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11. Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era.

Author

Liou, Lathan, Mostofsky, Elizabeth, Lehman, Laura, Salia, Soziema, Barrera, Francisco J., Wei, Ying, Cheema, Amal, Lala, Anuradha, Beam, Andrew, and Mittleman, Murray A.

Abstract

Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 2018 donor heart allocation policy change is warranted. This study assessed seven statistical and machine learning algorithms–Lasso, Ridge, Elastic Net, Cox Gradient Boost, Extreme Gradient Boost Linear, Extreme Gradient Boost Tree, and Random Survival Forests in a post-policy cohort of 7,160 adult heart-only transplant recipients in the Scientific Registry of Transplant Recipients (SRTR) database who received their first transplant on or after October 18, 2018. A cross-validation framework was designed in mlr. Model performance was also compared in a seasonally-matched pre-policy cohort. In the post-policy cohort, Random Survival Forests and Cox Gradient Boost had the highest performances with C-indices of 0.628 and 0.627. The relative importance of some predictive variables differed between the pre-policy and post-policy cohorts, such as the absence of ECMO in the post-policy cohort. Survival machine learning models provide reasonable prediction of 1-year posttransplant mortality outcomes and continual updating of prediction models is warranted in the contemporary era. [ABSTRACT FROM AUTHOR]

Published
2025
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15. The relationship between traffic-related air pollution exposures and allostatic load score among youth with type 1 diabetes in the SEARCH cohort

Author

Montresor-López, Jessica A., Reading, Stephanie R., Yanosky, Jeffrey D., Mittleman, Murray A., Bell, Ronny A., Crume, Tessa L., Dabelea, Dana, Dolan, Lawrence, D'Agostino, Ralph B., Jr., Marcovina, Santica M., Pihoker, Catherine, Reynolds, Kristi, Urbina, Elaine, Liese, Angela D., Quirós-Alcalá, Lesliam, Smith, J. Carson, Bueno de Mesquita, P. Jacob, and Puett, Robin C.

Published
2021
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19. Environmental and occupational particulate matter exposures and ectopic heart beats in welders

Author

Cavallari, Jennifer M, Fang, Shona C, Eisen, Ellen A, Mittleman, Murray A, and Christiani, David C

Subjects
Epidemiology, Health Sciences, Clinical Research, Cardiovascular, Prevention, Adult, Air Pollutants, Electrocardiography, Ambulatory, Environmental Exposure, Environmental Monitoring, Heart Rate, Humans, Linear Models, Longitudinal Studies, Male, Middle Aged, Occupational Exposure, Particulate Matter, Risk Factors, Self Report, Smoking, Tachycardia, Ventricular Premature Complexes, Welding, Young Adult, Clinical Sciences, Public Health and Health Services, Other Commerce, Management, Tourism and Services, Environmental & Occupational Health, Human resources and industrial relations, Public health
Abstract

ObjectivesLinks between arrhythmias and particulate matter exposures have been found among sensitive populations. We examined the relationship between personal particulate matter ≤2.5 µm aerodynamic diameter (PM2.5) exposures and ectopy in a panel study of healthy welders.MethodsSimultaneous ambulatory ECG and personal PM2.5 exposure monitoring with DustTrak Aerosol Monitor was performed on 72 males during work and non-work periods for 5-90 h (median 40 h). ECGs were summarised hourly for supraventricular ectopy (SVE) and ventricular ectopy (VE). PM2.5 exposures both work and non-work periods were averaged hourly with lags from 0 to 7 h. Generalised linear mixed-effects models with a random participant intercept were used to examine the relationship between PM2.5 exposure and the odds of SVE or VE. Sensitivity analyses were performed to assess whether relationships differed by work period and among current smokers.ResultsParticipants had a mean (SD) age of 38 (11) years and were monitored over 2993 person-hours. The number of hourly ectopic events was highly skewed with mean (SD) of 14 (69) VE and 1 (4) SVE. We found marginally significant increases in VE with PM2.5 exposures in the sixth and seventh hour lags, yet no association with SVE. For every 100 μg/m(3) increase in sixth hour lagged PM2.5, the adjusted OR (95% CI) for VE was 1.03 (1.00 to 1.05). Results persisted in work or non-work exposure periods and non-smokers had increased odds of VE associated with PM2.5 as compared with smokers.ConclusionsA small increase in the odds of VE with short-term PM2.5 exposure was observed among relatively healthy men with environmental and occupational exposures.

