The Pandemic Response Commons.

Objectives: A data commons is a software platform for managing, curating, analyzing, and sharing data with a community. The Pandemic Response Commons (PRC) is a data commons designed to provide a data platform for researchers studying an epidemic or pandemic. Methods: The PRC was developed using the open source Gen3 data platform and is based upon consortium, data, and platform agreements developed by the not-for-profit Open Commons Consortium. A formal consortium of Chicagoland area organizations was formed to develop and operate the PRC. Results: The consortium developed a general PRC and an instance of it for the Chicagoland region called the Chicagoland COVID-19 Commons. A Gen3 data platform was set up and operated with policies, procedures, and controls for a NIST SP 800-53 revision 4 Moderate system. A consensus data model for the commons was developed, and a variety of datasets were curated, harmonized and ingested, including statistical summary data about COVID cases, patient level clinical data, and SARS-CoV-2 viral variant data. Discussion and conclusions: Given the various legal and data agreements required to operate a data commons, a PRC is designed to be in place and operating at a low level prior to the occurrence of an epidemic, with the activities increasing as required during an epidemic. A regional instance of a PRC can also be part of a broader data ecosystem or data mesh consisting of multiple regional commons supporting pandemic response through sharing regional data.

Random variation drives a critical bias in the comparison of healthcare-associated infections.

OBJECTIVE: To evaluate random effects of volume (patient days or device days) on healthcare-associated infections (HAIs) and the standardized infection ratio (SIR) used to compare hospitals. DESIGN: A longitudinal comparison between publicly reported quarterly data (2014-2020) and volume-based random sampling using 4 HAI types: central-line-associated bloodstream infections, catheter-associated urinary tract infections, Clostridioides difficile infections, methicillin-resistant Staphylococcus aureus infections. METHODS: Using 4,268 hospitals with reported SIRs, we examined relationships of SIRs to volume and compared distributions of SIRs and numbers of reported HAIs to the outcomes of simulated random sampling. We included random expectations into SIR calculations to produce a standardized infection score (SIS). RESULTS: Among hospitals with volumes less than the median, 20%-33% had SIRs of 0, compared to 0.3%-5% for hospitals with volumes higher than the median. Distributions of SIRs were 86%-92% similar to those based on random sampling. Random expectations explained 54%-84% of variation in numbers of HAIs. The use of SIRs led hundreds of hospitals with more infections than either expected at random or predicted by risk-adjusted models to rank better than other hospitals. The SIS mitigated this effect and allowed hospitals of disparate volumes to achieve better scores while decreasing the number of hospitals tied for the best score. CONCLUSIONS: SIRs and numbers of HAIs are strongly influenced by random effects of volume. Mitigating these effects drastically alters rankings for HAI types and may further alter penalty assignments in programs that aim to reduce HAIs and improve quality of care.

Three-Month Symptom Profiles Among Symptomatic Adults With Positive and Negative Severe Acute Respiratory Syndrome Coronavirus 2 Tests: A Prospective Cohort Study From the INSPIRE Group.

BACKGROUND: Long-term symptoms following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are a major concern, yet their prevalence is poorly understood. METHODS: We conducted a prospective cohort study comparing adults with SARS-CoV-2 infection (coronavirus disease-positive [COVID+]) with adults who tested negative (COVID-), enrolled within 28 days of a Food and Drug Administration (FDA)-approved SARS-CoV-2 test result for active symptoms. Sociodemographic characteristics, symptoms of SARS-CoV-2 infection (assessed with the Centers for Disease Control and Prevention [CDC] Person Under Investigation Symptom List), and symptoms of post-infectious syndromes (ie, fatigue, sleep quality, muscle/joint pains, unrefreshing sleep, and dizziness/fainting, assessed with CDC Short Symptom Screener for myalgic encephalomyelitis/chronic fatigue syndrome) were assessed at baseline and 3 months via electronic surveys sent via text or email. RESULTS: Among the first 1000 participants, 722 were COVID+ and 278 were COVID-. Mean age was 41.5 (SD 15.2); 66.3% were female, 13.4% were Black, and 15.3% were Hispanic. At baseline, SARS-CoV-2 symptoms were more common in the COVID+ group than the COVID- group. At 3 months, SARS-CoV-2 symptoms declined in both groups, although were more prevalent in the COVID+ group: upper respiratory symptoms/head/eyes/ears/nose/throat (HEENT; 37.3% vs 20.9%), constitutional (28.8% vs 19.4%), musculoskeletal (19.5% vs 14.7%), pulmonary (17.6% vs 12.2%), cardiovascular (10.0% vs 7.2%), and gastrointestinal (8.7% vs 8.3%); only 50.2% and 73.3% reported no symptoms at all. Symptoms of post-infectious syndromes were similarly prevalent among the COVID+ and COVID- groups at 3 months. CONCLUSIONS: Approximately half of COVID+ participants, as compared with one-quarter of COVID- participants, had at least 1 SARS-CoV-2 symptom at 3 months, highlighting the need for future work to distinguish long COVID. CLINICAL TRIALS REGISTRATION: NCT04610515.

