Prosthetic Hip Dislocations in Direct Anterior Versus Posterior Approach in Patients With Instrumented Lumbar Fusion.

BACKGROUND: Instrumented posterior lumbar spinal fusion (IPLSF) has been demonstrated to contribute to instability following total hip arthroplasty (THA). It is unclear whether a supine direct anterior (DA) approach reduces the risk of instability. METHODS: A retrospective review of 1,773 patients who underwent THA through either a DA approach or a posterior approach at our institution over a 7-year period was performed. Radiographic and chart reviews were then used to identify our primary group of interest comprised of 111 patients with previous IPLSF. Radiographic review, chart review, and phone survey was performed. Dislocation rates in each approach group were then compared within this cohort of patients with IPLSF. RESULTS: Within the group of patients with IPLSF, 33.3% (n = 37) received a DA approach while 66.6% (n = 74) received a posterior approach. None of the 9 total dislocations in the DA group had IPLSF, whereas 4 of the 16 total dislocations in the posterior approach group had IPLSF (P = .78). When examining the larger group of patients, including those without IPLSF, patients undergoing a DA approach had a lower BMI and were likely have a smaller head size implanted (P < .001 for both). Using Fischer's exact test, fusion was associated with dislocation in the posterior approach group (P < .01), whereas fusion was not associated with dislocation in the anterior approach group (P = 1.0). CONCLUSIONS: While there was no significant difference in dislocation rates between posterior and anterior approach groups, in patients with IPLSF, the anterior approach had a lower percentage of dislocation events compared to the posterior approach.

Artificial Intelligence for Automated Implant Identification in Total Hip Arthroplasty: A Multicenter External Validation Study Exceeding Two Million Plain Radiographs.

BACKGROUND: The surgical management of complications after total hip arthroplasty (THA) necessitates accurate identification of the femoral implant manufacturer and model. Automated image processing using deep learning has been previously developed and internally validated; however, external validation is necessary prior to responsible application of artificial intelligence (AI)-based technologies. METHODS: We trained, validated, and externally tested a deep learning system to classify femoral-sided THA implants as one of the 8 models from 2 manufacturers derived from 2,954 original, deidentified, retrospectively collected anteroposterior plain radiographs across 3 academic referral centers and 13 surgeons. From these radiographs, 2,117 were used for training, 249 for validation, and 588 for external testing. Augmentation was applied to the training set (n = 2,117,000) to increase model robustness. Performance was evaluated by area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Implant identification processing speed was calculated. RESULTS: The training and testing sets were drawn from statistically different populations of implants (P < .001). After 1,000 training epochs by the deep learning system, the system discriminated 8 implant models with a mean area under the receiver operating characteristic curve of 0.991, accuracy of 97.9%, sensitivity of 88.6%, and specificity of 98.9% in the external testing dataset of 588 anteroposterior radiographs. The software classified implants at a mean speed of 0.02 seconds per image. CONCLUSION: An AI-based software demonstrated excellent internal and external validation. Although continued surveillance is necessary with implant library expansion, this software represents responsible and meaningful clinical application of AI with immediate potential to globally scale and assist in preoperative planning prior to revision THA.

Artificial Intelligence for Automated Implant Identification in Knee Arthroplasty: A Multicenter External Validation Study Exceeding 3.5 Million Plain Radiographs.

BACKGROUND: Surgical management of complications following knee arthroplasty demands accurate and timely identification of implant manufacturer and model. Automated image processing using deep machine learning has been previously developed and internally validated; however, external validation is essential prior to scaling clinical implementation for generalizability. METHODS: We trained, validated, and externally tested a deep learning system to classify knee arthroplasty systems as one of the 9 models from 4 manufacturers derived from 4,724 original, retrospectively collected anteroposterior plain knee radiographs across 3 academic referral centers. From these radiographs, 3,568 were used for training, 412 for validation, and 744 for external testing. Augmentation was applied to the training set (n = 3,568,000) to increase model robustness. Performance was determined by the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Implant identification processing speed was calculated. The training and testing sets were drawn from statistically different populations of implants (P < .001). RESULTS: After 1,000 training epochs by the deep learning system, the system discriminated 9 implant models with a mean area under the receiver operating characteristic curve of 0.989, accuracy of 97.4%, sensitivity of 89.2%, and specificity of 99.0% in the external testing dataset of 744 anteroposterior radiographs. The software classified implants at a mean speed of 0.02 seconds per image. CONCLUSION: An artificial intelligence-based software for identifying knee arthroplasty implants demonstrated excellent internal and external validation. Although continued surveillance is necessary with implant library expansion, this software represents a responsible and meaningful clinical application of artificial intelligence with immediate potential to globally scale and assist in preoperative planning prior to revision knee arthroplasty.

