Quantitative image analysis pipeline for detecting circulating hybrid cells in immunofluorescence images with human-level accuracy.

Circulating hybrid cells (CHCs) are a newly discovered, tumor-derived cell population found in the peripheral blood of cancer patients and are thought to contribute to tumor metastasis. However, identifying CHCs by immunofluorescence (IF) imaging of patient peripheral blood mononuclear cells (PBMCs) is a time-consuming and subjective process that currently relies on manual annotation by laboratory technicians. Additionally, while IF is relatively easy to apply to tissue sections, its application to PBMC smears presents challenges due to the presence of biological and technical artifacts. To address these challenges, we present a robust image analysis pipeline to automate the detection and analysis of CHCs in IF images. The pipeline incorporates quality control to optimize specimen preparation protocols and remove unwanted artifacts, leverages a ß-variational autoencoder (VAE) to learn meaningful latent representations of single-cell images, and employs a support vector machine (SVM) classifier to achieve human-level CHC detection. We created a rigorously labeled IF CHC data set including nine patients and two disease sites with the assistance of 10 annotators to evaluate the pipeline. We examined annotator variation and bias in CHC detection and provided guidelines to optimize the accuracy of CHC annotation. We found that all annotators agreed on CHC identification for only 65% of the cells in the data set and had a tendency to underestimate CHC counts for regions of interest (ROIs) containing relatively large amounts of cells (>50,000) when using the conventional enumeration method. On the other hand, our proposed approach is unbiased to ROI size. The SVM classifier trained on the ß-VAE embeddings achieved an F1 score of 0.80, matching the average performance of human annotators. Our pipeline enables researchers to explore the role of CHCs in cancer progression and assess their potential as a clinical biomarker for metastasis. Further, we demonstrate that the pipeline can identify discrete cellular phenotypes among PBMCs, highlighting its utility beyond CHCs.

Optimizing inpatient bed management in a rural community-based hospital: a quality improvement initiative.

BACKGROUND: Appropriate use of available inpatient beds is an ongoing challenge for US hospitals. Historical capacity goals of 80% to 85% may no longer serve the intended purpose of maximizing the resources of space, staff, and equipment. Numerous variables affect the input, throughput, and output of a hospital. Some of these variables include patient demand, regulatory requirements, coordination of patient flow between various systems, coordination of processes such as bed management and patient transfers, and the diversity of departments (both inpatient and outpatient) in an organization. METHODS: Mayo Clinic Health System in the Southwest Minnesota region of the US, a community-based hospital system primarily serving patients in rural southwestern Minnesota and part of Iowa, consists of 2 postacute care and 3 critical access hospitals. Our inpatient bed usage rates had exceeded 85%, and patient transfers from the region to other hospitals in the state (including Mayo Clinic in Rochester, Minnesota) had increased. To address these quality gaps, we used a blend of Agile project management methodology, rapid Plan-Do-Study-Act cycles, and a proactive approach to patient placement in the medical-surgical units as a quality improvement initiative. RESULTS: During 2 trial periods of the initiative, the main hub hospital (Mayo Clinic Health System hospital in Mankato) and other hospitals in the region increased inpatient bed usage while reducing total out-of-region transfers. CONCLUSION: Our novel approach to proactively managing bed capacity in the hospital allowed the region's only tertiary medical center to increase capacity for more complex and acute cases by optimizing the use of historically underused partner hospital beds.

Is the Lifetime Malignancy Risk in United States Military Personnel Sustaining Combat-related Trauma Increased Because of Radiation Exposure From Diagnostic Imaging?

