Improving Readiness to Manage Intimate Partner Violence in Family Medicine Clinics by Collaboration With a Community Organization

Background and Objectives

Primary care clinicians are in a unique position to address intimate partner violence (IPV) in routine clinical practice. The purpose of this study was to improve clinician readiness to identify and manage IPV in four family medicine residency practice sites on the west side of Chicago by partnering with a local domestic violence organization.

Methods

Practice sites included three federally qualified health centers and one hospital-based office. Eligible clinicians included resident and faculty physicians, nurse practitioners, and certified nurse midwives. We assessed readiness using the validated Physician Readiness to Manage Intimate Partner Violence Survey (PREMIS). We used initial survey results (n=53, 73%) to develop a targeted clinician educational intervention by a community organization. We administered the PREMIS tool postintervention at 1 and 6 months, measuring perceived and actual knowledge, preparedness, and practice issues. We performed comparison statistics to assess aggregate change.

Results

PREMIS response rates were n=53 (72%), n=32 (47%), and n=36 (49%), for preintervention, 1, and 6 months postintervention, respectively. Mean clinician preparedness score improved significantly at 1 and 6 months (P<.001, P<.009). Mean self-perceived knowledge score improved significantly at 1 month (P<.001) and trended toward improvement at 6 months (P=.07). Actual knowledge trended toward improvement at 1 month (P=.07) and after 6 months (P=.05). Mean practice issues scores did not improve significantly.

Conclusions

Participation in a 45-minute targeted educational intervention improved clinician readiness to manage IPV. Collaborating with a community partner builds a relationship for further referrals and advocacy for patients.

Development of an Electronic Algorithm to Identify in Real Time Adults Hospitalized With Suspected Community-Acquired Pneumonia

Background

Community-acquired pneumonia (CAP) is a major driver of hospital antibiotic use. Efficient methods to identify patients treated for CAP in real time using the electronic health record (EHR) are needed. Automated identification of these patients could facilitate systematic tracking, intervention, and feedback on CAP-specific metrics such as appropriate antibiotic choice and duration.

Methods

Using retrospective data, we identified suspected CAP cases by searching for patients who received CAP antibiotics AND had an admitting International Classification of Diseases, Tenth Revision (ICD-10) code for pneumonia OR chest imaging within 24 hours OR bacterial urinary antigen testing within 48 hours of admission (denominator query). We subsequently explored different structured and natural language processing (NLP)–derived data from the EHR to identify CAP cases. We evaluated combinations of these electronic variables through receiver operating characteristic (ROC) curves to assess which best identified CAP cases compared to cases identified by manual chart review. Exclusion criteria were age <18 years, absolute neutrophil count <500 cells/mm³, and admission to an oncology unit.

Results

Compared to the gold standard of chart review, the area under the ROC curve to detect CAP was 0.63 (95% confidence interval [CI], .55–.72; P < .01) using structured data (ie, laboratory and vital signs) and 0.83 (95% CI, .77–.90; P < .01) when NLP-derived data from radiographic reports were included. The sensitivity and specificity of the latter model were 80% and 81%, respectively.

Conclusions

Creating an electronic tool that effectively identifies CAP cases in real time is possible, but its accuracy is dependent on NLP-derived radiographic data.

OP0039 ALPN-303, AN ENHANCED, POTENT DUAL BAFF/APRIL ANTAGONIST ENGINEERED BY DIRECTED EVOLUTION FOR THE TREATMENT OF SYSTEMIC LUPUS ERYTHEMATOSUS (SLE) AND OTHER B CELL-RELATED AUTOIMMUNE DISEASES

Background:

BAFF and APRIL are TNF superfamily members that form homo- and heteromultimers that bind TACI and BCMA on B cells; BAFF also binds BAFF-R. BAFF and APRIL support B cell development, differentiation, and survival, particularly for plasmablasts and plasma cells, and play critical roles in the pathogenesis of B cell-related autoimmune diseases. In nonclinical models, inhibition of either BAFF or APRIL alone mediates relatively modest effects, whereas their co-neutralization dramatically reduces B cell function, including antibody production. Fc fusions of wild-type (WT) TACI (e.g. atacicept and telitacicept) target both BAFF and APRIL and have demonstrated promising clinical potential in e.g. systemic lupus erythematosus (SLE) and IgA nephropathy but have not yet clearly exhibited long-term and/or complete disease remissions.

Objectives:

To generate a dual BAFF/APRIL antagonist with inhibitory activity superior to WT TACI and BCMA and with the potential to improve clinical outcomes in B cell-mediated diseases.