Meaningless Applications and Misguided Methodologies in Artificial Intelligence-Related Orthopaedic Research Propagates Hype Over Hope.

There exists great hope and hype in the literature surrounding applications of artificial intelligence (AI) to orthopaedic surgery. Between 2018 and 2021, a total of 178 AI-related articles were published in orthopaedics. However, for every 2 original research papers that apply AI to orthopaedics, a commentary or review is published (30.3%). AI-related research in orthopaedics frequently fails to provide use cases that offer the uninitiated an opportunity to appraise the importance of AI by studying meaningful questions, evaluating unknown hypotheses, or analyzing quality data. The hype perpetuates a feed-forward cycle that relegates AI to a meaningless buzzword by rewarding those with nascent understanding and rudimentary technical knowhow into committing several basic errors: (1) inappropriately conflating vernacular ("AI/machine learning"), (2) repackaging registry data, (3) prematurely releasing internally validated algorithms, (4) overstating the "black box phenomenon" by failing to provide weighted analysis, (5) claiming to evaluate AI rather than the data itself, and (6) withholding full model architecture code. Relevant AI-specific guidelines are forthcoming, but forced application of the original Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines designed for regression analyses is irrelevant and misleading. To safeguard meaningful use, AI-related research efforts in orthopaedics should be (1) directed toward administrative support over clinical evaluation and management, (2) require the use of the advanced model, and (3) answer a question that was previously unknown, unanswered, or unquantifiable.

Operative times in primary total hip arthroplasty will remain stable up to the year 2027: prediction models based on 85,808 cases.

PURPOSE: Recently, the Centers for Medicare and Medicaid have announced the decision to review "potentially misvalued" Current Procedural Terminology codes, including those for primary total hip arthroplasty (THA). While recent studies have suggested that THA operative times have remained stable in recent years, there is an absence of information regarding how operative times are expected to change in the future. Therefore, the purpose of our analysis was to produce 2- and 10-year prediction models developed from contemporary operative time data. METHODS: Utilizing the American College of Surgeons National Surgical Quality Improvement patient database, all primary THA procedures performed between January 1st, 2008 and December 31st, 2017 were identified (n = 85,808 THA patients). Autocorrelation fit significance was determined through Box-Ljung lack of fit tests. Time series stationarity was evaluated using augmented Dickey-Fuller tests. After adjusting non-stationary time series for seasonality-dependent changes, 2-year and 10-year operative times were predicted using Autoregressive integrated moving average forecasting models. RESULTS: Our models indicate that operative time will continue to remain stable. Specifically, operative time for ASA Class 2 is projected to fall within 1 min of the previously calculated weighted mean. Additionally, ASA Class 3 projections fall within 3 min of this value. CONCLUSION: Operative time will remain within 3 min of the most recently reported mean up to the year 2027. Therefore, our findings do not support lowering physician compensation based on this metric. Future analyses should evaluate if operative times adjust over in light of changing patient demographics and alternative reimbursement models.

Clinical and Research Medical Applications of Artificial Intelligence.

Artificial intelligence (AI), including machine learning (ML), has transformed numerous industries through newfound efficiencies and supportive decision-making. With the exponential growth of computing power and large datasets, AI has transitioned from theory to reality in teaching machines to automate tasks without human supervision. AI-based computational algorithms analyze "training sets" using pattern recognition and learning from inputted data to classify and predict outputs that otherwise could not be effectively analyzed with human processing or standard statistical methods. Though widespread understanding of the fundamental principles and adoption of applications have yet to be achieved, recent applications and research efforts implementing AI have demonstrated great promise in predicting future injury risk, interpreting advanced imaging, evaluating patient-reported outcomes, reporting value-based metrics, and augmenting telehealth. With appreciation, caution, and experience applying AI, the potential to automate tasks and improve data-driven insights may be realized to fundamentally improve patient care. The purpose of this review is to discuss the pearls, pitfalls, and applications associated with AI.

Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Knee.

BACKGROUND: Revisions and reoperations for patients who have undergone total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), and distal femoral replacement (DFR) necessitates accurate identification of implant manufacturer and model. Failure risks delays in care, increased morbidity, and further financial burden. Deep learning permits automated image processing to mitigate the challenges behind expeditious, cost-effective preoperative planning. Our aim was to investigate whether a deep-learning algorithm could accurately identify the manufacturer and model of arthroplasty implants about the knee from plain radiographs. METHODS: We trained, validated, and externally tested a deep-learning algorithm to classify knee arthroplasty implants from one of 9 different implant models from retrospectively collected anterior-posterior (AP) plain radiographs from four sites in one quaternary referral health system. The performance was evaluated by calculating the area under the receiver-operating characteristic curve (AUC), sensitivity, specificity, and accuracy when compared with a reference standard of implant model from operative reports. RESULTS: The training and validation data sets were comprised of 682 radiographs across 424 patients and included a wide range of TKAs from the four leading implant manufacturers. After 1000 training epochs by the deep-learning algorithm, the model discriminated nine implant models with an AUC of 0.99, accuracy 99%, sensitivity of 95%, and specificity of 99% in the external-testing data set of 74 radiographs. CONCLUSIONS: A deep learning algorithm using plain radiographs differentiated between 9 unique knee arthroplasty implants from four manufacturers with near-perfect accuracy. The iterative capability of the algorithm allows for scalable expansion of implant discriminations and represents an opportunity in delivering cost-effective care for revision arthroplasty.

Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Hip.

BACKGROUND: The surgical management of complications surrounding patients who have undergone hip arthroplasty necessitates accurate identification of the femoral implant manufacturer and model. Failure to do so risks delays in care, increased morbidity, and further economic burden. Because few arthroplasty experts can confidently classify implants using plain radiographs, automated image processing using deep learning for implant identification may offer an opportunity to improve the value of care rendered. METHODS: We trained, validated, and externally tested a deep-learning system to classify total hip arthroplasty and hip resurfacing arthroplasty femoral implants as one of 18 different manufacturer models from 1972 retrospectively collected anterior-posterior (AP) plain radiographs from 4 sites in one quaternary referral health system. From these radiographs, 1559 were used for training, 207 for validation, and 206 for external testing. Performance was evaluated by calculating the area under the receiver-operating characteristic curve, sensitivity, specificity, and accuracy, as compared with a reference standard of implant model from operative reports with implant serial numbers. RESULTS: The training and validation data sets from 1715 patients and 1766 AP radiographs included 18 different femoral components across four leading implant manufacturers and 10 fellowship-trained arthroplasty surgeons. After 1000 training epochs by the deep-learning system, the system discriminated 18 implant models with an area under the receiver-operating characteristic curve of 0.999, accuracy of 99.6%, sensitivity of 94.3%, and specificity of 99.8% in the external-testing data set of 206 AP radiographs. CONCLUSIONS: A deep-learning system using AP plain radiographs accurately differentiated among 18 hip arthroplasty models from four industry leading manufacturers.

Enhancers: bridging the gap between gene control and human disease.

Enhancers are a class of regulatory elements essential for precise spatio-temporal control of gene expression during development and in terminally differentiated cells. This review highlights signature features of enhancer elements as well as new advances that provide mechanistic insights into enhancer-mediated gene control in the context of three-dimensional chromatin. We detail the various ways in which non-coding mutations can instigate aberrant gene control and cause a variety of Mendelian disorders, common diseases and cancer.

The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty.

