Work

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

Year: 2017

Type: article

Abstract: 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 χ2 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.

Source: Open Forum Infectious Diseases

DOI: 10.1093/ofid/ofx163.806