Published
2016

23. Long-Term Exposure to Fine Particulate Matter, Residential Proximity to Major Roads and Measures of Brain Structure

Author

Wilker, Elissa H, Preis, Sarah R, Beiser, Alexa S, Wolf, Philip A, Au, Rhoda, Kloog, Itai, Li, Wenyuan, Schwartz, Joel, Koutrakis, Petros, DeCarli, Charles, Seshadri, Sudha, and Mittleman, Murray A

Subjects
Epidemiology, Health Sciences, Behavioral and Social Science, Vascular Cognitive Impairment/Dementia, Acquired Cognitive Impairment, Climate-Related Exposures and Conditions, Neurosciences, Neurodegenerative, Alzheimer's Disease Related Dementias (ADRD), Clinical Research, Dementia, Aging, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Cerebrovascular, Biomedical Imaging, Brain Disorders, Social Determinants of Health, Neurological, Age Factors, Aged, Air Pollutants, Atrophy, Brain, Cerebral Infarction, Environmental Exposure, Female, Hippocampus, Humans, Male, Middle Aged, Particulate Matter, Socioeconomic Factors, White Matter, air pollution, brain infarcts, neuroimaging, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Neurology & Neurosurgery, Clinical sciences, Allied health and rehabilitation science
Abstract

Background and purposeLong-term exposure to ambient air pollution is associated with cerebrovascular disease and cognitive impairment, but whether it is related to structural changes in the brain is not clear. We examined the associations between residential long-term exposure to ambient air pollution and markers of brain aging using magnetic resonance imaging.MethodsFramingham Offspring Study participants who attended the seventh examination were at least 60 years old and free of dementia and stroke were included. We evaluated associations between exposures (fine particulate matter [PM2.5] and residential proximity to major roadways) and measures of total cerebral brain volume, hippocampal volume, white matter hyperintensity volume (log-transformed and extensive white matter hyperintensity volume for age), and covert brain infarcts. Models were adjusted for age, clinical covariates, indicators of socioeconomic position, and temporal trends.ResultsA 2-μg/m(3) increase in PM2.5 was associated with -0.32% (95% confidence interval, -0.59 to -0.05) smaller total cerebral brain volume and 1.46 (95% confidence interval, 1.10 to 1.94) higher odds of covert brain infarcts. Living further away from a major roadway was associated with 0.10 (95% confidence interval, 0.01 to 0.19) greater log-transformed white matter hyperintensity volume for an interquartile range difference in distance, but no clear pattern of association was observed for extensive white matter.ConclusionsExposure to elevated levels of PM2.5 was associated with smaller total cerebral brain volume, a marker of age-associated brain atrophy, and with higher odds of covert brain infarcts. These findings suggest that air pollution is associated with insidious effects on structural brain aging even in dementia- and stroke-free persons.

Published
2015

48. Air Pollution and Risk of Stroke

Author

Lokken, R Peter, Wellenius, Gregory A, Coull, Brent A, Burger, Mary R, Schlaug, Gottfried, Suh, Helen H, and Mittleman, Murray A

Subjects
Epidemiology, Health Sciences, Stroke, Brain Disorders, Cerebrovascular, Clinical Research, Climate-Related Exposures and Conditions, 2.2 Factors relating to the physical environment, Aged, Air Pollutants, Boston, Computer Simulation, Cross-Over Studies, Environmental Exposure, Female, Humans, Male, Middle Aged, Models, Statistical, Regression Analysis, Risk Assessment, Time Factors, Statistics, Public Health and Health Services, Public health
Abstract

BackgroundEpidemiologic studies linking ambient air pollution to the onset of acute cardiovascular events often rely on date of hospital admission for exposure assessment.MethodsWe investigated the extent of exposure misclassification resulting from assigning exposure to particulate matter based on (1) date of hospital admission, or (2) time of hospital presentation compared with particulate matter exposure based on time of stroke symptom onset. We performed computer simulations to evaluate the impact of this source of exposure misclassification on estimates of air pollution health effects in the context of a time-stratified case-crossover study.ResultsAmong 1101 patients admitted for a confirmed acute ischemic stroke to a Boston area hospital, symptom onset occurred a median of 1 calendar day before hospital admission (range = 0-30 days). The difference between ambient particulate matter exposure based on the calendar day of admission versus time of symptom onset ranged from -47 to 36 microg/m3 (-0.1 +/- 7.1 microg/m3; mean +/- SD). The simulation study indicated that for nonnull associations, exposure assessment based on hospitalization date led to estimates that were biased toward the null by 60%-66%, whereas assessment based on time of hospital presentation yielded estimates that were biased toward the null by 37%-42%.ConclusionsEpidemiologic studies of air pollution-related risk of acute cardiovascular events that assess exposure based on date of hospitalization likely underestimate the strength of associations. Using data on time of hospital presentation would marginally attenuate, but not eliminate, this important source of bias.

Published
2009

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