Can machine learning models predict failure of revision total hip arthroplasty?

Machine learning model outperforms the ACS Risk Calculator in predicting non-home discharge following primary total knee arthroplasty

PURPOSE

Despite the increase in outpatient total knee arthroplasty (TKA) procedures, many patients are still discharged to non-home locations following index surgery. The ability to accurately predict non-home discharge (NHD) following TKAs has the potential to promote a reduction in associated adverse events and excess healthcare costs. This study aimed to evaluate whether a machine learning (ML) model could outperform the American College of Surgeons (ACS) Risk Calculator in predicting NHD following TKA, using the same set of clinical variables. We hypothesised that the ML model would outperform the ACS Risk Calculator.

METHODS

Data from 365,240 patients who underwent a primary TKA between 2013 and 2020 were extracted from the ACS-National Surgical Quality Improvement Program database and used to develop an artificial neural network (ANN) to predict discharge disposition following primary TKA. The ANN and ACS calculator were assessed and compared using discrimination, calibration and decision curve analysis.

RESULTS

Age (>68 years), BMI (>35.5 kg/m2) and ASA Class (≥2) were found to be the most important variables in predicting NHD following TKA. When compared to the ACS calculator, the ANN model demonstrated a significantly superior ability to distinguish the area under the receiver operating characteristic curve (AUC) among NHD patients and provided probability predictions well aligned with the true outcomes (AUCANN = 0.69, AUCACS = 0.50, p = 0.002, slopeANN = 0.85, slopeACS = 4.46, interceptANN = 0.04, and interceptACS = 0.06).

CONCLUSION

Our findings support the hypothesis that machine learning models outperform the ACS Risk Calculator in predicting non-home discharge after TKA, even when constrained to the same clinical variables. Our findings underscore the potential benefits of integrating machine learning models into clinical practice for improving preoperative patient risk identification, optimisation, counselling and clinical decision-making.

LEVEL OF EVIDENCE

Level III.

Prediction of implant failure risk due to periprosthetic femoral fracture after primary elective total hip arthroplasty : a simplified and validated model based on 154,519 total hip arthroplasties from the Swedish Arthroplasty Register

Aims

While cementless fixation offers potential advantages over cemented fixation, such as a shorter operating time, concerns linger over its higher cost and increased risk of periprosthetic fractures. If the risk of fracture can be forecasted, it would aid the shared decision-making process related to cementless stems. Our study aimed to develop and validate predictive models of periprosthetic femoral fracture (PPFF) necessitating revision and reoperation after elective total hip arthroplasty (THA).

Methods

We included 154,519 primary elective THAs from the Swedish Arthroplasty Register (SAR), encompassing 21 patient-, surgical-, and implant-specific features, for model derivation and validation in predicting 30-day, 60-day, 90-day, and one-year revision and reoperation due to PPFF. Model performance was tested using the area under the curve (AUC), and feature importance was identified in the best-performing algorithm.

Results

The Lasso regression excelled in predicting 30-day revisions (area under the receiver operating characteristic curve (AUC) = 0.85), while the Gradient Boosting Machine (GBM) model outperformed other models by a slight margin for all remaining endpoints (AUC range: 0.79 to 0.86). Predictive factors for revision and reoperation were identified, with patient features such as increasing age, higher American Society of Anesthesiologists grade (> III), and World Health Organization obesity classes II to III associated with elevated risks. A preoperative diagnosis of idiopathic necrosis increased revision risk. Concerning implant design, factors such as cementless femoral fixation, reverse-hybrid fixation, hip resurfacing, and small (< 35 mm) or large (> 52 mm) femoral heads increased both revision and reoperation risks.

Conclusion

This is the first study to develop machine-learning models to forecast the risk of PPFF necessitating secondary surgery. Future studies are required to externally validate our algorithm and assess its applicability in clinical practice.

