Shovel Test Pit Paperwork of Judgemental Tests from Quarterman (8BR223)

This document contains the field notes taken during phase 1 survey for the judgemental tests

Screening risk assessment tools for assessing the environmental impact in an abandoned pyritic mine in Spain

This is the author’s version of a work that was accepted for publication in Science of the Total Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Science of the Total Environment 409.4 (2011): 692-703 http://dx.doi.org/10.1016/j.scitotenv.2010.10.056This paper describes a new methodology for assessing site-specific environmental impact of contaminants. The proposed method integrates traditional risk assessment approaches with real and variable environmental characteristics at a local scale. Environmental impact on selected receptors was classified for each environmental compartment into 5 categories derived from the whole (chronic and acute) risk assessment using 8 risk levels. Risk levels were established according to three hazard quotients (HQs) which represented the ratio of exposure to acute and chronic toxicity values. This tool allowed integrating in only one impact category all the elements involved in the standard risk assessment. The methodology was applied to an abandoned metal mine in Spain, where high levels of As, Cd, Zn and Cu were detected. Risk affecting potential receptors such as aquatic and soil organisms and terrestrial vertebrates were assessed. Whole results showed that impact to the ecosystem is likely high and further investigation or remedial actions are necessary. Some proposals to refine the risk assessment for a more realistic diagnostic are included.This work has been financed by Madrid Community through EIADES Project S-505/AMB/0296, and by Spanish MinistryfEducation and Science, project CTM-2007-66401-CO2/TECN

Modelling and prediction of physiological behaviour in critically ill patients

For patients with acute respiratory distress syndrome (ARDS), mechanical ventilation (MV) is an essential therapy in the intensive care unit. ARDS is diverse condition, the impact of which varies across patients. Every patient has different optimal ventilator settings that may change over the course of treatment, and there is no consensus on how these optimal settings should be found. In particular, the optimal level of positive end expiratory pressure (PEEP) is widely debated. PEEP that is too high or low can cause damage to healthy alveoli, leading to ventilator induced lung injury (VILI). VILI is associated with increased mortality, extended ICU stay, and high cost. The use of mathematical models to determine patient-specific ventilator settings can reduce the incidence of VILI. There have been many models developed to capture pulmonary mechanics, but they have limitations in lack of ability to capture all relevant physiology, or in complexity and difficulty of implementation. The focus of this research is the development of a model of pulmonary mechanics that does not suffer from many of the disadvantages of previous models. A nonlinear autoregressive (NARX) model was developed using a complex data set, and contains terms that enable it to fit to all features of the pressure waveform. It captures recruitment and distension across many increasing PEEP steps via an elastance vs. pressure curve that is defined by basis functions. Flow dependent terms allow it to capture viscoelastic effects and fit to an end-inspiratory pause. This model, and slight variations on it were tested on three cohorts of data in total. In many cases the model was compared with the well validated and extensively used first order model (FOM). Various investigations supported the choice of the NARX model terms. This included using the model for interpolation across a recruitment manoeuvre. The interpolated NARX model fit was consistent across different types of patients, while the FOM performed worse in patients experiencing over-distension at high pressure. Another comparison with the FOM found that the NARX model could more reliably capture expected changes in resistance with PEEP. The NARX model could also identify independent inspiratory and expiratory elastance, due to the flow dependent terms that the FOM does not have. The NARX model is flexible in its implementation. While it is normally identified in real time using the simple linear least squares method, it was also able to be combined with a modified Gauss – Newton parameter identification method for a spontaneous breathing application. In this case, anomalies in the pressure waveform caused by intermittent patient efforts were able to be removed to enable a more accurate identification of patient parameters. Aside from patient-specific parameter identification, the main potential clinical use of the NARX model is in predicting the effects of changes in PEEP. An extrapolation of the elastance curve allowed pressure at higher PEEP levels to be predicted. By using partial recruitment manoeuvres as the training data, the NARX model predicted pressure waveforms at higher PEEP levels with significantly lower residuals than the FOM. Since large PEEP changes are not recommended clinically, the most relevant results were the predictions for small PEEP increases of 2 cmH2O. In this scenario the NARX model accuracy was very high. A statistical classification analysis used the prediction methodology to test the ability of the NARX model to detect when alveolar over-distension is likely to occur with PEEP increases. The analysis considered a pressure threshold above which the risk of over-distension is high. False negatives are potentially much more harmful to patients than false positives, as a false negative means a failure to detect when over-distension will occur with a PEEP increase. Thus, sensitivity was a more important metric than specificity in the analysis. In most scenarios, the NARX model threshold detection had a very high sensitivity and outperformed the FOM, even when compared to a separate method designed to produce the best prediction outcomes from the FOM. However, on one cohort, the parameterisation of the NARX model had to be reduced by reducing the number of basis functions in order to outperform the FOM over large prediction horizons. An adaptation of the NARX model aimed to capture differences between COPD patients with resultant high auto-PEEP, and non-COPD patients. The adaptation replaced the flow dependent terms with basis functions that enabled linear resistance changes to be captured throughout a recruitment manoeuvre. The model parameters were able to distinguish between the two groups. At low pressure, the high auto-PEEP group had significantly higher modelled resistance, and had elastance curves that indicated a greater proportion of un-recruited lung units. Both of these outcomes were expected due to the airway narrowing and airway closures known to occur in COPD patients. As in MV, model based glycaemic control can allow personalised care, reduce mortality and improve clinical outcomes. Hence, a side project was undertaken to investigate whether the basis function approaches developed for MV could have potential applications in glycaemic control. The concept was applied to a glucose model to identify a time varying insulin sensitivity (SI) in ICU patients over multiple days. Parameterisation of the model was varied by varying the ratio of basis functions to data points, and this ratio influenced the identified SI profiles that were used to build SI prediction distributions. An analysis determined the appropriate level of parameterisation that resulted in accurate and precise predictions. The glucose model, the NARX model, and its adaptations all captured clinically relevant patient-specific parameters. The NARX model in particular overcame many of the limitations of previous models, due to the novel use of basis functions to describe elastance, and the use of terms that fit an end-inspiratory relaxation. It achieved this over a range of cohorts that represented a wide variety of patient physiologies and ventilation protocols. The data fitting and prediction outcomes indicate that it has high potential to be useful in diagnosis and disease tracking in a clinical setting

