Prediction of 30-Day Mortality Following Revision Total Hip and Knee Arthroplasty: Machine Learning Algorithms Outperform CARDE-B, 5-Item, and 6-Item Modified Frailty Index Risk Scores.

BACKGROUND: Although risk calculators are used to prognosticate postoperative outcomes following revision total hip and knee arthroplasty (total joint arthroplasty [TJA]), machine learning (ML) based predictive tools have emerged as a promising alternative for improved risk stratification. This study aimed to compare the predictive ability of ML models for 30-day mortality following revision TJA to that of traditional risk-assessment indices such as the CARDE-B score (congestive heart failure, albumin (< 3.5 mg/dL), renal failure on dialysis, dependence for daily living, elderly (> 65 years of age), and body mass index (BMI) of < 25 kg/m2), 5-item modified frailty index (5MFI), and 6MFI. METHODS: Adult patients undergoing revision TJA between 2013 and 2020 were selected from the American College of Surgeons National Surgical Quality Improvement Program database and randomly split 80:20 to compose the training and validation cohorts. There were 3 ML models - extreme gradient boosting, random forest, and elastic-net penalized logistic regression (NEPLR) - that were developed and evaluated using discrimination, calibration metrics, and accuracy. The discrimination of CARDE-B, 5MFI, and 6MFI scores was assessed individually and compared to that of ML models. RESULTS: All models were equally accurate (Brier score = 0.005) and demonstrated outstanding discrimination with similar areas under the receiver operating characteristic curve (AUCs, extreme gradient boosting = 0.94, random forest = NEPLR = 0.93). The NEPLR was the best-calibrated model overall (slope = 0.54, intercept = -0.004). The CARDE-B had the highest discrimination among the scores (AUC = 0.89), followed by 6MFI (AUC = 0.80), and 5MFI (AUC = 0.68). Albumin < 3.5 mg/dL and BMI (< 30.15) were the most important predictors of 30-day mortality following revision TJA. CONCLUSIONS: The ML models outperform traditional risk-assessment indices in predicting postoperative 30-day mortality after revision TJA. Our findings highlight the utility of ML for risk stratification in a clinical setting. The identification of hypoalbuminemia and BMI as prognostic markers may allow patient-specific perioperative optimization strategies to improve outcomes following revision TJA.

A concerted neuron-astrocyte program declines in ageing and schizophrenia.

Human brains vary across people and over time; such variation is not yet understood in cellular terms. Here we describe a relationship between people's cortical neurons and cortical astrocytes. We used single-nucleus RNA sequencing to analyse the prefrontal cortex of 191 human donors aged 22-97 years, including healthy individuals and people with schizophrenia. Latent-factor analysis of these data revealed that, in people whose cortical neurons more strongly expressed genes encoding synaptic components, cortical astrocytes more strongly expressed distinct genes with synaptic functions and genes for synthesizing cholesterol, an astrocyte-supplied component of synaptic membranes. We call this relationship the synaptic neuron and astrocyte program (SNAP). In schizophrenia and ageing-two conditions that involve declines in cognitive flexibility and plasticity1,2-cells divested from SNAP: astrocytes, glutamatergic (excitatory) neurons and GABAergic (inhibitory) neurons all showed reduced SNAP expression to corresponding degrees. The distinct astrocytic and neuronal components of SNAP both involved genes in which genetic risk factors for schizophrenia were strongly concentrated. SNAP, which varies quantitatively even among healthy people of similar age, may underlie many aspects of normal human interindividual differences and may be an important point of convergence for multiple kinds of pathophysiology.

Concerted neuron-astrocyte gene expression declines in aging and schizophrenia.

