Optimal Implementation of Heart Failure Guidelines and Standards Gregg C. Fonarow, MD, FACC, FAHA

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1 Optimal Implementation of Heart Failure Guidelines and Standards Gregg C. Fonarow, MD, FACC, FAHA Eliot Corday Professor of Cardiovascular Medicine and Science UCLA Division of Cardiology Director, Ahmanson-UCLA Cardiomyopathy Center Co-Chief, UCLA Division of Cardiology

2 Presenter Disclosure Information Optimizing Care of Heart Failure I will not discuss off label use of medications or devices DISCLOSURE INFORMATION: The following relationships exist related to this presentation: Gregg C. Fonarow, MD, FACC, FAHA NHLBI, AHRQ, Novartis, Medtronic, Gambro: Research, Consultant OPTIMIZE-HF Registry: Sponsored by GSK ADHERE Registry: Sponsored by Scios IMPROVE HF: Sponsored by Medtronic GWTG-HF: Sponsored by American Heart Association

3 Domains of Outcomes Research What Works Discovery (for patients and populations) System Performance Translation (getting what works implemented in patients and populations) Patient Alignment Preference (how to apply in ways that are patient centered) Value

4 Background on Heart Failure Prevalence Incidence Mortality 5,100, ,000 50% at five years Hospital Discharges Outpatient Visits 1,023, million Cost $39.8 billion Heart failure results in substantial morbidity, mortality and healthcare expenditures Marked gaps, variations, disparities in the quality of care for heart failure in inpatient and outpatient settings exists There are substantial opportunities to improve care and outcomes for heart failure 1 American Heart Association Heart and Stroke Statistical Update. Dallas, Tex: American Heart Association; Hunt SA et al. ACC/AHA guidelines for the evaluation and management of chronic heart failure in the adult

5 Prognosis with Heart Failure Overall 5-year mortality 50% Hospitalized Patients 1-year mortality: Mild to Moderate Symptoms 10-20% Severe Symptoms 30-60% Survival % Women Men Years Survival after the onset of congestive heart failure in Framingham Heart Study subjects Ho Circulation 1993;88:

6 Outcomes During and After HF Hospitalization In-hospital Length of stay (mean) 6.2 days Mortality rate 4.1% Hospital readmissions 20% at 30 days 50% at 6 months Longer-term mortality 11.6% at 30 days 33.1% at 12 months Fonarow GC et al. J Card Failure. 2003;9:S79 Jong P et al. Arch Intern Med. 2002;162:1689

7 Evidence-Based, Guideline-Recommended Therapies for Heart Failure Guideline Relative Risk Number Needed NNT for Mortality Relative Risk Recommended Reduction in to Treat for (standardized to Reduction in HF Therapy Mortality Mortality 36 months) Hospitalizations ACEI/ARB 17% 22 over 42 months 26 31% Beta-blocker 34% 28 over 12 months 9 41% Aldosterone Antagonist 30% 9 over 24 months 6 35% Hydralazine/Nitrate 43% 25 over 10 months 7 33% CRT 36% 12 over 24 months 8 52% ICD 23% 14 over 60 months 23 NA Fonarow GC, et al. Am Heart J 2011;161:

8 Long-Term Trends in Mortality With Heart Failure Temporal Trends in Age-Adjusted Mortality After the Onset of Heart Failure* 30-Day Mortality, 1-Year Mortality, 5-Year Mortality, Period % (95% CI) % (95% CI) % (95% CI) Men Women Men Women Men Women (4-19) 18 (7-27) 30 (18-40) 28 (16-39) 70 (57-79) 57 (43-67) (7-23) 16 (6-24) 41 (29-51) 28 (17-38) 75 (65-83) 59 (45-69) (5-18) 10 (4-16) 33 (23-42) 27 (17-35) 65 (54-73) 51 (39-60) (4-17) 10 (3-15) 28 (18-36) 24 (14-33) 59 (47-68) 45 (33-55) *All values were adjusted for age (<55, 55 64, 65 74, 75 84, and 85 years). Levy D et al. N Engl J Med. 2002;347:

9 National Trends in Outcomes Among Patients Hospitalized with HF Trends in Crude and Adjusted Mortality Rates Year N Crude Mortality (%) Adjusted Mortality (OR, 95% CI) 30-day 1-year 30-day 1-year , NA NA , (referent) 1.00 (referent) , ( ) 0.91 ( ) , ( ) 0.91 ( ) , ( ) 0.91 ( ) , ( ) 0.92 ( ) , ( ) 0.93 ( ) , ( ) 0.93 ( ) 1998 to 2008: 1-year mortality 31.7% to 32.0%, adjusted OR 0.94 National sample of 3,957,520 Medicare beneficiaries >65 who were hospitalized with HF between 1992 and 1999 Kosiborod AJM 2006;119:e1-e7. Sample of 55 million Medicare beneficiaries hospitalized with HF between 1998 and 2008 Chen JAMA 2011;306:

