Factors That Predict Short-term Complication Rates After Total Hip Arthroplasty

Background: There remains uncertainty regarding the relative importance of patient factors such as comorbidity and provider factors such as hospital volume in predicting complication rates after total hip arthroplasty (THA). Purpose: We therefore identified patient and provider factors predicting complications after THA. Methods: We reviewed discharge data from 138,399 patients undergoing primary THA in California from 1995 to 2005. The rate of complications during the first 90 days postoperatively (mortality, infection, dislocation, revision, perioperative fracture, neurologic injury, and thromboembolic disease) was regressed against a variety of independent variables, including patient factors (age, gender, race/ethnicity, income, Charlson comorbidity score) and provider variables (hospital volume, teaching status, rural location). Results: Compared with patients treated at high-volume hospitals (above the 20th percentile), patients treated at low-volume hospitals (below the 60th percentile) had a higher aggregate risk of having short-term complications (odds ratio, 2.00). A variety of patient factors also had associations with an increased risk of complications: increased Charlson comorbidity score, diabetes, rheumatoid arthritis, advanced age, male gender, and black race. Hispanic and Asian patients had lower risks of complications. Conclusions: Patient and provider characteristics affected the risk of a short-term complication after THA. These results may be useful for educating patients and anticipating perioperative risks of THA in different patient populations. Level of Evidence: Level II, prognostic study. See Guidelines for Authors for a complete description of levels of evidence. © 2010 The Author(s)

Short-Term Outcomes and Long-Term Implant Survival After Inpatient Surgical Management of Geriatric Proximal Humerus Fractures

IntroductionThe most common surgical options for geriatric proximal humerus fractures are open reduction and internal fixation (ORIF), hemiarthroplasty (HA), and reverse total shoulder arthroplasty. We used a longitudinal inpatient discharge database to determine the cumulative incidence of conversion to arthroplasty after ORIF of geriatric proximal humerus fractures. The rates of short-term complications and all-cause reoperation were also compared.Patients and methodsAll patients 65 or older who sustained a proximal humerus fracture and underwent either ORIF, HA, or shoulder arthroplasty (SA) as an inpatient from 2000 through 2017 were identified. Survival analysis was performed with ORIF conversion to arthroplasty and all-cause reoperation as the endpoints of interest. Rates of 30-day readmission and short-term complications were compared. Trends in procedure choice and outcomes over the study period were analyzed.ResultsA total of 27 102 geriatric patients that underwent inpatient surgical management of proximal humerus fractures were identified. Among geriatric patients undergoing ORIF, the cumulative incidence of conversion to arthroplasty within 10 years was 8.2%. The 10-year cumulative incidence of all-cause reoperation was 12.1% for ORIF patients and less than 4% for both HA and SA patients. Female sex was associated with increased risk of ORIF conversion and younger age was associated with higher all-cause reoperation. ORIF was associated with higher 30-day readmission and short-term complication rates. Over the study period, the proportion of patients treated with ORIF or SA increased while the proportion of patients treated with HA decreased. Short-term complication rates were similar between arthroplasty and ORIF patients in the later cohort (2015-2017).ConclusionThe 10-year cumulative incidence of conversion to arthroplasty for geriatric patients undergoing proximal humerus ORIF as an inpatient was found to be 8.2%. All-cause reoperations, short-term complications, and 30-day readmissions were all significantly lower among patients undergoing arthroplasty, but the difference in complication rate between arthroplasty and ORIF was attenuated in more recent years. Younger age was a risk factor for reoperation and female sex was associated with increased risk of requiring conversion to arthroplasty after ORIF

Pitfalls of using performance measures to evaluate the quality of hip fracture care

