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Transformation of the GWTG – Stroke Patient Registry to into a National Representative Database of Acute Ischemic Strokes (AIS) in the U.S.

Stroke remains a leading cause of death and disability in the United States.1  Approximately 800,000 people in the United States have a stroke every year.1  Eighty percent of all strokes can be prevented by screening for and treating known risk factors (hypertension, tobacco smoking, and atrial fibrillation).2 Recurrent strokes can also be prevented with proper management of these risk factors.3 Disease surveillance is crucial to the prevention of stroke, particularly in high-risk groups. Blacks and Hispanics report increasing stroke rates.4  Deprived populations within high-income countries are less likely to receive good-quality acute hospital and rehabilitation care than people with higher socioeconomic status.5  Findings from robust surveillance systems can be useful as healthcare providers can make informed decisions in the better medical management of strokes. Policymakers can work towards the development of aggressive campaigns to decrease the incidence of strokes in our communities and associated disparities in ethnic minorities and low-income groups.4,6  We can further estimate progress made towards the reduction and elimination of common risk factors of stroke.

Previously, the Institutes of Medicine recommended the development of surveillance systems in efforts to monitor the incidence and associated disabling burden from cardiovascular disease and strokes.7-8 The CDC’s Division for Heart Disease and Stroke Prevention (DHDSP) supports state, local, and tribal efforts to prevent, manage, and reduce risk factors related to stroke. The CDC has supported the implementation of stroke programs through cooperative agreements at these levels (CDC, 2020).9 However, due to the voluntary nature of these agreements, stroke surveillance data has been limited to only participant states. Therefore, it has been difficult to estimate the burden of a stroke at the national level.

A recent study by Ziaeian and colleagues presented the transformation of The Get With The Guidelines (GWTG) Stroke Patient Registry into a nationally representative database.10  This is the first study that has transformed a patient registry using post-stratification weights to represent a larger population of interest. The ability to translate observations from large registries to a national scale fills a considerable gap in the surveillance of the clinical characteristics, quality of care, and outcomes for Acute Ischemic Strokes (AIS) hospitalizations nationally.10  An acute stroke quality registry that is integrated with a guideline-based support tool can be a powerful tool for measuring and improving the quality of stroke care.11  Here we provide a summary of this recent study.10

Study population: The target population for the post-stratification weighting procedure is the total AIS presenting to U.S. hospitals by year. The NIS defines the AIS burden nationally stratified between the years of 2012 and 2014 and the nine U.S. Census regions – preserving the smallest sampling unit recommended by the NIS sponsors.  The National Inpatient Sample (NIS) is a weighted structured random sample of U.S. hospitalizations to represent national hospital utilization. However, the database does not include detailed clinical data such as stroke severity, laboratory data, medical treatments received, and patient-reported outcomes. The NIS is sponsored by the Agency for Healthcare Research and Quality’s Healthcare Cost and Utilization Project.

GWTG Stroke Patient Registry History: The GWTG – Stroke Patient Registry is a voluntary registry and continuous quality improvement initiative that collects data on patient characteristics, hospital adherence to guidelines and inpatient outcomes.  It was developed as part of a strategic goal of the American Heart Association (AHA) to reduce stroke and its associated risks, and Healthy People 2010 (HP2010) established national goals for stroke prevention and management.  The implementation of the GWTG Stroke registry has led to the implementation of evidence-based care and improved patient outcomes in many hospitals, acute care, and long-term care settings.6

GWTG hospitals comprise a mix of Joint Commission-certified stroke centers, PCNASR hospitals and small and large hospitals in urban and rural settings across the USA and Puerto Rico. Based on previous studies, the population of patients enrolled in GWTG is similar in age and racial makeup to the US population according to the U.S. census 2000.  Medicare beneficiaries linked to the GWTG registry are similar in demographics, comorbidities, and in-hospital outcomes compared with Medicare beneficiaries who are not linked.6,10

Methods: Ziaeian and colleagues integrated two data sources, The National Inpatient Sample (NIS), a structured random sample of U.S. hospitalizations weighted to represent national hospital utilization.10 The AHA-sponsored Get With The Guidelines Program (GWTG) program includes rich clinical data for quality improvement and research analyses.  They transformed these non-representative databases into a representative one with the use of post-stratification weights to rebalance over and underrepresented segments of the U.S. acute ischemic stroke (AIS) population. The approach described in the present paper is a far more robust estimation of the characteristics of stroke presentation and the quality of hospital care nationally.

