrics were associated with provider, patient, or encounter characteristics. For example, problem list updates were more likely for new patients than established ones, and alert acceptance was negatively correlated with alert frequency.

Conclusions

Providers using the same EHR developed personalized patterns of use of EHR features. We conclude that physician-level usage of EHR features may be a valuable additional predictor in research on the effects of EHRs on healthcare quality and costs.     

Impact of a web-based personally controlled health management system on influenza vaccination and health services utilization rates: a randomized controlled trial

Objective

To assess the impact of a web-based personally controlled health management system (PCHMS) on the uptake of seasonal influenza vaccine and primary care service utilization among university students and staff.

Materials and methods

A PCHMS called Healthy.me was developed and evaluated in a 2010 CONSORT-compliant two-group (6-month waitlist vs PCHMS) parallel randomized controlled trial (RCT) (allocation ratio 1:1). The PCHMS integrated an untethered personal health record with consumer care pathways, social forums, and messaging links with a health service provider.

Results

742 university students and staff met inclusion criteria and were randomized to a 6-month waitlist (n=372) or the PCHMS (n=370). Amongst the 470 participants eligible for primary analysis, PCHMS users were 6.7% (95% CI: 1.46 to 12.30) more likely than the waitlist to receive an influenza vaccine (waitlist: 4.9% (12/246, 95% CI 2.8 to 8.3) vs PCHMS: 11.6% (26/224, 95% CI 8.0 to 16.5); χ²=7.1, p=0.008). PCHMS participants were also 11.6% (95% CI 3.6 to 19.5) more likely to visit the health service provider (waitlist: 17.9% (44/246, 95% CI 13.6 to 23.2) vs PCHMS: 29.5% (66/224, 95% CI: 23.9 to 35.7); χ²=8.8, p=0.003). A dose–response effect was detected, where greater use of the PCHMS was associated with higher rates of vaccination (p=0.001) and health service provider visits (p=0.003).

Discussion

PCHMS can significantly increase consumer participation in preventive health activities, such as influenza vaccination.

Conclusions

Integrating a PCHMS into routine health service delivery systems appears to be an effective mechanism for enhancing consumer engagement in preventive health measures.

Trial registration

Australian New Zealand Clinical Trials Registry ACTRN12610000386033. http://www.anzctr.org.au/trial_view.aspx?id=335463.     

Using statistical text classification to identify health information technology incidents

Objective

To examine the feasibility of using statistical text classification to automatically identify health information technology (HIT) incidents in the USA Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience (MAUDE) database.

Design

We used a subset of 570 272 incidents including 1534 HIT incidents reported to MAUDE between 1 January 2008 and 1 July 2010. Text classifiers using regularized logistic regression were evaluated with both ‘balanced’ (50% HIT) and ‘stratified’ (0.297% HIT) datasets for training, validation, and testing. Dataset preparation, feature extraction, feature selection, cross-validation, classification, performance evaluation, and error analysis were performed iteratively to further improve the classifiers. Feature-selection techniques such as removing short words and stop words, stemming, lemmatization, and principal component analysis were examined.

Measurements

κ statistic, F1 score, precision and recall.

Results

Classification performance was similar on both the stratified (0.954 F1 score) and balanced (0.995 F1 score) datasets. Stemming was the most effective technique, reducing the feature set size to 79% while maintaining comparable performance. Training with balanced datasets improved recall (0.989) but reduced precision (0.165).

Conclusions

Statistical text classification appears to be a feasible method for identifying HIT reports within large databases of incidents. Automated identification should enable more HIT problems to be detected, analyzed, and addressed in a timely manner. Semi-supervised learning may be necessary when applying machine learning to big data analysis of patient safety incidents and requires further investigation.     

Factors contributing to an increase in duplicate medication order errors after CPOE implementation

Objective

To evaluate the incidence of duplicate medication orders before and after computerized provider order entry (CPOE) with clinical decision support (CDS) implementation and identify contributing factors.

Design

CPOE with duplicate medication order alerts was implemented in a 400-bed Northeastern US community tertiary care teaching hospital. In a pre-implementation post-implementation design, trained nurses used chart review, computer-generated reports of medication orders, provider alerts, and staff reports to identify medication errors in two intensive care units (ICUs).

Measurement

Medication error data were adjudicated by a physician and a human factors engineer for error stage and type. A qualitative analysis of duplicate medication ordering errors was performed to identify contributing factors.

