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BackgroundPandemic-related restrictions are expected to continue to shape end-of-life care and impact the experiences of dying hospitalised patients and their families.ObjectiveTo understand families' experiences of loss and bereavement during and after the death of their loved one amidst the SARS-CoV-2 (COVID-19) pandemic.DesignQualitative descriptive study.SettingThree acute care units in a Canadian tertiary care hospital.ParticipantsFamily members of 28 hospitalised patients who died from March-July 2020.Main outcome measuresQualitative semistructured interviews conducted 6-16 months after patient death inquired about family experiences before and beyond the death of their loved one and garnered suggestions to improve end-of-life care.ResultsPandemic restrictions had consequences for families of dying hospitalised patients. Most family members described an attitude of acquiescence, some framing their experience as a sacrifice made for the public good. Families appreciated how clinicians engendered trust in the name of social solidarity while trying to mitigate the negative impact of family separation. However, fears about the patient's experience of isolation and changes to postmortem rituals also created despair and contributed to long-lasting grief.ConclusionProfound loss and enduring grief were described by family members whose final connections to their loved one were constrained by pandemic circumstances. Families observed solidarity among clinical staff and experienced a sense of unity with staff, which alleviated some distress. Their suggestions to improve end-of-life care given pandemic restrictions included frequent, flexible communication, exceptions for family presence when safe, and targeted efforts to connect patients whose isolation is intensified by functional impairment or limited technological access.Trial registration numberNCT04602520; Results.

BackgroundThe 3 Wishes Project (3WP) is an end-of-life program that honors the dignity of dying patients by fostering meaningful connections among patients, families, and clinicians. Since 2013, it has become embedded in the culture of end-of-life care in over 20 ICUs across North America. The purpose of the current study is to describe the variation in implementation of 3WP across sites, in order to ascertain which factors facilitated multicenter implementation, which factors remain consistent across sites, and which may be adapted to suit local needs.MethodsUsing the methodology of qualitative description, we collected interview and focus group data from 85 clinicians who participated in the successful initiation and sustainment of 3WP in 9 ICUs. We describe the transition between different models of 3WP implementation, from core clinical program to the incorporation of various research activities. We describe various sources of financial and in-kind resources accessed to support the program.ResultsBeyond sharing a common goal of improving end-of-life care, sites varied considerably in organizational context, staff complement, and resources. Despite these differences, the program was successfully implemented at each site and eventually evolved from a clinical or research intervention to a general approach to end-of-life care. Key to this success was flexibility and the empowerment of frontline staff to tailor the program to address identified needs with available resources. This adaptability was fueled by cross-pollination of ideas within and outside of each site, resulting in the establishment of a network of like-minded individuals with a shared purpose.ConclusionsThe successful initiation and sustainment of 3WP relied on local adaptations to suit organizational needs and resources. The semi-structured nature of the program facilitated these adaptations, encouraged creative and important ways of relating within local clinical cultures, and reinforced the main tenet of the program: meaningful human connection at the end of life. Local adaptations also encouraged a team approach to care, supplementing the typical patient-clinician dyad by explicitly empowering the healthcare team to collectively recognize and respond to the needs of dying patients, families, and each other.Trial registrationNCT04147169 , retrospectively registered with clinicaltrials.gov on October 31, 2019.

OBJECTIVES:. Proliferation of COVID-19 research underscored the need for improved awareness among investigators, research staff and bedside clinicians of the operational details of clinical studies. The objective was to describe the genesis, goals, participation, procedures, and outcomes of two research operations committees in an academic ICU during the COVID-19 pandemic. DESIGN:. Two-phase, single-center multistudy cohort. SETTING:. University-affiliated ICU in Hamilton, ON, Canada. PATIENTS:. Adult patients in the ICU, medical stepdown unit, or COVID-19 ward. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. An interprofessional COVID Collaborative was convened at the pandemic onset within our department, to proactively coordinate studies, help navigate multiple authentic consent encounters by different research staff, and determine which studies would be suitable for coenrollment. From March 2020 to May 2021, five non-COVID trials continued, two were paused then restarted, and five were launched. Over 15 months, 161 patients were involved in 215 trial enrollments, 110 (51.1%) of which were into a COVID treatment trial. The overall informed consent rate (proportion agreed of those eligible and approached including a priori and deferred consent models) was 83% (215/259). The informed consent rate was lower for COVID-19 trials (110/142, 77.5%) than other trials (105/117, 89.7%; p = 0.01). Patients with COVID-19 were significantly more likely to be coenrolled in two or more studies (29/77, 37.7%) compared with other patients (13/84, 15.5%; p = 0.002). Review items for each new study were collated, refined, and evolved into a modifiable checklist template to set up each study for success. The COVID Collaborative expanded to a more formal Department of Critical Care Research Operations Committee in June 2021, supporting sustainable research operations during and beyond the pandemic. CONCLUSIONS:. Structured coordination and increased communication about research operations among diverse research stakeholders cultivated a sense of shared purpose and enhanced the integrity of clinical research operations.

