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Background The CLUSTER trial assessed the impact of prospective identification of clusters coupled with a response protocol on the containment of hospital clusters. Methods This 82-hospital CRT in 16 states compared clusters of bacterial and fungal healthcare pathogens using a statistical outbreak detection tool (WHONET-SaTScan) coupled with a standardized response protocol (automated cluster detection arm) compared to routine surveillance with the response protocol (control arm). Trial periods: 24 mo Baseline (2/17–1/19); 5 mo Phase-in (2/19–6/19); 30 mo Intervention (7/19–1/22). The primary outcome was the number of additional cases occurring after initial cluster detection. Analyses used generalized linear mixed models to assess differences in additional cases between the intervention vs baseline periods across arms, clustering by hospital. Results were assessed overall and, to account for the effect of COVID-19 on hospital operations, stratified into pre-COVID-19 (7/19–6/20) and during COVID-19 (7/20–1/22) intervention periods. We also assessed the probability that a patient was in a cluster. Results In the baseline period, the automated cluster detection and control arms had 0.09 and 0.07 additional cluster cases/1000 admissions, respectively. The automated cluster detection arm had a 22% greater relative reduction in additional cluster cases in the intervention vs baseline period compared to control (P=0.5). Within the intervention period, the automated cluster detection arm had a significant 64% relative reduction pre-COVID-19 (P< 0.05) and a non-significant 6% relative reduction during COVID-19 (P=0.9) compared to control (Figure). When evaluating patient risk of being part of a cluster across the entire intervention period, the automated cluster detection arm had a significant 35% relative reduction vs control (P< 0.01). Conclusion A statistical automated tool coupled with a response protocol improved cluster containment by 64% pre-COVID-19 but not during COVID-19; there were no significant differences between the arms when using the entire intervention period. Automated cluster detection may substantially improve outbreak containment in non-pandemic periods when infection prevention programs are able to optimize containment protocols. Disclosures Susan S. Huang, MD, MPH, Medline: Conducted studies in which hospitals and nursing homes received contributed antiseptic and/or environmental cleaning products|Molnlyke: Conducted clinical studies in which hospitals received contributed antiseptic product|Stryker: Conducted clinical studies in which hospitals and nursing homes received contributed antiseptic products|Xttrium Laboratories: Conducted clinical studies in which hospitals and nursing homes received contributed antiseptic product.

The US Food and Drug Administration (FDA) created the Sentinel System in response to a requirement in the FDA Amendments Act of 2007 that the agency establish a system for monitoring risks associated with drug and biologic products using data from disparate sources. The Sentinel System has completed hundreds of analyses, including many that have directly informed regulatory decisions. The Sentinel System also was designed to support a national infrastructure for a learning health system. Sentinel governance and guiding principles were designed to facilitate Sentinel’s role as a national resource. The Sentinel System infrastructure now supports multiple non-FDA projects for stakeholders ranging from regulated industry to other federal agencies, international regulators, and academics. The Sentinel System is a working example of a learning health system that is expanding with the potential to create a global learning health system that can support medical product safety assessments and other research.

Concerns regarding both the limited generalizability and the slow pace of traditional randomized trials have led to calls for greater use of real-world evidence (RWE) in the evaluation of new treatments or products. The RWE label has been used to refer to a variety of departures from the methods of traditional randomized controlled trials. Recognizing this complexity and potential confusion, the National Academies of Science, Engineering, and Medicine convened a series of workshops to clarify and address questions regarding the use of RWE to evaluate new medical treatments. Those workshops identified three specific dimensions in which RWE studies might differ from traditional clinical trials: use of real-world data (data extracted from health system records or data captured by mobile devices), delivery of real-world treatment (open-label treatments delivered in community settings by community practitioners), and real-world treatment assignment (including nonrandomized comparisons and variations on random assignment such as before-after or stepped-wedge designs). For any RWE study, decisions regarding each of these dimensions depends on the specific research question, characteristics of the potential study settings, and characteristics of the settings where study results would be applied.

Objective:To compare strategies for hospital ranking based on colon surgical-site infection (SSI) rate by combining all colon procedures versus stratifying by surgical approach (ie, laparoscopic vs open).Design:Retrospective cohort study.Methods:We identified SSIs among Medicare beneficiaries undergoing colon surgery from 2009 through 2013 using previously validated methods. We created a risk prediction model for SSI using age, sex, race, comorbidities, surgical approach (laparoscopy vs open), and concomitant colon and noncolon procedures. Adjusted SSI rates were used to rank hospitals. Subanalyses were performed for common colon procedures and procedure types for which there were both open and laparoscopic procedures. We generated ranks using only open and only laparoscopic procedures, overall and for each subanalysis. Rankings were compared using a Spearman correlation coefficient.Results:In total, 694,813 colon procedures were identified among 508,135 Medicare beneficiaries. The overall SSI rate was 7.6%. The laparoscopic approach was associated with lower SSI risk (OR, 0.5; 95% CI, 0.4–0.5), and higher SSI risk was associated with concomitant abdominal surgeries (OR, 1.4; 95% CI, 1.4–1.5) and higher Elixhauser score (OR, 1.1; 95% CI, 1.0–1.1). Hospital rankings for laparascopic procedures were poorly correlated with rankings for open procedures (r = 0.23).Conclusions:Hospital rankings based on total colon procedures fail to account for differences in SSI risk from laparoscopic vs open procedures. Stratifying rankings by surgical approach yields a more equitable comparison of surgical performance.

