Low energy gunshot injuries: Does removal of retained bullet fragmentation at the time of internal fixation reduce the risk of fracture related infection?

OBJECTIVE: To examine the effects of RBF (Retained Bullet Fragment) removal at the time of long bone fixation on FRI (fracture related infection) rates in low energy GSI (Gunshot Injury) related fractures. DESIGN: Retrospective Cohort Study SETTING: Level 1 Academic Trauma Center INTERVENTION: Retrospective review of the impact of RBFs on the risk of FRI when employing internal fixation in low energy GSI (Gunshot Injury) related fractures. In situations where the injury pattern requires surgical fixation, the question arises as to whether or not the RBFs need to be removed to prevent FRI. MAIN OUTCOME MEASURES: Whether or not the RBFs removed in our patient population prevented short- and long-term fracture related infection after low-energy gunshot injury (FRI-LGI). RESULTS: Of the 2,136 GSI related fractures, 131 patients met inclusion criteria, 81 patients underwent removal (R) of RBFs at the time of internal fixation while 50 patients did not undergo any removal (NR) at time of internal fixation. Among the patients who underwent surgical intervention, (Open Reduction Internal Fixation) ORIF was performed in 55 cases (R: 39; NR: 16), and (Intramedullary Nail) IMN was performed in 76 cases (R: 42; NR: 34). The overall rate of deep FRI-LGI was 6.9 % of the 131-patient cohort. We found that removal of RBFs had a statistically significant impact on the rate of deep FRI-LGI when compared to the NR group (p = 0.031). In the RBF removal group, only two patients (2.4 %) developed deep FRI-LGIs, whereas in the NR group, seven patients (14.0 %) developed deep FRI-LGIs. The incidence of early FRI-LGI was higher in the NR group (median 0.6 months) compared to the R group, which was associated with late FRI-LGIs (median 10.1 months) when they occurred. DISCUSSION: In our study population, we found a statistically significantly increased incidence of deep and early FRI-LGI when RBFs are not removed at the time of extra-articular long bone internal fixation. The presence of retained bullet fragments following internal fixation may pose a risk factor for future development of deep FRI-LGI. We believe a surgeon should use their best judgment as to whether a RBF can safely be removed at the time of long bone fixation. Based on our findings, if safely permitted, RBF removal should be considered at the time of GSI long bone fixation resulting from low energy hand gun injuries.

Food-specific attribution of selected gastrointestinal illnesses: estimates from a Canadian expert elicitation survey.

The study used a structured expert elicitation survey to derive estimates of food-specific attribution for nine illnesses caused by enteric pathogens in Canada. It was based on a similar survey conducted in the United States and focused on Campylobacter spp., Escherichia coli O157:H7, Listeria monocytogenes, nontyphoidal Salmonella enterica, Shigella spp., Vibrio spp., Yersinia enterocolitica, Cryptosporidium parvum, and Norwalk-like virus. A snowball approach was used to identify food safety experts within Canada. Survey respondents provided background information as well as self-assessments of their expertise for each pathogen and the 12 food categories. Depending on the pathogen, food source attribution estimates were based on responses from between 10 and 35 experts. For each pathogen, experts divided their estimates of total foodborne illness across 12 food categories and they provided a best estimate for each category as well as 5th and 95th percentile limits for foods considered to be vehicles. Their responses were treated as triangular probability distributions, and linear aggregation was used to combine the opinions of each group of experts for each pathogen-food source group. Across the 108 pathogen-food groups, a majority of experts agreed on 30 sources and 48 nonsources for illness. The number of food groups considered to be pathogen sources by a majority of experts varied by pathogen from a low of one food source for Vibrio spp. (seafood) and C. parvum (produce) to a high of seven food sources for Salmonella spp. Beta distributions were fitted to the aggregated opinions and were reasonable representations for most of the pathogen-food group attributions. These results will be used to quantitatively assess the burden of foodborne illness in Canada as well as to analyze the uncertainty in our estimates.

Foodborne proportion of gastrointestinal illness: estimates from a Canadian expert elicitation survey.

The study used a structured expert elicitation survey to derive estimates of the foodborne attributable proportion for nine illnesses caused by enteric pathogens in Canada. It was based on a similar study conducted in the United States and focused on Campylobacter, Escherichia coli O157:H7, Listeria monocytogenes, nontyphoidal Salmonella enterica, Shigella spp., Vibrio spp., Yersinia enterocolitica, Cryptosporidium parvum, and Norwalk-like virus. For each pathogen, experts were asked to provide their best estimate and low and high limits for the proportion of foodborne illness relative to total cases. In addition, they provided background information with regard to food safety experience, including self-evaluated expertise for each pathogen on a 5-point scale. A snowball approach was used to identify 152 experts within Canada. The experts' background details were summarized using descriptive statistics. Factor analysis was used to determine whether the variability in best estimates was related to self-assessed level of expertise or other background information. Cluster analysis followed by beta function fitting was undertaken on best estimates from experts who self-evaluated their expertise 3 or higher. In parallel, Monte Carlo resampling was run using triangular distributions based on each expert's best estimate and its limits. Sixty-six experts encompassing various academic backgrounds, fields of expertise, and experiences relevant to food safety provided usable data. Considerable variation between experts in their estimated foodborne attributable proportions was observed over all diseases, without any relationship to the expert's background. Uncertainty about their estimate (measured by the low and high limits) varied between experts and between pathogens as well. Both cluster analysis and Monte Carlo resampling clearly indicated disagreement between experts for Campylobacter, E. coli O157, L. monocytogenes, Salmonella, Vibrio, and Y. enterocolitica. In the absence of more reliable estimates, the observed discrepancy between experts must be explored and understood before one can judge which opinion is the best.

