Calcification in the soft tissues of the chest after thoracotomy

We have observed soft-tissue calcification of the chest in 4 of 54 patients following thoracotomy in the neonatal period for a Blalock-Taussig or a Blalock-Hanlon procedure. The time of appearance ranged from 2 weeks to 11 months. This calcification is similar to myositis ossificans in having a traumatic origin. In one of the patients, it resolved spontaneously.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46812/1/256_2004_Article_BF00347331.pd

Fine Particulate Matter and Lung Function among Burning-Exposed Deepwater Horizon Oil Spill Workers

BACKGROUND: During the 2010 Deepwater Horizon (DWH) disaster, controlled burning was conducted to remove oil from the water. Workers near combustion sites were potentially exposed to increased fine particulate matter [with aerodynamic diameter ≤2:5 lm (PM2:5 )] levels. Exposure to PM2:5 has been linked to decreased lung function, but to our knowledge, no study has examined exposure encountered in an oil spill cleanup. OBJECTIVE: We investigated the association between estimated PM2:5 only from burning/flaring of oil/gas and lung function measured 1–3 y after the DWH disaster. METHODS: We included workers who participated in response and cleanup activities on the water during the DWH disaster and had lung function measured at a subsequent home visit (n = 2,316). PM2:5 concentrations were estimated using a Gaussian plume dispersion model and linked to work histories via a job-exposure matrix. We evaluated forced expiratory volume in 1 s (FEV1; milliliters), forced vital capacity (FVC; milliliters), and their ratio (FEV1/FVC; %) in relation to average and cumulative daily maximum exposures using multivariable linear regressions. RESULTS: We observed significant exposure–response trends associating higher cumulative daily maximum PM2:5 exposure with lower FEV1 (p-trend = 0:04) and FEV1/FVC (p-trend = 0:01). In comparison with the referent group (workers not involved in or near the burning), those with higher cumulative exposures had lower FEV1 [−166:8 mL, 95% confidence interval (CI): −337:3, 3.7] and FEV1/FVC (−1:7, 95% CI: −3:6, 0.2). We also saw nonsignificant reductions in FVC (high vs. referent: −120:9, 95% CI: −319:4, 77.6; p-trend = 0:36). Similar associations were seen for average daily maximum PM2:5 exposure. Inverse associations were also observed in analyses stratified by smoking and time from exposure to spirom-etry and when we restricted to workers without prespill lung disease. CONCLUSIONS: Among oil spill workers, exposure to PM2:5 specifically from controlled burning of oil/gas was associated with significantly lower FEV1 and FEV1/FVC when compared with workers not involved in burning. https://doi.org/10.1289/EHP8930

Associations between airborne crude oil chemicals and symptom-based asthma

Rationale: The 2010 Deepwater Horizon (DWH) oil spill response and cleanup (OSRC) workers were exposed to airborne total hydrocarbons (THC), benzene, toluene, ethylbenzene, o-, m-, and p-xylenes and n-hexane (BTEX-H) from crude oil and PM2.5 from burning/flaring oil and natural gas. Little is known about asthma risk among oil spill cleanup workers. Objectives: We assessed the relationship between asthma and several oil spill-related exposures including job classes, THC, individual BTEX-H chemicals, the BTEX-H mixture, and PM2.5 using data from the Gulf Long-Term Follow-up (GuLF) Study, a prospective cohort of 24,937 cleanup workers and 7,671 nonworkers following the DWH disaster. Methods: Our analysis largely focused on the 19,018 workers without asthma before the spill who had complete exposure, outcome, and covariate information. We defined incident asthma 1–3 years following exposure using both self-reported wheeze and self-reported physician diagnosis of asthma. THC and BTEX-H were assigned to participants based on measurement data and work histories, while PM2.5 used modeled estimates. We used modified Poisson regression to estimate risk ratios (RR) and 95% confidence intervals (CIs) for associations between spill-related exposures and asthma and a quantile-based g-computation approach to explore the joint effect of the BTEX-H mixture on asthma risk. Results: OSRC workers had greater asthma risk than nonworkers (RR: 1.60, 95% CI: 1.38, 1.85). Higher estimated THC exposure levels were associated with increased risk in an exposure-dependent manner (linear trend test p < 0.0001). Asthma risk also increased with increasing exposure to individual BTEX-H chemicals and the chemical mixture: A simultaneous quartile increase in the BTEX-H mixture was associated with an increased asthma risk of 1.45 (95% CI: 1.35,1.55). With fewer cases, associations were less apparent for physician-diagnosed asthma alone. Conclusions: THC and BTEX-H were associated with increased asthma risk defined using wheeze symptoms as well as a physician diagnosis