IMPACT-AFib was an 80,000-patient randomized clinical trial implemented by five US insurance companies (health plans) aimed at increasing the use of oral anticoagulants by individuals with atrial fibrillation who were at high risk of stroke and not on treatment. The underlying thesis was that patients could be change agents to initiate prescribing discussions with their providers. We tested the effect of mailing information to both patients and their providers. We used administrative medical claims and pharmacy dispensing data to identify eligible patients, to randomize them to an early or delayed intervention, and to assess clinical outcomes. The core data were analysis-ready datasets each site had created and curated for the FDA’s Sentinel System, supplemented by updated “fresh” pharmacy and enrollment data to ensure eligibility at the time of intervention. Following mutually agreed upon procedures, sites linked to additional internal source data to implement the intervention—educational information mailed to patients and their providers in the early intervention arm, and to providers of patients in the delayed intervention arm approximately 12 months later. The primary analysis compares the early intervention arm to the delayed intervention arm, prior to the delayed intervention being conducted (i.e. compares intervention to non-intervention). The endpoints of interest were evidence of initiation of anticoagulation (primary) as well as clinical endpoints, including stroke and hospitalization for bleeding. Major challenges, some unanticipated, identified during the planning phase include convening multi-stakeholder investigator teams and advisors, addressing ethical concerns about not intervening in a usual care comparison group, and identifying and avoiding interference with sites’ routine programs that were similar to the intervention. Needs and challenges during the implementation phase included the fact that even limited site-specific programming greatly increased time and effort, the need to refresh research data extracts immediately before outreach to patients and providers, potential difficulty identifying low-cost medications such as warfarin that may not be reimbursed by health plans and so not discoverable in dispensing data, the need to develop workarounds when “providers” in claims data were facilities, difficulty addressing clustering of patients by provider because providers can have multiple identifiers within and between health plans, and the need to anticipate loss to follow up because of health plan disenrollment or change in benefits. As pragmatic trials begin to shape evidence generation within clinical practice, investigators should anticipate issues inherent to claims data and working with multiple large sites. In IMPACT-AFib, we found that investing in collaboration and communication among all parties throughout all phases of the study helped ensure common understanding, early identification of challenges, and streamlined actual implementation.

Aim To assess whether dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 receptor agonists are associated with an increased lung cancer risk among individuals with type 2 diabetes. Methods We conducted a population-based cohort study using the UK Clinical Practice Research Datalink. We identified 130 340 individuals newly treated with antidiabetes drugs between January 2007 and March 2017, with follow-up until March 2018. We used a time-varying approach to model use of dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 receptor agonists compared with use of other second- or third-line antidiabetes drugs. We used Cox proportional hazards models to estimate the adjusted hazard ratios, with 95% CIs, of incident lung cancer associated with use of dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 receptor agonists, separately, by cumulative duration of use, and by time since initiation. Results A total of 790 individuals were newly diagnosed with lung cancer (median follow-up 4.6 years, incidence rate 1.5/1000 person-years, 95% CI 1.4-1.6). Compared with use of second-/third-line drugs, use of dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 receptor agonists was not associated with an increased lung cancer risk (hazard ratio 1.07, 95% CI 0.87-1.32, and hazard ratio 1.02, 95% CI 0.68-1.54, respectively). There was no evidence of duration-response relationships. Conclusions In individuals with type 2 diabetes, use of incretin-based drugs was not associated with increased lung cancer risk.

The randomized controlled trial (RCT) is the gold standard for evaluating the causal effects of medications. Limitations of RCTs have led to increasing interest in using real-world evidence (RWE) to augment RCT evidence and inform decision making on medications. Although RWE can be either randomized or nonrandomized, nonrandomized RWE can capitalize on the recent proliferation of large healthcare databases and can often answer questions that cannot be answered in randomized studies due to resource constraints. However, the results of nonrandomized studies are much more likely to be impacted by confounding bias, and the existence of unmeasured confounders can never be completely ruled out. Furthermore, nonrandomized studies require more complex design considerations which can sometimes result in design-related biases. We discuss questions that can help investigators or evidence consumers evaluate the potential impact of confounding or other biases on their findings: Does the design emulate a hypothetical randomized trial design? Is the comparator or control condition appropriate? Does the primary analysis adjust for measured confounders? Do sensitivity analyses quantify the potential impact of residual confounding? Are methods open to inspection and (if possible) replication? Designing a high-quality nonrandomized study of medications remains challenging and requires broad expertise across a range of disciplines, including relevant clinical areas, epidemiology, and biostatistics. The questions posed in this paper provide a guiding framework for assessing the credibility of nonrandomized RWE and could be applied across many clinical questions.

Background The profound changes wrought by coronavirus disease 2019 (COVID-19) on routine hospital operations may have influenced performance on hospital measures, including healthcare-associated infections (HAIs). We aimed to evaluate the association between COVID-19 surges and HAI and cluster rates. Methods In 148 HCA Healthcare-affiliated hospitals, from 1 March 2020 to 30 September 2020, and a subset of hospitals with microbiology and cluster data through 31 December 2020, we evaluated the association between COVID-19 surges and HAIs, hospital-onset pathogens, and cluster rates using negative binomial mixed models. To account for local variation in COVID-19 pandemic surge timing, we included the number of discharges with a laboratory-confirmed COVID-19 diagnosis per staffed bed per month. Results Central line-associated blood stream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), and methicillin-resistant Staphylococcus aureus (MRSA) bacteremia increased as COVID-19 burden increased. There were 60% (95% confidence interval [CI]: 23–108%) more CLABSI, 43% (95% CI: 8–90%) more CAUTI, and 44% (95% CI: 10–88%) more cases of MRSA bacteremia than expected over 7 months based on predicted HAIs had there not been COVID-19 cases. Clostridioides difficile infection was not significantly associated with COVID-19 burden. Microbiology data from 81 of the hospitals corroborated the findings. Notably, rates of hospital-onset bloodstream infections and multidrug resistant organisms, including MRSA, vancomycin-resistant enterococcus, and Gram-negative organisms, were each significantly associated with COVID-19 surges. Finally, clusters of hospital-onset pathogens increased as the COVID-19 burden increased. Conclusions COVID-19 surges adversely impact HAI rates and clusters of infections within hospitals, emphasizing the need for balancing COVID-related demands with routine hospital infection prevention.

Background The Centers for Medicare and Medicaid Services (CMS) use colon surgical site infection (SSI) rates to rank hospitals and apply financial penalties. The CMS’ risk-adjustment model omits potentially impactful variables that might disadvantage hospitals with complex surgical populations. Methods We analyzed adult patients who underwent colon surgery within facilities associated with HCA Healthcare from 2014 to 2016. SSIs were identified from National Health Safety Network (NHSN) reporting. We trained and validated 3 SSI prediction models, using (1) current CMS model variables, including hospital-specific random effects (HCA-adapted CMS model); (2) demographics and claims-based comorbidities (expanded-claims model); and (3) demographics, claims-based comorbidities, and NHSN variables (claims-plus–electronic health record [EHR] model). Discrimination, calibration, and resulting rankings were compared among all models and the current CMS model with published coefficient values. Results We identified 39 468 colon surgeries in 149 hospitals, resulting in 1216 (3.1%) SSIs. Compared to the HCA-adapted CMS model, the expanded-claims model had similar performance (c-statistic, 0.65 vs 0.67, respectively), while the claims-plus-EHR model was more accurate (c-statistic, 0.70; 95% confidence interval, .67–.73; P = .004). The sampling variation, due to the low surgical volume and small number of infections, contributed 74% of the total variation in observed SSI rates between hospitals. When CMS model rankings were compared to those from the expanded-claims and claims-plus-EHR models, 18 (15%) and 26 (22%) hospitals changed quartiles, respectively, and 10 (8.3%) and 12 (10%) hospitals changed into or out of the lowest-performing quartile, respectively. Conclusions An expanded set of variables improved colon SSI risk predictions and quartile assignments, but low procedure volumes and SSI events remain a barrier to effectively comparing hospitals., Using additional variables can improve risk adjustments of hospital-level colon surgical site infection rates, but have limited impact on financial penalties. Low procedure and infection rates contribute to much of the between-hospital variation.

