Trends in Empiric Broad-Spectrum Antibiotic Use for Suspected Community-Onset Sepsis in US Hospitals.

Importance: Little is known about the degree to which suspected sepsis drives broad-spectrum antibiotic use in hospitals, what proportion of antibiotic courses are unnecessarily broad in retrospect, and whether these patterns are changing over time. Objective: To describe trends in empiric broad-spectrum antibiotic use for suspected community-onset sepsis. Design, Setting, and Participants: This cross-sectional study used clinical data from adults admitted to 241 US hospitals in the PINC AI Healthcare Database. Eligible participants were aged 18 years or more and were admitted between 2017 and 2021 with suspected community-onset sepsis, defined by a blood culture draw, lactate measurement, and intravenous antibiotic administration on admission. Exposures: Empiric anti-methicillin-resistant Staphylococcus aureus (MRSA) and/or antipseudomonal ß-lactam agent use. Main Outcomes and Measures: Annual rates of empiric anti-MRSA and/or antipseudomonal ß-lactam agent use and the proportion that were likely unnecessary in retrospect based on the absence of ß-lactam resistant gram-positive or ceftriaxone-resistant gram-negative pathogens from clinical cultures obtained through hospital day 4. Annual trends were calculated using mixed-effects logistic regression models, adjusting for patient and hospital characteristics. Results: Among 6 272 538 hospitalizations (median [IQR] age, 66 [53-78] years; 443 465 male [49.6%]; 106 095 Black [11.9%], 65 763 Hispanic [7.4%], 653 907 White [73.1%]), 894 724 (14.3%) had suspected community-onset sepsis, of whom 582 585 (65.1%) received either empiric anti-MRSA (379 987 [42.5%]) or antipseudomonal ß-lactam therapy (513 811 [57.4%]); 311 213 (34.8%) received both. Patients with suspected community-onset sepsis accounted for 1 573 673 of 3 141 300 (50.1%) of total inpatient anti-MRSA antibiotic days and 2 569 518 of 5 211 745 (49.3%) of total antipseudomonal ß-lactam days. Between 2017 and 2021, the proportion of patients with suspected sepsis administered anti-MRSA or antipseudomonal therapy increased from 63.0% (82 731 of 131 275 patients) to 66.7% (101 003 of 151 435 patients) (adjusted OR [aOR] per year, 1.03; 95% CI, 1.03-1.04). However, resistant organisms were isolated in only 65 434 cases (7.3%) (30 617 gram-positive [3.4%], 38 844 gram-negative [4.3%]) and the proportion of patients who had any resistant organism decreased from 9.6% to 7.3% (aOR per year, 0.87; 95% CI, 0.87-0.88). Most patients with suspected sepsis treated with empiric anti-MRSA and/or antipseudomonal therapy had no resistant organisms (527 356 of 582 585 patients [90.5%]); this proportion increased from 88.0% in 2017 to 91.6% in 2021 (aOR per year, 1.12; 95% CI, 1.11-1.13). Conclusions and Relevance: In this cross-sectional study of adults admitted to 241 US hospitals, empiric broad-spectrum antibiotic use for suspected community-onset sepsis accounted for half of all anti-MRSA or antipseudomonal therapy; the use of these types of antibiotics increased between 2017 and 2021 despite resistant organisms being isolated in less than 10% of patients treated with broad-spectrum agents.

Validation of Multi-State EHR-Based Network for Disease Surveillance (MENDS) Data and Implications for Improving Data Quality and Representativeness.

Introduction: Surveillance modernization efforts emphasize the potential use of electronic health record (EHR) data to inform public health surveillance and prevention. However, EHR data streams vary widely in their completeness, accuracy, and representativeness. Methods: We developed a validation process for the Multi-State EHR-Based Network for Disease Surveillance (MENDS) pilot project to identify and resolve data quality issues that could affect chronic disease prevalence estimates. We examined MENDS validation processes from December 2020 through August 2023 across 5 data-contributing organizations and outlined steps to resolve data quality issues. Results: We identified gaps in the EHR databases of data contributors and in the processes to extract, map, integrate, and analyze their EHR data. Examples of source-data problems included missing data on race and ethnicity and zip codes. Examples of data processing problems included duplicate or missing patient records, lower-than-expected volumes of data, use of multiple fields for a single data type, and implausible values. Conclusion: Validation protocols identified critical errors in both EHR source data and in the processes used to transform these data for analysis. Our experience highlights the value and importance of data validation to improve data quality and the accuracy of surveillance estimates that use EHR data. The validation process and lessons learned can be applied broadly to other EHR-based surveillance efforts.

