Aims

This study aimed to investigate the risk of postoperative complications in COVID-19-positive patients undergoing common orthopaedic procedures.

Aims

Delirium is associated with adverse outcomes following hip fracture, but the prevalence and significance of delirium for the prognosis and ongoing rehabilitation needs of patients admitted from home is less well studied. Here, we analyzed relationships between delirium in patients admitted from home with 1) mortality; 2) total length of hospital stay; 3) need for post-acute inpatient rehabilitation; and 4) hospital readmission within 180 days.

Aims

To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA.

Aims

In the last decade, perioperative advancements have expanded the use of outpatient primary total knee arthroplasty (TKA). Despite this, there remains limited data on expedited discharge after revision TKA. This study compared 30-day readmissions and reoperations in patients undergoing revision TKA with a hospital stay greater or less than 24 hours. The authors hypothesized that expedited discharge in select patients would not be associated with increased 30-day readmissions and reoperations.

To describe the longitudinal trends in patients with obesity and Metabolic Syndrome (MetS) undergoing TKA and the associated impact on complications and lengths of hospital stay. We identified patients who underwent primary TKA between 2006 – 2017 within the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database. We recorded patient demographics, length of stay (LOS), and 30-day major and minor complications. We labelled those with an obese Body Mass Index (BMI ≥ 30), hypertension, and diabetes as having MetS. We evaluated mean BMI, LOS, and 30-day complication rates in all patients, obese patients, and those with MetS from 2006-2017. We used multivariable regression to evaluate the trends in BMI, complications, and LOS over time in all patients and those with MetS, and the effect of BMI and MetS on complication rates and LOS, stratified by year. 270,846 patients underwent primary TKA at hospitals participating in the NSQIP database. 63.71% of patients were obese (n = 172,333), 15.21% were morbidly obese (n = 41,130), and 12.37% met criteria for MetS (n = 33,470). Mean BMI in TKA patients increased at a rate of 0.03 per year (0.02-0.05; p < 0 .0001). Despite this, the rate of adverse events in obese patients decreased: major complications by an odds ratio (OR) of 0.94 (0.93-0.96; p < 0 .0001) and minor complications by 0.94 (0.93-0.95; p < 0 .001). LOS also decreased over time at an average rate of −0.058 days per year (-0.059 to −0.057; p < 0 .0001). The proportion of patients with MetS did not increase, however similar improvements in major complications (OR 0.94 [0.91-0.97] p < 0 .0001), minor complications (OR 0.97 [0.94-1.00]; p < 0 .0330), and LOS (mean −0.055 [-0.056 to −0.054] p < 0 .0001) were found. In morbidly obese patients (BMI ≥ 40), there was a decreased proportion per year (OR 0.989 [0.98-0.994] p < 0 .0001). Factors specifically associated with major complications in obese patients included COPD (OR 1.75 [1.55-2.00] p < 0.0001) and diabetes (OR 1.10 [1.02-1.1] p = 0.017). Hypertension (OR 1.12 [1.03-1.21] p = 0.0079) was associated with minor complications. Similarly, in patients with MetS, major complications were associated with COPD (OR 1.72 [1.35-2.18] p < 0.0001). Neuraxial anesthesia was associated with a lower risk for major complications in the obese cohort (OR 0.87 [0.81-0.92] p < 0.0001). BMI ≥ 40 was associated with a greater risk for minor complications (OR 1.37 [1.26-1.50] p < 0.0001), major complications (1.11 [1.02-1.21] p = 0.015), and increased LOS (+0.08 days [0.07-0.09] p < 0.0001). Mean BMI in patients undergoing primary TKA increased from 2006 - 2017. MetS comorbidities such as diabetes and hypertension elevated the risk for complications in obese patients. COPD contributed to higher rates of major complications. The obesity-specific risk reduction with spinal anesthesia suggests an improved post-anesthetic clinical course in obese patients with pre-existing pulmonary pathology. Encouragingly, the overall rates of complications and LOS in patients with obesity and MetS exhibited a longitudinal decline. This finding may be related to the decreased proportion of patients with BMI ≥ 40 treated over the same period, possibly the result of quality improvement initiatives aimed at delaying high-risk surgery in morbidly obese patients until healthy weight loss is achieved. These findings may also reflect increased awareness and improved management of these patients and their elevated risk profiles