The US Sentinel System and the Canadian Network for Observational Drug Effect Studies (CNODES) are two medical product safety surveillance networks. Using Sentinel's preprogrammed, parameterizable analytic tools, we reproduced two protocol-based studies conducted by CNODES to assess the risks of acute pancreatitis and heart failure (HF) associated with the use of incretin-based drugs, compared with use of ≥ 2 oral hypoglycemic agents. Results from the replication new-user cohort analyses aligned with those from the CNODES nested case-control studies. The adjusted hazard ratios were 0.95 (0.81-1.12; vs. 1.03 (0.87-1.22) in CNODES) for acute pancreatitis and 0.91 (0.84-1.00; vs. 0.82 (0.67-1.00) in CNODES) for HF among patients without HF history. The CNODES's common protocol approach allows studies tailored to specific safety questions, whereas the Sentinel's common data model plus pretested program approach enables more rapid analysis. Despite these differences, it is possible to obtain comparable results using both approaches.

In 2012, a U.S. Institute of Medicine report called for a different approach to health care: "Left unchanged, health care will continue to underperform; cause unnecessary harm; and strain national, state, and family budgets." The answer, they suggested, would be a "continuously learning" health system. Ethicists and researchers urged the creation of "learning health organizations" that would integrate knowledge from patient-care data to continuously improve the quality of care. Our experience with an ongoing research study on atrial fibrillation-a trial known as IMPACT-AFib-gave us some insight into one of the challenges that will have to be dealt with in creating these organizations. Although the proposed educational intervention study placed no restrictions on what providers and health plans could do, the oversight team argued that the ethical principle of beneficence did not allow the researchers to be "bystanders" in relation to a control group receiving suboptimal care. In response, the researchers designed a "workaround" that allowed the project to go forward. We believe the experience suggests that what we call "bystander ethics" will create challenges for the kinds of quality improvement research that LHOs are designed to do.

Is Learning Worth the Trouble? To help address important health issues, embedded pragmatic clinical trials could generate real-world evidence without the limitations associated with observational s...

In group or cluster-randomized trials (GRTs), matching is a technique that can be used to improve covariate balance. When baseline data are available, we suggest a strategy that can be used to achieve the desired balance between treatment and control groups across numerous potential confounding variables. This strategy minimizes the overall within-pair Mahalanobis distance; and involves iteratively: 1) making pairs that minimize the distance between pairs of clusters with respect to potentially confounding variables; 2) visually assessing the potential effects of these pairs and resulting possible randomizations; and 3) reweighting variables of selecting weights to make pairs of clusters. In step 2, we plot the between-arm differences with a parallel-coordinates plot. Investigators can compare plots of different weighting schemes to determine the one that best suits their needs prior to the actual, final, randomization. We demonstrate application of the approach with the Mupirocin-Iodophor Swap Out trial. A webapp is provided.

Background The profound changes wrought by COVID-19 on routine hospital operations may have influenced performance on hospital measures, including healthcare-associated infections (HAIs). Objective Evaluate the association between COVID-19 surges and HAI or cluster rates Methods Design: Prospective cohort study Setting 148 HCA Healthcare-affiliated hospitals, 3/1/2020-9/30/2020, and a subset of hospitals with microbiology and cluster data through 12/31/2020 Patients All inpatients Measurements We evaluated the association between COVID-19 surges and HAIs, hospital-onset pathogens, and cluster rates using negative binomial mixed models. To account for local variation in COVID-19 pandemic surge timing, we included the number of discharges with a laboratory-confirmed COVID-19 diagnosis per staffed bed per month at each hospital. Results Central line-associated blood stream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), and methicillin-resistant Staphylococcus aureus (MRSA) bacteremia increased as COVID-19 burden increased (P ≤ 0.001 for all), with 60% (95% CI, 23 to 108%) more CLABSI, 43% (95% CI, 8 to 90%) more CAUTI, and 44% (95% CI, 10 to 88%) more cases of MRSA bacteremia than expected over 7 months based on predicted HAIs had there not been COVID-19 cases. Clostridioides difficile infection (CDI) was not significantly associated with COVID-19 burden. Microbiology data from 81 of the hospitals corroborated the findings. Notably, rates of hospital-onset bloodstream infections and multidrug resistant organisms, including MRSA, vancomycin-resistant enterococcus and Gram-negative organisms were each significantly associated with COVID-19 surges (P < 0.05 for all). Finally, clusters of hospital-onset pathogens increased as the COVID-19 burden increased (P = 0.02). Limitations Variations in surveillance and reporting may affect HAI data. Table 1. Effect of an increase in number of COVID-19 discharges on HAIs and hospital-onset pathogens Figure 1. Predicted mean HAI rates as COVID-19 discharges increase Predicted mean HAI rate by increasing monthly COVID-19 discharges. Panel a. CLABSI, Panel b, CAUTI Panel c. MRSA Bacteremia, Panel d. CDI. Data are stratified by small, medium and large hospitals. Figure 2. Monthly comparison of COVID discharges to clusters COVID-19 discharges and the number of clusters of hospital-onset pathogens are correlated throughout the pandemic. Conclusion COVID-19 surges adversely impact HAI rates and clusters of infections within hospitals, emphasizing the need for balancing COVID-related demands with routine hospital infection prevention. Disclosures Kenneth Sands, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Susan S. Huang, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Xttrium (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products) Ken Kleinman, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Edward Septimus, MD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Eunice J. Blanchard, MSN RN, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Russell Poland, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Micaela H. Coady, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Deborah S. Yokoe, MD, MPH, Nothing to disclose Julia Moody, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Richard Platt, MD, MSc, Medline (Research Grant or Support, Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Jonathan B. Perlin, MD, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)

