Seán Cournane

Review of ultrasound elastography quality control and training test phantoms

While the rapid development of ultrasound elastography techniques in recent decades has sparked its prompt implementation in the clinical setting adding new diagnostic information to conventional imaging techniques, questions still remain as to its full potential and efficacy in the hospital environment. A limited number of technical studies have objectively assessed the full capabilities of the different elastography approaches, perhaps due, in part, to the scarcity of suitable tissue-mimicking materials (TMMs) and appropriately designed phantoms available. Few commercially available elastography phantoms possess the necessary test target characteristics or mechanical properties observed clinically, or indeed reflect the lesion-to-background elasticity ratio encountered during clinical scanning. Thus, while some phantoms may prove useful, they may not fully challenge the capabilities of the different elastography techniques, proving limited when it comes to quality control (QC) and/or training purposes. Although a variety of elastography TMMs, such as agar and gelatine dispersions, co-polymer in oil and poly(vinyl) alcohol cryogel, have been developed for specific research purposes, such work is yet to produce appropriately designed phantoms to adequately challenge the variety of current commercially available elastography applications. Accordingly, there is a clear need for the further development of elastography TMMs and phantoms to keep pace with the rapid developments in elastography technology, to ensure that the performance of these new diagnostic approaches are validated, and for clinical training purposes.

Social deprivation, population dependency ratio and an extended hospital episode — Insights from acute medicine

BACKGROUND Patients from deprived backgrounds have a higher in-patient mortality following an emergency medical admission; this study aimed to investigate the extent to which Deprivation status and the population Dependency Ratio influenced extended hospital episodes. METHODS All Emergency Medical admissions (75,018 episodes of 41,728 patients) over 12 years (2002-2013) categorized by quintile of Deprivation Index and Population Dependency Rates (proportion of non-working/working) were evaluated against length of stay (LOS). Patients with an Extended LOS (ELOS), >30 days, were investigated, by Deprivation status, Illness Severity and Co-morbidity status. Univariate and multi-variable risk estimates (Odds Rates or Incidence Rate Ratios) were calculated, using truncated Poisson regression. RESULTS Hospital episodes with ELOS had a frequency of 11.5%; their median LOS (IQR) was 55.0 (38.8, 97.6) days utilizing 57.6% of all bed days by all 75,018 emergency medical admissions. The Deprivation Index independently predicted the rate of such ELOS admissions; these increased approximately five-fold (rate/1000 population) over the Deprivation Quintiles with model adjusted predicted admission rates of for Q1 0.93 (95% CI: 0.86, 0.99), Q22.63 (95% CI: 2.55, 2.71), Q3 3.84 (95% CI: 3.77, 3.91), Q4 3.42 (95% CI: 3.37, 3.48) and Q5 4.38 (95% CI: 4.22, 4.54). Similarly the Population Dependency Ratio Quintiles (dependent to working structure of the population by small area units) independently predicted extended LOS admissions. CONCLUSION The admission of patients with an ELOS is strongly influenced by the Deprivation status and the population Dependency Ratio of the catchment area. These factors interact, with both high deprivation and Dependency cohorts having a major influence on the numbers of emergency medical admission patients with an extended hospital episode.

Deprivation index and dependency ratio are key determinants of emergency medical admission rates

BACKGROUND Patients from deprived backgrounds have a higher in-patient mortality following an emergency medical admission; there has been debate as to the extent to which deprivation and population structure influences hospital admission rate. METHODS All emergency medical admissions to an Irish hospital over a 12-year period (2002-2013) categorized by quintile of Deprivation Index and Dependency Ratio (proportion of population <15 or ≥ 65 years) from small area population statistics (SAPS), were evaluated against hospital admission rates. Univariate and multivariable risk estimates (Odds Ratios (OR) or Incidence Rate Ratios (IRR)) were calculated, using logistic or zero truncated Poisson regression as appropriate. RESULTS 66,861 admissions in 36,214 patients occured during the study period. The Deprivation Index quintile independently predicted the admission rate/1000 population, Q1 9.4 (95%CI 9.2 to 9.7), Q2 16.8 (95%CI 16.6 to 17.0), Q3 33.8 (95%CI 33.5 to 34.1), Q4 29.6 (95%CI 29.3 to 29.8) and Q5 45.4 (95%CI 44.5 to 46.2). Similarly the population Dependency Ratio was an independent predictor of the admission rate with adjusted predicted rates of Q1 20.8 (95%CI 20.5 to 21.1), Q2 19.2 (95%CI 19.0 to 19.4), Q3 27.6 (95%CI 27.3 to 27.9), Q4 43.9 (95%CI 43.5 to 44.4) and Q5 34.4 (95%CI 34.1 to 34.7). A high concurrent Deprivation Index and Dependency Ratio were associated with very high admission rates. CONCLUSION Deprivation Index and population Dependency Ratio are key determinants of the rate of emergency medical admissions.

