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Introduction Antimicrobial resistance poses a significant global healthcare challenge in the management of bacterial infections, which is frequently attributed to rapid bacterial adaptations. This study aims to develop an antibiogram for a tertiary care hospital, providing comprehensive antibiotic sensitivity profiles for Gram-positive and Gram-negative bacteria. It informs healthcare providers of antibiotic resistance trends, enabling informed treatment decisions and enhanced infection control measures. Methods We conducted a six-month prospective observational study, during which we gathered and analyzed data from the microbiology laboratory to identify patterns of antimicrobial sensitivity. Subsequently, the data underwent analysis and interpretation using the respected WHONET software, a readily available tool designed for this specific task. Our methodology adhered to the guidelines established by the Clinical & Laboratory Standards Institute for the standardization of antibiogram generation procedures, and these guidelines are easily integrated into the WHONET software for analytical purposes. Results There were a total of 357 isolates across various hospital departments, comprising 13 distinct bacterial species. Among them, nine were identified as Gram-negative bacteria, accounting for 262 (73.3%) isolates. Escherichia coli accounted for 131 (36.6%) isolates, while Klebsiella accounted for 62 (17.3%), emerging as the predominant species among them. The remaining four bacterial species were identified as Gram-positive bacteria, totaling 95 (26.6%) isolates, with Staphylococcus aureus being the most frequently isolated species at 51 (14.2%), followed by Enterococcus at 26 (7.2%). Subsequent analysis using the WHONET software facilitated the creation of an antibiogram. Among the Gram-negative bacteria, E. coli displayed high sensitivity (100%) to aztreonam and clindamycin, followed by nitrofurantoin (98%), imipenem (94%), and meropenem (95%). However, it exhibited decreased sensitivity to ampicillin (25%), cefuroxime (34%), and ceftriaxone (39%). Conversely, among the Gram-positive bacteria, S. aureus demonstrated 100% sensitivity to ampicillin, amoxiclav, cefazolin, teicoplanin, linezolid, rifampicin, nitrofurantoin, and cefotaxime. However, it exhibited zero sensitivity to vancomycin and only 6% sensitivity to cotrimoxazole. Conclusion This study advances the understanding of antibiotic susceptibility in a tertiary care setting and provides an invaluable tool for optimizing treatment strategies, enhancing infection control measures, and combating antibiotic resistance.
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BACKGROUND: Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems. OBJECTIVE: To assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score. DESIGN: Retrospective, cohort study. PARTICIPANTS: Data from 6764 adults with HF were abstracted from EHRs at a large integrated health system from 1/1/10 to 12/31/19. MAIN MEASURES: One-year survival from time of first cardiology or primary care visit was estimated using MARKER-HF, SHFM, and MAGGIC. Discrimination was measured by the area under the receiver operating curve (AUC). Calibration was assessed graphically. KEY RESULTS: Compared to MARKER-HF, both SHFM and MAGGIC required a considerably larger amount of data engineering and imputation to generate risk score estimates. MARKER-HF, SHFM, and MAGGIC exhibited similar discriminations with AUCs of 0.70 (0.69-0.73), 0.71 (0.69-0.72), and 0.71 (95% CI 0.70-0.73), respectively. All three scores showed good calibration across the full risk spectrum. CONCLUSIONS: These findings suggest that MARKER-HF, which uses readily available clinical and lab measurements in the EHR and required less imputation and data engineering than SHFM and MAGGIC, is an easier tool to identify high-risk patients in ambulatory clinics who could benefit from referral to a HF specialist.