10 Heart Failure Hospitalizations The number of heart failure hospitalizations is increasing in both men and women 600, ,000 Discharges 400, , , ,000 0 Women Men '79 '81 '83 '85 '87 '89 '91 '93 '95 ' CDC/NCHS: Hospital discharges include patients both living and dead. AHA, 1998 Heart and Statistical Update NCHS, National Center for Health Statistics AHA Heart and Stroke Statistical Update 2006

11 Utilization of Evidence Based HF Therapies IMPROVEMENT International Survey Percent of Patients ACE Inhibitors Beta-Blockers ACEI + BB Aldosterone Antagonist International survey: 15 countries, 1363 physicians, 11,062 patients: Year Outpatient regimen in patients with Stage C HF, documented systolic dysfunction. Cleland Lancet :

12 Risk-Treatment Mismatch in HF At Hospital Discharge 90 Day Follow-Up 1 Year Follow-Up Patients (%) ACEI ACEI or ARB β- Blocker ACEI ACEI or ARB β- Blocker 1 Year Mortality Rate Low Risk Average Risk High Risk Use rates in absence of contraindications. For all drug classes, P<.001 for trend. Lee, D. JAMA. 2005;294:

contraindications or other exceptions Median 33.

13 Variation in HF Quality of Care in US Cardiology Practices LVEF 35, NYHA Class III-IV, QRS 120 ms, No contraindications or other exceptions Median 33.3 Mean 37.3 Fonarow GC et al. Circ: Heart Fail 2008;1:98-106

Female Black Female Black male White female Black female 0

14 Race and Sex Disparities in ICD Use at Discharge Among Eligible Patients With HF White Male Black Male White Female Black Female Black male White female Black female Hernandez, A. F. et al. JAMA 2007;298:

15 CMS/TJC: Quality-of-Care Indicators for HF HF-1: Discharge Instructions 1. Daily weights 4. What to do if Sx worsen 2. 2 gram sodium diet 5. Follow-up appointment 3. Activity Rx 6. List of medications HF-2: HF-3: HF-4: Assessment of LV Function ACEI/ARB at Discharge in Appropriate Patients with LVSD Smoking Cessation Advice/Counseling

16 ADHERE: Quality of Care Conformity to JCAHO HF Performance Indicators HF-1, % Discharge instructions HF-2, % LV function HF-3, % Discharge ACEI Rx HF-4, % Smoking cessation counseling All Patients (N=54,639) Patients at Academic Hospitals (N=18.934) Patients at Non-Academic Hospitals (N=35,705) P Value < <.14 ADHERE, Acute Decompensated Heart Failure National Registry. Fonarow GC, et al. Arch Intern Med. 2005;165:

17 ADHERE: Variation in ACEI/ARB Use CMS Core Measure: HF 3 - LVEF < 40% prescribed ACEI/ARB at discharge Rate (%) ADHERE Hospitals ADHERE: admissions between 6/ /2003 at 223 hospitals (subset with LVEF <.40, no CI) Fonarow GC et al. Arch Intern Med 2005;165:

18 ADHERE Quality of Care Conformity to JCAHO HF Performance Indicators Lagging Centers Leading Centers % Utilization 100% 80% 60% 40% 70% 72% 97% 58% 88% 85% % 0% 1% Discharge Instructions LV Function Measurement ACEI use 8% Smoking Cessation Length of Stay (median) 1.4 Mortality admissions between 6/ /2003 at 223 hospitals Grouped by Leading (90 th percentile) and Lagging (10 th percentile) Fonarow GC et al. Arch Intern Med 2005;165: All P<0.0001

19 Failure of Usual Care in Heart Failure Failure to prescribe evidence-based medications Failure to discontinue medication that may exacerbate HF Failure to titrate medications to target doses Failure to adhere to prescribed medications Failure to adequately address comorbidities Failure to consider device therapies Failure to provide adequate dietary counseling Failure to comply with dietary regimen Failure to seek early care with escalating symptoms Failure of adequate discharge planning Failure of adequate follow-up Failure of adequate monitoring Failure of patient social support systems Failure to address patient and care-giver needs

20 Reasons for Underutilization of Evidence-Based Therapies Gaps in knowledge and awareness Lack of systems RCTs study patient populations that are too narrow in scope Uncertainty regarding effectiveness Ongoing risk concerns Questions regarding: drug/product safety Bias Costs

21 Potential Evidence of Overuse Al-Khatib SM et al JAMA. 2011;305(1):43-49

Implement evidencebased care Improve communications Ensure compliance Improve quality of

22 Bridging the Gap Between Knowledge and Routine Clinical Practice ACC/AHA/HFSA Guidelines Systems Clinical Practice I IIa IIb III Clinical trial evidence National guidelines Implement evidencebased care Improve communications Ensure compliance Improve quality of care Improve outcomes Adapted from the American Heart Association. Get With The Guidelines; 2001.

