A pilot study to identify factors associated with frailty within the World Trade Center general responder cohort.

BACKGROUND: Given the significant exposures experienced by the World Trade Center (WTC) general responders, there is increasing interest in understanding the effect of these exposures on aging in this population. We aim to identify factors that may be associated with frailty, a clinical syndrome characterized by a decrease in one's reserve that has been linked to poor health outcomes. METHODS: WTC general responders enrolled in the WTC Health Program aged 50 and older provided informed consent. Validated frailty assessments, the Frailty Phenotype (with the Johns Hopkins Frailty Assessment Calculator) along with the FRAIL scale, categorized nonfrail from prefrail/frail. Fall risk, functional status, and cognition were also assessed. WTC variables, including an identified WTC-certified condition, were utilized. The risk of frailty was estimated using log binomial regression analysis. A 95% confidence interval (CI) was used to estimate the prevalence ratio (PR). RESULTS: One hundred and six participants were included; 38 (35.8%) were classified as pre-frail or frail. More of the pre-frail/frail group were obese (57.9% vs. 25%; p = 0.004) and had a WTC-certified condition (78.9% vs. 58.8%; p = 0.036). Obesity (PR = 2.43, 95% CI = 1.31, 4.53), a WTC-certified condition (PR = 1.77, 95% CI = 1.09, 2.89), and risk of falling (PR = 1.97, 95% CI = 1.01, 3.84) were independently associated with frailty. CONCLUSIONS: Obesity and having a WTC-certified condition were found to be risk factors for frailty in our pilot study. Future work may focus on further identifying risk factors for frailty in the larger WTC general responder population.

Lagged WQS regression for mixtures with many components.

The developmental timing of exposures to toxic chemicals or combinations of chemicals may be as important as the dosage itself. This concept is called "critical windows of exposure." The time boundaries of such windows can be detected if exposure data are collected repeatedly in short time intervals. The development of tooth-matrix biomarkers which provide prenatal and postnatal exposure measures in repeated intervals can provide such data. Using teeth, we use reverse distributed lagged models (DLMs) to incorporate weekly prenatal and postnatal measures of exposures to estimate time-varying associations with developmental effects. The analysis of such data using lagged weighted quantile sum (WQS) regression as an extension to reverse DLMs for complex mixtures was first proposed by Bello et al. This prior algorithm was not operationally generalizable to large numbers of components (say, more than five or six). We propose a revised algorithm that may be useful for larger mixtures by combining time-specific WQS(t) indices in a reverse DLM. We demonstrate the new algorithm using tooth data in association with a neurodevelopmental score and in simulated data from 3 cases wherein different components of a mixture have time varying associations and in the case where none have associations. The new algorithm correctly detects the simulated associations when the number of samples within the time-specific analyses is moderate to large.

The adoption of generic immunosuppressant medications in kidney, liver, and heart transplantation among recipients in Colorado or nationally with Medicare part D.

The transplant community is divided regarding whether substitution with generic immunosuppressants is appropriate for organ transplant recipients. We estimated the rate of uptake over time of generic immunosuppressants using US Medicare Part D Prescription Drug Event (PDE) and Colorado pharmacy claims (including both Part D and non-Part D) data from 2008 to 2013. Data from 26 070 kidney, 15 548 liver, and 6685 heart recipients from Part D, and 1138 kidney and 389 liver recipients from Colorado were analyzed. The proportions of patients with PDEs or claims for generic and brand-name tacrolimus or mycophenolate mofetil were calculated over time by transplanted organ and drug. Among Part D kidney, liver, and heart beneficiaries, the proportion dispensed generic tacrolimus reached 50%-56% at 1 year after first generic approval and 78%-81% by December 2013. The proportion dispensed generic mycophenolate mofetil reached 70%-73% at 1 year after generic market entry and 88%-90% by December 2013. There was wide interstate variability in generic uptake, with faster uptake in Colorado compared with most other states. Overall, generic substitution for tacrolimus and mycophenolate mofetil for organ transplant recipients increased rapidly following first availability, and utilization of generic immunosuppressants exceeded that of brand-name products within a year of market entry.

Assessment of cumulative health risk in the World Trade Center general responder cohort.