Association of Initial SARS-CoV-2 Test Positivity With Patient-Reported Well-being 3 Months After a Symptomatic Illness.

Importance: Long-term sequelae after symptomatic SARS-CoV-2 infection may impact well-being, yet existing data primarily focus on discrete symptoms and/or health care use. Objective: To compare patient-reported outcomes of physical, mental, and social well-being among adults with symptomatic illness who received a positive vs negative test result for SARS-CoV-2 infection. Design, Setting, and Participants: This cohort study was a planned interim analysis of an ongoing multicenter prospective longitudinal registry study (the Innovative Support for Patients With SARS-CoV-2 Infections Registry [INSPIRE]). Participants were enrolled from December 11, 2020, to September 10, 2021, and comprised adults (aged ≥18 years) with acute symptoms suggestive of SARS-CoV-2 infection at the time of receipt of a SARS-CoV-2 test approved by the US Food and Drug Administration. The analysis included the first 1000 participants who completed baseline and 3-month follow-up surveys consisting of questions from the 29-item Patient-Reported Outcomes Measurement Information System (PROMIS-29; 7 subscales, including physical function, anxiety, depression, fatigue, social participation, sleep disturbance, and pain interference) and the PROMIS Short Form-Cognitive Function 8a scale, for which population-normed T scores were reported. Exposures: SARS-CoV-2 status (positive or negative test result) at enrollment. Main Outcomes and Measures: Mean PROMIS scores for participants with positive COVID-19 tests vs negative COVID-19 tests were compared descriptively and using multivariable regression analysis. Results: Among 1000 participants, 722 (72.2%) received a positive COVID-19 result and 278 (27.8%) received a negative result; 406 of 998 participants (40.7%) were aged 18 to 34 years, 644 of 972 (66.3%) were female, 833 of 984 (84.7%) were non-Hispanic, and 685 of 974 (70.3%) were White. A total of 282 of 712 participants (39.6%) in the COVID-19-positive group and 147 of 275 participants (53.5%) in the COVID-19-negative group reported persistently poor physical, mental, or social well-being at 3-month follow-up. After adjustment, improvements in well-being were statistically and clinically greater for participants in the COVID-19-positive group vs the COVID-19-negative group only for social participation (ß = 3.32; 95% CI, 1.84-4.80; P < .001); changes in other well-being domains were not clinically different between groups. Improvements in well-being in the COVID-19-positive group were concentrated among participants aged 18 to 34 years (eg, social participation: ß = 3.90; 95% CI, 1.75-6.05; P < .001) and those who presented for COVID-19 testing in an ambulatory setting (eg, social participation: ß = 4.16; 95% CI, 2.12-6.20; P < .001). Conclusions and Relevance: In this study, participants in both the COVID-19-positive and COVID-19-negative groups reported persistently poor physical, mental, or social well-being at 3-month follow-up. Although some individuals had clinically meaningful improvements over time, many reported moderate to severe impairments in well-being 3 months later. These results highlight the importance of including a control group of participants with negative COVID-19 results for comparison when examining the sequelae of COVID-19.

Economical Utilization of Health Information with Learning Healthcare System Data Commons.

Finding, accessing, sharing, and analyzing patient data from a clinical setting for collaborative research has continually proven to be a challenge in healthcare organizations. The human and technological architecture required to perform these services exist at the largest academic institutions but are usually under-funded. At smaller, less academically focused healthcare organizations across the United States, where the majority of care is delivered, they are generally absent. Here we propose a solution called the Learning Healthcare System Data Commons where cost is usage-based and the most basic elements are designed to be extensible, allowing it to evolve with the changing landscape of healthcare. Herein we also discuss our reference implementation of this platform tailored specifically for operational sustainability and governance using the data generated in a hospital setting for research, quality, and educational purposes.