Electronic Health Record Data for Lyme Disease Surveillance, Massachusetts, USA, 2017-2018.

Lyme disease surveillance based on provider and laboratory reports underestimates incidence. We developed an algorithm for automating surveillance using electronic health record data. We identified potential Lyme disease markers in electronic health record data (laboratory tests, diagnosis codes, prescriptions) from January 2017-December 2018 in 2 large practice groups in Massachusetts, USA. We calculated their sensitivities and positive predictive values (PPV), alone and in combination, relative to medical record review. Sensitivities ranged from 57% (95% CI 47%-69%) for immunoassays to 87% (95% CI 70%-100%) for diagnosis codes. PPVs ranged from 53% (95% CI 43%-61%) for diagnosis codes to 58% (95% CI 50%-66%) for immunoassays. The combination of a diagnosis code and antibiotics within 14 days or a positive Western blot had a sensitivity of 100% (95% CI 86%-100%) and PPV of 82% (95% CI 75%-89%). This algorithm could make Lyme disease surveillance more efficient and consistent.

The Impact of Common Variations in Sequential Organ Failure Assessment Score Calculation on Sepsis Measurement Using Sepsis-3 Criteria: A Retrospective Analysis Using Electronic Health Record Data.

OBJECTIVES: To assess the impact of different methods of calculating Sequential Organ Failure Assessment (SOFA) scores using electronic health record data on the incidence, outcomes, agreement, and predictive validity of Sepsis-3 criteria. DESIGN: Retrospective observational study. SETTING: Five Massachusetts hospitals. PATIENTS: Hospitalized adults, 2015 to 2022. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We defined sepsis as a suspected infection (culture obtained and antibiotic administered) with a concurrent increase in SOFA score by greater than or equal to 2 points (Sepsis-3 criteria). Our reference SOFA implementation strategy imputed normal values for missing data, used Pao2/Fio2 ratios for respiratory scores, and assumed normal baseline SOFA scores for community-onset sepsis. We then implemented SOFA scores using different missing data imputation strategies (averaging worst values from preceding and following days vs. carrying forward nonmissing values), imputing respiratory scores using Spo2/Fio2 ratios, and incorporating comorbidities and prehospital laboratory data into baseline SOFA scores. Among 1,064,459 hospitalizations, 297,512 (27.9%) had suspected infection and 141,052 (13.3%) had sepsis with an in-hospital mortality rate of 10.3% using the reference SOFA method. The percentage of patients missing SOFA components for at least 1 day in the infection window was highest for Pao2/Fio2 ratios (98.6%), followed by Spo2/Fio2 ratios (73.5%), bilirubin (68.5%), and Glasgow Coma Scale scores (57.2%). Different missing data imputation strategies yielded near-perfect agreement in identifying sepsis (kappa 0.99). However, using Spo2/Fio2 imputations yielded higher sepsis incidence (18.3%), lower mortality (8.1%), and slightly lower predictive validity for mortality (area under the receiver operating curves [AUROC] 0.76 vs. 0.78). For community-onset sepsis, incorporating comorbidities and historical laboratory data into baseline SOFA score estimates yielded lower sepsis incidence (6.9% vs. 11.6%), higher mortality (13.4% vs. 9.6%), and higher predictive validity (AUROC 0.79 vs. 0.75) relative to the reference SOFA implementation. CONCLUSIONS: Common variations in calculating respiratory and baseline SOFA scores, but not in handling missing data, lead to substantial differences in observed incidence, mortality, agreement, and predictive validity of Sepsis-3 criteria.

Electronic Health Record-Based Algorithm for Monitoring Respiratory Virus-Like Illness.

Viral respiratory illness surveillance has traditionally focused on single pathogens (e.g., influenza) and required fever to identify influenza-like illness (ILI). We developed an automated system applying both laboratory test and syndrome criteria to electronic health records from 3 practice groups in Massachusetts, USA, to monitor trends in respiratory viral-like illness (RAVIOLI) across multiple pathogens. We identified RAVIOLI syndrome using diagnosis codes associated with respiratory viral testing or positive respiratory viral assays or fever. After retrospectively applying RAVIOLI criteria to electronic health records, we observed annual winter peaks during 2015-2019, predominantly caused by influenza, followed by cyclic peaks corresponding to SARS-CoV-2 surges during 2020-2024, spikes in RSV in mid-2021 and late 2022, and recrudescent influenza in late 2022 and 2023. RAVIOLI rates were higher and fluctuations more pronounced compared with traditional ILI surveillance. RAVIOLI broadens the scope, granularity, sensitivity, and specificity of respiratory viral illness surveillance compared with traditional ILI surveillance.