The NIS lacks detailed clinical data such as stroke severity, laboratory data, medical treatments received, and patient-reported outcomes. It is not nationally representative and inadequate to measure stroke burden and quality of care nationally.  The GWTG-Stroke patient registry captures 58% of all strokes nationally. The GWTG program registries with volunteer hospitals are not proportionally representative of the entire nation.10  Ziaeian and colleagues used the GWTG-Stroke registry from 2012 to 2014 to evaluate post-stratification weighting procedures to represent the entire US AIS population.10

To determine the total number of AIS hospitalizations in the U.S. and marginal population characteristics for post-stratification weights, the investigators used target population counts from the NIS database. The NIS sampled 20% of the administrative discharge records from all participating hospitals (approximately 4300 hospitals) covering 95% of the U.S. population and 94% of all community hospital discharges from 2010 to 2014.  Raking and Bayesian interpolation, two parallel methods to estimate post-stratification survey weights, were used and their distribution was analyzed with histograms and treemaps to provide a perspective on the skewed representation of the GWTG-Stroke raw sample.

Results:  There were an estimated 1,388,296 AIS hospitalizations between 2012 to 2014 in the U.S. For the raking method, anchored characteristics in the weighted GWTG-Stroke sample matched the exact population totals estimated from the NIS. On admission, 49.2% of stroke patients nationally were using antiplatelet medications, 15.5% anticoagulants, 69.1% antihypertensives, 43.6% cholesterol-lowering medications, and 27.4% used diabetic medications. Approximately 48% of patients were discharged home, 40.2% to transitional care facilities, and 4.6% with hospice-related services.

Conclusions and Implications:  This research demonstrated the integration of two valuable data systems to make better population wide clinical estimates of acute ischemic stroke in the U.S., the GWTG Stroke Patient Registry and the NIS.  Their work demonstrates that methods exist to marry existing databases to make more reliable statistical inferences of population health and health services utilization. Understanding the effectiveness of hospital systems at a national and regional level is critical to insure consistency and timeliness in the receipt of evidence-based care. With the expansion of patient registries, the inclusion of clinical outcomes in these registries, and advanced statistical methods are available to transform non-random samples into representative population estimates.

References:

  1. Centers for Disease Control and Prevention. Underlying Cause of Death, 1999–2018. CDC WONDER Online Database. Atlanta, GA: Centers for Disease Control and Prevention; 2018. Accessed March 5, 2020.
  2. George MG, Fischer L, Koroshetz W, et al. CDC Grand Rounds: Public Health Strategies to Prevent and Treat Strokes. MMWR Morb Mortal Wkly Rep 2017;66:479–481. DOI: http://dx.doi.org/10.15585/mmwr.mm6618a5external icon.
  3. Caprio FZ, Sorond FA. Cerebrovascular Disease: Primary and Secondary Stroke Prevention. Med Clin North Am. 2019;103(2):295-308. doi:10.1016/j.mcna.2018.10.001
  4. Skolarus LE, Sharrief A, Gardener H, Jenkins C, Boden-Albala B. Considerations in Addressing Social Determinants of Health to Reduce Racial/Ethnic Disparities in Stroke Outcomes in the United States. Stroke. 2020;51(11):3433-3439. doi:10.1161/STROKEAHA.120.030426
  5. Marshall IJ, Wang Y, Crichton S, McKevitt C, Rudd AG, Wolfe CD. The effects of socioeconomic status on stroke risk and outcomes. Lancet Neurol. 2015;14(12):1206-1218. doi:10.1016/S1474-4422(15)00200-8.
  6. Ormseth CH, Sheth KN, Saver JL, Fonarow GC, Schwamm LH. The American Heart Association’s Get With the Guidelines (GWTG)-Stroke development and impact on stroke care. Stroke Vasc Neurol. 2017;2(2):94-105. Published 2017 May 29. doi:10.1136/svn-2017-000092
  7. Committee on a National Surveillance System for Cardiovascular and Select Chronic Diseases; Institute of Medicine, IOM (Institute of Medicine). A Nationwide Framework for Surveillance of Cardiovascular and Chronic Lung Diseases. Washington: National Academies Press; 2011. 201 p. Available from: http://www.nap.edu/catalog/13145
  8. Sidney S, Rosamond WD, Howard VJ, Luepker RV. The “Heart Disease and Stroke Statistics–2013 Update” and the Need for a National Cardiovascular Surveillance System. Circulation. 2013;127(1):21–3 Available from: http://www.ncbi.nlm.nih.gov/pubmed/23239838.
  9. Centers for Disease Control and Prevention. Division for Heart Disease and Stroke Prevention. About the State, Local, and Tribal Programs. Atlanta, GA: Centers for Disease Control and Prevention; 2020. Accessed March 5, 2020. Available from: https://www.cdc.gov/dhdsp/programs/spha/overview.htm
  10. Ziaeian B, Xu H, Matsouaka RA, et al. National surveillance of stroke quality of care and outcomes by applying post-stratification survey weights on the Get With The Guidelines-Stroke patient registry. BMC Med Res Methodol. 2021;21(1):23. Published 2021 Feb 4. doi:10.1186/s12874-021-01214-z
  11. Shahraki AD, Ghabaee M, Shahmoradi L, Malak JS, Jazani MR, Safdari R. Smart Acute Stroke Quality Registry Design-Data Elements Identification. J Registry Manag. 2018;45(1):43-47.

