Screening for and prophylaxis of venous thromboembolism in severe carbon monoxide poisoning?

Risk of Venous Thromboembolism After Carbon Monoxide Poisoning: A Nationwide Population-Based Study

STUDY OBJECTIVE

Few studies have investigated the association between carbon monoxide (CO) poisoning and risk of venous thromboembolism. We aim to identify the risk of pulmonary embolism and deep venous thrombosis after CO poisoning.

METHODS

We conducted a nationwide cohort-crossover study using administrative claims data in Korea. We compared the risk of venous thromboembolism (pulmonary embolism or deep venous thrombosis) in the cohort period after CO poisoning to that of the same period 1 year later (crossover period), using conditional logistic regression analysis.

RESULTS

We included 22,699 patients with a diagnosis of CO poisoning during the study period between 2004 and 2015. The risk of venous thromboembolism was significantly elevated during days 0 to 90 after CO poisoning (odds ratio 3.96; 95% confidence interval 2.50 to 6.25). However, this risk was not significantly elevated during subsequent postexposure periods through 360 days. During days 0 to 30 after CO poisoning, the risks of pulmonary embolism (odds ratio 22.00; 95% confidence interval 5.33 to 90.75) and deep venous thrombosis (odds ratio 10.33; 95% confidence interval 3.16 to 33.80) were significantly elevated.

CONCLUSION

We found that the risk of venous thromboembolism persisted for up to 90 days after CO poisoning. The risk was increased 22-fold for pulmonary embolism and 10-fold for deep venous thrombosis, especially in the first month after CO poisoning. Patients should be monitored for venous thromboembolism risk after CO poisoning.

A review of AirQ Models and their applications for forecasting the air pollution health outcomes

Even though clean air is considered as a basic requirement for the maintenance of human health, air pollution continues to pose a significant health threat in developed and developing countries alike. Monitoring and modeling of classic and emerging pollutants is vital to our knowledge of health outcomes in exposed subjects and to our ability to predict them. The ability to anticipate and manage changes in atmospheric pollutant concentrations relies on an accurate representation of the chemical state of the atmosphere. The task of providing the best possible analysis of air pollution thus requires efficient computational tools enabling efficient integration of observational data into models. A number of air quality models have been developed and play an important role in air quality management. Even though a large number of air quality models have been discussed or applied, their heterogeneity makes it difficult to select one approach above the others. This paper provides a brief review on air quality models with respect to several aspects such as prediction of health effects.