Increased severity of anemia is associated with postoperative complications following primary total shoulder arthroplasty

Author(s):  
Kevin Y. Wang ◽  
Theodore Quan ◽  
Alex Gu ◽  
Matthew J. Best ◽  
Monica Stadecker ◽  
...  
2015 ◽  
Vol 24 (10) ◽  
pp. 1567-1573 ◽  
Author(s):  
Gregory L. Cvetanovich ◽  
William W. Schairer ◽  
Bryan D. Haughom ◽  
Gregory P. Nicholson ◽  
Anthony A. Romeo

2017 ◽  
Vol 14 (1) ◽  
pp. 23-28 ◽  
Author(s):  
Venkat Boddapati ◽  
Michael C. Fu ◽  
William W. Schairer ◽  
Lawrence V. Gulotta ◽  
David M. Dines ◽  
...  

2019 ◽  
Vol 28 (12) ◽  
pp. e410-e421 ◽  
Author(s):  
Anirudh K. Gowd ◽  
Avinesh Agarwalla ◽  
Nirav H. Amin ◽  
Anthony A. Romeo ◽  
Gregory P. Nicholson ◽  
...  

2021 ◽  
Vol 5 ◽  
pp. 247154922110381
Author(s):  
Sai K. Devana ◽  
Akash A. Shah ◽  
Changhee Lee ◽  
Varun Gudapati ◽  
Andrew R. Jensen ◽  
...  

Background Reverse total shoulder arthroplasty (rTSA) offers tremendous promise for the treatment of complex pathologies beyond the scope of anatomic total shoulder arthroplasty but is associated with a higher rate of major postoperative complications. We aimed to design and validate a machine learning (ML) model to predict major postoperative complications or readmission following rTSA. Methods We retrospectively reviewed California's Office of Statewide Health Planning and Development database for patients who underwent rTSA between 2015 and 2017. We implemented logistic regression (LR), extreme gradient boosting (XGBoost), gradient boosting machines, adaptive boosting, and random forest classifiers in Python and trained these models using 64 binary, continuous, and discrete variables to predict the occurrence of at least one major postoperative complication or readmission following primary rTSA. Models were validated using the standard metrics of area under the receiver operating characteristic (AUROC) curve, area under the precision–recall curve (AUPRC), and Brier scores. The key factors for the top-performing model were determined. Results Of 2799 rTSAs performed during the study period, 152 patients (5%) had at least 1 major postoperative complication or 30-day readmission. XGBoost had the highest AUROC and AUPRC of 0.681 and 0.129, respectively. The key predictive features in this model were patients with a history of implant complications, protein-calorie malnutrition, and a higher number of comorbidities. Conclusion Our study reports an ML model for the prediction of major complications or 30-day readmission following rTSA. XGBoost outperformed traditional LR models and also identified key predictive features of complications and readmission.


2019 ◽  
Vol 3 (2) ◽  
pp. 99-107 ◽  
Author(s):  
Brandon E. Lung ◽  
Shrey Kanjiya ◽  
Michael Bisogno ◽  
David E. Komatsu ◽  
Edward D. Wang

2021 ◽  
pp. 175857322110273
Author(s):  
Puneet Gupta ◽  
Theodore Quan ◽  
Zachary R Zimmer

Background Octogenarians are at an increased risk of morbidity and mortality following various surgeries, but this has not yet been well explored in octogenarians undergoing revision total shoulder arthroplasty (RTSA). Thus, the purpose of this study was to analyze whether octogenarians undergoing RTSA are at an increased risk of 30-day postoperative complications, readmissions, and mortality relative to the younger geriatric population. Methods Data of patients who underwent RTSA from 2013 to 2018 were obtained from a large de-identified database. Patients were divided into two cohorts: ages 65–79 and ages 80–89. Demographic data, comorbidities, and postoperative complications were collected and compared between the two cohorts. Bivariate and multivariate analyses were performed. Results On bivariate analyses, patients aged 80–89 were more likely to develop pulmonary embolism (p = 0.014) and extended length of stay more than 3 days (p = 0.006) compared to the cohort aged 65–79. Following adjustment on multivariate analyses, 80–89 years old patients no longer had an increased likelihood of pulmonary embolism or extended length of stay compared to the 65–79 age group. Octogenarians were not found to have higher rates of 30-day readmissions (p = 0.782), mortality (p = 0.507), reoperation (p = 0.785), pneumonia (p = 0.417), urinary tract infection (p = 0.739), or sepsis (p = 0.464) compared to the cohort aged 65–79 following RTSA. Conclusion Age greater than 80 should not be used independently as a factor for evaluating whether a geriatric patient is a proper candidate for RTSA.


2019 ◽  
pp. 175857321987657
Author(s):  
Jacob M Wilson ◽  
Russell E Holzgrefe ◽  
Christopher A Staley ◽  
Spero Karas ◽  
Michael B Gottschalk ◽  
...  

Background Total shoulder arthroplasty has been demonstrated to be an effective treatment for arthritis of the glenohumeral joint. Prior studies have identified longer operative times as a risk factor for complications after numerous types of procedures. We hypothesized that increased operative time, in 20-min intervals, would be associated with complications following total shoulder arthroplasty. Methods Patients undergoing total shoulder arthroplasty from 2006 to 2015 were identified from the ACS-NSQIP database. Patient demographic information, perioperative parameters, and 30-day outcomes were retrieved. Pearson's Chi-square test and multivariate Poisson regression with robust error variance were used to analyze the relationship of operative time and outcomes. Results A total of 10,082 patients were included. Multivariate analysis revealed that for each increase in 20 min of operative time, there were significantly increased rates of any complication (relative risk (RR) 1.24, 95% confidence interval (CI) 1.19–1.26), anemia requiring transfusion (RR 1.33, 95%CI 1.26–1.4), peripheral nerve injury (RR 1.88, 95%CI 1.53–2.31), and urinary tract infection (RR 1.24, 95%CI 1.09–1.41). Discussion This study indicates that increasing operative time confers increased risk for postoperative complications following total shoulder arthroplasty. We anticipate the results of this manuscript will be used for provider education, policy decision-making, and potentially to derive algorithms that can improve safety and efficiency in total shoulder arthroplasty. Level of evidence III.


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