Holter-detected arrhythmias and associated risk factors in hospitalized elderly patients with diagnosed heart failure | BMC Cardiovascular Disorders

This study provides important insights into the clinical characteristics, arrhythmic burden, and associated risk factors among hospitalized elderly patients with newly diagnosed heart failure, using a combination of clinical, echocardiographic, and Holter monitoring data. Notably, both AF and PVCs, classified by Lown grade III–V, were relatively prevalent and linked to distinct clinical and laboratory parameters. The study used a threshold of LVEF <50% to define systolic dysfunction, which does not fully align with the latest guideline classifications. According to AHA/ACC/HFSA 2022, only LVEF <40% qualifies as HFrEF, while values from 41–49% fall under HFmrEF. This discrepancy should be considered when interpreting the findings.

In our study, echocardiographic evaluation revealed significant cardiac structural abnormalities among elderly patients with HF, including a reduced left ventricular ejection fraction (mean EF = 33.3%) and a high prevalence of regional wall motion abnormalities (62.5%). These findings are consistent with previous literature, where the majority of elderly HF patients were classified as having HFrEF. For instance, Mani Teja K et al. (2022) reported that 79.2% of elderly HF patients had LVEF < 50% [14]. The mean left atrial (LA) diameter in our cohort was 38.9 mm, suggesting LA enlargement, which is recognized as a marker of atrial remodeling and a risk factor for AF [15, 16]. The prevalence of AF in our sample was 19.4%, which is slightly lower than the 22–44% range reported in large HF registries [17, 18], but still clinically relevant. The observed elevated pulmonary artery systolic pressure (mean = 35.8 mmHg) may further contribute to the atrial pressure overload and arrhythmogenesis. Notably, no cases of non-sustained ventricular tachycardia (NSVT) or supraventricular tachycardia (SVT) were recorded, which contrasts with a prior study suggesting higher rates of complex arrhythmias in HF patients [19]. However, this discrepancy may be attributed to variations in patient selection, HF severity, or detection methods. Overall, our echocardiographic findings support the importance of comprehensive cardiac imaging in the elderly HF population, as it provides crucial information on structural remodeling, hemodynamic burden, and arrhythmic risk.

Holter monitoring revealed substantial arrhythmic burden in this cohort, with a high number of PVCs (median (25th−75th)= 1955 (54-38514)) and a majority of patients falling into high-risk Lown grades IVa (38.8%) and IVb (18.1%). Although the Lown grading system was originally developed for risk stratification in patients with ischemic heart disease [20], it has also been employed in broader heart failure populations in contemporary studies [21]. In our cohort, only 18.1% of patients had documented coronary artery disease; thus, applying the Lown classification to patients with non-ischemic etiologies may have limitations. However, it still provides a practical framework for characterizing the burden of ventricular ectopy and has been shown to correlate with prognosis in various heart failure subtypes. This adaptation should be interpreted with caution, and further validation in non-ischemic cohorts is warranted. These findings align with prior reports that Holter monitoring offers superior sensitivity in detecting transient arrhythmias compared to surface ECG [6]. In that study, Holter monitoring identified AF in nearly 25% of elderly HF patients—higher than typical detection rates via standard ECG—supporting its role in comprehensive rhythm surveillance. Our data also reinforce the association between HF and a high burden of ventricular arrhythmias. The mean QTc interval in our sample was prolonged at 476.41 ms, and QRS durations were widened, both of which are known markers of increased sudden cardiac death risk [22, 23]. These results highlight the role of Holter monitoring not only for arrhythmia detection but also for risk stratification in elderly HF patients.

Multivariable regression analysis identified several clinical factors independently associated with AF and high-grade PVCs. For AF, increased BMI and elevated systolic pulmonary artery pressure (sPAP) emerged as significant predictors. These findings are supported by prior studies indicating that obesity contributes to atrial structural remodeling and increased atrial pressure, facilitating AF development [24, 25]. Elevated sPAP, reflecting pulmonary hypertension, has also been linked to atrial pressure overload and AF incidence in HF patients [26]. Although age, LDL-C, and glucose levels showed trends toward significance, they did not reach statistical thresholds in our cohort. For high-grade PVCs (Lown grade III–V), elevated sPAP, lower creatinine, and higher sodium levels were significant predictors. The association between sPAP and PVCs likely reflects the underlying hemodynamic stress and myocardial strain in HF, while the inverse relationship with creatinine was unexpected and warrants further investigation—possibly reflecting confounding or survivor bias. The positive association between sodium and PVCs aligns with literature suggesting that electrolyte imbalances may influence ventricular excitability. Overall, these findings emphasize the multifactorial nature of arrhythmogenesis in HF and the need for individualized risk assessment [27–29].

The findings of this study have important clinical implications for the management of elderly patients with heart failure. The high prevalence of arrhythmias—particularly atrial fibrillation and high-grade premature ventricular contractions—underlines the necessity of routine rhythm surveillance, especially using extended monitoring such as Holter ECG. Identifying independent predictors such as increased BMI, elevated systolic pulmonary artery pressure, and altered electrolyte levels enables clinicians to better stratify arrhythmic risk and tailor monitoring and treatment strategies accordingly. These results also support integrating echocardiographic and laboratory parameters into comprehensive risk models to improve early detection and prevention of adverse cardiac events.

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