Results

Data were collected for 4147 patient-days pre-implementation and 4013 patient-days post-implementation. Duplicate medication ordering errors increased after CPOE implementation (pre: 48 errors, 2.6% total; post: 167 errors, 8.1% total; p<0.0001). Most post-implementation duplicate orders were either for the identical order or the same medication. Contributing factors included: (1) provider ordering practices and computer availability, for example, two orders placed within minutes by different providers on rounds; (2) communication and hand-offs, for example, duplicate orders around shift change; (3) CDS and medication database design, for example confusing alert content, high false-positive alert rate, and CDS algorithms missing true duplicates; (4) CPOE data display, for example, difficulty reviewing existing orders; and (5) local CDS design, for example, medications in order sets defaulted as ordered.

Conclusions

Duplicate medication order errors increased with CPOE and CDS implementation. Many work system factors, including the CPOE, CDS, and medication database design, contributed to their occurrence.     

Does user-centred design affect the efficiency,usability and safety of CPOE order sets?

Background

Application of user-centred design principles to Computerized provider order entry (CPOE) systems may improve task efficiency, usability or safety, but there is limited evaluative research of its impact on CPOE systems.

Objective

We evaluated the task efficiency, usability, and safety of three order set formats: our hospital''s planned CPOE order sets (CPOE Test), computer order sets based on user-centred design principles (User Centred Design), and existing pre-printed paper order sets (Paper).

Participants

27staff physicians, residents and medical students.

Setting

Sunnybrook Health Sciences Centre, an academic hospital in Toronto, Canada.

Methods

Participants completed four simulated order set tasks with three order set formats (two CPOE Test tasks, one User Centred Design, and one Paper). Order of presentation of order set formats and tasks was randomized. Users received individual training for the CPOE Test format only.

Main Measures

Completion time (efficiency), requests for assistance (usability), and errors in the submitted orders (safety).

Results

27 study participants completed 108 order sets. Mean task times were: User Centred Design format 273 s, Paper format 293 s (p=0.73 compared to UCD format), and CPOE Test format 637 s (p<0.0001 compared to UCD format). Users requested assistance in 31% of the CPOE Test format tasks, whereas no assistance was needed for the other formats (p<0.01). There were no significant differences in number of errors between formats.

Conclusions

The User Centred Design format was more efficient and usable than the CPOE Test format even though training was provided for the latter. We conclude that application of user-centred design principles can enhance task efficiency and usability, increasing the likelihood of successful implementation.     

Development and Psychometric Evaluation of the Impact of Health Information Technology (I-HIT) Scale

Objective

The use of health information technology (HIT) for the support of communication processes and data and information access in acute care settings is a relatively new phenomenon. A means of evaluating the impact of HIT in hospital settings is needed. The purpose of this research was to design and psychometrically evaluate the Impact of Health Information Technology scale (I-HIT). I-HIT was designed to measure the perception of nurses regarding the ways in which HIT influences interdisciplinary communication and workflow patterns and nurses’ satisfaction with HIT applications and tools.

Design

Content for a 43-item tool was derived from the literature, and supported theoretically by the Coiera model and by nurse informaticists. Internal consistency reliability analysis using Cronbach’s alpha was conducted on the 43-item scale to initiate the item reduction process. Items with an item total correlation of less than 0.35 were removed, leaving a total of 29 items.

Measurements

Item analysis, exploratory principal component analysis and internal consistency reliability using Cronbach’s alpha were used to confirm the 29-item scale.

Results

Principal components analysis with Varimax rotation produced a four-factor solution that explained 58.5% of total variance (general advantages, information tools to support information needs, information tools to support communication needs, and workflow implications). Internal consistency of the total scale was 0.95 and ranged from 0.80-0.89 for four subscales.

Conclusion

I-HIT demonstrated psychometric adequacy and is recommended to measure the impact of HIT on nursing practice in acute care settings.     

Developing High-specificity Anti-hypertensive Alerts by Therapeutic State Analysis of Electronic Prescribing Records

Objective

This paper presents a model for analysis of chronic disease prescribing action over time in terms of transitions in status of therapy as indicated in electronic prescribing records. The quality of alerts derived from these therapeutic state transitions is assessed in the context of antihypertensive prescribing.

Design

A set of alert criteria is developed based on analysis of state-transition in past antihypertensive prescribing of a rural Australian General Practice. Thirty active patients coded as hypertensive with alerts on six months of previously un-reviewed prescribing, and 30 hypertensive patients without alerts, are randomly sampled and independently reviewed by the practice’s two main general practice physicians (GPs), each GP reviewing 20 alert and 20 non-alert cases (providing 10 alert and 10 non-alert cases for agreement assessment).

Measurements

GPs provide blind assessment of quality of hypertension management and retrospective assessment of alert relevance.