BACKGROUND: Patients with complex polytrauma in the military and civilian settings are often exposed to substantial diagnostic medical radiation because of serial imaging studies for injury diagnosis and subsequent management. This cumulative radiation exposure may increase the risk of subsequent malignancy. This is particularly true for combat-injured servicemembers who receive care at a variety of facilities worldwide. Currently, there is no coordinated effort to track the amount of radiation exposure each servicemember receives, nor a surveillance program to follow such patients in the long term. It is important to assess whether military servicemembers are exposed to excessive diagnostic radiation to mitigate or prevent such occurrences and monitor for carcinogenesis, when necessary. The cumulative amount of radiation exposure for combat-wounded and noncombat-wounded servicemembers has not been described, and it remains unknown whether diagnostic radiation exposure meets thresholds for an increased risk of carcinogenesis. QUESTIONS/PURPOSES: We performed this study to (1) quantify the amount of exposure for combat-wounded servicemembers based on medical imaging in the first year after injury and compare those exposures with noncombat-related trauma, and (2) determine whether the cumulative dose of radiation correlates to the Injury Severity Score (ISS) across the combat-wounded and noncombat-wounded population combined. METHODS: We performed a retrospective study of servicemembers who sustained combat or noncombat trauma and were treated at Walter Reed National Military Medical Center from 2005 to 2018. We evaluated patients using the Department of Defense Trauma Registry. After consolidating redundant records, the dataset included 3812 unique servicemember encounters. Three percent (104 of 3812) were excluded because of missing radiation exposure data in the electronic medical record. The final cohort included 3708 servicemembers who had combat or noncombat injury trauma, with a mean age at the time of injury of 26 ± 6 years and a mean ISS of 18 ± 12. The most common combat trauma mechanisms of injury were blast (in 65% [2415 of 3708 patients]), followed by high-velocity gunshot wounds (in 22% [815 of 3708 patients]). We calculated the cumulative diagnostic radiation dose exposure at 1 year post-traumatic injury in patients with combat-related trauma and those with noncombat trauma. We did this by multiplying the number of imaging studies by the standardized effective radiation dose for each imaging study type. We then performed analysis of variance for four data subsets (battle combat trauma, nonbattle civilian trauma, high ISS, and high radiation exposure [> 50 mSv]) independently. To evaluate whether the total number of imaging studies, radiation exposure, and ISS values differed between battle-wounded and nonbattle-wounded patients, we performed a pairwise t-test. RESULTS: The mean radiation exposure for combat-related injuries was 35 ± 26 mSv while the mean radiation exposure for noncombat-related injuries was 22 ± 33 mSv in the first year after injury. In the first year after trauma, 44% of patients (1626 of 3708) were exposed to high levels of radiation that were greater than 20 mSv, and 23% (840 of 3708) were exposed to very high levels of radiation that were greater than 50 mSv. Servicemembers with combat trauma-related injuries had eight more imaging studies than those who sustained noncombat injuries. Servicemembers with combat trauma injuries (35 ± 26 mSv) were exposed to more radiation (approximately 4 mSv) than patients treated for noncombat injuries (22 ± 33 mSv) (p = 0.01). We found that servicemembers with combat injuries had a higher ISS than servicemembers with noncombat trauma (p < 0.001). We found a positive correlation between radiation exposure and ISS for servicemembers. The positive relationship between radiation exposure and ISS held for combat trauma (r 2 = 0.24; p < 0.001), noncombat trauma (r 2 = 0.20; p < 0.001), servicemembers with a high ISS (r 2 = 0.10; p < 0.001), and servicemembers exposed to high doses of radiation (r 2 = 0.09; p < 0.001). CONCLUSION: These data should be used during clinical decision-making and patient counseling at military treatment facilities and might provide guidance to the Defense Health Agency. These recommendations will help determine whether the benefits of further imaging outweigh the risk of carcinogenesis. If not, we need to develop interdisciplinary clinical practice guidelines to reduce or minimize radiation exposure. It is important for treating physicians to seriously weigh the risk and benefits of every imaging study ordered because each test does not come without a cumulative risk. LEVEL OF EVIDENCE: Level III, therapeutic study.

Survival of anterior cruciate ligament reconstructions in active-duty military populations.

PURPOSE: Anterior cruciate ligament tears and anterior cruciate ligament reconstruction (ACLR) are common in young athletes. The modifiable and non-modifiable factors contributing to ACLR failure and reoperation are incompletely understood. The purpose of this study was to determine ACLR failure rates in a physically high-demand population and identify the patient-specific risk factors, including prolonged time between diagnosis and surgical correction, that portend failure. METHODS: A consecutive series of military service members with ACLR with and without concomitant procedures (meniscus [M] and/or cartilage [C]) done at military facilities between 2008 and 2011 was completed via the Military Health System Data Repository. This was a consecutive series of patients without a history of knee surgery for two years prior to the primary ACLR. Kaplan-Meier survival curves were estimated and evaluated with Wilcoxon test. Cox proportional hazard models calculated hazard ratios (HR) with 95% confidence intervals (95% CI) to identify demographic and surgical factors that influenced ACLR failure. RESULTS: Of the 2735 primary ACLRs included in the study, 484/2,735 (18%) experienced ACLR failure within four years, including (261/2,735) (10%) undergoing revision ACLR and (224/2,735) (8%) due to medical separation. The factors that increased failure include Army Service (HR 2.19, 95% CI 1.67, 2.87), > 180 days from injury to ACLR (HR 1.550, 95% CI 1.157, 2.076), tobacco use (HR 1.429 95% CI 1.174, 1.738), and younger patient age (HR 1.024, 95% CI 1.004, 1.044). CONCLUSION: The overall clinical failure rate of service members with ACLR is 17.7% with minimum four-year follow-up, where more patients are likely to fail due to revision surgery than medical separation. The cumulative probability of survival at 4 years was 78.5%. Smoking cessation and treating ACLR patients promptly are modifiable risk factors impacting either graft failure or medical separation. LEVEL OF EVIDENCE: Level III.