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BACKGROUND: Persistent multi-organ symptoms after coronavirus disease 2019 (COVID-19) have been termed "long COVID" or "post-acute sequelae of SARS-CoV-2 infection." The complexity of these clinical manifestations posed challenges early in the pandemic as different ambulatory models formed out of necessity to manage the influx of patients. Little is known about the characteristics and outcomes of patients seeking care at multidisciplinary post-COVID centers. METHODS: We performed a retrospective cohort study of patients evaluated at our multidisciplinary comprehensive COVID-19 center in Chicago, Ill, between May 2020 and February 2022. We analyzed specialty clinic utilization and clinical test results according to severity of acute COVID-19. RESULTS: We evaluated 1802 patients a median of 8 months from acute COVID-19 onset, including 350 post-hospitalization and 1452 non-hospitalized patients. Patients were seen in 2361 initial visits in 12 specialty clinics, with 1151 (48.8%) in neurology, 591 (25%) in pulmonology, and 284 (12%) in cardiology. Among the patients tested, 742/878 (85%) reported decreased quality of life, 284/553 (51%) had cognitive impairment, 195/434 (44.9%) had alteration of lung function, 249/299 (83.3%) had abnormal computed tomography chest scans, and 14/116 (12.1%) had elevated heart rate on rhythm monitoring. Frequency of cognitive impairment and pulmonary dysfunction was associated with severity of acute COVID-19. Non-hospitalized patients with positive SARS-CoV-2 testing had findings similar to those with negative or no test results. CONCLUSIONS: The experience at our multidisciplinary comprehensive COVID-19 center shows common utilization of multiple specialists by long COVID patients, who harbor frequent neurologic, pulmonary, and cardiologic abnormalities. Differences in post-hospitalization and non-hospitalized groups suggest distinct pathogenic mechanisms of long COVID in these populations.
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Sodium-glucose cotransporter-2 inhibitors (SGLT2is) and glucagon-like peptide-1 receptor agonists (GLP1-RAs) reduce cardiovascular events and mortality in patients with type 2 diabetes mellitus (T2DM). We sought to describe trends in prescribing for SGLT2is and GLP1-RAs in diverse care settings, including (1) the outpatient clinics of a midwestern integrated health system and (2) small- and medium-sized community-based primary care practices and health centers in 3 midwestern states. We included adults with T2DM and ≥1 outpatient clinic visit. The outcomes of interest were annual active prescription rates for SGLT2is and GLP1-RAs (separately). In the integrated health system, 22,672 patients met the case definition of T2DM. From 2013 to 2019, the overall prescription rate for SGLT2is increased from 1% to 15% (absolute difference [AD] 14%, 95% confidence interval [CI] 13% to 15%, p <0.01). The GLP1-RA prescription rate was stable at 10% (AD 0%, 95% CI -1% to 1%, p = 0.9). In community-based primary care practices, 43,340 patients met the case definition of T2DM. From 2013 to 2017, the SGLT2i prescription rate increased from 3% to 7% (AD 4%, 95% CI 3% to 6%, p <0.01), whereas the GLP1-RA prescription rate was stable at 2% to 3% (AD 1%, 95% CI -1 to 1%, p = 0.40). In a fully adjusted regression model, non-Hispanic Black patients had lower odds of SGLT2i or GLP1-RA prescription (odds ratio 0.56, 95% CI 0.34 to 0.89, p = 0.016). In conclusion, the increase in prescription rates was greater for SGLT2is than for GLP1-RAs in patients with T2DM in a large integrated medical center and community primary care practices. Overall, prescription rates for eligible patients were low, and racial disparities were observed.
Subject(s)
Cardiovascular Diseases , Diabetes Mellitus, Type 2 , Glucagon-Like Peptide-1 Receptor , Sodium-Glucose Transporter 2 Inhibitors , Adult , Humans , Cardiovascular Diseases/complications , Diabetes Mellitus, Type 2/complications , Diabetes Mellitus, Type 2/drug therapy , Glucagon-Like Peptide-1 Receptor/agonists , Hypoglycemic Agents/therapeutic use , Sodium-Glucose Transporter 2 Inhibitors/therapeutic use , Sodium-Glucose Transporter 2 Inhibitors/pharmacology , Drug PrescriptionsABSTRACT
As COVID-19 cases begin to decrease in the USA, learning from the pandemic experience will provide insights regarding disparities of care delivery. We sought to determine if specific populations hospitalized with COVID-19 are equally likely to be enrolled in clinical trials. We examined patients hospitalized with COVID-19 at centers participating in the American Heart Association's COVID-19 CVD Registry. The primary outcome was odds of enrollment in a clinical trial, according to sex, race, and ethnicity. Among 14,397 adults hospitalized with COVID-19, 9.5% (n = 1,377) were enrolled in a clinical trial. The proportion of enrolled patients was the lowest for Black patients (8%); in multivariable analysis, female and Black patients were less likely to be enrolled in a clinical trial related to COVID-19 compared to men and other racial groups, respectively. Determination of specific reasons for the disparities in trial participation related to COVID-19 in these populations should be further investigated.
