In college student-athletes, risk of sudden death due to cardiovascular disease is relatively low, with mortality rates similar to suicide and drug abuse, but less than expected in the general population, although highest in African-American athletes. A substantial minority of confirmed cardiovascular deaths would not likely have been reliably detected by pre-participation screening with 12-lead electrocardiograms.

Within this large forensic registry of competitive athletes, cardiovascular sudden deaths due to genetic and/or congenital heart diseases were uncommon in females and more common in African Americans/other minorities than in whites. Hypertrophic cardiomyopathy is an under-appreciated cause of sudden death in male minority athletes.

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Venous thromboembolism (VTE) is a common health condition with a high mortality and morbidity as well as significant health cost. Traditional treatment with parenteral heparin followed by vitamin K antagonist (VKA) has helped to decrease both morbidity and mortality over years. However, difficulties with warfarin such as INR monitoring, drug-drug interactions, and dietary restrictions has led to research for new anticoagulants. Thus, novel anticoagulants such as direct thrombin and factor X inhibitors have been developed and studied for various indications including the management of VTE. There is now good evidence that some novel anticoagulants are at least as effective as traditional anticoagulation therapy with probably safer outcomes. We have reviewed the literature on the medical management of VTE with the focus on the role of dabigatran, rivaroxaban, apixaban and edoxaban for this indication.

Background:Retrospective chart review studies may be delayed by inability to export clean clinical data from an electronic medical record (EMR) or data repository. Macros are preprogrammed procedures that can be used in Microsoft Excel to help streamline the process of cleaning clinical datasets. Objectives: To demonstrate how macros may be useful for researchers at community hospitals and smaller academic health centers that lack informatics support. Methods: Using an intrinsic function of our institution's EMR, vital signs and lab results from 20 individual hospitalizations were exported to a spreadsheet. Two macros were developed to sort through these datasets and output them into a specified format. The speed of macroassisted data cleaning was compared to manual transcription. Results: Time spent on data cleaning was significantly reduced when using macro-assisted sorting compared to the manual approach for both vital signs (46.5 seconds versus 12.3 minutes per record, a 94% reduction; P < 0.001) and labs (13.7 seconds versus 2.6 minutes per record, a 91% reduction; P < 0.001). Conclusions:Macros offer a flexible and efficient tool for cleaning large sets of clinical data, particularly when an institution lacks informatics support or EMR functionality to export clinical data in an analysis-ready format.

Retrospective chart review (RCR) studies rely on the collection and analysis of documented clinical data, a process that can be prone to errors. The aim of this study was to develop a defined set of criteria to evaluate RCR datasets for potential data errors. The Data Error Criteria (DEC) were developed by identifying data coding and data entry errors via literature review and then classifying them based on error types. Three components comprise the DEC: general errors, numerical-specific errors, and categorical variable-specific errors. Two reviewers independently applied these criteria via a manual review process to an existing de-identified database. A total of 10,168 errors were identified out of a total of 28,656 data points. The total number of errors included redundancies as certain errors may be included in multiple categories. These included 2515 general errors, 39 numerical-specific errors, and 7614 categorical variable-specific errors. Input-related categorical variable-specific errors occurred most frequently, followed by errors secondary to blank cells. Inter-rater agreement was near perfect for all categories. Identifying errors outlined in the DEC can be crucial for the data analysis stage as they can lead to inaccurate calculations and delay study timelines. The DEC offers a framework to evaluate datasets while reducing time and efforts needed to create high-quality RCR-related databases.

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