BACKGROUND: Multiple comorbidities have been reported among rescue/recovery workers responding to the 9/11/2001 WTC disaster. In this study, we developed an index that quantifies the cumulative physiological burden of comorbidities and predicts life expectancy in this cohort. METHODS: A machine learning approach (gradient boosting) was used to model the relationship between mortality and several clinical parameters (laboratory test results, blood pressure, pulmonary function measures). This model was used to construct a risk index, which was validated by assessing its association with a number of health outcomes within the WTC general responder cohort. RESULTS: The risk index showed significant associations with mortality, self-assessed physical health, and onset of multiple chronic conditions, particularly COPD, hypertension, asthma, and sleep apnea. CONCLUSION: As an aggregate of several clinical parameters, this index serves as a cumulative measure of physiological dysregulation and could be utilized as a prognostic indicator of life expectancy and morbidity risk.

Extending the Distributed Lag Model framework to handle chemical mixtures.

Distributed Lag Models (DLMs) are used in environmental health studies to analyze the time-delayed effect of an exposure on an outcome of interest. Given the increasing need for analytical tools for evaluation of the effects of exposure to multi-pollutant mixtures, this study attempts to extend the classical DLM framework to accommodate and evaluate multiple longitudinally observed exposures. We introduce 2 techniques for quantifying the time-varying mixture effect of multiple exposures on an outcome of interest. Lagged WQS, the first technique, is based on Weighted Quantile Sum (WQS) regression, a penalized regression method that estimates mixture effects using a weighted index. We also introduce Tree-based DLMs, a nonparametric alternative for assessment of lagged mixture effects. This technique is based on the Random Forest (RF) algorithm, a nonparametric, tree-based estimation technique that has shown excellent performance in a wide variety of domains. In a simulation study, we tested the feasibility of these techniques and evaluated their performance in comparison to standard methodology. Both methods exhibited relatively robust performance, accurately capturing pre-defined non-linear functional relationships in different simulation settings. Further, we applied these techniques to data on perinatal exposure to environmental metal toxicants, with the goal of evaluating the effects of exposure on neurodevelopment. Our methods identified critical neurodevelopmental windows showing significant sensitivity to metal mixtures.

Association of 2,4-dichlorophenol urinary concentrations and olfactory dysfunction in a national sample of middle-aged and older U.S. adults.

Olfaction is a key sensory mechanism in humans. Deficits in this chemosensory function have wide-ranging impacts on overall health and quality of life. This study examines the role of environmental phenols as risk factors for olfactory dysfunction among a random sample of 839 middle-aged and older U.S. adults. Olfactory function assessment was carried out using a short 8-item test, scores on which were used to classify subjects into normal or impaired olfactory function groups. Logistic regression models were used to test for associations between olfactory impairment and creatinine-adjusted urinary levels of 8 common environmental phenols, adjusting for potentially confounding covariates. A statistically significant association between 2,4-dichlorophenol levels and olfactory impairment (OR = 1.02 [95 % CI: (1.003, 1.04)]; p = 0.02) was found. 2,4-dichlorophenol is a hazardous pollutant with widespread exposure via industrial and indoor air pollution, diet, and the use of pesticides and herbicides. This study is the first to reveal its role in olfactory impairment.

Frequency and prioritization of patient health risks from a structured health risk assessment.

PURPOSE: To describe the frequency and patient-reported readiness to change, desire to discuss, and perceived importance of 13 health risk factors in a diverse range of primary care practices. METHODS: Patients (n = 1,707) in 9 primary care practices in the My Own Health Report (MOHR) trial reported general, behavioral, and psychosocial risk factors (body mass index [BMI], health status, diet, physical activity, sleep, drug use, stress, anxiety or worry, and depression). We classified responses as "at risk" or "healthy" for each factor, and patients indicated their readiness to change and/or desire to discuss identified risk factors with providers. Patients also selected 1 of the factors they were ready to change as most important. We then calculated frequencies within and across these factors and examined variation by patient characteristics and across practices. RESULTS: On average, patients had 5.8 (SD = 2.12; range, 0-13) unhealthy behaviors and mental health risk factors. About 55% of patients had more than 6 risk factors. On average, patients wanted to change 1.2 and discuss 0.7 risks. The most common risks were inadequate fruit/vegetable consumption (84.5%) and overweight/obesity (79.6%). Patients were most ready to change BMI (33.3%) and depression (30.7%), and most wanted to discuss depression (41.9%) and anxiety or worry (35.2%). Overall, patients rated health status as most important. CONCLUSIONS: Implementing routine comprehensive health risk assessments in primary care will likely identify a high number of behavioral and psychosocial health risks. By soliciting patient priorities, providers and patients can better manage counseling and behavior change.