Variation in the Reporting of Elective Surgeries and Its Influence on Patient Safety Indicators.

BACKGROUND: US hospital safety is routinely measured via Patient Safety Indicators (PSIs). Receiving a score for most PSIs requires a minimum number of qualifying cases to meet specific criteria; for example, whether an admission was elective. Because admission type is determined by hospitals' internal policies, the study team suspected that hospitals may be exempted from elective-based PSI scores as a result of their internal admission classification policies. METHODS: Multiple regression was combined with machine learning to analyze Medicare inpatient claims data reported by 3,484 hospitals during the 2015-2017 PSI measurement period. The researchers examined the average percentage of elective admissions across surgical diagnosis-related groups (DRGs) (average percent elective [APE]) in relation to hospital characteristics, surgical claims volumes, and numbers and types of surgical DRGs. This study asked whether hospitals with exceptionally low APE shared particular characteristics, reported claims for similar DRGs, or were disproportionately exempted from elective-based PSIs. RESULTS: Cross-validated multiple regression explained 73.9% of variation in APE among hospitals and identified surgical claims volume and 16 surgical DRGs as consistently important variables. However, the exceptionally low APE of 96 hospitals could not be explained by surgical claims volume, surgical DRGs among claims, or hospital characteristics. These outliers were disproportionately exempt from elective-based PSI scores. CONCLUSION: Some hospitals may have classified admissions in a way that exempted them from elective-based PSI scores. Transparency into admission classification policies is needed to ensure fair and reliable use of PSIs when ranking hospitals and adjusting payments. Alternatively, PSIs may need modifications to rely on externally validated criteria.

A Standard-Based Citywide Health Information Exchange for Public Health in Response to COVID-19: Development Study.

BACKGROUND: Disease surveillance is a critical function of public health, provides essential information about the disease burden and the clinical and epidemiologic parameters of disease, and is an important element of effective and timely case and contact tracing. The COVID-19 pandemic demonstrates the essential role of disease surveillance in preserving public health. In theory, the standard data formats and exchange methods provided by electronic health record (EHR) meaningful use should enable rapid health care data exchange in the setting of disruptive health care events, such as a pandemic. In reality, access to data remains challenging and, even if available, often lacks conformity to regulated standards. OBJECTIVE: We sought to use regulated interoperability standards already in production to generate awareness of regional bed capacity and enhance the capture of epidemiological risk factors and clinical variables among patients tested for SARS-CoV-2. We described the technical and operational components, governance model, and timelines required to implement the public health order that mandated electronic reporting of data from EHRs among hospitals in the Chicago jurisdiction. We also evaluated the data sources, infrastructure requirements, and the completeness of data supplied to the platform and the capacity to link these sources. METHODS: Following a public health order mandating data submission by all acute care hospitals in Chicago, we developed the technical infrastructure to combine multiple data feeds from those EHR systems-a regional data hub to enhance public health surveillance. A cloud-based environment was created that received ELR, consolidated clinical data architecture, and bed capacity data feeds from sites. Data governance was planned from the project initiation to aid in consensus and principles for data use. We measured the completeness of each feed and the match rate between feeds. RESULTS: Data from 88,906 persons from CCDA records among 14 facilities and 408,741 persons from ELR records among 88 facilities were submitted. Most (n=448,380, 90.1%) records could be matched between CCDA and ELR feeds. Data fields absent from ELR feeds included travel histories, clinical symptoms, and comorbidities. Less than 5% of CCDA data fields were empty. Merging CCDA with ELR data improved race, ethnicity, comorbidity, and hospitalization information data availability. CONCLUSIONS: We described the development of a citywide public health data hub for the surveillance of SARS-CoV-2 infection. We were able to assess the completeness of existing ELR feeds, augment those feeds with CCDA documents, establish secure transfer methods for data exchange, develop a cloud-based architecture to enable secure data storage and analytics, and produce dashboards for monitoring of capacity and the disease burden. We consider this public health and clinical data registry as an informative example of the power of common standards across EHRs and a potential template for future use of standards to improve public health surveillance.