Total knee and hip arthroplasty (TKA and THA) are two of the highest volume and resource intensive surgical procedures. Key drivers of the cost of surgical care are duration of surgery (DOS) and postoperative inpatient length of stay (LOS). The ability to predict TKA and THA DOS and LOS has substantial implications for hospital finances, scheduling and resource allocation. The goal of this study was to predict DOS and LOS for elective unilateral TKAs and THAs using machine learning models (MLMs) constructed on preoperative patient factors using a large North American database. The American College of Surgeons (ACS) National Surgical and Quality Improvement (NSQIP) database was queried for elective unilateral TKA and THA procedures from 2014-2019. The dataset was split into training, validation and testing based on year. Multiple conventional and deep MLMs such as linear, tree-based and multilayer perceptrons (MLPs) were constructed. The models with best performance on the validation set were evaluated on the testing set. Models were evaluated according to 1) mean squared error (MSE), 2) buffer accuracy (the number of times the predicted target was within a predesignated buffer of the actual target), and 3) classification accuracy (the number of times the correct class was predicted by the models). To ensure useful predictions, the results of the models were compared to a mean regressor. A total of 499,432 patients (TKA 302,490; THA 196,942) were included. The MLP models had the best MSEs and accuracy across both TKA and THA patients. During testing, the TKA MSEs for DOS and LOS were 0.893 and 0.688 while the THA MSEs for DOS and LOS were 0.895 and 0.691. The TKA DOS 30-minute buffer accuracy and ≤120 min, >120 min classification accuracy were 78.8% and 88.3%, while the TKA LOS 1-day buffer accuracy and ≤2 days, >2 days classification accuracy were 75.2% and 76.1%. The THA DOS 30-minute buffer accuracy and ≤120 min, >120 min classification accuracy were 81.6% and 91.4%, while the THA LOS 1-day buffer accuracy and ≤2 days, >2 days classification accuracy were 78.3% and 80.4%. All models across both TKA and THA patients were more accurate than the mean regressors for both DOS and LOS predictions across both buffer and classification accuracies. Conventional and deep MLMs have been effectively implemented to predict the DOS and LOS of elective unilateral TKA and THA patients based on preoperative patient factors using a large North American database with a high level of accuracy. Future work should include using operational factors to further refine these models and improve predictive accuracy. Results of this work will allow institutions to optimize their resource allocation, reduce costs and improve surgical scheduling. Acknowledgements:. The American College of Surgeons National Surgical Quality Improvement Program and the hospitals participating in the ACS NSQIP are the source of the data used herein; they have not verified and are not responsible for the statistical validity of the data analysis or the conclusions derived by the authors

Aims

To compare rates of serious adverse events in patients undergoing revision knee arthroplasty with consideration of the indication for revision (urgent versus elective indications), and compare these with primary arthroplasty and re-revision arthroplasty.

Aims

This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA).

Aims

While preoperative bloodwork is routinely ordered, its value in determining which patients are at risk of postoperative readmission following total knee arthroplasty (TKA) and total hip arthroplasty (THA) is unclear. The objective of this study was to determine which routinely ordered preoperative blood markers have the strongest association with acute hospital readmission for patients undergoing elective TKA and THA.

Aims

The purpose of this study was to assess total knee arthroplasty (TKA) volume and rates of early complications in morbidly obese patients over the last decade, where the introduction of quality models influencing perioperative care pathways occurred.

Delayed management of high energy femoral shaft fractures is associated with increased complication rates. It has been suggested that there is less urgency to stabilize lower energy femoral shaft fractures. The purpose of this study was to evaluate the effect of surgical delay on 30-day complications following fixation of lower energy femoral shaft fractures. Patients ≥ 18 years who underwent either plate or nail fixation of low energy (falls from standing or up to three steps' height) femoral shaft fractures from 2005 – 2016 were identified from the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) via procedural codes. Patients with pathologic fractures, fractures of the distal femur or femoral neck were excluded. Patients were categorized into early (< 2 4 hours) or delayed surgery (2–30 days) groups. Bivariate analyses were used to compare demographics and unadjusted rates of complications between groups. A multivariable logistic regression was used to compare the rate of major and minor complications between groups, while adjusting for relevant covariables. Head injury patients and polytrauma patients are not included in the NSQIP database. Of 2,716 lower energy femoral shaft fracture patients identified, 2,412 (89%) were treated within 1 day of hospital admission, while 304 (11.2%) were treated between 2 and 30 days post hospital admission. Patient age, American Society of Anesthesiologists (ASA) classification score, presence of diabetes, functional status, smoking status, and surgery type (nail vs. plate) were significantly different between groups (p After adjusting for all relevant covariables, delayed surgery significantly increased the odds of 30-day minor complications (p=0.02, OR = 1.48 95%CI 1.01–2.16), and 30-day mortality (p < 0 .001), OR = 1.31 (95%CI 1.03–2.14). The delay of surgical fixation of femoral shaft fractures appears to significantly increase patients' risk of minor adverse events as well as increase mortality. With only 89% of patients being treated in the 24 hour timeframe that constitutes best practice for treatment of femoral shaft fractures, there remains room for improvement. These results suggest that early treatment of all femoral shaft fractures, even those with a lower energy mechanism of injury, leads to improved outcomes

