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.

Improving Sepsis Outcomes in the Era of Pay-for-Performance and Electronic Quality Measures: A Joint IDSA/ACEP/PIDS/SHEA/SHM/SIDP Position Paper.

The Centers for Medicare & Medicaid Services (CMS) introduced the Severe Sepsis/Septic Shock Management Bundle (SEP-1) as a pay-for-reporting measure in 2015 and is now planning to make it a pay-for-performance measure by incorporating it into the Hospital Value-Based Purchasing Program. This joint IDSA/ACEP/PIDS/SHEA/SHM/SIPD position paper highlights concerns with this change. Multiple studies indicate that SEP-1 implementation was associated with increased broad-spectrum antibiotic use, lactate measurements, and aggressive fluid resuscitation for patients with suspected sepsis but not with decreased mortality rates. Increased focus on SEP-1 risks further diverting attention and resources from more effective measures and comprehensive sepsis care. We recommend retiring SEP-1 rather than using it in a payment model and shifting instead to new sepsis metrics that focus on patient outcomes. CMS is developing a community-onset sepsis 30-day mortality electronic clinical quality measure (eCQM) that is an important step in this direction. The eCQM preliminarily identifies sepsis using systemic inflammatory response syndrome (SIRS) criteria, antibiotic administrations or diagnosis codes for infection or sepsis, and clinical indicators of acute organ dysfunction. We support the eCQM but recommend removing SIRS criteria and diagnosis codes to streamline implementation, decrease variability between hospitals, maintain vigilance for patients with sepsis but without SIRS, and avoid promoting antibiotic use in uninfected patients with SIRS. We further advocate for CMS to harmonize the eCQM with the Centers for Disease Control and Prevention's (CDC) Adult Sepsis Event surveillance metric to promote unity in federal measures, decrease reporting burden for hospitals, and facilitate shared prevention initiatives. These steps will result in a more robust measure that will encourage hospitals to pay more attention to the full breadth of sepsis care, stimulate new innovations in diagnosis and treatment, and ultimately bring us closer to our shared goal of improving outcomes for patients.

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.

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.

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.

Mycobacterium abscessus Cluster in Cardiac Surgery Patients Potentially Attributable to a Commercial Water Purification System.

BACKGROUND: Nontuberculous mycobacteria are water-avid pathogens that are associated with nosocomial infections. OBJECTIVE: To describe the analysis and mitigation of a cluster of Mycobacterium abscessus infections in cardiac surgery patients. DESIGN: Descriptive study. SETTING: Brigham and Women's Hospital, Boston, Massachusetts. PARTICIPANTS: Four cardiac surgery patients. INTERVENTION: Commonalities among cases were sought, potential sources were cultured, patient and environmental specimens were sequenced, and possible sources were abated. MEASUREMENTS: Description of the cluster, investigation, and mitigation. RESULTS: Whole-genome sequencing confirmed homology among clinical isolates. Patients were admitted during different periods to different rooms but on the same floor. There were no common operating rooms, ventilators, heater-cooler devices, or dialysis machines. Environmental cultures were notable for heavy mycobacterial growth in ice and water machines on the cluster unit but little or no growth in ice and water machines in the hospital's other 2 inpatient towers or in shower and sink faucet water in any of the hospital's 3 inpatient towers. Whole-genome sequencing confirmed the presence of a genetically identical element in ice and water machine and patient specimens. Investigation of the plumbing system revealed a commercial water purifier with charcoal filters and an ultraviolet irradiation unit leading to the ice and water machines in the cluster tower but not the hospital's other inpatient towers. Chlorine was present at normal levels in municipal source water but was undetectable downstream from the purification unit. There were no further cases after high-risk patients were switched to sterile and distilled water, ice and water machine maintenance was intensified, and the commercial purification system was decommissioned. LIMITATION: Transmission pathways were not clearly characterized. CONCLUSION: Well-intentioned efforts to modify water management systems may inadvertently increase infection risk for vulnerable patients. PRIMARY FUNDING SOURCE: National Institutes of Health.