Human brains vary across people and over time; such variation is not yet understood in cellular terms. Here we describe a striking relationship between people's cortical neurons and cortical astrocytes. We used single-nucleus RNA-seq to analyze the prefrontal cortex of 191 human donors ages 22-97 years, including healthy individuals and persons with schizophrenia. Latent-factor analysis of these data revealed that in persons whose cortical neurons more strongly expressed genes for synaptic components, cortical astrocytes more strongly expressed distinct genes with synaptic functions and genes for synthesizing cholesterol, an astrocyte-supplied component of synaptic membranes. We call this relationship the Synaptic Neuron-and-Astrocyte Program (SNAP). In schizophrenia and aging - two conditions that involve declines in cognitive flexibility and plasticity 1,2 - cells had divested from SNAP: astrocytes, glutamatergic (excitatory) neurons, and GABAergic (inhibitory) neurons all reduced SNAP expression to corresponding degrees. The distinct astrocytic and neuronal components of SNAP both involved genes in which genetic risk factors for schizophrenia were strongly concentrated. SNAP, which varies quantitatively even among healthy persons of similar age, may underlie many aspects of normal human interindividual differences and be an important point of convergence for multiple kinds of pathophysiology.

Do preoperative PROMIS scores independently predict 90-day readmission following primary total knee arthroplasty?

INTRODUCTION: The rising demand for total knee arthroplasty (TKA) is expected to increase the total number of TKA-related readmissions, presenting significant public health and economic burden. With the increasing use of Patient-Reported Outcomes Measurement Information System (PROMIS) scores to inform clinical decision-making, this study aimed to investigate whether preoperative PROMIS scores are predictive of 90-day readmissions following primary TKA. MATERIALS AND METHODS: We retrospectively reviewed a consecutive series of 10,196 patients with preoperative PROMIS scores who underwent primary TKA. Two comparison groups, readmissions (n = 79; 3.6%) and non-readmissions (n = 2091; 96.4%) were established. Univariate and multivariate logistic regression analyses were then performed with readmission as the outcome variable to determine whether preoperative PROMIS scores could predict 90-day readmission. RESULTS: The study cohort consisted of 2170 patients overall. Non-white patients (OR = 3.53, 95% CI [1.16, 10.71], p = 0.026) and patients with cardiovascular or cerebrovascular disease (CVD) (OR = 1.66, 95% CI [1.01, 2.71], p = 0.042) were found to have significantly higher odds of 90-day readmission after TKA. Preoperative PROMIS-PF10a (p = 0.25), PROMIS-GPH (p = 0.38), and PROMIS-GMH (p = 0.07) scores were not significantly associated with 90-day readmission. CONCLUSION: This study demonstrates that preoperative PROMIS scores may not be used to predict 90-day readmission following primary TKA. Non-white patients and patients with CVD are 3.53 and 1.66 times more likely to be readmitted, highlighting existing racial disparities and medical comorbidities contributing to readmission in patients undergoing TKA.

Patient-Reported Outcomes Measurement Information System Physical Function and Global Physical Health Subscale Strongly Correlate and Perform Similarly to the Knee Injury and Osteoarthritis Outcome Score-Physical Function Short Form.

BACKGROUND: Arthroplasty surgeons use a variety of patient-reported outcome measures (PROMs) to assess functional well-being, including the Knee Injury and Osteoarthritis Outcome Score (KOOS) Physical Function short form (KOOS-PS), Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function Short Form 10a (PROMIS PF SF 10a), and PROMIS Global-10 Physical Health subscale. However, there is a paucity of literature assessing their concurrent validity and performance. METHODS: Between June 2016 and December 2020, patient visits at an arthroplasty clinic for knee concerns were identified. Patients who completed KOOS-PS, PROMIS PF SF 10a, and PROMIS Global-10, including its physical and mental health subscales, at the same visit were identified. Spearman rho (ρ) correlations were calculated and ceiling and floor effects identified. Overall, 5,303 patient encounters were included. RESULTS: Among physical function domains, strong correlation existed between the KOOS-PS and PROMIS PF SF 10a (ρ = 0.76, P < .001), KOOS-PS and PROMIS Global Physical Health (ρ = 0.71, P < .001), and PROMIS PF SF 10a and PROMIS Global Physical Health (ρ = 0.78, P < .001). No physical function-focused PROM had an appreciable floor effect (ie, at or more than 1%). The KOOS-PS had a small but measurable ceiling effect (n = 105 [2.0%]). CONCLUSIONS: All of the examined PROMs are acceptable to measure the functional status of patients with knee pathology, with the PROMIS Global-10 also being able to capture elements of mental health too. The PROMIS Global-10 may be of most value of the PROMs assessed, as the United States Centers for Medicare and Medicaid Services already incorporate the mental health component into new alternative payment models.