The objective of this study was to determine feasibility of using RAND quality indicators to evaluate hip fracture care. Retrospective chart review was used to determine the adherence to quality indicators and the location of documentation of compliance. A chart abstraction tool was created for systematic extraction of data from multiple chart components. A total of 111 patients underwent operative treatment of a hip fracture and met inclusion criteria in either 1998 or 2003. The main outcome measure was the rate of compliance with quality measures. Overall, compliance was 88% for the 7 performance measures. Physician notes were the most accurate chart component but, if examined alone, would have only resulted in a reported rate of 81% adherence to indicators. Review of the nursing notes, ancillary service notes, results, and orders was required to fully document quality of care. Ceiling effects were noted for 4 of the 7 quality indicators as noncompliance was rare for these measures. The results of this study highlight the need for a thorough method of abstracting multiple chart components to accurately report quality of care. This is an important consideration for any pay-for-performance program. Specifically, the failure to review all chart components may lead to incorrect conclusions about the quality of care delivered by individual providers. In addition, the selection of quality measures subject to ceiling effects may limit the usefulness of quality reporting initiatives. Copyright ® 2009 SLACK Incorporated. All rights reserved

Patient Use of Cost and Quality Data When Choosing a Joint Replacement Provider in the Context of Reference Pricing

Health plans are encouraging consumerism among joint replacement patients by reporting information on hospital costs and quality. Little is known about how the proliferation of such initiatives impacts patients’ selection of a surgeon and hospital. We performed a qualitative analysis of semistructured interviews with 13 patients who recently received a hip or knee replacement surgery. Patients focused on the choice of a surgeon as opposed to a hospital, and the surgeon choice was primarily made based on reputation. Most patients had long-standing relationships with an orthopedic surgeon and tended to stay with that surgeon for their replacement. Despite growing availability of cost and quality information, patients almost never used such information to make a decision

Patient Use of Cost and Quality Data When Choosing a Joint Replacement Provider in the Context of Reference Pricing

Health plans are encouraging consumerism among joint replacement patients by reporting information on hospital costs and quality. Little is known about how the proliferation of such initiatives impacts patients’ selection of a surgeon and hospital. We performed a qualitative analysis of semistructured interviews with 13 patients who recently received a hip or knee replacement surgery. Patients focused on the choice of a surgeon as opposed to a hospital, and the surgeon choice was primarily made based on reputation. Most patients had long-standing relationships with an orthopedic surgeon and tended to stay with that surgeon for their replacement. Despite growing availability of cost and quality information, patients almost never used such information to make a decision

Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Primary Anatomic Total Shoulder Replacements.

BACKGROUND: The demand and incidence of anatomic total shoulder arthroplasty (aTSA) procedures is projected to increase substantially over the next decade. There is a paucity of accurate risk prediction models which would be of great utility in minimizing morbidity and costs associated with major post-operative complications. Machine learning is a powerful predictive modeling tool and has become increasingly popular, especially in orthopedics. We aimed to build a ML model for prediction of major complications and readmission following primary aTSA. METHODS: A large California administrative database was retrospectively reviewed for all adults undergoing primary aTSA between 2015 to 2017. The primary outcome was any major complication or readmission following aTSA. A wide scope of standard ML benchmarks, including Logistic regression (LR), XGBoost, Gradient boosting, AdaBoost and Random Forest were employed to determine their power to predict outcomes. Additionally, important patient features to the prediction models were indentified. RESULTS: There were a total of 10,302 aTSAs with 598 (5.8%) having at least one major post-operative complication or readmission. XGBoost had the highest discriminative power (area under receiver operating curve AUROC of 0.689) of the 5 ML benchmarks with an area under precision recall curve AURPC of 0.207. History of implant complication, severe chronic kidney disease, teaching hospital status, coronary artery disease and male sex were the most important features for the performance of XGBoost. In addition, XGBoost identified teaching hospital status and male sex as markedly more important predictors of outcomes compared to LR models. CONCLUSION: We report a well calibrated XGBoost ML algorithm for predicting major complications and 30-day readmission following aTSA. History of prior implant complication was the most important patient feature for XGBoost performance, a novel patient feature that surgeons should consider when counseling patients