Ventilator-Associated Pneumonia, Ventilator-Associated Events, and Nosocomial Respiratory Viral Infections on the Leeside of the Pandemic.

The COVID-19 pandemic has had an unprecedented impact on population health and hospital operations. Over 7 million patients have been hospitalized for COVID-19 thus far in the United States alone. Mortality rates for hospitalized patients during the first wave of the pandemic were > 30%, but as we enter the fifth year of the pandemic hospitalizations have fallen and mortality rates for hospitalized patients with COVID-19 have plummeted to 5% or less. These gains reflect lessons learned about how to optimize respiratory support for different kinds of patients, targeted use of therapeutics for patients with different manifestations of COVID-19 including immunosuppressants and antivirals as appropriate, and high levels of population immunity acquired through vaccines and natural infections. At the same time, the pandemic has helped highlight some longstanding sources of harm for hospitalized patients including hospital-acquired pneumonia, ventilator-associated events (VAEs), and hospital-acquired respiratory viral infections. We are, thankfully, on the leeside of the pandemic at present; but the large increases in ventilator-associated pneumonia (VAP), VAEs, bacterial superinfections, and nosocomial respiratory viral infections associated with the pandemic beg the question of how best to prevent these complications moving forward. This paper reviews the burden of hospitalization for COVID-19, the intersection between COVID-19 and both VAP and VAEs, the frequency and impact of hospital-acquired respiratory viral infections, new recommendations on how best to prevent VAP and VAEs, and current insights into effective strategies to prevent nosocomial spread of respiratory viruses.

Validity of inpatient electronic health record-based measures of oxygen-related therapy in the United States: Lessons applicable for studying COVID-19 endpoints.

INTRODUCTION: During the COVID-19 pandemic, inpatient electronic health records (EHRs) have been used to conduct public health surveillance and assess treatments and outcomes. Invasive mechanical ventilation (MV) and supplemental oxygen (O2) use are markers of severe illness in hospitalized COVID-19 patients. In a large US system (n = 142 hospitals), we assessed documentation of MV and O2 use during COVID-19 hospitalization in administrative data versus nursing documentation. METHODS: We identified 319 553 adult hospitalizations with a COVID-19 diagnosis, February 2020-October 2022, and extracted coded, administrative data for MV or O2. Separately, we developed classification rules for MV or O2 supplementation from semi-structured nursing documentation. We assessed MV and O2 supplementation in administrative data versus nursing documentation and calculated ordinal endpoints of decreasing COVID-19 disease severity. Nursing documentation was considered the gold standard in sensitivity and positive predictive value (PPV) analyses. RESULTS: In nursing documentation, the prevalence of MV and O2 supplementation among COVID-19 hospitalizations was 14% and 75%, respectively. The sensitivity of administrative data was 83% for MV and 41% for O2, with both PPVs above 91%. Concordance between sources was 97% for MV (κ = 0.85), and 54% for O2 (κ = 0.21). For ordinal endpoints, administrative data accurately identified intensive care and MV but underestimated hospitalizations with O2 requirements (42% vs. 18%). CONCLUSIONS: In comparison to nursing documentation, administrative data under-ascertained O2 supplementation but accurately estimated severe endpoints such as MV. Nursing documentation improved ascertainment of O2 among COVID-19 hospitalizations and can capture oxygen requirements in adults hospitalized with COVID-19 or other respiratory illnesses.

Electronic Health Records Versus Survey Small Area Estimates for Public Health Surveillance.

INTRODUCTION: Electronic health records (EHRs) are increasingly being leveraged for public health surveillance. EHR-based small area estimates (SAEs) are often validated by comparison to survey data such as the Behavioral Risk Factor Surveillance System (BRFSS). However, survey and EHR-based SAEs are expected to differ. In this cross-sectional study, SAEs were generated using MDPHnet, a distributed EHR-based surveillance network, for all Massachusetts municipalities and zip code tabulation areas (ZCTAs), compared to BRFSS PLACES SAEs, and reasons for differences explored. METHODS: This study delineated reasons a priori for how SAEs derived using EHRs may differ from surveys by comparing each strategy's case classification criteria and reviewing the literature. Hypertension, diabetes, obesity, asthma, and smoking EHR-based SAEs for 2021 in all ZCTAs and municipalities in Massachusetts were estimated with Bayesian mixed effects modeling and poststratification in the summer/fall of 2023. These SAEs were compared to BRFSS PLACES SAEs published by the U.S. Centers for Disease Control and Prevention. RESULTS: Mean prevalence was higher in EHR data versus BRFSS in both municipalities and ZCTAs for all outcomes except asthma. ZCTA and municipal symmetric mean absolute percentages ranged from 12.0 to 38.2% and 13.1 to 39.8%, respectively. There was greater variability in EHR-based SAEs versus BRFSS PLACES in both municipalities and ZCTAs. CONCLUSIONS: EHR-based SAEs tended to be higher than BRFSS and more variable. Possible explanations include detection of undiagnosed cases and over-classification using EHR data, and under-reporting within BRFSS. Both EHR and survey-based surveillance have strengths and limitations that should inform their preferred uses in public health surveillance.