Los tumores carcinoides son los tumores endócrinos del intestino más frecuentes, la incidencia de la población general es de 1.5 casos por cada 100.000 habitantes, la distribución por edades incluye de 10 a 90 años con una frecuencia máxima sexta y séptima década de la vida. El tumor carcinoide de ampolla de Vater es raro, la clínica es inespecífica y hormonalmente inactivo, se pone de manifiesto por la colestasis, el diagnóstico correcto es dado por la histología. El tratamiento es la pancreatoduodenectomía radical o local según el tamaño tumoral y el seguimiento es a largo plazo.

Abstract Background Scaling-up and sustaining healthcare interventions can be challenging. Our objective was to describe how the 3 Wishes Project (3WP), a personalized end-of-life intervention, was scaled-up and sustained in an intensive care unit (ICU). Methods In a longitudinal mixed-methods study from January 12,013 - December 31, 2018, dying patients and families were invited to participate if the probability of patient death was > 95% or after a decision to withdraw life support. A research team member or bedside clinician learned more about each of the patients and their family, then elicited and implemented at least 3 personalized wishes for patients and/or family members. We used a qualitative descriptive approach to analyze interviews and focus groups conducted with 25 clinicians who cared for the enrolled patients. We used descriptive statistics to summarize patient, wish, and clinician characteristics, and analyzed outcome data in quarters using Statistical Process Control charts. The primary outcome was enrollment of terminally ill patients and respective families; the secondary outcome was the number of wishes per patient; tertiary outcomes included wish features and stakeholder involvement. Results Both qualitative and quantitative analyses suggested a three-phase approach to the scale-up of this intervention during which 369 dying patients were enrolled, having 2039 terminal wishes implemented. From a research project to clinical program to an approach to practice, we documented a three-fold increase in enrolment with a five-fold increase in total wishes implemented, without a change in cost. Beginning as a study, the protocol provided structure; starting gradually enabled frontline staff to experience and recognize the value of acts of compassion for patients, families, and clinicians. The transition to a clinical program was marked by handover from the research staff to bedside staff, whereby project catalysts mentored project champions to create staff partnerships, and family engagement became more intentional. The final transition involved empowering staff to integrate the program as an approach to care, expanding it within and beyond the organization. Conclusions The 3WP is an end-of-life intervention which was implemented as a study, scaled-up into a clinical program, and sustained by becoming integrated into practice as an approach to care.

Abstract Background The 3 Wishes Project (3WP) is an end-of-life program that honors the dignity of dying patients by fostering meaningful connections among patients, families, and clinicians. Since 2013, it has become embedded in the culture of end-of-life care in over 20 ICUs across North America. The purpose of the current study is to describe the variation in implementation of 3WP across sites, in order to ascertain which factors facilitated multicenter implementation, which factors remain consistent across sites, and which may be adapted to suit local needs. Methods Using the methodology of qualitative description, we collected interview and focus group data from 85 clinicians who participated in the successful initiation and sustainment of 3WP in 9 ICUs. We describe the transition between different models of 3WP implementation, from core clinical program to the incorporation of various research activities. We describe various sources of financial and in-kind resources accessed to support the program. Results Beyond sharing a common goal of improving end-of-life care, sites varied considerably in organizational context, staff complement, and resources. Despite these differences, the program was successfully implemented at each site and eventually evolved from a clinical or research intervention to a general approach to end-of-life care. Key to this success was flexibility and the empowerment of frontline staff to tailor the program to address identified needs with available resources. This adaptability was fueled by cross-pollination of ideas within and outside of each site, resulting in the establishment of a network of like-minded individuals with a shared purpose. Conclusions The successful initiation and sustainment of 3WP relied on local adaptations to suit organizational needs and resources. The semi-structured nature of the program facilitated these adaptations, encouraged creative and important ways of relating within local clinical cultures, and reinforced the main tenet of the program: meaningful human connection at the end of life. Local adaptations also encouraged a team approach to care, supplementing the typical patient-clinician dyad by explicitly empowering the healthcare team to collectively recognize and respond to the needs of dying patients, families, and each other. Trial registration NCT04147169 , retrospectively registered with clinicaltrials.gov on October 31, 2019.