Subject(s)
COVID-19 , Male , Adult , Humans , Female , United States/epidemiology , American Heart Association , Registries , Ethnicity , Racial GroupsABSTRACT
BACKGROUND: Low rates of heart failure (HF) hospitalizations were observed during the 2020 peak of the COVID-19 pandemic. Additionally, posthospitalization follow-up transitioned to a predominantly telemedicine model. It is unknown whether the shift to telemedicine impacted disparities in posthospitalization follow-up or HF readmissions. OBJECTIVE: The aim of this paper is to determine whether the shift to telemedicine impacted racial and ethnic as well as socioeconomic disparities in acute decompensated heart failure (ADHF) follow-up and HF readmissions. We additionally sought to investigate the impact of the COVID-19 pandemic on the severity of ADHF hospitalizations. METHODS: This was a retrospective cohort study of HF admissions across 8 participating hospitals during the initial peak of the COVID-19 pandemic (March 15 to June 1, 2020), compared to the same time frame in 2019. Patients were stratified by race, ethnicity, and median neighborhood income. Hospital and intensive care unit (ICU) admission rates, inpatient mortality, 7-day follow-up, and 30-day readmissions were assessed. RESULTS: From March 15, 2019, to June 1, 2020, there were 1162 hospitalizations for ADHF included in the study. There were significantly fewer admissions for ADHF in 2020, compared with 2019 (442 vs 720; P<.001). Patients in 2020 had higher rates of ICU admission, compared with 2019 (15.8% vs 11.1%; P=.02). This trend was seen across all subgroups and was significant for patients from the highest income quartile (17.89% vs 10.99%; P=.02). While there was a trend toward higher inpatient mortality in 2020 versus 2019 (4.3% vs 2.8%; P=.17), no difference was seen among different racial and socioeconomic groups. Telemedicine comprised 81.6% of 7-day follow-up in 2020, with improvement in 7-day follow-up rates (40.5% vs 29.6%; P<.001). Inequities in 7-day follow-up for patients from non-Hispanic Black racial backgrounds compared to those from non-Hispanic White backgrounds decreased during the pandemic. Additionally, those with telemedicine follow-up were less likely to be readmitted in 30 days when compared to no follow-up (13.8% vs 22.4%; P=.03). CONCLUSIONS: There were no major differences in HF ICU admissions or inpatient mortality for different racial and socioeconomic groups during the COVID-19 pandemic. Inequalities in 7-day follow-up were reduced with the advent of telemedicine and decreased 30-day readmission rates for those who had telemedicine follow-up.
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Conventional cancer therapies are associated with toxicity toward healthy cells, which need to be addressed by novel therapeutic approaches. US2021/0230592 patent application discloses a carbon nanotube-based approach for tumor targeting, wherein a self-assembling single-wall nanotube complex has been developed and functionalized to deliver a molecule to a cancerous cell. The radiolabeled nanotubes exhibited markedly reduced toxicity as they did not depict any 'over toxicity' up to radioactivity value of 1350 nCi. The single-wall carbon nanotube conjugates were covalently connected to several chelators, therapeutic or diagnostic radionuclides, and showed promising results in the effective cancer management. Besides, this invention with further modifications paves an ideal pathway to researchers in effective diagnosis and treatment of cancer.
Subject(s)
Nanotubes, Carbon , Neoplasms , Humans , Neoplasms/metabolismABSTRACT
Background: COVID-19 outbreak is considered to be a major public health concern as it has a negative impact on the patient's psychological health. In addition, patients under home isolation might be more panic and in stress. In this study, we examined the effect of Bhramari Pranayama (Bhr.P) intervention on patients' psychological distress during home isolation. Methods: Ninety-two asymptomatic COVID-19 patients were recruited from the host hospital and willing patients who satisfied the inclusion criteria (n = 42) were selected for the study. The patients were given Bhr.P intervention (20 min) through online for 15 days. Participants were assessed with Depression Anxiety and Stress Scale-21 (DASS-21), Pittsburgh Sleep Quality Index (PSQI), and Quality of life (WHOQOL-BREF) at baseline and post-intervention. Results: Bhr.P practice has shown a significant (P < 0.05) reduction in DASS-21 score of depression, anxiety and stress. In addition, the patients stated significant improvement in quality of sleep (PSQI; p < 0.05) and quality of life (WHOQOL-BREF; p < 0.05) after the intervention. Conclusion: Our findings indicate that Bhr.P intervention had a positive impact on psychological health as well as quality of sleep among the COVID-19 patients during home isolation. However, it needs to be confirmed by multi-site randomized controlled trials.Clinical trial registration: CTRI/2021/04/032845.