Neuroinvasive Bacillus cereus Infection in Immunocompromised Hosts: Epidemiologic Investigation of 5 Patients With Acute Myeloid Leukemia.

Background: Bacillus cereus is a ubiquitous gram-positive rod-shaped bacterium that can cause sepsis and neuroinvasive disease in patients with acute leukemia or neutropenia. Methods: A single-center retrospective review was conducted to evaluate patients with acute leukemia, positive blood or cerebrospinal fluid test results for B cereus, and abnormal neuroradiographic findings between January 2018 and October 2022. Infection control practices were observed, environmental samples obtained, a dietary case-control study completed, and whole genome sequencing performed on environmental and clinical Bacillus isolates. Results: Five patients with B cereus neuroinvasive disease were identified. All patients had acute myeloid leukemia (AML), were receiving induction chemotherapy, and were neutropenic. Neurologic involvement included subarachnoid or intraparenchymal hemorrhage or brain abscess. All patients were treated with ciprofloxacin and survived with limited or no neurologic sequelae. B cereus was identified in 7 of 61 environmental samples and 1 of 19 dietary protein samples-these were unrelated to clinical isolates via sequencing. No point source was identified. Ciprofloxacin was added to the empiric antimicrobial regimen for patients with AML and prolonged or recurrent neutropenic fevers; no new cases were identified in the ensuing year. Conclusions: B cereus is ubiquitous in the hospital environment, at times leading to clusters with unrelated isolates. Fastidious infection control practices addressing a range of possible exposures are warranted, but their efficacy is unknown and they may not be sufficient to prevent all infections. Thus, including B cereus coverage in empiric regimens for patients with AML and persistent neutropenic fever may limit the morbidity of this pathogen.

COVID-19 Severity in People With HIV Compared With Those Without HIV.

BACKGROUND: People with HIV (PWH) may be at risk for more severe COVID-19 outcomes. We compared risk for severe COVID-19 in PWH with matched individuals without HIV. METHODS: We identified adults in Massachusetts with a positive SARS-CoV-2 test, March 2020-July 2022, using electronic medical record data from 3 large clinical practice groups. We then used regression models to compare outcomes among PWH versus propensity score-matched people without HIV (matched 20:1) for severe COVID-19 (pneumonia or acute respiratory distress syndrome), hospitalization, and hospital length of stay. RESULTS: We identified 171,058 individuals with COVID-19; among them, 768 PWH were matched to 15,360 individuals without HIV. Overall, severe COVID-19 and hospitalization were similar in PWH and those without HIV (severe COVID-19: 3.8% vs 3.0%, adjusted odds ratio [OR] 1.27, 95% confidence interval [CI]: 0.86-1.87; hospitalization: 12.1% vs 11.3%, adjusted OR: 1.08, 95% CI: 0.87 to 1.35). Compared with people without HIV, PWH with low CD4 T-cell counts (<200 cells/mm 3 ) had more severe COVID-19 (adjusted OR: 3.99, 95% CI: 2.06 to 7.74) and hospitalization (adjusted OR: 2.26, 95% CI: 1.35 to 3.80), but PWH with high CD4 counts had lower odds of hospitalization (adjusted OR: 0.73, 95% CI: 0.52 to 1.03). CONCLUSIONS: PWH with low CD4 T-cell counts had worse COVID-19 outcomes compared with people without HIV, but outcomes for those with high CD4 counts were similar to, or better than, those without HIV. It is unclear whether these findings are generalizable to settings where PWH have less access to and engagement with health care.

The next frontier of healthcare-associated infection (HAI) surveillance metrics: Beyond device-associated infections.