“The views, opinions and positions expressed within this blog are those of the author(s) alone and do not represent those of the American Heart Association. The accuracy, completeness and validity of any statements made within this article are not guaranteed. We accept no liability for any errors, omissions or representations. The copyright of this content belongs to the author and any liability with regards to infringement of intellectual property rights remains with them. The Early Career Voice blog is not intended to provide medical advice or treatment. Only your healthcare provider can provide that. The American Heart Association recommends that you consult your healthcare provider regarding your personal health matters. If you think you are having a heart attack, stroke or another emergency, please call 911 immediately.”

 

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The Sweet Spot in Treatment of Heart Failure With Reduced Ejection Fraction: SGLT2 Inhibitors

I am pleased to have the opportunity to summarize an important recent paper on the use of sodium-glucose co-transporter 2 (SGLT2) inhibitors by Drs. Muthiah Vaduganathan, Gregg Fonarow, and colleagues in JAMA Cardiology,1 that was published simultaneously with AHA20.

Background:

SGLT2 inhibitors are a class of medications that were initially developed for management of diabetes but were serendipitously found to be effective in treating individuals with heart failure. In May 2020, dapagliflozin became the first SGLT2 inhibitor approved by the US Food and Drug Administration (FDA) for use in patients with heart failure with reduced ejection fraction (HFrEF) after the pivotal Dapagliflozin and Prevention of Adverse Outcomes in Heart Failure (DAPA-HF) trial, which showed that dapagliflozin reduced heart failure events and mortality.2 In the EMPEROR-Reduced (EMPagliflozin outcomE tRial in Patients With chrOnic heaRt Failure With Reduced Ejection Fraction) trial, use of another SGLT2 inhibitor, empagliflozin, was also found to reduce risk of cardiovascular death and heart failure hospitalizations.3

Major Question Addressed in the Paper: What proportion of contemporary patients with HFrEF in the US are potentially eligible for initiation of dapagliflozin based on the FDA label?

Approach: The investigators studied patients with HFrEF (EF≤40%) who were in the AHA Get With The Guidelines-Heart Failure (GWTG-HF) registry. They assessed patients admitted between January 2014 to September 2019 at 529 sites (started with 586,580 patients). Patients were excluded if they had any of the following based on the FDA label for dapagliflozin: estimated glomerular filtration rate [eGFR]<30 mL/min/1.73 m2 at discharge, dialysis (either history of chronic dialysis or required dialysis during hospitalization), and/or type 1 diabetes. After excluding patients who met the aforementioned criteria and those who had missing discharge eGFR or vital signs, the primary study cohort consisted of 154,714 patients at 406 sites.

Major Results:

  • Of the 154,714 patients studied in the GWTG-HF registry, 125,497 (81.1%) were candidates for initiation of dapagliflozin based on the FDA label.
  • When only looking at sites with ≥10 hospitalizations (355 sites that enrolled 154,522 patients), the median proportion of dapagliflozin candidates was still 81.1% (25th-75th percentiles 77.8-84.6%).
  • A higher proportion of patients without type 2 diabetes than with type 2 diabetes were candidates for dapagliflozin (85.5% vs. 75.6%).
  • The most frequent reason for not meeting the FDA label was eGFR<30 mL/min/1.73 m2, which was met more frequently in patients with a history of or new diagnosis of diabetes than those without diabetes (23.9% vs. 14.3%).
  • There was lower use of evidence-based heart failure therapies in the GWTG-HF patients compared to patients in the DAPA-HF trial.

