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Smart Implantable Device Use Is Associated With Decreased Postdischarge Hospital Utilization in Total Knee
Andrew Nakla1, Patrick M Aubin2, Samuel Ademisoye1
1Department of Orthopaedics and Sports Medicine, Cedars Sinai Medical Center, Los Angeles, California.
Background:
Studies have demonstrated an increased risk of postoperative complications in patients who have metabolic syndrome undergoing total knee arthroplasty (TKA). Smart implantable devices (SIDs) and remote monitoring have shown promise in improving outcomes. The purpose of this study was to evaluate whether patients who have metabolic syndrome and an SID TKA had decreased postdischarge hospital utilization.
Methods:
This was a retrospective cohort study using data from a deidentified national administrative health insurance database. All patients were diagnosed with metabolic syndrome, defined as diabetes mellitus type II plus at least two of the following: body mass index greater than 30, hypertension, or hyperlipidemia. There were 142 TKAs in 134 patients who had metabolic syndrome and received an SID, who were propensity score-matched to 1,420 TKAs in 1,247 patients who had metabolic syndrome but did not have an SID. The matched dataset was analyzed to compare emergency department (ED) visits, hospital readmissions, and reasons for unplanned acute postoperative care.
Results:
Of the 1,562 TKAs that were performed, there were 250 (16.0%) ED visits and 165 (10.6%) readmissions within the first 90 days postoperatively. The cumulative incidence of ED visits was 9.9% in the SID group compared to 16.6% in controls (P = 0.036). Hospital readmissions were significantly lower in the SID group (5.6%) compared to controls (11.1%; P = 0.045). Patients who did not have an SID had a higher rate of medical complications (23%) compared to those who had an SID (10%; P = 0.0001).
Conclusions:
In patients who have metabolic syndrome undergoing primary TKA, the use of an SID was associated with significantly decreased postdischarge hospital utilization. Further work is needed to determine the mechanism by which smart implants may improve outcomes and reduce complications in high-risk populations.