HYPOTHESIS/PURPOSE: The objective is to develop and validate an artificial intelligence model, specifically an artificial neural network (ANN), to predict length of stay (LOS), discharge disposition, and inpatient charges for primary anatomic total (aTSA), reverse total (rTSA), and hemi- (HSA) shoulder arthroplasty to establish internal validity in predicting patient-specific value metrics. METHODS: Using data from the National Inpatient Sample between 2003 and 2014, 4 different ANN models to predict LOS, discharge disposition, and inpatient costs using 39 preoperative variables were developed based on diagnosis and arthroplasty type: primary chronic/degenerative aTSA, primary chronic/degenerative rTSA, primary traumatic/acute rTSA, and primary acute/traumatic HSA. Models were also combined into diagnosis type only. Outcome metrics included accuracy and area under the curve (AUC) for a receiver operating characteristic curve. RESULTS: A total of 111,147 patients undergoing primary shoulder replacement were included. The machine learning algorithm predicting the overall chronic/degenerative conditions model (aTSA, rTSA) achieved accuracies of 76.5%, 91.8%, and 73.1% for total cost, LOS, and disposition, respectively; AUCs were 0.75, 0.89, and 0.77 for total cost, LOS, and disposition, respectively. The overall acute/traumatic conditions model (rTSA, HSA) had accuracies of 70.3%, 79.1%, and 72.0% and AUCs of 0.72, 0.78, and 0.79 for total cost, LOS, and discharge disposition, respectively. CONCLUSION: Our ANN demonstrated fair to good accuracy and reliability for predicting inpatient cost, LOS, and discharge disposition in shoulder arthroplasty for both chronic/degenerative and acute/traumatic conditions. Machine learning has the potential to preoperatively predict costs, LOS, and disposition using patient-specific data for expectation management between health care providers, patients, and payers.

Have Total Hip Arthroplasty Operative Times Changed Over the Past Decade? An Analysis of 157,574 Procedures.

BACKGROUND: With the recent reevaluation of surgeon reimbursement for total hip arthroplasty (THA) by the Centers for Medicare and Medicaid Services, there is increasing need for information regarding trends in operative time. While single-institutional analyses exist, there is a lack of large-scale, nationally representative, multi-institutional data. Therefore, the purpose of our study is to (1) evaluate past/present operative time trends for THA and (2) investigate factors influencing operative times from a 10-year, large multi-institutional database. METHODS: All primary THAs conducted between 2008 and 2018 were queried using Current Procedural Terminology code 27130 from the American College of Surgeons-National Surgical Quality Improvement Program database, yielding 157,574 patients. Operative time, demographics, and comorbidity data were collected and analyzed. Multivariable linear models were created, and trend analyses were used where appropriate. RESULTS: Median operative time was 87 minutes. Operative time was stable across included study years, with all calculated values within 5 minutes of the median (range, 86-92 minutes). Operative time was statistically stable over the last 3 years (P = .121). Age, body mass index, resident involvement, modified Charlson comorbidity index, and preoperative laboratory values influenced operative time (P < .001). Length of stay, readmission, superficial wound infection, and sepsis decreased over the study period. Nonelective procedures were statistically longer than elective (P < .0001). CONCLUSION: While numerous factors influence the duration of THA, this study found that THA operative time has remained stable in recent years. Therefore, revaluation for THA based on intraservice time is not supported. Future analyses should continue to analyze factors that influence operative time in order to ensure patient safety and maintain positive outcomes.

What Factors Influence Operative Time in Total Knee Arthroplasty? A 10-Year Analysis in a National Sample.

BACKGROUND: Changes in reimbursement in total knee arthroplasty (TKA) by Centers for Medicare and Medicaid Services (CMS) have been tied to a perceived decrease in the total surgical time required to perform these operations. However, little information is available to CMS about recorded surgical times for TKA across the United States and the variables that drive these values. Therefore, the purpose of our study, is to evaluate (1) changes in operative time over time and (2) factors associated with variations in operative time. METHODS: The National Surgical Quality Improvement Program database was queried to identify all primary TKAs conducted between January 1, 2008, and December 31, 2017. All TKAs conducted within our study period that had operative time data available were included. Multivariable linear models were created to assess factors that influence operative time over the study period. RESULTS: Our final analysis included 140,890 TKAs. The mean operative time across the study period was found to be 92.60 minutes. Examining quarterly values, operative time stayed within 5 minutes of this mean (range, 89.80-97.51 minutes). Age, sex, functional status, anesthesia type, body mass index, operative year, transfusion requirements, and preoperative laboratory findings significantly influenced operative time (P < .05 for all). CONCLUSION: Our analysis indicates that while there are numerous factors that influence procedure duration, operative times have remained stable. This information should be heavily considered in regard to physician reimbursement, because providers are maintaining operative times and work effort while mitigating factors that influence outcomes in the perioperative period.

