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.

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.

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.

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.

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.

Development and validation of a simple lupus severity index using ACR criteria for classification of SLE.

OBJECTIVE: To develop a simple systemic lupus erythematosus (SLE) severity index that requires knowledge of only American College of Rheumatology (ACR) criteria and subcriteria. METHODS: This study used demographic, mortality and medical records data of 1915 patients with lupus from the Lupus Family Registry and Repository. The data were randomly split (2:1 ratio) into independent training and validation sets. A logistic regression with ridge penalty was used to model the probability of being prescribed major immunosuppressive drugs-a surrogate indicator of lupus severity. ACR criteria and subcriteria were used as predictor variables in this model, and the resulting regression coefficient estimates obtained from the training data were used as item weightings to construct the severity index. RESULTS: The resulting index was tested on the independent validation dataset and was found to have high predictive accuracy for immunosuppressive use and early mortality. The index was also found to be strongly correlated with a previously existing severity score for lupus. In addition, demographic factors known to influence lupus severity (eg, age of onset, gender and ethnicity) all showed robust associations with our severity index that were consistent with observed clinical trends. CONCLUSIONS: This new index can be easily computed using ACR criteria, which may be among the most readily available data elements from patient medical records. This tool may be useful in lupus research, especially large dataset analyses to stratify patients by disease severity, an important prognostic indicator in SLE.

Development and Validation of a Clinical Risk-Assessment Tool Predictive of All-Cause Mortality.

In clinical settings, the diagnosis of medical conditions is often aided by measurement of various serum biomarkers through the use of laboratory tests. These biomarkers provide information about different aspects of a patient's health and overall function of multiple organ systems. We have developed a statistical procedure that condenses the information from a variety of health biomarkers into a composite index, which could be used as a risk score for predicting all-cause mortality. It could also be viewed as a holistic measure of overall physiological health status. This health status metric is computed as a function of standardized values of each biomarker measurement, weighted according to their empirically determined relative strength of association with mortality. The underlying risk model was developed using the biomonitoring and mortality data of a large sample of US residents obtained from the National Health and Nutrition Examination Survey (NHANES) and the National Death Index (NDI). Biomarker concentration levels were standardized using spline-based Cox regression models, and optimization algorithms were used to estimate the weights. The predictive accuracy of the tool was optimized by bootstrap aggregation. We also demonstrate how stacked generalization, a machine learning technique, can be used for further enhancement of the prediction power. The index was shown to be highly predictive of all-cause mortality and long-term outcomes for specific health conditions. It also exhibited a robust association with concurrent chronic conditions, recent hospital utilization, and current health status as assessed by self-rated health.

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.

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.