The objectives of the study were to develop a multivariate framework for analyzing energy balance data from lactating cows and investigate potential changes in maintenance requirements and partial efficiencies of energy utilization by lactating cows over the years. The proposed model accounted for the fact that metabolizable energy intake, milk energy output, and tissue energy balance are random variables that interact mutually. The model was specified through structural equations implemented in a Bayesian framework. The structural equations, along with a model traditionally used to estimate energetic parameters, were fitted to a large database of indirect calorimetry records from lactating cows. Maintenance requirements and partial efficiencies for both models were similar to values reported in the literature. In particular, the estimated parameters (with 95% credible interval in parentheses) for the proposed model were: net energy requirement for maintenance equal to 0.36 (0.34, 0.38) MJ/kg of metabolic body weight·day; the efficiency of utilizing dietary energy for milk production and tissue gain were 0.63 (0.61, 0.64) and 0.70 (0.68, 0.72), respectively; the efficiency of utilizing body stores for milk production was 0.89 (0.87, 0.91). Furthermore, additional analyses were conducted for which energetic parameters were allowed to depend on the decade in which studies were conducted. These models investigated potential changes in maintenance requirements and partial efficiencies over the years. Canonical correlation analysis was used to investigate the association between changes in energetic parameters with additional dietary and animal characteristics available in the database. For both models, net energy requirement for maintenance and the efficiency of utilizing dietary energy for milk production and tissue gain increased in the more recent decades, whereas the efficiency of utilizing body stores for milk production remained unchanged. The increase in maintenance requirements in modern milk production systems is consistent with the literature that describes increased fasting heat production in cows of higher genetic merit. The increase in utilization of dietary energy for milk production and tissue gain was partially attributed to the changes in dietary composition, in particular to the increase in dietary ether extract to levels closer to currently observed in modern milk production systems. Therefore, the estimated energetic parameters from this study can be used to update maintenance requirements and partial efficiencies of energy utilization in North American feeding systems for lactating cows.

Six classical growth functions (monomolecular, Schumacher, Gompertz, logistic, Richards, and Morgan) were fitted to individual and average (by parity) cumulative milk production curves of Canadian Holstein dairy cows. The data analyzed consisted of approximately 91,000 daily milk yield records corresponding to 122 first, 99 second, and 92 third parity individual lactation curves. The functions were fitted using nonlinear regression procedures, and their performance was assessed using goodness-of-fit statistics (coefficient of determination, residual mean squares, Akaike information criterion, and the correlation and concordance coefficients between observed and adjusted milk yields at several days in milk). Overall, all the growth functions evaluated showed an acceptable fit to the cumulative milk production curves, with the Richards equation ranking first (smallest Akaike information criterion) followed by the Morgan equation. Differences among the functions in their goodness-of-fit were enlarged when fitted to average curves by parity, where the sigmoidal functions with a variable point of inflection (Richards and Morgan) outperformed the other 4 equations. All the functions provided satisfactory predictions of milk yield (calculated from the first derivative of the functions) at different lactation stages, from early to late lactation. The Richards and Morgan equations provided the most accurate estimates of peak yield and total milk production per 305-d lactation, whereas the least accurate estimates were obtained with the logistic equation. In conclusion, classical growth functions (especially sigmoidal functions with a variable point of inflection) proved to be feasible alternatives to fit cumulative milk production curves of dairy cows, resulting in suitable statistical performance and accurate estimates of lactation traits.

