Development of Machine-Learning Algorithms to Predict Attainment of Minimal Clinically Important Difference After Hip Arthroscopy for Femoroacetabular Impingement Yield Fair Performance and Limited Clinical Utility

A Glossary of Terms in Artificial Intelligence for Healthcare

In recent decades, artificial intelligence (AI) has infiltrated a variety of domains, including media, education, and medicine. There exists no glossary, lexicon, or reference for the uninitiated medical professional to explore the new terminology. As AI-driven technologies and applications become more available for clinical use in healthcare settings, an understanding of basic components, models, and tasks related to AI is crucial for clinical and academic appraisal. Here, we present a glossary of AI definitions that healthcare professionals can utilize to augment personal understanding of AI during this fourth industrial revolution. LEVEL OF EVIDENCE: Level V, expert opinion.

Predicting patient reported outcome measures: a scoping review for the artificial intelligence-guided patient preference predictor

Background

The algorithmic patient preference predictor (PPP) has been proposed to aid in decision making for incapacitated patients in the absence of advanced directives. Ethical and legal challenges aside, multiple practical barriers exist for building a personalized PPP. Here, we examine previous work using machine learning to predict patient reported outcome measures (PROMs) for capacitated patients undergoing diverse procedures, therapies, and life events. Demonstrating robust performance in predicting PROMs for capacitated patients could suggest opportunities for developing a model tailored to incapacitated ones.

Methods

We performed a scoping review of PubMed, Embase, and Scopus using the PRISMA-ScR guidelines to capture studies using machine learning to predict PROMs following a medical event alongside qualitative studies exploring a theoretical PPP.

Results

Sixty-eight studies used machine learning to evaluate PROMs; an additional 20 studies focused on a theoretical PPP. For PROMs, orthopedic surgeries (n = 33) and spinal surgeries (n = 12) were the most common medical event. Studies used demographic (n = 30), pre-event PROMs (n = 52), comorbidities (n = 29), social determinants of health (n = 30), and intraoperative variables (n = 124) as predictors. Thirty-four different PROMs were used as the target outcome. Evaluation metrics varied by task, but performance was overall poor to moderate for the best reported scores. In models that used feature importance, pre-event PROMs were the most predictive of post-event PROMs. Fairness assessments were rare (n = 6). These findings reinforce the necessity of the integrating patient values and preferences, beyond demographic factors, to improve the development of personalized PPP models for incapacitated patients.

Conclusion

The primary objective of a PPP is to estimate patient-reported quality of life following an intervention. Use of machine learning to predict PROMs for capacitated patients introduces challenges and opportunities for building a personalized PPP for incapacitated patients without advanced directives.

Several factors predict the achievement of the patient acceptable symptom state and minimal clinically important difference for patient-reported outcome measures following anterior cruciate ligament reconstruction: A systematic review

PURPOSE

To summarize the predictors of the patient acceptable symptom state (PASS), minimal clinically important difference (MCID) and minimal important change (MIC) for patient-reported outcome measures (PROMs) following anterior cruciate ligament reconstruction (ACLR).

METHODS

MEDLINE, PubMed and Embase were searched from inception to 5 January 2024. The authors adhered to PRISMA/R-AMSTAR guidelines, and the Cochrane Handbook for Systematic Reviews of Interventions. Data on statistical associations between predictive factors and PROMs were extracted. Inverse odds ratios (ORs) and confidence intervals (reverse group comparison) were calculated when appropriate to ensure comparative consistency.

RESULTS

Thirteen studies comprising 21,235 patients (48.1% female) were included (mean age 29.3 years). Eight studies comprising 3857 patients identified predictors of PASS, including lateral extra-articular tenodesis (LET) (OR = 11.08, p = 0.01), hamstring tendon (HT) autografts (OR range: 2.02-2.63, p ≤ 0.011), age over 30 (OR range: 1.37-2.28, p ≤ 0.02), male sex (OR range: 1.03-1.32, p ≤ 0.01) and higher pre-operative PROMs (OR range: 1.04-1.21). Eight studies comprising 18,069 patients identified negative predictors of MCID or MIC, including female sex (OR = 0.93, p = 0.034), absence of HT autografts (OR = 0.70, p < 0.0001), higher pre-operative PROMs (OR = 0.76-0.84, p ≤ 0.01), meniscectomy (OR = 0.67, p = 0.014) and collision sports (OR = 0.02-0.60, p ≤ 0.05).

CONCLUSION

Higher pre-operative PROMs, age over 30, male sex, LETs and HT autografts predicted PASS achievement. Lower pre-operative PROMs, male sex, non-collision sports, and lack of meniscectomies predicted MCID/MIC achievement. This review provides a comprehensive understanding of the predictors of clinically significant post-ACLR outcomes, thus improving clinical decision-making and the management of patient expectations.

LEVEL OF EVIDENCE

Level IV.

Artificial Intelligence for Clinically Meaningful Outcome Prediction in Orthopedic Research: Current Applications and Limitations

PURPOSE OF REVIEW

Patient-reported outcome measures (PROM) play a critical role in evaluating the success of treatment interventions for musculoskeletal conditions. However, predicting which patients will benefit from treatment interventions is complex and influenced by a multitude of factors. Artificial intelligence (AI) may better anticipate the propensity to achieve clinically meaningful outcomes through leveraging complex predictive analytics that allow for personalized medicine. This article provides a contemporary review of current applications of AI developed to predict clinically significant outcome (CSO) achievement after musculoskeletal treatment interventions.

RECENT FINDINGS

The highest volume of literature exists in the subspecialties of total joint arthroplasty, spine, and sports medicine, with only three studies identified in the remaining orthopedic subspecialties combined. Performance is widely variable across models, with most studies only reporting discrimination as a performance metric. Given the complexity inherent in predictive modeling for this task, including data availability, data handling, model architecture, and outcome selection, studies vary widely in their methodology and results. Importantly, the majority of studies have not been externally validated or demonstrate important methodological limitations, precluding their implementation into clinical settings. A substantial body of literature has accumulated demonstrating variable internal validity, limited scope, and low potential for clinical deployment. The majority of studies attempt to predict the MCID-the lowest bar of clinical achievement. Though a small proportion of models demonstrate promise and highlight the utility of AI, important methodological limitations need to be addressed moving forward to leverage AI-based applications for clinical deployment.

Recommended Requirements and Essential Elements for Proper Reporting of the Use of Artificial Intelligence Machine Learning Tools in Biomedical Research and Scientific Publications

Essential elements required for proper use of artificial intelligence machine learning tools in biomedical research and scientific publications include (1) explanation justifying why a machine learning approach contributes to the purpose of the study; (2) description of the adequacy of the data (input) to produce the desired results (output); (3) details of the algorithmic (i.e., computational) approach including methods for organizing the data (preprocessing); the machine learning computational algorithm(s) assessed; on what data the models were trained; the presence of bias and efforts to mitigate these effects; and the methods for quantifying the variables (features) most influential in determining the results (e.g., Shapley values); (4) description of methods, and reporting of results, quantitating performance in terms of both model accuracy and model calibration (level of confidence in the model's predictions); (5) availability of the programming code (including a link to the code when available-ideally, the code should be available); (6) discussion of model internal validation (results applicable and sensitive to the population investigated and data on which the model was trained) and external validation (were the results investigated as to whether they are generalizable to different populations? If not, consideration of this limitation and discussion of plans for external validation, i.e., next steps). As biomedical research submissions using artificial intelligence technology increase, these requirements could facilitate purposeful use and comprehensive methodological reporting.