Comparison of Different Analytical Methods for the Determination of Carbon Monoxide in Postmortem Blood

Comparative Evaluation of Carboxyhemoglobin Quantification in Postmortem Whole Blood by CO-Oximetry and Headspace Gas Chromatography with Flame Ionization Detection and Atom Absorption Spectrophotometry

A comparative evaluation of two methods used for carboxyhemoglobin (COHb) determination in postmortem whole blood was performed: carbon monoxide (CO)-oximetry measuring at 128 wavelengths and headspace gas chromatography with flame ionization detection (HS-GC--FID) where CO was determined after catalytic reduction of CO to CH4 and Fe was determined by atom absorption spectrophotometry (AAS, 248.3 nm). An aliquot of 100 µL whole blood was loaded into the CO-oximetry module. In the HS-GC--FID analysis, to 1.0 mL of whole blood, 3.0 mL of saponin solution was added, mixed and centrifuged. To 20 mL HS vials, 400 µL of the supernatant was added and the vials were immediately sealed. One milliliter of potassium hexacyanoferrat (III) solution was added through the HS septum and mixed. The samples were incubated at 70°C for 5 min. CO was separated using He as carrier gas and a CP-Molsieve 5 Å PLOT capillary column. Fe was determined using 400 µL of the saponin supernatant diluted to 10 mL by water. During a period of ∼3 years, 124 postmortem whole blood samples were analyzed. Bland-Altman method comparison showed satisfactory agreement and no significant bias between the methods for the whole saturation range (5 to 85% COHb). Five samples, all with %COHb >40, showed deviations of more than 10% COHb in absolute terms. One sample, in the lower COHb range <10%, was false negative on the CO-oximetry method. The between-assay accuracy, reported as bias, at 60% COHb was -0.8% and -9.0%, and precision, reported as relative standard deviation, was 1.6% and 7.7%, for the CO-oximetry and HS-GC--FID-AAS methods, respectively. Both methods obtained satisfactory results in proficiency testing rounds, with z-scores <±2 (n = 11). This study showed that the CO-oximetry method based on the 128-wavelength principle and the HS-GC--FID-AAS method are comparable and satisfactory for %COHb determination in postmortem whole blood.

Acute carbon monoxide poisoning with low saturation of carboxyhaemoglobin: a forensic retrospective study in Shanghai, China

Carbon monoxide (CO) poisoning is a common cause of death, leading to morbidity and mortality worldwide. Features of the CO poisoning with low carboxyhemoglobin (COHb) levels remain to be characterized. This study collected a total of 307 CO poisoning cases from Shanghai Public Security Bureau, an official organization that handles the most complicated and life-threatening cases across Shanghai municipality in China, and regrouped these cases into three categories: group 1, 10% < COHb% < 30% (n = 58); group 2, 30% ≤ COHb% < 50% (n = 79); group 3, COHb% ≥ 50% (n = 170). Epidemiological, demographic, and forensic aspects of the CO poisoning cases, particularly those with low COHb levels, were analyzed. Our results showed that group 2 and 3 were mostly observed in younger victims (≤ 30 years), while group 1 equally distributed to all age groups (p = 0.03). All the CO poisoning from group 2 and 3 occurred in enclosed spaces, whereas cases from group 1 died additionally in outdoor spaces (p = 0.01). 81.03% of group 1 cases died in fire circumstances, while only 45.57% from group 2 and 30.59% from group 3 were fire-related (p = 0.00). Accordingly, group 1 was mostly related with fire burns, while group 2 or 3 were largely associated with gas leakage (p = 0.00). A combination with alcohol, but not other psychotropic drugs, associated with significant higher levels of blood COHb% in fire-unrelated (p = 0.021) but not fire-related cases (p = 0.23). Five extremely low COHb% (< 30%)-related poisoning deaths were negative of any cardiopulmonary pathology and psychoactive substances. In conclusion, CO poisoning with low COHb% significantly associates with fire circumstances and outdoor spaces and has no age preference. Further diagnostic markers mandates to be identified in order to avoid disputes in cases of extremely low COHb%-related poisoning.

Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran

This study was aimed to investigate the air pollutants impact on heart patient's hospital admission rates in Yazd for the first time. Modeling was done by time series, multivariate linear regression, and artificial neural network (ANN). During 5 years, the mean concentrations of PM₁₀, SO₂, O₃, NO₂, and CO were 98.48 μg m^-3, 8.57 ppm, 19.66 ppm, 18.14 ppm, and 4.07 ppm, respectively. The total number of cardiovascular disease (CD) patients was 12,491, of which 57% and 43% were related to men and women, respectively. The maximum correlation of air pollutants was observed between CO and PM₁₀ (R = 0.62). The presence of SO₂ and NO₂ can be dependent on meteorological parameters (R = 0.48). Despite there was a positive correlation between age and CD (p = 0.001), the highest correlation was detected between SO₂ and CD (R = 0.4). The annual variation trend of SO₂, NO₂, and CO concentrations was more similar to the variations trend in meteorological parameters. Moreover, the temperature had also been an effective factor in the O₃ variation rate at lag = 0. On the other hand, SO₂ has been the most effective contaminant in CD patient admissions in hospitals (R = 0.45). In the monthly database classification, SO₂ and NO₂ were the most prominent factors in the CD (R = 0.5). The multivariate linear regression model also showed that CO and SO₂ were significant contaminants in the number of hospital admissions (R = 0.46, p = 0.001) that both pollutants were a function of air temperature (p = 0.002). In the ANN nonlinear model, the 14, 12, 10, and 13 neurons in the hidden layer were formed the best structure for PM, NO₂, O₃, and SO₂, respectively. Thus, the R_all rate for these structures was 0.78-0.83. In these structures, according to the autocorrelation of error in lag = 0, the series are stationary, which makes it possible to predict using this model. According to the results, the artificial neural network had a good ability to predict the relationship between the effect of air pollutants on the CD in a 5 years' time series.