A mechanistic model (COWPOLL) was used to estimate enteric methane (CH4) emissions from beef production systems in Chile. The results expressed as a proportion of gross energy intake (GEI) were compared with enteric fermentation data reported in the last Chilean greenhouse gases inventory, which utilized an earlier the Intergovernmental Panel on Climate Change Tier 2 approach. The simulation analysis was based on information from feedstuffs, dry matter intake (DMI), body weight (BW) and average daily gain (ADG) of steers raised and finished at two research facilities located in Central and Southern Chile, as well as three simulated scenarios for grass-based finishing systems in Southern Chile. Data for feedlot production systems in the central region were assessed by considering steers fed a forage : concentrate ratio of 23 : 77 using maize silage and wheat straw as roughage sources during the stages of backgrounding and fattening. Average DMI were 7.3±0.62 and 9.2±0.55 kg/day per steer for backgrounding and fattening, respectively, whereas ADG were 1.1±0.22 and 1.3±0.37 kg/day for backgrounding and fattening. For the Southern Chilean fattening production systems, the forage : concentrate ratio was 56 : 44 with ryegrass pasture as the sole forage source. In this case, average DMI was 9.97±0.51 and ADG was 1.1±0.24 kg/day per steer. Two of the grass-based scenarios used the same initial BW information as that used for the Central and Southern Chilean systems, but feedlot diets were replaced by ryegrass pasture. The third grass-based scenario used an initial BW of 390 kg. In all the grass-based scenarios an ADG of 0.90 kg/day, with maximum DMI estimated as a proportion of BW (0.01 of NDF, kg/kg BW), was assumed. The results of the simulation analysis showed that emission factors (Ym; fraction of GEI) ranged from 0.062 to 0.079 of GEI. Smaller values were associated with finishing systems that included a lower proportion of forage in the diet due to higher propionate production, which serves as a sink for hydrogen in the rumen. Cattle finished in feedlot systems had an average of 0.062 of GEI lost as CH4, whereas grass-based cattle had losses of 0.079 of GEI. Enteric CH4 emissions for the systems using grass-based and concentrate diets were 261 and 159 g/kg weight gain, respectively. The Chilean CH4 inventory employs a fixed Ym of 0.060 to estimate enteric fermentation for all cattle. This value is lower than the average Ym obtained in the current simulation analysis (0.071 of GEI), which results in underestimation of enteric CH4 emissions from beef cattle. However, these results need to be checked against field measurements of CH4 emissions. Implementation of mechanistic models in the preparation of national greenhouse gas inventories is feasible if appropriate information is provided, allowing dietary characteristics and regional particularities to be taken into consideration.

Reliable estimates of fresh manure water output from dairy cows help to improve storage design, enhance efficiency of land application, quantify the water footprint, and predict nutrient transformations during manure storage. The objective of the study was to construct a mechanistic, dynamic, and deterministic mathematical model to quantify urinary and fecal water outputs (kg/d) from individual lactating dairy cows. The model contained 4 body water pools: reticulorumen (QRR), post-reticulorumen (QPR), extracellular (QEC), and intracellular (QIC). Dry matter (DM) intake, dietary forage, DM, crude protein, acid detergent fiber and ash contents, milk yield, and milk fat and protein contents, days in milk, and body weight were input variables to the model. A set of linear equations was constructed to determine drinking, feed, and saliva water inputs to QRR and fractional water passage from QRR to QPR. Water transfer via the rumen wall was subjected to changes in QEC and total water input to QRR. Post-reticulorumen water passage was adjusted for DM intake. Metabolic water production and respiratory cutaneous water losses were estimated with functions of heat production in the model. Water loss in urine was driven by absorbed N left after being removed via milk. Model parameters were estimated simultaneously using observed fecal and urinary water output data from lactating Holstein cows (n=670). The model was evaluated with data that were not used for model development and optimization (n=377). The observations in both data sets were related to thermoneutral conditions. The model predicted drinking water intake, fecal, urinary, and total fresh manure water output with root mean square prediction errors as a percentage of average values of 18.1, 15.6, 30.6, and 14.6%, respectively. In all cases, >97% of the prediction error was due to random variability of data. The model can also be used to determine saliva production, heat and metabolic water production, respiratory cutaneous water losses, and size of major body water pools in lactating Holstein cows under thermoneutral conditions.

