Medical Comanagement of Hip Fracture Patients Is Not Associated with Superior Perioperative Outcomes: A Propensity Score-Matched Retrospective Cohort Analysis of the National Surgical Quality Improvement Project

The primary finding of this study is that even once propensity score matching eliminated nearly all discernible baseline differences between the cohorts of hip fracture patients with and without medical comanagement during their hospitalization, and comanagement was not associated with superior (and in fact was associated with still inferior) perioperative outcomes.

As is evident from the baseline differences shown in Table 2, medical comanagement is utilized in a patient population that has significant comorbidities and adverse patient factors. The NSQIP provides a robust opportunity to remove the effects of these confounding variables because of the richness of variables in the dataset. For instance, some studies used a summary score for patient frailty, which has been an apparent predictor of worse clinical outcomes in this population.^18,19 The NSQIP analyzes each component of the frailty score (diabetic status, history of COPD or current pneumonia, congestive heart failure, hypertension requiring medication, and nonindependent functional status) as well as to add additional variables (eg, low serum albumin level) and propensity score matching on each of these variables individually.

It is also important to note that although prior analyses have demonstrated that SHFPs are associated with better outcomes in this database,¹⁷ comanagement did not correlate with the use of an SHFP, nor did comanagement demonstrate any association with better outcomes in the subgroup who participated in an SHFP or in the subgroup who did not.

This retrospective cohort analysis cannot, of course, demonstrate causation. Several limitations are worth noting. The ability to use any retrospective dataset depends on the quality of the variable definitions and the data quality contained in it. Although the NSQIP has demonstrated high validity and interobserver variability compared with other data sources, some imperfections and heterogeneity (for instance, in the way two different institutions may define comanagement) may be present.

It is important to note that any propensity score-matched analysis incurs the risk of residual/unmeasured confounding, since the power of this technique still depends on the presence of measured variables to match, and no match is ever perfect. For instance, some variables remain imperfectly balanced in the matched cohorts (eg, hypoalbuminemia and fracture type, Table 3). These differences may reach statistical significance because of large sample size without obvious clinical significance, but they illustrate the point that residual confounding may persist. It is also possible that some detection bias is present in the comanagement cohort, if dedicated comanagement personnel are more likely to diagnose complications (eg, pneumonia, PE) that require some clinical suspicion to be identified. We doubt that this plays a dominant role, for the NSQIP is relatively robust to this potential bias because of its rigorous process of relying on a trained clinical reviewer at each site (as opposed, for instance, to using billing codes), and several components of the composite morbidity endpoint (eg, reintubation, prolonged mechanical ventilation, stroke, cardiac arrest, or death) would be difficult to miss even if clinicians have low clinical suspicion or attentiveness. However, some potential remains.

It is also possible that comanagement is applied to sicker patients and functions more as a marker of that population than an intervention that improves results. To take a similar example, past literature has demonstrated a strong association between do-not-resuscitate (DNR) status and adverse outcomes.^20-24 In all likelihood, the DNR status does not directly cause worse outcomes so much as it marks a sick and vulnerable population. Selection bias at the individual patient level may contribute to an association between comanagement and worse outcomes.

Similarly, institutions that routinely apply comanagement may care for a sicker patient population. To this end, institution-level variables may modulate the relationship between comanagement, SHFP participation, and outcomes. Comanagement and SHFP participation may cluster according to the surgeon, the institution, or the patient subtype (eg, ICU vs ward status). Unfortunately, individual hospital and surgeon identifiers are explicitly excluded from the publicly available NSQIP PUF to protect program and patient confidentiality, so that advanced hierarchical modeling techniques cannot explore these relationships with this dataset.

Beyond these limitations, one plausible explanation for the lack of an association between comanagement and improved outcomes is that standardization and other continuous quality improvement processes have already accomplished a great deal, and the addition of comanagement of individual patients is not having an appreciably positive additional impact. Although the acuity and prevalence of comorbidities in the hip fracture population are high, many of their issues may be stereotyped enough that thoughtful, well-designed algorithms and protocols may serve them nearly as well, if not better than individual comanagement.

This admittedly speculative explanation has significant implications for resource utilization and patient care. Medical comanagement involves a heavy investment of time, energy, and money on the part of a second medical team to deliberately duplicate some aspects of daily care with the intended goal of improving patient outcomes. The results of this study may provide motivation for efforts to hybridize or modify the involvement of comanaging physicians and teams—for instance, to guide and refine the creation and revision of SHFP protocols without providing daily comanagement to each individual patient and/or to implement more iterative, continuous process improvement initiatives.²⁵ Our results may also help direct healthcare systems to focus elsewhere in the search for modifiable process and care delivery variables that can move the needle on the significant morbidity and mortality that still exist in this population.