Tumor-infiltrating lymphocytes in necrotic tumors after melanoma neoadjuvant anti-PD1 therapy correlate with pathological response and recurrence-free survival.

PURPOSE: Neoadjuvant anti-PD1 therapy in melanoma may increase tumor-infiltrating lymphocytes (TILs), and more TILs are associated with better treatment response. A major pathological response (MPR) in melanoma after neoadjuvant anti-PD1 therapy usually comprises tumor necrosis and fibrosis. The role of TILs in necrotic tumor necrosis (nTILs) has not been explored. EXPERIMENTAL DESIGN: We performed CD3 and CD8 immunohistochemical stains on 41 melanomas with geographic necrosis. 14 were immunotherapy-naïve, and 27 had been treated with one dose of neoadjuvant anti-PD-1 in two clinical trials. CD3+ and CD8+ nTILs were graded as absent/minimal or moderate/brisk. The percentage of necrotic areas in the tumor bed before and after treatment was quantified. Endpoints were MPR and 5-year recurrence-free survival (RFS). RESULTS: In the immunotherapy-naïve cohort, 3/14 (21%) specimens had moderate/brisk CD3+, and 2/14 (14%) had moderate/brisk CD8+ nTILs. In the treated cohort, 16/27 (59%) specimens had moderate/brisk CD3+, and 15/27 (56%) had moderate/brisk CD8+ nTILs, higher than the naïve cohort (CD3, p=0.046; CD8, p=0.018). Tumor necrosis was significantly increased after anti-PD1 therapy (p=0.007). In the treated cohort, moderate/brisk CD3+ and CD8+ nTILs correlated with MPR (p=0.042, p=0.019, respectively). Treated patients with moderate/brisk CD3+ nTILs had higher 5-year RFS than those with absent/minimal nTILs (69% versus 0%; p=0.006). This persisted on multivariate analysis (HR 0.16, 95% CI 0.03-0.84, p=0.03), adjusted for pathologic response, which was borderline significant (HR 0.26, 95% CI 0.07-1.01, p=0.051). CONCLUSIONS: CD3+ and CD8+ nTILs are associated with pathological response and 5-year RFS in melanoma patients after neoadjuvant anti-PD1 therapy.

LAG-3 sustains TOX expression and regulates the CD94/NKG2-Qa-1b axis to govern exhausted CD8 T cell NK receptor expression and cytotoxicity.

Exhausted CD8 T (Tex) cells in chronic viral infection and cancer have sustained co-expression of inhibitory receptors (IRs). Tex cells can be reinvigorated by blocking IRs, such as PD-1, but synergistic reinvigoration and enhanced disease control can be achieved by co-targeting multiple IRs including PD-1 and LAG-3. To dissect the molecular changes intrinsic when these IR pathways are disrupted, we investigated the impact of loss of PD-1 and/or LAG-3 on Tex cells during chronic infection. These analyses revealed distinct roles of PD-1 and LAG-3 in regulating Tex cell proliferation and effector functions, respectively. Moreover, these studies identified an essential role for LAG-3 in sustaining TOX and Tex cell durability as well as a LAG-3-dependent circuit that generated a CD94/NKG2+ subset of Tex cells with enhanced cytotoxicity mediated by recognition of the stress ligand Qa-1b, with similar observations in humans. These analyses disentangle the non-redundant mechanisms of PD-1 and LAG-3 and their synergy in regulating Tex cells.

Prevalence and Risk Factors for Chronic Postsurgical Pain After Thoracic Surgery: A Prospective Cohort Study.