Background Up to 40% of hospitalized patients receive unnecessary or inappropriately broad antibiotics despite a low risk of multidrug-resistant organism (MDRO) infection. Empiric standard spectrum antibiotic use would reduce extended-spectrum (ES) antibiotic exposure and future resistance. We evaluated whether computerized prescriber order entry prompts providing patient-specific MDRO risk estimates could reduce ES antibiotic use compared to routine stewardship practices in patients hospitalized with urinary tract infection (UTI). Methods This 59-hospital cluster randomized trial compared: 1) INSPIRE prompts providing patient-specific MDRO UTI risk estimates at order entry and recommended standard spectrum antibiotics for risk < 10% versus 2) routine stewardship practices. Prompt used an absolute MDRO risk algorithm based on a 140 hospital data set. Trial population included adults treated with antibiotics for UTI in ED or non-ICU wards in first 3 days of admission (empiric days); prompt was triggered if ES antibiotics were ordered. Prescribers received feedback on prompt response. Trial periods: 18-month Baseline (Apr 2017–Sept 2018); 6-month Phase-in (Oct 2018–Mar 2019); 15-month Intervention (Apr 2019 – June 2020). Primary outcome was ES antibiotic days of therapy (ES-DOT) per empiric day; secondary outcomes were a) vancomycin and b) anti-pseudomonal DOT per empiric day. Unadjusted, as-randomized analyses used generalized linear mixed effects models to assess differences in ES-DOT rates between the intervention vs baseline period across arms (difference in differences), while clustering by patient and hospital. Results Results: We randomized 59 hospitals in 12 states, with 87,749 and 66,996 non-ICU UTI admissions in baseline and intervention periods, respectively. Intervention group had a a 21% reduction in ES-DOT compared to routine care. Vancomycin and anti-pseudomonal DOT were similarly reduced in the intervention group by 17% and 23%, respectively (Table). Conclusion Conclusion: INSPIRE order entry prompts providing real-time, patient-specific MDRO risk estimates with recommendation to use standard spectrum antibiotics in low risk patients significantly reduced empiric ES prescribing in adults admitted with UTI. Disclosures Shruti K. Gohil, MD, MPH, Medline (Other Financial or Material Support, Co-Investigator in studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnycke (Other Financial or Material Support, Co-Investigator in studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Co-Investigator in studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products) Edward Septimus, MD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Ken Kleinman, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Lauren Heim, MPH, Medline (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Stryker (Sage) (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product)Xttrium (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product) Syma Rashid, MD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Stryker (Sage) (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product)Xttrium (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product) Taliser R. Avery, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Kenneth Sands, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Julia Moody, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Kimberly N. Smith, MBA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Brandon Carver, BA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Caren Spencer-Smith, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Russell Poland, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Jason Hickok, MBA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Arjun Srinivasan, MD, Nothing to disclose John A. Jernigan, MD, MS, Nothing to disclose Jonathan B. Perlin, MD, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Richard Platt, MD, MSc, Medline (Research Grant or Support, Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Susan S. Huang, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Xttrium (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)

Background Up to 40% of hospitalized patients receive unnecessary or inappropriately broad antibiotics despite a low risk of multidrug-resistant organism (MDRO) infection. Empiric standard spectrum antibiotic use would reduce extended-spectrum (ES) antibiotic exposure and future resistance. We evaluated whether computerized prescriber order entry prompts providing patient-specific MDRO risk estimates could reduce ES antibiotic use compared to routine stewardship practices in patients hospitalized with pneumonia. Methods This 59 hospital cluster-randomized trial compared: 1) INSPIRE prompts providing patient-specific MDRO pneumonia risk estimates at order entry and recommended standard spectrum antibiotics for risk < 10% versus 2) routine stewardship practices. Prompt used an absolute MDRO risk algorithm based on a 140 hospital data set. Trial population included adults treated with antibiotics for pneumonia in ED or non-ICU wards in first 3 days of admission (empiric days); prompt was triggered if ES antibiotics were ordered. Prescribers received feedback on prompt response. Trial periods: 18-month Baseline (Apr 2017–Sept 2018); 6-month Phase-in (Oct 2018–Mar 2019); 15-month Intervention (Apr 2019 – June 2020). Primary outcome was ES antibiotic days of therapy (ES-DOT) per empiric day; secondary outcomes were a) vancomycin and b) anti-pseudomonal DOT per empiric day. Unadjusted, as-randomized analyses used generalized linear mixed effects models to assess differences in ES-DOT rates between the intervention vs baseline period across arms (difference in differences), while clustering by patient and hospital. Results We randomized 59 hospitals in 12 states, with 59,897 and 51,486 non-ICU pneumonia admissions in baseline and intervention periods, respectively. Intervention group had a 33% reduction in ES-DOT compared to routine care. Vancomycin and anti-pseudomonal DOT were similarly reduced in the intervention group by 27% and 33%, respectively (Table). Conclusion INSPIRE order entry prompts providing real-time, patient-specific MDRO risk estimates with recommendation to use standard spectrum antibiotics in low risk patients significantly reduced empiric ES prescribing in adults admitted with pneumonia. Disclosures Shruti K. Gohil, MD, MPH, Medline (Other Financial or Material Support, Co-Investigator in studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnycke (Other Financial or Material Support, Co-Investigator in studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Co-Investigator in studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products) Edward Septimus, MD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Ken Kleinman, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Lauren Heim, MPH, Medline (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Stryker (Sage) (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product)Xttrium (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product) Syma Rashid, MD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Stryker (Sage) (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product)Xttrium (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product) Taliser R. Avery, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Kenneth Sands, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Julia Moody, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Micaela H. Coady, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Kimberly N. Smith, MBA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Brandon Carver, BA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Caren Spencer-Smith, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Russell Poland, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Jason Hickok, MBA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Arjun Srinivasan, MD, Nothing to disclose John A. Jernigan, MD, MS, Nothing to disclose Jonathan B. Perlin, MD, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Richard Platt, MD, MSc, Medline (Research Grant or Support, Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Susan S. Huang, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Xttrium (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)