Degree of articular injury as measured by CT cross sectional area is associated with physical function following the treatment of bicondylar tibial plateau fractures.

BACKGROUND: Bicondylar tibial plateau fractures are complex injuries that commonly require surgical repair. Long-term clinical outcome has been associated with discrepancies in leg alignment, instability and condylar width abnormalities. While intuitive, the degree of articular damage at time of injury has not been linked to outcomes in patients with bicondylar tibial plateau fractures. The aim of this study was to quantify percentage of articular surface cross sectional area disruption and assess for correlation between the degree of articular injury and patient reported physical function. METHODS: Retrospective cohort study at two level 1 trauma centers. 57 consecutive patients undergoing surgical repair for bicondylar tibial plateau fractures between 2013 and 2016. MAIN OUTCOME MEASURE: Preoperative CT scans were reviewed, and the percentage of articular surface disruption cross sectional area was calculated. PROMIS® scores were collected from patients at a minimum of 2 years. RESULTS: 57 patients with an average age of 58 ± 14.3 years were included. The average PROMIS® score was 45.5. There was a correlation between percentage of articular surface disruption and total PROMIS® scores (0.4, CI: 0.2-0.5, p = .007) and the physical function of the PROMIS® score (0.4, CI: 0.2-0.6, p < .001). CONCLUSION: Our method for calculating articular surface disruption on CT is a simple, reproducible and accurate method for assessing the degree of articular damage in patients with bicondylar tibial plateau fractures. We found that the percentage of cross-sectional articular surface disruption correlates with patient reported outcomes and physical function.

A global federated real-world data and analytics platform for research.

Objective: This article describes a scalable, performant, sustainable global network of electronic health record data for biomedical and clinical research. Materials and Methods: TriNetX has created a technology platform characterized by a conservative security and governance model that facilitates collaboration and cooperation between industry participants, such as pharmaceutical companies and contract research organizations, and academic and community-based healthcare organizations (HCOs). HCOs participate on the network in return for access to a suite of analytics capabilities, large networks of de-identified data, and more sponsored trial opportunities. Industry participants provide the financial resources to support, expand, and improve the technology platform in return for access to network data, which provides increased efficiencies in clinical trial design and deployment. Results: TriNetX is a growing global network, expanding from 55 HCOs and 7 countries in 2017 to over 220 HCOs and 30 countries in 2022. Over 19 000 sponsored clinical trial opportunities have been initiated through the TriNetX network. There have been over 350 peer-reviewed scientific publications based on the network's data. Conclusions: The continued growth of the TriNetX network and its yield of clinical trial collaborations and published studies indicates that this academic-industry structure is a safe, proven, sustainable path for building and maintaining research-centric data networks.

Validation and Generalizability of Machine Learning Models for the Prediction of Discharge Disposition Following Revision Total Knee Arthroplasty.

BACKGROUND: Postoperative discharge to facilities account for over 33% of the $ 2.7 billion revision total knee arthroplasty (TKA)-associated annual expenditures and are associated with increased complications when compared to home discharges. Prior studies predicting discharge disposition using advanced machine learning (ML) have been limited due to a lack of generalizability and validation. This study aimed to establish ML model generalizability by externally validating its prediction for nonhome discharge following revision TKA using national and institutional databases. METHODS: The national and institutional cohorts comprised 52,533 and 1,628 patients, respectively, with 20.6 and 19.4% nonhome discharge rates. Five ML models were trained and internally validated (five-fold cross-validation) on a large national dataset. Subsequently, external validation was performed on our institutional dataset. Model performance was assessed using discrimination, calibration, and clinical utility. Global predictor importance plots and local surrogate models were used for interpretation. RESULTS: The strongest predictors of nonhome discharge were patient age, body mass index, and surgical indication. The area under the receiver operating characteristic curve increased from internal to external validation and ranged between 0.77 and 0.79. Artificial neural network was the best predictive model for identifying patients at risk for nonhome discharge (area under the receiver operating characteristic curve = 0.78), and also the most accurate (calibration slope = 0.93, intercept = 0.02, and Brier score = 0.12). CONCLUSION: All five ML models demonstrated good-to-excellent discrimination, calibration, and clinical utility on external validation, with artificial neural network being the best model for predicting discharge disposition following revision TKA. Our findings establish the generalizability of ML models developed using data from a national database. The integration of these predictive models into clinical workflow may assist in optimizing discharge planning, bed management, and cost containment associated with revision TKA.