OBJECTIVE: Thoracic surgery is associated with one of the highest rates of chronic postsurgical pain (CPSP) among all surgical subtypes. Chronic postsurgical pain carries significant medical, psychological, and economic consequences, and further interventions are needed to prevent its development. This study aimed to determine the prevalence, characteristics, and risk factors associated with CPSP after thoracic surgery. DESIGN: A prospective cohort study. SETTING: Single-center tertiary care hospital. PARTICIPANTS: This study included 285 adult patients who underwent thoracic surgery at Toronto General Hospital in Toronto, Canada, between 2012 and 2020. MEASUREMENTS AND MAIN RESULTS: Demographic, psychological, and clinical data were collected perioperatively, and follow-up evaluations were administered at 3, 6, and 12 months after surgery to assess CPSP. Chronic postsurgical pain was reported in 32.4%, 25.4%, and 18.2% of patients at 3, 6, and 12 months postoperatively, respectively. Average CPSP pain intensity was rated to be 3.37 (SD 1.82) at 3 months. Features of neuropathic pain were present in 48.7% of patients with CPSP at 3 months and 71% at 1 year. Multivariate logistic regression models indicated that independent predictors for CPSP at 3 months were scores on the Hospital Anxiety and Depression Scale (adjusted odds ratio [aOR] of 1.07, 95% CI of 1.02 to 1.14, p = 0.012) and acute postoperative pain (aOR of 2.75, 95% CI of 1.19 to 6.36, p = 0.018). INTERVENTIONS: None. CONCLUSIONS: Approximately 1 in 3 patients will continue to have pain at 3 months after surgery, with a large proportion reporting neuropathic features. Risk factors for pain at 3 months may include preoperative anxiety and depression and acute postoperative pain.

Combination anti-PD-1 and anti-CTLA-4 therapy generates waves of clonal responses that include progenitor-exhausted CD8+ T cells.

Combination checkpoint blockade with anti-PD-1 and anti-CTLA-4 antibodies has shown promising efficacy in melanoma. However, the underlying mechanism in humans remains unclear. Here, we perform paired single-cell RNA and T cell receptor (TCR) sequencing across time in 36 patients with stage IV melanoma treated with anti-PD-1, anti-CTLA-4, or combination therapy. We develop the algorithm Cyclone to track temporal clonal dynamics and underlying cell states. Checkpoint blockade induces waves of clonal T cell responses that peak at distinct time points. Combination therapy results in greater magnitude of clonal responses at 6 and 9 weeks compared to single-agent therapies, including melanoma-specific CD8+ T cells and exhausted CD8+ T cell (TEX) clones. Focused analyses of TEX identify that anti-CTLA-4 induces robust expansion and proliferation of progenitor TEX, which synergizes with anti-PD-1 to reinvigorate TEX during combination therapy. These next generation immune profiling approaches can guide the selection of drugs, schedule, and dosing for novel combination strategies.

Comparison of model feature importance statistics to identify covariates that contribute most to model accuracy in prediction of insomnia.

IMPORTANCE: Sleep is critical to a person's physical and mental health and there is a need to create high performing machine learning models and critically understand how models rank covariates. OBJECTIVE: The study aimed to compare how different model metrics rank the importance of various covariates. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional cohort study was conducted retrospectively using the National Health and Nutrition Examination Survey (NHANES), which is publicly available. METHODS: This study employed univariate logistic models to filter out strong, independent covariates associated with sleep disorder outcome, which were then used in machine-learning models, of which, the most optimal was chosen. The machine-learning model was used to rank model covariates based on gain, cover, and frequency to identify risk factors for sleep disorder and feature importance was evaluated using both univariable and multivariable t-statistics. A correlation matrix was created to determine the similarity of the importance of variables ranked by different model metrics. RESULTS: The XGBoost model had the highest mean AUROC of 0.865 (SD = 0.010) with Accuracy of 0.762 (SD = 0.019), F1 of 0.875 (SD = 0.766), Sensitivity of 0.768 (SD = 0.023), Specificity of 0.782 (SD = 0.025), Positive Predictive Value of 0.806 (SD = 0.025), and Negative Predictive Value of 0.737 (SD = 0.034). The model metrics from the machine learning of gain and cover were strongly positively correlated with one another (r > 0.70). Model metrics from the multivariable model and univariable model were weakly negatively correlated with machine learning model metrics (R between -0.3 and 0). CONCLUSION: The ranking of important variables associated with sleep disorder in this cohort from the machine learning models were not related to those from regression models.

Top-Down Proteomics Identifies Plasma Proteoform Signatures of Liver Cirrhosis Progression.