FoxP1 orchestration of ASD-relevant signaling pathways in the striatum.

Mutations in the transcription factor Forkhead box p1 (FOXP1) are causative for neurodevelopmental disorders such as autism. However, the function of FOXP1 within the brain remains largely uncharacterized. Here, we identify the gene expression program regulated by FoxP1 in both human neural cells and patient-relevant heterozygous Foxp1 mouse brains. We demonstrate a role for FoxP1 in the transcriptional regulation of autism-related pathways as well as genes involved in neuronal activity. We show that Foxp1 regulates the excitability of striatal medium spiny neurons and that reduction of Foxp1 correlates with defects in ultrasonic vocalizations. Finally, we demonstrate that FoxP1 has an evolutionarily conserved role in regulating pathways involved in striatal neuron identity through gene expression studies in human neural progenitors with altered FOXP1 levels. These data support an integral role for FoxP1 in regulating signaling pathways vulnerable in autism and the specific regulation of striatal pathways important for vocal communication.

Machine learning algorithms to estimate 10-Year survival in patients with bone metastases due to prostate cancer: toward a disease-specific survival estimation tool.

BACKGROUND: Prognostic indicators, treatments, and survival estimates vary by cancer type. Therefore, disease-specific models are needed to estimate patient survival. Our primary aim was to develop models to estimate survival duration after treatment for skeletal-related events (SREs) (symptomatic bone metastasis, including impending or actual pathologic fractures) in men with metastatic bone disease due to prostate cancer. Such disease-specific models could be added to the PATHFx clinical-decision support tool, which is available worldwide, free of charge. Our secondary aim was to determine disease-specific factors that should be included in an international cancer registry. METHODS: We analyzed records of 438 men with metastatic prostate cancer who sustained SREs that required treatment with radiotherapy or surgery from 1989-2017. We developed and validated 6 models for 1-, 2-, 3-, 4-, 5-, and 10-year survival after treatment. Model performance was evaluated using calibration analysis, Brier scores, area under the receiver operator characteristic curve (AUC), and decision curve analysis to determine the models' clinical utility. We characterized the magnitude and direction of model features. RESULTS: The models exhibited acceptable calibration, accuracy (Brier scores < 0.20), and classification ability (AUCs > 0.73). Decision curve analysis determined that all 6 models were suitable for clinical use. The order of feature importance was distinct for each model. In all models, 3 factors were positively associated with survival duration: younger age at metastasis diagnosis, proximal prostate-specific antigen (PSA) < 10 ng/mL, and slow-rising alkaline phosphatase velocity (APV). CONCLUSIONS: We developed models that estimate survival duration in patients with metastatic bone disease due to prostate cancer. These models require external validation but should meanwhile be included in the PATHFx tool. PSA and APV data should be recorded in an international cancer registry.

FOXP1 negatively regulates intrinsic excitability in D2 striatal projection neurons by promoting inwardly rectifying and leak potassium currents.

Heterozygous loss-of-function mutations in the transcription factor FOXP1 are strongly associated with autism. Dopamine receptor 2 expressing (D2) striatal projection neurons (SPNs) in heterozygous Foxp1 (Foxp1+/-) mice have higher intrinsic excitability. To understand the mechanisms underlying this alteration, we examined SPNs with cell-type specific homozygous Foxp1 deletion to study cell-autonomous regulation by Foxp1. As in Foxp1+/- mice, D2 SPNs had increased intrinsic excitability with homozygous Foxp1 deletion. This effect involved postnatal mechanisms. The hyperexcitability was mainly due to down-regulation of two classes of potassium currents: inwardly rectifying (KIR) and leak (KLeak). Single-cell RNA sequencing data from D2 SPNs with Foxp1 deletion indicated the down-regulation of transcripts of candidate ion channels that may underlie these currents: Kcnj2 and Kcnj4 for KIR and Kcnk2 for KLeak. This Foxp1-dependent regulation was neuron-type specific since these same currents and transcripts were either unchanged, or very little changed, in D1 SPNs with cell-specific Foxp1 deletion. Our data are consistent with a model where FOXP1 negatively regulates the excitability of D2 SPNs through KIR and KLeak by transcriptionally activating their corresponding transcripts. This, in turn, provides a novel example of how a transcription factor may regulate multiple genes to impact neuronal electrophysiological function that depends on the integration of multiple current types - and do this in a cell-specific fashion. Our findings provide initial clues to altered neuronal function and possible therapeutic strategies not only for FOXP1-associated autism but also for other autism forms associated with transcription factor dysfunction.

