A single-center prospective cohort study among patients recently discharged for MI showed that remote evaluation of HF symptoms using the KCCQ could identify those at risk of death.

This summary is based on the publication of Wohlfahrt P, Jenča D, Melenovský V, et al. - Remote Heart Failure Symptoms Assessment After Myocardial Infarction Identifies Patients at Risk for Death. J Am Heart Assoc. 2024 Jan 16;13(2):e032505. doi: 10.1161/JAHA.123.032505

Introduction and methods

Background

Up to 40% of patients with an MI will develop HF [1], which increases their mortality risk [2]. Early recognition of HF symptoms could identify MI patients at increased mortality risk and improve risk stratification beyond the clinical variables used in the Global Registry of Acute Coronary Events (GRACE) risk score. The KCCQ is an HF-specific patient‐reported outcome that predicts adverse events in patients with acute or chronic HF [3-5]. However, it is unknown whether the KCCQ can be used in a general population of MI patients to identify those at increased mortality risk.

Aim of the study

The authors examined the association of the KCCQ – Overall Summary Score (OSS) with all-cause mortality risk in patients hospitalized for MI.

Methods

The authors used data from 1135 consecutive patients hospitalized for MI at a large tertiary heart center in Prague, Czech Republic between June 2017 and September 2022. These data had been collected in the prospective Institute for Clinical and Experimental Medicine Acute Myocardial Infarction Registry. One month after hospital discharge, study participants completed the 23-item KCCQ remotely. Based on the KCCQ-OS, patients’ health status was categorized as follows: The health status was very poor to poor (score of <25) for 30 patients (2.6%), it was poor to fair (25–49) for 114 (10.0%), 274 patients (24.1%) had a fair to good health status (50–74), and 717 (63.2%) had a good to excellent health status (≥75).

Outcome

The primary endpoint was all-cause mortality.

Main results

• During a median follow-up duration of 46 months (IQR: 29–61), 146 patients (12.9%) died. In a nonlinear analysis adjusted for age, the mortality risk increased with decreasing KCCQ-OSS.
• Adjustment for gender, LVEF, and the components of the validated GRACE risk score (i.e., age, heart rate and systolic blood pressure at hospital admission, creatinine, maximal troponin level (double log‐transformed value), STEMI, cardiac arrest at admission, and Killip class) indicated that KCCQ-OSS <50 was independently associated with increased mortality risk. Compared with KCCQ-OSS ≥50, the HR was 6.05 (95%CI: 3.43–10.68; P<0.001) for patients with KCCQ-OSS <25 and 2.66 (95%CI: 1.70–4.17; P<0.001) for those with KCCQ-OSS 25–49.
• There were no significant interactions between the KCCQ-OSS categories and age (P for interaction=0.86), sex (P for interaction=0.72), or systolic dysfunction with LVEF <40% at discharge (P for interaction=0.53).
• When assessing mortality risk at 2 years after MI, the area under the curve (AUC) for the combined KCCQ-OSS categories <25, 25–49, and ≥50 was 67.9 (95%CI: 61.9–73.9).
• Addition of these 3 KCCQ-OSS categories to components of the GRACE risk score associated with the outcome (i.e., age, Killip class, STEMI, heart rate, and creatinine) improved the C index (AUC increased from 82.6; 95%CI: 78.0–87.3 to 85.3; 95%CI: 80.5–90.0; delta AUC: 2.6; 95%CI: 0.3–5.0; P=0.03). This addition also improved the Brier score by −0.6 (95%CI: −1.0 to −0.2; P=0.01) and the continuous net reclassification improvement by 0.71 (95%CI: 0.45–1.04).
• Forward stepwise Cox regression analysis adjusted for age showed that the KCCQ items walking impairment, leg swelling, and change in symptoms during the past 2 weeks were most strongly associated with mortality after MI.

Conclusion

This Czech, single-center, prospective cohort study among patients recently discharged for MI showed that lower KCCQ-OSS (particularly <50) was independently associated with increased mortality risk. This indicated that remote evaluation of HF symptoms using the KCCQ can identify those MI patients at risk of death. The 3 most predictive KCCQ items were walking impairment, leg swelling, and change in symptoms. The authors think “[t]he KCCQ can be part of a toolkit for risk stratification after [MI].”

References

1. Jenča D, Melenovský V, Stehlik J, Staněk V, Kettner J, Kautzner J, Adámková V, Wohlfahrt P. Heart failure after myocardial infarction: incidence and predictors. ESC Heart Fail. 2021;8:222–237. doi: 10.1002/ehf2.13144

2. Gerber Y, Weston SA, Enriquez-Sarano M, Berardi C, Chamberlain AM, Manemann SM, Jiang R, Dunlay SM, Roger VL. Mortality associated with heart failure after myocardial infarction: a contemporary community perspective. Circ Heart Fail. 2016;9:e002460. doi: 10.1161/CIRCHEARTFAILURE.115.002460

3. Heidenreich PA, Spertus JA, Jones PG, Weintraub WS, Rumsfeld JS, Rathore SS, Peterson ED, Masoudi FA, Krumholz HM, Havranek EP, et al. Health status identifies heart failure outpatients at risk for hospitalization or death. J Am Coll Cardiol. 2006;47:752–756. doi: 10.1016/j.jacc.2005.11.021

4. Hu D, Liu J, Zhang L, Bai X, Tian A, Huang X, Zhou K, Gao M, Ji R, Miao F, et al. Health status predicts short-and long-term risk of composite clinical outcomes in acute heart failure. JACC: Heart Failure. 2021;9:861–873. doi: 10.1016/j.jchf.2021.06.015

5. Parissis JT, Nikolaou M, Farmakis D, Paraskevaidis IA, Bistola V, Venetsanou K, Katsaras D, Filippatos G, Kremastinos DT. Self-assessment of health status is associated with inflammatory activation and predicts long-term outcomes in chronic heart failure. Eur J Heart Fail. 2009;11:163–169. doi: 10.1093/eurjhf/hfn0

Find this article online at J Am Heart Assoc.