Elevated ESR and CRP Prior to Second-Stage Reimplantation Knee Revision Surgery for Periprosthetic Joint Infection Are Associated with Increased Reinfection Rates.

Although two-stage revision surgery is considered as the most effective treatment for managing chronic periprosthetic joint infection (PJI), there is no current consensus on the predictors of optimal timing to second-stage reimplantation. This study aimed to compare clinical outcomes between patients with elevated erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) prior to second-stage reimplantation and those with normalized ESR and CRP prior to second-stage reimplantation. We retrospectively reviewed 198 patients treated with two-stage revision total knee arthroplasty for chronic PJI. Cohorts included patients with: (1) normal level of serum ESR and CRP (n = 96) and (2) elevated level of serum ESR and CRP prior to second-stage reimplantation (n = 102). Outcomes including reinfection rates and readmission rates were compared between both cohorts. At a mean follow-up of 4.4 years (2.8-6.5 years), the elevated ESR and CRP cohort demonstrated significantly higher reinfection rates compared with patients with normalized ESR and CRP prior to second-stage reimplantation (33.3% vs. 14.5%, p < 0.01). Patients with both elevated ESR and CRP demonstrated significantly higher reinfection rates, when compared with patients with elevated ESR and normalized CRP (33.3% vs. 27.6%, p = 0.02) as well as normalized ESR and elevated CRP (33.3% vs. 26.3%, p < 0.01). This study demonstrates that elevated serum ESR and/or CRP levels prior to reimplantation in two-stage knee revision surgery for chronic PJI are associated with increased reinfection rate after surgery. Elevation of both ESR and CRP were associated with a higher risk of reinfection compared with elevation of either ESR or CRP, suggesting the potential benefits of normalizing ESR and CRP prior to reimplantation in treatment of chronic PJI.

The Use of Artificial Neural Networks for the Prediction of Surgical Site Infection Following TKA.

This is a retrospective study. Surgical site infection (SSI) is associated with adverse postoperative outcomes following total knee arthroplasty (TKA). However, accurately predicting SSI remains a clinical challenge due to the multitude of patient and surgical factors associated with SSI. This study aimed to develop and validate machine learning models for the prediction of SSI following primary TKA. This is a retrospective study for patients who underwent primary TKA. Chart review was performed to identify patients with superficial or deep SSIs, defined in concordance with the criteria of the Musculoskeletal Infection Society. All patients had a minimum follow-up of 2 years (range: 2.1-4.7 years). Five machine learning algorithms were developed to predict this outcome, and model assessment was performed by discrimination, calibration, and decision curve analysis. A total of 10,021 consecutive primary TKA patients was included in this study. At an average follow-up of 2.8 ± 1.1 years, SSIs were reported in 404 (4.0%) TKA patients, including 223 superficial SSIs and 181 deep SSIs. The neural network model achieved the best performance across discrimination (area under the receiver operating characteristic curve = 0.84), calibration, and decision curve analysis. The strongest predictors of the occurrence of SSI following primary TKA, in order, were Charlson comorbidity index, obesity (BMI >30 kg/m2), and smoking. The neural network model presented in this study represents an accurate method to predict patient-specific superficial and deep SSIs following primary TKA, which may be employed to assist in clinical decision-making to optimize outcomes in at-risk patients.