Methods:

Our directed evolution platform was used to identify a potent variant TNFR domain (vTD) of TACI that exhibits significantly enhanced affinity for BAFF and APRIL as compared to WT TACI; this TACI vTD domain was fused to a human IgG Fc to generate the therapeutic candidate ALPN-303. ALPN-303 was evaluated for functional activity in: 1) human lymphocyte assays, 2) the NOD.Aec1Aec2 spontaneous model of Sjogren’s syndrome (SjS), 3) the bm12-induced mouse model of lupus, 4) the (NZB/NZW)F1 spontaneous model of lupus, and 5) preclinical rodent and cynomolgus monkey pharmacokinetic/pharmacodynamic studies.

Results:

ALPN-303 inhibited BAFF- and APRIL-mediated signaling in vitro in human lymphocyte assays, with significantly lower IC50 values than WT TACI-Fc and belimumab comparators. In all mouse models evaluated, administration of ALPN-303 rapidly and significantly reduced key lymphocyte subsets including plasma cells, germinal center B cells, and follicular T helper cells. ALPN-303 significantly reduced autoantibodies and sialadenitis in the spontaneous SjS model, inhibited glomerular IgG deposition in the bm12-induced model of lupus, and potently suppressed anti-dsDNA autoAbs, blood urea nitrogen levels, proteinuria, sialadenitis, kidney lesions, and renal immune complex deposition in the NZB/W lupus model. As compared to WT TACI-Fc, ALPN-303 exhibited higher serum exposure and significantly and persistently decreased titers of serum IgM, IgG, and IgA antibodies in mice and cynomolgus monkeys (Figure 1).

Figure 1.

ALPN-303 induces more potent suppression, as compared to WT TACI-Fc, of serum immunoglobulins following a single 9 mg/kg IV infusion (on Day 0; arrows) in female cynomolgus monkeys.

Conclusion:

ALPN-303 is a potent BAFF/APRIL antagonist derived from our directed evolution platform that consistently demonstrates encouraging immunomodulatory activity and efficacy in vitro and in vivo, superior in preclinical studies to anti-BAFF antibody and WT TACI-Fc. This novel Fc fusion molecule demonstrates favorable preliminary developability characteristics, including higher serum exposures and more potent immunosuppressive activities, which may enable lower clinical doses and/or longer dosing intervals than WT TACI-Fc therapeutics. ALPN-303 may thus be an attractive development candidate for the treatment of multiple autoimmune and inflammatory diseases, particularly B cell-related diseases such as SLE, SjS, and other connective tissue diseases. Preclinical development is underway to enable the initiation of clinical trials later this year.

Disclosure of Interests:

Stacey R. Dillon Shareholder of: Alpine Immune Sciences, Bristol Myers Squibb, Employee of: Alpine Immune Sciences, Bristol Myers Squibb, Lawrence S. Evans Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Katherine E. Lewis Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Jing Yang Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Mark W. Rixon Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Joe Kuijper Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Dan Demonte Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Janhavi Bhandari Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Steve Levin Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Kayla Kleist Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Sherri Mudri Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Susan Bort Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Daniel Ardourel Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Michelle A. Seaberg Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Rachel Wang Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Chelsea Gudgeon Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Russell Sanderson Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Martin F. Wolfson Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Jan Hillson Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Stanford L. Peng Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences

Responding to a Pandemic: COVID-19 Projects in the Malone Center

As the scope and scale of the COVID-19 pandemic became clear in early March of 2020, the faculty of the Malone Center engaged in several projects aimed at addressing both immediate and long-term implications of COVID-19. In this article, we briefly outline the processes that we engaged in to identify areas of need, the projects that emerged, and the results of those projects. As we write, some of these projects have reached a natural termination point, whereas others continue. We identify some of the factors that led to projects that moved to implementation, as well as factors that led projects to fail to progress or to be abandoned.

Factors associated with missed appointments by adults with type 2 diabetes mellitus: a systematic review

Keeping regular medical appointments is a key indicator of patient engagement in diabetes care. Nevertheless, a significant proportion of adults with type 2 diabetes mellitus (T2DM) miss their regular medical appointments. In order to prevent and delay diabetes-related complications, it is essential to understand the factors associated with missed appointments among adults with T2DM. We synthesized evidence concerning factors associated with missed appointments among adults with T2DM. Using five electronic databases, including PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, PsycINFO and Web of Science, a systematic literature search was done to identify studies that describe factors related to missed appointments by adults with T2DM. A total of 18 articles met the inclusion criteria. The majority of studies included in this review were cohort studies using medical records. While more than half of the studies were of high quality, the operational definitions of missed appointments varied greatly across studies. Factors associated with missed appointments were categorized as patient characteristics, healthcare system and provider factors and interpersonal factors with inconsistent findings. Patient characteristics was the most commonly addressed category, followed by health system and provider factors. Only three studies addressed interpersonal factors, two of which were qualitative. An increasing number of people live with one or more chronic conditions which require more careful attention to patient-centered care and support. Future research is warranted to address interpersonal factors from patient perspectives to better understand the underlying causes of missed appointments among adults with T2DM.