Engaging primary care patients to use a patient-centered personal health record.

PURPOSE: Health care leaders encourage clinicians to offer portals that enable patients to access personal health records, but implementation has been a challenge. Although large integrated health systems have promoted use through costly advertising campaigns, other implementation methods are needed for small to medium-sized practices where most patients receive their care. METHODS: We conducted a mixed methods assessment of a proactive implementation strategy for a patient portal (an interactive preventive health record [IPHR]) offered by 8 primary care practices. The practices implemented a series of learning collaboratives with practice champions and redesigned workflow to integrate portal use into care. Practice implementation strategies, portal use, and factors influencing use were assessed prospectively. RESULTS: A proactive and customized implementation strategy designed by practices resulted in 25.6% of patients using the IPHR, with the rate increasing 1.0% per month over 31 months. Fully 23.5% of IPHR users signed up within 1 day of their office visit. Older patients and patients with comorbidities were more likely to use the IPHR, but blacks and Hispanics were less likely. Older age diminished as a factor after adjusting for comorbidities. Implementation by practice varied considerably (from 22.1% to 27.9%, P <.001) based on clinician characteristics and workflow innovations adopted by practices to enhance uptake. CONCLUSIONS: By directly engaging patients to use a portal and supporting practices to integrate use into care, primary care practices can match or potentially surpass the usage rates achieved by large health systems.

Machine Learning as a Tool for Early Detection: A Focus on Late-Stage Colorectal Cancer across Socioeconomic Spectrums.

PURPOSE: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. METHODS: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. RESULTS: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, year of diagnosis, age, proximity to superfund sites, and primary payer. Spatio-temporal clusters highlighted geographic areas with a statistically significant high probability of late-stage diagnoses, emphasizing the need for targeted healthcare interventions. CONCLUSIONS: This research underlines the potential of ML in enhancing the prognostic predictions in oncology, particularly in CRC. The gradient boosting model, with its robust performance, holds promise for deployment in healthcare systems to aid early detection and formulate localized cancer prevention strategies. The study's methodology demonstrates a significant step toward utilizing AI in public health to mitigate disparities and improve cancer care outcomes.

Interactive preventive health record to enhance delivery of recommended care: a randomized trial.

PURPOSE: Americans receive only one-half of recommended preventive services. Information technologies have been advocated to engage patients. We tested the effectiveness of an interactive preventive health record (IPHR) that links patients to their clinician's record, explains information in lay language, displays tailored recommendations and educational resources, and generates reminders. METHODS: This randomized controlled trial involved 8 primary care practices. Four thousand five hundred patients were randomly selected to receive a mailed invitation to use the IPHR or usual care. Outcomes were measured using patient surveys and electronic medical record data and included IPHR use and service delivery. Comparisons were made between invited and usual-care patients and between users and nonusers among those invited to use the IPHR. RESULTS: At 4 and 16 months, 229 (10.2%) and 378 (16.8%) of invited patients used the IPHR. The proportion of patients up-to-date with all services increased between baseline and 16 months by 3.8% among intervention patients (from 11.4% to 15.2%, P <.001) and by 1.5% among control patients (from 11.1% to 12.6%, P = .07), a difference of 2.3% (P = .05). Greater increases were observed among patients who used the IPHR. At 16 months, 25.1% of users were up-to-date with all services, double the rate among nonusers. At 4 months, delivery of colorectal, breast, and cervical cancer screening increased by 19%, 15%, and 13%, respectively, among users. CONCLUSIONS: Information systems that feature patient-centered functionality, such as the IPHR, have potential to increase preventive service delivery. Engaging more patients to use systems could have important public health benefits.

Development and Validation of a Clinical Frailty Index for the World Trade Center General Responder Cohort.

Objectives: To develop and validate a clinical frailty index to characterize aging among responders to the 9/11 World Trade Center (WTC) attacks. Methods: This study was conducted on health monitoring data on a sample of 6197 responders. A clinical frailty index, WTC FI-Clinical, was developed according to the cumulative deficit model of frailty. The validity of the resulting index was assessed using all-cause mortality as an endpoint. Its association with various cohort characteristics was evaluated. Results: The sample's median age was 51 years. Thirty items were selected for inclusion in the index. It showed a strong correlation with age, as well as significant adjusted associations with mortality, 9/11 exposure severity, sex, race, pre-9/11 occupation, education, and smoking status. Discussion: The WTC FI-Clinical highlights effects of certain risk factors on aging within the 9/11 responder cohort. It will serve as a useful instrument for monitoring and tracking frailty within this cohort.

