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Orthopaedic Proceedings
Vol. 102-B, Issue SUPP_9 | Pages 40 - 40
1 Oct 2020
Barsoum WK
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Introduction

Implementing operative eligibility thresholds based on body mass index (BMI) alone risks restricting access to improved pain, function, and quality-of-life. The purpose of this study was to: 1) investigate the relationship between BMI and improvements in 1-year patient reported outcome measures (PROMs), and 2) determine how many patients would have been denied 1-year improvements with specific BMI cut-offs.

Methods

Data were collected on a prospective cohort of 3,214 TKA patients from 2015–2018. Clinically meaningful 1-year improvements were defined as 15 points for Knee Injury and Osteoarthritis Outcome Scores (KOOS) pain and Physical Function Shortform (PS), and 14 points for Knee-Related Quality-of-Life (KRQOL). For specific BMI cut-offs, the positive predictive value for predicting a failure to improve and number of patients denied surgery to avoid one failed improvement was calculated.


Orthopaedic Proceedings
Vol. 102-B, Issue SUPP_10 | Pages 40 - 40
1 Oct 2020
Girbino KL Klika AK Barsoum WK Rueda CAH Piuzzi NS
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Introduction

With the removal of total hip arthroplasty (THA) from the Centers for Medicare & Medicaid Services (CMS) inpatient-only list, understanding predictors of length of stay (LOS) after THA is critical. Thus, we aimed to determine the influence of patient- and procedure-related risk factors as predictors of >1-day LOS after THA.

Methods

A prospective cohort of 5,281 patients underwent primary THA between January 2016 and April 2019. Risk factors increased LOS were categorized as patient-related (demographics, smoking status, baseline Veterans RAND 12 Item Health Survey Mental Component Summary score [VR-12 MCS], Charlson Comorbidity Index [CCI], surgical indication, baseline Hip Injury and Osteoarthritis Outcome Score [HOOS] pain subscore and baseline HOOS physical function shortform (HOOS-PS), range of motion, and predicted discharge disposition) or procedure-related (hospital site, surgeon, approach, day of surgery, and surgery start time). By using the Akaike information criterion (AIC) and internally-validated concordance probabilities (C-index) for discriminating a 1-day LOS from a >1-day LOS, we compared performance between a patient-related risk factors only model and a model containing both patient- and procedure-related risk factors.


Orthopaedic Proceedings
Vol. 100-B, Issue SUPP_12 | Pages 14 - 14
1 Oct 2018
Barsoum WK Anis H Faour M Klika AK Mont MA Molloy RM Rueda CAH
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Introduction

Antibiotic-impregnated bone cement (AIBC) has been used for decades to treat and prevent post-operative infections in joint arthroplasty. Local delivery of antibiotics may theoretically have a bactericidal effect, however evidence supporting this is controversial and literature suggests its prophylactic use in primary total knee arthroplasty (TKA) is seldom justified. With evolving standards of care, historical data is no longer relevant in addressing the efficacy of AIBC in the contemporary TKA. The purpose of this study was to evaluate outcomes following primary TKA using AIBC and regular non-AIBC by comparing rates of surgical site infection (SSI) and prosthetic joint infection (PJI).

Methods

A retrospective review was conducted of all cemented primary TKA procedures from a large institutional database between January 1, 2015 and December 31st, 2016. This identified 6,073 cases, n=2,613 in which AIBC was used and n=3,460 cases using bone cement without antibiotics. Patients were stratified into low risk and high-risk groups based on age (>65 years), BMI (>40), and Charlson Comorbidity Index (CCI; >3). Medical records were reviewed for diagnoses of SSI (skin and superficial wound infections) and PJI (deep joint infections requiring surgery) over a 2-year postoperative period. Univariate analysis and multivariate regression models were used to ascertain the effects of cement type, patient factors (age, gender, BMI, CCI), operative time, and length of stay on infection rates. Additionally, mixed models (adjusted for gender, age, race, BMI, and CCI) were built to account for surgeon variability.


Orthopaedic Proceedings
Vol. 100-B, Issue SUPP_13 | Pages 12 - 12
1 Oct 2018
Barsoum WK Villa JM Higuera-Rueda CA Patel PD
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Introduction

Perioperative hospital adverse events are an issue that every surgeon endeavors to avoid and minimize as much as possible. Even “minor events” such as fever or tachycardia may lead to significant costs due to workup tests, inter-consultations, and/or increased hospital stay. The objective of this study was compare perioperative outcomes (hospital length of stay [LOS], discharge disposition), rates of in-hospital adverse events and transfusion, and postoperative readmission and reoperation rates for simultaneous and staged bilateral direct anterior total hip arthroplasty (DA-THA) patients.

