This was a prospective observational cohort study. Reporting of the presented study is performed based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Between January 2019 and June 2025, patients within the nonischemic cardiomyopathy spectrum but without LV dilatation were enrolled. Inclusion criteria evolved over time: from 2019 to 2023, patients were included based on the 2016 HNDC criteria, while from 2023 onward, the updated definition of NDLVC was applied. Only patients fulfilling the updated NDLVC criteria were included in the NDLVC cohort; those who did not meet these criteria were excluded from the present analysis. Patients were recruited at our tertiary care center (General Hospital of Athens, Ippokrateio) either through the emergency department or during routine outpatient evaluation at the HF or arrhythmia departments. Eligibility was determined based on predefined clinical and diagnostic criteria at the time of their initial assessment, as described later on. Clinical outcomes were systematically collected and analyzed in the NDLVC subgroup, whereas the DCM cohort served as a comparator for baseline clinical, imaging, and electrophysiological characteristics. The study was performed according to the criteria set by the Declaration of Helsinki and received Institutional Review Board approval from the institution (22843/30-12-2022). All patients provided written informed consent.

NDLVC was defined in the absence of LV dilatation on cardiac magnetic resonance (CMR), based on age, sex, and body surface area, and the presence of a nonischemic LV scar or an LVEF <50% without scar. DCM was defined as the presence of LV dilatation and global or regional systolic dysfunction unexplained solely by abnormal loading conditions or coronary artery disease.

All patients underwent evaluation, and somatometric, clinical, and pharmacological data were collected. All patients underwent initial CMR imaging with gadolinium delayed enhancement, from which left and right ventricular end systolic and diastolic volumes, ejection fraction, and late gadolinium enhancement presence were calculated. CMR studies were performed on 1.5- or 3.0-T scanners, with a cardiac phased-array receiver surface coil, ECG gating, and breath-hold technique, using a dedicated cardiac software.

Furthermore, all patients underwent baseline 24-hour Holter monitoring (CardioMem CM 4000, GETEMED), resting and signal-averaged ECG (MAC 5500 HD, GE Healthcare) in order to fully evaluate the electrophysiological substrate of each patient. Seven electrocardiographic parameters were evaluated: (1) QRS duration and morphology on surface resting ECG, where patients with a QRS >140 msec were further categorized as having left bundle branch block (LBBB), right bundle branch block or nonspecific intraventricular conduction delay, (2) late potentials (LPS) on 45‐minute ECG recording during resting, when 2/3 were present, this criterion was considered positive. Three criteria are used to detect LPS: filtered ECG QRS duration >114 ms, the duration of the terminal part of the QRS complex with an amplitude below 40 μV and the root mean square amplitude of the last 40 ms of the signal <20 μV. Modified criteria for patients with a QRS duration >140 ms were also used as described by Gatzoulis et al, (3) PVCs/hour on 24‐hour electrocardiography, (4) presence of nonsustained ventricular tachycardia (NSVT) on 24‐hour electrocardiography as well as NSVT rate. ^, NSVT was defined as ≥3 consecutive ventricular premature beats with a rate >100 beats/min, lasting <30 seconds, (5) standard deviation of normal RR intervals (SDNN) ≤75 ms on the 24‐hour electrocardiography. Heart rate variability reflects the variation in time intervals between consecutive heartbeats and is an indicator of autonomic nervous system function. SDNN quantifies the overall variability in beat-to-beat timing over a recording period. Higher SDNN values generally indicate healthier autonomic regulation with greater parasympathetic (vagal) activity, while lower values are associated with impaired autonomic function and increased cardiovascular risk. (6) Fridericia QTc derived from 24‐hour electrocardiography with QTc prolongation defined as >440 ms (men) or >450 ms (women) and (7) Ambulatory T‐wave alternans ≥65 μV in two Holter channels, on 24‐hour. T-wave alternans refers to a beat-to-beat variation in the amplitude or shape of the T-wave on an ECG, and is considered a marker of electrical instability and increased risk for VA.

The endpoints of the present study were

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  The identification of variables that differentiate between NDLVC and DCM.

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  HF hospitalization, which was defined as admission for ≥24 hours with a primary diagnosis of HF, with ≥1 symptom and ≥2 physical examination, laboratory, or invasive findings of HF, and the patient received HF-specific treatment.

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  Hospitalization for VA, including VT/VF occurrence or SCD. SCD was defined as unexpected death due to cardiac causes with or without documented VA, death within 1 hour of acute symptoms, or nocturnal death with no antecedent history of immediate worsening symptoms. VT/VF occurrence included sustained (>30 seconds) VT causing hemodynamic instability or appropriate ICD interventions, defined as a shock for termination of rapid (>173 beats/min) sustained VT or VF.

All patients underwent systematic phenotyping and follow-up. In-person follow-up visits were scheduled for all participants at 6 and 12 months and then annually following study inclusion. During each visit, patients underwent standardized clinical assessment, ECG, and transthoracic echocardiography. In addition, participants were instructed to promptly notify the study team of any interim hospitalizations to ensure accurate event capture.

All analyses were performed with R statistical software (v. 4.4.2). Categorical variables are presented with absolute frequencies and percentages (%), while continuous variables with mean and standard deviation when following the normal distribution, or as median with interquartile range (IQR) when non-normally distributed. Normality was visually assessed with histograms and formally tested with the Shapiro-Wilk test. The analyzed dataset did not contain missing values. Continuous variables were tested for differences using the t test when normally distributed or the Wilcoxon–Mann–Whitney test when non-normally distributed. Respectively, the chi-squared test or Fisher’s exact test was used for categorical variables, as appropriate. To identify variables that differentiate between NDLVC and DCM, univariate and multivariable logistic regression modelling were used to estimate odds ratio (OR) with 95% confidence intervals (CI). Variables identified from univariate analysis (p value <0.20) were combined with significant predictors identified in the refs. ^, and were inserted into the initial model. Subsequently, via backward elimination and comparison of nested models using ANOVA, a final model was created. Finally, the same model-building strategy was used for the outcomes of HF and VA hospitalization. Variables reported to be significantly associated with HF ^, and VA hospitalization ^,, were identified in the literature and were inserted into the initial model along with variables identified via univariate modelling (p value <0.20). The Firth’s penalization was used to resolve issues related to model convergence in the outcomes of HF and VA hospitalization. All final multivariable logistic regression models were assessed for the validity of logistic regression assumptions. A two-sided p value <0.05 was considered statistically significant for all analyses.