A systematic review of engagement in care and health care utilization among older adults living with HIV and non-communicable diseases.

It is critical to understand health care engagement and utilization among older persons living with HIV (OPWH) who may have greater burden for non-communicable diseases. Following the PRISMA guidelines, a systematic review using 5 electronic databases was conducted to appraise and synthesize the current literature on the relationship of non-communicable diseases on engagement in care and health care utilization among OPWH. Original studies published in English between 2009 and 2019 were included, yielding 16 relevant articles. Overall, having co-morbid non-communicable diseases was associated with a decreased likelihood of initiating and adhering to ART. Being on ART and viral suppression were associated with better engagement in non-communicable disease care. Findings also suggest that an increasing number of co-morbidities is associated with higher health care utilization and financial burden. This review underscores the need for preventing and managing co-morbidities to enhance engagement in HIV care and that health care practitioners need to ensure that OPWH are engaged in care for both HIV and their co-morbid conditions by providing coordinated and integrated care.

A Machine-Learning Algorithm to Predict the Likelihood of Prolonged Opioid Use Following Arthroscopic Hip Surgery.

PURPOSE: To develop a machine-learning algorithm and clinician-friendly tool predicting the likelihood of prolonged opioid use (>90 days) following hip arthroscopy. METHODS: The Military Data Repository was queried for all adult patients undergoing arthroscopic hip surgery between 2012 and 2017. Demographic, health history, and prescription records were extracted for all included patients. Opioid use was divided into preoperative use (30-365 days before surgery), perioperative use (30 days before surgery through 14 days after surgery), postoperative use (14-90 days after surgery), and prolonged postoperative use (90-365 days after surgery). Six machine-learning algorithms (Naïve Bayes, Gradient Boosting Machine, Extreme Gradient Boosting, Random Forest, Elastic Net Regularization, and artificial neural network) were developed. Area under the receiver operating curve and Brier scores were calculated for each model. Decision curve analysis was applied to assess clinical utility. Local-Interpretable Model-Agnostic Explanations were used to demonstrate factor weights within the selected model. RESULTS: A total of 6,760 patients were included, of whom 2,762 (40.9%) filled at least 1 opioid prescription >90 days after surgery. The artificial neural network model showed superior discrimination and calibration with area under the receiver operating curve = 0.71 (95% confidence interval 0.68-0.74) and Brier score = 0.21 (95% confidence interval 0.20-0.22). Postsurgical opioid use, age, and preoperative opioid use had the most influence on model outcome. Lesser factors included the presence of a psychological comorbidity and strong history of a substance use disorder. CONCLUSIONS: The artificial neural network model shows sufficient validity and discrimination for use in clinical practice. The 5 identified factors (age, preoperative opioid use, postoperative opioid use, presence of a mental health comorbidity, and presence of a preoperative substance use disorder) accurately predict the likelihood of prolonged opioid use following hip arthroscopy. LEVEL OF EVIDENCE: III, retrospective comparative prognostic trial.

Synthesis, Biological Evaluation and Docking Studies of Ring-Opened Analogues of Ipomoeassin F.

The plant-derived macrocyclic resin glycoside ipomoeassin F (Ipom-F) binds to Sec61α and significantly disrupts multiple aspects of Sec61-mediated protein biogenesis at the endoplasmic reticulum, ultimately leading to cell death. However, extensive assessment of Ipom-F as a molecular tool and a therapeutic lead is hampered by its limited production scale, largely caused by intramolecular assembly of the macrocyclic ring. Here, using in vitro and/or in cellula biological assays to explore the first series of ring-opened analogues for the ipomoeassins, and indeed all resin glycosides, we provide clear evidence that macrocyclic integrity is not required for the cytotoxic inhibition of Sec61-dependent protein translocation by Ipom-F. Furthermore, our modeling suggests that open-chain analogues of Ipom-F can interact with multiple sites on the Sec61α subunit, most likely located at a previously identified binding site for mycolactone and/or the so-called lateral gate. Subsequent in silico-aided design led to the discovery of the stereochemically simplified analogue 3 as a potent, alternative lead compound that could be synthesized much more efficiently than Ipom-F and will accelerate future ipomoeassin research in chemical biology and drug discovery. Our work may also inspire further exploration of ring-opened analogues of other resin glycosides.