Histogram from Vaduganathan et al. evaluating the proportion of patients meeting the dapagliflozin FDA label criteria from hospitals with at least 10 eligible HFrEF hospitalizations.

Major Study Limitations: Since the GWTG-HF data are de-identified, only unique hospitalization episodes were presented so some patients may be represented more than once in this study. Glycated hemoglobin levels were not measured in a protocolized way, thus type 2 diabetes could be underdiagnosed in this study. Data regarding post-discharge labs and the use of therapies were not available.

Key Take Home Message: This study using a large AHA registry (GWTG-HF) strikingly found that 4 out of 5 adults with HFrEF (regardless of whether the patient has type 2 diabetes) may be eligible for initiation of dapagliflozin, supporting the broad applicability of this therapy in US clinical practice.

For further learning, there are several great OnDemand sessions from AHA20 on SGLT2 inhibitors.

AHA20 OnDemand Sessions on SGLT-2 inhibitors:

  • New Glucose-Lowering Agents with CV Benefits: Working… But How?
  • SGLT2i for Non-Diabetic Indications: Updates from Mega-Trials and Mechanistic Insights
  • Novel Anti-Diabetic Agents: A Tidal Wave of Change in the Cardiovascular Care of Patients with CKD
  • The Heart, the Kidney, and SGLT2 Inhibition: For Clinical Trials to Patient Care

Potential Future Research Directions:

  • Determine the mechanisms leading to the efficacy of SGLT2 inhibitors in HFrEF.
  • Investigate the renal effects of SGLT2 inhibitors and whether SGLT2 inhibitors can be safely used in patients with more severe chronic kidney disease.
    • DAPA-CKD4 (Dapagliflozin and Prevention of Adverse Outcomes in Chronic Kidney Disease), which included patients with eGFR as low as 25 mL/min/1.73 m2, showed that dapagliflozin reduced risk of sustained eGFR decline of at least 50%, end-stage kidney disease, or death from renal or cardiovascular causes regardless of the presence or absence of type 2 diabetes.
    • EMPEROR-Reduced included HFrEF patients with eGFR as low as 20 mL/min/1.73 m2.
  • Evaluate whether SGLT2 inhibitors are beneficial in patients with heart failure with preserved ejection fraction (HFpEF). Current ongoing/future clinical trials with HFpEF patients include DELIVER (NCT03619213), EMPEROR-Preserved (NCT03057951), EMPA-HEART 2 (NCT04461041), PRESERVED-HF (NCT03030235), and EMBRACE-HF (NCT03030222).
  • Assess the effects of simultaneous use of SGLT2 inhibitors and another class of diabetic medications that have shown beneficial cardiovascular disease (CVD) effects, glucagon-like peptide-1 receptor agonists (GLP-1RA) and determine which of these two classes of medications should be prioritized in drug-naïve patients with type 2 diabetes and atherosclerotic cardiovascular disease (ASCVD).

Potential mechanisms underlying the beneficial effects of SGLT2 inhibitors. Figure from Dr. Subodh Verma’s talk entitled “SGLT2 inhibitors: Why do they work” in the “New Glucose-Lowering Agents with CV Benefits: Working… But How?” session at AHA20.

 