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.

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.

Impact of SARS-CoV-2 Prevention Measures on Non-SARS-CoV-2 Hospital-Onset Respiratory Viral Infections: An Incidence Trend Analysis From 2015-2023.

We reviewed hospital-onset respiratory viral infections, 2015-2023, in one hospital to determine whether Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmission prevention measures prevented non-SARS-CoV-2 respiratory viral infections. Masking, employee symptom attestations, and screening patients and visitors for symptoms were associated with a 44%-53% reduction in hospital-onset influenza and respiratory syncytial virus (RSV), accounting for changes in community incidence.

Ultra-Short-Course Antibiotics for Suspected Pneumonia With Preserved Oxygenation.

BACKGROUND: Suspected pneumonia is the most common indication for antibiotics in hospitalized patients but is frequently overdiagnosed. We explored whether normal oxygenation could be used as an indicator to support early discontinuation of antibiotics. METHODS: We retrospectively identified all patients started on antibiotics for pneumonia in 4 hospitals with oxygen saturations ≥95% on ambient air, May 2017-February 2021. We propensity-matched patients treated 1-2 days vs 5-8 days and compared hospital mortality and time to discharge using subdistribution hazard ratios (SHRs). Secondary outcomes included readmissions, 30-day mortality, Clostridioides difficile infections, hospital-free days, and antibiotic-free days. RESULTS: Among 39 752 patients treated for possible pneumonia, 10 012 had median oxygen saturations ≥95% without supplemental oxygen. Of these, 2871 were treated 1-2 days and 2891 for 5-8 days; 4478 patients were propensity-matched. Patients treated 1-2 vs 5-8 days had similar hospital mortality (2.1% vs 2.8%; SHR, 0.75 [95% confidence interval {CI}, .51-1.09]) but less time to discharge (6.1 vs 6.6 days; SHR, 1.13 [95% CI, 1.07-1.19]) and more 30-day hospital-free days (23.1 vs 22.7; mean difference, 0.44 [95% CI, .09-.78]). There were no significant differences in 30-day readmissions (16.0% vs 15.8%; odds ratio [OR], 1.01 [95% CI, .86-1.19]), 30-day mortality (4.6% vs 5.1%; OR, 0.91 [95% CI, .69-1.19]), or 90-day C. difficile infections (1.3% vs 0.8%; OR, 1.67 [95% CI, .94-2.99]). CONCLUSIONS: One-quarter of hospitalized patients treated for pneumonia had oxygenation saturations ≥95% on ambient air. Outcomes were similar with 1-2 vs 5-8 days of antibiotics. Normal oxygenation levels may help identify candidates for early antibiotic discontinuation. Prospective trials are warranted.

Risk of Misleading Conclusions in Observational Studies of Time-to-Antibiotics and Mortality in Suspected Sepsis.

BACKGROUND: Influential studies conclude that each hour until antibiotics increases mortality in sepsis. However, these analyses often (1) adjusted for limited covariates, (2) included patients with long delays until antibiotics, (3) combined sepsis and septic shock, and (4) used linear models presuming each hour delay has equal impact. We evaluated the effect of these analytic choices on associations between time-to-antibiotics and mortality. METHODS: We retrospectively identified 104 248 adults admitted to 5 hospitals from 2015-2022 with suspected infection (blood culture collection and intravenous antibiotics ≤24 h of arrival), including 25 990 with suspected septic shock and 23 619 with sepsis without shock. We used multivariable regression to calculate associations between time-to-antibiotics and in-hospital mortality under successively broader confounding-adjustment, shorter maximum time-to-antibiotic intervals, stratification by illness severity, and removing assumptions of linear hourly associations. RESULTS: Changing covariates, maximum time-to-antibiotics, and severity stratification altered the magnitude, direction, and significance of observed associations between time-to-antibiotics and mortality. In a fully adjusted model of patients treated ≤6 hours, each hour was associated with higher mortality for septic shock (adjusted odds ratio [aOR]: 1.07; 95% CI: 1.04-1.11) but not sepsis without shock (aOR: 1.03; .98-1.09) or suspected infection alone (aOR: .99; .94-1.05). Modeling each hour separately confirmed that every hour of delay was associated with increased mortality for septic shock, but only delays >6 hours were associated with higher mortality for sepsis without shock. CONCLUSIONS: Associations between time-to-antibiotics and mortality in sepsis are highly sensitive to analytic choices. Failure to adequately address these issues can generate misleading conclusions.