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BACKGROUND: Timely referral for specialist evaluation in patients with advanced heart failure (HF) is a Class 1 recommendation. However, the transition from stage C HF to advanced or stage D HF often goes undetected in routine care, resulting in delayed referral and higher mortality rates. OBJECTIVES: The authors sought to develop an augmented intelligence-enabled workflow using machine learning to identify patients with stage D HF and streamline referral. METHODS: We extracted data on HF patients with encounters from January 1, 2007, to November 30, 2020, from a HF registry within a regional, integrated health system. We created an ensemble machine learning model to predict stage C or stage D HF and integrated the results within the electronic health record. RESULTS: In a retrospective data set of 14,846 patients, the model had a good positive predictive value (60%) and low sensitivity (25%) for identifying stage D HF in a 100-person, physician-reviewed, holdout test set. During prospective implementation of the workflow from April 1, 2021, to February 15, 2022, 416 patients were reviewed by a clinical coordinator, with agreement between the model and the coordinator in 50.3% of stage D predictions. Twenty-four patients have been scheduled for evaluation in a HF clinic, 4 patients started an evaluation for advanced therapies, and 1 patient received a left ventricular assist device. CONCLUSIONS: An augmented intelligence-enabled workflow was integrated into clinical operations to identify patients with advanced HF. Endeavors such as this require a multidisciplinary team with experience in design thinking, informatics, quality improvement, operations, and health information technology, as well as dedicated resources to monitor and improve performance over time.
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There is little data describing trends in the use of hydroxychloroquine for COVID-19 following publication of randomized trials that failed to demonstrate a benefit of this therapy. We identified 13,957 patients admitted for active COVID-19 at 85 U.S. hospitals participating in a national registry between March 1 and August 31, 2020. The overall proportion of patients receiving hydroxychloroquine peaked at 55.2% in March and April and decreased to 4.8% in May and June and 0.8% in July and August. At the hospital-level, median use was 59.4% in March and April (IQR 48.5-71.5%, range 0-100%) and decreased to 0.3% (IQR 0-5.4%, range 0-100%) by May and June and 0% (IQR 0-1.3%, range 0-36.4%) by July and August. The rate and hospital-level uniformity in deimplementation of this ineffective therapy for COVID-19 reflects a rapid response to evolving clinical information and further study may offer strategies to inform deimplementation of ineffective clinical care.
Subject(s)
Antirheumatic Agents/therapeutic use , COVID-19 Drug Treatment , Cardiovascular Diseases/drug therapy , Hydroxychloroquine/therapeutic use , Aged , COVID-19/complications , COVID-19/mortality , Cardiovascular Diseases/complications , Cross-Sectional Studies , Female , Hospitalization , Humans , Male , Middle Aged , RegistriesABSTRACT
Despite decades of improvement in the quality and outcomes of cardiovascular care, significant gaps remain. Existing quality improvement strategies are often limited in scope to specific clinical conditions and episodic care. Health services and outcomes research is essential to inform gaps in care but rarely results in the development and implementation of care delivery solutions. Although individual health systems are engaged in projects to improve the quality of care delivery, these efforts often lack a robust study design or implementation evaluation that can inform generalizability and further dissemination. Aligning the work of health care systems and health services and outcomes researchers could serve as a strategy to overcome persisting gaps in cardiovascular quality and outcomes. We describe the inception of the Cardiovascular Quality Improvement and Care Innovation Consortium that seeks to rapidly improve cardiovascular care by (1) developing, implementing, and evaluating multicenter quality improvement projects using innovative care designs; (2) serving as a resource for quality improvement and care innovation partners; and (3) establishing a presence within existing quality improvement and care innovation structures. Success of the collaborative will be defined by projects that result in changes to care delivery with demonstrable impacts on the quality and outcomes of care across multiple health systems. Furthermore, insights gained from implementation of these projects across sites in Cardiovascular Quality Improvement and Care Innovation Consortium will inform and promote broad dissemination for greater impact.