169. Development of a Real Time Electronic Algorithm to Identify Hospitalized Patients with Community-Acquired Pneumonia

Background

Syndrome-based antibiotic stewardship can be limited by difficulty in finding cases for evaluation. We developed an electronic extraction algorithm to prospectively identify CAP patients.

Methods

We included non-oncology patients ≥18 years old admitted to The Johns Hopkins Hospital from 12/2018 to 3/2019 who 1) received common CAP antibiotics for ≥48 hours after admission and 2) had a bacterial urinary antigen and chest imaging ordered within 48 hours of admission that was not for assessment of endotracheal tube or central line placement. Charts of patients meeting these criteria were reviewed by 2 authors to identify true cases of CAP based on IDSA guidelines. Cases identified in 12/2018 (n=111) were used to explore potential indicators of CAP, and cases identified 1–3/2019 (n=173) were used to evaluate combinations of indicators that could identify patients treated for CAP who did have CAP (true CAP) and did not have CAP (false CAP). This cohort was divided into a training and a validation set (2/3 and 1/3, respectively). Potential indicators included vitals signs, laboratory data and free text extracted via natural language processing (NLP). Predictive performance of composite indicators for true CAP were assessed using receiver-operating characteristics (ROC) curves. The Hosmer-Lemeshow goodness fit test was used to test model fit and the Akaike Information Criteria was used to determine model selection.

Results

True CAP was observed in 41% (71/173) of cases and 14 potential individual indicators were identified (Table). These were combined to make 45 potential composite indicators. ROC curves for selected composite indicators are shown in the Figure. Models without use of NLP-derived variables had poor discriminative ability. The best model included fever, hypoxemia, leukocytosis, and “consolidation” on imaging with a sensitivity and positive predictive value 78.7% and specificity and negative predictive value of 85.7%.

Table. Indicators evaluated to identify patients with CAP

Figure. ROC curves for composite indicators

Conclusion

Patients with CAP can be identified using electronic data but use of NLP-derived radiographic criteria is required. These data can be linked with data on antibiotic use and duration to develop reports for clinicians regarding appropriate CAP diagnosis and treatment.

Disclosures

All Authors: No reported disclosures

Targeted rapid testing for SARS-CoV-2 in the emergency department is associated with large reductions in uninfected patient exposure time

Opportunity exists to decrease healthcare-related exposure to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), preserve infection control resources, and increase care capacity by reducing the time to diagnosis of coronavirus disease 2019 (COVID-19). A retrospective cohort analysis was undertaken to measure the effect of targeted rapid molecular testing for SARS-CoV-2 on these outcomes. In comparison with standard platform testing, rapid testing was associated with a 65.6% reduction (12.6 h) in the median time to removal from the isolation cohort for patients with negative diagnostic results. This translated to an increase in COVID-19 treatment capacity of 3028 bed-hours and 7500 fewer patient interactions that required the use of personal protective equipment per week.

Artificial Intelligence-Based Clinical Decision Support for COVID-19-Where Art Thou?

The COVID-19 crisis has brought about new clinical questions, new workflows, and accelerated distributed healthcare needs. Although artificial intelligence (AI)-based clinical decision support seemed to have matured, the application of AI-based tools for COVID-19 has been limited to date. In this perspective piece, the opportunities and requirements for AI-based clinical decision support systems are identified and challenges that impact "AI readiness" for rapidly emergent healthcare challenges are highlighted.

THU0222 B CELL MODULATORY VARIANT TNF RECEPTOR DOMAINS (VTDS) IDENTIFIED BY DIRECTED EVOLUTION TO INHIBIT BAFF AND APRIL, ALONE OR COMBINED WITH VARIANT IG DOMAINS (VIGD™) THAT INHIBIT T CELL COSTIMULATION, FOR THE TREATMENT OF SEVERE AUTOIMMUNE AND/OR INFLAMMATORY DISEASE

Background:

BAFF and APRIL are TNF superfamily members that bind both TACI and BCMA on B cells; BAFF also binds BAFF-R. Together, BAFF and APRIL support B cell development, differentiation, and survival. Their co-neutralization dramatically reduces B cell function, including antibody production, whereas inhibition of either BAFF or APRIL alone mediates relatively modest effects.