Association of Deepwater Horizon Oil Spill Response and Cleanup Work With Risk of Developing Hypertension

Importance: Exposure to hydrocarbons, fine particulate matter (PM2.5), and other chemicals from the April 20, 2010, Deepwater Horizon disaster may be associated with increased blood pressure and newly detected hypertension among oil spill response and cleanup workers. Objective: To determine whether participation in cleanup activities following the disaster was associated with increased risk of developing hypertension. Design, Setting, and Participants: This cohort study was conducted via telephone interviews and in-person home exams. Participants were 6846 adults who had worked on the oil spill cleanup (workers) and 1505 others who had completed required safety training but did not do cleanup work (nonworkers). Eligible participants did not have diagnosed hypertension at the time of the oil spill. Statistical analyses were performed from June 2018 to December 2021. Exposures: Engagement in cleanup activities following the Deepwater Horizon oil spill disaster, job classes, quintiles of cumulative total hydrocarbons exposure level, potential exposure to burning or flaring oil, and estimated PM2.5were examined. Main Outcomes and Measures: Systolic and diastolic blood pressure measurements were collected during home exams from 2011 to 2013 using automated oscillometric monitors. Newly detected hypertension was defined as antihypertensive medication use or elevated blood pressure since the spill. Log binomial regression was used to calculate prevalence ratios (PR) and 95% CIs for associations between cleanup exposures and hypertension. Multivariable linear regression was used to estimate exposure effects on continuous blood pressure levels. Results: Of 8351 participants included in this study, 6484 (77.6%) were male, 517 (6.2%) were Hispanic, 2859 (34.2%) were non-Hispanic Black, and 4418 (52.9%) were non-Hispanic White; the mean (SD) age was 41.9 (12.5) years at enrollment. Among workers, the prevalence of newly detected hypertension was elevated in all quintiles (Q) of cumulative total hydrocarbons above the first quintile (PR for Q3, 1.29 [95% CI, 1.13-1.46], PR for Q4, 1.25 [95% CI, 1.10-1.43], and PR for Q5, 1.31 [95% CI, 1.15-1.50]). Both exposure to burning and/or flaring oil and gas (PR, 1.16 [95% CI, 1.02-1.33]) and PM2.5from burning (PR, 1.26 [95% CI, 0.89-1.71]) for the highest exposure category were associated with increased risk of newly detected hypertension, as were several types of oil spill work including cleanup on water (PR, 1.34 [95% CI, 1.08-1.66]) and response work (PR, 1.51 [95% CI, 1.20-1.90]). Conclusions and Relevance: Oil spill exposures were associated with newly detected hypertension after the Deepwater Horizon disaster. These findings suggest that blood pressure screening should be considered for workers with occupational hydrocarbon exposure

Lichenometric dating (lichenometry) and the biology of the lichen genus rhizocarpon:challenges and future directions