Intravenous to Oral Antibiotic Switch Therapy Among Patients Hospitalized With Community-Acquired Pneumonia.

BACKGROUND: Community-acquired pneumonia (CAP) is a leading cause of hospital admissions and antimicrobial use. Clinical practice guidelines recommend switching from intravenous (IV) to oral antibiotics once patients are clinically stable. METHODS: We conducted a retrospective cohort study of adults admitted with CAP and initially treated with IV antibiotics at 642 US hospitals from 2010 through 2015. Switching was defined as discontinuation of IV and initiation of oral antibiotics without interrupting therapy. Patients switched by hospital day 3 were considered early switchers. We compared length of stay (LOS), in-hospital 14-day mortality, late deterioration (intensive care unit [ICU] transfer), and hospital costs between early switchers and others, controlling for hospital characteristics, patient demographics, comorbidities, initial treatments, and predicted mortality. RESULTS: Of 378 041 CAP patients, 21 784 (6%) were switched early, most frequently to fluoroquinolones. Patients switched early had fewer days on IV antibiotics, shorter duration of inpatient antibiotic treatment, shorter LOS, and lower hospitalization costs, but no significant excesses in 14-day in-hospital mortality or late ICU admission. Patients at a higher mortality risk were less likely to be switched. However, even in hospitals with relatively high switch rates, <15% of very low-risk patients were switched early. CONCLUSIONS: Although early switching was not associated with worse outcomes and was associated with shorter LOS and fewer days on antibiotics, it occurred infrequently. Even in hospitals with high switch rates, <15% of very low-risk patients were switched early. Our findings suggest that many more patients could be switched early without compromising outcomes.

Evaluation of an Opt-Out Protocol for Antibiotic De-Escalation in Patients With Suspected Sepsis: A Multicenter, Randomized, Controlled Trial.

BACKGROUND: Sepsis guidelines recommend daily review to de-escalate or stop antibiotics in appropriate patients. This randomized, controlled trial evaluated an opt-out protocol to decrease unnecessary antibiotics in patients with suspected sepsis. METHODS: We evaluated non-intensive care adults on broad-spectrum antibiotics despite negative blood cultures at 10 US hospitals from September 2018 through May 2020. A 23-item safety check excluded patients with ongoing signs of systemic infection, concerning or inadequate microbiologic data, or high-risk conditions. Eligible patients were randomized to the opt-out protocol vs usual care. Primary outcome was post-enrollment antibacterial days of therapy (DOT). Clinicians caring for intervention patients were contacted to encourage antibiotic discontinuation using opt-out language. If continued, clinicians discussed the rationale for continuing antibiotics and de-escalation plans. To evaluate those with zero post-enrollment DOT, hurdle models provided 2 measures: odds ratio of antibiotic continuation and ratio of mean DOT among those who continued antibiotics. RESULTS: Among 9606 patients screened, 767 (8%) were enrolled. Intervention patients had 32% lower odds of antibiotic continuation (79% vs 84%; odds ratio, 0.68; 95% confidence interval [CI], .47-.98). DOT among those who continued antibiotics were similar (ratio of means, 1.06; 95% CI, .88-1.26). Fewer intervention patients were exposed to extended-spectrum antibiotics (36% vs 44%). Common reasons for continuing antibiotics were treatment of localized infection (76%) and belief that stopping antibiotics was unsafe (31%). Thirty-day safety events were similar. CONCLUSIONS: An antibiotic opt-out protocol that targeted patients with suspected sepsis resulted in more antibiotic discontinuations, similar DOT when antibiotics were continued, and no evidence of harm. CLINICAL TRIALS REGISTRATION: NCT03517007.