23 Ahmanson-UCLA Cardiomyopathy Center Comprehensive HF Disease Management Program UCLA Multidisciplinary Team: Advance Practice Nurses, HF Specialists, CT Surgery, MSW, Others Comprehensive assessment Optimization of heart failure treatment regimen Detailed patient and family education Daily measuring and recording of weights Sodium restricted diet with detailed guidelines Two liter (64 oz) fluid restriction (if congestion) Patient self-monitored flexible-loop diuretic regimen Alcohol and smoking abstinence Progressive walking exercise program Vigilant monitoring, care coordination, and follow-up by advance practice nurses and physicians Fonarow GC et al. J Am Coll Cardiol. 1997;30:

24 HF Disease Management Program: Impact on Treatment and Hospitalizations ACE Inhibitor Use Patients (%) 77 Conventional Management 6 Months Pre-comprehensive *P=0.05 vs conventional management 95 * 92 * HF Management System at Discharge HF Management 6 Months Post-comprehensive Cumulative Hospitalizations Conventional Care (6 months) 85% Reduction in Hospitalizations Post- P< comprehensive 63 Rx Patients, 6 months conventional treatment pre- vs 6 months post-comprehensive management. Total medical costs: Pre ($18,808) vs Post ($9,555), P< Fonarow GC. et al. J Am Coll Cardiol. 1997;30:

25 Randomized Trials of Disease Management Programs for Heart Failure Sensitivity analysis Mortality All-cause readmission HF-related readmission OR CI OR CI OR CI Overall High quality studies Low quality studies Multidisciplinary Nurse Short intervention (0 3 m) Medium intervention (3 6 m) Long intervention (> 6 m) Randomized Trials, 5308 patients Roccaforte EJHF 2005;7: P <0.01

26 Benefits and Drawbacks of Heart Failure Disease Management Programs Benefits Improved use of evidencebased therapy Improved symptom status and functional capacity Usual Care 96% Drawbacks Improved QOL Reduction in hospitalization Decrease in total medical costs Improved survival suggested in some studies 4% HF Disease Management Programs Moser DK, Mann DL. Circulation. 2002;105:

27 Why a Hospital-Based System for HF Management? Patients Patient capture point Have patient s/family s attention: teachable moment Predictor of care in community Hospital structure Standardized processes/protocols/ orders/teams Accrediting bodies for standards of care Centers for Medicare and Medicaid Services (CMS) and peer-review organizations Joint Commission (in-hospital) HEDIS and NQF (postdischarge) Fonarow GC, et al. Am Heart J. 2004;148:43-51.

28 Institutional Heart Failure Discharge Medication Program Reduces Readmissions and Mortality * Pre-Intervention (n=11,038) Post-Intervention (n=8,045) Treatment Rates (%) HR 0.80 p< * HR 0.77 p< * 0 ACEI Rx Readmissions 1 year Mortality Intermountain Health Care: 10 Hospitals Pre 1/96-12/98 n=11,038 to 1/99-3/00 n=8,045 Pearson Circulation 2001;104:II-838

29 OPTIMIZE-HF: Program Objectives OPTIMIZE-HF is a national performance improvement initiative to improve guidelines adherence in patients hospitalized with HF Overall OPTIMIZE-HF program objectives: Improve medical care and education of patients hospitalized with HF Accelerate initiation of HF evidence-based, guidelinerecommended therapies by starting these therapies before hospital discharge in appropriate patients without contraindications Increase understanding of barriers to use of ACEI/ARBs, β-blockers, and other guidelinerecommended therapies in eligible HF patients Fonarow GC, et al. Am Heart J. 2004;148:43-51.

30 OPTIMIZE-HF Participating Hospitals (N=259) Washington New Hampshire Montana North Dakota Minnesota Vermont Maine Oregon Idaho South Dakota Wisconsin Michigan New York Massachusetts Wyoming Rhode Island California Nevada Utah Colorado Nebraska Kansas Iowa Missouri Illinois Ohio Indiana Kentucky West Virginia Pennsylvania Virginia North Carolina New Jersey Connecticut Delaware Arizona New Mexico Oklahoma Arkansas Tennessee South Carolina Maryland Alabama Georgia Texas Louisiana Alaska Mississippi Florida Hawaii Puerto Rico Virgin Islands

Process of Care Improvement Program Customizable toolkit to assist

31 Implementing OPTIMIZE-HF OPTIMIZE-HF + Web-based Registry Measure and benchmark heart failure quality of care and meaningful outcomes Process of Care Improvement Program Customizable toolkit to assist hospitals in improving care and regional meetings to train hospital teams

materials Wall charts Flash cards PowerPoint presentations

32 Process-of-Care Improvement Component Hospital Tool Kit Algorithms Care paths Standing orders Patient education materials Wall charts Flash cards PowerPoint presentations Structured Educational Opportunities Regional educational forums Access to faculty

Performance Improvement Process Select Target

Report Evaluate and Communicate Results to Team

QUALITY Create Action Plan Cardiologists Nurses

Implement Improvement Action Plan Improve Use of

33 Performance Improvement Process Select Target Performance Measures Review Practice Profile Report Evaluate and Communicate Results to Team Develop Solutions to Implementation Barriers QUALITY Create Action Plan Cardiologists Nurses Administrative Support Staff Impact Opportunity Implement Improvement Action Plan Improve Use of Evidence- Based Therapies Fonarow GC et al. Am Heart J. 2004;148:43 51.