References

  1. Vaduganathan M, Greene SJ, Zhang S, Grau-Sepulveda M, DeVore AD, Butler J, Heidenreich PA, Huang JC, Kittleson MM, Joynt Maddox KE, McDermott JJ, Owens AT, Peterson PN, Solomon SD, Vardeny O, Yancy CW, Fonarow GC. Applicability of us food and drug administration labeling for dapagliflozin to patients with heart failure with reduced ejection fraction in us clinical practice: The get with the guidelines-heart failure (gwtg-hf) registry. JAMA Cardiol. 2020
  2. McMurray JJV, Solomon SD, Inzucchi SE, Køber L, Kosiborod MN, Martinez FA, Ponikowski P, Sabatine MS, Anand IS, Bělohlávek J, Böhm M, Chiang CE, Chopra VK, de Boer RA, Desai AS, Diez M, Drozdz J, Dukát A, Ge J, Howlett JG, Katova T, Kitakaze M, Ljungman CEA, Merkely B, Nicolau JC, O’Meara E, Petrie MC, Vinh PN, Schou M, Tereshchenko S, Verma S, Held C, DeMets DL, Docherty KF, Jhund PS, Bengtsson O, Sjöstrand M, Langkilde AM, Investigators D-HTCa. Dapagliflozin in patients with heart failure and reduced ejection fraction. N Engl J Med. 2019;381:1995-2008
  3. Packer M, Anker SD, Butler J, Filippatos G, Pocock SJ, Carson P, Januzzi J, Verma S, Tsutsui H, Brueckmann M, Jamal W, Kimura K, Schnee J, Zeller C, Cotton D, Bocchi E, Böhm M, Choi DJ, Chopra V, Chuquiure E, Giannetti N, Janssens S, Zhang J, Gonzalez Juanatey JR, Kaul S, Brunner-La Rocca HP, Merkely B, Nicholls SJ, Perrone S, Pina I, Ponikowski P, Sattar N, Senni M, Seronde MF, Spinar J, Squire I, Taddei S, Wanner C, Zannad F, Investigators E-RT. Cardiovascular and renal outcomes with empagliflozin in heart failure. N Engl J Med. 2020;383:1413-1424
  4. Heerspink HJL, Stefánsson BV, Correa-Rotter R, Chertow GM, Greene T, Hou FF, Mann JFE, McMurray JJV, Lindberg M, Rossing P, Sjöström CD, Toto RD, Langkilde AM, Wheeler DC, Investigators D-CTCa. Dapagliflozin in patients with chronic kidney disease. N Engl J Med. 2020;383:1436-1446

 

“The views, opinions and positions expressed within this blog are those of the author(s) alone and do not represent those of the American Heart Association. The accuracy, completeness and validity of any statements made within this article are not guaranteed. We accept no liability for any errors, omissions or representations. The copyright of this content belongs to the author and any liability with regards to infringement of intellectual property rights remains with them. The Early Career Voice blog is not intended to provide medical advice or treatment. Only your healthcare provider can provide that. The American Heart Association recommends that you consult your healthcare provider regarding your personal health matters. If you think you are having a heart attack, stroke or another emergency, please call 911 immediately.”

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The Clock is Ticking: Door-to-Needle Time in Acute Ischemic Stroke

Lay of the Land

In 2008, after years of being the third-leading cause of death in the United States, stroke dropped to fourth. In part, this reflected the results of a commitment made by the American Heart Association/American Stroke Association (AHA/ASA) more than a decade prior to reduce stroke, coronary heart disease, and cardiovascular risk by 25% by the year 2010 (a goal met a year early in 2009). The reason for the success, although multifactorial, can largely be attributed to improved prevention and improved care within the first hours of acute strokes.1 As early as 2000, the benefits of time-dependent administration of intravenous tissue plasminogen activator (tPA) in patients with acute ischemic stroke were well supported (Figure 1).2

Figure 1. Graph of model estimating OR for favorable outcome at 3 months in recombinant tissue-type plasminogen activator (rt-PA) treated patients compared to placebo treated patients by time from stroke onset to treatment (onset-to-treatment time [OTT]) with 95% confidence intervals, adjusting for the baseline NIH Stroke Scale. OR > 1 indicates greater odds that rt-PA treated patients will have a favorable outcome at 3 months compared to the placebo treated patients. Range of OTT was 58 to 180 minutes with mean (μ) of 119.7 minutes.2

Guidelines began recommending a door-to-needle time for tPA administration of 60 minutes or less, however, studies found that less than 30% of US patients were treated within this time window. The Target: Stroke initiative was launched in 2010 to assist hospitals in providing timely tPA. As a result, the proportion of tPA administered within 60 minutes increased from 26.5% during the preintervention period to 41.3% after implementation. Despite national initiatives, shorter door-to-needle times have not been as quickly adopted as door-to-balloon times for percutaneous coronary intervention in acute coronary syndromes (Figure 2).4 Part of the problem is a lack of robust mortality outcomes data to support trends observed in the (only) two randomized trials conducted to assess long term outcomes with tPA in acute ischemic stroke; neither of which was powered to probe for mortality effects.