Prospective Genomic Surveillance Reveals Cryptic MRSA Outbreaks with Local to International Origins among NICU Patients.

Methicillin-resistant Staphylococcus aureus (MRSA) infections cause substantive morbidity and mortality in neonates. Using publicly available resources from the National Center of Biotechnology Information (NCBI) and Food and Drug Administration's (FDA) GalaxyTrakr pipeline, we illustrate the dynamics of MRSA colonization and infection in neonates. Over 217 days of prospective surveillance, analyses revealed concurrent MRSA transmission chains affecting 11 of 17 MRSA-colonized patients (65%), with two clusters that demonstrated intervals of more than a month among the appearance of isolates. All MRSA infected neonates (n = 3) showed previous colonization with the infecting strain. GalaxyTrakr clustering of the NICU strains, in the context of 21,521 international isolates deposited in NCBI's Pathogen Detection Resource, revealed NICU isolates to be distinct from adult MRSA strains seen locally and internationally. Clustering of the NICU strains within an international context enhanced the resolution of strain clusters and supported the rule-out of suspected, local transmission events within the NICU. Analyses also identified sequence type 1535 isolates, emergent in the Middle East, carrying a unique SCCmec with fusC and aac(6')-Ie/aph(2'')-1a that provided a multidrug-resistant phenotype. NICU genomic pathogen surveillance, leveraging public repositories and outbreak detection tools, supports rapid identification of cryptic MRSA clusters, and can inform infection prevention interventions for this vulnerable patient population. Results demonstrate that sporadic infections in the NICU may be indicative of hidden chains of asymptomatic transmission best identified with sequenced-based approaches.

Reliability of Admission Procalcitonin Testing for Capturing Bacteremia Across the Sepsis Spectrum: Real-World Utilization and Performance Characteristics, 65 U.S. Hospitals, 2008-2017.

OBJECTIVES: Serum procalcitonin is often ordered at admission for patients with suspected sepsis and bloodstream infections (BSIs), although its performance characteristics in this setting remain contested. This study aimed to evaluate use patterns and performance characteristics of procalcitonin-on-admission in patients with suspected BSI, with or without sepsis. DESIGN: Retrospective cohort study. SETTING: Cerner HealthFacts Database (2008-2017). PATIENTS: Adult inpatients (≥ 18 yr) who had blood cultures and procalcitonin drawn within 24 hours of admission. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Testing frequency of procalcitonin was determined. Sensitivity of procalcitonin-on-admission for detecting BSI due to different pathogens was calculated. Area under the receiver operating characteristic curve (AUC) was calculated to assess discrimination by procalcitonin-on-admission for BSI in patients with and without fever/hypothermia, ICU admission and sepsis defined by Centers for Disease Control and Prevention Adult Sepsis Event criteria. AUCs were compared using Wald test and p values were adjusted for multiple comparisons. At 65 procalcitonin-reporting hospitals, 74,958 of 739,130 patients (10.1%) who had admission blood cultures also had admission procalcitonin testing. Most patients (83%) who had admission day procalcitonin testing did not have a repeat procalcitonin test. Median procalcitonin varied considerably by pathogen, BSI source, and acute illness severity. At a greater than or equal to 0.5 ng/mL cutoff, sensitivity for BSI detection was 68.2% overall, ranging between 58.0% for enterococcal BSI without sepsis and 96.4% for pneumococcal sepsis. Procalcitonin-on-admission displayed moderate discrimination at best for overall BSI (AUC, 0.73; 95% CI, 0.72-0.73) and showed no additional utility in key subgroups. Empiric antibiotic use proportions were not different between blood culture sampled patients with a positive procalcitonin (39.7%) and negative procalcitonin (38.4%) at admission. CONCLUSIONS: At 65 study hospitals, procalcitonin-on-admission demonstrated poor sensitivity in ruling out BSI, moderate-to-poor discrimination for both bacteremic sepsis and occult BSI and did not appear to meaningfully alter empiric antibiotic usage. Diagnostic stewardship of procalcitonin-on-admission and risk assessment of admission procalcitonin-guided clinical decisions is warranted.