Blood manganese levels during pregnancy and postpartum depression: A cohort study among women in Mexico.

BACKGROUND: Occupational studies have shown an association between elevated Mn exposure and depressive symptoms. Blood Mn (BMn) naturally rises during pregnancy due to mobilization from tissues, suggesting it could contribute to pregnancy and postpartum depressive symptoms. OBJECTIVES: To assess the association between BMn levels during pregnancy and postpartum depression (PPD), creating opportunities for possible future interventions. METHODS: We studied 561 women from the reproductive longitudinal Programming Research in Obesity, Growth, Environment, and Social Stressors (PROGRESS) cohort in Mexico City. BMn was measured at the 2nd and 3rd trimesters, as well as delivery. The Edinburgh Postnatal Depression Scale (EPDS) was used to assess PPD symptoms at 12-months postpartum. We used a generalized linear model assuming a Poisson distribution to assess the association between BMn levels and PPD, with adjustments for age, stress and depressive symptoms during pregnancy, education, socioeconomic status, and contemporaneous blood lead levels. RESULTS: The mean ±â€¯standard deviation (SD) EPDS score at 12-months postpartum was 6.51 ±â€¯5.65, and 17.11% of women met the criteria for possible PPD (score ≥ 13). In adjusted models, BMn during the 3rd trimester (ß: 0.13, 95% CI: 0.04-0.21) and BMn levels averaged at the 2nd and 3rd trimester (ß: 0.14, 95% CI: 0.02-0.26) had a positive association with EPDS scores at 12 months postpartum. BMn at the 2nd trimester (ß: 0.07, 95% CI: -0.09-0.22) and delivery (ß: 0.03, 95% CI: -0.04-0.10) had a non-significant positive association with EPDS scores at 12-months postpartum. Stress and depressive symptoms during pregnancy was associated with higher EPDS scores at 12-months postpartum in all of the adjusted models but were only significant when either BMn during 3rd trimester or BMn averaged across 2nd and 3rd trimester was assessed as the exposure. DISCUSSION: Our results demonstrate that elevated BMn levels during pregnancy predict PPD symptoms and could be a potential pathway for intervention and prevention of PPD.

Early life metal dysregulation in amyotrophic lateral sclerosis.

OBJECTIVE: Deficiencies and excess of essential elements and toxic metals are implicated in amyotrophic lateral sclerosis (ALS), but the age when metal dysregulation appears remains unknown. This study aims to determine whether metal uptake is dysregulated during childhood in individuals eventually diagnosed with ALS. METHODS: Laser ablation-inductively coupled plasma-mass spectrometry was used to obtain time series data of metal uptake using biomarkers in teeth from autopsies or dental extractions of ALS (n = 36) and control (n = 31) participants. Covariate data included sex, smoking, occupational exposures, and ALS family history. Case-control differences were identified in temporal profiles of metal uptake for individual metals using distributed lag models. Weighted quantile sum (WQS) regression was used for metals mixture analyses. Similar analyses were performed on an ALS mouse model to further verify the relevance of dysregulation of metals in ALS. RESULTS: Metal levels were higher in cases than in controls: 1.49 times for chromium (1.11-1.82; at 15 years), 1.82 times for manganese (1.34-2.46; at birth), 1.65 times for nickel (1.22-2.01; at 8 years), 2.46 times for tin (1.65-3.30; at 2 years), and 2.46 times for zinc (1.49-3.67; at 6 years). Co-exposure to 11 elements indicated that childhood metal dysregulation was associated with ALS. The mixture contribution of metals to disease outcome was likewise apparent in tooth biomarkers of an ALS mouse model, and differences in metal distribution were evident in ALS mouse brains compared to brains from littermate controls. INTERPRETATION: Overall, our study reveals direct evidence that altered metal uptake during specific early life time windows is associated with adult-onset ALS.

Cell-bound complement activation products associate with lupus severity in SLE.