Background ICU universal decolonization with daily chlorhexidine (CHG) baths plus mupirocin nasal decolonization reduces all-cause bloodstream infections (BSI) and MRSA clinical cultures. We assessed nasal iodophor, an antiseptic less susceptible to resistance, in place of mupirocin. Methods We conducted a cluster randomized non-inferiority trial in ICUs, comparing universal decolonization with: 1) Mupirocin-CHG: daily CHG baths and 5 days of twice daily nasal mupirocin, to 2) Iodophor-CHG: same regimen, substituting twice daily 10% povidone-iodine for mupirocin. All adult ICUs in a hospital were assigned to the same strategy. We compared each hospital’s outcomes during the 18-month intervention (Nov 2017-Apr 2019) to its own baseline (May 2015-Apr 2017), during which all hospitals used mupirocin-CHG. The primary outcome was ICU-attributable S. aureus clinical isolates. Secondary outcomes included ICU-attributable MRSA clinical isolates and all-cause BSI. As randomized and as treated analyses used unadjusted proportional hazards models assessing differences in outcomes between baseline and intervention periods across the two groups, accounting for clustering by hospital and patient. Results We randomized 137 hospitals with 233 ICUs in 18 states. There were 442,544 admissions in the baseline period and 349,262 in the intervention period. Median ICU length of stay was 4 days. ICU types included mixed medical surgical (56%), medical (9%), surgical (11%), cardiac (15%), and neurologic (9%). CHG adherence was similar in both arms (85%), but adherence was greater for mupirocin (90%) than iodophor (82%). Primary as-randomized results (Table, Figure) exceeded the non-inferiority margin in favor of mupirocin, for S. aureus clinical cultures (21% superiority, P< 0.001) and for MRSA clinical cultures (20% superiority, P< 0.001). The regimens had similar BSI hazards. Analyses of fully adherent patients are in progress. Figure - Primary and Secondary Outcomes of Mupirocin Iodophor Swap Out Trial Conclusion Universal iodophor-CHG was equivalent to mupirocin-CHG for ICU BSI prevention. Mupirocin-CHG was superior to iodophor-CHG for S. aureus and MRSA clinical isolates, potentially due to greater adherence to mupirocin. Disclosures Susan S. Huang, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Xttrium (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products) Edward Septimus, MD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Ken Kleinman, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Lauren Heim, MPH, Medline (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Stryker (Sage) (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product)Xttrium (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product) Julia Moody, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Taliser R. Avery, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Syma Rashid, MD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Stryker (Sage) (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product)Xttrium (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product) Katherine Haffenreffer, BS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Lauren Shimelman, BA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Caren Spencer-Smith, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Selsebil Sljivo, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Ed Rosen, BS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Russell Poland, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Micaela H. Coady, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Eunice J. Blanchard, MSN RN, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Kimberly Reddish, DNP, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Brandon Carver, BA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Kimberly N. Smith, MBA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Jason Hickok, MBA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Karen Lolans, BS, Medline (Research Grant or Support) Nadia Khan, BS, Medline (Research Grant or Support) John A. Jernigan, MD, MS, Nothing to disclose Kenneth Sands, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Jonathan B. Perlin, MD, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Richard Platt, MD, MSc, Medline (Research Grant or Support, Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)

The US Food and Drug Administration's Sentinel System was established in 2009 to use routinely collected electronic health data for improving the national capability to assess post‐market medical product safety. Over more than a decade, Sentinel has become an integral part of FDA's surveillance capabilities and has been used to conduct analyses that have contributed to regulatory decisions. FDA's role in the COVID‐19 pandemic response has necessitated an expansion and enhancement of Sentinel. Here we describe how the Sentinel System has supported FDA's response to the COVID‐19 pandemic. We highlight new capabilities developed, key data generated to date, and lessons learned, particularly with respect to working with inpatient electronic health record data. Early in the pandemic, Sentinel developed a multi‐pronged approach to support FDA's anticipated data and analytic needs. It incorporated new data sources, created a rapidly refreshed database, developed protocols to assess the natural history of COVID‐19, validated a diagnosis‐code based algorithm for identifying patients with COVID‐19 in administrative claims data, and coordinated with other national and international initiatives. Sentinel is poised to answer important questions about the natural history of COVID‐19 and is positioned to use this information to study the use, safety, and potentially the effectiveness of medical products used for COVID‐19 prevention and treatment.

Concerns regarding both the limited generalizability and the slow pace of traditional randomized trials have led to calls for greater use of real-world evidence (RWE) in the evaluation of new treatments or products. RWE studies often rely on real-world data (RWD), including data extracted from healthcare records or data captured by mobile phones or other consumer devices. Global assessments of RWD sources are not helpful in assessing whether any specific RWD element is fit for any specific purpose. Instead, evidence generators and evidence consumers should clearly identify the specific health state or clinical phenomenon of interest and then consider each step between that clinical phenomenon and its representation in a research database. We propose specific questions regarding potential error or bias affecting each of those steps: Would a person experiencing this clinical phenomenon present for care in this setting or interact with this recording device? Would this clinical phenomenon be accurately recognized or assessed? How might the recording environment or tools affect accurate and consistent recording of this clinical phenomenon? Can data elements from different sources be harmonized, both technically (same format) and semantically (same meaning)? Can the original data elements be consistently reduced to a useful clinical phenotype? Addressing these questions requires a range of clinical, organizational, and technical expertise. Transparency regarding each step in the creation of RWD is essential if evidence consumers are to rely on RWE studies.

Concerns regarding both the limited generalizability and the slow pace of traditional randomized trials have led to calls for greater use of real-world evidence in the evaluation of new treatments or products. Real-world clinical trials or pragmatic trials often differ from traditional clinical trials in the use of open-label or nonblinded treatments delivered by real-world clinicians in community practice settings. Blinding and standardization of treatment may sometimes be necessary for internal validity, but they may also obscure or distort meaningful differences between treatments. When investigators consider whether blinding of clinicians, patients, or assessors is necessary, we suggest they consider several specific questions: Will clinicians, patients, and assessors have expectations or preferences regarding benefits or adverse effects? How might those expectations affect treatment uptake, treatment adherence, or assessment of outcomes? Will expectations differ in the settings where trial results will be applied? How would blinding of treatment reduce biases? How would blinding obscure true differences between treatments? How would procedures necessary for blinding reduce acceptability or increase risk of trial participation? When investigators consider how strictly treatments should be standardized, we suggest they consider several specific questions: How would treatment effectiveness or safety vary according to clinician experience or expertise? What level of experience or expertise is available in potential trial settings and settings where trial results would be applied? Is some level of standardization necessary for valid inference? Considering any special vulnerabilities of the study population, is some level of standardization necessary to assure participant safety?