Objectives:

While CTLA-4-based therapeutics that block T cell costimulation provide safe and moderately effective T cell inhibition in many disease settings, and while B cell targeting therapies have demonstrated promising therapeutic potential, we postulate that improved, combined BAFF and APRIL inhibition, either alone or coupled with inhibition of T cell costimulation, will provide more effective and durable relief from severe B cell-related autoimmune diseases like SLE.

Methods:

We used our directed evolution platform to identify variant domains of the TNF family receptors TACI or BCMA that exhibit enhanced affinity for BAFF and APRIL as compared to their wild-type (WT) counterparts. These variant TACI or BCMA domains (vTD), alone or together with platform-derived CTLA-4 domains (vIgD), were fused to a modified human IgG1 Fc lacking effector function, yielding a panel of immunomodulatory molecules: TACI vTD-Fc, BCMA vTD-Fc, TACI vTD/CTLA-4 vIgD-Fc, & BCMA vTD/CTLA-4 vIgD-Fc. All were evaluated for functional activity: 1)in vitroin primary human B cell & MLR assays and in a Jurkat/NF-kB reporter cell line expressing TACI, and 2)in vivoin standard immunization models, and in the bm12-induced and NZB/NZW spontaneous mouse models of lupus.

Results:

The novel engineered TACI vTD-Fc or BCMA vTD-Fc fusion proteins significantly inhibited BAFF- and APRIL-mediated signalingin vitroin TACI+Jurkat cells. TACI (or BCMA) vTD/CTLA-4 vIgD-Fc proteins also attenuated T cell activation in primary human lymphocyte assays. When administered to mice, these molecules rapidly and potently reduced key B and T cell subsets, including plasma cells, follicular T helper cells, germinal center cells, & memory T cells. Treatment with TACI vTD-Fc or TACI vTD/CTLA-4 vIgD-Fc proteins also significantly reduced titers of antigen-specific antibodies in immunized mice more so than abatacept or WT TACI-Fc, and potently suppressed anti-dsDNA autoantibodies, blood urea nitrogen levels, proteinuria, and renal immune complex deposition in the bm12 & NZB/W lupus models.

Conclusion:

Directed evolution of TNFR and IgSF domains has successfully facilitated the development of Fc fusion proteins containing TACI or BCMA vTDs, with or without fusion to CTLA-4 vIgDs. These novel immunomodulators consistently demonstrate potent immunosuppressive activity and efficacyin vitroandin vivo, appearing superior to existing and/or approved immunomodulators like belimumab, abatacept, or atacicept. Such biologics may therefore be attractive candidates for the treatment of serious autoimmune diseases, particularly B cell-related diseases such as SLE, Sjogren’s syndrome, etc.

Disclosure of Interests: :

Stacey R. Dillon Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Lawrence S. Evans Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Mark W. Rixon Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Joe Kuijper Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Dan Demonte Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Katherine E. Lewis Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Steve Levin Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Kayla Kleist Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Sherri Mudri Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Susan Bort Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Janhavi Bhandari Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Fariha Ahmed-Qadri Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Jing Yang Shareholder of: Alpine Immune Sciences, Inc., Employee of: Alpine Immune Sciences, Inc., Michelle A. Seaberg Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Rachel Wang Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Russell Sanderson Shareholder of: Alpine Immune Sciences, Inc., Employee of: Alpine Immune Sciences, Inc., Martin F. Wolfson Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Jan Hillson Shareholder of: Alpine Immune Sciences, Inc., Employee of: Alpine Immune Sciences, Inc., Stanford L. Peng Shareholder of: Alpine Immune Sciences, Inc., Employee of: CMO and President of Alpine Immune Sciences, Inc., Kristine M. Swiderek Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc.

Factors Influencing Time-Dependent Quality Indicators for Patients With Suspected Acute Coronary Syndrome

OBJECTIVES

Rapid risk stratification and timely treatment are critical to favorable outcomes for patients with acute coronary syndrome (ACS). Our objective was to identify patient and system factors that influence time-dependent quality indicators (QIs) for patients with unstable angina/non-ST elevation myocardial infarction (NSTEMI) in the emergency department (ED).