Study protocol for the Innovative Support for Patients with SARS-COV-2 Infections Registry (INSPIRE): A longitudinal study of the medium and long-term sequelae of SARS-CoV-2 infection.

BACKGROUND: Reports on medium and long-term sequelae of SARS-CoV-2 infections largely lack quantification of incidence and relative risk. We describe the rationale and methods of the Innovative Support for Patients with SARS-CoV-2 Registry (INSPIRE) that combines patient-reported outcomes with data from digital health records to understand predictors and impacts of SARS-CoV-2 infection. METHODS: INSPIRE is a prospective, multicenter, longitudinal study of individuals with symptoms of SARS-CoV-2 infection in eight regions across the US. Adults are eligible for enrollment if they are fluent in English or Spanish, reported symptoms suggestive of acute SARS-CoV-2 infection, and if they are within 42 days of having a SARS-CoV-2 viral test (i.e., nucleic acid amplification test or antigen test), regardless of test results. Recruitment occurs in-person, by phone or email, and through online advertisement. A secure online platform is used to facilitate the collation of consent-related materials, digital health records, and responses to self-administered surveys. Participants are followed for up to 18 months, with patient-reported outcomes collected every three months via survey and linked to concurrent digital health data; follow-up includes no in-person involvement. Our planned enrollment is 4,800 participants, including 2,400 SARS-CoV-2 positive and 2,400 SARS-CoV-2 negative participants (as a concurrent comparison group). These data will allow assessment of longitudinal outcomes from SARS-CoV-2 infection and comparison of the relative risk of outcomes in individuals with and without infection. Patient-reported outcomes include self-reported health function and status, as well as clinical outcomes including health system encounters and new diagnoses. RESULTS: Participating sites obtained institutional review board approval. Enrollment and follow-up are ongoing. CONCLUSIONS: This study will characterize medium and long-term sequelae of SARS-CoV-2 infection among a diverse population, predictors of sequelae, and their relative risk compared to persons with similar symptomatology but without SARS-CoV-2 infection. These data may inform clinical interventions for individuals with sequelae of SARS-CoV-2 infection.

Genomic Update of Phenotypic Prediction Rule for Methicillin-Resistant Staphylococcus aureus (MRSA) USA300 Discloses Jail Transmission Networks with Increased Resistance.

Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of health care-associated (HA) and community-associated (CA) infections. USA300 strains are historically CA-MRSA, while USA100 strains are HA-MRSA. Here, we update an antibiotic prediction rule to distinguish these two genotypes based on antibiotic resistance phenotype using whole-genome sequencing (WGS), a more discriminatory methodology than pulsed-field gel electrophoresis (PFGE). MRSA clinical isolates collected from 2007 to 2017 underwent WGS; associated epidemiologic data were ascertained. In developing the rule, we examined MRSA isolates that included a population with a history of incarceration. Performance characteristics of antibiotic susceptibility for predicting USA300 compared to USA100, as defined by WGS, were examined. Phylogenetic analysis was performed to examine resistant USA300 clades. We identified 275 isolates (221 USA300, 54 USA100). Combination susceptibility to clindamycin or levofloxacin performed the best overall (sensitivity 80.7%, specificity 75.9%) to identify USA300. The average number of antibiotic classes with resistance was higher for USA100 (3 versus 2, P < 0.001). Resistance to ≤2 classes was predictive for USA300 (area under the curve (AUC) 0.84, 95% confidence interval 0.78 to 0.90). Phylogenetic analysis identified a cluster of USA300 strains characterized by increased resistance among incarcerated individuals. Using a combination of clindamycin or levofloxacin susceptibility, or resistance to ≤2 antibiotic classes, was predictive of USA300 as defined by WGS. Increased resistance was observed among individuals with incarceration exposure, suggesting circulation of a more resistant USA300 clade among at-risk community networks. Our phenotypic prediction rule could be used as an epidemiologic tool to describe community and nosocomial shifts in USA300 MRSA and quickly identify emergence of lineages with increased resistance. IMPORTANCE Methicillin-resistant Staphylococcus aureus (MRSA) is an important cause of health care-associated (HA) and community-associated (CA) infections, but the epidemiology of these strains (USA100 and USA300, respectively) now overlaps in health care settings. Although sequencing technology has become more available, many health care facilities still lack the capabilities to perform these analyses. In this study, we update a simple prediction rule based on antibiotic resistance phenotype with integration of whole-genome sequencing (WGS) to predict strain type based on antibiotic resistance profiles that can be used in settings without access to molecular strain typing methods. This prediction rule has many potential epidemiologic applications, such as analysis of retrospective data sets, regional monitoring, and ongoing surveillance of CA-MRSA infection trends. We demonstrate application of this rule to identify an emerging USA300 strain with increased antibiotic resistance among incarcerated individuals that deviates from the rule.