Association of Early Opioid Withdrawal Treatment Strategy and Patient-Directed Discharge Among Hospitalized Patients with Opioid Use Disorder.

BACKGROUND: Medical hospitalizations for people with opioid use disorder (OUD) frequently result in patient-directed discharges (PDD), often due to untreated pain and withdrawal. OBJECTIVE: To investigate the association between early opioid withdrawal management strategies and PDD. DESIGN: Retrospective cohort study using three datasets representing 362 US hospitals. PARTICIPANTS: Adult patients hospitalized between 2009 and 2015 with OUD (as identified using ICD-9-CM codes or inpatient buprenorphine administration) and no PDD on the day of admission. INTERVENTIONS: Opioid withdrawal management strategies were classified based on day-of-admission receipt of any of the following treatments: (1) medications for OUD (MOUD) including methadone or buprenorphine, (2) other opioid analgesics, (3) adjunctive symptomatic medications without opioids (e.g., clonidine), and (4) no withdrawal treatment. MAIN MEASURES: PDD was assessed as the main outcome and hospital length of stay as a secondary outcome. KEY RESULTS: Of 6,715,286 hospitalizations, 127,158 (1.9%) patients had OUD and no PDD on the day of admission, of whom 7166 (5.6%) had a later PDD and 91,051 (71.6%) patients received some early opioid withdrawal treatment (22.3% MOUD; 43.4% opioid analgesics; 5.9% adjunctive medications). Compared to no withdrawal treatment, MOUD was associated with a lower risk of PDD (adjusted odds ratio [aOR] = 0.73, 95%CI 0.68-0.8, p < .001), adjunctive treatment alone was associated with higher risk (aOR = 1.13, 95%CI: 1.01-1.26, p = .031), and treatment with opioid analgesics alone was associated with similar risk (aOR 0.95, 95%CI: 0.89-1.02, p = .148). Among those with PDD, both MOUD (adjusted incidence rate ratio [aIRR] = 1.24, 95%CI: 1.17-1.3, p < .001) and opioid analgesic treatments (aIRR = 1.39, 95%CI: 1.34-1.45, p < .001) were associated with longer hospital stays. CONCLUSIONS: MOUD was associated with decreased risk of PDD but was utilized in < 1 in 4 patients. Efforts are needed to ensure all patients with OUD have access to effective opioid withdrawal management to improve the likelihood they receive recommended hospital care.

Update on Sepsis Epidemiology in the Era of COVID-19.

Timely and accurate data on the epidemiology of sepsis is essential to inform public policy, clinical practice, and research priorities. Recent studies have illuminated several ongoing questions about sepsis epidemiology, including the incidence and outcomes of sepsis in non-Western countries and in specialized populations such as surgical patients, patients with cancer, and the elderly. There have also been new insights into the limitations of current surveillance methods using administrative data and increasing experience tracking sepsis incidence and outcomes using "big data" approaches that take advantage of detailed electronic health record data. The COVID-19 pandemic, however, has fundamentally changed the landscape of sepsis epidemiology. It has increased sepsis rates, helped highlight ongoing controversies about how to define sepsis, and intensified debate about the possible unintended consequences of overly rigid sepsis care bundles. Despite these controversies, there is a growing consensus that severe COVID-19 causing organ dysfunction is appropriate to label as sepsis, even though it is treated very differently from bacterial sepsis, and that surveillance strategies need to be modified to reliably identify these cases to fully capture and delineate the current burden of sepsis. This review will summarize recent insights into the epidemiology of sepsis and highlight several urgent questions and priorities catalyzed by COVID-19.