Inspiratory and expiratory elastance in a non-linear autoregressive model of pulmonary mechanics

For patients with acute respiratory distress syndrome (ARDS), the use of mathematical models to determine patient-specific ventilator settings can reduce ventilator induced lung injury and improve patient outcomes. A non-linear autoregressive model of pulmonary mechanics was used to identify inspiratory and expiratory pressure-dependent elastance (Ei and Ee) as independent variables. The analysis was implemented on 19 data sets of recruitment manoeuvres (RMs) that were performed on 10 mechanically ventilated patients. At pressures p = 15–20 cmH2O the agreement between Ei and Ee was low. However, Ei was a well-matched predictor of Ee for p = 25–40 cmH2O, with R2 ≥ 0.78, and there was no significant bias in the difference between Ei and Ee. Since many other models cannot uniquely identify Ei and Ee, the outcome may provide further insight into the characteristics of ARDS lungs in sedated patients

Resistance in a non-linear autoregressive model of pulmonary mechanics

Respiratory system modelling can enable patient-specific mechanical ventilator settings to be found, and can thus reduce the incidence of ventilator induced lung injury in the intensive care unit. The resistance of a simple first order model (FOM) of pulmonary mechanics was compared with a flow dependent term of a non-linear autoregressive (NARX) model. Model parameters were identified for consecutive non-overlapping windows of length 20 breaths. The analysis was performed over recruitment manoeuvres for 25 sedated mechanically ventilated patients. The NARX model term, b1, consistently decreased as positive end expiratory pressure (PEEP) increased, while the FOM resistance behaviour varied. Overall the NARX b1 behaviour is more in-line with expected trends in airway resistance as PEEP increases. This work has further verified the physiologically descriptive capability of the NARX model

Prediction of high airway pressure using a non-linear autoregressive model of pulmonary mechanics

Abstract Background For mechanically ventilated patients with acute respiratory distress syndrome (ARDS), suboptimal PEEP levels can cause ventilator induced lung injury (VILI). In particular, high PEEP and high peak inspiratory pressures (PIP) can cause over distension of alveoli that is associated with VILI. However, PEEP must also be sufficient to maintain recruitment in ARDS lungs. A lung model that accurately and precisely predicts the outcome of an increase in PEEP may allow dangerous high PIP to be avoided, and reduce the incidence of VILI. Methods and results Sixteen pressure-flow data sets were collected from nine mechanically ventilated ARDs patients that underwent one or more recruitment manoeuvres. A nonlinear autoregressive (NARX) model was identified on one or more adjacent PEEP steps, and extrapolated to predict PIP at 2, 4, and 6 cmH2O PEEP horizons. The analysis considered whether the predicted and measured PIP exceeded a threshold of 40 cmH2O. A direct comparison of the method was made using the first order model of pulmonary mechanics (FOM(I)). Additionally, a further, more clinically appropriate method for the FOM was tested, in which the FOM was trained on a single PEEP prior to prediction (FOM(II)). The NARX model exhibited very high sensitivity (> 0.96) in all cases, and a high specificity (> 0.88). While both FOM methods had a high specificity (> 0.96), the sensitivity was much lower, with a mean of 0.68 for FOM(I), and 0.82 for FOM(II). Conclusions Clinically, false negatives are more harmful than false positives, as a high PIP may result in distension and VILI. Thus, the NARX model may be more effective than the FOM in allowing clinicians to reduce the risk of applying a PEEP that results in dangerously high airway pressures