Using Existing Data to Address Important Clinical Questions in Critical Care

Abstract

Objective With important technological advances in healthcare delivery and the internet, clinicians and scientists now have access to overwhelming number of available databases capturing patients with critical illness. Yet investigators seeking to answer important clinical or research questions with existing data have few resources that adequately describe the available sources and the strengths and limitations of each. This article reviews an approach to selecting a database to address health services and outcomes research questions in critical care, examines several databases that are commonly used for this purpose, and briefly describes some strengths and limitations of each. Data Sources Narrative review of the medical literature. Summary The available databases that collect information on critically ill patients are numerous and vary in the types of questions they can optimally answer. Selection of a data source must not only consider accessibility, but also the quality of the data contained within the database, and the extent to which it captures the necessary variables for the research question. Questions seeking causal associations (e.g. effect of treatment on mortality) usually either require secondary data that contain detailed information about demographics, laboratories, and physiology to best address non-random selection or sophisticated study design. Purely descriptive questions (e.g. incidence of respiratory failure) can often be addressed using secondary data with less detail such as administrative claims. Though each database has its own inherent limitations, all secondary analyses will be subject to the same challenges of appropriate study design and good observational research. Conclusion The literature demonstrates that secondary analyses can have significant impact on critical care practice. While selection of the optimal database for a particular question is a necessary part of high-quality analyses, it is not sufficient to guarantee an unbiased study. Thoughtful and well-constructed study design and analysis approaches remain equally important pillars of robust science. Only through responsible use of existing data will investigators ensure that their study has the greatest impact on critical care practice and outcomes.

Can We Trust Observational Studies Using Propensity Scores in the Critical Care Literature? A Systematic Comparison With Randomized Clinical Trials*

“…Interest in observational research is driven by legislation promoting comparative effectiveness research (e.g., the Affordable Care Act) (3), the emergence of large administrative and electronic healthcare record databases offering data mining opportunities (4,5), and the development and adoption of novel statistical methods for analyzing observational data. Many of these methods, such as propensity scores (PSs) and other exposure modeling methods, attempt to emulate aspects of RCTs and can reduce confounding in studies with nonrandom treatment assignment (6).…”

Abdominal Computed Tomography Utilization and 30-day Revisitation in Emergency Department Patients Presenting With Abdominal Pain

“…Prior studies investigating ED revisits have found multiple associated factors, including advanced age, 8 higher triage acuity, 8 and lack of access to primary care. 9 However, administrative and other claims data often lack clinical data at the patient level, limiting the value of such research to provider organizations and clinicians; 10 it is unclear in which direction this influences these results. Large electronic health records offer the opportunity to integrate previously disparate data sources, enabling analyses that seek to identify the complex linkages between clinical details and resource utilization.…”

Long-Term Outcomes in Critically Ill Septic Patients Who Survived Cardiopulmonary Resuscitation*

“…Nevertheless, our study has some limitations that merit consideration. First, this was a retrospective claims database analysis, so there was the possibility of residual confounding (20,21). Second, we did not have information on the severity index of acute illness such as Acute Physiology and Chronic Health Evaluation II or Sequential Organ Failure Assessment scores.…”