Predictors of disparities in retention in care among African Americans living with HIV.

Limited health literacy may contribute to racial disparities in retention in HIV care. The purpose of this study was to evaluate the effects of health literacy and patient and social-level factors on retention in care among African Americans living with HIV. This study included 699 participants recruited from outpatient HIV clinics and retention in care was defined as visit adherence. Multivariable logistic regression models were used to assess predictors of visit adherence among persons with 100% visit adherence compared to less than 100% visit adherence. Controlling for demographic factors, the odds of 100% visit adherence was greater among non-African Americans compared to African Americans. In models that included health literacy, race was no longer significant and health literacy was a significant predictor of 100% visit adherence. Among participants with less than 100% visit adherence, health literacy was not a significant predictor of visit adherence; however, age, marital status, and patient attitudes towards the health care provider were significant predictors. Findings suggest that health literacy may mediate the relationship between race and visit adherence. Future studies should further examine these relationships and develop interventions that target modifiable factors, with a goal of improving health equity and minimizing disparities.

Survival in Pediatric, Adolescent, and Young Adult Patients With Sarcoma in the Military Health System: Comparison With the SEER Population.

BACKGROUND: We sought to compare survival outcomes of sarcomas in the pediatric and adolescent/young adult populations with universal care access in the Military Health System (MHS) to those from the United States general population. METHODS: We compared data from the Department of Defense's (DoD) Automated Central Tumor Registry (ACTUR) and the National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) program on the overall survival of patients 24 years or younger with histologically or microscopically confirmed sarcoma between diagnosed between January 1, 1987, and December 31, 2013. The Kaplan-Meier survival curves were used to compare survival between the 2 patient populations. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) comparing ACTUR relative to SEER. RESULTS: The final analysis included 309 and 1236 bone sarcoma cases and 465 and 1860 soft tissue sarcoma cases from ACTUR and SEER, respectively. Cox proportional hazards analysis showed soft tissue sarcoma patients in ACTUR had significantly better overall (HR=0.73, 95% CI=0.55-0.98) and 5-year overall (HR=0.63, 95% CI=0.46-0.86) survival compared with SEER patients, but no significant difference in overall or 5-year overall survival between ACTUR and SEER patients with bone sarcoma. CONCLUSION: Survival data from the ACTUR database demonstrated significantly improved overall survival for soft tissue sarcomas and equivalent survival in bone sarcomas compared with that reported by SEER.

Expressions of Resilience: Social Media Responses to a Flooding Event.

Concerns over the resilience of individuals within communities impacted by extreme weather events have heightened in recent years due to the increasing frequency and intensity of these events. Individuals' participation in communicative activities is an integral part of how they prepare for and respond to natural disasters. This study focuses on how individuals express resilience in social media posts from Twitter before, during, and after a regional flooding event in Colorado in 2013 (N = 210,303). Findings show that both negative and positive emotional responses spike at the start of the event, with positive emotions remaining high in the weeks following the event. Uses of language related to social connections, as well as references to home and work, increased during and after the event. Tweets used pronouns focused on the self during the event but shifted to pronouns focused on the collective after the event. This study points the importance of language for understanding the lasting impact extreme weather events can have on individuals, as well as when and how to reach individuals with information about recovery. The increased focus on collective language after the event provides an opportunity for calls to action in collecting and distributing shared resources.

Derepression of sonic hedgehog signaling upon Gpr161 deletion unravels forebrain and ventricular abnormalities.