Modelling the reworking effects of bioturbation on the incorporation of radionuclides into the sediment column: implications for the fate of particle-reactive radionuclides in Irish Sea sediments

A microcosm laboratory experiment was conducted to determine the impact of biological reworking by the ragworm Nereis diversicolor on the redistribution of particle-bound radionuclides deposited at the sediment-water interface. Over the course of the 40-day experiment, as much as 35% of a (137)Cs-labelled particulate tracer deposited on the sediment surface was redistributed to depths of up to 11 cm by the polychaete. Three different reworking models were employed to model the profiles and quantify the biodiffusion and biotransport coefficients: a gallery-diffuser model, a continuous sub-surface egestion model and a biodiffusion model. Although the biodiffusion coefficients obtained for each model were quite similar, the continuous sub-surface egestion model provided the best fit to the data. The average biodiffusion coefficient, at 1.8 +/- 0.9 cm(2) y(-1), is in good agreement with the values quoted by other workers on the bioturbation effects of this polychaete species. The corresponding value for the biotransport coefficient was found to be 0.9 +/- 0.4 cm y(-1). The effects of non-local mixing were incorporated in a model to describe the temporal evolution of measured (99)Tc and (60)Co radionuclide sediment profiles in the eastern Irish Sea, influenced by radioactive waste discharged from the Sellafield reprocessing plant. Reworking conditions in the sediment column were simulated by considering an upper mixed layer, an exponentially decreasing diffusion coefficient, and appropriate biotransport coefficients to account for non-local mixing. The diffusion coefficients calculated from the (99)Tc and (60)Co cores were in the range 2-14 cm(2) y(-1), which are consistent with the values found by other workers in the same marine area, while the biotransport coefficients were similar to those obtained for a variety of macrobenthic organisms in controlled laboratories and field studies.

Social deprivation and hospital admission rates, length of stay and readmissions in emergency medical admissions

BACKGROUND Patients from deprived backgrounds have a higher in-patient mortality following an emergency medical admission. How deprivation relates to the admission or readmission incidence rates, episode length of stay (LOS) and ancillary resource utilization is less clear. METHODS All emergency medical admissions (66,861 episodes in 36,214 patients) between 2002 and 2013, categorized by quintile of Irish National Deprivation Index were assessed against admission or readmission incidence rates (/1000 local population by electoral division), LOS and utilization of five ancillary services. Univariate and multi-variable risk estimates (odds ratios (OR) or incidence rate ratios (IRR)) were calculated, using truncated Poisson regression. RESULTS The deprivation index quintile was strongly correlated with the emergency medical admission rate with IRR (as compared with quintile 1) as follows: Q2 1.99 (95% CI: 1.96, 2.01), Q3 3.45 (95% CI: 3.41, 3.49), Q4 3.27 (95% CI: 3.23, 3.31) and Q5 4.29 (95% CI: 4.23, 4.35). LOS was not influenced by deprivation status; although increasing deprivation resulted in increased utilization of social services (OR 1.04: 95% CI: 1.03, 1.06), with a lower requirement for occupational therapy (OR 0.94: 95% CI: 0.93, 0.96) and speech/language services (OR 0.83: 95% CI: 0.80, 0.86). There was a rather decreased use of ancillary services with increasing deprivation; however, the readmission rate was strongly predicted by deprivation status. CONCLUSION Deprivation status strongly influenced the admission and readmission rates for medical patients admitted as emergencies; however, ancillary resource utilization was not increased. Deprivation index will increase demand on hospital resources due to the aggregate effect on both admission and readmission incidence rates.

Factors associated with length of stay following an emergency medical admission

BACKGROUND Hospitals are under pressure to use resources in the most efficient manner. We have examined the factors predicting Length of Stay (LOS) in one institution, using a database of all episodes of emergency medical admissions prospectively collected over 12 years. AIM To examine the ability to predict hospital LOS following an emergency medical hospital admission. METHODS All emergency admissions (66,933 episodes; 36,271 patients) to St. Jamess Hospital, Dublin, Ireland over a 12-year period (2002-2013) were evaluated in relation to LOS. Predictor variables (identified univariately) were entered into a multiple logistic regression model to predict a longer or shorter LOS (bivariate at the median). The data was also modelled as count data (absolute LOS), using zero truncated Poisson regression methodology. Appropriate post-estimation techniques for model fit were then applied to assess the resulting model. RESULTS The major predictors of LOS included Acute Illness Severity (biochemical laboratory score at admission), Charlson co-morbidity, Manchester Triage Category at admission, Diagnosis Related Group, sepsis status (based on blood culture result), and Chronic Disease Score Indicator. The full model to predict a LOS above or below the median had an Area Under Receiver Operating Characteristic (AUROC) of 0.71 (95% CI: 0.70, 0.71). The truncated Poisson model appeared to achieve a good model fit (R(2) statistic=0.76). CONCLUSION Predictor variables strongly correlated with LOS; there were linear increases within categories and summation between variables. More predictor variables may improve model reliability but predicting LOS ranges or quantiles may be more realistic, based on these results.