Risk Factors Associated With SARS-CoV-2 Seropositivity Among US Health Care Personnel.

Importance: Risks for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among health care personnel (HCP) are unclear. Objective: To evaluate the risk factors associated with SARS-CoV-2 seropositivity among HCP with the a priori hypothesis that community exposure but not health care exposure was associated with seropositivity. Design, Setting, and Participants: This cross-sectional study was conducted among volunteer HCP at 4 large health care systems in 3 US states. Sites shared deidentified data sets, including previously collected serology results, questionnaire results on community and workplace exposures at the time of serology, and 3-digit residential zip code prefix of HCP. Site-specific responses were mapped to a common metadata set. Residential weekly coronavirus disease 2019 (COVID-19) cumulative incidence was calculated from state-based COVID-19 case and census data. Exposures: Model variables included demographic (age, race, sex, ethnicity), community (known COVID-19 contact, COVID-19 cumulative incidence by 3-digit zip code prefix), and health care (workplace, job role, COVID-19 patient contact) factors. Main Outcome and Measures: The main outcome was SARS-CoV-2 seropositivity. Risk factors for seropositivity were estimated using a mixed-effects logistic regression model with a random intercept to account for clustering by site. Results: Among 24 749 HCP, most were younger than 50 years (17 233 [69.6%]), were women (19 361 [78.2%]), were White individuals (15 157 [61.2%]), and reported workplace contact with patients with COVID-19 (12 413 [50.2%]). Many HCP worked in the inpatient setting (8893 [35.9%]) and were nurses (7830 [31.6%]). Cumulative incidence of COVID-19 per 10 000 in the community up to 1 week prior to serology testing ranged from 8.2 to 275.6; 20 072 HCP (81.1%) reported no COVID-19 contact in the community. Seropositivity was 4.4% (95% CI, 4.1%-4.6%; 1080 HCP) overall. In multivariable analysis, community COVID-19 contact and community COVID-19 cumulative incidence were associated with seropositivity (community contact: adjusted odds ratio [aOR], 3.5; 95% CI, 2.9-4.1; community cumulative incidence: aOR, 1.8; 95% CI, 1.3-2.6). No assessed workplace factors were associated with seropositivity, including nurse job role (aOR, 1.1; 95% CI, 0.9-1.3), working in the emergency department (aOR, 1.0; 95% CI, 0.8-1.3), or workplace contact with patients with COVID-19 (aOR, 1.1; 95% CI, 0.9-1.3). Conclusions and Relevance: In this cross-sectional study of US HCP in 3 states, community exposures were associated with seropositivity to SARS-CoV-2, but workplace factors, including workplace role, environment, or contact with patients with known COVID-19, were not. These findings provide reassurance that current infection prevention practices in diverse health care settings are effective in preventing transmission of SARS-CoV-2 from patients to HCP.

Clinical Course and Factors Associated With Hospitalization and Critical Illness Among COVID-19 Patients in Chicago, Illinois.