Lichenometric dating (lichenometry) involves the use of lichen measurements to estimate the age of exposure of various substrata. Because of low radial growth rates and considerable longevity, species of the crustose lichen genus Rhizocarpon have been the most useful in lichenometry. The primary assumption of lichenometry is that colonization, growth and mortality of Rhizocarpon are similar on surfaces of known and unknown age so that the largest thalli present on the respective faces are of comparable age. This review describes the current state of knowledge regarding the biology of Rhizocarpon and considers two main questions: (1) to what extent does existing knowledge support this assumption; and (2) what further biological observations would be useful both to test its validity and to improve the accuracy of lichenometric dates? A review of the Rhizocarpon literature identified gaps in knowledge regarding early development, the growth rate/size curve, mortality, regeneration, competitive effects, colonization, and succession on rock surfaces. The data suggest that these processes may not be comparable on different rock surfaces, especially in regions where growth rates and thallus turnover are high. In addition, several variables could differ between rock surfaces and influence maximum thallus size, including rate and timing of colonization, radial growth rates, environmental differences, thallus fusion, allelopathy, thallus mortality, colonization and competition. Comparative measurements of these variables on surfaces of known and unknown age may help to determine whether the basic assumptions of lichenometry are valid. Ultimately, it may be possible to take these differences into account when interpreting estimated dates

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations

Clustering identifies endotypes of traumatic brain injury in an intensive care cohort: a CENTER-TBI study

Background While the Glasgow coma scale (GCS) is one of the strongest outcome predictors, the current classification of traumatic brain injury (TBI) as ‘mild’, ‘moderate’ or ‘severe’ based on this fails to capture enormous heterogeneity in pathophysiology and treatment response. We hypothesized that data-driven characterization of TBI could identify distinct endotypes and give mechanistic insights. Methods We developed an unsupervised statistical clustering model based on a mixture of probabilistic graphs for presentation (< 24 h) demographic, clinical, physiological, laboratory and imaging data to identify subgroups of TBI patients admitted to the intensive care unit in the CENTER-TBI dataset (N = 1,728). A cluster similarity index was used for robust determination of optimal cluster number. Mutual information was used to quantify feature importance and for cluster interpretation. Results Six stable endotypes were identified with distinct GCS and composite systemic metabolic stress profiles, distinguished by GCS, blood lactate, oxygen saturation, serum creatinine, glucose, base excess, pH, arterial partial pressure of carbon dioxide, and body temperature. Notably, a cluster with ‘moderate’ TBI (by traditional classification) and deranged metabolic profile, had a worse outcome than a cluster with ‘severe’ GCS and a normal metabolic profile. Addition of cluster labels significantly improved the prognostic precision of the IMPACT (International Mission for Prognosis and Analysis of Clinical trials in TBI) extended model, for prediction of both unfavourable outcome and mortality (both p < 0.001). Conclusions Six stable and clinically distinct TBI endotypes were identified by probabilistic unsupervised clustering. In addition to presenting neurology, a profile of biochemical derangement was found to be an important distinguishing feature that was both biologically plausible and associated with outcome. Our work motivates refining current TBI classifications with factors describing metabolic stress. Such data-driven clusters suggest TBI endotypes that merit investigation to identify bespoke treatment strategies to improve care

Serum metabolome associated with severity of acute traumatic brain injury

Complex metabolic disruption is a crucial aspect of the pathophysiology of traumatic brain injury (TBI). Associations between this and systemic metabolism and their potential prognostic value are poorly understood. Here, we aimed to describe the serum metabolome (including lipidome) associated with acute TBI within 24 h post-injury, and its relationship to severity of injury and patient outcome. We performed a comprehensive metabolomics study in a cohort of 716 patients with TBI and non-TBI reference patients (orthopedic, internal medicine, and other neurological patients) from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) cohort. We identified panels of metabolites specifically associated with TBI severity and patient outcomes. Choline phospholipids (lysophosphatidylcholines, ether phosphatidylcholines and sphingomyelins) were inversely associated with TBI severity and were among the strongest predictors of TBI patient outcomes, which was further confirmed in a separate validation dataset of 558 patients. The observed metabolic patterns may reflect different pathophysiological mechanisms, including protective changes of systemic lipid metabolism aiming to maintain lipid homeostasis in the brain