METHODS

A retrospective, cohort study was conducted during a 42-month period of all patients 24 years or older suspected of having ACS as defined by receiving an electrocardiogram and at least 1 cardiac biomarker test. Cox regression was used to model the effects of patient characteristics, ancillary service use, staffing provisions, equipment availability, and ED and hospital crowding on ACS QIs.

RESULTS

Emergency department adherence rates to national standards for electrocardiogram readout time and biomarker turnaround time were 42% and 37%, respectively. Cox regression models revealed that chief complaints without chest pain and the timing of stress testing and medication administration were associated with the most significant delays.

CONCLUSIONS

Patient and system factors both significantly influenced QI times in this cohort with unstable angina/NSTEMI. These results illustrate both the complexity of diagnosing patients with NSTEMI and the competing effects of clinical and system factors on patient flow through the ED.

577. The Role of Healthcare Worker-Mediated Contact Networks in the Transmission of Vancomycin-Resistant Enterococci

Background

Healthcare workers (HCWs) commonly contact multiple patients daily and serve as an important vector for transmission of pathogens such as vancomycin-resistant enterococci (VRE). Characterizing the HCW-patient network is difficult, which limits understanding of the role of HCWs in the horizontal transmission of pathogens. Electronic health records (EHR) present an opportunity to generate HCW-mediated contact networks and evaluate their impact on transmission.

Methods

Retrospective analysis of patients (PT) admitted to a medical intensive care unit and solid-organ transplant unit between July 2016 and June 2017. Clinical and demographic information, including VRE surveillance swab outcomes, were extracted from the hospital EHR system. PT-HCW-PT connections were defined as HCW contacts with a patient within an hour of another patient. Multi-variable logistic regression was used to analyze factors associated with unit-acquired VRE colonization incidence.

Results

A total of 2,336 patients had a recorded interaction with 4,956 unique HCWs. 146 patients were colonized with VRE on unit-admission, and 29 patients had unit-acquired VRE colonization. HCWs had contact with ~2 (range: 1–23) patients a day and ~6 (range: 1–58) contacts with patients per day (Figure 1), though rates varied by HCW-type. Patients were contacted by ~7 different HCWs resulting in ~28 contacts per day, with nurses being the most common (Figure 2). This resulted in approximately 10 PT-HCW-PT connections per day (range: 1–33) to an average of 3 other patients. After adjusting for known VRE acquisition risk factors, HCW connections to other patients with VRE significantly increased the risk of VRE acquisition (odds ratio = 1.32; 95% CI: 1.20–1.44; Table 1).

Conclusion

Understanding how HCWs connect patients can elucidate how pathogens, such as VRE, spread in the hospital. We demonstrated how EHR data can inform how HCWs connect patients to spread HAIs and the impact of those connections on the spread of VRE. Though EHR data have limitations, as certain activities and contacts are not logged into the system, they provide a scalable and generalizable source for understanding how patients are connected and can be utilized to reduce the spread of nosocomial infections.

Disclosures

All authors: No reported disclosures.

Readmission Risk Trajectories for Patients With Heart Failure Using a Dynamic Prediction Approach: Retrospective Study

Background

Patients hospitalized with heart failure suffer the highest rates of 30-day readmission among other clinically defined patient populations in the United States. Investigation into the predictability of 30-day readmissions can lead to clinical decision support tools and targeted interventions that can help care providers to improve individual patient care and reduce readmission risk.

Objective

This study aimed to develop a dynamic readmission risk prediction model that yields daily predictions for patients hospitalized with heart failure toward identifying risk trajectories over time and identifying clinical predictors associated with different patterns in readmission risk trajectories.

Methods

A two-stage predictive modeling approach combining logistic and beta regression was applied to electronic health record data accumulated daily to predict 30-day readmission for 534 hospital encounters of patients with heart failure over 2750 patient days. Unsupervised clustering was performed on predictions to uncover time-dependent trends in readmission risk over the patient’s hospital stay. We used data collected between September 1, 2013, and August 31, 2015, from a community hospital in Maryland (United States) for patients with a primary diagnosis of heart failure. Patients who died during the hospital stay or were transferred to other acute care hospitals or hospice care were excluded.

