The Journal of Arthroplasty, Volume 34, Issue 7, S108 - S113

Preoperative Optimization Checklists Within the Comprehensive Care for Joint Replacement Bundle Have Not Decreased Hospital Returns for Total Knee Arthroplasty

Ryan, Sean P. et al.
Knee

Background

The Comprehensive Care for Joint Replacement (CJR) model has resulted in the evolution of preoperative optimization programs to decrease costs and hospital returns. At the investigating institution, one center was not within the CJR bundle and has dedicated fewer resources to this effort. The remaining centers have adopted an 11 metric checklist designed to identify and mitigate modifiable preoperative risks. We hypothesized that this checklist would improve postoperative metrics that impact costs for total knee arthroplasty (TKA) patients eligible for participation in CJR.

Methods

Patients undergoing TKA from 2014 to 2018 were retrospectively reviewed. Only patients with eligible participation in CJR were included. Outcome variables including length of stay, disposition, 90-day emergency department visits, and hospital readmissions were explored. Analysis was performed to determine differences in outcomes between CJR participating and non-CJR participating hospitals within the healthcare system.

Results

In total, 2308 TKA patients including 1564 from a CJR participating center and 744 from a non-CJR center were analyzed. There was no significant difference in patient age or gender. Patients at the non-CJR hospital had significantly higher body mass index ( P < .001) and American Society of Anesthesiologists scores ( P < .001), while those in the CJR network had fewer skilled nursing facility discharges ( P = .028) and shorter length of stay ( P < .001). However, there was no reduction in 90-day emergency department visits or readmissions.

Conclusion

The resources utilized at CJR participating hospitals, including patient optimization checklists, did not effectively alter patient outcomes following discharge. Likely, a checklist alone is insufficient for risk mitigation and detailed optimization protocols for modifiable risk factors must be investigated.

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