Figure 2. Trend in percentage of patients with door-to-balloon (D2B) time <90 minutes over 6 years.4

This brings us to the study published earlier this week in JAMA Man S et al. (corresponding author Fonarow GC) titled “Association Between Thrombolytic Door-to-Needle Time and 1-Year Mortality and Readmission in Patients With Acute Ischemic Stroke.” This nationwide study of US patients treated with intravenous tPA for acute ischemic stroke demonstrated that shorter door-to-needle times were significantly associated with better long-term outcomes, including lower 1-year all-cause mortality, 1-year all-cause readmission, and the composite of all-cause mortality or readmission at 1 year.5

Study Design

This US cohort included Medicare beneficiaries aged 65 years or older who were treated with intravenous tPA for acute ischemic stroke at Get With The Guidelines (GWTG)–Stroke participating hospitals between January 1, 2006, and December 31, 2016, with 1-year follow-up through December 31, 2017. Patient clinical data were obtained from the GWTG-Stroke database. Study entry criteria required patients to (1) have been aged 65 years or older; (2) have a discharge diagnosis of acute ischemic stroke; (3) have been treated with intravenous tPA within 4.5 hours of the time they were last known to be well; (4) have had a documented door-to-needle time; (5) not have been treated with a concomitant therapy with intra-arterial reperfusion techniques; (6) have had the admission be the first for stroke during the study period; and (7) not have been transferred to another acute care hospital, left against medical advice, or without a documented site of discharge disposition.5 Overall, 61426 participants met the inclusion criteria for the study.

The prespecified primary outcomes included 1-year all-cause mortality, 1-year all-cause readmission, and the composite of all-cause mortality or readmission at 1 year. One-year cardiovascular readmission was a prespecified secondary outcome and was defined as a readmission with a primary discharge diagnosis of hypertension, coronary artery disease, myocardial infarction, heart failure, abdominal or aortic aneurysm, valvular disease, and cardiac arrhythmia. Recurrent stroke readmission, a post hoc secondary outcome, was defined as a readmission for transient ischemic attack, ischemic and hemorrhagic stroke, carotid endarterectomy or stenting, but not for direct complications of index stroke.5

Door-to-needle time was first analyzed using the prespecified times of within 45 minutes and within 60 minutes versus longer than those targets, in line with prior studies on this topic. The authors also ingeniously also evaluated time as a continuous variable, as a categorical variable in 15-minute increments using within 30 minutes as the reference group, and in 45-minute and 60-minute increments. Cox proportional hazards models were used to examine the associations of door-to-needle timeliness and each 1-year outcome with robust variance estimation to ac- count for the clustering of patients within hospitals.5 On hours were defined as 7:00 AM to 6:00 PM on any weekday. Off hours were defined as any other time, including evenings, nights, weekends, and national holidays. The authors did this because prior studies using this prespecified time cutoff have shown that presenting during off hours was associated with inferior quality of care, inferior intravenous thrombolytic treatment, and in-hospital mortality.5

Results

Among the 61426 Medicare beneficiaries treated with intravenous tPA within 4.5 hours of the time they were last known to be well at the 1651 GWTG-Stroke participating hospitals, the median age was 80 years, 43.5% were male, 82.0% were non-Hispanic white, 8.7% were non-Hispanic black, 4.0% were Hispanic, and 5.3% were of other race/ethnicity. More patients that arrived during off hours were treated within longer door-to-needle times (40.7% for ≤30 minutes, 45.6% for 31-45 minutes, 50.6% for 46-60 minutes, 53.5% for 61-75 minutes, and 56.3% for >75 minutes; P < .001). Despite having longer onset-to-arrival times, some patients had shorter onset-to-needle and door-to-needle times.5

Most patients were treated at teaching hospitals (77.7%) and primary stroke centers (73.2%); 3% were treated at rural hospitals. More patients who were treated at teaching hospitals, but not at primary stroke centers, were treated within shorter door-to-needle times. The median door-to-needle time was 65 minutes, with 5.6% of patients treated with tPA within 30 minutes of hospital arrival, 20.8% within 45 minutes, and 44.1% within 60 minutes.5

Patients who received tPA after 45 minutes of hospital arrival had worse long-term outcomes than those treated within 45 minutes of hospital arrival, including significantly higher all-cause mortality (35.0% vs 30.8%, respectively; adjusted hazard ratio [HR], 1.13 [95% CI, 1.09- 1.18]), higher all-cause readmission (40.8% vs 38.4%; ad- justed HR, 1.08 [95% CI, 1.05-1.12]), higher all-cause mortality or readmission (56.0% vs 52.1%; adjusted HR, 1.09 [95% CI, 1.06-1.12]), and higher cardiovascular readmission (secondary outcome) (19.8% vs 18.4%; adjusted HR, 1.05 [95% CI, 1.00- 1.10]), but not significantly higher recurrent stroke readmission (a post hoc secondary outcome) (9.3% vs 8.8%; adjusted HR, 1.05 [95% CI, 0.98-1.12]).