Cirrhosis, advanced liver disease, affects 2-5 million Americans. While most patients have compensated cirrhosis and may be fairly asymptomatic, many decompensate and experience life-threatening complications such as gastrointestinal bleeding, confusion (hepatic encephalopathy), and ascites, reducing life expectancy from 12 to less than 2 years. Among patients with compensated cirrhosis, identifying patients at high risk of decompensation is critical to optimize care and reduce morbidity and mortality. Therefore, it is important to preferentially direct them towards specialty care which cannot be provided to all patients with cirrhosis. We used discovery Top-down Proteomics (TDP) to identify differentially expressed proteoforms (DEPs) in the plasma of patients with progressive stages of liver cirrhosis with the ultimate goal to identify candidate biomarkers of disease progression. In this pilot study, we identified 209 DEPs across three stages of cirrhosis (compensated, compensated with portal hypertension, and decompensated), of which 115 derived from proteins enriched in the liver at a transcriptional level and discriminated the three stages of cirrhosis. Enrichment analyses demonstrated DEPs are involved in several metabolic and immunological processes known to be impacted by cirrhosis progression. We have preliminarily defined the plasma proteoform signatures of cirrhosis patients, setting the stage for ongoing discovery and validation of biomarkers for early diagnosis, risk stratification, and disease monitoring.

One-year opioid consumption trajectories among individuals receiving multidisciplinary postsurgical care: a single-center observational study from the Toronto General Hospital Transitional Pain Service.

INTRODUCTION: The Transitional Pain Service (TPS) is an innovative, personalized approach to postsurgical opioid consumption and pain management. The objectives of this study were to identify trajectories of opioid consumption and pain intensity within 12 months after initiating treatment through the TPS, identify biopsychosocial factors associated with trajectory membership, and examine the relationship between trajectory membership and other outcomes of interest over the same 12-month period. METHODS: Consecutive patients referred to the TPS were included in the present study (n=466). After providing informed consent, they completed self-report questionnaires at the initial visit at the TPS (either pre surgery or post surgery) and at every TPS visit until 12 months. Growth mixture modeling was used to derive trajectories and identify associated factors. RESULTS: Results showed three distinct opioid consumption trajectories for both presurgical opioid consumers and opioid-naïve patients. These trajectories all decreased over time and among those who were consuming opioids before surgery that returned to presurgical levels. Being man, having a substance use disorder, or reporting higher levels of pain interference were associated with higher daily opioid consumption for presurgical opioid consumers. For presurgical opioid-naïve individuals, higher opioid consumption trajectories were associated with higher levels of psychological distress. Five pain intensity trajectories were identified, and there were no significant association between opioid consumption and pain intensity trajectories. CONCLUSIONS: Results suggest that opioid consumption and pain intensity trajectories mostly decrease after surgery in a high-risk population enrolled in a TPS. Results also show heterogeneity in postsurgical recovery and highlight the importance of using personalized interventions to optimize individual trajectories.

Combined JAK inhibition and PD-1 immunotherapy for non-small cell lung cancer patients.

Persistent inflammation driven by cytokines such as type-one interferon (IFN-I) can cause immunosuppression. We show that administration of the Janus kinase 1 (JAK1) inhibitor itacitinib after anti-PD-1 (programmed cell death protein 1) immunotherapy improves immune function and antitumor responses in mice and results in high response rates (67%) in a phase 2 clinical trial for metastatic non-small cell lung cancer. Patients who failed to respond to initial anti-PD-1 immunotherapy but responded after addition of itacitinib had multiple features of poor immune function to anti-PD-1 alone that improved after JAK inhibition. Itacitinib promoted CD8 T cell plasticity and therapeutic responses of exhausted and effector memory-like T cell clonotypes. Patients with persistent inflammation refractory to itacitinib showed progressive CD8 T cell terminal differentiation and progressive disease. Thus, JAK inhibition may improve the efficacy of anti-PD-1 immunotherapy by pivoting T cell differentiation dynamics.

Application of a transparent artificial intelligence algorithm for US adults in the obese category of weight.