Results

Readmission occurred in 107 (107/534, 20.0%) encounters. The out-of-sample area under curve for the 2-stage predictive model was 0.73 (SD 0.08). Dynamic clinical predictors capturing laboratory results and vital signs had the highest predictive value compared with demographic, administrative, medical, and procedural data included. Unsupervised clustering identified four risk trajectory groups: decreasing risk (131/534, 24.5% encounters), high risk (113/534, 21.2%), moderate risk (177/534, 33.1%), and low risk (113/534, 21.2%). The decreasing risk group demonstrated change in average probability of readmission from admission (0.69) to discharge (0.30), whereas the high risk (0.75), moderate risk (0.61), and low risk (0.39) groups maintained consistency over the hospital course. A higher level of hemoglobin, larger decrease in potassium and diastolic blood pressure from admission to discharge, and smaller number of past hospitalizations are associated with decreasing readmission risk (P<.001).

Conclusions

Dynamically predicting readmission and quantifying trends over patients’ hospital stay illuminated differing risk trajectory groups. Identifying risk trajectory patterns and distinguishing predictors may shed new light on indicators of readmission and the isolated effects of the index hospitalization.

Evaluation of multidisciplinary collaboration in pediatric trauma care using EHR data

OBJECTIVES

The study sought to identify collaborative electronic health record (EHR) usage patterns for pediatric trauma patients and determine how the usage patterns are related to patient outcomes.

MATERIALS AND METHODS

A process mining-based network analysis was applied to EHR metadata and trauma registry data for a cohort of pediatric trauma patients with minor injuries at a Level I pediatric trauma center. The EHR metadata were processed into an event log that was segmented based on gaps in the temporal continuity of events. A usage pattern was constructed for each encounter by creating edges among functional roles that were captured within the same event log segment. These patterns were classified into groups using graph kernel and unsupervised spectral clustering methods. Demographics, clinical and network characteristics, and emergency department (ED) length of stay (LOS) of the groups were compared.

RESULTS

Three distinct usage patterns that differed by network density were discovered: fully connected (clique), partially connected, and disconnected (isolated). Compared with the fully connected pattern, encounters with the partially connected pattern had an adjusted median ED LOS that was significantly longer (242.6 [95% confidence interval, 236.9-246.0] minutes vs 295.2 [95% confidence, 289.2-297.8] minutes), more frequently seen among day shift and weekday arrivals, and involved otolaryngology, ophthalmology services, and child life specialists.

DISCUSSION

The clique-like usage pattern was associated with decreased ED LOS for the study cohort, suggesting greater degree of collaboration resulted in shorter stay.

CONCLUSIONS

Further investigation to understand and address causal factors can lead to improvement in multidisciplinary collaboration.

Predicting probability of perirectal colonization with carbapenem-resistant Enterobacteriaceae (CRE) and other carbapenem-resistant organisms (CROs) at hospital unit admission

BACKGROUND

Targeted screening for carbapenem-resistant organisms (CROs), including carbapenem-resistant Enterobacteriaceae (CRE) and carbapenemase-producing organisms (CPOs), remains limited; recent data suggest that existing policies miss many carriers.

OBJECTIVE

Our objective was to measure the prevalence of CRO and CPO perirectal colonization at hospital unit admission and to use machine learning methods to predict probability of CRO and/or CPO carriage.

METHODS

We performed an observational cohort study of all patients admitted to the medical intensive care unit (MICU) or solid organ transplant (SOT) unit at The Johns Hopkins Hospital between July 1, 2016 and July 1, 2017. Admission perirectal swabs were screened for CROs and CPOs. More than 125 variables capturing preadmission clinical and demographic characteristics were collected from the electronic medical record (EMR) system. We developed models to predict colonization probabilities using decision tree learning.

RESULTS

Evaluating 2,878 admission swabs from 2,165 patients, we found that 7.5% and 1.3% of swabs were CRO and CPO positive, respectively. Organism and carbapenemase diversity among CPO isolates was high. Despite including many characteristics commonly associated with CRO/CPO carriage or infection, overall, decision tree models poorly predicted CRO and CPO colonization (C statistics, 0.57 and 0.58, respectively). In subgroup analyses, however, models did accurately identify patients with recent CRO-positive cultures who use proton-pump inhibitors as having a high likelihood of CRO colonization.

CONCLUSIONS

In this inpatient population, CRO carriage was infrequent but was higher than previously published estimates. Despite including many variables associated with CRO/CPO carriage, models poorly predicted colonization status, likely due to significant host and organism heterogeneity.

Linking Electronic Health Record and Trauma Registry Data: Assessing the Value of Probabilistic Linkage

BACKGROUND

Electronic health record (EHR) systems contain large volumes of novel heterogeneous data that can be linked to trauma registry data to enable innovative research not possible with either data source alone.

OBJECTIVE

This article describes an approach for linking electronically extracted EHR data to trauma registry data at the institutional level and assesses the value of probabilistic linkage.

