医学
置信区间
假体周围
优势比
回顾性队列研究
逻辑回归
内科学
关节置换术
外科
作者
Majd Tarabichi,Noam Shohat,Michael M. Kheir,Muyibat A. Adelani,David P. Brigati,Sean M. Kearns,Pankajkumar Patel,John C. Clohisy,Carlos A. Higuera,Brett R. Levine,Ran Schwarzkopf,Javad Parvizi,William A. Jiranek
标识
DOI:10.1016/j.arth.2017.04.065
摘要
Abstract
Background
Although HbA1c is commonly used for assessing glycemic control before surgery, there is no consensus regarding its role and the appropriate threshold in predicting adverse outcomes. This study was designed to evaluate the potential link between HbA1c and subsequent periprosthetic joint infection (PJI), with the intention of determining the optimal threshold for HbA1c. Methods
This is a multicenter retrospective study, which identified 1645 diabetic patients who underwent primary total joint arthroplasty (1004 knees and 641 hips) between 2001 and 2015. All patients had an HbA1c measured within 3 months of surgery. The primary outcome of interest was a PJI at 1 year based on the Musculoskeletal Infection Society criteria. Secondary outcomes included orthopedic (wound and mechanical complications) and nonorthopedic complications (sepsis, thromboembolism, genitourinary, and cardiovascular complications). A regression analysis was performed to determine the independent influence of HbA1c for predicting PJI. Results
Overall 22 cases of PJI occurred at 1 year (1.3%). HbA1c at a threshold of 7.7 was distinct for predicting PJI (area under the curve, 0.65; 95% confidence interval, 0.51-0.78). Using this threshold, PJI rates increased from 0.8% (11 of 1441) to 5.4% (11 of 204). In the stepwise logistic regression analysis, PJI remained the only variable associated with higher HbA1c (odds ratio, 1.5; confidence interval, 1.2-2.0; P = .0001). There was no association between high HbA1c levels and other complications assessed. Conclusion
High HbA1c levels are associated with an increased risk for PJI. A threshold of 7.7% seems to be more indicative of infection than the commonly used 7% and should perhaps be the goal in preoperative patient optimization.
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