OBJECTIVE AND AIMS: Identification of associations between the obese category of weight in the general US population will continue to advance our understanding of the condition and allow clinicians, providers, communities, families, and individuals make more informed decisions. This study aims to improve the prediction of the obese category of weight and investigate its relationships with factors, ultimately contributing to healthier lifestyle choices and timely management of obesity. METHODS: Questionnaires that included demographic, dietary, exercise and health information from the US National Health and Nutrition Examination Survey (NHANES 2017-2020) were utilized with BMI 30 or higher defined as obesity. A machine learning model, XGBoost predicted the obese category of weight and Shapely Additive Explanations (SHAP) visualized the various covariates and their feature importance. Model statistics including Area under the receiver operator curve (AUROC), sensitivity, specificity, positive predictive value, negative predictive value and feature properties such as gain, cover, and frequency were measured. SHAP explanations were created for transparent and interpretable analysis. RESULTS: There were 6,146 adults (age > 18) that were included in the study with average age 58.39 (SD = 12.94) and 3122 (51%) females. The machine learning model had an Area under the receiver operator curve of 0.8295. The top four covariates include waist circumference (gain = 0.185), GGT (gain = 0.101), platelet count (gain = 0.059), AST (gain = 0.057), weight (gain = 0.049), HDL cholesterol (gain = 0.032), and ferritin (gain = 0.034). CONCLUSION: In conclusion, the utilization of machine learning models proves to be highly effective in accurately predicting the obese category of weight. By considering various factors such as demographic information, laboratory results, physical examination findings, and lifestyle factors, these models successfully identify crucial risk factors associated with the obese category of weight.

Exposure to environmentally relevant concentrations of Bisphenol-A linked to loss of visual lateralization in adult zebrafish (Danio rerio).

Weak, but environmentally relevant concentrations of contaminants can have subtle, yet important, impacts on organisms, which are often overlooked due to the lack of acute impacts and the timing of exposure. Thus, recognizing simple, non-invasive markers of contamination events is essential for early detection and addressing the effects of exposure to weak environmental contaminants. Here, we tested whether exposure to an environmentally relevant concentration of Bisphenol-A (BPA), a common and persistent contaminant in aquatic systems, affects the lateralization of adult zebrafish (Danio rerio), a widely used model organism in ecotoxicology. We found that 73.5% of adult zebrafish displayed a left-side bias when they approached a visual cue, but that those exposed to weak BPA (0.02 mg/L) for 7 days did not exhibit laterality. Only 47.1% displayed a left-side bias. We found no differences in activity level and visual sensitivity, motor and sensory mechanisms, that regulate lateralized responses and that were unaffected by weak BPA exposure. These findings indicate the reliability of laterality as a simple measure of contaminant exposure and for future studies of the detailed mechanisms underlying subtle and complex behavioral effects to pollutants.

The V-BRCH Project: Strengthening HIV Research Capacity in Nigeria through Intensive Workshops in Implementation Science and Grant Writing.

As persons with HIV live longer as the result of antiretroviral therapy, morbidity from HIV-associated noncommunicable diseases (NCDs) is increasing. The Vanderbilt-Nigeria Building Research Capacity in HIV and Noncommunicable Diseases program is a training platform created with the goal of training a cohort of successful Nigerian investigators to become leaders in HIV-associated NCD research. We describe survey findings from two week-long workshops in Kano, Nigeria, where trainees received instruction in implementation science and grant writing. Surveys assessed participants' self-perceived knowledge and confidence in topics taught during these workshops. Thirty-seven participants (all assistant professors) attended the implementation science workshop; 30 attended the grant-writing workshop. Response rates for the implementation science workshop were 89.2% for the preworkshop survey and 91.9% for the postworkshop survey. For the grant-writing workshop, these values were 88.2% and 85.3%, respectively. Improvement in participant knowledge and confidence was observed in every domain measured for both workshops. On average, a 101.4% increase in knowledge and a 118.0% increase in confidence was observed across measured domains among participants in the implementation science workshop. For the grant-writing workshop, there was a 68.8% increase in knowledge and a 70.3% increase in confidence observed. Participants rated the workshops and instructors as effective for both workshops. These workshops improved participants' knowledge and competence in implementation science and grant writing, and provide a model for training programs that aim to provide physician scientists with the skills needed to compete for independent funding, conduct locally relevant research, and disseminate research findings.

Where you live matters: Area deprivation predicts poor survival and liver transplant waitlisting.