Results

Alerts were found on 66 of 611 cases with coded hypertension with 37 alerts on the 30 sampled alert cases. GPs assessed alerting sensitivity as 74% (CI 52% - 89%) and specificity as 61% (CI 45% - 74%) for the sample, which is estimated as 26% sensitivity and 93% specificity for the antihypertensive population. Agreement between the GPs on assessment of alert relevance was fair (kappa = 0.37).

Conclusions

Data-driven development of alerts from electronic prescribing records using analysis of therapeutic state transition shows promise for derivation of high-specificity alerts to improve the quality of chronic disease management activities.     

Clinical decision support implemented with academic detailing improves prescribing of key renally cleared drugs in the hospital setting

Objective

Lack of dose adjustment for renally cleared drugs in the presence of poor renal function is a common problem in the hospital setting. The absence of a clinical decision support system (CDSS) from direct clinician workflows such as computerized provider order entry (CPOE) hinders the uptake of CDSS. This study implemented CDSS in an environment independent of CPOE, introduced to prescribers via academic detailing, to address the dosing of renally cleared drugs.

Design

GFR+ was designed to automatically calculate and update renal function, doses of key drugs adjusted for renal function, and highlight clinically significant decreases in renal function. Prescribers were made aware of GFR+, its navigation, and surrounding clinical issues, using academic detailing.

Measurement

The rate of dosing conformity and management for key renally cleared drugs in hospitalized patients, before and after GFR+ implementation.

Results

Improvements were seen in dosing conformity for enoxaparin (from 68% to 86%, p=0.03), gentamicin (63–87%, p=0.01), and vancomycin (47–77%, p=0.07), as well as the appropriate use of gentamicin therapeutic drug monitoring (70–90%, p=0.02). During episodes of acute renal impairment, renally cleared drugs were held on 38% of instances in the pre-intervention period compared with 62% post-intervention (p=0.01).

Conclusion

Clinical decision support implemented with academic detailing improved dosing conformity and management of key renally cleared drugs in a hospitalized population. Academic detailing should be strongly considered to facilitate the introduction of CDSS systems that cannot be placed directly into the clinician workflow.     

The military health system's personal health record pilot with Microsoft HealthVault and Google Health

Objective

To design, build, implement, and evaluate a personal health record (PHR), tethered to the Military Health System, that leverages Microsoft^® HealthVault and Google^® Health infrastructure based on user preference.

Materials and methods

A pilot project was conducted in 2008–2009 at Madigan Army Medical Center in Tacoma, Washington. Our PHR was architected to a flexible platform that incorporated standards-based models of Continuity of Document and Continuity of Care Record to map Department of Defense-sourced health data, via a secure Veterans Administration data broker, to Microsoft^® HealthVault and Google^® Health based on user preference. The project design and implementation were guided by provider and patient advisory panels with formal user evaluation.

Results

The pilot project included 250 beneficiary users. Approximately 73.2% of users were <65 years of age, and 38.4% were female. Of the users, 169 (67.6%) selected Microsoft^® HealthVault, and 81 (32.4%) selected Google^® Health as their PHR of preference. Sample evaluation of users reflected 100% (n=60) satisfied with convenience of record access and 91.7% (n=55) satisfied with overall functionality of PHR.

Discussion

Key lessons learned related to data-transfer decisions (push vs pull), purposeful delays in reporting sensitive information, understanding and mapping PHR use and clinical workflow, and decisions on information patients may choose to share with their provider.

Conclusion

Currently PHRs are being viewed as empowering tools for patient activation. Design and implementation issues (eg, technical, organizational, information security) are substantial and must be thoughtfully approached. Adopting standards into design can enhance the national goal of portability and interoperability.     

Use of a support vector machine for categorizing free-text notes: assessment of accuracy across two institutions

Background

Electronic health record (EHR) users must regularly review large amounts of data in order to make informed clinical decisions, and such review is time-consuming and often overwhelming. Technologies like automated summarization tools, EHR search engines and natural language processing have been shown to help clinicians manage this information.

Objective

To develop a support vector machine (SVM)-based system for identifying EHR progress notes pertaining to diabetes, and to validate it at two institutions.

Materials and methods

We retrieved 2000 EHR progress notes from patients with diabetes at the Brigham and Women''s Hospital (1000 for training and 1000 for testing) and another 1000 notes from the University of Texas Physicians (for validation). We manually annotated all notes and trained a SVM using a bag of words approach. We then used the SVM on the testing and validation sets and evaluated its performance with the area under the curve (AUC) and F statistics.

Results

The model accurately identified diabetes-related notes in both the Brigham and Women''s Hospital testing set (AUC=0.956, F=0.934) and the external University of Texas Faculty Physicians validation set (AUC=0.947, F=0.935).