BACKGROUND: SARS-CoV-2 is a global pandemic associated with significant morbidity and mortality. However, information from United States cohorts is limited. Understanding predictors of admission and critical illness in these patients is essential to guide prevention and risk stratification strategies. METHODS: This was a retrospective, registry-based cohort study including all patients presenting to Rush University Medical Center in Chicago, Illinois, with COVID-19 from March 4, 2020 to June 21, 2020. Demographic, clinical, laboratory, and treatment data were obtained from the registry and compared between hospitalized and nonhospitalized patients as well as those with critical illness. We used logistic regression modeling to explore risk factors associated with hospitalization and critical illness. RESULTS: A total of 8,673 COVID-19 patients were included in the study, of whom 1,483 (17.1%) were admitted to the hospital and 528 (6.1%) were admitted to the intensive care unit. Risk factors for hospital admission included advanced age, male sex (odds ratio [OR] = 1.69, 95% confidence interval [CI] = 1.44 to 1.98), Hispanic/Latino ethnicity (OR = 1.52, 95% CI = 1.18 to 1.92), hypertension (OR = 1.77, 95% CI = 1.46 to 2.16), diabetes mellitus (OR = 1.84, 95% CI = 1.53 to 2.22), prior CVA (OR = 3.20, 95% CI = 1.99 to 5.14), coronary artery disease (OR = 1.45, 95% CI = 1.03 to 2.06), heart failure (OR = 1.79, 95% CI = 1.23 to 2.61), chronic kidney disease (OR = 2.60, 95% CI = 1.77 to 3.83), end-stage renal disease (OR = 2.22, 95% CI = 1.12 to 4.41), cirrhosis (OR = 2.03, 95% CI = 1.42 to 2.91), fever (OR = 1.43, 95% CI = 1.19 to 1.71), and dyspnea (OR = 4.53, 95% CI = 3.75 to 5.47). Factors associated with critical illness included male sex (OR = 1.45, 95% CI = 1.12 to 1.88), congestive heart failure (OR = 1.45, 95% CI = 1.00 to 2.12), obstructive sleep apnea (OR = 1.58, 95% CI = 1.07 to 2.33), blood-borne cancer (OR = 3.53, 95% CI = 1.26 to 9.86), leukocytosis (OR = 1.53, 95% CI = 1.15 to 2.17), elevated neutrophil-to-lymphocyte ratio (OR = 1.61, 95% CI = 1.20 to 2.17), hypoalbuminemia (OR = 1.80, 95% CI = 1.39 to 2.32), elevated AST (OR = 1.66, 95% CI = 1.20 to 2.29), elevated lactate (OR = 1.95, 95% CI = 1.40 to 2.73), elevated D-Dimer (OR = 1.44, 95% CI = 1.05 to 1.97), and elevated troponin (OR = 3.65, 95% CI = 2.03 to 6.57). CONCLUSION: There are a number of factors associated with hospitalization and critical illness. Clinicians should consider these factors when evaluating patients with COVID-19.

The Postencounter Form System: Viewpoint on Efficient Data Collection Within Electronic Health Records.

Electronic health records (EHRs) offer opportunities for research and improvements in patient care. However, challenges exist in using data from EHRs due to the volume of information existing within clinical notes, which can be labor intensive and costly to transform into usable data with existing strategies. This case report details the collaborative development and implementation of the postencounter form (PEF) system into the EHR at the Road Home Program at Rush University Medical Center in Chicago, IL to address these concerns with limited burden to clinical workflows. The PEF system proved to be an effective tool with over 98% of all clinical encounters including a completed PEF within 5 months of implementation. In addition, the system has generated over 325,188 unique, readily-accessible data points in under 4 years of use. The PEF system has since been deployed to other settings demonstrating that the system may have broader clinical utility.

An interactive tool to forecast US hospital needs in the coronavirus 2019 pandemic.

OBJECTIVE: We developed an application (https://rush-covid19.herokuapp.com/) to aid US hospitals in planning their response to the ongoing Coronavirus Disease 2019 (COVID-19) pandemic. MATERIALS AND METHODS: Our application forecasts hospital visits, admits, discharges, and needs for hospital beds, ventilators, and personal protective equipment by coupling COVID-19 predictions to models of time lags, patient carry-over, and length-of-stay. Users can choose from 7 COVID-19 models, customize 23 parameters, examine trends in testing and hospitalization, and download forecast data. RESULTS: Our application accurately predicts the spread of COVID-19 across states and territories. Its hospital-level forecasts are in continuous use by our home institution and others. DISCUSSION: Our application is versatile, easy-to-use, and can help hospitals plan their response to the changing dynamics of COVID-19, while providing a platform for deeper study. CONCLUSION: Empowering healthcare responses to COVID-19 is as crucial as understanding the epidemiology of the disease. Our application will continue to evolve to meet this need.

Prognostic significance of ischemic electrocardiographic changes with regadenoson stress myocardial perfusion imaging.

