Lessons Learned through Research: Do Hospitals and Ambulatory Centers Follow Guideline Recommended Care Nancy Albert, PhD, RN, CCNS, CCRN, NE-BC, FAHA, FCCM Senior Director Nursing Research and Innovation; CNS, Kaufman Center for Heart Failure May 2012

2 Presenter Disclosure Information Nancy M. Albert PhD, CCNS, CHFN, CCEN, FAHA, FCCM Lessons Learned through Research: Do Hospitals and Ambulatory Centers Follow Guideline Recommended Care 2 FINANCIAL DISCLOSURE: No relevant financial relationship exists

National Trends in Readmission Rates after HF Hospitalization Ross JS, et al. Circ Heart Fail 2010;3: RSRR, Risk-standardized 30 D all-cause readmission rate RSRR

All Cause Mortality After Each Subsequent Hospitalization for HF Setoguchi et al. Am Heart J. 2007;154: Time Since Admission Kaplan-Meier Cun. Mortality CHF 1 st Admission (n = 14,374) 2 nd Admission (n = 3,358) 3 rd admission (n = 1,123) 4 th Admission (n = 417) 1 st hospitalization: 30 d mortality = 12%; 1 yr = 34%

Risk-Treatment Mismatch in HF: Canadian EFFECT Study Use rates in absence of contraindications. For all drug classes, P <.001 for trend. EFFECT, Enhanced Feedback for Effective Cardiac Treatment. Lee D. JAMA. 2005;294: At Hospital Discharge90-Day Follow-Up1-Year Follow-Up Low RiskAverage RiskHigh Risk ACEIACEI or ARB - Blocker 1-Year Mortality Rate Patients, % ACEIACEI or ARB - Blocker

ACADEMIC DETAILING 734 physicians surveyed Guidelines helped a moderate (47%) or great amount (23%) in clinical decision making – 21% respondents reported ACE-I are contraindicated if s cr. > 2.0 mg/dL – 27% thought intermittent inotropic therapy is reasonable practice per guidelines – 33.9% underestimated HF prevalence in US – 75.2% underestimated 1-year mortality for Medicare patients Hauptman PJ, et al. Am J Medicine. 2008;121;

Timeline from Concept to Clinical Adoption of Neurohormonal Treatments in Heart Failure ACE Inhibitors -Blockers Concept1st DataDefinitiveApproval50% use Adapted from M Konstam, HFSA Aldosterone Antagonists X

PACE of Improvement From to performance on the typical CMS inpatient/outpatient measures improved from 69.5% to 73.4%, a 12.8% relative improvement At this rate, by 2024, we will have 95% performance on the measures that were current in 2002

ADHERE: Variation in ACEI Use ADHERE: Dec 2002, 206 Hospitals; 23,193 patients (subset with LVEF <.40) ORYX Core Measure: HF 3 - LVEF < 40% prescribed ACEI at discharge Rate (%) ADHERE Hospitals

ADHERE: Variation in Beta Blocker Use ADHERE: Dec 2002, 206 Hospitals (Subset with LVEF < 0.40) ADHERE Hospitals Rate (%) Use of Beta blocker at Discharge for Patients with LVEF <= 40%

GWTG-HF- Aldosterone Antagonists 34% of patients received Tx by end of 2007 Albert NM, et al JAMA; 2009;302:1658. In 140 hospitals w 10/more patients meeting criteria, use was highly variable: median, 28.3%

Albert NM, et al. JAMA 2009; 302:1658 GWTG-HF Aldosterone Ant. Tx Use over time, N = 12,565; P = % 34.5%

GWTG-HF Aldosterone Antagonist Tx- Trends in Adherence Over Time Albert NM, et al. JAMA 2009; 302:1658 Aldosterone Ant. Users (n / N) Jan- Jun 05 % Jul- Dec 05 % Jan- Jun 06 % Jul- Dec 06 % Jan- Jun 07 % Jul- Dec 07 % P value* Inappropriate use overall (n= 640; 7.43%) Serum K+ >5.5 & <6.0 mEq/L (n=18; 0.21%) Serum Cr. 2.5 & < 3.0 mg/dL (n= 233; 2.71%) EF >40% and without HTN (n= 396; 4.60%)

Albert NM, et al. JAMA 2009; 302:1658 GWTG-HF Aldosterone Ant. Tx Yes, Aldosterone Ant No, Aldosterone Ant. %, Performance Measure Conformity

GWTG-HF: Warfarin at Hospital Discharge Among Pts Admitted for HF w Atrial Fibrillation 72,534 pts from 01/2005 – 03/2008 – 255 hospitals Results: – 20.5%, atrial fib on admission (n=14,901) – 13.7%, prior Hx atrial fib but SR at adm. (n=9,918) – Contraindications to warfarin Tx were documented in 9.2% – Median prevalence of warfarin Tx, 64.9% Piccini JP et al. JACC 2009;54:

Warfarin at Hospital Discharge Among Pts Admitted for HF w AF Median prevalence of warfarin Tx, 64.9% Trend over time, P = Piccini JP et al. JACC 2009;54:

GWTG-HF: Warfarin at Hospital Discharge Among Pts Admitted for HF w Atrial Fib Site 1815 Warfarin Discharge (%)

GWTG-HF: Warfarin at Hospital Discharge Among Pts Admitted for HF w Atrial Fib Piccini JP et al. JACC 2009;54: CHADS 2 score: CHF, HTN, age > 75, DM, prior stroke/TIA Warfarin Discharge (%) Chads2 Score P<.0001 for trend Are hospitals delivering optimal evidence-based recommendations?

Hernandez, A. F. et al. JAMA 2007;298: GWTG-HF: Race and Gender Disparities in ICD Use at Discharge Among Eligible Patients With HF Black female White female Black male White Male Black Male White Female Black Female N= 13,034 pts

GWTG-HF: Hospital Variation and Characteristics of ICD Use January 2005 – June 2007 New or Discharge prescription for ICD Tx in patients with EF 30% without documented contraindication – 54,750 pts from 234 hospitals – Of 12,693 pts, 2545 had prior ICD (20% use) – Of 10,148 (134 hospitals): – Overall Use/Planned implementation = 20% Shah B, et al. JACC 2009;53:

Hospital Variation in ICD Use Patient Factors High Use N=48 Med. Use N=42 Low Use N=44 P Value Female34%38%36%<0.03 Race-AA22%33%32%<0.001 High Chol38%32%30%<0.001 Hx MI15%9%11%<0.001 Hx HTN63%66%64%0.004 Shah B, et al. JACC 2009;53: Are disparities in care present by patient features?

New or Planned ICD Use-GWTG-HF Shah B, et al. JACC 2009;53: GWTG - HF Hospital Site Hospital ICD rate (%)

Hospital ICD Use Associations with Hospital Characteristics Shah B, et al. JACC 2009;53: Academic Adjusted ICD rate (%) Non-academic Heart transplants No heart transplants PCI capable No PCI CABG capable No CABG Northeast Midwest South West Beds <100 Beds Beds Beds Beds >500 Beds Are disparities in care present by hospital features?

Median, 49.1 Mean, 50.7 IMPROVE-HF: Variation in OPD HF Care Fonarow GC, et al. Circ Heart Fail. 2008;1:98–106.

ADHERE-HF: Rates of Conformity by Practice Setting and Differences in Hospital Level Outcomes; > 80,000 Hosp. Admissions Fonarow GC, et al. Arch Intern Med 2005; 165:1469–1477

Median, 33.3 Mean, 35.0 IMPROVE-HF: Variation in OPD HF Care Fonarow GC, et al. Circ Heart Fail. 2008;1:98–106.

Median, 33.3 Mean, 37.3 IMPROVE-HF: Variation in OPD HF Care Fonarow GC, et al. Circ Heart Fail. 2008;1:98–106.

Median, 60.7 Mean, 59.8 IMPROVE-HF: Variation in OPD HF Care Fonarow GC, et al. Circ Heart Fail. 2008;1:98–106. Are cardiologist practices delivering optimal evidence-based recommendations?

IMPROVE-HF: Improvement in Quality Measures at 24 Months (Pt. Level Analysis) Eligible Patients Treated Fonarow GC, et al. Circulation. 2010;122: P-values are for relative change; *, P <0.001 vs. baseline Baseline N= 15,177; 24 Mo. N= 11,621; 167 practices

IMPROVE-HF: Baseline Measure Conformity: Alive vs. Dead at 24-Months The baseline process measure conformity was significantly lower among patients who died compared with those who survived for 5 of 7 individual measures. Fonarow GC, et al. Circulation. 2011;123(15):

IMPROVE-HF Nested Case-Control Analysis: Baseline Use of Guideline Recommended Therapies in Cases (N=1376; Dead) and Controls (N= 2752; Alive) at 24-months (matched at 1:2 ratio) P< Fonarow GC, et al. J Am Heart Assoc 2012;1:16-26.

IMPROVE-HF Nested Case-Control Analysis: Mortality Reduction Based on Number of Guideline-Recommended Therapies at Baseline 24 Month Mortality Adjusted Odds Ratios (95% CI) Number of Therapies (vs. 0 or 1 therapy) 2 therapies 3 therapies 4 therapies 5, 6, or 7 therapies Odds Ratio (95% confidence interval) 0.63 ( ) (P =0.0026) 0.38 ( ) (P <0.0001) 0.30 ( ) (P <0.0001) 0.31 ( ) (P <0.0001) Fonarow GC, et al. J Am Heart Assoc 2012;1:16-26.