Subject(s)
Delivery of Health Care , Quality Improvement , Humans , Research DesignABSTRACT
BACKGROUND: The electronic medical record contains a wealth of information buried in free text. We created a natural language processing algorithm to identify patients with atrial fibrillation (AF) using text alone. METHODS AND RESULTS: We created 3 data sets from patients with at least one AF billing code from 2010 to 2017: a training set (n=886), an internal validation set from site no. 1 (n=285), and an external validation set from site no. 2 (n=276). A team of clinicians reviewed and adjudicated patients as AF present or absent, which served as the reference standard. We trained 54 algorithms to classify each patient, varying the model, number of features, number of stop words, and the method used to create the feature set. The algorithm with the highest F-score (the harmonic mean of sensitivity and positive predictive value) in the training set was applied to the validation sets. F-scores and area under the receiver operating characteristic curves were compared between site no. 1 and site no. 2 using bootstrapping. Adjudicated AF prevalence was 75.1% at site no. 1 and 86.2% at site no. 2. Among 54 algorithms, the best performing model was logistic regression, using 1000 features, 100 stop words, and term frequency-inverse document frequency method to create the feature set, with sensitivity 92.8%, specificity 93.9%, and an area under the receiver operating characteristic curve of 0.93 in the training set. The performance at site no. 1 was sensitivity 92.5%, specificity 88.7%, with an area under the receiver operating characteristic curve of 0.91. The performance at site no. 2 was sensitivity 89.5%, specificity 71.1%, with an area under the receiver operating characteristic curve of 0.80. The F-score was lower at site no. 2 compared with site no. 1 (92.5% [SD, 1.1%] versus 94.2% [SD, 1.1%]; P<0.001). CONCLUSIONS: We developed a natural language processing algorithm to identify patients with AF using text alone, with >90% F-score at 2 separate sites. This approach allows better use of the clinical narrative and creates an opportunity for precise, high-throughput cohort identification.
Subject(s)
Atrial Fibrillation/diagnosis , Diagnosis, Computer-Assisted , Electronic Health Records , Natural Language Processing , Aged , Aged, 80 and over , Atrial Fibrillation/classification , Atrial Fibrillation/epidemiology , Chicago/epidemiology , Female , Humans , Male , Middle Aged , Predictive Value of Tests , Prevalence , Reproducibility of Results , Utah/epidemiologyABSTRACT
Process improvement begins with the process view: understanding patient care from the patient's point of view. Organizations must also clearly articulate for themselves how they define operational excellence so that the tradeoffs taken in process improvement can be clearly made. Constructing a process map allows application of powerful analytical tools, such as Little's law, which in turn uncovers targets for process improvement from the patient's point of view. Often tradeoffs among process performance metrics, such as quality, cost, time, personalization, and innovation, must be made when deciding upon improvements to be made in certain processes.
Subject(s)
Delivery of Health Care/standards , Disease Management , Heart Failure/therapy , Quality Improvement/organization & administration , HumansABSTRACT
The transition from hospitalization to outpatient care is a vulnerable time for patients with heart failure. This requires specific focus on the transitional care period. Here the authors propose a framework to guide process improvement in the transitional care period. The authors extend this framework by (1) examining the role new technology might play in transitional care, and (2) offering practical advice for teams building transitional care programs.