Patients who received tPA after 60 minutes of hospital arrival vs within 60 minutes of hospital arrival had significantly higher adjusted all-cause mortality (35.8% vs 32.1%, respectively; adjusted HR, 1.11 [95% CI, 1.07-1.14]), higher all-cause readmission (41.3% vs 39.1%; adjusted HR, 1.07 [95% CI, 1.04-1.10]), higher all-cause mortality or readmission (56.8% vs 53.1%; adjusted HR, 1.08 [95% CI, 1.05-1.10]), and higher cardiovascular readmission (secondary outcome) (20.2% vs 18.6%; adjusted HR, 1.06 [95% CI, 1.01-1.10]), but not significantly higher recurrent stroke readmission (a post hoc secondary outcome) (9.3% vs 8.9%; adjusted HR, 1.03 [95% CI, 0.97-1.09]).

The absolute differences in outcomes increased with longer door-to-needle times. The cumulative incidence curves showed that approximately 42% of the deaths or readmissions occurred within 30 days.

Every 15-minute increase in door-to-needle times was significantly associated with higher all-cause mortality (adjusted HR, 1.04 [95% CI, 1.02-1.05] for door-to-needle time within 90 minutes of arrival. However, this association did not persist beyond 90 minutes of hospital arrival. Every 15-minute increase in door-to-needle times was significantly associated with higher all-cause readmission (adjusted HR, 1.02 [95% CI, 1.01- 1.03]) and higher all-cause mortality or readmission (adjusted HR, 1.02 [95% CI, 1.01-1.03]). Every 15-minute increase in door-to-needle times after 60 minutes of hospital arrival was significantly associated with higher cardiovascular readmission (secondary outcome) (adjusted HR, 1.02 [95% CI, 1.01- 1.04]) and higher stroke readmission (a post hoc secondary out- come) (adjusted HR, 1.02 [95% CI, 1.00-1.04]); however, these associations were not statistically significant for the door-to-needle times within 60 minutes of hospital arrival.

My Take

I would first like to commend the authors on this undertaking. The fact that early door-to-balloon time is still questionable seems contrary to our understanding of ischemic events and time to cell necrosis. This high-quality study further supports the notion that “time is muscle,” as seen in other ischemic events such as acute myocardial infarction and acute limb ischemia. However, the limitations of the study affects its generalizability and application to real world scenarios. The patients in this study are all over the age of 65, largely non-Hispanic whites, all with recorded times of last seen normal and mostly treated in academic centers with stroke units. Nonetheless, the authors have certainly progressed the field of stroke treatment, if even incrementally, in the right direction.

References:

  1. Jauch EC, Saver  JL, Adams  HP  Jr,  et al; American Heart Association Stroke Council; Council on Cardiovascular Nursing; Council on Peripheral Vascular Disease; Council on Clinical Cardiology.  Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association.  Stroke. 2013;44(3):870-947.
  2. Marler JR, Tilley  BC, Lu  M,  et al.  Early stroke treatment associated with better outcome: the NINDS rt-PA stroke study.  Neurology. 2000;55(11):1649-1655.
  3. Fonarow GC, Zhao X, Smith EE, et al. Door-to-needle times for tissue plasminogen activator administration and clinical outcomes in acute ischemic stroke before and after a quality improvement initiative. JAMA. 2014;311(16):1632- 1640. doi:10.1001/jama.2014.3203
  4. Krumholz HM, et al. Improvements in door-to-balloon time in the United States, 2005-2010. Circulation 2011;124:1038-45.
  5. Man S, Xian Y, Holmes DN, et al. Association Between Thrombolytic Door-to-Needle Time and 1-Year Mortality and Readmission in Patients With Acute Ischemic Stroke. JAMA. 2020;323(21):1-15. doi:10.1001/jama.2020.5697

“The views, opinions and positions expressed within this blog are those of the author(s) alone and do not represent those of the American Heart Association. The accuracy, completeness and validity of any statements made within this article are not guaranteed. We accept no liability for any errors, omissions or representations. The copyright of this content belongs to the author and any liability with regards to infringement of intellectual property rights remains with them. The Early Career Voice blog is not intended to provide medical advice or treatment. Only your healthcare provider can provide that. The American Heart Association recommends that you consult your healthcare provider regarding your personal health matters. If you think you are having a heart attack, stroke or another emergency, please call 911 immediately.”