Revolutionizing total hip arthroplasty: The role of artificial intelligence and machine learning

Purpose

There has been substantial growth in the literature describing the effectiveness of artificial intelligence (AI) and machine learning (ML) applications in total hip arthroplasty (THA); these models have shown the potential to predict post-operative outcomes using algorithmic analysis of acquired data and can ultimately optimize clinical decision-making while reducing time, cost and complexity. The aim of this review is to analyze the most updated articles on AI/ML applications in THA as well as present the potential of these tools in optimizing patient care and THA outcomes.

Methods

A comprehensive search was completed through August 2024, according to the PRISMA guidelines. Publications were searched using the Scopus, Medline, EMBASE, CENTRAL and CINAHL databases. Pertinent findings and patterns in AI/ML methods utilization, as well as their applications, were quantitatively summarized and described using frequencies, averages and proportions. This study used a modified eight-item Methodological Index for Non-Randomized Studies (MINORS) checklist for quality assessment.

Results

Nineteen articles were eligible for this study. The selected studies were published between 2016 and 2024. Out of the various ML algorithms, four models have proven to be particularly significant and were used in almost 20% of the studies, including elastic net penalized logistic regression, artificial neural network, convolutional neural network (CNN) and multiple linear regression. The highest area under the curve (=1) was reported in the preoperative planning outcome variable and utilized CNN. All 20 studies demonstrated a high level of quality and low risk of bias, with a modified MINORS score of at least 7/8 (88%).

Conclusions

Developments in AI/ML prediction models in THA are rapidly increasing. There is clear potential for these tools to assist in all stages of surgical care as well as in challenges at the broader hospital administrative level and patient-specific level.

Level of Evidence

Level III.

Postoperative Thrombocytopenia after Revision Arthroplasty: Features, Diagnostic and Therapeutic Considerations

We present the case of a 66 year-old male patient who developed severe postoperative thrombocytopenia after revision total hip arthroplasty. The patient underwent surgery in a dedicated orthopedics hospital and was initially managed in the intensive care unit. Upon the development of thrombocytopenia, he was referred to a dedicated hematology clinic for investigation and advanced management. A thorough diagnostic algorithm was employed in order to rule out the main causes of thrombocytopenia. By exclusion, we diagnosed the patient as suffering from a rare and severe form of postoperative thrombocytopenia through an immune mechanism. Although postoperative thrombocytopenia is relatively frequent but transitory and no treatment is required, this condition was refractory to corticosteroids and substitution therapy; however, it quickly responded to treatment with thrombopoietin receptor agonists. The patient recovered and was successfully discharged with normal platelet values. While rare occurrences, alternative causes of thrombocytopenia such as infection, drug-induced, or immune should be considered in patients developing postoperative thrombocytopenia.

Emerging Innovations in Preoperative Planning and Motion Analysis in Orthopedic Surgery

In recent years, preoperative planning has undergone significant advancements, with a dual focus: improving the accuracy of implant placement and enhancing the prediction of functional outcomes. These breakthroughs have been made possible through the development of advanced processing methods for 3D preoperative images. These methods not only offer novel visualization techniques but can also be seamlessly integrated into computer-aided design models. Additionally, the refinement of motion capture systems has played a pivotal role in this progress. These "markerless" systems are more straightforward to implement and facilitate easier data analysis. Simultaneously, the emergence of machine learning algorithms, utilizing artificial intelligence, has enabled the amalgamation of anatomical and functional data, leading to highly personalized preoperative plans for patients. The shift in preoperative planning from 2D towards 3D, from static to dynamic, is closely linked to technological advances, which will be described in this instructional review. Finally, the concept of 4D planning, encompassing periarticular soft tissues, will be introduced as a forward-looking development in the field of orthopedic surgery.

Bibliometric analysis of machine learning trends and hotspots in arthroplasty literature over 31 years

Background

Artificial intelligence has demonstrated utility in orthopedic research. Algorithmic models derived from machine learning have demonstrated adaptive learning with predictive application towards outcomes, leading to increased traction in the literature. This study aims to identify machine learning arthroplasty research trends and anticipate emerging key terms.