34 OPTIMIZE-HF Hospital Characteristics Total Hospitals (N=259), n (%) Follow-Up Hospitals (N=91), n (%) Bed size: 0 to (12) 9 (10) 100 to (22) 21 (23) 250 to (40) 40 (44) 500 to (15) 13 (14) (5) 4 (4) Unknown 16 (6) 4 (4) Academic 118 (48) 48 (55) Transplant program 34 (14) 9 (10) Interventional (CABG/PCI) 163 (67) 62 (70) Midwest 68 (27) 27 (30) Northeast 44 (17) 14 (16) South 87 (34) 34 (38) West 56 (22) 15 (17) Fonarow GC, et al. Arch Intern Med. 2007;167:

35 OPTIMIZE-HF Patient Characteristics Hospital Cohort (N=48,612) Follow-Up Cohort (N=5,791) Mean age (years) Sex (% male) Caucasian (%) Ischemic etiology (%) Mean LVEF (%) LVSD (% of those assessed) Insulin-treated diabetes (%) Non insulin-treated diabetes (%) Hypertension (%) Rales (%) Mean SBP (mmhg) Mean HR (bpm) Mean Na (meq/l) Mean SCr (mg/dl) Mean Hb (g/dl) Fonarow GC, et al. Arch Intern Med. 2007;167:

36 Distribution of Patients Hospitalized with HF by Age and Gender 5,000 Female patients Male patients 4,000 Patients (n) 3,000 2,000 1,000 0 < Age (years) Fonarow G, et al. J Am Coll Cardiol. 2005;45:339A. Poster presented at ACC Fonarow G, et al. J Am Coll Cardiol. 2005;45:340A. Poster presented at ACC 2005.

37 OPTIMIZE-HF Comorbidities Patients (%) Fonarow GC, et al. Arch Intern Med. 2007;167:

38 Frequency of One or More Precipitating Factors in OPTIMIZE-HF Precipitating Factors Frequency (%) Arrhythmia, % 13.5 Uncontrolled hypertension, % 10.7 Ischemia/acute coronary syndromes, % 14.7 Worsening renal function, % 6.8 Pneumonia/respiratory process, % 15.3 Medication noncompliance, % 8.9 Dietary noncompliance, % 5.2 Other, % 12.7 No precipitating factors, % 38.7 One precipitating factors, % 42.2 Two precipitating factors, % 13.6 Three precipitating factors, % 4.2 Four or more precipitating factors, % 1.4 Fonarow et al. Arch Intern Med. 2008;168:

39 OPTIMIZE-HF Clinical Outcomes Hospital Cohort (N=48,612) Follow-Up Cohort (N=5,791) Length of stay, median (days) Length of stay, mean (days) In-hospital mortality (%) day readmission rate (%) day postdischarge mortality rate (%) day postdischarge mortality/rehospitalization (%) Fonarow GC, et al. Arch Intern Med. 2007;167:

One Year Mortality Rates in HF Clinical Trials vs. Registries 40 37.5% 39.6% 35 32.9% 1 Year Mortality Rate, % 30 25 20 15 10 6.5% 8.2% 9.8% 19.

40 One Year Mortality Rates in HF Clinical Trials vs. Registries % 39.6% % 1 Year Mortality Rate, % % 8.2% 9.8% 19.0% 5 0 SCD-HEFT (n = 2521) CHARM (n = 7601) MERIT-HF (n = 3991) COMPANION (n = 1520) EFFECT (n = 4031) Worcester (n = 2445) OPTIMIZE-HF (n = 5791) Annualized Clinical Trials Registries

41 Use of Evidence-Based HF Therapy at Discharge in Eligible Patients 100 Hospital Discharge Eligible Patients Treated (%) ACEI/ARB at Discharge β-blocker at Discharge Evidence- Based β-blocker Aldosterone Antagonist Statin For CVD/DM Warfarin For AF 11,976/14,493 13,032/15,675 10,248/15,675 3,621/20,118 14,904/38,066 6,571/12,560 ACEI/ARB, β-blocker, and aldosterone antagonist use in eligible patients with LVSD; statin in CAD, PVD, CVD and/or diabetes; and warfarin use in patients with atrial fibrillation. Fonarow GC, et al. Arch Intern Med. 2007;167:

42 Changes in Performance and Quality Measures Over Time Eligible Patients Treated (%) P<.0001 P<.0001 P=.1848 P<.0001 P<.0001 HF-1 HF-2 HF-3 HF-4 Any β- Blocker Discharge Instructions LV Function Measured ACEI in LVSD Smoking Cessation Q Q Q Q Q Q Q Q Fonarow GC, et al. Arch Intern Med. 2007;167:

Sex-Related Disparity in Use of Evidence- Based HF Therapy at Discharge Eligible Patients Treated (%) P=.0062 P=.5028 P=.1281 P<.0001 Women (n=25,075) Men (n=23,537) P<.0001 P=.

