Debridement options for the interprofessional team.

ABSTRACT: Debridement is a core component of chronic wound management. Although various debridement methods exist, each carries a unique patient risk level. This article discusses the different normal tissue components that are critical to safe debridement practice, various methods of wound debridement for nurses, and the importance of an interprofessional team and consulting a wound specialist.

Executive Summary: Debridement: Canadian Best Practice Recommendations for Nurses Developed by Nurses Specialized in Wound, Ostomy and Continence Canada (NSWOCC).

Debridement is described in the literature as having a high level of clinical risk and may result in patient harm when performed by untrained nurses. As a result, specialized knowledge, skills, and competencies are required to initiate, direct, and perform safe and effective debridement. This executive summary provides an overview of Debridement: Canadian Best Practice Recommendations for Nurses from the Nurses Specialized in Wound, Ostomy and Continence Canada (NSWOCC). The primary objective of these recommendations is to positively influence patient outcomes and enhance safety. The 12 recommendations place the safety of the patient and nurse at the forefront and highlight the educational, competency, certification, preceptor/mentorship, and legal requirements for nurses to initiate, direct, and perform all methods of debridement. We designed these recommendations to be circulated and implemented widely by nurses of various professional levels across the continuum of care and advocate for organizations and government agencies to clearly define debridement in their policies and legislative regulations.

Practical Use of Computationally Frugal Model Analysis Methods.

Three challenges compromise the utility of mathematical models of groundwater and other environmental systems: (1) a dizzying array of model analysis methods and metrics make it difficult to compare evaluations of model adequacy, sensitivity, and uncertainty; (2) the high computational demands of many popular model analysis methods (requiring 1000's, 10,000 s, or more model runs) make them difficult to apply to complex models; and (3) many models are plagued by unrealistic nonlinearities arising from the numerical model formulation and implementation. This study proposes a strategy to address these challenges through a careful combination of model analysis and implementation methods. In this strategy, computationally frugal model analysis methods (often requiring a few dozen parallelizable model runs) play a major role, and computationally demanding methods are used for problems where (relatively) inexpensive diagnostics suggest the frugal methods are unreliable. We also argue in favor of detecting and, where possible, eliminating unrealistic model nonlinearities-this increases the realism of the model itself and facilitates the application of frugal methods. Literature examples are used to demonstrate the use of frugal methods and associated diagnostics. We suggest that the strategy proposed in this paper would allow the environmental sciences community to achieve greater transparency and falsifiability of environmental models, and obtain greater scientific insight from ongoing and future modeling efforts.

Simulation of water-table aquifers using specified saturated thickness.

Simulating groundwater flow in a water-table (unconfined) aquifer can be difficult because the saturated thickness available for flow depends on model-calculated hydraulic heads. It is often possible to realize substantial time savings and still obtain accurate head and flow solutions by specifying an approximate saturated thickness a priori, thus linearizing this aspect of the model. This specified-thickness approximation often relies on the use of the "confined" option in numerical models, which has led to confusion and criticism of the method. This article reviews the theoretical basis for the specified-thickness approximation, derives an error analysis for relatively ideal problems, and illustrates the utility of the approximation with a complex test problem. In the transient version of our complex test problem, the specified-thickness approximation produced maximum errors in computed drawdown of about 4% of initial aquifer saturated thickness even when maximum drawdowns were nearly 20% of initial saturated thickness. In the final steady-state version, the approximation produced maximum errors in computed drawdown of about 20% of initial aquifer saturated thickness (mean errors of about 5%) when maximum drawdowns were about 35% of initial saturated thickness. In early phases of model development, such as during initial model calibration efforts, the specified-thickness approximation can be a very effective tool to facilitate convergence. The reduced execution time and increased stability obtained through the approximation can be especially useful when many model runs are required, such as during inverse model calibration, sensitivity and uncertainty analyses, multimodel analysis, and development of optimal resource management scenarios.

Effects of sediment-associated extractable metals, degree of sediment grain sorting, and dissolved organic carbon upon Cryptosporidium parvum removal and transport within riverbank filtration sediments, Sonoma County, California.

Oocysts of the protozoan pathogen Cryptosporidium parvum are of particular concern for riverbank filtration (RBF) operations because of their persistence, ubiquity, and resistance to chlorine disinfection. At the Russian River RBF site (Sonoma County, CA), transport of C. parvum oocysts and oocyst-sized (3 µm) carboxylate-modified microspheres through poorly sorted (sorting indices, σ(1), up to 3.0) and geochemically heterogeneous sediments collected between 2 and 25 m below land surface (bls) were assessed. Removal was highly sensitive to variations in both the quantity of extractable metals (mainly Fe and Al) and degree of grain sorting. In flow-through columns, there was a log-linear relationship (r(2) = 0.82 at p < 0.002) between collision efficiency (α, the probability that colloidal collisions with grain surfaces would result in attachment) and extractable metals, and a linear relationship (r(2) = 0.99 at p < 0.002) between α and σ(1). Collectively, variability in extractable metals and grain sorting accounted for ∼83% of the variability in α (at p < 0.0002) along the depth profiles. Amendments of 2.2 mg L(-1) of Russian River dissolved organic carbon (DOC) reduced α for oocysts by 4-5 fold. The highly reactive hydrophobic organic acid (HPOA) fraction was particularly effective in re-entraining sediment-attached microspheres. However, the transport-enhancing effects of the riverine DOC did not appear to penetrate very deeply into the underlying sediments, judging from high α values (∼1.0) observed for oocysts being advected through unamended sediments collected at ∼2 m bls. This study suggests that in evaluating the efficacy of RBF operations to remove oocysts, it may be necessary to consider not only the geochemical nature and size distribution of the sediment grains, but also the degrees of sediment sorting and the concentration, reactivity, and penetration of the source water DOC.