IMPROVE-HF: Incremental Benefits with HF Therapies (Cumulative % Reduction in Odds of Death at 24 Months) -28% to -49% P< % to -71% P< % to -81% P< % to -86% P< % to -88% P< % to -87% P< Fonarow GC, et al. J Am Heart Assoc 2012;1:16-26.

IMPROVE-HF: Incremental Benefit with HF Therapies (Cumulative % Reduction in Odds of Death at 24 Months Associated with Sequential Treatments) +20% to -68% P= % to -91% P< % to -96% P< Fonarow GC, et al. J Am Heart Assoc 2012;1:16-26.

Hospitalization for HF : CV / Medical Conditions Klapholz, et al. JACC 2004;43: Reasons for Clinical Decompensation were Identified in only ½ of Patients Syst. BP >200 mmHg Non-Compliance MR / AR >3+ ACS Renal Insuff. Afib / Flutter / SVT Sev. COPD / Asthma Pneumonia AS / MS <1.0 cm 2 Sepsis Patients (%) Did we assess patient knowledge, psychosocial, behavioral, economic needs/issues?

Primary endpoint was: Mortality or Readmission for heart failure COACH Study: Self-Care Compliance

COACH study: Self-Care Compliance Van der Wal MH, et al. Eur Heart J 2010;31: Compliance = scoring mostly or always in following 3 recommendations: 1) Sodium-restricted diet 2) Fluid restriction 3) Exercise Compliance = daily weight monitoring three times per week to daily Compliance measured 1 month after hospital discharge & followed for 18 months 48% (N=830 patients) Cum survival Time to primary endpoint Overall non-compliant Overall compliant HR 1.40 ( ); P=0.01

Van der Wal MH, et al. Eur Heart J 2010;31: Compliance with advice on weight monitoring, low sodium diet, fluid restriction and exercise *, P< 0.01; **P<0.05 COACH study: Self-Care Compliance % primary endpoint Total compliance score (0-4) 4321 or 0 25% 38% 36% 45% * ** *

Non-compliance adjusted HR (95% CI) Primary endpoint: 1.48 ( ) P=0.002 Time to: -- HF readmission: 1.55 ( ) P= Death: 1.24 ( ) P=0.20 Non-compliance adjusted HR (95% CI) Primary endpoint: 1.20 ( ) P=0.27 Time to: -- HF readmission:.93 ( ) P= Death: 1.58 ( ) P=0.02 Van der Wal MH, et al. Eur Heart J 2010;31: COACH study: Self-Care Compliance Cum survival Time to primary endpoint Non-compliant exercise Compliant exercise Time to primary endpoint Non-compliant weighing Compliant weighing Compliant with exerciseCompliant with daily weighing

COACH study: Self-Care Compliance Conclusion: Advice and compliance in all 4 self-care behaviors are important Time to primary endpoint Non-compliant exercise Compliant exercise Time to primary endpoint Non-compliant fluid Compliant fluid Compliant with diet Cum survival Compliant with fluid restriction Van der Wal MH, et al. Eur Heart J 2010;31: Do we assess patients adherence to self-care and learn reasons for non-adherence?

Performance Measures & Quality Based on nationally established guidelines Include outcomes and processes of care known to positively influence overall outcomes of care – Institution and system level – Hospital – Healthcare practitioners/care providers – Incorporate risk-adjustment methodology to account for significant differences in patient populations among institutions

Performance Measures & Quality Often, we question the reliability of performance measures in specific settings – Documentation of patient education – Checking a box does not reflect delivery of quality care 1. Koelling TM, et al. Circulation 2005;111: Krumholz HM et al. JACC 2002;39: :1 RN-delivered comprehensive HF education during a 1-hour hospital session decreased 6- month days hospitalized or days dead 1 RN delivered, 1 hour, comprehensive hospital education + ongoing 1 year telephone-based support decreased 1-year hospital readmission rate and hospital costs 2

Performance Measures & Quality Public release of performance data in changing the behavior of healthcare consumers, professionals or organizations – > 35,000 consumers & 1560 hospitals Conclusions: – The small body of evidence available provides no consistent evidence that the public release of performance data changes consumer behavior or improves care. – Evidence that the public release of performance data may have an impact on the behavior of healthcare professionals or organizations is lacking. Ketelaar NA, et al. Cochrane Database Syst Rev. 2011;11:CD

PROGRAMS Developed to Improve Performance Measures Usually designed to enhance quality of patient care that promotes adoption of evidence-based, guideline- recommended therapies – Force a deeper look into actions and practices – Requires: – Leadership – Evidence-based clinical decision support (algorithms; pocket cards, customizable order sets...) – Patient education resources – Regular review of data; benchmarking – Process improvement model of change