Over 300,000 total hip arthroplasties (THA) are performed annually in the USA. Surgical Site Infections (SSI) are one of the most common complications and are associated with increased morbidity, mortality and cost. Risk factors for SSI include obesity, diabetes and smoking, but few studies have reported on the predictive value of pre-operative blood markers for SSI. The purpose of this study was to create a clinical prediction model for acute SSI (classified as either superficial, deep and overall) within 30 days of THA based on commonly ordered pre-operative lab markers and using data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. All adult patients undergoing an elective unilateral THA for osteoarthritis from 2011–2016 were identified from the NSQIP database using Current Procedural Terminology (CPT) codes. Patients with active or chronic, local or systemic infection/sepsis or disseminated cancer were excluded. Multivariate logistic regression was used to determine coefficients, with manual stepwise reduction. Receiver Operating Characteristic (ROC) curves were also graphed. The SSI prediction model included the following covariates: body mass index (BMI) and sex, comorbidities such as congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), smoking, current/previous steroid use, as well as pre-operative blood markers, albumin, alkaline phosphate, blood urea nitrogen (BUN), creatinine, hematocrit, international normalized ratio (INR), platelets, prothrombin time (PT), sodium and white blood cell (WBC) levels. Since the data met logistic assumption requirements, bootstrap estimation was used to measure internal validity. The area under the ROC curve for final derivations along with McFadden's R-squared were utilized to compare prediction models. A total of 130,619 patients were included with the median age of patients at time of THA was 67 years (mean=66.6+11.6 years) with 44.8% (n=58,757) being male. A total of 1,561 (1.20%) patients had a superficial or deep SSI (overall SSI). Of all SSI, 45.1% (n=704) had a deep SSI and 55.4% (n=865) had a superficial SSI. The incidence of SSI occurring annually decreased from 1.44% in 2011 to 1.16% in 2016. Area under the ROC curve for the SSI prediction model was 0.79 and 0.78 for deep and superficial SSI, respectively and 0.71 for overall SSI. CHF had the largest effect size (Odds Ratio(OR)=2.88, 95% Confidence Interval (95%CI): 1.56 – 5.32) for overall SSI risk. Albumin (OR=0.44, 95% CI: 0.37 – 0.52, OR=0.31, 95% CI: 0.25 – 0.39, OR=0.48, 95% CI: 0.41 – 0.58) and sodium (OR=0.95, 95% CI: 0.93 – 0.97, OR=0.94, 95% CI: 0.91 – 0.97, OR=0.95, 95% CI: 0.93 – 0.98) levels were consistently significant in all clinical prediction models for superficial, deep and overall SSI, respectively. In terms of pre-operative blood markers, hypoalbuminemia and hyponatremia are both significant risk factors for superficial, deep and overall SSI. In this large NSQIP database study, we were able to create an SSI prediction model and identify risk factors for predicting acute superficial, deep and overall SSI after THA. To our knowledge, this is the first clinical model whereby pre-operative hyponatremia (in addition to hypoalbuminemia) levels have been predictive of SSI after THA. Although the model remains without external validation, it is a vital starting point for developing a risk prediction model for SSI and can help physicians mitigate risk factors for acute SSI post THA