Only about half of patients with atrial fibrillation (AF) who are at increased risk for stroke are treated with an oral anticoagulant (OAC), despite guideline recommendations for their use. Educating patients with AF about prevention of stroke with OACs may enable them as agents of change to initiate OAC treatment.To determine whether an educational intervention directed to patients and their clinicians stimulates the use of OACs in patients with AF who are not receiving OACs.The Implementation of a Randomized Controlled Trial to Improve Treatment With Oral Anticoagulants in Patients With Atrial Fibrillation (IMPACT-AFib) trial was a prospective, multicenter, open-label, pragmatic randomized clinical trial conducted from September 25, 2017, to May 1, 2019, embedded in health plans that participate in the US Food and Drug Administration's Sentinel System. It used the distributed database comprising health plan members to identify eligible patients, their clinicians, and outcomes. IMPACT-AFib enrolled patients with AF, a CHA2DS2-VASc (cardiac failure or dysfunction, hypertension, age 65-74 [1 point] or ≥75 years [2 points], diabetes, and stroke, transient ischemic attack or thromboembolism [2 points]-vascular disease, and sex category [female]) score of 2 or more, no evidence of OAC prescription dispensing in the preceding 12 months, and no hospitalization-related bleeding event within the prior 6 months.Randomization to a single mailing of patient and/or clinician educational materials vs control.Analysis was performed on a modified intention-to-treat basis. The primary end point was the proportion of patients with at least 1 OAC prescription dispensed or at least 4 international normalized ratio test results within 1 year of the intervention.Among 47 333 patients, there were 24 909 men (52.6%), the mean (SD) age was 77.9 (9.7) years, mean (SD) CHA2DS2-VASc score was 4.5 (1.7), 22 404 patients (47.3%) had an ATRIA (Anticoagulation and Risk Factors in Atrial Fibrillation) bleeding risk score of 5 or more, and 8890 patients (18.8%) had a history of hospitalization for bleeding. There were 2328 of 23 546 patients (9.9%) in the intervention group with initiation of OAC at 1 year compared with 2330 of 23 787 patients (9.8%) in the control group (adjusted OR, 1.01 [95% CI, 0.95-1.07]; P = .79).Among a large population with AF with a guideline indication for OACs for stroke prevention who were randomized to a mailed educational intervention or to usual care, there was no clinically meaningful, numerical, or statistically significant difference in rates of OAC initiation. More-intensive interventions are needed to try and address the public health issue of underuse of anticoagulation for stroke prevention among patients with AF.ClinicalTrials.gov Identifier: NCT03259373.

Objectives Persons living with Alzheimer's disease (AD) may be at increased risk for prescribing cascades due to greater multimorbidity, polypharmacy, and the need for more complex care. Our objective was to assess the proportion of the antidopaminergic-antiparkinsonian medication prescribing cascades among persons living with Alzheimer's disease. Setting Two large administrative claims databases in the United States. Participants We identified patients aged ≥50 on January 1, 2017, who were dispensed a drug used to treat Alzheimer's disease for at least 1 day in the 365 days prior to or on cohort entry date and who had medical and pharmacy coverage in the 365 days prior to the cohort entry date. We excluded individuals with a recent institutional stay. We identified incident antidopaminergic (antipsychotic/metoclopramide) use in the 183 days following cohort entry and identified subsequent incident antiparkinsonian drug use within 8 to 365 days. Results There were 121,538 patients with Alzheimer's disease eligible for inclusion. Approximately 62% were women with a mean age of 79.5 (SD ± 8.6). The mean number of drugs dispensed was 9.2 (SD ± 4.9). There were 36 incident antiparkinsonian users among 4,534 incident antipsychotic/metoclopramide users (0.8%). Conclusion We determined that the proportion of antidopaminergic-antiparkinsonian medication prescribing cascades, widely considered as high-priority, was low. Our approach can be used to assess the proportion of prescribing cascades in populations considered to be at high risk and to prioritize system-level interventional efforts to improve medication safety in these patients.

The Sentinel System is a national electronic postmarketing resource established by the US Food and Drug Administration to support assessment of the safety and effectiveness of marketed medical products. It has built a large, multi-institutional, distributed data network that contains comprehensive electronic health data, covering about 700 million person-years of longitudinal observation time nationwide. With its sophisticated infrastructure and a large selection of flexible analytic tools, the Sentinel System permits rapid and secure analyses, while preserving patient privacy and health-system autonomy. The Sentinel System also offers enhanced capabilities, including accessing full-text medical records, supporting randomized clinical trials embedded in healthcare delivery systems, and facilitating effective collection of patient-reported data using mobile devices, among many other research programs. The nephrology research community can use the infrastructure, tools, and data that this national resource offers for evidence generation. This review summarizes the Sentinel System and its ability to rapidly generate high-quality, real-world evidence; discusses the program's experience in, and potential for, addressing gaps in kidney care; and outlines avenues for conducting research, leveraging this national resource in collaboration with Sentinel investigators.

BackgroundNational data from diverse institutions across the United States are critical for guiding policymakers as well as clinical and public health leaders. This study characterized a large national cohort of patients diagnosed with COVID-19 in the U.S., compared to patients diagnosed with viral pneumonia and influenza.Methods and FindingsWe captured cross-sectional information from 36 large healthcare systems in 29 U.S. states, participating in PCORnet®, the National Patient-Centered Clinical Research Network. Patients included were those diagnosed with COVID-19, viral pneumonia and influenza in any care setting, starting from January 1, 2020. Using distributed queries executed at each participating institution, we acquired information for patients on care setting (any, ambulatory, inpatient or emergency department, mechanical ventilator), age, sex, race, state, comorbidities (assessed with diagnostic codes), and medications used for treatment of COVID-19 (hydroxychloroquine with or without azithromycin; corticosteroids, anti-interleukin-6 agents).During this time period, 24,516 patients were diagnosed with COVID-19, with 42% in an emergency department or inpatient hospital setting; 79,639 were diagnosed with viral pneumonia (53% inpatient/ED) and 163,984 with influenza (41% inpatient/ED). Among COVID-19 patients, 68% were 20 to Conclusion and RelevancePCORnet leverages existing data to capture information on one of the largest U.S. cohorts to date of patients diagnosed with COVID-19 compared to patients diagnosed with viral pneumonia and influenza.

