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Data quality monitoring plays a crucial role in multicenter prospective registries. By maintaining high data accuracy, completeness, and consistency, researchers can improve the overall quality and reliability of the registry data, enabling meaningful conclusions and supporting evidence-based decisions. The purpose of the present study was to evaluate data quality metrics (completeness, accuracy, and temporal plausibility) of a Multicenter Registry of Cardiac Implantable Electronic Devices (CIEDs) and to perform a direct data audit of a random sample of records to assess the agreement levels with the source documents. The CIED Registry was a prospective, multicenter, real-world observational study carried out from January 2020 to December 2022 in five designated centers across Sao Paulo, Brazil. We assessed the data quality of the CIED Registry by using two distinct approaches: • Dynamic data monitoring using features of the REDCap (Research Electronic Data Capture) software, including data reports and data quality rules • Direct data audit in which information from a random sample of 10 % of cases from the coordinating center was compared with original source documentsOur findings suggest that the methodological approach applied to the CIED Registry resulted in high data completeness, accuracy, temporal plausibility, and excellent agreement levels with the source documents.

OBJECTIVES: We designed a cohort study to describe characteristics and outcomes of patients with coronavirus disease (COVID-19) admitted to the intensive care unit (ICU) in the largest public hospital in Sao Paulo, Brazil, as Latin America becomes the epicenter of the pandemic. METHODS: This is the protocol for a study being conducted at an academic hospital in Brazil with 300 adult ICU beds dedicated to COVID-19 patients. We will include adult patients admitted to the ICU with suspected or confirmed COVID-19 during the study period. The main outcome is ICU survival at 28 days. Data will be collected prospectively and retrospectively by trained investigators from the hospital’s electronic medical records, using an electronic data capture tool. We will collect data on demographics, comorbidities, severity of disease, and laboratorial test results at admission. Information on the need for advanced life support and ventilator parameters will be collected during ICU stay. Patients will be followed up for 28 days in the ICU and 60 days in the hospital. We will plot Kaplan-Meier curves to estimate ICU and hospital survival and perform survival analysis using the Cox proportional hazards model to identify the main risk factors for mortality. ClinicalTrials.gov: NCT04378582. RESULTS: We expect to include a large sample of patients with COVID-19 admitted to the ICU and to be able to provide data on admission characteristics, use of advanced life support, ICU survival at 28 days, and hospital survival at 60 days. CONCLUSIONS: This study will provide epidemiological data about critically ill patients with COVID-19 in Brazil, which could inform health policy and resource allocation in low- and middle-income countries.