Social determinants of health (SDOH) are important predictors of poor clinical outcomes in chronic diseases, but their associations among the general cirrhosis population and liver transplantation (LT) are limited. We conducted a retrospective, multiinstitutional analysis of adult (≥18-years-old) patients with cirrhosis in metropolitan Chicago to determine the associations of poor neighborhood-level SDOH on decompensation complications, mortality, and LT waitlisting. Area deprivation index and covariates extracted from the American Census Survey were aspects of SDOH that were investigated. Among 15 101 patients with cirrhosis, the mean age was 57.2 years; 6414 (42.5%) were women, 6589 (43.6%) were non-Hispanic White, 3652 (24.2%) were non-Hispanic Black, and 2662 (17.6%) were Hispanic. Each quintile increase in area deprivation was associated with poor outcomes in decompensation (sHR [subdistribution hazard ratio] 1.07; 95% CI 1.05-1.10; P < .001), waitlisting (sHR 0.72; 95% CI 0.67-0.76; P < .001), and all-cause mortality (sHR 1.09; 95% CI 1.06-1.12; P < .001). Domains of SDOH associated with a lower likelihood of waitlisting and survival included low income, low education, poor household conditions, and social support (P < .001). Overall, patients with cirrhosis residing in poor neighborhood-level SDOH had higher decompensation, and mortality, and were less likely to be waitlisted for LT. Further exploration of structural barriers toward LT or optimizing health outcomes is warranted.

FDG PET/CT Imaging 1 Week after a Single Dose of Pembrolizumab Predicts Treatment Response in Patients with Advanced Melanoma.

PURPOSE: Immunologic response to anti-programmed cell death protein 1 (PD-1) therapy can occur rapidly with T-cell responses detectable in as little as one week. Given that activated immune cells are FDG avid, we hypothesized that an early FDG PET/CT obtained approximately 1 week after starting pembrolizumab could be used to visualize a metabolic flare (MF), with increased tumor FDG activity due to infiltration by activated immune cells, or a metabolic response (MR), due to tumor cell death, that would predict response. PATIENTS AND METHODS: Nineteen patients with advanced melanoma scheduled to receive pembrolizumab were prospectively enrolled. FDG PET/CT imaging was performed at baseline and approximately 1 week after starting treatment. FDG PET/CT scans were evaluated for changes in maximum standardized uptake value (SUVmax) and thresholds were identified by ROC analysis; MF was defined as >70% increase in tumor SUVmax, and MR as >30% decrease in tumor SUVmax. RESULTS: An MF or MR was identified in 6 of 11 (55%) responders and 0 of 8 (0%) nonresponders, with an objective response rate (ORR) of 100% in the MF-MR group and an ORR of 38% in the stable metabolism (SM) group. An MF or MR was associated with T-cell reinvigoration in the peripheral blood and immune infiltration in the tumor. Overall survival at 3 years was 83% in the MF-MR group and 62% in the SM group. Median progression-free survival (PFS) was >38 months (median not reached) in the MF-MR group and 2.8 months (95% confidence interval, 0.3-5.2) in the SM group (P = 0.017). CONCLUSIONS: Early FDG PET/CT can identify metabolic changes in melanoma metastases that are potentially predictive of response to pembrolizumab and significantly correlated with PFS.

Stochastic modeling of obesity status in United States adults using Markov Chains: A nationally representative analysis of population health data from 2017-2020.

Importance: The prevalence of obesity among United States adults has increased from 34.9% in 2013-2014 to 42.8% in 2017-2018. Developing methods to model the increase of obesity over-time is a necessity to know how to accurately quantify its cost and to develop solutions to combat this national public health emergency. Methods: A cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017-2020) was conducted in individuals who completed the weight questionnaire and had accurate data for both weight at the time of survey and weight 10 years ago. To model the dynamics of obesity, a Markov transition state matrix was created, which allowed for the analysis of weight transitions over time. Bootstrap simulation was incorporated to account for uncertainty and generate multiple simulated datasets, providing a more robust estimation of the prevalence and trends in obesity within the cohort. Results: Of the 6146 individuals who met the inclusion criteria, 3024 (49%) individuals were male and 3122 (51%) were female. There were 2252 (37%) White individuals, 1257 (20%) Hispanic individuals, 1636 (37%) Black individuals, and 739 (12%) Asian individuals. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight. A total of 2411 (39%) individuals lost weight, and 3735 (61%) individuals gained weight. 87 (1%) individuals were underweight (BMI <18.5), 2058 (33%) were normal weight (18.5 ≤ BMI <25), 1376 (22%) were overweight (25 ≤ BMI <30) and 2625 (43%) were in the obese category (BMI >30). Conclusion: United States adults are at risk of transitioning from normal weight to the overweight or obese category. Markov modeling combined with bootstrap simulations can accurately model long-term weight status.

