Machine-learning Models Predict 30-Day Mortality, Cardiovascular Complications, and Respiratory Complications After Aseptic Revision Total Joint Arthroplasty

Abstract

Background Aseptic revision THA and TKA are associated with an increased risk of adverse outcomes compared with primary THA and TKA. Understanding the risk profiles for patients undergoing aseptic revision THA or TKA may provide an opportunity to decrease the risk of postsurgical complications. There are risk stratification tools for postoperative complications after aseptic revision TKA or THA; however, current tools only include nonmodifiable risk factors, such as medical comorbidities, and do not include modifiable risk factors. Questions/purposes (1) Can machine learning predict 30day mortality and complications for patients undergoing aseptic revision THA or TKA using a cohort from the American College of Surgeons National Surgical Quality Improvement Program database? (2) Which patient variables are the most relevant in predicting complications? Methods This was a temporally validated, retrospective study analyzing the 2014 to 2019 National Surgical Quality Improvement Program database, as this database captures a large cohort of aseptic revision THA and TKA patients across a broad range of clinical settings and includes preoperative laboratory values. The training data set was 2014 to 2018, and 2019 was the validation data set. Given that predictive models learn expected prevalence of outcomes, this split allows assessment of model performance in contemporary patients. Between 2014 and 2019, a total of 24,682 patients underwent aseptic revision TKA and 17,871 patients underwent aseptic revision THA. Of those, patients with CPT codes corresponding to aseptic revision TKA or THA were considered as potentially eligible. Based on excluding procedures involving unclean wounds, 78% (19,345 of 24,682) of aseptic revision TKA procedures and 82% (14,711 of 17,871) of aseptic revision THA procedures were eligible. Ten percent of patients in each of the training and validation cohorts had missing predictor variables. Most of these missing data were preoperative Each author certifies that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request. Ethical approval for this study was obtained from the institutional review board at the Naval Medical Center Portsmouth, VA, USA.

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence

“…For institutional resources, many health care systems are partnering with large companies with cloud-based servers and technical AI resources, such as Amazon Web Services. Failure to have established a business relationship to obtain such resources, or lack of technology to facilitate large-scale processes (i.e., 143 THA Identification of patients at high risk of experiencing hyponatremia after THA Buddhiraju et al 130 THA Prediction of blood transfusion after primary and revision THA Ding et al 133 THA Prediction of DVT and PE after THA Shah et al 145 THA Prediction of major complications following THA Chen et al 131 THA Prediction of postoperative delirium after THA Knee Jung et al 138 TKA Prediction of postoperative delirium after TKA Jo et al 137 TKA Development of a web-based predictive model for transfusion after TKA Kolin et al 142 TKA Prediction of risk for postoperative anemia and blood transfusion after TKA Tsai et al 147 TKA Identification of patients at high-risk of prolonged opioid prescription after TKA Klemt et al 141 TKA Prediction of extended opioid use after primary TKA Hip & Knee Shohat et al 146 TKA, THA Prediction of VTE and MBE after TKA and THA Gabriel et al 135 TKA, THA Prediction of persistent opioid use among patients undergoing TKA and THA Onishchenko et al 144 TKA, THA Prediction of major adverse cardiac events after TKA and THA Dai et al 132 TKA, THA Prediction of ischemic stroke risk in patients after TKA and THA Huang et al 136 TKA, THA Prediction of risk for postoperative blood transfusion after TKA and THA Harris et al 30 TKA, THA Prediction of 30-day mortality and cardiac complications after TJA Abraham et al 129 rTKA, rTHA Prediction of 30-day mortality, cardiac complications, and respiratory complications after rTKA and rTHA Spine Kim et al 140 Posterior lumbar spinal fusion Prediction of complications after posterior lumbar spinal fusion Kim et al 139 ASD Prediction of surgical complications after ASD surgery Durand et al 134 ASD Prediction of blood transfusion requirement after ASD surgery *ASD 5 adult spinal deformity, DVT 5 deep venous thrombosis, MBE 5 major bleeding event, PE 5 pulmonary embolism, rTHA 5 revision total hip arthroplasty, rTKA 5 revision total knee arthroplasty, THA 5 total hip arthroplasty, TJA 5 total joint arthroplasty, TKA 5 total knee arthroplasty, and VTE 5 venous thromboembolism.…”

Knee

“…Authors from Portsmouth (Virginia, USA) have employed this technology to successfully develop a predictive model for 30-day cardiovascular and pulmonary complications along with mortality after aseptic revision total joint arthroplasty using the National Surgical Quality Improvement Program (NSQIP) database from the US. 1 They identified over 34,000 patients from 2014 to 2019 who met criteria for inclusion. They used data from 27,011 patients (2014 to 2018) as the training cohort to develop the model and 7,045 patients (2019) for model validation.…”

CORR Insights®: Machine-learning Models Predict 30-day Mortality, Cardiovascular Complications, and Respiratory Complications After Aseptic Revision Total Joint Arthroplasty

“…The current study in Clinical Orthopaedics and Related Research ® by Abraham et al [1] expands on this concept by predicting 30-day postoperative morbidity and mortality in patients undergoing aseptic revision THA and TKA. Previous studies used machine learning to assess factors that predict 30-day mortality and morbidity after primary THA and TKA based on medical comorbidities and laboratory parameters.…”

“…In the present study [1], the XGBoost tool was used to create a scoring tool for 30-day adverse outcomes. This tool is freely available and very accessible.…”

“…In the present study [1], using the XGBoost tool, the training dataset used data from 2014 to 2018, and the validation dataset used data from 2019. A post hoc analysis showed that the use of 2020 data did not improve the calibration of the 2019 validation dataset.…”