BACKGROUND: In patients undergoing regadenoson SPECT myocardial perfusion imaging (MPI), the prognostic value of ischemic ST-segment depression (ST↓) and the optimal ST↓ threshold have not been studied. METHODS: A retrospective cohort study of consecutive patients referred for regadenoson stress MPI was conducted. Patients with uninterpretable ECG were excluded. Two diagnostic thresholds of horizontal or downsloping ST↓ were studied, ≥ 0.5 mm and ≥ 1.0 mm. The primary endpoint was the composite major adverse cardiac events (MACE) of cardiac death, myocardial infarction, or coronary revascularization. RESULTS: Among 8615 subjects (mean age 62 ± 13 years; 55% women), 89 (1.0%) had ST↓ ≥ 1.0 mm and 133 (1.5%) had ST↓ ≥ 0.5 mm. Regadenoson-induced ST↓ was more common in women (P < .001). Mean follow-up was 2.5 ± 2.2 years. After multivariate adjustment, ST↓ ≥ 1.0 mm was associated with a non-significant increase in MACE risk (P = .069), irrespective to whether MPI was abnormal (P = .162) or normal (P = .214). Ischemic ST↓ ≥ 0.5 mm was independently associated with MACE in the entire cohort (HR 2.14; CI 1.38-3.32; P = .001), whether MPI is normal (HR 2.07; CI 1.07-4.04; P = .032) or abnormal (HR 2.24; CI 1.23-4.00; P = .007), after adjusting for clinical and imaging covariates. An ST↓ threshold of ≥ 0.5 mm provided greater incremental prognostic value beyond clinical and imaging parameters (Δχ2 = 12.78; P < .001) than ≥ 1.0 mm threshold (Δχ2 = 3.72; P = .093). CONCLUSION: Regadenoson-induced ischemic ST↓ is more common in women and it provides a modest independent prognostic value beyond MPI and clinical parameters. ST↓ ≥ 0.5 mm is a better threshold than ≥ 1.0 mm to define ECG evidence for regadenoson-induced myocardial ischemia.

Disagreement Between Hospital Rating Systems: Measuring the Correlation of Multiple Benchmarks and Developing a Quality Composite Rank.

In the United States, hospital rating system usefulness is limited by heterogeneity and conflicting results. US News Best Hospitals, Vizient Quality and Accountability Study, Centers for Medicare & Medicaid Services (CMS) Star Rating, Leapfrog Hospital Safety Grade, and the Truven Top 100 Hospitals ratings were compared using Spearman correlations. Rank aggregation was used to combine the scores generating a Quality Composite Rank (QCR). The highest correlation between rating systems was shown between the Leapfrog Safety Grade and the CMS Star Rating. In a proportional odds logistic regression, a greater discordance between the CMS Star Rating, Vizient rank, US News, and Leapfrog was associated with a lower overall rank in the QCR. Lack of transparency and understanding about the differences and similarities for these hospital ranking systems complicates use of the measures. By combining the results of these ranking systems into a composite, the measurement of hospital quality can be simplified.

Frequent Methicillin-Resistant Staphylococcus aureus Introductions Into an Inner-city Jail: Indications of Community Transmission Networks.

BACKGROUND: Jails may facilitate spread of methicillin-resistant Staphylococcus aureus (MRSA) in urban areas. We examined MRSA colonization upon entrance to a large urban jail to determine if there are MRSA transmission networks preceding incarceration. METHODS: Males incarcerated in Cook County Jail (Chicago) were enrolled, with enrichment for people living with human immunodeficiency virus (PLHIV), within 72 hours of intake. Surveillance cultures assessed prevalence of MRSA colonization. Whole-genome sequencing (WGS) identified preincarceration transmission networks.We examined methicillin-resistant Staphylococcus aureus (MRSA) isolates to determine if there are transmission networks that precede incarceration. A large proportion of individuals enter jail colonized with MRSA. Molecular epidemiology and colonization risk factors provide clues to community reservoirs for MRSA. RESULTS: There were 718 individuals (800 incarcerations) enrolled; 58% were PLHIV. The prevalence of MRSA colonization at intake was 19%. In multivariate analysis, methamphetamine use, unstable housing, current/recent skin infection, and recent injection drug use were predictors of MRSA. Among PLHIV, recent injection drug use, current skin infection, and HIV care at outpatient clinic A that emphasizes comprehensive care to the lesbian, gay, bisexual, transgender community were predictors of MRSA. Fourteen (45%) of 31 detainees with care at clinic A had colonization. WGS revealed that this prevalence was not due to clonal spread in clinic but rather to an intermingling of distinct community transmission networks. In contrast, genomic analysis supported spread of USA500 strains within a network. Members of this USA500 network were more likely to be PLHIV (P < .01), men who have sex with men (P < .001), and methamphetamine users (P < .001). CONCLUSIONS: A large proportion of individuals enter jail colonized with MRSA. Molecular epidemiology and colonization risk factors provide clues to identify colonized detainees entering jail and potential community reservoirs of MRSA.