Methods

Published literature focused on machine learning in arthroplasty from 1992 to 2023 was selected through the Web of Science Core Collection of Clarivate Analytics. Following that, bibliometric indicators were attained and brought in to perform an additional examination using Bibliometrix and VOSviewer to identify historical and present patterns within the literature.

Results

A total of 235 documents were obtained through bibliometric sourcing based on machine learning applications within the arthroplasty literature. Thirty-four countries published articles on the topic, and the United States was demonstrated to be the largest global contributor. Four hundred-five institutions internationally contributed articles, with Harvard Medical School and the University of California system as the most relevant institutes, with 75 and 44 articles produced, respectively. Kwon YM was the most productive author, while Haeberle HS and Ramkumar PN were the most impactful based on h-index. The Thematic map and Co-occurrence visualization helped identify both major and niche themes present in the scientific databases.

Conclusions

Machine learning in arthroplasty research continues to gain traction with a growing annual production rate and contributions from international authors and institutions. Institutions and authors based in the United States are the leading contributors to machine learning applications within arthroplasty research. This research discerns trends that have occurred, are presently ongoing, and are emerging within this field, aiming to inform future hotspot development.

Fully automated assessment of the knee alignment on long leg radiographs following corrective knee osteotomies in patients with valgus or varus deformities

INTRODUCTION

The assessment of the knee alignment on long leg radiographs (LLR) postoperative to corrective knee osteotomies (CKOs) is highly dependent on the reader's expertise. Artificial Intelligence (AI) algorithms may help automate and standardise this process. The study aimed to analyse the reliability of an AI-algorithm for the evaluation of LLRs following CKOs.

MATERIALS AND METHODS

In this study, we analysed a validation cohort of 110 postoperative LLRs from 102 patients. All patients underwent CKO, including distal femoral (DFO), high tibial (HTO) and bilevel osteotomies. The agreement between manual measurements and the AI-algorithm was assessed for the mechanical axis deviation (MAD), hip knee ankle angle (HKA), anatomical-mechanical-axis-angle (AMA), joint line convergence angle (JLCA), mechanical lateral proximal femur angle (mLPFA), mechanical lateral distal femoral angle (mLDFA), mechanical medial proximal tibia angle (mMPTA) and mechanical lateral distal tibia angle (mLDTA), using the intra-class-correlation (ICC) coefficient between the readers, each reader and the AI and the mean of the manual reads and the AI-algorithm and Bland-Altman Plots between the manual reads and the AI software for the MAD, HKA, mLDFA and mMPTA.

RESULTS

In the validation cohort, the AI software showed excellent agreement with the manual reads (ICC: 0.81-0.99). The agreement between the readers (Inter-rater) showed excellent correlations (ICC: 0.95-0. The mean difference in the DFO group for the MAD, HKA, mLDFA and mMPTA were 0.50 mm, - 0.12°, 0.55° and 0.15°. In the HTO group the mean difference for the MAD, HKA, mLDFA and mMPTA were 0.36 mm, - 0.17°, 0.57° and 0.08°, respectively. Reliable outputs were generated in 95.4% of the validation cohort.

CONCLUSION

 he application of AI-algorithms for the assessment of lower limb alignment on LLRs following CKOs shows reliable and accurate results.

LEVEL OF EVIDENCE

Diagnostic Level III.

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.

AI-based hip prosthesis failure prediction through evolutional radiological indices

INTRODUCTION

This study aimed to develop artificial intelligence models for predicting hip implant failure from radiological features. Analyzing the evolution of the periprosthetic bone and implant's position throughout the entire follow-up period has shown the potential to be more relevant in outcome prediction than simply considering the latest radiographic images. Thus, we investigated an AI-based model employing a small set of evolutional parameters derived from conventional radiological features to predict hip prosthesis failure.