Embedded pragmatic clinical trials (ePCTs) are embedded in healthcare systems as well as their data environments. For people living with dementia (PLWD), settings of care can be different from the general population and involve additional people whose information is also important. The ePCT designs have the opportunity to leverage data that becomes available through the normal delivery of care. They may be particularly valuable in Alzheimer's disease and Alzheimer's disease-related dementia (AD/ADRD), given the complexity of case identification and the diversity of care settings. Grounded in the objectives of the Data and Technical Core of the newly established National Institute on Aging Imbedded Pragmatic Alzheimer's Disease and AD-Related Dementias Clinical Trials Collaboratory (IMPACT Collaboratory), this article summarizes the state of the art in using existing data sources (eg, Medicare claims, electronic health records) in AD/ADRD ePCTs and approaches to integrating them in real-world settings. J Am Geriatr Soc 68:S49-S54, 2020.

Objective:To assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms.Design:Multicenter retrospective cohort study.Setting:The study included 43 hospitals using a common infection prevention surveillance system.Methods:A space–time permutation scan statistic was applied to hospital microbiology, admission, discharge, and transfer data to identify clustering of microorganisms within hospital locations and services. Infection preventionists were asked to rate the importance of each cluster. A convenience sample of 10 hospitals also provided information about clusters previously identified through their usual surveillance methods.Results:We identified 230 clusters in 43 hospitals involving Gram-positive and -negative bacteria and fungi. Half of the clusters progressed after initial detection, suggesting that early detection could trigger interventions to curtail further spread. Infection preventionists reported that they would have wanted to be alerted about 81% of these clusters. Factors associated with clusters judged to be moderately or highly concerning included high statistical significance, large size, and clusters involving Clostridioides difficile or multidrug-resistant organisms. Based on comparison data provided by the convenience sample of hospitals, only 9 (18%) of 51 clusters detected by usual surveillance met statistical significance, and of the 70 clusters not previously detected, 58 (83%) involved organisms not routinely targeted by the hospitals’ surveillance programs. All infection prevention programs felt that an automated outbreak detection tool would improve their ability to detect outbreaks and streamline their work.Conclusions:Automated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.

Introduction: Prescribing cascades occur when the side effect of a drug is misinterpreted as a new medical condition, and a second drug is prescribed to address the side effect. Persons with Alzheimer’s disease (AD) are at increased risk of prescribing cascades due to greater multimorbidity, polypharmacy, and complexity of care. The objective of this study was to evaluate educational materials about prescribing cascades in persons with AD, and elicit input on their use in a future trial. Methods: We interviewed community-dwelling adults with either an AD diagnosis or a prescription drug used to treat AD ( n = 12), caregivers of patients meeting the same criteria ( n = 14), and providers ( n = 15). We coded interview transcripts and organized themes according to the communication–human information processing model. We revised the materials based on the interviews, and surveyed participating caregivers and providers for their reactions to the revised materials. Results: Analysis of patients’, caregivers’, and providers’ comments suggest: (a) Providers had conflicting views about the messaging of materials; (b) Caregivers were likely to read letters addressed to patients; (c) Providers were likely to ignore letters, but were receptive to patient/caregiver-initiated conversations; (d) Patients and caregivers had difficulty understanding prescribing cascades; (e) Providers worried that mailed materials would undermine trust; (f) Participants had mixed views on how materials might affect the clinical encounter; (g) Participants felt that materials would improve patient/caregiver engagement. When surveyed, most providers found the revised materials informative and actionable, and most caregivers found them understandable and useful. Conclusions: This evaluation of educational materials about prescribing cascades in patients with AD provides strong support for engaging caregivers to communicate with providers about prescribing cascades. By giving patients and caregivers a basic description of the prescribing cascade concept, our educational materials may help them prepare for a conversation with the provider, who can then tailor the discussion of the possible cascade to the specific needs of the individual patient and caregiver. However, evidence on whether materials can stimulate such conversations awaits testing in a future trial. Lay summary Patient, caregiver and provider thoughts on educational materials about prescribing and medication safety Prescribing cascades occur when the side effect of a medication is misinterpreted as a new medical condition, and a second medication is prescribed to treat the side effect. Persons with Alzheimer’s disease (AD) are at increased risk of prescribing cascades because they often have more medical conditions, more medications, and more complex care. The goal of this study was to evaluate mailed educational materials about prescribing cascades in persons with AD, and get input on their use in a future study. We interviewed 12 adults with AD, or prescribed a medication to treat AD, 14 caregivers of persons with AD, and 15 providers. We reviewed the interview transcripts to identify important findings about our educational materials. We edited the materials based on the interviews, and sent participating caregivers and providers a questionnaire to get their reactions to the new materials. Important findings from the interviews suggest: (a) Providers had conflicting views about the recommendations given; (b) Caregivers were likely to read letters addressed to patients; (c) Providers were likely to ignore letters, but were receptive to patients/caregivers introducing the topic; (d) Patients and caregivers had difficulty understanding prescribing cascades; (e) Providers worried mailed materials would undermine trust; (f) Participants had mixed views on how materials might affect a doctor’s appointment; (g) Participants felt strongly that materials would improve patient/caregiver engagement. When surveyed, almost all providers found the revised materials informative and actionable; and most caregivers found them understandable and useful. These findings provide strong support for engaging caregivers to communicate with providers about prescribing cascades. The educational materials may help patients and caregivers prepare for a conversation with the provider, who can then tailor the discussion of the possible cascade to the specific needs of the individual patient and caregiver. However, evidence on whether materials can stimulate such conversations awaits testing in a future study.