Methods

A retrospective chart review was conducted on a consecutive series of 411 primary bilateral DA-THAs performed between 2010 and 2016 at a single institution by two fellowship trained surgeons. These were categorized as: (1) simultaneous (same anesthesia, n=122) and (2) staged (different hospitalizations, n=289). The mean time between staged surgeries was 468 days (± 414 days). Baseline patient demographics as well as hospital LOS, discharge disposition (home vs. other), hospital adverse events (i.e., nausea, vomiting, tachycardia, fever, confusion, pulmonary embolism, etc.), blood transfusions, and unplanned hospital readmissions and reoperations within 90 days were collected. Groups were compared using independent –tests, Fisher's exact test, and Pearson Chi-Square.


Orthopaedic Proceedings
Vol. 92-B, Issue SUPP_I | Pages 12 - 12
1 Mar 2010
Klika A Barsoum WK Gad B Styron J Green K Bershadsky B Pifer M
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Purpose: The current health care climate encourages an early discharge directly home. Efforts to increase efficiency and decrease length of stay require accurate pre-planning of patient discharge following total joint arthroplasty (TJA). The purpose of this study was to develop and evaluate an easily administered form to preoperatively predict patient discharge disposition following TJA.

Method: A form was generated by a multidisciplinary group of clinicians which identified a set of preoperative factors relevant to patient discharge including age, gender, body mass index, comorbidities, preoperative ambulatory status, projected postoperative weight bearing, home environment and location, and caregiver assistance. Data were collected from a retrospective review of 516 medical charts for patients that had undergone primary total knee arthroplasty (TKA) (n=103), revision TKA (n=104), bilateral TKA (n=102), primary total hip arthroplasty (THA) (n=106), and revision THA (n=101). A stepwise multinomial logistic regression model was used to identify predictors of discharge to a skilled nursing facility (SNF), rehabilitation facility, or home, using SPSS version 11.5 statistical software (SPSS Inc., Chicago, IL).

Results: Patients were more likely to be discharged to either a SNF or rehabilitation facility if they underwent bilateral TKA (p< 0.001); were female (p< 0.001), have their heart disease monitored (p=0.003); or are older (p< 0.001). Patients are more likely to be discharged home if preoperatively they are capable of independent ambulation in the community (p=0.014). Patients discharged to either a SNF or rehabilitation facility were not significantly different except patients undergoing bilateral TKA were more likely to be discharged to a rehabilitation facility (p< 0.001).

Conclusion: We identified factors associated with discharge to a SNF, rehabilitation facility, or home following elective joint replacement surgery. With further validation, this model may be a useful tool for preoperatively predicting a patient’s discharge disposition, which is valuable to the hospital, clinicians, patients, and families in efficiently preparing for postoperative care.


Orthopaedic Proceedings
Vol. 92-B, Issue SUPP_I | Pages 30 - 30
1 Mar 2010
Klika A Barsoum WK Lee HH Krebs V Bershadsky B
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Purpose: There is a paucity of literature describing clinical outcomes following hip arthroscopy. Variables associated with short or prolonged recovery are undefined. This presents a challenge to surgeons in preoperatively communicating with patients about expectations after surgery. The goals of this study are to identify predictors of recovery and to develop models which will facilitate the proper counseling of patients prior to hip arthroscopy. In this study, we define a normal recovery after hip arthroscopy, determine the predictive values of preoperative and intraoperative variables for recovery and for progression to total hip arthroplasty (THA) after hip arthroscopy.

Method: A retrospective review of 216 individuals treated with hip arthroscopy at a tertiary medical center was conducted by a single reviewer. Univariate analysis was used to identify independent variables that correlated with prolonged or short recovery following hip arthroscopy and also on variables correlated with progression to THA. Binary logistic regression analysis was used to develop and test multivariate models for predicting prolonged recovery and progression to THA.

Results: Univariate analyses revealed multiple variables (spanning demographics, past medical history, radiographic findings, physical examination findings, and intraoperative findings) which were significantly (p≤0.05) correlated with prolonged recovery (13 significant predictors) and also with progression to THA (14 significant predictors). A multivariate predictive algorithm was generated using 5 significant predictors of prolonged recovery, which included Workman’s compensation involvement, female gender, use of pain medications, presence of a limp, and presence of a lateral labral tear. This algorithm was tested successfully using an independent sample of 25 individuals. Three multivariate predictors of progression to THA after hip arthroscopy were identified, including radiographic presence of arthritis, female gender and the presence of grade 4 chondral lesions, and a predictive algorithm was generated.

Conclusion: We generated and initially validated a multivariate algorithm to predict prolonged recovery following hip arthroscopy. If validated in larger sample, this model may allow a surgeon to appropriately counsel patients regarding expectations for recovery after hip arthroscopy.