43 Sex-Related Disparity in Use of Evidence- Based HF Therapy at Discharge Eligible Patients Treated (%) P=.0062 P=.5028 P=.1281 P<.0001 Women (n=25,075) Men (n=23,537) P<.0001 P=.0406 ACEI ACEI/ARB β-blocker Warfarin Statin Aldosterone Antagonist ACEI/ARB, β-blocker, and aldosterone antagonist use in eligible patients with LVSD; statin in CAD, PVD, CVD, and/or diabetes; and warfarin use in patients with atrial fibrillation. Fonarow GC, et al. J Am Coll Cardiol. 2005;45:339A (Updated July 2005). The OPTIMIZE-HF Registry [database]. Final Data Report. Duke Clinical Research Institute. July 2005.

44 Age-Related Disparity in Use of Evidence-Based HF Therapy at Discharge Eligible Patients Treated (%) P<.0001 P<.0001 P<.0001 P<.0001 Age 70 y (n=16,973) Age >70 y ( n=31,558) P<.0001 P<.0001 ACEI ACEI/ARB β-blocker Warfarin Statin Aldosterone Antagonist ACEI/ARB, β-blocker, and aldosterone antagonist use in eligible patients with LVSD; statin in CAD, PVD, CVD, and/or diabetes; and warfarin use in patients with atrial fibrillation. Fonarow GC, et al. J Am Coll Cardiol. 2005;45:339A (updated July 2005). The OPTIMIZE-HF Registry [database]. Final Data Report. Duke Clinical Research Institute. July 2005.

45 HF Measures at Hospital Discharge by Race P=.0003 P<.0001 P<.0001 P<.0001 Complete Discharge Instructions LVEF Assessed African American Discharge ACEI Non African American Smoking Cessation Advice Yancy et al. J Am Coll Cardiol. 2008;51:

46 Changes in Clinical Outcomes Over Time with OPTIMIZE-HF Clinical Outcome Q Q P Value Length of stay (days) mean <0.001 In-hospital mortality, % Post-discharge mortality, % Post-discharge mortality/rehospitalization, % Fonarow GC, et al. Arch Intern Med. 2007;167:

47 In-Hospital and Follow-Up Outcomes by Process of Care Improvement (PrCI) Tool Use In-Hospital Mortality 60- to 90-Day Mortality and Rehospitalization Patients (%) P< Patients (%) P< PrCI Tool Use No PrCI Tool Use 0 PrCI Tool Use No PrCI Tool Use PrCI tool use (admission order set or discharge checklist) was reported during hospitalization in 45.3% of patients (n=22,017/48,612). Fonarow GC, et al. Arch Intern Med. 2007;167:

Impact of Evidence-Based HF Therapy Use at Hospital Discharge on Treatment Rates During Follow-Up 60- to 90-Day Postdischarge Follow-Up Eligible Patients Treated at Follow-Up (%) 100 93.

48 Impact of Evidence-Based HF Therapy Use at Hospital Discharge on Treatment Rates During Follow-Up 60- to 90-Day Postdischarge Follow-Up Eligible Patients Treated at Follow-Up (%) OR 30.6 (95% CI, ) P< OR (95% CI ) P< β-blocker at Discharge YES β-blocker at Discharge NO ACEI/ARB at Discharge YES ACEI/ARB at Discharge NO (1,579/1,697) (94/309) (1,329/1,861) (75/382) Fonarow GC et al. J Card Fail 2007;13:722-31

49 Impact of Discharge Use of Beta Blocker on Early Clinical Outcomes in Heart Failure Survival Probability P<0.01 P= Beta-Blocker No Beta-Blocker Days After Hospital Discharge Patients at Risk Beta-blocker 1,946 1,855 1, No Beta-blocker *Only subset of patients with 60- to 90-day follow-up are included. Patients with beta-blocker contraindications are excluded. Fonarow et al. J Am Coll Cardiol. 2008;52:

Mean Dose and Frequency of Dose Changes of β-blockade Post HF Hospital Discharge Mean Daily Dose (mg) At Discharge At 60-90 day Follow-up Carvedilol 17.8 ± 17.5 20.3 ± 17.

50 Mean Dose and Frequency of Dose Changes of β-blockade Post HF Hospital Discharge Mean Daily Dose (mg) At Discharge At day Follow-up Carvedilol 17.8 ± ± 17.3 Newly started on carvedilol 12.5 ± ± 15.2 Metoprolol succinate 68.3 ± ± 52.2 Newly started on metoprolol succinate Immediate-release metoprolol tartrate 57.5 ± ± ± ± 56.4 Atenolol 43.7 ± ± 37.8 Fonarow GC, et al. Am J Cardiol 2008;102:

51 Trends in Quality of Care and Outcomes in ADHERE: Q to Q ,168 enrollments from 285 ADHERE Hospitals Q n = 8,198 Q n = 11,289 Q n = 14,520 Q n = 17,041 Q n = 17,878 Q n = 16,839 Q n = 14,206 Q n = 11,447 Q n = 14,006 Q n = 12,519 Q n = 11,529 Q n = 9,610 Baseline Characteristics Similar All 12 Quarters Baseline In-hospital Mortality Risk by CART Tool Similar All 12 Quarters Fonarow GC et al. Am Heart J 2007;153:

52 Trends in Treatment and Clinical Outcomes in ADHF: ADHERE Medications During Q Q Difference P Value Hospitalization (n = 8220) (n = 9610) IV Diuretic 88.0% 87.3% - 0.7% 0.16 IV Inotrope 14.7% 7.9% - 6.8% < IV Nitroglycerin 9.1% 8.7% + 0.4% 0.36 IV Nesiritide 5.1% 21.4% % < PO ACEI/ARB 81.8% 81.0% - 0.8% 0.41 PO Beta Blocker 61.9% 80.1% +18.2% < PO Aldosterone Ant 29.1% 34.6% + 5.5% < PO Digoxin 53.0% 40.0% % < ADHERE 12 Consecutive quarters, Inpatients medications, PO Meds in LVSD patients without contraindications Fonarow GC et al. Am Heart J 2007;153:

53 Trends in Clinical Outcomes for HF in ADHERE: Q to Q Q Q RR 0.64 P< Mechanical Ventilation P< P< RR 0.71 P< ICU LOS Hospital LOS In-Hospital (days, mean) (days, mean) Mortality Expected Mortality ADHERE 12 Consecutive quarters, 159,168 patient episodes from 285 Hospitals Extrapolated to all of US: 21,000 fewer MV, 880,000 less hospital days, 14,300 less deaths per year Fonarow GC et al. Am Heart J 2007;153:

January 2005; currently over 500 US hospitals participating, over 500,000 patient HF hospitalizations Opportunity for

54 American Heart Association: Get With the Guidelines HF The AHA s in-hospital quality-improvement program aims at ensuring that every patient with HF receives the best possible care Continuity of data and hospital tools with OPTIMIZE-HF Launched January 2005; currently over 500 US hospitals participating, over 500,000 patient HF hospitalizations Opportunity for hospitals to achieve national recognition through participation Opportunity for advanced heart failure certification via The Joint Commission

55 AHA GWTG-HF Web Based Patient Management Tool

56 AHA GWTG-HF PMT, On-Demand Quality Reporting

57 GWTG-HF: Performance Measures % 90.0% 80.0% 70.0% Compliance 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Discharge Instructions LV Function Measurement ACEI or ARB at D/C for LVSD Beta Blocker at D/C for LVSD Smoking Cessation Counseling Composite Performance Measure 100% Compliance Measure Baseline 69.7% 90.1% 81.2% 87.3% 77.4% 80.0% 60.1% Current 93.3% 98.8% 94.7% 96.1% 98.9% 95.6% 90.5% Achievement Measure Baseline = Admissions Jan2005 Dec2005 January 2012 Current = Admissions Jan2011-Dec2011

58 GWTG-HF Participation, Quality of Care and Clinical Outcomes Measure GWTG Hospitals (n=355) Non-GWTG Hospitals (n=3909) P-Value LVEF documented 92.8% 83.0% < ACEI/ARB in LVSD 85.6% 81.4% Discharge Instructions Smoking Cessation Counseling 67.7% 55.3% < % 81.3% 0.04 Risk-adjusted 30-day mortality for HF was lower in GWTG PAA hospitals compared to non-gwtg hospitals Hospital Compared data Heidenreich PA et al Am Heart J 2009;158:546-53

IMPROVE-HF Registry to Improve the Use of Evidence-Based Heart Failure

evidence-based heart failure therapies in the outpatient setting via a

for medicine, devices, and patient education Identify treatment gaps as

Advance understanding of best approaches to identify appropriate HF

closing treatment gaps by offering practical information and disease

59 IMPROVE-HF Registry to Improve the Use of Evidence-Based Heart Failure Therapies in the Outpatient Setting Improve treatment rates for evidence-based heart failure therapies in the outpatient setting via a performance improvement registry Track utilization (via chart reviews) for medicine, devices, and patient education Identify treatment gaps as compared to treatment guidelines and evidence from large clinical trials Advance understanding of best approaches to identify appropriate HF patients for indicated medical and device therapies Assist practices in closing treatment gaps by offering practical information and disease management tools 167 US Cardiology Practices, 35,000 Patients. Fonarow GC et al. Am Heart J. 2007;154:12-38.

Clinical Assessment and Management Forms Patient Education Materials www.