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Another (Louder) Call to Improve the Care We Provide Heart Failure Patients

I am always taken aback when I recommend a switch to sacubitril/valsartan in a patient with heart failure with reduced ejection fraction (HFrEF) and the response is “my patient feels fine”. This is a common response and certainly not a good enough reason to not optimize guideline directed medical therapy (GDMT) in patients with HFrEF. Optimization of GDMT in HFrEF, known to improve morbidity and mortality (1,2), is dismal. The Change the Management of Patients with Heart Failure (CHAMP-HF) registry included patients in the United States with chronic HFrEF receiving at least one oral medication for management of HF and showed >25% of eligible patients are not prescribed angiotensin converting enzyme inhibitor/angiotensin receptor blocker/angiotensin receptor neprilysin inhibitor, >33% are not prescribed a beta blocker, >50% are not prescribed a mineralocorticoid receptor antagonist. Remarkably, even among those receiving GDMT fewer than 25% are prescribed target doses and only 1% of eligible patients are simultaneously on target doses of all 3 classes of GDMT (3,4).

The mechanisms for suboptimal prescription of GDMT in HFrEF are complex and undertreatment is even more evident among women, minority patient populations, and patients from economically disadvantaged backgrounds, among others. Cost is certainly an issue, especially with more novel HF therapies and co-pay assistance programs are not always available to our most vulnerable patients. There are not enough HF cardiologists to take care of the continuously increasing population of HF patients and therefore, optimization of GDMT needs to be done by general cardiologists and primary care clinicians as well. We should also become creative and use telemedicine to optimize GDMT more efficiently. We do our patients a disservice by not optimizing GDMT that improves HF morbidity and mortality.

And just as optimization of GDMT is not ideal, neither is our evaluation of etiology of HF. Optimization of GDMT and determination of etiology of HF whose management may change disease trajectory should be undertaken in all patients with new-onset HF. This begins with a fundamental understanding of the various etiologies of HF, the laboratory and imaging testing needed, and the best treatment strategy for the underlying etiology discovered- if any (cue, “idiopathic” cardiomyopathy). O’Connor and colleagues’ observational cohort study from the Get With The Guidelines- Heart Failure (GWTG-HF) registry demonstrates the need to improve the testing we perform to exclude coronary artery disease (CAD) as the underlying etiology of new-onset HF.4

Why is this important? Well, of course for treatment, which involves deciding whether medical therapy (aspirin, statins) or revascularization (surgical or percutaneous) is a more optimal strategy. And most important to improve disease trajectory as continued ischemia will lead to worsening HF. O’Connor and colleagues found that the majority of  17,185 patients hospitalized for new-onset HF did not receive testing for CAD either during the hospitalization or in the 90 days before and after, despite data demonstrating that 60% (!!!) of HF patients have concomitant significant CAD.4 And consistent with disparities I mentioned earlier regarding the undertreatment of women with GDMT, men were more likely to be tested for CAD.

Diagnosing and treating CAD provides an opportunity to discuss risk factor modification with patients such as smoking cessation, diabetes control, exercise, healthy diets etc.… to further mitigate future risk. The importance of optimization of GDMT in patients with HFrEF cannot be understated and analogous to this, is the importance of examining the underlying etiology of HF in patients with new-onset HF with preserved, borderline, or reduced EF to improve disease trajectory. Furthermore, inequities in both aspects of the care of HF patients in terms of identification of etiology and optimization of GDMT, must be addressed on a national level. We have plenty of data illustrating suboptimal optimization of GDMT in those with established HFrEF and suboptimal testing for CAD in those with new-onset HF. The next steps are understanding the mechanisms and implementing strategies to improve care. The need for this is critical to reduce morbidity and mortality in all HF patients.

References

  1. Yancy CW, Jessup M, Bozkurt B et al. 2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. Circulation 2017;137.
  2. Yancy CW, Januzzi JL, Allen LA et al. 2017 ACC Expert Consensus Decision Pathway for Optimization of Heart Failure Treatment: Answers to 10 Pivotal Issues About Heart Failure With Reduced Ejection Fraction. Journal of the American College of Cardiology 2017.
  3. Greene SJ, Butler J, Albert NM et al. Contemporary Utilization and Dosing of Guideline-Directed Medical Therapy for Heart Failure with Reduced Ejection Fraction: From the CHAMP-HF Registry. Journal of the American College of Cardiology 2018.
  4. O’Connor, Kyle D., et al. “Testing for Coronary Artery Disease in Older Patients With New-Onset Heart Failure.” Circulation: Heart Failure, vol. 13, no. 4, 2020, doi:10.1161/circheartfailure.120.006963.

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