MATERIALS AND METHODS

One hundred sixty-nine radiological features were annotated from historical anteroposterior and lateral radiographs for 162 total hip arthroplasty patients, 32 of which later underwent implant failure. Linear regression on each patient's chronologically sorted radiological features was employed to derive 169 corresponding evolutional parameters per image. Three sets of machine learning predictors were developed: one employing the original features (standard model), one the evolutional ones (evolutional model), and the last their union (hybrid model). Each set included a model employing all the available features (full model) and a model employing the few most predictive ones according to Gini importance (minimal model).

RESULTS

The evolutional and hybrid predictors resulted highly effective (area under the ROC curve (AUC) of full models = 0.94), outperforming the standard one, whose AUC was only 0.82. The minimal hybrid model, employing just four features, three of which evolutional, scored an AUC of 0.95, proving even more accurate than the full one, exploiting 173 features. This tool could be shaped to be either highly specific (sensitivity: 80%, specificity: 98.6%) or highly sensitive (sensitivity: 90%, specificity: 92.4%).

CONCLUSION

The proposed predictor may represent a highly sensitive screening tool for clinicians, capable to predict THA failure with an advance between a few months and more than a year through only four radiological parameters, considering either their value at the latest visit or their evolution through time.

The Use of Artificial Intelligence for the Prediction of Periprosthetic Joint Infection Following Aseptic Revision Total Knee Arthroplasty

Periprosthetic joint infection (PJI) following revision total knee arthroplasty (TKA) for aseptic failure is associated with poor outcomes, patient morbidity, and high health care expenditures. The aim of this study was to develop novel machine learning algorithms for the prediction of PJI following revision TKA for patients with aseptic indications for revision surgery. A single-institution database consisting of 1,432 consecutive revision TKA patients with aseptic etiologies was retrospectively identified. The patient cohort included 208 patients (14.5%) who underwent re-revision surgery for PJI. Three machine learning algorithms (artificial neural networks, support vector machines, k-nearest neighbors) were developed to predict this outcome and these models were assessed by discrimination, calibration, and decision curve analysis. This is a retrospective study. Among the three machine learning models, the neural network model achieved the best performance across discrimination (area under the receiver operating characteristic curve = 0.78), calibration, and decision curve analysis. The strongest predictors for PJI following revision TKA for aseptic reasons were prior open procedure prior to revision surgery, drug abuse, obesity, and diabetes. This study utilized machine learning as a tool for the prediction of PJI following revision TKA for aseptic failure with excellent performance. The validated machine learning models can aid surgeons in patient-specific risk stratifying to assist in preoperative counseling and clinical decision making for patients undergoing aseptic revision TKA.

Validation of Machine Learning Model Performance in Predicting Blood Transfusion After Primary and Revision Total Hip Arthroplasty

BACKGROUND

The rates of blood transfusion following primary and revision total hip arthroplasty (THA) remain as high as 9% and 18%, respectively, contributing to patient morbidity and healthcare costs. Existing predictive tools are limited to specific populations, thereby diminishing their clinical applicability. This study aimed to externally validate our previous institutionally developed machine learning (ML) algorithms to predict the risk of postoperative blood transfusion following primary and revision THA using national inpatient data.

METHODS

Five ML algorithms were trained and validated using data from 101,266 primary THA and 8,594 revision THA patients from a large national database to predict postoperative transfusion risk after primary and revision THA. Models were assessed and compared based on discrimination, calibration, and decision curve analysis.

RESULTS

The most important predictors of transfusion following primary and revision THA were preoperative hematocrit (<39.4%) and operation time (>157 minutes), respectively. All ML models demonstrated excellent discrimination (area under the curve (AUC) >0.8) in primary and revision THA patients, with artificial neural network (AUC = 0.84, slope = 1.11, intercept = -0.04, Brier score = 0.04), and elastic-net-penalized logistic regression (AUC = 0.85, slope = 1.08, intercept = -0.01, and Brier score = 0.12) performing best, respectively. On decision curve analysis, all 5 models demonstrated a higher net benefit than the conventional strategy of intervening for all or no patients in both patient cohorts.

CONCLUSIONS

This study successfully validated our previous institutionally developed ML algorithms for the prediction of blood transfusion following primary and revision THA. Our findings highlight the potential generalizability of predictive ML tools developed using nationally representative data in THA patients.

