Development and Internal Validation of Machine Learning Algorithms for Predicting Hyponatremia After TJA

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review

» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.

Hyponatremia Is an Overlooked Sign of Trouble Following Total Joint Arthroplasty

BACKGROUND

Hyponatremia is a common electrolyte abnormality in arthroplasty patients. This issue, underrecognized by surgeons, can impact the postoperative course of patients. There are, however, little data on the implications of sodium disturbances following total joint arthroplasty (TJA). The primary aims of this study were to (1) report the rate of hyponatremia following TJA, and (2) examine the impact of hyponatremia on the perioperative course of TJA patients.

METHODS

This was a retrospective analysis of 3,071 primary and revision TJAs performed between 2015 and 2017. Based on preoperative and postoperative sodium values (pre-post), patients were classified into 4 groups: normonatremic-normonatremic (Group 1), normonatremic-hyponatremic (Group 2), hyponatremic-normonatremic (Group 3), and hyponatremic-hyponatremic (Group 4). Primary end points were length of stay (LOS), postoperative discharge, in-hospital complications, and 90-day readmissions.

RESULTS

The distribution of cases was 84.6% Group 1, 9.4% Group 2, 2.1% Group 3, and 3.8% Group 4. Overall, 13.2% of patients had hyponatremia after TJA. Older age, hip arthroplasty, general anesthesia, higher Charlson Comorbidity Index, congestive heart failure, revision surgery, and history of stroke, liver disease, and chronic kidney disease were risk factors for postoperative hyponatremia. Patients with postoperative hyponatremia (Groups 2 and 4) had greater likelihoods of having a 90-day complication and non-home discharge and greater LOS.

CONCLUSIONS

Postoperative hyponatremia was a relatively common occurrence in patients undergoing TJA, and was associated with greater LOS, complications, and non-home discharge. Surgeons should identify patients at risk for developing sodium abnormalities in order to optimize these patients and avoid increased resource utilization.

LEVEL OF EVIDENCE

Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Concerns surrounding application of artificial intelligence in hip and knee arthroplasty

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.

Cite this article: Bone Joint J 2022;104-B(12):1292–1303.