Inverse gradients of transcriptional repressors antagonize the transcriptional effector response to morphogens. However, the role of such inverse regulation might not manifest solely from lack of repressors. Sonic hedgehog (Shh) patterns the forebrain by being expressed ventrally; however, absence of antagonizing Gli3 repressor paradoxically cause insufficient pathway activation. Interestingly, lack of the primary cilia-localized G-protein-coupled receptor, Gpr161 increases Shh signaling in the mouse neural tube from coordinated lack of Gli3 repressor and Smoothened-independent activation. Here, by deleting Gpr161 in mouse neuroepithelial cells and radial glia at early mid-gestation we detected derepression of Shh signaling throughout forebrain, allowing determination of the pathophysiological consequences. Accumulation of cerebrospinal fluid (hydrocephalus) was apparent by birth, although usual causative defects in multiciliated ependymal cells or aqueduct were not seen. Rather, the ventricular surface was expanded (ventriculomegaly) during embryogenesis from radial glial overproliferation. Cortical phenotypes included polymicrogyria in the medial cingulate cortex, increased proliferation of intermediate progenitors and basal radial glia, and altered neocortical cytoarchitectonic structure with increased upper layer and decreased deep layer neurons. Finally, periventricular nodular heterotopia resulted from disrupted neuronal migration, while the radial glial scaffold was unaffected. Overall, suppression of Shh pathway during early mid-gestation prevents ventricular overgrowth, and regulates cortical gyration and neocortical/periventricular cytoarchitecture.

Controlled aliasing for improved parallel imaging with a 3D spiral staircase trajectory.

PURPOSE: To introduce a modified 3D stack-of-spirals trajectory and efficient SENSE reconstruction for improved through-plane undersampling, while maintaining SNR efficiency and other benefits of spiral acquisitions. METHODS: A novel spiral staircase trajectory is introduced. This trajectory is a modified stack of spirals, in which spiral arms are distributed between partitions along kz . The trajectory maintains the efficient separable reconstruction with a Cartesian fast Fourier transform along the kz direction, followed by a 2D slice-by-slice gridding reconstruction. An additional intermediate step introduces a phase correction to collapse the spiral arms into the prescribed slice planes. For data undersampled through plane, this produces aliasing with reduced coherence, controlled by the arm-ordering. Undersampled data can then be reconstructed with reduced g-factor using a conjugate gradient-based iterative SENSE algorithm. RESULTS: The trajectory significantly improves g-factor for through-plane accelerated acquisitions. Improvement manifests through both reduced overall g-factor and reduced structure in the g-factor maps. In the presented experiments, the mean g-factor decreased from 1.26 to 0.93 and the maximum g-factor decreased from 3.89 to 1.15 for R = 2 spiral staircase when compared with stack of spirals, and the mean g-factor decreased from 2.51 to 0.94 and the maximum g-factor decreased from 8.26 to 1.35 for R = 3 spiral staircase when compared with stack of spirals. CONCLUSION: The novel spiral staircase trajectory offers improved aliasing characteristics for through-plane parallel imaging acceleration in 3D spiral acquisitions.

A Path Analysis of Patient and Social-Level Factors on Health Literacy and Retention in Care Among African Americans Living with HIV.

Low health literacy and poor retention in care may contribute to HIV health disparities among African Americans, but causal pathways have not been examined. We utilized an adapted health literacy model to examine the role of health literacy on racial disparities in retention in care. Retention in care for 699 participants was assessed 24-months post survey and operationalized as 100% visit adherence versus less than 100% visit adherence. Most participants were African American (60%) and virally suppressed (93%). Results from a path analysis revealed that non-African American race was related to greater health literacy (p = .023) and to 100% visit adherence (p = .024). Greater health literacy was associated with 100% visit adherence (p = .008), which was in turn related to viral suppression (p < .001). Findings indicate that health literacy partially mediates the relationship between race and retention in care and are among the first to suggest these causal pathways.

Disparities in Retention in Care Among Adults Living with HIV/AIDS: A Systematic Review.

As national HIV prevention goals aim to increase the proportion of persons living with HIV, determining existing disparities in retention in care will allow for targeted intervention. The purpose of this systematic review was to identify existing disparities in retention in care. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) 2015 guided this systematic review. Electronic databases, including PubMed/MEDLINE, CINAHL, Sociological Collection, PsychInfo, and Cab Direct/Global Health, were systematically searched and twenty studies were included. This review identified disparities in retention in care that have been documented by race, gender, age, HIV exposure, incarceration history, place of birth, and U.S. geographic location. Research is necessary to further identify existing disparities in retention in care and to better understand determinants of health disparities. Additionally, interventions must be tailored to meet the needs of health disparate populations and should be assessed to determine their effectiveness in reducing health disparities.

External Validation of PATHFx Version 3.0 in Patients Treated Surgically and Nonsurgically for Symptomatic Skeletal Metastases.