A multifactorial risk prioritization framework for foodborne pathogens.

We develop a prioritization framework for foodborne risks that considers public health impact as well as three other factors (market impact, consumer risk acceptance and perception, and social sensitivity). Canadian case studies are presented for six pathogen-food combinations: Campylobacter spp. in chicken; Salmonella spp. in chicken and spinach; Escherichia coli O157 in spinach and beef; and Listeria monocytogenes in ready-to-eat meats. Public health impact is measured by disability-adjusted life years and the cost of illness. Market impact is quantified by the economic importance of the domestic market. Likert-type scales are used to capture consumer perception and acceptance of risk and social sensitivity to impacts on vulnerable consumer groups and industries. Risk ranking is facilitated through the development of a knowledge database presented in the format of info cards and the use of multicriteria decision analysis (MCDA) to aggregate the four factors. Three scenarios representing different stakeholders illustrate the use of MCDA to arrive at rankings of pathogen-food combinations that reflect different criteria weights. The framework provides a flexible instrument to support policymakers in complex risk prioritization decision making when different stakeholder groups are involved and when multiple pathogen-food combinations are compared.

A neural network approach to predict survival/death and growth/no-growth interfaces for Escherichia coli O157:H7.

An artificial neural network (ANN) model was developed to predict survival/death and growth/no-growth interfaces for Escherichia coli O157:H7 in a mayonnaise-type system. Temperature, pH, acetic acid, sucrose and salt were the inputs to a three-layer back-propagation neural network. The ANN model was trained using the data-set of McKellar et al. [2002. A probability model describing the interface between survival and death of E. coli O157:H7 in a mayonnaise model system. Food Microbiol. 19, 235-247] that consisted of 1,820 treatment combinations from controlled experiments with a cocktail of five strains of E. coli O157:H7. After training, the model correctly predicted the growth/no-growth in 1,810 combinations (99.5%) with 8 false positives and 2 false negatives, and survival/death in 1,804 combinations (99.1%) with 13 false positives and 3 false negatives. Classification accuracy was validated using additional literature data-sets for growth of E. coli O157:H7 under various environmental conditions. The ANN model accurately predicted the survival/death in 27 of 30 cases (90%) in experimental mayonnaise inoculated with E. coli O157:H7, with 3 fail-positive predictions and all observed growth (100%). Simulations were used to estimate the influence of incubation temperature on survival and growth for specific combinations of acetic acid, salt, pH and sucrose. The ANN model is recommended as an alternative tool for classification of survival and growth conditions in predictive microbiology.

Comparison of Monte Carlo and fuzzy math simulation methods for quantitative microbial risk assessment.

The objective of food safety risk assessment is to quantify levels of risk for consumers as well as to design improved processing, distribution, and preparation systems that reduce exposure to acceptable limits. Monte Carlo simulation tools have been used to deal with the inherent variability in food systems, but these tools require substantial data for estimates of probability distributions. The objective of this study was to evaluate the use of fuzzy values to represent uncertainty. Fuzzy mathematics and Monte Carlo simulations were compared to analyze the propagation of uncertainty through a number of sequential calculations in two different applications: estimation of biological impacts and economic cost in a general framework and survival of Campylobacter jejuni in a sequence of five poultry processing operations. Estimates of the proportion of a population requiring hospitalization were comparable, but using fuzzy values and interval arithmetic resulted in more conservative estimates of mortality and cost, in terms of the intervals of possible values and mean values, compared to Monte Carlo calculations. In the second application, the two approaches predicted the same reduction in mean concentration (-4 log CFU/ ml of rinse), but the limits of the final concentration distribution were wider for the fuzzy estimate (-3.3 to 5.6 log CFU/ml of rinse) compared to the probability estimate (-2.2 to 4.3 log CFU/ml of rinse). Interval arithmetic with fuzzy values considered all possible combinations in calculations and maximum membership grade for each possible result. Consequently, fuzzy results fully included distributions estimated by Monte Carlo simulations but extended to broader limits. When limited data defines probability distributions for all inputs, fuzzy mathematics is a more conservative approach for risk assessment than Monte Carlo simulations.

Fuzzy logic applications.

Fuzzy logic is a modeling method well suited for the control of complex and non-linear systems. This paper illustrates some of the power of fuzzy logic through a simple control example. For the analytical chemist, fuzzy logic incorporates imprecision from measurement noise as well as from linguistic process descriptions to produce operational control systems.

Flow Characteristics and Holding Time Calculations of Ice Cream Mixes in HTST Holding Tubes.

In order to determine the potential for development of laminar flow and consequential underholding in the holding tubes of HTST pasteurizers, a study on the relationship between ice cream mix viscosity and shear rate at 80°C has been conducted. Typical shear rates at the wall were calculated for HTST holding tubes of standard industry sizes and flow rates. Shear rates in the holding tube were found to vary from 50 to 180 s-1, depending on the conditions. Viscosity of ice cream mixes as a function of shear rate, stabilizer type, and stabilizer concentration were measured. Ice cream mix was found to be non-Newtonian and pseudoplastic. Viscosities ranged from 8.7 cP in an unstabilized mix at high shear rate (relative to the inside of the holding tube) to 103 cP for 0.25% carboxymethyl cellulose at low shear rate. Generalized Reynolds numbers inside the holding tubes varied from 100 to 1700, indicating a strong potential for the development of laminar flow. The apparent viscosities required to result in a minimum generalized Reynolds number of 2100 are very near to or less than the actual viscosities of stabilized ice cream mixes, and thus the potential for a laminar flow pattern within the holding tube needs to be addressed in determining holding tube lengths for a required holding time.