Using Group-Based Trajectory Temperature Modeling to Predict Postoperative Infections after Total Knee Arthroplasty

BACKGROUND: Fever is common in the postoperative setting and frequently physiologic. Despite this, roughly half of febrile patients undergo testing for infectious complications, of which only a few reveal infection. We analyzed whether temperature trajectories could help optimize postoperative (post-op) risk assessment in total knee arthroplasty (TKA) patients. METHODS: We included adult patients who underwent primary TKA between January 1, 2007–December 31, 2013 within NorthShore University HealthSystem. Patients were excluded if infection was suspected before/during surgery. Patient data were extracted from the Database Warehouse. A physician verified post-op complications by chart review. We performed group-based trajectory modeling (GBTM) with covariates: age, BMI, gender, co-morbid conditions and procedure time (STATA). We compared complications per group by χ(2) test and evaluated associations with any post-op complication by multivariable (MV) logistic regression (SPSS). RESULTS: We identified 5495 independent patients, following three distinct temperature trajectories (Figure 1) – low (group 1), medium (group 2), high (group 3). Noninfectious complications were more likely than infectious complications, and complications were 5x more common in group 3 vs. group 1 (Table 1). In MV logistic regression, membership in group 3 was independently associated with developing a post-op complication, adjusting for age, presence of renal failure and presence of a cardiac arrhythmia (OR 4.4, 95% CI 3.2–6.0, P < 0.01). CONCLUSION: GBTM may help identify TKA patients at increased risk of a post-op complication in real-time, thus helping clinicians avoid unnecessary testing and antibiotics in the post-op setting. DISCLOSURES: All authors: No reported disclosures.