OBJECTIVES: To evaluate the association between lupus severity and cell-bound complement activation products (CB-CAPs) or low complement proteins C3 and C4. METHODS: All subjects (n=495) fulfilled the American College of Rheumatology (ACR) classification criteria for SLE. Abnormal CB-CAPs (erythrocyte-bound C4d or B-lymphocyte-bound C4d levels >99th percentile of healthy) and complement proteins C3 and C4 were determined using flow cytometry and turbidimetry, respectively. Lupus severity was estimated using the Lupus Severity Index (LSI). Statistical analysis consisted of multivariable linear regression and groups comparisons. RESULTS: Abnormal CB-CAPs were more prevalent than low complement values irrespective of LSI levels (62% vs 38%, respectively, p<0.0001). LSI was low (median 5.44, IQR: 4.77-6.93) in patients with no complement abnormality, intermediate in patients with abnormal CB-CAPs (median 6.09, IQR: 5.31-8.20) and high in the group presenting with both abnormal CB-CAPs and low C3 and/or C4 (median 7.85, IQR: 5.51-8.37). Odds of immunosuppressant use was higher in subjects with LSI ≥5.95 compared with subjects with LSI <5.95 (1.60 vs 0.53, p<0.0001 for both). Multivariable regression analysis revealed that higher LSI scores associated with abnormal CB-CAPs-but not low C3/C4-after adjusting for younger age, race and longer disease duration (p=0.0001), which were also independent predictors of disease severity (global R2=0.145). CONCLUSION: Abnormalities in complement activation as measured by CB-CAPs are associated with increased LSI.

Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach.

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-refined bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localization tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, a refinement step is designed to explicitly impose shape prior knowledge and improve segmentation quality. This step is effective for overcoming image artifacts (e.g., due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialize atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution, and anatomically smooth bi-ventricular 3D models, despite the presence of artifacts in input CMR volumes.

Deep learning cardiac motion analysis for human survival prediction.

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p = .0012) for our model C=0.75 (95% CI: 0.70 - 0.79) than the human benchmark of C=0.59 (95% CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival.

Development of a Physiological Frailty Index for the World Trade Center General Responder Cohort.

Responders to the 9/11/2001 WTC attacks were exposed to multiple toxic pollutants. Since 2002, the health of the responder cohort has been continuously tracked by the WTC Health Monitoring Program. However, no assessments have been made of frailty, an important health metric given the current average age of the WTC responder cohort (55 years). In this study, we use laboratory test results and other physiological parameters to construct a physiological frailty index (FI-Lab) for this cohort. The study sample comprised responders aged 40 years or older who completed a health monitoring visit at Mount Sinai Center within the past 5 years. For each subject, FI-Lab was computed as the proportion of 20 physiological parameters (lab tests, pulmonary function, and blood pressure) on which the subject had abnormal values. Using negative binomial regression models, we tested FI-Lab's association with the SF-12 wellbeing score and various demographic characteristics. FI-Lab showed strong associations with the physical and mental components of the SF-12 as well as age, race, and smoking status. Using a cutoff of 0.25 to define presence of physiological/preclinical frailty, we found frailty prevalence in the study sample to be approximately 12%. This study demonstrates the feasibility of assessing preclinical frailty in the WTC responder cohort.

A Random Forest-Induced Distance-Based Measure of Physiologic al Dysregulation.

BACKGROUND: Aging involves gradual, multisystemic Physiological Dysregulation (PD) which increases risk of age-related comorbidities. Ability to quantify age-related PD could provide insights into biological mechanisms underlying the aging process. One approach to measuring PD exploits the fact that increasing PD manifests as a gradual deviation of physiological parameters away from normal levels. A recent geometric approach for quantifying PD uses Mahalanobis distance to measure the extent to which an individual's physiological parameters (measured via biomarkers from clinical blood biochemistry panels) deviate from normal levels. While useful, this approach has shortcomings that may impact its accuracy, primarily the incorrect assumption of multivariate normality among biomarkers, and identical weighting of biomarkers. Herein, we develop a more robust multivariate distance-based measure of PD. METHOD: Proximity matrices induced by survival tree ensembles (Random Survival Forests) were used to compute a robust distance metric for quantifying how abnormal an individual's biomarker profile is. This approach requires no distributional assumptions and allows differential weighting of biomarkers based on association with mortality. Using receiver operating characteristic analysis and model fit statistics we compared performance of our measure to the standard approach based on Mahalanobis distance. RESULTS & CONCLUSION: Our new metric showed statistically significant improvements in predicting mortality, health status and biological age, compared to the standard approach. Additional advantages offered by our method are the ability to handle missing values in biomarkers and to accommodate categorical risk factors. These results suggest our approach could provide greater precision in the evaluation of PD, which could enable better characterization of the extent and impact of degenerative processes resulting from aging.