Understanding risk for early dislocation resulting in reoperation within 90 days of reverse total shoulder arthroplasty: extreme rare event detection through cost-sensitive machine learning

BACKGROUND

Reliable prediction of postoperative dislocation after reverse total shoulder arthroplasty (RSA) would inform patient counseling as well as surgical and postoperative decision making. Understanding interactions between multiple risk factors is important to identify those patients most at risk of this rare but costly complication. To better understand these interactions, a game theory-based approach was undertaken to develop machine learning models capable of predicting dislocation-related 90-day readmission following RSA.

MATERIAL & METHODS

A retrospective review of the Nationwide Readmissions Database was performed to identify patients who underwent RSA between 2016 and 2018 with a subsequent readmission for prosthetic dislocation. Of the 74,697 index procedures included in the data set, 740 (1%) experienced a dislocation resulting in hospital readmission within 90 days. Five machine learning algorithms were evaluated for their ability to predict dislocation leading to hospital readmission within 90 days of RSA. Shapley additive explanation (SHAP) values were calculated for the top-performing models to quantify the importance of features and understand variable interaction effects, with hierarchical clustering used to identify cohorts of patients with similar risk factor combinations.

RESULTS

Of the 5 models evaluated, the extreme gradient boosting algorithm was the most reliable in predicting dislocation (C statistic = 0.71, F2 score = 0.07, recall = 0.84, Brier score = 0.21). SHAP value analysis revealed multifactorial explanations for dislocation risk, with presence of a preoperative humerus fracture; disposition involving discharge or transfer to a skilled nursing facility, intermediate care facility, or other nonroutine facility; and Medicaid as the expected primary payer resulting in strong, positive, and unidirectional effects on increasing dislocation risk. In contrast, factors such as comorbidity burden, index procedure complexity and duration, age, sex, and presence or absence of preoperative glenohumeral osteoarthritis displayed bidirectional influences on risk, indicating potential protective effects for these variables and opportunities for risk mitigation. Hierarchical clustering using SHAP values identified patients with similar risk factor combinations.

CONCLUSION

Machine learning can reliably predict patients at risk for postoperative dislocation resulting in hospital readmission within 90 days of RSA. Although individual risk for dislocation varies significantly based on unique combinations of patient characteristics, SHAP analysis revealed a particularly at-risk cohort consisting of young, male patients with high comorbidity burdens who are indicated for RSA after a humerus fracture. These patients may require additional modifications in postoperative activity, physical therapy, and counseling on risk-reducing measures to prevent early dislocation after RSA.

The utility of machine learning algorithms for the prediction of patient-reported outcome measures following primary hip and knee total joint arthroplasty

BACKGROUND

Patient-reported outcome measures (PROMs) are increasingly used as quality benchmark in total hip and knee arthroplasty (THA; TKA) due to bundled payment systems that aim to provide a patient-centered, value-based treatment approach. However, there is a paucity of predictive tools for postoperative PROMs. Therefore, this study aimed to develop and validate machine learning models for the prediction of numerous patient-reported outcome measures following primary hip and knee total joint arthroplasty.

METHODS

A total of 4526 consecutive patients (2137 THA; 2389 TKA) who underwent primary hip and knee total joint arthroplasty and completed both pre- and postoperative PROM scores was evaluated in this study. The following PROM scores were included for analysis: HOOS-PS, KOOS-PS, Physical Function SF10A, PROMIS SF Physical and PROMIS SF Mental. Patient charts were manually reviewed to identify patient demographics and surgical variables associated with postoperative PROM scores. Four machine learning algorithms were developed to predict postoperative PROMs following hip and knee total joint arthroplasty. Model assessment was performed through discrimination, calibration and decision curve analysis.

RESULTS

The factors most significantly associated with the prediction of postoperative PROMs include preoperative PROM scores, Charlson Comorbidity Index, American Society of Anaesthesiology score, insurance status, age, length of hospital stay, body mass index and ethnicity. The four machine learning models all achieved excellent performance across discrimination (AUC > 0.83), calibration and decision curve analysis.