Purpose Prescribing cascades occur when a physician prescribes a new drug to address the side-effect of another drug. Persons with Alzheimer's disease and related dementias (ADRD) are at increased risk for prescribing cascades. Our objective was to develop an approach to estimating the proportion of calcium channel blocker-diuretic (CCB-diuretic) prescribing cascades among persons with ADRD in two U.S. health plans. Methods We identified patients aged ≥50 on 1/1/2017, dispensed a drug to treat ADRD in the 365-days prior to/on cohort entry date. Patients had medical/pharmacy coverage for 1 year before and through cohort entry. We excluded individuals with an institutional stay encounter in the 45 days prior to cohort entry and censored patients based on: disenrollment from coverage, death, or end of data. We identified incident and prevalent CCB use in the 183-days following cohort entry, and identified subsequent incident diuretic use among incident and prevalent CCB-users within 365-days from cohort entry. Results There were 121 538 eligible patients. Approximately 62% were female, with a mean age of 79.5 (SD ±8.6). Overall 2.1% of the cohort experienced a prevalent CCB-diuretic prescribing cascade with 1586 incident diuretic-users among 36 462 prevalent CCB-users (4.3%, 95% CI 4.1%-4.6%]); and there were161 incident diuretic-users among 3304 incident CCB-users (4.9%, 95% CI 4.2%-5.7%) (incident CCB-diuretic cascade). Conclusions We describe an approach to identify prescribing cascades in persons with ADRD, which can be used to assess the proportion of prescribing cascades in large cohorts. We determined the proportion of CCB-diuretic prescribing cascades was low. This article is protected by copyright. All rights reserved.

Objective To investigate the association between obstetric haemorrhage and cardiovascular disease up to three decades after pregnancy. Design Population-based cohort study. Setting and population All women who delivered between 1989 and 2016 in Quebec, Canada. Methods Using hospital admissions data, 1 224 975 women were followed from their first delivery until March 2018. The main exposure measures were antenatal (placenta praevia, placental abruption, peripartum haemorrhage) or postpartum haemorrhage, with or without transfusion. Adjusted Cox regression models were used to assess the association between obstetric haemorrhage and future cardiovascular disease. Main outcome measure Cardiovascular hospitalisation. Results Among 104 291 (8.5%) women with haemorrhage, 4612 (4.4%) required transfusion. Women with haemorrhage had a higher incidence of cardiovascular hospitalisation than women without haemorrhage (15.5 versus 14.1 per 10 000 person-years; 2437 versus 28 432 events). Risk of cardiovascular hospitalisation was higher for obstetric haemorrhage, with or without transfusion, compared with no haemorrhage (adjusted hazard ratio [aHR] 1.06, 95% CI 1.02-1.10). Women with haemorrhage and transfusion had a substantially greater risk of cardiovascular hospitalisation (aHR 1.47, 95% CI 1.23-1.76). Among transfused women, placental abruption (aHR 1.79, 95% CI 1.06-3.00) and postpartum haemorrhage (aHR 1.38, 95% CI 1.13-1.68) were both associated with risk of cardiovascular hospitalisation. Antenatal haemorrhage with transfusion was associated with 2.46 times the risk of cardiovascular hospitalisation at 5 years (95% CI 1.59-3.80) and 2.14 times the risk at 10 years (95% CI 1.47-3.12). Conclusions Obstetric haemorrhage requiring transfusion is associated with maternal cardiovascular disease. The benefit of cardiovascular risk prevention in pregnant women with obstetric haemorrhage requires further investigation. Tweetable abstract Risk of future cardiovascular disease is increased for women with obstetric haemorrhage who require transfusion.

Introduction One‐third of Medicare beneficiaries are enrolled in Medicare Advantage (MA). Yet, little is known about MA beneficiaries diagnosed with Alzheimer's disease (AD) and AD‐related dementias (AD/ADRD). Methods We calculated the prevalence of AD/ADRD diagnoses in 2014 and 2016 in three MA plans. We determined the demographic characteristics of beneficiaries diagnosed with AD/ADRD, and whether they disenrolled from the MA plan for any reason within 364 days from the index date. Results In 2014 and 2016, the overall prevalence of AD/ADRD diagnoses was 5.6% and 6.5%, respectively. In 2016, AD/ADRD beneficiaries were on average 82.4 (SD = 7.4) years of age, 61.8% female, and had multiple comorbidities. By 364 days post‐index date, 32% of beneficiaries with AD/ADRD had disenrolled from their plan. The demographic characteristics of 2014 beneficiaries with diagnosed AD/ADRD were similar to their 2016 counterparts. Discussion The prevalence of AD/ADRD diagnosis in MA is lower than rates reported in Medicare fee‐for‐service.

Several pharmacoepidemiology networks have been developed over the past decade that use a distributed approach, implementing the same analysis at multiple data sites, to preserve privacy and minimize data sharing. Distributed networks are efficient, by interrogating data on very large populations. The structure of these networks can also be leveraged to improve replicability, increase transparency, and reduce bias. We describe some features of distributed networks using, as examples, the Canadian Network for Observational Drug Effect Studies, the Sentinel System in the USA, and the European Research Network of Pharmacovigilance and Pharmacoepidemiology. Common protocols, analysis plans, and data models, with policies on amendments and protocol violations, are key features. These tools ensure that studies can be audited and repeated as necessary. Blinding and strict conflict of interest policies reduce the potential for bias in analyses and interpretation. These developments should improve the timeliness and accuracy of information used to support both clinical and regulatory decisions.

Objective To describe PCORnet, a clinical research network developed for patient-centered outcomes research on a national scale. Study Design and Setting Descriptive study of the current state and future directions for PCORnet. We conducted cross-sectional analyses of the health systems and patient populations of the 9 Clinical Research Networks and 2 Health Plan Research Networks that are part of PCORnet. Results Within the Clinical Research Networks electronic health data are currently collected from 337 hospitals, 169,695 physicians, 3,564 primary care practices, 338 emergency departments, and 1,024 community clinics. Patients can be recruited for prospective studies from any of these clinical sites. The Clinical Research Networks have accumulated data from 80 million patients with at least 1 visit from 2009-2018. The PCORnet Health Plan Research Network population of individuals with a valid enrollment segment from 2009-2019 exceeds 60 million individuals, who on average have 2.63 years of follow-up. Conclusion PCORnet’s infrastructure comprises clinical data from a diverse cohort of patients and has the capacity to rapidly access these patient populations for pragmatic clinical trials, epidemiological research, and patient-centered research on rare diseases., Highlights • PCORnet is a national network-of-networks developed to conduct patient-centered outcomes research. • There are 9 Clinical Research Networks and 2 Health Plan Research Networks within PCORnet. • The Clinical Research Networks have collected EHR data for a cohort of 80 million individuals, and the Health Plan Network have collected enrollment and claims files on over 60 million individuals. • PCORnet infrastructure can support large-scale pragmatic clinical trials and observational research using its distributed data network.