Stat5 opposes the transcription factor Tox and rewires exhausted CD8+ T cells toward durable effector-like states during chronic antigen exposure.

Rewiring exhausted CD8+ T (Tex) cells toward functional states remains a therapeutic challenge. Tex cells are epigenetically programmed by the transcription factor Tox. However, epigenetic remodeling occurs as Tex cells transition from progenitor (Texprog) to intermediate (Texint) and terminal (Texterm) subsets, suggesting development flexibility. We examined epigenetic transitions between Tex cell subsets and revealed a reciprocally antagonistic circuit between Stat5a and Tox. Stat5 directed Texint cell formation and re-instigated partial effector biology during this Texprog-to-Texint cell transition. Constitutive Stat5a activity antagonized Tox and rewired CD8+ T cells from exhaustion to a durable effector and/or natural killer (NK)-like state with superior anti-tumor potential. Temporal induction of Stat5 activity in Tex cells using an orthogonal IL-2:IL2Rß-pair fostered Texint cell accumulation, particularly upon PD-L1 blockade. Re-engaging Stat5 also partially reprogrammed the epigenetic landscape of exhaustion and restored polyfunctionality. These data highlight therapeutic opportunities of manipulating the IL-2-Stat5 axis to rewire Tex cells toward more durably protective states.

Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension.

Machine learning methods are widely used within the medical field to enhance prediction. However, little is known about the reliability and efficacy of these models to predict long-term medical outcomes such as blood pressure using lifestyle factors, such as diet. The authors assessed whether machine-learning techniques could accurately predict hypertension risk using nutritional information. A cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) between January 2017 and March 2020. XGBoost was used as the machine-learning model of choice in this study due to its increased performance relative to other common methods within medical studies. Model prediction metrics (e.g., AUROC, Balanced Accuracy) were used to measure overall model efficacy, covariate Gain statistics (percentage each covariate contributes to the overall prediction) and SHapely Additive exPlanations (SHAP, method to visualize each covariate) were used to provide explanations to machine-learning output and increase the transparency of this otherwise cryptic method. Of a total of 9650 eligible patients, the mean age was 41.02 (SD = 22.16), 4792 (50%) males, 4858 (50%) female, 3407 (35%) White patients, 2567 (27%) Black patients, 2108 (22%) Hispanic patients, and 981 (10%) Asian patients. From evaluation of model gain statistics, age was found to be the single strongest predictor of hypertension, with a gain of 53.1%. Additionally, demographic factors such as poverty and Black race were also strong predictors of hypertension, with gain of 4.33% and 4.18%, respectively. Nutritional Covariates contributed 37% to the overall prediction: Sodium, Caffeine, Potassium, and Alcohol intake being significantly represented within the model. Machine Learning can be used to predict hypertension.

Technical Report: Machine-Learning Pipeline for Medical Research and Quality-Improvement Initiatives.

Machine-learning techniques have been increasing in popularity within medicine during the past decade. However, these computational techniques are not presented in statistical lectures throughout medical school and are perceived to have a high barrier to entry. The objective is to develop a concise pipeline with publicly available data to decrease the learning time towards using machine learning for medical research and quality-improvement initiatives. This report utilized a publicly available machine-learning data package in R (MLDataR) and computational packages (XGBoost) to highlight techniques for machine-learning model development and visualization with SHaply Additive exPlanations (SHAP). A simple six-step process along with example code was constructed to build and visualize machine-learning models. A concrete set of three steps was developed to help with interpretation. Further teaching of these methods could benefit researchers by providing alternative methods for data analysis in medical studies. These could help researchers without computational experience to get a feel for machine learning to better understand the literature and technique.

Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends.