Subject(s)
Ambulatory Care/methods , Heart Failure/therapy , Hospitalization/statistics & numerical data , Transitional Care/organization & administration , HumansABSTRACT
OBJECTIVES: The quality of hospital discharge care and patient factors (health and sociodemographic) impact the rates of unplanned readmissions. This study aims to measure the effects of controlling for the patient factors when using readmission rates to quantify the weighted edges between health care providers in a collaboration network. This improved understanding may inform strategies to reduce hospital readmissions, and facilitate quality-improvement initiatives. METHODS: We extracted 4 years of patient, provider, and activity data related to cardiology discharge workflow. A Weibull model was developed to predict the risk of unplanned 30-day readmission. A provider-patient bipartite network was used to connect providers by shared patient encounters. We built collaboration networks and calculated the Shared Positive Outcome Ratio (SPOR) to quantify the relationship between providers by the relative rate of patient outcomes, using both risk-adjusted readmission rates and unadjusted readmission rates. The effect of risk adjustment on the calculation of the SPOR metric was quantified using a permutation test and descriptive statistics. RESULTS: Comparing the collaboration networks consisting of 2,359 provider pairs, we found that SPOR values with risk-adjusted outcomes are significantly different than unadjusted readmission as an outcome measure (p-value = 0.025). The two networks classified the same provider pairs as high-scoring 51.5% of the time, and the same low scoring provider pairs 85.6% of the time. The observed differences in patient demographics and disease characteristics between high-scoring and low-scoring provider pairs were reduced by applying the risk-adjusted model. The risk-adjusted model reduced the average variation across each individual's SPOR scored provider connections. CONCLUSIONS: Risk adjusting unplanned readmission in a collaboration network has an effect on SPOR-weighted edges, especially on classifying high-scoring SPOR provider pairs. The risk-adjusted model reduces the variance of providers' connections and balances shared patient characteristics between low- and high-scoring provider pairs. This indicates that the risk-adjusted SPOR edges better measure the impact of collaboration on readmissions by accounting for patients' risk of readmission.
Subject(s)
Cooperative Behavior , Health Personnel , Humans , Outcome Assessment, Health Care , Patient Readmission , Risk FactorsABSTRACT
BACKGROUND: Outcome trials in patients with type 2 diabetes mellitus have demonstrated reduced hospitalizations for heart failure (HF) with sodium-glucose co-transporter-2 inhibitors. However, few of these patients had HF, and those that did were not well-characterized. Thus, the effects of sodium-glucose co-transporter-2 inhibitors in patients with established HF with reduced ejection fraction, including those with and without type 2 diabetes mellitus, remain unknown. METHODS: DEFINE-HF (Dapagliflozin Effects on Biomarkers, Symptoms and Functional Status in Patients with HF with Reduced Ejection Fraction) was an investigator-initiated, multi-center, randomized controlled trial of HF patients with left ventricular ejection fraction ≤40%, New York Heart Association (NYHA) class II-III, estimated glomerular filtration rate ≥30 mL/min/1.73m2, and elevated natriuretic peptides. In total, 263 patients were randomized to dapagliflozin 10 mg daily or placebo for 12 weeks. Dual primary outcomes were (1) mean NT-proBNP (N-terminal pro b-type natriuretic peptide) and (2) proportion of patients with ≥5-point increase in HF disease-specific health status on the Kansas City Cardiomyopathy Questionnaire overall summary score, or a ≥20% decrease in NT-proBNP. RESULTS: Patient characteristics reflected stable, chronic HF with reduced ejection fraction with high use of optimal medical therapy. There was no significant difference in average 6- and 12-week adjusted NT-proBNP with dapagliflozin versus placebo (1133 pg/dL (95% CI 1036-1238) vs 1191 pg/dL (95% CI 1089-1304), P=0.43). For the second dual-primary outcome of a meaningful improvement in Kansas City Cardiomyopathy Questionnaire overall summary score or NT-proBNP, 61.5% of dapagliflozin-treated patients met this end point versus 50.4% with placebo (adjusted OR 1.8, 95% CI 1.03-3.06, nominal P=0.039). This was attributable to both higher proportions of patients with ≥5-point improvement in Kansas City Cardiomyopathy Questionnaire overall summary score (42.9 vs 32.5%, adjusted OR 1.73, 95% CI 0.98-3.05), and ≥20% reduction in NT-proBNP (44.0 vs 29.4%, adjusted OR 1.9, 95% CI 1.1-3.3) by 12 weeks. Results were consistent among patients with or without type 2 diabetes mellitus, and other prespecified subgroups (all P values for interaction=NS). CONCLUSIONS: In patients with heart failure and reduced ejection fraction, use of dapagliflozin over 12 weeks did not affect mean NT-proBNP but increased the proportion of patients experiencing clinically meaningful improvements in HF-related health status or natriuretic peptides. Benefits of dapagliflozin on clinically meaningful HF measures appear to extend to patients without type 2 diabetes mellitus. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT02653482.