Deep Learning Preoperatively Predicts Value Metrics for Primary Total Knee Arthroplasty: Development and Validation of an Artificial Neural Network Model.

BACKGROUND: The objective is to develop and validate an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition before primary total knee arthroplasty (TKA). The secondary objective applied the ANN to propose a risk-based, patient-specific payment model (PSPM) commensurate with case complexity. METHODS: Using data from 175,042 primary TKAs from the National Inpatient Sample and an institutional database, an ANN was developed to predict LOS, charges, and disposition using 15 preoperative variables. Outcome metrics included accuracy and area under the curve for a receiver operating characteristic curve. Model uncertainty was stratified by All Patient Refined comorbidity indices in establishing a risk-based PSPM. RESULTS: The dynamic model demonstrated "learning" in the first 30 training rounds with areas under the curve of 74.8%, 82.8%, and 76.1% for LOS, charges, and discharge disposition, respectively. The PSPM demonstrated that as patient comorbidity increased, risk increased by 2.0%, 21.8%, and 82.6% for moderate, major, and severe comorbidities, respectively. CONCLUSION: Our deep learning model demonstrated "learning" with acceptable validity, reliability, and responsiveness in predicting value metrics, offering the ability to preoperatively plan for TKA episodes of care. This model may be applied to a PSPM proposing tiered reimbursements reflecting case complexity.

Preoperative Prediction of Value Metrics and a Patient-Specific Payment Model for Primary Total Hip Arthroplasty: Development and Validation of a Deep Learning Model.

BACKGROUND: The primary objective was to develop and test an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition for total hip arthroplasty. The secondary objective was to create a patient-specific payment model (PSPM) accounting for patient complexity. METHODS: Using 15 preoperative variables from 78,335 primary total hip arthroplasty cases for osteoarthritis from the National Inpatient Sample and our institutional database, an ANN was developed to predict LOS, charges, and disposition. Validity metrics included accuracy and area under the curve of the receiver operating characteristic curve. Predictive uncertainty was stratified by All Patient Refined comorbidity cohort to establish the PSPM. RESULTS: The dynamic model demonstrated "learning" in the first 30 training rounds with areas under the curve of 82.0%, 83.4%, and 79.4% for LOS, charges, and disposition, respectively. The proposed PSPM established a risk increase of 2.5%, 8.9%, and 17.3% for moderate, major, and severe comorbidities, respectively. CONCLUSION: The deep learning ANN demonstrated "learning" with good reliability, responsiveness, and validity in its prediction of value-centered outcomes. This model can be applied to implement a PSPM for tiered payments based on the complexity of the case.

Development and Validation of a Machine Learning Algorithm After Primary Total Hip Arthroplasty: Applications to Length of Stay and Payment Models.

BACKGROUND: Value-based payment programs in orthopedics, specifically primary total hip arthroplasty (THA), present opportunities to apply forecasting machine learning techniques to adjust payment models to a specific patient or population. The objective of this study is to (1) develop and validate a machine learning algorithm using preoperative big data to predict length of stay (LOS) and patient-specific inpatient payments after primary THA and (2) propose a risk-adjusted patient-specific payment model (PSPM) that considers patient comorbidity. METHODS: Using an administrative database, we applied 122,334 patients undergoing primary THA for osteoarthritis between 2012 and 16 to a naïve Bayesian model trained to forecast LOS and payments. Performance was determined using area under the receiver operating characteristic curve and percent accuracy. Inpatient payments were grouped as <$12,000, $12,000-$24,000, and >$24,000. LOS was grouped as 1-2, 3-5, and 6+ days. Payment model uncertainty was applied to a proposed risk-based PSPM. RESULTS: The machine learning algorithm required age, race, gender, and comorbidity scores ("risk of illness" and "risk of morbidity") to demonstrate excellent validity, reliability, and responsiveness with an area under the receiver operating characteristic curve of 0.87 and 0.71 for LOS and payment. As patient complexity increased, error for predicting payment increased in tiers of 3%, 12%, and 32% for moderate, major, and extreme comorbidities, respectively. CONCLUSION: Our preliminary machine learning algorithm demonstrated excellent construct validity, reliability, and responsiveness predicting LOS and payment prior to primary THA. This has the potential to allow for a risk-based PSPM prior to elective THA that offers tiered reimbursement commensurate with case complexity. LEVEL OF EVIDENCE: III.