Computer-Assisted Antimicrobial Recommendations for Optimal Therapy: Analysis of Prescribing Errors in an Antimicrobial Stewardship Trial.

Clinician education and prospective audit and feedback interventions, deployed separately and concurrently, did not reduce antimicrobial use errors or rates compared to a control group of general medicine inpatients at our public hospital. Additional research is needed to define the optimal scope and intensity of hospital antimicrobial stewardship interventions. Infect Control Hosp Epidemiol 2017;38:857-859.

Genomic and Epidemiological Evidence for Community Origins of Hospital-Onset Methicillin-Resistant Staphylococcus aureus Bloodstream Infections.

Background: We examined whether disparities existed in hospital-onset (HO) Staphylococcus aureus bloodstream infections (BSIs) and used whole-genome sequencing (WGS) to identify factors associated with USA300 transmission networks. Methods: We evaluated HO methicillin-susceptible S. aureus (MSSA) and HO methicillin-resistant S. aureus (MRSA) BSIs for 2009-2013 at 2 hospitals and used an adjusted incidence for modeling. WGS and phylogenetic analyses were performed on a sample of USA300 BSI isolates. Epidemiologic data were analyzed in the context of phylogenetic reconstructions. Results: On multivariate analysis, male sex, African-American race, and non-Hispanic white race/ethnicity were significantly associated with HO-MRSA BSIs whereas Hispanic ethnicity was negatively associated (rate ratio, 0.41; P = .002). Intermixing of community-onset and HO-USA300 strains on the phylogenetic tree indicates that these strains derive from a common pool. African-American race was the only factor associated with genomic clustering of isolates. Conclusions: In a multicenter assessment of HO-S. aureus BSIs, African-American race was significantly associated with HO-MRSA but not MSSA BSIs. There appears to be a nexus of USA300 community and hospital transmission networks, with a community factor being the primary driver. Our data suggest that HO-USA300 BSIs likely are due to colonizing strains acquired in the community before hospitalization. Therefore, prevention efforts may need to extend to the community for maximal benefit.

Consumer Rankings and Health Care: Toward Validation and Transparency.

BACKGROUND: Differences between the Centers for Medicare & Medicaid Services (CMS)-measured rates of safety events for Rush University Medical Center (RUMC; Chicago) and the U. S. News & World Report (USNWR)-deter mined patient safety score were evaluated in an attempt to validate the USNWR patient safety score-based ranking. METHODS: The USNWR findings for Patient Safety Indicators (PSIs) were compared with findings derived from RUMC internal billing data, and sensitivity analyses were conducted using a simulated data set derived from the Healthcare Cost and Utilization Project (HCUP) state inpatient data sets. RESULTS: Discrepancies were found for PSIs 3 (Pressure Ulcer Rate), 9 (Perioperative Hemorrhage or Hematoma Rate), and 11 (Postoperative Respiratory Failure Rate)-an excess of 0.72, 0.63, and 0.26 cases/1,000 admissions, in USNWR versus RUMC, respectively). The sensitivity analysis, which included missing present on admission (POA) flags and dates, resulted in an increase of rates by 1.83 (95% confidence interval [CI] = 1.10-2.56) cases/1,000 hospital- izations, 2.72 (CI = 0.00-5.90) cases/1,000 hospitalizations, and 3.89 (CI = 1.60-6.20) cases/1,000 hospitalizations for PSI 3, 9, and 11, respectively. Regression modeling showed that each 1% increase in transfers was associated with an in- crease of 0.06 cases of PSI 3/1,000 admissions; each 1,000 increase in admissions was associated with an increase of 0.04 cases of PSI 9/1,000 admissions. CONCLUSION: The USNWR data set produced inaccurate PSI rates for RUMC, and false-positive event rates were more common among high-transfer and high-volume hos- pitals. More transparency and validation is needed for con- sumer-based benchmarking methods. In response to these findings and concerns raised by others, in 2016 USNWR made changes to its methodology and data sources and reported them in announcing its 2016-17 Best Hospitals.