2019
DOI: 10.2106/jbjs.18.00758
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The Main Predictors of Length of Stay After Total Knee Arthroplasty

Abstract: Background: Often, differences in length of stay after total knee arthroplasty are solely attributed to patient factors. Therefore, our aim was to determine the influence of patient-related and procedure or structural-related risk factors as predictors of length of stay after total knee arthroplasty. Methods: A prospective cohort of 4,509 patients (54.6% of whom had Medicare for insurance) underwent primary total knee arthroplasty across 4 facilities in… Show more

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Cited by 64 publications
(19 citation statements)
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“…[6,10] However, to reach purpose of reducing LOS is not such simple as many patient characteristics are associated with it, like age, sex, marital status, co-morbidity and early rehabilitation. [6,11] Clinical pathway at our institution focused on early rehabilitation and we believe that this factor was the primarily responsible for reducing LOS. We showed there was a significant increase in the number of patients starting physical therapy within 24 hours of surgery.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…[6,10] However, to reach purpose of reducing LOS is not such simple as many patient characteristics are associated with it, like age, sex, marital status, co-morbidity and early rehabilitation. [6,11] Clinical pathway at our institution focused on early rehabilitation and we believe that this factor was the primarily responsible for reducing LOS. We showed there was a significant increase in the number of patients starting physical therapy within 24 hours of surgery.…”
Section: Discussionmentioning
confidence: 99%
“…Besides patient-related factors, as already mentioned, there are also structural-related risk factors influencing LOS. [11] Piuzzi et al [11] have reported patient-related factors such as sex, age and comorbidities are important but are not the main drivers of the LOS. Our results corroborate with their results, since the two groups did not differ regarding demographic characteristics despite difference found in the LOS.…”
Section: Plos Onementioning
confidence: 99%
“…In comparison, obesity and high BMI may increase LOS after TKA (Piuzzi et al. 2019 , Shah et al. 2019 ).…”
Section: Discussionmentioning
confidence: 99%
“…The Akaike information criterion (AIC) was used to evaluate the different models. Variable importance plots were obtained by ranking each variable by the increase in AIC upon its removal [12]. AIC ≥2 represents a statistically better model [13].…”
Section: Proportional Odds Logistic Regression Modelsmentioning
confidence: 99%