Predicting Inpatient Payments Prior to Lower Extremity Arthroplasty Using Deep Learning: Which Model Architecture Is Best?

BACKGROUND: Recent advances in machine learning have given rise to deep learning, which uses hierarchical layers to build models, offering the ability to advance value-based healthcare by better predicting patient outcomes and costs of a given treatment. The purpose of this study is to compare the performance of 2 common deep learning models, traditional multilayer perceptron (MLP), and the newer dense neural network (DenseNet), in predicting outcomes for primary total hip arthroplasty (THA) and total knee arthroplasty (TKA) as a foundation for future musculoskeletal studies seeking to utilize machine learning. METHODS: Using 295,605 patients undergoing primary THA and TKA from a New York State inpatient administrative database from 2009 to 2016, 2 neural network designs (MLP vs DenseNet) with different model regularization techniques (dropout, batch normalization, and DeCovLoss) were applied to compare model performance on predicting inpatient procedural cost using the area under the receiver operating characteristic curve (AUC). Models were implemented to identify high-cost surgical cases. RESULTS: DenseNet performed similarly to or better than MLP across the different regularization techniques in predicting procedural costs of THA and TKA. Applying regularization to DenseNet resulted in a significantly higher AUC as compared to DenseNet alone (0.813 vs 0.792, P = .011). When regularization methods were applied to MLP, the AUC was significantly lower than without regularization (0.621 vs 0.791, P = 1.1 × 10-15). When the optimal MLP and DenseNet models were compared in a head-to-head fashion, they performed similarly at cost prediction (P > .999). CONCLUSION: This study establishes that in predicting costs of lower extremity arthroplasty, DenseNet models improve in performance with regularization, whereas simple neural network models perform significantly worse without regularization. In light of the resource-intensive nature of creating and testing deep learning models for orthopedic surgery, particularly for value-centric procedures such as arthroplasty, this study establishes a set of key technical features that resulted in better prediction of inpatient surgical costs. We demonstrated that regularization is critically important for neural networks in arthroplasty cost prediction and that future studies should utilize these deep learning techniques to predict arthroplasty costs. LEVEL OF EVIDENCE: III.

Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Review.

BACKGROUND: Driven by the rapid development of big data and processing power, artificial intelligence and machine learning (ML) applications are poised to expand orthopedic surgery frontiers. Lower extremity arthroplasty is uniquely positioned to most dramatically benefit from ML applications given its central role in alternative payment models and the value equation. METHODS: In this report, we discuss the origins and model specifics behind machine learning, consider its progression into healthcare, and present some of its most recent advances and applications in arthroplasty. RESULTS: A narrative review of artificial intelligence and ML developments is summarized with specific applications to lower extremity arthroplasty, with specific lessons learned from osteoarthritis gait models, joint-specific imaging analysis, and value-based payment models. CONCLUSION: The advancement and employment of ML provides an opportunity to provide data-driven, high performance medicine that can rapidly improve the science, economics, and delivery of lower extremity arthroplasty.

Discovery of Age-Related Protein Folding Stability Differences in the Mouse Brain Proteome.

Described here is the application of thermodynamic stability measurements to study age-related differences in the folding and stability of proteins in a rodent model of aging. Thermodynamic stability profiles were generated for 809 proteins in brain cell lysates from mice, aged 6 (n = 7) and 18 months (n = 9) using the Stability of Proteins from Rates of Oxidation (SPROX) technique. The biological variability of the protein stability measurements was low and within the experimental error of SPROX. A total of 83 protein hits were detected with age-related stability differences in the brain samples. Remarkably, the large majority of the brain protein hits were destabilized in the old mice, and the hits were enriched in proteins that have slow turnover rates (p < 0.07). Furthermore, 70% of the hits have been previously linked to aging or age-related diseases. These results help validate the use of thermodynamic stability measurements to capture relevant age-related proteomic changes and establish a new biophysical link between these proteins and aging.