It has been shown that the preoperative modification of risk factors associated with obesity may reduce complications after total knee arthroplasty (TKA). However, the optimal method of doing so remains unclear. The aim of this study was to investigate whether a preoperative Risk Stratification Tool (RST) devised in our institution could reduce unexpected intensive care unit (ICU) transfers and 90-day emergency department (ED) visits, readmissions, and reoperations after TKA in obese patients. We retrospectively reviewed 1,614 consecutive patients undergoing primary unilateral TKA. Their mean age was 65.1 years (17.9 to 87.7) and the mean BMI was 34.2 kg/m2 (SD 7.7). All patients underwent perioperative optimization and monitoring using the RST, which is a validated calculation tool that provides a recommendation for postoperative ICU care or increased nursing support. Patients were divided into three groups: non-obese (BMI < 30 kg/m2, n = 512); obese (BMI 30 kg/m2 to 39.9 kg/m2, n = 748); and morbidly obese (BMI > 40 kg/m2, n = 354). Logistic regression analysis was used to evaluate the outcomes among the groups adjusted for age, sex, smoking, and diabetes.Aims
Methods
The aim of this study was to determine the impact of the severity of anaemia on postoperative complications following total hip arthroplasty (THA) and total knee arthroplasty (TKA). A retrospective cohort study was conducted using the American College of Surgeons National Quality Improvement Program (ACS-NSQIP) database. All patients who underwent primary TKA or THA between January 2012 and December 2017 were identified and stratified based upon hematocrit level. In this analysis, we defined anaemia as packed cell volume (Hct) < 36% for women and < 39% for men, and further stratified anaemia as mild anaemia (Hct 33% to 36% for women, Hct 33% to 39% for men), and moderate to severe (Hct < 33% for both men and women). Univariate and multivariate analyses were used to evaluate the incidence of multiple adverse events within 30 days of arthroplasty.Aims
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