An Audit of a Hospital-Based Doppler Ultrasound Quality Control Protocol Using a Commercial String Doppler Phantom

Results from a four-year audit of a Doppler quality assurance (QA) program using a commercially available Doppler string phantom are presented. The suitability of the phantom was firstly determined and modifications were made to improve the reliability and quality of the measurements. QA of Doppler ultrasound equipment is very important as data obtained from these systems is used in patient management. It was found that if the braided-silk filament of the Doppler phantom was exchanged with an O-ring rubber filament and the velocity range below 50 cm/s was avoided for Doppler quality control (QC) measurements, then the maximum velocity accuracy (MVA) error and intrinsic spectral broadening (ISB) results obtained using this device had a repeatability of 18 ± 3.3% and 19 ± 3.5%, respectively. A consistent overestimation of the MVA of between 12% and 56% was found for each of the tested ultrasound systems. Of more concern was the variation of the overestimation within each respective transducer category: MVA errors of the linear, curvilinear and phased array probes were in the range 12.3-20.8%, 32.3-53.8% and 27-40.7%, respectively. There is a dearth of QA data for Doppler ultrasound; it would be beneficial if a multicentre longitudinal study was carried out using the same Doppler ultrasound test object to evaluate sensitivity to deterioration in performance measurements.

Radiology imaging delays as independent predictors of length of hospital stay for emergency medical admissions

AIM To investigate the extent to which the time to completion for computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound could be shown to influence the length of stay and costs incurred while in hospital, while accounting for patient acuity. MATERIALS AND METHODS All emergency admissions, totalling 25,326 imaging investigations between 2010-2014 were evaluated. The 50(th), 75(th), and 90(th) centiles of completion times for each imaging type was entered into a multivariable truncated Poisson regression model predicting the length of hospital stay. Estimates of risk (odds or incidence rate ratios [IRRs]) of the regressors were adjusted for acute illness severity, Charlson comorbidity index, chronic disabling disease score, and sepsis status. Quantile regression analysis was used to examine the impact of imaging on total hospital costs. RESULTS For all imaging examinations, longer hospital lengths of stay were shown to be related to delays in imaging time. Increased delays in CT and MRI were shown to be associated with increased hospital episode costs, while ultrasound did not independently predict increased hospital costs. The magnitude of the effect of imaging delays on episode costs were equivalent to some measures of illness severity. CONCLUSION CT, MRI, and ultrasound are undertaken in patients with differing clinical complexity; however, even with adjustment for complexity, the time delay in a more expeditious radiological service could potentially shorten the hospital episode and reduce costs.

A Business Analytics Software Tool for Monitoring and Predicting Radiology Throughput Performance.

Business analytics (BA) is increasingly being utilised by radiology departments to analyse and present data. It encompasses statistical analysis, forecasting and predictive modelling and is used as an umbrella term for decision support and business intelligence systems. The primary aim of this study was to determine whether utilising BA technologies could contribute towards improved decision support and resource management within radiology departments. A set of information technology requirements were identified with key stakeholders, and a prototype BA software tool was designed, developed and implemented. A qualitative evaluation of the tool was carried out through a series of semi-structured interviews with key stakeholders. Feedback was collated, and emergent themes were identified. The results indicated that BA software applications can provide visibility of radiology performance data across all time horizons. The study demonstrated that the tool could potentially assist with improving operational efficiencies and management of radiology resources.

Time patterns in mortality after an emergency medical admission; relationship to weekday or weekend admission

BACKGROUND The aim of this study was to detail the time profile and frequency distribution of mortality following an emergency admission and to compare these for weekday and weekend admissions. METHODS We profiled in-hospital deaths following emergency medical admission between 2002 and 2014. We determined the frequency distribution, time pattern, causality and influence of day of admission on mortality out to 120days. We utilized a multivariable regression model (logistic for in-hospital mortality and truncated Poisson for count data) to adjust for major predictor variables. RESULTS There were 82,368 admissions in 44,628 patients with 4587 in-hospital deaths. The 30-day in-hospital mortality declined from 8.2% in 2002 to 3.7% in 2014. The mortality pattern showed an exponential decay over time; the time to death was best described by the three-parameter Weibull model. The calculated time to death for the 5th, 10th, 25th, 50th, 75th, and 90th centiles were 0.5, 1.2, 3.8, 11.1, 26.3 and 49.3days. Acute Illness Severity Score, Chronic Disabling Disease Score, Charlson Co-Morbidity Index and Sepsis status were associated with mortality. The risk of death was initially high, lower by day 3, and showed a cumulative increase over time. The mortality pattern was very similar between a weekday or weekend admission; however, the risk of death was greater at all time points between 0 and 120days for patients admitted at a weekend OR 1.08 (95% CI 1.01-1.15). CONCLUSION We have demonstrated the pattern of mortality following an emergency admission. The underlying pattern is similar between weekday and weekend admissions.