60 IMPROVE HF Practice Specific Education and Implementation Tools Evidence Based Algorithms and Pocket Cards Clinical Trials and Current Guidelines Clinical Assessment and Management Forms Patient Education Materials Dissemination of best practices: - Webcasts - Online Education - Newsletters

61 IMPROVE HF Performance Intervention: Benchmarked Practice Profile Report Adherence to Guidelines Practice or Single Physician On-Demand Performance Measures across all physicians within practice Benchmarking Benchmarking Capability: region, practice, individual physician

62 IMPROVE HF: Baseline Quality of Care 100% Baseline Eligible Patients Treated 80% 60% 40% 80% 86% 34% 69% 38% 49% 62% 20% 0% ACEI/ARB ß-blocker Aldosterone Antagonist Anticoagulant for AF CRT ICD HF Education 167 Cardiology/Multispecialty Practices, 15,138 patients Fonarow GC, et al. Circulation. 2010;122:

63 IMPROVE HF Primary Results: Improvement in Quality Measures at 24 Months Significant Improvement in 6 of 7 Quality Measures at 12 and 24 Months Eligible Patients Treated 100% 80% 60% 40% * * 87% 84% 80% 86% * * 93% 94% Baseline 12 months 24 months * 51% * 62% 69% 69% 69% 69% 38% 34% * 58% * 49% * 71% * 79% * P<0.001 vs. baseline 62% * * 71% 69% 20% 0% ACEI/ARB ß-blocker Aldosterone Antagonist Anticoagulant for AF CRT ICD HF Education 167 practices, 34,810 heart failure patients enrolled Fonarow GC, et al. Circulation. 2010;122:

64 IMPROVE HF Composite Quality Measures Substantially Improved at the Patient Level Composite Score: % of total indicated quality measures provided 17.0% relative increase, p < All-or-None Care: % of patients receiving each indicated quality measure 80.6% relative increase, p < % 80.1% 43.9% 24.3% Baseline (n=167) Patient level analysis 24 months (n=155) Baseline (n=167) Patient level analysis 24 months (n=155) Fonarow GC, et al. Circulation. 2010;122:

65 Improvement in Quality Measures at 24 Months Improved Treatment or Documentation of Exceptions? Quality Measure Newly documented contraindication/ Intolerance at 24 mo. in patients initially eligible at baseline (N=7,605), % Newly treated at 24 mo. in patients initially eligible at baseline (N=7,605), % Newly treated at 24 mo. in patients not initially eligible at baseline, but eligible at 24 mo. ACEI/ARB 9.8% (699/7138) 7.6% (546/7138) 67.1% (49/73) ß-blocker 5.5% (381/6905) 6.3% (434/6905) 83.9% (208/248) Aldosterone antagonist 16.4% (210/1278) 10.3% (132/1278) 54.2% (396/730) Anticoagulation for AF 8.8% (181/2061) 6.9% (143/2061) 58.1% (493/848) CRT-P/CRT-D 1.8% (12/673) 23.5% (158/673) 59.3% (377/636) ICD/CRT-D 3.9% (198/5028) 15.3% (769/5028) 71.1% (857/1205) HF education 0.0% (0/7605) 26.3% (2003/7605) 0.0% (0/0) Fonarow GC, et al. Circulation. 2010;122:

66 Reduced Practice Variation in Guideline Directed ICD Use with IMPROVE HF Mehra et al. Pacing Clin Electrophysiol. 2012;35:

67 Absolute Improvement in Quality Measures for Women and Men Men Women P Value Quality Measure (expressed in %) N=10, mo N=5,422 Δ Baseline Baseline N=4, mo N=2,181 Δ Base -line* 24 mo* Δ CRT-P/CRT-D < ICD/CRT-D <.001 <.001 <.001 Similar for 5 quality measures, but significantly better in women for CRT, ICD, and composite care * Comparison of % of men vs. women receiving quality measure Difference in absolute change percentage between sexes Walsh MN, et al. J Cardiac Fail. 2010;122: Sex and Improvements in HF Therapies

68 Race Based Improvements in IMPROVE HF Performance Measure Black White Not Documented P- values Baseline (n = 1401) 24- Months (n = 686) Percent Absolute Improve ment Baseline (n = 6430) 24- Months (n = 3238) Percent Absolute Improvem ent Baseline (n = 7081) 24- Months (n = 3537) Percent Absolute Improvem ent B vs W B vs ND W vs ND CRT-P/-D 39.3% 67.3% % 71.3% % 65.6% ICD/-D 47.8% 80.8% % 80.1% % 78.0% * P-value for differences in absolute improvement. Reynolds D et al J Natl Med Assoc May-Jun;104(5-6):287-98

69 Temporal Changes in ICD Use in GWTG-HF Al-Khatib S M et al. Circulation 2012;125:

70 Does Increased Use of Guideline Recommended Therapies Improve Clinical Outcomes?

71 Associations Between Outpatient Heart Failure Process of Care Measures and Mortality Process Measure at Baseline Deaths within 24 Months with Measure Conformity, % Deaths within 24 Months without Measure Conformity, % Adjusted Odds Ratio, 95% CI P value ACEI/ARB 18.8% 37.0% 0.51 ( ) < Beta-blocker 20.7% 38.0% 0.45 ( ) < Aldosterone antagonist 28.9% 28.9% 1.36 ( ) Anticoagulation for AF 25.4% 33.8% 0.69 ( ) ICD/-D 20.4% 27.8% 0.62 ( ) < CRT-P/-D 28.8% 38.3% 0.64 ( ) HF education 22.3% 29.4% 0.69 ( ) Fonarow GC, et al. Circulation 2011; 123:

72 Improved Adherence to HF Guidelines Translates to Improved Clinical Outcomes in Real World Patients Each 10% improvement in guideline recommended composite care was associated with a 13% lower odds of 24-month mortality (adjusted OR 0.87; 95% CI, 0.84 to 0.90; P<0.0001). ACC/AHA Guideline Directed Therapy for Heart Failure Improves Outcomes Fonarow GC, et al. Circulation. 2011;123:

73 Cumulative Benefits of Established, Guideline- Recommended HF Therapies Fonarow GC et al J Am Heart Assoc 2012;1:16-26

74 IMPROVE HF Clinical Implications IMPROVE HF is the largest outpatient cardiology HF practice performance improvement program ever untaken. Implementation of this defined, scalable, and sustainable performance improvement intervention improves the use of evidence-based, guideline-recommended HF therapies in real-world cardiology and multispecialty practices. In all care settings, programs to provide practitioners with useful reminders based on the guidelines and to continuously assess the success achieved in providing these recommended therapies to eligible patients should be implemented. Fonarow GC, et al. Circulation. 2010;122:

75 Change in Mortality in the Past 20 Years in Chronic Heart Failure Clinical Trials Outcome of Placebo Arms of Randomized Controlled HF with Reduced LVEF Clinical Trials Time Frame # of Trials NYHA Class Cardiac HR Non- Cardiac HR Total Mortality HR Over the past 20 years, overall mortality rates for HF patients have decreased by 63%, while cardiac mortality in HF trials has decreased by almost 70% Bryg RJ et al. J Card Fail 2011;116:s91

76 Temporal Trends in Survival in HF Patients With and Without Reduced EF Patient with Reduced Ejection Fraction Patient with Preserved Ejection Fraction Survival Survival P = Year P = Year No. at Risk No. at Risk Owan TE, et al. N Engl J Med. 2006;355:

77 Challenges to Implement a Heart Failure Performance Improvement System This will not work in my practice or hospital The physicians will not agree to this We cannot get a consensus The managed care organization will not pay for it Patients do not want to be on a lot of medications There is not enough time It will cost too much It may not be safe to start β-blocker medications in heart failure patients CRT and ICD don t work This will benefit the competition The administration will not pay for it What about the liability? It will take too much time All my patients are too complex for this The patients should all be followed by someone else It is too hard to get things through the practice committee The physicians do not like cookbook medicine We do not have anyone to do this

78 Costs for Evidence-Based Heart Failure Therapies Therapy Specific Regimen Annual Cost ACE inhibitor Lisinopril 20 mg daily $40 Beta blocker Carvedilol 25 mg BID $40 Aldosterone antagonist Spironolactone 25 mg daily $40 CRT-D CRT-D device placement ($45,000, one time)

79 Key Elements to Quality Improvement: Why Do Some Programs Succeed? Access to current and accurate data on treatment and outcomes Have stated goals Administrative support Support among clinicians Use of care maps and pathways Use of data to provide feedback Bradley. JAMA. 2001;285:

80 Potential Impact of Optimal Implementation of Evidence-Based HF Therapies on Mortality Guideline HF Patient Current HF Potential Lives Potential Lives Recommended Population Population Saved per Year Saved per Year Therapy Eligible for Eligible and (Sensitivity Range*) Treatment, n* Untreated, n (%) ACEI/ARB 2,459, ,767 (20.4) 6516 ( ,260) Beta-blocker 2,512, ,809 (14.4) 12,922 ( ,329) Aldosterone Antagonist 603, ,326 (63.9) 21,407 (10,960-36,991) Hydralazine/Nitrate 150, ,749 (92.7) 6655 ( ,500) CRT 326, ,604 (61.2) 8317 ( ,372) ICD 1,725, ,512 (49.4) 12,179 ( ,045) Total ,996 (34, ,497) Fonarow GC, et al. Am Heart J 2011;161:

81 Cumulative Impact of Heart Failure Therapies Relative-risk 2 yr Mortality None % ACE Inhibitor 23% 27% Aldosterone Ant 30% 19% Beta Blocker 35% 12% CRT ± ICD 36% 8% Cumulative risk reduction if all five therapies are used: 77% Absolute risk reduction: 27%, NNT = 4 Fonarow GC. Lancet 2008;372:

82 Humanity s greatest advances are not in its discoveries but in how those discoveries are applied... Bill Gates, June 7, 2007 Harvard Commencement Address

83 The Approach to Heart Failure The burden of HF continues to grow and HF is one of the single most deadly and expensive health care problems Medical therapies and nonpharmacologic measures for HF that can impact HF patients need for rehospitalization, costs of care, health status, and survival are underutilized in conventional practice settings Hospital and outpatient performance improvement systems and outpatient disease management programs are needed to optimize care and outcomes for HF

Get With The Guidelines: Lessons for National Healthcare Improvement Programs

Get With The Guidelines: Lessons for National Healthcare Improvement Programs Gregg C. Fonarow, MD, FACC, FAHA, FHFSA Eliot Corday Professor of Cardiovascular Medicine and Science UCLA Division of Cardiology