Discussion

Overall, the model we developed was quite accurate. Furthermore, it generalized, without loss of accuracy, to another institution with a different EHR and a distinct patient and provider population.

Conclusions

It is possible to use a SVM-based classifier to identify EHR progress notes pertaining to diabetes, and the model generalizes well.     

Patient Web Services Integrated with a Shared Medical Record: Patient Use and Satisfaction

Objectives

This study sought to describe the evolution, use, and user satisfaction of a patient Web site providing a shared medical record between patients and health professionals at Group Health Cooperative, a mixed-model health care financing and delivery organization based in Seattle, Washington.

Design

This study used a retrospective, serial, cross-sectional study from September 2002 through December 2005 and a mailed satisfaction survey of a random sampling of 2,002 patients.

Measurements

This study measured the adoption and use of a patient Web site (MyGroupHealth) from September 2002 through December 2005.

Results

As of December 2005, 25% (105,047) of all Group Health members had registered and completed an identification verification process enabling them to use all of the available services on MyGroupHealth. Identification verification was more common among patients receiving care in the Integrated Delivery System (33%) compared with patients receiving care in the network (7%). As of December 2005, unique monthly user rates per 1,000 adult members were the highest for review of medical test results (54 of 1,000), medication refills (44 of 1,000), after-visit-summaries (32 of 1,000), and patient–provider clinical messaging (31 of 1,000). The response rate for the patient satisfaction survey was 46% (n = 921); 94% of survey respondents were satisfied or very satisfied with MyGroupHealth overall. Patients reported highest satisfaction (satisfied or very satisfied) for medication refills (96%), patient–provider messaging (93%), and medical test results (86%).

Conclusion

Use and satisfaction with MyGroupHealth were greatest for accessing services and information involving ongoing, active care and patient–provider communication. Tight integration of Web services with clinical information systems and patient–provider relationships may be important in meeting the needs of patients.     

The influence of computerized decision support on prescribing during ward-rounds: are the decision-makers targeted?

Objective

To assess whether a low level of decision support within a hospital computerized provider order entry system has an observable influence on the medication ordering process on ward-rounds and to assess prescribers'' views of the decision support features.

Methods

14 specialty teams (46 doctors) were shadowed by the investigator while on their ward-rounds and 16 prescribers from these teams were interviewed.

Results

Senior doctors were highly influential in prescribing decisions during ward-rounds but rarely used the computerized provider order entry system. Junior doctors entered the majority of medication orders into the system, nearly always ignored computerized alerts and never raised their occurrence with other doctors on ward-rounds. Interviews with doctors revealed that some decision support features were valued but most were not perceived to be useful.

Discussion and conclusion

The computerized alerts failed to target the doctors who were making the prescribing decisions on ward-rounds. Senior doctors were the decision makers, yet the junior doctors who used the system received the alerts. As a result, the alert information was generally ignored and not incorporated into the decision-making processes on ward-rounds. The greatest value of decision support in this setting may be in non-ward-round situations where senior doctors are less influential. Identifying how prescribing systems are used during different clinical activities can guide the design of decision support that effectively supports users in different situations. If confirmed, the findings reported here present a specific focus and user group for designers of medication decision support.     

PARP inhibition in atherosclerosis and its effects on dendritic cells,T cells and auto-antibody levels

Objective

Atherosclerosis is a chronic inflammatory process. Poly(ADP-ribose) polymerase-1 (PARP), a nuclear enzyme linked to DNA repair, has been shown to be involved in atherogenesis; however, the effects on dendritic cells, T cells and serum auto-antibody levels are not fully understood.

Methods

Male Apoe^-/- mice on a western diet were treated with the PARP inhibitor 1NO-1001 (n = 15), while the control group (n = 15) received 5% glucose solution for 10 weeks.

Results

Inhibition of PARP markedly reduced atherosclerotic lesion development (p = 0.001). Immunohistochemistry and mRNA analysis revealed a reduced inflammatory compound inside the lesion. Focusing on dendritic cells, INO-1001 reduced number of cells (p = 0.04), grade of activation, represented by I/12 (p = 0.04) and Cd83 (p = 0.03), and grade of attraction, represented by Mip3α (p = 0.02) in the plaque. Furthermore, INO-1001 decreased number of T lymphocyte (p = 0.003) in the lesion and grade of activation after stimulation with oxLDL in vitro. Moreover, serum IgM antibody levels to oxLDL were significantly lower in INO-1001 treated mice (p = 0.03).

Conclusions

Functional blockade of PARP by INO-1001 reduces atherosclerotic lesion development. The anti-atherogenic effect is beside already known mechanisms also moderated due to modulation of DC and T cell invasion and activation, DC attraction as well as IgM antibody levels to oxLDL.