COVID-19 advanced cardiovascular and clinical phenotyping dataset

This article describes a structured clinical dataset collected from 100 adult patients who were previously hospitalized due to moderate-to-severe COVID-19 infection. Data were obtained prospectively at a second-level care hospital in Mexicali, Baja California, Mexico. The dataset includes demographic characteristics, anthropometric measurements, traditional cardiovascular risk factors, general blood test, Questionnaire-derived variables, 12-lead electrocardiographic (ECG) parameters, signal-averaged electrocardiogram (SAECG) measurements, and heart rate variability (HRV) metrics. Questionnaire-derived variables include medical history, cardiovascular risk factors, medication use, lifestyle-related factors, and COVID-19 vaccination information. Laboratory variables include metabolic, renal, lipid, electrolyte, and hematological parameters obtained from routine clinical blood analyses. The 12-lead ECG data include cardiac rhythm classification, measurements of wave and interval durations, electrical axis parameters, and precordial voltage amplitudes. SAECG variables are provided as raw numerical measurements, including total QRS duration, low-amplitude signal duration below 40 µV (LAS40), and root mean square voltage of the last 40 ms (RMS40), together with a variable indicating the presence or absence of ventricular late potentials. HRV parameters are available as continuous time-domain and frequency-domain values, allowing future researchers to apply different clinical thresholds or computational approaches. The dataset is organized into multiple structured Comma-Separated Values (CSV) files distributed across dedicated subdirectories within the Mendeley Data repository. The repository also includes supporting PDF documentation, such as the ethics approval document containing the protocol number, the questionnaire applied during data collection, and the informed consent form. An overview of each variable is provided, and only anonymized patient identifiers are included; no personally identifiable information is contained in the dataset. These data may support future exploratory studies on cardiovascular alterations in post-COVID populations and machine learning applications. The structured format is intended to facilitate secondary data analyses, such as correlational studies and the identification of associations between clinical and electrocardiographic variables.