METHODS

Encounter data were independently obtained from the EHR data warehouse (n = 1,632) and the pediatric trauma registry (n = 1,829) at a Level I pediatric trauma center. Deterministic linkage was attempted using nine different combinations of medical record number (MRN), encounter identity (ID) (visit ID), age, gender, and emergency department (ED) arrival date. True matches from the best performing variable combination were used to create a gold standard, which was used to evaluate the performance of each variable combination, and to train a probabilistic algorithm that was separately used to link records unmatched by deterministic linkage and the entire cohort. Additional records that matched probabilistically were investigated via chart review and compared against records that matched deterministically.

RESULTS

Deterministic linkage with exact matching on any three of MRN, encounter ID, age, gender, and ED arrival date gave the best yield of 1,276 true matches while an additional probabilistic linkage step following deterministic linkage yielded 110 true matches. These records contained a significantly higher number of boys compared to records that matched deterministically and etiology was attributable to mismatch between MRNs in the two data sets. Probabilistic linkage of the entire cohort yielded 1,363 true matches.

CONCLUSION

The combination of deterministic and an additional probabilistic method represents a robust approach for linking EHR data to trauma registry data. This approach may be generalizable to studies involving other registries and databases.

Prolonged wait time is associated with increased mortality for Chilean waiting list patients with non-prioritized conditions

BACKGROUND

Most data on mortality and prognostic factors of universal healthcare waiting lists come from North America, Australasia, and Europe, with little information from South America. We aimed to determine the relationship between medical center-specific waiting time and waiting list mortality in Chile.

METHOD

Using data from all new patients listed in medical specialist waitlists for non-prioritized health problems from 2008 to 2015 in three geographically distant regions of Chile, we constructed hierarchical multivariate survival models to predict mortality risk at two years after registration for each medical center. Kendall rank correlation analysis was used to measure the association between medical center-specific mortality hazard ratio and waiting times.

RESULT

There were 987,497 patients waiting for care at 77 medical centers, including 33,546 (3.40%) who died within two years after registration. Male gender (hazard ratio [HR] = 1.17, 95% confidence interval [CI] 1.1-1.24), older age (HR = 2.88, 95% CI 2.72-3.05), urban residence (HR = 1.19, 95% CI 1.09-1.31), tertiary care (HR = 2.2, 95% CI 2.14-2.26), oncology (HR = 3.57, 95% CI 3.4-3.76), and hematology (HR = 1.6, 95% CI 1.49-1.73) were associated with higher risk of mortality at each medical center with large region-to-region variations. There was a statistically significant association between waiting time variability and death (Z = 2.16, P = 0.0308).

CONCLUSION

Patient wait time for non-prioritized health conditions was associated with increased mortality in Chilean hospitals.

Comparing Electron Energetics and UV Brightness in Jupiter's Northern Polar Region During Juno Perijove 5

We compare electron and UV observations mapping to the same location in Jupiter's northern polar region, poleward of the main aurora, during Juno perijove 5. Simultaneous peaks in UV brightness and electron energy flux are identified when observations map to the same location at the same time. The downward energy flux during these simultaneous observations was not sufficient to generate the observed UV brightness; the upward energy flux was. We propose that the primary acceleration region is below Juno's altitude, from which the more intense upward electrons originate. For the complete interval, the UV brightness peaked at ~240 kilorayleigh (kR); the downward and upward energy fluxes peaked at 60 and 700 mW/m2, respectively. Increased downward energy fluxes are associated with increased contributions from tens of keV electrons. These observations provide evidence that bidirectional electron beams with broad energy distributions can produce tens to hundreds of kilorayleigh polar UV emissions.

Comparing Electron Energetics and UV Brightness in Jupiter's Northern Polar Region During Juno Perijove 5

We compare electron and UV observations mapping to the same location in Jupiter's northern polar region, poleward of the main aurora, during Juno perijove 5. Simultaneous peaks in UV brightness and electron energy flux are identified when observations map to the same location at the same time. The downward energy flux during these simultaneous observations was not sufficient to generate the observed UV brightness; the upward energy flux was. We propose that the primary acceleration region is below Juno's altitude, from which the more intense upward electrons originate. For the complete interval, the UV brightness peaked at ~240 kilorayleigh (kR); the downward and upward energy fluxes peaked at 60 and 700 mW/m², respectively. Increased downward energy fluxes are associated with increased contributions from tens of keV electrons. These observations provide evidence that bidirectional electron beams with broad energy distributions can produce tens to hundreds of kilorayleigh polar UV emissions.

