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The Bone & Joint Journal
Vol. 103-B, Issue 6 Supple A | Pages 45 - 50
1 Jun 2021
Kerbel YE Johnson MA Barchick SR Cohen JS Stevenson KL Israelite CL Nelson CL

Aims

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.

Methods

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.


The Bone & Joint Journal
Vol. 102-B, Issue 9 | Pages 1183 - 1193
14 Sep 2020
Anis HK Strnad GJ Klika AK Zajichek A Spindler KP Barsoum WK Higuera CA Piuzzi NS

Aims

The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors.

Methods

Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model accuracies within the imputed data set were assessed through cross-validation with root mean square errors (RMSEs) and mean absolute errors (MAEs) for the LOS and PROMs models, and the index of prediction accuracy (IPA), and area under the curve (AUC) for the readmission models. Model accuracies in new patient data sets were assessed with AUC.


The Bone & Joint Journal
Vol. 95-B, Issue 11_Supple_A | Pages 144 - 147
1 Nov 2013
Jones RE Russell RD Huo MH

Satisfactory primary wound healing following total joint replacement is essential. Wound healing problems can have devastating consequences for patients. Assessment of their healing capacity is useful in predicting complications. Local factors that influence wound healing include multiple previous incisions, extensive scarring, lymphoedema, and poor vascular perfusion. Systemic factors include diabetes mellitus, inflammatory arthropathy, renal or liver disease, immune compromise, corticosteroid therapy, smoking, and poor nutrition. Modifications in the surgical technique are necessary in selected cases to minimise potential wound complications. Prompt and systematic intervention is necessary to address any wound healing problems to reduce the risks of infection and other potential complications.

Cite this article: Bone Joint J 2013;95-B, Supple A:144–7.