The practical use of simplicity in developing ground water models.

The advantages of starting with simple models and building complexity slowly can be significant in the development of ground water models. In many circumstances, simpler models are characterized by fewer defined parameters and shorter execution times. In this work, the number of parameters is used as the primary measure of simplicity and complexity; the advantages of shorter execution times also are considered. The ideas are presented in the context of constructing ground water models but are applicable to many fields. Simplicity first is put in perspective as part of the entire modeling process using 14 guidelines for effective model calibration. It is noted that neither very simple nor very complex models generally produce the most accurate predictions and that determining the appropriate level of complexity is an ill-defined process. It is suggested that a thorough evaluation of observation errors is essential to model development. Finally, specific ways are discussed to design useful ground water models that have fewer parameters and shorter execution times.

Comparison of local grid refinement methods for MODFLOW.

Many ground water modeling efforts use a finite-difference method to solve the ground water flow equation, and many of these models require a relatively fine-grid discretization to accurately represent the selected process in limited areas of interest. Use of a fine grid over the entire domain can be computationally prohibitive; using a variably spaced grid can lead to cells with a large aspect ratio and refinement in areas where detail is not needed. One solution is to use local-grid refinement (LGR) whereby the grid is only refined in the area of interest. This work reviews some LGR methods and identifies advantages and drawbacks in test cases using MODFLOW-2000. The first test case is two dimensional and heterogeneous; the second is three dimensional and includes interaction with a meandering river. Results include simulations using a uniform fine grid, a variably spaced grid, a traditional method of LGR without feedback, and a new shared node method with feedback. Discrepancies from the solution obtained with the uniform fine grid are investigated. For the models tested, the traditional one-way coupled approaches produced discrepancies in head up to 6.8% and discrepancies in cell-to-cell fluxes up to 7.1%, while the new method has head and cell-to-cell flux discrepancies of 0.089% and 0.14%, respectively. Additional results highlight the accuracy, flexibility, and CPU time trade-off of these methods and demonstrate how the new method can be successfully implemented to model surface water-ground water interactions.

Parameter and observation importance in modelling virus transport in saturated porous media-investigations in a homogenous system.

This paper evaluates the importance of seven types of parameters to virus transport: hydraulic conductivity, porosity, dispersivity, sorption rate and distribution coefficient (representing physical-chemical filtration), and in-solution and adsorbed inactivation (representing virus inactivation). The first three parameters relate to subsurface transport in general while the last four, the sorption rate, distribution coefficient, and in-solution and adsorbed inactivation rates, represent the interaction of viruses with the porous medium and their ability to persist. The importance of four types of observations to estimate the virus-transport parameters are evaluated: hydraulic heads, flow, temporal moments of conservative-transport concentrations, and virus concentrations. The evaluations are conducted using one- and two-dimensional homogeneous simulations, designed from published field experiments, and recently developed sensitivity-analysis methods. Sensitivity to the transport-simulation time-step size is used to evaluate the importance of numerical solution difficulties. Results suggest that hydraulic conductivity, porosity, and sorption are most important to virus-transport predictions. Most observation types provide substantial information about hydraulic conductivity and porosity; only virus-concentration observations provide information about sorption and inactivation. The observations are not sufficient to estimate these important parameters uniquely. Even with all observation types, there is extreme parameter correlation between porosity and hydraulic conductivity and between the sorption rate and in-solution inactivation. Parameter estimation was accomplished by fixing values of porosity and in-solution inactivation.

Numerical methods for improving sensitivity analysis and parameter estimation of virus transport simulated using sorptive-reactive processes.

Using one- and two-dimensional homogeneous simulations, this paper addresses challenges associated with sensitivity analysis and parameter estimation for virus transport simulated using sorptive-reactive processes. Head, flow, and conservative- and virus-transport observations are considered. The paper examines the use of (1) observed-value weighting, (2) breakthrough-curve temporal moment observations, and (3) the significance of changes in the transport time-step size. The results suggest that (1) sensitivities using observed-value weighting are more susceptible to numerical solution variability, (2) temporal moments of the breakthrough curve are a more robust measure of sensitivity than individual conservative-transport observations, and (3) the transport-simulation time step size is more important than the inactivation rate in solution and about as important as at least two other parameters, reflecting the ease with which results can be influenced by numerical issues. The approach presented allows more accurate evaluation of the information provided by observations for estimation of parameters and generally improves the potential for reasonable parameter-estimation results.

Determining extreme parameter correlation in ground water models.

In ground water flow system models with hydraulic-head observations but without significant imposed or observed flows, extreme parameter correlation generally exists. As a result, hydraulic conductivity and recharge parameters cannot be uniquely estimated. In complicated problems, such correlation can go undetected even by experienced modelers. Extreme parameter correlation can be detected using parameter correlation coefficients, but their utility depends on the presence of sufficient, but not excessive, numerical imprecision of the sensitivities, such as round-off error. This work investigates the information that can be obtained from parameter correlation coefficients in the presence of different levels of numerical imprecision, and compares it to the information provided by an alternative method called the singular value decomposition (SVD). Results suggest that (1) calculated correlation coefficients with absolute values that round to 1.00 were good indicators of extreme parameter correlation, but smaller values were not necessarily good indicators of lack of correlation and resulting unique parameter estimates; (2) the SVD may be more difficult to interpret than parameter correlation coefficients, but it required sensitivities that were one to two significant digits less accurate than those that required using parameter correlation coefficients; and (3) both the SVD and parameter correlation coefficients identified extremely correlated parameters better when the parameters were more equally sensitive. When the statistical measures fail, parameter correlation can be identified only by the tedious process of executing regression using different sets of starting values, or, in some circumstances, through graphs of the objective function.