Background: Certain medications interfere with the bone remodeling process and may potentially increase the risk of complications after total knee arthroplasty (TKA). As patients undergoing TKA may be taking these bone mineral density (BMD)-reducing medications, it is unclear as to whether and which medications impact TKA outcomes. Therefore, the purpose of this study was to observe the impact of various BMD-reducing medications on 2-year implant-related complications following TKA.
Methods: A retrospective analysis of patients undergoing primary TKA was conducted using a national administrative claims database. Patients were identified if they were taking any known BMD-reducing medication and were compared to control patients. To control for confounders associated with taking multiple agents, multivariable logistic regression analyses were conducted for each 2-year outcome (all-cause revision, loosening-indicated revision, and periprosthetic fracture--indicated revision), with the output recorded as odds ratios (ORs).
Results: In our study, 502,927 of 1,276,209 TKA patients (39.4%) were taking at least one BMD-reducing medication perioperatively. On multivariable analysis, medications associated with a higher likelihood of 2-year all-cause revision included first- and second-generation antipsychotics (SGAs) (OR: 1.42 and 1.26, respectively), selective serotonin reuptake inhibitors (SSRIs) (OR: 1.14), glucocorticoids (1.13), and proton pump inhibitors (PPIs) (OR: 1.23) (P < .05 for all). Medications associated with a higher likelihood of 2-year periprosthetic fracture included SGAs (OR: 1.51), SSRIs (OR: 1.27), aromatase inhibitors (OR: 1.29), and PPIs (OR: 1.42) (P < .05 for all).
Conclusions: Of the drug classes observed, the utilization of perioperative PPIs, SSRIs, glucocorticoids, first-generation antipsychotics, and SGAs was associated with the highest odds of all-cause revision. Our findings suggest a relationship between these medications and BMD-related complications; however, further studies should seek to determine the causality of these relationships.
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