CONCLUSION

This study developed machine learning models for the prediction of patient-reported outcome measures at 1-year following primary hip and knee total joint arthroplasty. The study findings show excellent performance on discrimination, calibration and decision curve analysis for all four machine learning models, highlighting the potential of these models in clinical practice to inform patients prior to surgery regarding their expectations of postoperative functional outcomes following primary hip and knee total joint arthroplasty.

LEVEL OF EVIDENCE

Level III, case control retrospective analysis.

Predicting surgical operative time in primary total knee arthroplasty utilizing machine learning models

BACKGROUND

Prolonged surgical operative time is associated with postoperative adverse outcomes following total knee arthroplasty (TKA). Increasing operating room efficiency necessitates the accurate prediction of surgical operative time for each patient. One potential way to increase the accuracy of predictions is to use advanced predictive analytics, such as machine learning. The aim of this study is to use machine learning to develop an accurate predictive model for surgical operative time for patients undergoing primary total knee arthroplasty.

METHODS

A retrospective chart review of electronic medical records was conducted to identify patients who underwent primary total knee arthroplasty at a tertiary referral center. Three machine learning algorithms were developed to predict surgical operative time and were assessed by discrimination, calibration and decision curve analysis. Specifically, we used: (1) Artificial Neural Networks (ANNs), (2) Random Forest (RF), and (3) K-Nearest Neighbor (KNN).

RESULTS

We analyzed the surgical operative time for 10,021 consecutive patients who underwent primary total knee arthroplasty. The neural network model achieved the best performance across discrimination (AUC = 0.82), calibration and decision curve analysis for predicting surgical operative time. Based on this algorithm, younger age (< 45 years), tranexamic acid non-usage, and a high BMI (> 40 kg/m²) were the strongest predictors associated with surgical operative time.

CONCLUSIONS

This study shows excellent performance of machine learning models for predicting surgical operative time in primary total knee arthroplasty. The accurate estimation of surgical duration is important in enhancing OR efficiency and identifying patients at risk for prolonged surgical operative time.

LEVEL OF EVIDENCE

Level III, case control retrospective analysis.

The utilization of artificial neural networks for the prediction of 90-day unplanned readmissions following total knee arthroplasty

BACKGROUND

A reliable predictive tool to predict unplanned readmissions has the potential to lower readmission rates through targeted pre-operative counseling and intervention with respect to modifiable risk factors. This study aimed to develop and internally validate machine learning models for the prediction of 90-day unplanned readmissions following total knee arthroplasty.

METHODS

A total of 10,021 consecutive patients underwent total knee arthroplasty. Patient charts were manually reviewed to identify patient demographics and surgical variables that may be associated with 90-day unplanned hospital readmissions. Four machine learning algorithms (artificial neural networks, support vector machine, k-nearest neighbor, and elastic-net penalized logistic regression) were developed to predict 90-day unplanned readmissions following total knee arthroplasty and these models were evaluated using ROC AUC statistics as well as calibration and decision curve analysis.

RESULTS

Within the study cohort, 644 patients (6.4%) were readmitted within 90 days. The factors most significantly associated with 90-day unplanned hospital readmissions included drug abuse, surgical operative time, and American Society of Anaesthesiologist Physical Status (ASA) score. The machine learning models all achieved excellent performance across discrimination (AUC > 0.82), calibration, and decision curve analysis.

CONCLUSION

This study developed four machine learning models for the prediction of 90-day unplanned hospital readmissions in patients following total knee arthroplasty. The strongest predictors for unplanned hospital readmissions were drug abuse, surgical operative time, and ASA score. The study findings show excellent model performance across all four models, highlighting the potential of these models for the identification of high-risk patients prior to surgery for whom coordinated care efforts may decrease the risk of subsequent hospital readmission.

LEVEL OF EVIDENCE

Level III, case-control retrospective analysis.