CORR Insights^®: No Decrease in Knee Survivorship or Outcomes Scores for Patients With HIV Infection Who Undergo TKA

Where Are We Now?

Issa and colleagues performed a retrospective review of patients who were human immunodeficiency virus (HIV) positive and underwent primary TKA at one of three urban centers in the United States between 2005 and 2011, and compared them to a matched cohort of patients without HIV. Among patients with well-managed HIV and no concurrent diagnosis of hemophilia, the authors of the current study found no decrease in survivorship or increase in complications at a mean followup of 6 years. This is in contrast to a number of earlier studies [4, 5, 7, 9] examining joint arthroplasty in patients with HIV, which suggested a greater risk of complications and poorer implant survival in this patient group.

The discrepancy between the authors’ work and these earlier studies is, to some extent, a testament to the advances in the management of HIV. Once a disease uniformly associated with serious morbidity and early mortality, the use of contemporary pharmacologic highly-active antiretroviral therapy (HAART) has transformed HIV into a chronic disease with minimal morbidity and a life expectancy approaching that of the general population. The findings from Issa and colleagues stand in contrast to those of 20 years ago, when orthopaedic studies asked whether the potential benefits of surgery in this population outweighed its risks [6, 11].

More generally, the current study provides further data to inform shared decision making and risk optimization in the context of increasing personalization of care in the field of adult reconstruction. Contemporary joint replacement is characterized by excellent objective outcomes, including a decreasing overall revision risk over time [13], and 15-year survival rates of around 90% or greater [2, 8]. Given this, and with increasing emphasis on maximizing patient subjective outcomes, payor pressure to reduce adverse event rates, and the availability of “big data” for analysis, there is an ongoing trend toward personalizing risk assessment and perioperative medical management. An example of the results of this work is the American Joint Replacement Registry (AJRR) Total Joint Risk Calculator [1, 3], which allows surgeons to construct an individualized patient perioperative infection and mortality risk profile based on 37 different demographic and comorbid factors.

Where Do We Need To Go?

The work by Issa and colleagues alludes to the fact that not all patients with a given comorbidity may have the same clinical or health-resource utilization risk. In their study, all the patients with HIV were on contemporary HAART, were under the care of specialists with experiencing managing HIV, had T-cell tests above 200, and underwent preoperative optimization. It is reasonable to expect that these patients might have a more favorable risk profile when compared to others who were not on HAART (or who did not adhere to the regimen), did not have access to specialty care, and had more-severe immunodeficiency. This same principle applies to many other comorbidities commonly encountered in patients being considered for joint surgery such as the association between glycemic control in diabetes mellitus and postoperative infection risk [12], and may even extend to different phenotypes of osteoarthritis [10]. Thus, robust evidence that accounts for these types of differences between patients will be of critical importance in supporting the ongoing evolution of perioperative risk mitigation and the personalization of care.

However, the ability to account for these differences among patients in generalizable risk-assessment models remains limited. Individual institutions or practice groups may have clinical or research databases that include robust patient-level variables, but are limited both by patient numbers and their ability to follow patients across multiple episodes, practitioner groups, and health systems. In contrast, large data sets are powerful for detecting associations with relatively uncommon but clinically and/or economically important outcomes while controlling for large numbers of covariates. However, the currently available “big data” registries, administrative and claims databases, and large-scale outcomes databases typically lack detailed clinical data, longitudinal followup, or both.

How Do We Get There?

The ideal “big data” source would include nationwide patient, payor, and provider representation; detailed demographic, clinical, health resource utilization, and cost data to support all conceivable research questions; and longitudinal followup across all providers and health systems. While interesting to contemplate, the barriers to implementation of such a system are formidable in any country. Thus, a more attainable approach may be to form links between existing discrete but complimentary data sets. There are very real challenges associated with this, requiring extensive data sharing between different data holding groups, and the use of a unique common patient identifier to link across sources. At minimum, issues concerning patient privacy would have to be addressed and safeguards developed to allow linking while protecting patient confidentiality. In some jurisdictions such as the United States, the lack of a universal unique patient identifier such as a national health insurance number creates additional challenges around ensuring accurate data linkage. However, the ability to connect detailed clinical data from one or more health systems to national-level administrative data will be a powerful tool to enable researchers, surgeons and administrators to further increase the value of orthopaedic care.