Adult patients with acute respiratory distress syndrome (ARDS) require the inclusion of particular elements into a database. It is assumed that certain key data that are not connected to ARDS are already incorporated into relevant database tables. For instance, it is routine to consider gender and age, as well as socioeconomic and marriage statuses (Misulis & Frisse, 2019). Such variables are not discussed in detail in this paper because they are not ARDS-specific, but the features that can help to track the development of ARDS are going to be reviewed.
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First, it is important to include the information about the patient’s diagnosis, the disease’s progression, and related dates. ICD-10 codes provide the standardization that other methods do not; the result should consist of numeric data (Misulis & Frisse, 2019). For each patient, dates that mark ARDS onset, its progress (for instance, from acute to chronic), and its resolution are also to be collected (Bellani et al., 2016). ARDS status data can use some text information, and time-related elements should consist of date and time formats used by the database. In addition, it is logical to include the data about treatment, especially since ARDS is often undertreated (Bellani et al., 2016; Thompson, Chambers, & Liu, 2017); the variable should probably use text.
Other elements are supposed to cover the data needed to discern the progress of the illness. It is reasonable to collect the textual information about risk factors since, depending on the ARDS definition, they might be integral for the diagnosis (Thompson et al., 2017). Given the specifics of the disease, it is also necessary to include a text-based description of the test results used to determine the presence of lung infiltrates (for instance, radiography or tomography) (Sweeney & McAuley, 2016). ARDS severity should also be recorded with the help of severity categories (from mild to severe) or oxygenation criteria (Bellani et al., 2016; Sweeney & McAuley, 2016). It may be logical to introduce two variables here; one can be text-based and describe the category, and the other one may specify the number-based (ratio) value of oxygenation. Thus, ARDS in adult patients requires including some specific elements into a database in order to track the patients’ state. The data types depend on an individual variable, but it will not be necessary to make one variable use different types of data.
Bellani, G., Laffey, J. G., Pham, T., Fan, E., Brochard, L., Esteban, A.,… Ranieri, M. (2016). Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA, 315(8), 788-800. Web.
Misulis, K., & Frisse, M. (2019). Representation and organization of health information. In M. Frisse & K. Misulis (Eds.), Essentials of clinical informatics (pp. 45-52). New York, NY: Oxford University Press.
Sweeney, R., & McAuley, D. (2016). Acute respiratory distress syndrome. The Lancet, 388(10058), 2416-2430. Web.
Thompson, B., Chambers, R., & Liu, K. (2017). Acute respiratory distress syndrome. New England Journal of Medicine, 377(6), 562-572. Web.
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