In recent years, it has become increasingly evident that surveillance metrics for invasive device-associated infections (ie, central-line-associated bloodstream infections, ventilator-associated pneumonias, and catheter-associated urinary tract infections) do not capture all harms; they capture only a subset of healthcare-associated infections (HAIs). Although prevention of device-associated infections remains critical, we need to address the full spectrum of potential harms from device use and non-device-associated infections. These include complications associated with additional devices, such as peripheral venous and arterial catheters, non-device-associated infections such as nonventilator hospital-acquired pneumonia, and noninfectious device complications such as trauma, thrombosis, and acute lung injury. As authors of the device-associated infection sections in the SHEA/IDSA/APIC Compendium of Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals, we highlight catheter-associated urinary tract infection as an example of the strengths and limitations of the current emphasis on device-associated infection surveillance, suggest performance metrics that present a more comprehensive picture of patient harm, and provide a high-level overview of similar issues with other infection surveillance measures.

Cycle threshold dynamics of non-severe acute respiratory coronavirus virus 2 (SARS-CoV-2) respiratory viruses.

OBJECTIVE: Many providers use severe acute respiratory coronavirus virus 2 (SARS-CoV-2) cycle thresholds (Ct values) as approximate measures of viral burden in association with other clinical data to inform decisions about treatment and isolation. We characterized temporal changes in Ct values for non-SARS-CoV-2 respiratory viruses as a first step to determine whether cycle thresholds could play a similar role in the management of non-SARS-CoV-2 respiratory viruses. DESIGN: Retrospective cohort study. SETTING: Brigham and Women's Hospital, Boston. METHODS: We retrospectively identified all adult patients with positive nasopharyngeal PCRs for influenza, respiratory syncytial virus (RSV), parainfluenza, human metapneumovirus (HMPV), rhinovirus, or adenovirus between January 2022 and March 2023. We plotted Ct distributions relative to days since symptom onset, and we assessed whether distributions varied by immunosuppression and other comorbidities. RESULTS: We analyzed 1,863 positive samples: 506 influenza, 502 rhinovirus, 430 RSV, 219 HMPV, 180 parainfluenza, 26 adenovirus. Ct values were generally 25-30 on the day of symptom onset, lower over the ensuing 1-3 days, and progressively higher thereafter with Ct values ≥30 after 1 week for most viruses. Ct values were generally higher and more stable over time for rhinovirus. There was no association between immunocompromised status and median intervals from symptom onset until Ct values were ≥30. CONCLUSIONS: Ct values relative to symptom onset for influenza, RSV, and other non-SARS-CoV-2 respiratory viruses generally mirror patterns seen with SARS-CoV-2. Further data on associations between Ct values and viral viability, transmissibility, host characteristics, and response to treatment for non-SARS-CoV-2 respiratory viruses are needed to determine how clinicians and infection preventionists might integrate Ct values into treatment and isolation decisions.

Fostering Governance and Information Partnerships for Chronic Disease Surveillance: The Multi-State EHR-Based Network for Disease Surveillance.

CONTEXT: Electronic health records (EHRs) are an emerging chronic disease surveillance data source and facilitating this data sharing is complex. PROGRAM: Using the experience of the Multi-State EHR-Based Network for Disease Surveillance (MENDS), this article describes implementation of a governance framework that aligns technical, statutory, and organizational requirements to facilitate EHR data sharing for chronic disease surveillance. IMPLEMENTATION: MENDS governance was cocreated with data contributors and health departments representing Texas, New Orleans, Louisiana, Chicago, Washington, and Indiana through engagement from 2020 to 2022. MENDS convened a governance body, executed data-sharing agreements, and developed a master governance document to codify policies and procedures. RESULTS: The MENDS governance committee meets regularly to develop policies and procedures on data use and access, timeliness and quality, validation, representativeness, analytics, security, small cell suppression, software implementation and maintenance, and privacy. Resultant policies are codified in a master governance document. DISCUSSION: The MENDS governance approach resulted in a transparent governance framework that cultivates trust across the network. MENDS's experience highlights the time and resources needed by EHR-based public health surveillance networks to establish effective governance.

In Support of Universal Admission Testing for SARS-CoV-2 During Significant Community Transmission.

Many hospitals have stopped or are considering stopping universal admission testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We discuss reasons why admission testing should still be part of a layered system to prevent hospital-acquired SARS-CoV-2 infections during times of significant community transmission. These include the morbidity of SARS-CoV-2 in vulnerable patients, the predominant contribution of presymptomatic and asymptomatic people to transmission, the high rate of transmission between patients in shared rooms, and data suggesting surveillance testing is associated with fewer nosocomial infections. Preferences of diverse patient populations, particularly the hardest-hit communities, should be surveyed and used to inform prevention measures. Hospitals' ethical responsibility to protect patients from serious infections should predominate over concerns about costs, labor, and inconvenience. We call for more rigorous data on the incidence and morbidity of nosocomial SARS-CoV-2 infections and more research to help determine when to start, stop, and restart universal admission testing and other prevention measures.