Use of Systems Engineering to Design a Hospital Command Center

BACKGROUND

In hospitals and health systems across the country, patient flow bottlenecks delay care delivery-emergency department boarding and operating room exit holds are familiar examples. In other industries, such as oil, gas, and air traffic control, command centers proactively manage flow through complex systems.

METHODS

A systems engineering approach was used to analyze and maximize existing capacity in one health system, which led to the creation of the Judy Reitz Capacity Command Center. This article describes the key elements of this novel health system command center, which include strategic colocation of teams, automated visual displays of real-time data providing a global view, predictive analytics, standard work and rules-based protocols, and a clear chain of command and guiding tenets. Preliminary data are also shared.

RESULTS

With proactive capacity management, subcycle times decreased and allowed the health system's flagship hospital to increase occupancy from 85% to 92% while decreasing patient delays.

CONCLUSION

The command center was built with three primary goals-reducing emergency department boarding, eliminating operating room holds, and facilitating transfers in from outside facilities-but the command center infrastructure has the potential to improve hospital operations in many other areas.

The association between AB blood group and neonatal disease

BACKGROUND

Numerous studies have examined the association between ABO blood groups and adult disease states, but very few have studied the neonatal population. The objective of this study was to determine the relationship between AB blood group and the occurrence of common neonatal disorders such as neutropenia at birth, sepsis, respiratory distress syndrome (RDS), intraventricular hemorrhage (IVH), retinopathy of prematurity (ROP), and patent ductus arteriosus (PDA) compared to all other blood groups.

METHODS

We performed a retrospective review on 3,981 infants born at 22 0/7 to 42 6/7 weeks' gestational age and compared the relative risk of neonatal diseases in infants with AB blood group to that of infants with all other blood groups (A, B, and O).

RESULTS

When compared to all other blood groups, AB infants demonstrated an increased risk for developing negative clinical outcomes. AB blood group was significantly associated with a 14-89% increased risk of neutropenia at birth, sepsis, RDS, and ROP. Risks for IVH and PDA were not significant.

CONCLUSION

We hypothesize that the phenotypic expression of A and B antigens, rather than the antigens themselves, in the AB group may reveal an enhanced susceptibility to injury at the endothelial level resulting in an increased risk for disease development.

Triage Performance in Emergency Medicine: A Systematic Review

STUDY OBJECTIVE

Rapid growth in emergency department (ED) triage literature has been accompanied by diversity in study design, methodology, and outcome assessment. We aim to synthesize existing ED triage literature by using a framework that enables performance comparisons and benchmarking across triage systems, with respect to clinical outcomes and reliability.

METHODS

PubMed, EMBASE, Scopus, and Web of Science were systematically searched for studies of adult ED triage systems through 2016. Studies evaluating triage systems with evidence of widespread adoption (Australian Triage Scale, Canadian Triage and Acuity Scale, Emergency Severity Index, Manchester Triage Scale, and South African Triage Scale) were cataloged and compared for performance in identifying patients at risk for mortality, critical illness and hospitalization, and interrater reliability. This study was performed and reported in adherence to Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines.

RESULTS

A total of 6,160 publications were identified, with 182 meeting eligibility criteria and 50 with sufficient data for inclusion in comparative analysis. The Canadian Triage and Acuity Scale (32 studies), Emergency Severity Index (43), and Manchester Triage Scale (38) were the most frequently studied triage scales, and all demonstrated similar performance. Most studies (6 of 8) reported high sensitivity (>90%) of triage scales for identifying patients with ED mortality as high acuity at triage. However, sensitivity was low (<80%) for identification of patients who had critical illness outcomes and those who died within days of the ED visit or during the index hospitalization. Sensitivity varied by critical illness and was lower for severe sepsis (36% to 74%), pulmonary embolism (54%), and non-ST-segment elevation myocardial infarction (44% to 85%) compared with ST-segment elevation myocardial infarction (56% to 92%) and general outcomes of ICU admission (58% to 100%) and lifesaving intervention (77% to 98%). Some proportion of hospitalized patients (3% to 45%) were triaged to low acuity (level 4 to 5) in all studies. Reliability measures (κ) were variable across evaluations, with only a minority (11 of 42) reporting κ above 0.8.

CONCLUSION

We found that a substantial proportion of ED patients who die postencounter or are critically ill are not designated as high acuity at triage. Opportunity to improve interrater reliability and triage performance in identifying patients at risk of adverse outcome exists.