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Spine Registries
Summary of Key Points
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Registries and databases provide opportunities to capture “real-world” data.
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Capturing and studying “real world” outcomes of routinely performed spine surgery has a two-fold advantage:
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It allows us to optimize patient outcomes by identifying drivers of adverse events such as readmissions, reoperations, and complications.
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It helps optimize cost-effective strategies by allowing care teams and departments to identify drivers of low-value outcomes, such as prolonged length of stay and non-routine discharge.
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When using data derived from such databases and registries for research, it is important to remain cognizant of associated limitations and interpret the conclusions in the light of these limitations.
Clinical Data Systems in the Context of Value-Based Care
The healthcare reforms introduced as part of the Affordable Care Act of 2010 led to healthcare systems transitioning toward a value-based system, moving away from traditional fee for service models. Value-based healthcare models have been preferred because they prioritize patient-related factors and outcomes and reward healthcare providers for provision of “quality, as opposed to quantity.” , Value is typically defined as quality/cost but may be defined differently by multiple stakeholders in multiple contexts. For example, cost can refer to the expense of providing care or may relate to the direct and/or indirect medical expenses incurred by individuals, groups, or society more generally. Healthcare costs are an essential aspect of defining “value,” and are generally well-described for most healthcare services (even if cost information is not readily available to all participants in the healthcare delivery system). Healthcare outcome data are equally important for defining value but are generally more difficult to obtain and interpret than cost data (the latter being routinely collected as a core element of “administrative” healthcare datasets). Information related to observed and patient-reported outcomes is not typically part of administrative data systems, nor is it routinely included as an element in point-of-service health information technologies. Given the limitations of existing clinical data systems, along with the central importance of valid and reliable clinical outcomes data to objectively determining healthcare quality and effectiveness (and, by extension, evaluating truly “value-based,” as opposed to simply “cost-based,” care), an imperative exists to develop robust methods to acquire such information.
Randomized clinical trials (RCTs) have long been the “gold standard” for high-quality medical evidence, largely because of their ability to minimize confounders and sources of bias. , However, there are several limitations to using RCTs routinely to establish treatment safety and/or effectiveness, with some of the most important limitations being the challenges and costs of establishing adequate randomization; a lack of treatment equipoise (making conduct of RCTs impractical or even unethical); and the discontinuous nature of RCTs, preventing repeated period evaluations and assessments. Furthermore, findings from RCTs do not always translate into real-world practice outcomes. Specifically, efficacious treatments in research settings often fail to prove effective at the individual patient level when suboptimally applied in everyday care settings. ,
The 21st Century Cures Act of 2016 highlighted the need for “real-world evidence” to better define treatment outcomes, guide therapeutic research, and encourage quality improvement initiatives. One of the key components of synthesizing “real-world” evidence was described in this document as a growing need to gather information not only in research-intensive and academic environments but also in routine clinical, community, and home healthcare settings. One method of gathering and synthesizing such “real-world evidence” is a clinical registry, defined by the Agency of Healthcare Research and Quality (AHRQ) as “an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure and that serves a predetermined scientific, clinical, or policy purpose(s).”
Clinical Registries
Clinical registries are increasingly used to generate evidence and ascertain the effectiveness of clinical interventions in routine practice. Registries can also be used to identify patient or healthcare delivery system factors that are related to clinical outcomes, providing guidance not only for improving healthcare delivery but also toward identifying the more fundamental mechanisms of disease that could be driving such outcomes. Patient care registries can be cost-effective and easily scaled to accommodate numerous users, and can rapidly and efficiently yield vast amounts of clinical data. When registries are designed properly and implemented rigorously, the data that they generate can be both reliable and valid. Registries avoid the constraints of narrow eligibility criteria and may be used to directly evaluate a wide range of practice environments. In part for these reasons, healthcare policy makers, purchasers, and payers increasingly value high-quality registry data. Registries may represent the “next disruptive technology” in clinical research.
There has been a recent growth in both the number and quality of clinical registries in the United States and around the world. With regards to surgical specialties, the American College of Surgeons ‒ National Surgical Quality Improvement Program (ACS-NSQIP) is an important example of an existing large, collaborative data effort designed to define and promote better quality healthcare. However, ACS-NSQIP and other similar databases collect a relatively small amount of information relevant to neurosurgical disorders, and importantly do not include longitudinal or patient-related outcome information. Other national databases containing limited amounts of outcome data relevant to neurosurgery include the National Inpatient Sample, the National Readmissions Database, and other information services offered by the Healthcare Cost and Utilization Project, which is in turn run by the AHRQ. However, given the restrictions pertaining to use of International Classification of Diseases codes to accurately establish comparable procedural/diagnostic groupings, the use of these resources for reliable documentation of neurosurgical outcomes has been limited.
Clinical registries represent a promising approach to obtaining such information. However, existing external methods of clinical outcome observation and collection are inadequate to allow for the acquisition of meaningful, valid, and reliable data related to the short- and long-term outcomes of patients undergoing therapies for neurosurgical disorders. Thus, there has been widespread advocacy on the national front for the use of prospective registries as a way to drive healthcare reform, as evidenced by health reform packages funding and expanding the role of “comparative effectiveness research” in determining cost effectiveness. The formation of prospective registries for collecting data on healthcare outcomes and effectiveness is one avenue to facilitate evidence-based practices and value assessments in a generalizable and cost-effective manner. When appropriately designed, quality registries can be used to generate high-level evidence (level 1 prognostic/predictive evidence and level 2 evidence on effectiveness). The trend in spine surgery has been toward building and maintaining such registries to allow for high-quality data collection and analysis specific to a given procedure or surgical paradigm.
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