Total knee arthroplasty (TKA) is the most commonly performed elective orthopaedic procedure. With an increasingly aging population, the number of TKAs performed is expected to be ∼2,900 per 100,000 by 2050. Surgical Site Infections (SSI) after TKA can have significant morbidity and mortality. The purpose of this study was to construct a risk prediction model for acute SSI (classified as either superficial, deep and overall) within 30 days of a TKA based on commonly ordered pre-operative blood markers and using audited administrative data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. All adult patients undergoing an elective unilateral TKA for osteoarthritis from 2011–2016 were identified from the NSQIP database using Current Procedural Terminology (CPT) codes. Patients with active or chronic, local or systemic infection/sepsis or disseminated cancer were excluded. Multivariate logistic regression was conducted to estimate coefficients, with manual stepwise reduction to construct models. Bootstrap estimation was administered to measure internal validity. The SSI prediction model included the following co-variates: body mass index (BMI) and sex, comorbidities such as congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), smoking, current/previous steroid use, as well as pre-operative blood markers, albumin, alkaline phosphatase, blood urea nitrogen (BUN), creatinine, hematocrit, international normalized ratio (INR), platelets, prothrombin time (PT), sodium and white blood cell (WBC) levels. To compare clinical models, areas under the receiver operating characteristic (ROC) curves and McFadden's R-squared values were reported. The total number of patients undergoing TKA were 210,524 with a median age of 67 years (mean age of 66.6 + 9.6 years) and the majority being females (61.9%, N=130,314). A total of 1,674 patients (0.8%) had a SSI within 30 days of the index TKA, of which N=546 patients (33.2%) had a deep SSI and N=1,128 patients (67.4%) had a superficial SSI. The annual incidence rate of overall SSI decreased from 1.60% in 2011 to 0.68% in 2016. The final risk prediction model for SSI contained, smoking (OR=1.69, 95% CI: 1.31 – 2.18), previous/current steroid use (OR=1.66, 95% CI: 1.23 – 2.23), as well as the pre-operative lab markers, albumin (OR=0.46, 95% CI: 0.37 – 0.56), blood urea nitrogen (BUN, OR=1.01, 95% CI: 1 – 1.02), international normalized ratio (INR, OR=1.22, 95% CI:1.05 – 1.41), and sodium levels (OR=0.94, 95% CI: 0.91 – 0.98;). Area under the ROC curve for the final model of overall SSI was 0.64. Models for deep and superficial SSI had ROC areas of 0.68 and 0.63, respectively. Albumin (OR=0.46, 95% CI: 0.37 – 0.56, OR=0.33, 95% CI: 0.27 – 0.40, OR=0.75, 95% CI: 0.59 – 0.95) and sodium levels (OR=0.94, 95% CI: 0.91 – 0.98, OR=0.96, 95% CI: 0.93 – 0.99, OR=0.97, 95% CI: 0.96 – 0.99) levels were consistently significant in all prediction models for superficial, deep and overall SSI, respectively. Overall, hypoalbuminemia and hyponatremia are both significant risk factors for superficial, deep and overall SSI. To our knowledge, this is the first prediction model for acute SSI post TKA whereby hyponatremia (and hypoalbuminemia) are predictive of SSI. This prediction model can help fill an important gap for predicting risk factors for SSI after TKA and can help physicians better optimize patients prior to TKA

Aims

Currently, the US Center for Medicaid and Medicare Services (CMS) has been testing bundled payments for revision total joint arthroplasty (TJA) through the Bundled Payment for Care Improvement (BPCI) programme. Under the BPCI, bundled payments for revision TJAs are defined on the basis of diagnosis-related groups (DRGs). However, these DRG-based bundled payment models may not be adequate to account appropriately for the varying case-complexity seen in revision TJAs.

Aims

Natural Language Processing (NLP) offers an automated method to extract data from unstructured free text fields for arthroplasty registry participation. Our objective was to investigate how accurately NLP can be used to extract structured clinical data from unstructured clinical notes when compared with manual data extraction.

This study was designed to compare atypical hip fractures with a matched cohort of standard hip fractures to evaluate the difference in outcomes. Patients from the American College of Surgeons National Surgical Quality Improvement Program's (NSQIP) targeted hip fracture data file (containing a more comprehensive set of variables collected on 9,390 specially targeted hip fracture patients, including the differentiation of atypical from standard hip fractures) were merged with the standard 2016 NSQIP data file. Atypical hip fracture patients aged 18 years and older in 2016 were identified via the targeted hip fracture data file and matched to two standard hip fracture controls by age, sex, and fracture location. Patient demographics, length of hospital stay, 30-day mortality, major and minor complications, and other hip-specific variables were identified from the database. Binary outcomes were compared using the McNemar's test for paired groups, and continuous outcomes were compared using a paired t-test. Ninety-five atypical hip fractures were identified, and compared to 190 age, sex, and fracture location matched standard hip fracture controls. There was no statistical difference in body mass index (BMI), race, ASA score, smoking status, timing of fixation, or functional status between the two groups (P>0.05). Thirty-day mortality was significantly higher in the atypical hip fracture group (atypical 7.36%, standard 2.11% p. This is the first study, to our knowledge, that demonstrates an increase in the rate of mortality in atypical hip fractures. Comparing atypical hip fractures with a matched cohort of standard hip fractures revealed a significantly greater 30-day mortality rate with an odds ratio of 3.62 in atypical hip fractures (95% CI 1.03–12.68). Prospective, clinical studies are recommended to further investigate these findings

Aims

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).