Development of a Hypertension Electronic Phenotype for Chronic Disease Surveillance in Electronic Health Records: Key Analytic Decisions and Their Effects.

INTRODUCTION: Modernizing chronic disease surveillance with electronic health record (EHR) data may provide better data to improve hypertension prevention and control, but no consensus exists for an EHR-based surveillance definition for hypertension. The Multi-State EHR-Based Network for Disease Surveillance (MENDS) pilot surveillance system was used to develop and test an electronic phenotype for hypertension. METHODS: We used MENDS data from 1,671,279 patients in Louisiana to examine the effect of different analytic decisions on estimates of hypertension prevalence. Decisions included 1) whether to restrict surveillance to patients with recent blood pressure measurements, 2) varying the number and recency of encounters to define the population at risk of hypertension, 3) how to define hypertension (diagnosis codes, antihypertensive medication, blood pressure measurements, or combinations of these), and 4) how to handle multiple blood pressure measurements on the same day. Results were compared with independent estimates of hypertension prevalence in Louisiana from the Behavioral Risk Factor Surveillance System (BRFSS). RESULTS: Applying varying criteria resulted in hypertension prevalence estimates ranging from 19.7% to 59.3%. A hypertension surveillance strategy that includes a population with at least 1 clinical encounter with measured blood pressure in the previous 2 years and identifies hypertension using all available data (≥1 diagnosis code, ≥1 antihypertensive medication, and ≥2 elevated blood pressure values ≥140/90 mm Hg on separate days) generated estimates in line with population-based survey data. This definition estimated the crude 2019 hypertension prevalence in the state of Louisiana as 43.4% (age-adjusted, 41.0%), comparable with the crude BRFSS estimate of 39.7% (age adjusted, 37.1%). CONCLUSION: Applying different criteria to define hypertension using EHR data has a large effect on hypertension prevalence estimates. The proposed electronic phenotype generates hypertension prevalence estimates that align with independent estimates from BRFSS.

Leveraging Electronic Health Record Data for Timely Chronic Disease Surveillance: The Multi-State EHR-Based Network for Disease Surveillance.

CONTEXT: Electronic health record (EHR) data can potentially make chronic disease surveillance more timely, actionable, and sustainable. Although use of EHR data can address numerous limitations of traditional surveillance methods, timely surveillance data with broad population coverage require scalable systems. This report describes implementation, challenges, and lessons learned from the Multi-State EHR-Based Network for Disease Surveillance (MENDS) to help inform how others work with EHR data to develop distributed networks for surveillance. PROGRAM: Funded by the Centers for Disease Control and Prevention (CDC), MENDS is a data modernization demonstration project that aims to develop a timely national chronic disease sentinel surveillance system using EHR data. It facilitates partnerships between data contributors (health information exchanges, other data aggregators) and data users (state and local health departments). MENDS uses query and visualization software to track local emerging trends. The program also uses statistical and geospatial methods to generate prevalence estimates of chronic disease risk measures at the national and local levels. Resulting data products are designed to inform public health practice and improve the health of the population. IMPLEMENTATION: MENDS includes 5 partner sites that leverage EHR data from 91 health system and clinic partners and represents approximately 10 million patients across the United States. Key areas of implementation include governance, partnerships, technical infrastructure and support, chronic disease algorithms and validation, weighting and modeling, and workforce education for public health data users. DISCUSSION: MENDS presents a scalable distributed network model for implementing national chronic disease surveillance that leverages EHR data. Priorities as MENDS matures include producing prevalence estimates at various geographic and subpopulation levels, developing enhanced data sharing and interoperability capacity using international data standards, scaling the network to improve coverage nationally and among underrepresented geographic areas and subpopulations, and expanding surveillance of additional chronic disease measures and social determinants of health.