BACKGROUND: PATHFx is a clinical decision-support tool based on machine learning capable of estimating the likelihood of survival after surgery for patients with skeletal metastases. The applicability of any machine-learning tool depends not only on successful external validation in unique patient populations but also on remaining relevant as more effective systemic treatments are introduced. With advancements in the treatment of metastatic disease, it is our responsibility to patients to ensure clinical support tools remain contemporary and accurate. QUESTION/PURPOSES: Therefore, we sought to (1) generate updated PATHFx models using recent data from patients treated at one large, urban tertiary referral center and (2) externally validate the models using two contemporary patient populations treated either surgically or nonsurgically with external-beam radiotherapy alone for symptomatic skeletal metastases for symptomatic lesions. METHODS: After obtaining institutional review board approval, we collected data on 208 patients undergoing surgical treatment for pathologic fractures at Memorial Sloan Kettering Cancer Center between 2015 and 2018. These data were combined with the original PATHFx training set (n = 189) to create the final training set (n = 397). We then created six Bayesian belief networks designed to estimate the likelihood of 1-month, 3-month, 6-month, 12-month, 18-month, and 24-month survival after treatment. Bayesian belief analysis is a statistical method that allows data-driven learning to arise from conditional probabilities by exploring relationships between variables to estimate the likelihood of an outcome using observed data. For external validation, we extracted the records of patients treated between 2016 and 2018 from the International Bone Metastasis Registry and records of patients treated nonoperatively with external-beam radiation therapy for symptomatic skeletal metastases from 2012 to 2016 using the Military Health System Data Repository (radiotherapy-only group). From each record, we collected the date of treatment, laboratory values at the time of treatment initiation, demographic data, details of diagnosis, and the date of death. All records reported sufficient follow-up to establish survival (yes/no) at 24-months after treatment. For external validation, we applied the data from each record to the new PATHFx models. We assessed calibration (calibration plots), accuracy (Brier score), discriminatory ability (area under the receiver operating characteristic curve [AUC]). RESULTS: The updated PATHFx version 3.0 models successfully classified survival at each time interval in both external validation sets and demonstrated appropriate discriminatory ability and model calibration. The Bayesian models were reasonably calibrated to the Memorial Sloan Kettering Cancer Center training set. External validation with 197 records from the International Bone Metastasis Registry and 192 records from the Military Health System Data Repository for analysis found Brier scores that were all less than 0.20, with upper bounds of the 95% confidence intervals all less than 0.25, both for the radiotherapy-only and International Bone Metastasis Registry groups. Additionally, AUC estimates were all greater than 0.70, with lower bounds of the 95% CI all greater than 0.68, except for the 1-month radiotherapy-only group. To complete external validation, decision curve analysis demonstrated clinical utility. This means it was better to use the PATHFx models when compared to the default assumption that all or no patients would survive at all time periods except for the 1-month models. We believe the favorable Brier scores (< 0.20) as well as DCA indicate these models are suitable for clinical use. CONCLUSIONS: We successfully updated PATHFx using contemporary data from patients undergoing either surgical or nonsurgical treatment for symptomatic skeletal metastases. These models have been incorporated for clinical use on PATHFx version 3.0 (https://www.pathfx.org). Clinically, external validation suggests it is better to use PATHFx version 3.0 for all time periods except when deciding whether to give radiotherapy to patients with the life expectancy of less than 1 month. This is partly because most patients survived 1-month after treatment. With the advancement of medical technology in treatment and diagnosis for patients with metastatic bone disease, part of our fiduciary responsibility is to the main current clinical support tools. LEVEL OF EVIDENCE: Level III, therapeutic study.

Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?

