A total of 458 HFrEF (LVEF ≤40%) and 112 HFpEF (LVEF ≥50%) patients aged ≥60 years with NYHA class ≥II from TIME-CHF were included. Endpoints are 18-month overall and HF hospitalization-free survival. After correction for baseline characteristics that differed between the HF types, i.e. age, gender, body mass index, systolic blood pressure, cause of HF, and AF, HFpEF patients exhibited higher soluble interleukin 1 receptor-like 1 [ST2; 37.6 (28.5-54.7) vs. 35.7 (25.6-52.2), P = 0.02], high sensitivity C-reactive protein (hsCRP; 8.54 (3.39-25.86) vs. 6.66 (2.42-15.39), P = 0.01), and cystatin-C [1.94 (1.57-2.37) vs. 1.75 (1.39-2.12), P = 0.01]. In contrast, HFrEF patients exhibited higher NT-proBNP [2142 (1473-4294) vs. 4202 (2239, P < 0.001], high sensitivity troponin T [hsTnT;, P = 0.03], and haemoglobin [124 (110-135) vs. 134 (122-145), P < 0.001]. In addition to these clinical characteristics, NT-proBNP, haemoglobin, cystatin-C, hsTnT, and ST2 improved the area under the curve from 0.86 (0.82-0.89) to 0.91 (0.87-0.94; P < 0

Background: A systemic pro-inflammatory state has been hypothesized to mediate the association between comorbidities and abnormal cardiac structure/function in heart failure with preserved ejection fraction (HFpEF). We conducted a proteomic analysis to investigate this paradigm. Methods: In 228 HFpEF patients from the multicenter PROMIS-HFpEF study, 248 unique circulating proteins were quantified by a multiplex immunoassay (Olink) and used to recapitulate systemic inflammation. In a deductive approach, we performed principal component (PC) analysis to summarize 47 proteins known a priori to be involved in inflammation. In an inductive approach, we performed unbiased weighted co-expression network analyses of all 248 proteins to identify clusters of proteins that overrepresented inflammatory pathways. We defined comorbidity burden as the sum of 8 common HFpEF comorbidities. We used multivariable linear regression and statistical mediation analyses to determine whether and to what extent inflammation mediates the association of comorbidity burden with abnormal cardiac structure/function in HFpEF. We also externally validated our findings in an independent cohort of 117 HFpEF cases and 30 comorbidity controls without HF. Results: Comorbidity burden was associated with abnormal cardiac structure/function and with PCs/clusters of inflammation proteins. Systemic inflammation was also associated with increased mitral E velocity, E/e' ratio, and tricuspid regurgitation (TR) velocity; and worse right ventricular function (tricuspid annular plane systolic excursion [TAPSE] and right ventricular. [RV] free wall strain). Inflammation mediated the association between comorbidity burden and mitral E velocity (proportion mediated 19-35%), E/e' ratio (18-29%), TR velocity (27-41%), and tricuspid annular plane systolic excursion (13%) (P<0.05 for all) but not RV free wall strain. TNF-R1, UPAR, IGFBP-7 and GDF-15 were the top individual proteins that mediated the relationship between comorbidity burden and echocardiographic parameters. In the validation cohort, inflammation was upregulated in HFpEF cases versus controls, and the most prominent inflammation protein cluster identified in PROMIS-HFpEF was also present in HFpEF cases (but not controls) in the validation cohort. Conclusions: Proteins involved in inflammation form a conserved network in HFpEF across 2 independent cohorts and may mediate the association between comorbidity burden and echocardiographic indicators of worse hemodynamics and RV dysfunction. These findings support the comorbidity-inflammation paradigm in HFpEF.

Aims Diagnosing heart failure with preserved ejection fraction (HFpEF) is challenging. The newly proposed HFA‐PEFF algorithm entails a stepwise approach. Step 1, typically performed in the ambulatory setting, establishes a pre‐test likelihood. The second step calculates a score based on echocardiography and natriuretic peptides. The aim of this study is to validate the diagnostic value and establish the clinical impact of the second step of the HFA‐PEFF score. Methods and results The second step of the HFA‐PEFF score was evaluated in two independent, prospective cohorts, i.e. the Maastricht cohort (228 HFpEF patients and 42 controls) and the Northwestern Chicago cohort (459 HFpEF patients). In Maastricht, the HFA‐PEFF score categorizes 11 (4%) of the total cohort with suspected HFpEF in the low‐likelihood (0–1 points) and 161 (60%) in the high‐likelihood category (5–6 points). A high HFA‐PEFF score can rule in HFpEF with high specificity (93%) and positive predictive value (98%). A low score can rule out HFpEF with a sensitivity of 99% and a negative predictive value of 73%. The diagnostic accuracy of the score is 0.90 (0.84–0.96), by the area under the curve of the receiver operating characteristic curve. However, 98 (36%) are classified in the intermediate‐likelihood category, where additional testing is advised. The distribution of the score shows a similar pattern in the Northwestern (Chicago) and Maastricht HFpEF patients (53% vs. 65% high, 43% vs. 34% intermediate, 4.8% vs. 1.3% low). Conclusion This study validates and characterizes the HFA‐PEFF score in two independent, well phenotyped cohorts. We demonstrate that the HFA‐PEFF score is helpful in clinical practice for the diagnosis of HFpEF.