Ejection Fraction in Heart Failure: A Redefinition. Tarek Kashour King Fahad Cardiac Center King Saud University Riyadh, KSA

Transcription

1 Ejection Fraction in Heart Failure: A Redefinition Tarek Kashour King Fahad Cardiac Center King Saud University Riyadh, KSA

2 Word of caution!!! Incomplete understanding of a disease process may lead to incorrect classification with potential serious consequences Rabies was classified as a psychiatric illness before discovery of germ theory Lung cancer and brain cancers were lumped in one group early last century

3 Heart Failure Taxonomy Acute vs. Chronic Left vs. Right De Novo ADHF vs. acute on top of chronic ADHF Systolic vs. diastolic HFpEF vs. HFrEF Etc..

4 HFpEF vs. HFrEF Issues with definition i.e. what is the cut off value that dichotomizes the two syndromes? 40% 45% 50% Are these two syndromes distinct or consist of multiple overlapping phenotypes?

5 EF cut off values in clinical trials for HFpEF Trial EF cut off I-PRESERVE 45% DIG-PEF > 45% CHARM-Preserved > 40% PEP-CHF 40%

6 EF cut off values in population-based studies and registries Some studies used 40% (1) Others used 45% (2) Others used 50% (3,4) Others excluded the group 40% - 50% (5,6) 1- Mangala A et al, Am J Cardiol Ho et al, Circ heart fail 2011;4: Owan et al, NEJM 2006;355: Chinali M et al, Coron Artery Dis 2010;21: Bhatia et al, NEJM 2006;355: Goldberg R et al, Am J Cardiol 2013;111:

7 Do HFpEF and HFrEF represent: distinct or overlapping multiple phenotypes?

8

9 Are HFpEF and HFrEF overlapping or distinct phenotypes? One way to answer this question is to examine carefully the two extreme groups EF < 40% EF > 45-50% If each of these groups constitute homogenous patient population in terms of Patient characteristics Prognosis Response to therapy Then, the bimodal model has a merit

10 Does HFrEF represent a single disease entity? Analysis from HF-ACTION trial Total of 2331 patients with chronic HF EF 35% Cluster analysis using 45 variables Ahmad et al, JACC 2014;64:

11 Cluster Cluster 1 Features Older Caucasian males, Ischemic CMP 68%, 39% NYHA III-IV. Highest rates of comorbidities Lowest median peak VO2 and highest biomarker levels Cluster 2 Cluster 3 Cluster 4 Youngest, median age= 49, females 38%, highest BMI (34) 90% non-ischemic CMP Highest prior hospitalization, lowest biomarker levels and second highest peak VO2 80% ischemic CMP, Highest burden of angina (97%) Highest rates of prior PCI and CABG Second lowest peak VO2 and second highest biomarker level Second highest rate of prior hospitalizations Highest percentage of Caucasians (77%) and females 39%. Median age 55 years, 90% non-ischemic CMP. Lowest rate of prior hospitalization Lowest rates of risk factors and comorbidities Highest peak VO2, second lowest biomarker levels

12 Outcomes

13 This study indicate that HFrEF syndrome comprises multiple phenotypes

14 Does HFPEF represent a single disease entity? 379 with HFpEF (EF > 50%) Detailed clinical, lab, ECG and echo phenotyping 67 continuous variables Several statistical learning algorithms including unbiased cluster analysis Shah et al, Circulation 2015;131:269-79

15

16 Outcomes Shah et al, Circulation 2015;131:269-79

17 This study indicate that HFpEF syndrome comprises multiple phenotypes

18 Acute heart failure with preserved vs. reduced ejection fraction: Insights from HEARTS Regitry

19 HEARTS Registry Enrolled 2609 consecutive patients hospitalized with ADHF 18 hospitals from Saudi Arabia Between October 2009 and December 2010 Follow up until January 2013

20 HFpEF/HFrEF: LV function in HEARTS was categorized into 4 different groups: Normal LV function= >50% Mild dysfunction= 40-50% Moderate dysfunction= 30-40% Severe dysfunction= <30%

21 Distribution of LV Function Grades 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 45.5% 24.2% 13% 12.8% normal mild moderate severe

22 Baseline characteristics according to LVEF group

23 What is the pattern of baseline characteristics distribution among the 4 groups?

24 Demographics

25 Mean age according to LV function Proportion of patients above 70 years % 40% 35% 30% 25% 20% 15% 10% 5% 0%

26 Proportion of female patients BMI 70% 60% 50% 40% 30% 20% 10% 0%

27 Medical History

28 HTN Atrial fibrillation 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 30% 25% 20% 15% 10% 5% 0%

29 History of IHD COPD 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% 12% 10% 8% 6% 4% 2% 0%

30 Vital Signs

31 SBP DBP

32 Investigations

33 LBBB GFR 16% 14% 12% 10% 8% 6% 4% 2% 0%

34 In-Hospital Complications

35 Shock VT/VF requiring Tx 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% 6.0% 5.0% 4.0% 3.0% 2.0% 1.0% 0.0%

36 Heart Failure Phenotypic Continuum Normal EF Low HFpEF HFrEF

37 We used Fisher s linear discriminant analysis to assess how much overlap is there if in fact it exists Normal EF Low HFpEF HFrEF

38 Different models were explored based on Clinical, laboratory and ECG variables Two final models were selected First model included all variables except the noninvasive hemodynamic parameters Second model included only the non-invasive hemodynamic parameters (SBP, DBP, MBP, PP and PP/MBP)

39 Fisher s LDA using clinical and laboratory parameters

40 Fisher s LDA using non-invasive hemodynamic parameters

41

42 LDA function-based between-group distance Mild Moderate Severe Normal Mild Moderate 0.48

43 Conclusion from HEARTS Registry Our data indicates that HF patient population comprises multiple overlapping phenotypes when phenotypically characterized according to EF strata The patient population with mild LV dysfunction (EF 40%-50%) seems to be closer to the group with EF>50%

44 Take-home Message HF taxonomy according to EF has significant limitations HF comprises multiple overlapping phenotypes when classified according to EF There are two groups at the two ends of the spectrum with in-between intermediate groups Each EF stratum consists of multiple phenotypes New taxonomy is needed to better classify and characterize HF patients

45 Thank you