BACKGROUND: Machine-learning methods such as the Bayesian belief network, random forest, gradient boosting machine, and decision trees have been used to develop decision-support tools in other clinical settings. Opioid abuse is a problem among civilians and military service members, and it is difficult to anticipate which patients are at risk for prolonged opioid use. QUESTIONS/PURPOSES: (1) To build a cross-validated model that predicts risk of prolonged opioid use after a specific orthopaedic procedure (ACL reconstruction), (2) To describe the relationships between prognostic and outcome variables, and (3) To determine the clinical utility of a predictive model using a decision curve analysis (as measured by our predictive system's ability to effectively identify high-risk patients and allow for preventative measures to be taken to ensure a successful procedure process). METHODS: We used the Military Analysis and Reporting Tool (M2) to search the Military Health System Data Repository for all patients undergoing arthroscopically assisted ACL reconstruction (Current Procedure Terminology code 29888) from January 2012 through December 2015 with a minimum of 90 days postoperative follow-up. In total, 10,919 patients met the inclusion criteria, most of whom were young men on active duty. We obtained complete opioid prescription filling histories from the Military Health System Data Repository's pharmacy records. We extracted data including patient demographics, military characteristics, and pharmacy data. A total of 3.3% of the data was missing. To curate and impute all missing variables, we used a random forest algorithm. We shuffled and split the data into 80% training and 20% hold-out sets, balanced by outcome variable (Outcome90Days). Next, the training set was further split into training and validation sets. Each model was built on the training data set, tuned with the validation set as applicable, and finally tested on the separate hold-out dataset. We chose four predictive models to develop, at the end choosing the best-fit model for implementation. Logistic regression, random forest, Bayesian belief network, and gradient boosting machine models were the four chosen models based on type of analysis (classification). Each were trained to estimate the likelihood of prolonged opioid use, defined as any opioid prescription filled more than 90 days after anterior cruciate reconstruction. After this, we tested the models on our holdout set and performed an area under the curve analysis concordance statistic, calculated the Brier score, and performed a decision curve analysis for validation. Then, we chose the method that produced the most suitable analysis results and, consequently, predictive power across the three calculations. Based on the calculations, the gradient boosting machine model was selected for future implementation. We systematically selected features and tuned the gradient boosting machine to produce a working predictive model. We performed area under the curve, Brier, and decision curve analysis calculations for the final model to test its viability and gain an understanding of whether it is possible to predict prolonged opioid use. RESULTS: Four predictive models were successfully developed using gradient boosting machine, logistic regression, Bayesian belief network, and random forest methods. After applying the Boruta algorithm for feature selection based on a 100-tree random forest algorithm, features were narrowed to a final seven features. The most influential features with a positive association with prolonged opioid use are preoperative morphine equivalents (yes), particular pharmacy ordering sites locations, shorter deployment time, and younger age. Those observed to have a negative association with prolonged opioid use are particular pharmacy ordering sites locations, preoperative morphine equivalents (no), longer deployment, race (American Indian or Alaskan native) and rank (junior enlisted).On internal validation, the models showed accuracy for predicting prolonged opioid use with AUC greater than our benchmark cutoff 0.70; random forest were 0.76 (95% confidence interval 0.73 to 0.79), 0.76 (95% CI 0.73 to 0.78), 0.73 (95% CI 0.71 to 0.76), and 0.72 (95% CI 0.69 to 0.75), respectively. Although the results from logistic regression and gradient boosting machines were very similar, only one model can be used in implementation. Based on our calculation of the Brier score, area under the curve, and decision curve analysis, we chose the gradient boosting machine as the final model. After selecting features and tuning the chosen gradient boosting machine, we saw an incremental improvement in our implementation model; the final model is accurate, with a Brier score of 0.10 (95% CI 0.09 to 0.11) and area under the curve of 0.77 (95% CI 0.75 to 0.80). It also shows the best clinical utility in a decision curve analysis. CONCLUSIONS: These scores support our claim that it is possible to predict which patients are at risk of prolonged opioid use, as seen by the appropriate range of hold-out analysis calculations. Current opioid guidelines recommend preoperative identification of at-risk patients, but available tools for this purpose are crude, largely focusing on identifying the presence (but not relative contributions) of various risk factors and screening for depression. The power of this model is that it will permit the development of a true clinical decision-support tool, which risk-stratifies individual patients with a single numerical score that is easily understandable to both patient and surgeon. Probabilistic models provide insight into how clinical factors are conditionally related. Not only will this gradient boosting machine be used to help understand factors contributing to opiate misuse after ACL reconstruction, but also it will allow orthopaedic surgeons to identify at-risk patients before surgery and offer increased support and monitoring to prevent opioid abuse and dependency. LEVEL OF EVIDENCE: Level III, therapeutic study.

Preoperative Smoking Cessation as a Durable Form of Long-Term Smoking Cessation.

Smokers who undergo total joint arthroplasty (TJA) face increased rates of medical and surgical complications that can be reduced by preoperative smoking cessation. We investigated the long-term durability of preoperative smoking cessation among TJA patients. Twenty-seven TJA patients who were identified as having an active history of smoking at the preoperative appointment before TJA consented to telephone survey about their perioperative and current smoking status. Average time from operation to survey was 3.7 years. Of the 27 patients, 21 (77.8%) were identified as having quit smoking prior to surgery. Of these 21 patients, 10 (47.6%) self-reported continued abstinence from smoking at the time of survey. Our cessation rate was significantly lower than reported long-term smoking cessation rates with standard therapies (p < 0.001). Our results suggest that preoperative counseling and a requirement for smoking-cessation prior to elective TJA may have long-term durability that exceeds that of popular reported methods. (Journal of Surgical Orthopaedic Advances 29(2):103-105, 2020).