IMPORTANCE: The prevalence of obesity among United States adults has increased from 30.5% in 1999 to 41.9% in 2020. However, despite the recognition of long-term weight gain as an important public health issue, there is a paucity of studies studying the long-term weight gain and building models for long-term projection. METHODS: A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017-2020) was conducted in patients who completed the weight questionnaire and had accurate data for both weight at time of survey and weight ten years ago. Multistate gradient boost modeling classifiers were used to generate covariate dependent transition matrices and Markov chains were utilized for multistate modeling. RESULTS: Of the 6146 patients that met the inclusion criteria, 3024 (49%) of patients were male and 3122 (51%) of patients were female. There were 2252 (37%) White patients, 1257 (20%) Hispanic patients, 1636 (37%) Black patients, and 739 (12%) Asian patients. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight (Fig. 1). A total of 2411 (39%) patients lost weight, and 3735 (61%) patients gained weight (Table 1). We observed that 87 (1%) of patients were underweight (BMI < 18.5), 2058 (33%) were normal weight (18.5 ≤ BMI < 25), 1376 (22%) were overweight (25 ≤ BMI < 30) and 2625 (43%) were obese (BMI > 30). From analysis of the transitions between normal/underweight, overweight, and obese, we observed that after 10 years, of the patients who were underweight, 65% stayed underweight, 32% became normal weight, 2% became overweight, and 2% became obese. After 10 years, of the patients who were normal weight, 3% became underweight, 78% stayed normal weight, 17% became overweight, and 2% became obese. Of the patients who were overweight, 71% stayed overweight, 0% became underweight, 14% became normal weight, and 15% became obese. Of the patients who were obese, 84% stayed obese, 0% became underweight, 1% became normal weight, and 14% became overweight. CONCLUSIONS: United States adults are at risk of transitioning from normal weight to becoming overweight or obese. Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions.

Impact of a Transitional Pain Service on postoperative opioid trajectories: a retrospective cohort study.

INTRODUCTION: It has been well described that a small but significant proportion of patients continue to use opioids months after surgical discharge. We sought to evaluate postdischarge opioid use of patients who were seen by a Transitional Pain Service compared with controls. METHODS: We conducted a retrospective cohort study using administrative data of individuals who underwent surgery in Ontario, Canada from 2014 to 2018. Matched cohort pairs were created by matching Transitional Pain Service patients to patients of other academic hospitals in Ontario who were not enrolled in a Transitional Pain Service. Segmented regression was performed to assess changes in monthly mean daily opioid dosage. RESULTS: A total of 209 Transitional Pain Service patients were matched to 209 patients who underwent surgery at other academic centers. Over the 12 months after surgery, the mean daily dose decreased by an estimated 3.53 morphine milligram equivalents (95% CI 2.67 to 4.39, p<0.001) per month for the Transitional Pain Service group, compared with a decline of only 1.05 morphine milligram equivalents (95% CI 0.43 to 1.66, p<0.001) for the controls. The difference-in-difference change in opioid use for the Transitional Pain Service group versus the control group was -2.48 morphine milligram equivalents per month (95% CI -3.54 to -1.43, p=0.003). DISCUSSION: Patients enrolled in the Transitional Pain Service were able to achieve opioid dose reduction faster than in the control cohorts. The difficulty in finding an appropriate control group for this retrospective study highlights the need for future randomized controlled trials to determine efficacy.

Use of feature importance statistics to accurately predict asthma attacks using machine learning: A cross-sectional cohort study of the US population.

BACKGROUND: Asthma attacks are a major cause of morbidity and mortality in vulnerable populations, and identification of associations with asthma attacks is necessary to improve public awareness and the timely delivery of medical interventions. OBJECTIVE: The study aimed to identify feature importance of factors associated with asthma in a representative population of US adults. METHODS: A cross-sectional analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017-2020). All adult patients greater than 18 years of age (total of 7,922 individuals) with information on asthma attacks were included in the study. Univariable regression was used to identify significant nutritional covariates to be included in a machine learning model and feature importance was reported. The acquisition and analysis of the data were authorized by the National Center for Health Statistics Ethics Review Board. RESULTS: 7,922 patients met the inclusion criteria in this study. The machine learning model had 55 out of a total of 680 features that were found to be significant on univariate analysis (P<0.0001 used). In the XGBoost model the model had an Area Under the Receiver Operator Characteristic Curve (AUROC) = 0.737, Sensitivity = 0.960, NPV = 0.967. The top five highest ranked features by gain, a measure of the percentage contribution of the covariate to the overall model prediction, were Octanoic Acid intake as a Saturated Fatty Acid (SFA) (gm) (Gain = 8.8%), Eosinophil percent (Gain = 7.9%), BMXHIP-Hip Circumference (cm) (Gain = 7.2%), BMXHT-standing height (cm) (Gain = 6.2%) and HS C-Reactive Protein (mg/L) (Gain 6.1%). CONCLUSION: Machine Learning models can additionally offer feature importance and additional statistics to help identify associations with asthma attacks.