The purpose of this study was to evaluate prediction of methane emissions from finishing beef cattle using an extant mechanistic model with pH-independent or pH-dependent volatile fatty acid (VFA) stoichiometries, a recent stoichiometry adjustment for the use of monensin, and adaptation of the underlying model structure, to see if prediction improvements could be made for beef cattle. The database used for independent evaluation of methane predictions consisted of 74 animal means from six studies. For the "Bannink" stoichiometries, pH-dependence of stoichiometry improved the root mean square prediction error (RMSPE) statistic (38.8 to 36.6%) but the concordance correlation coefficient (CCC) statistic was reduced (0.509 to 0.469). Inclusion of monensin in the stoichiometry improved both pH-independent and pH-dependent predictions. For the "Murphy" stoichiometries, pH-dependence worsened the RMSPE (31.2 to 33.7%) as well as the CCC (0.611 to 0.465) statistic. Inclusion of monensin in the stoichiometry improved predictions with the pH-independent but not with the pH-dependent stoichiometry. Results indicate that although improvements have been made to the mechanistic model, further improvement in the representation of VFA stoichiometry, and likely the representation and prediction of pH and neutral detergent fiber digestibility, are required for more accurate prediction of methane emissions for finishing beef cattle. However, inclusion of an adjustment for monensin feeding generally lead to improved methane predictions.

Phytase, a widely used feed additive in poultry diets, increases P availability and subsequently reduces inorganic-P supplementation and P-excretion. Phytase supplementation effect on P-retention in poultry has been investigated, but the effect sizes were highly variable. The present study's objective was to conduct several meta-analyses to quantitatively summarize the phytase effect on P-retention in broilers and layers. Data from 103 and 26 controlled experiments testing the phytase effect on P-retention were included in 2 separate meta-analyses for broilers and layers, respectively. The mean difference calculated by subtracting the means of P-retention for the control group from the phytase-supplemented group was chosen as an effect size estimate. Between-study variability (heterogeneity) of mean difference was estimated using random-effect models and had a significant effect (P < 0.01) in both broilers and layers. Therefore, random-effect models were extended to mixed-effect models to explain heterogeneity and obtain final phytase effect size estimates. Available dietary and bird variables were included as fixed effects in the mixed-effect models. The final broiler mixed-effect model included phytase dose and Ca-to-total-P ratio (Ca:tP), explaining 15.6% of the heterogeneity. Other variables such as breed might further explain between-study variance. Broilers consuming control diets were associated with 48.4% P-retention. Exogenous phytase supplementation at 1,039 FTU/kg of diet increased P-retention by 8.6 percentage units on average. A unit increase of phytase dose and Ca:tP from their means further increased P-retention. For layers, the final mixed-effect models included dietary Ca, age, and experimental period length. The variables explained 65.9% of the heterogeneity. Layers receiving exogenous phytase at 371 FTU/kg were associated with a 5.02 percentage unit increase in P-retention. A unit increase in dietary Ca from its mean increased P-retention, whereas an increase in the experiment length and layer's age decreased P-retention. Phytase supplementation had a significant positive effect on P-retention in both broilers and layers, but effect sizes across studies were significantly heterogeneous due to differences in Ca contents, experiment length, bird age, and phytase dose.

Quantification of dairy cattle nitrogen (N) excretion and secretion is necessary to improve the efficiency with which feed N is converted to milk N (ENU). Faecal and urinary N excretion and milk N secretion are correlated with each other and thus are more accurately described by a multivariate model that can accommodate the covariance between the three observations than by three separate univariate models. Further, by simultaneously predicting the three routes of excretion and taking advantage of the mass balance relationships between them, covariate effects on N partitioning from feed to faeces and absorbed N and from absorbed N to milk and urine N and animal ENU can be estimated. A database containing 1094 lactating dairy cow observations collated from indirect calorimetry experiments was used for model development. Dietary metabolisable energy content (ME, MJ/kg DM) increased ENU at a decreasing rate, increased the efficiency with which feed N was converted to absorbed N and decreased the efficiency with which absorbed N was converted to milk N. However, the parameter estimate of the effect of ME on post-absorption efficiency was not different from zero when the model was fitted to a data subset in which net energy and metabolisable protein were at or above requirement. This suggests the effect of ME on post-absorption N use is dependent on the energy status of the animal.

Linear and non-linear models have been extensively utilised for the estimation of net and metabolisable energy requirements and for the estimation of the efficiencies of utilising dietary energy for maintenance and tissue gain. In growing animals, biological principles imply that energy retention rate is non-linearly related to the energy intake level because successive increments in energy intake above maintenance result in diminishing returns for tissue energy accretion. Heat production in growing cattle has been traditionally described by logarithmic regression and exponential models. The objective of the present study was to develop Bayesian models of energy retention and heat production in growing cattle using parametric and non-parametric techniques. Parametric models were used to represent models traditionally employed to describe energy use in growing steers and heifers whereas the non-parametric approach was introduced to describe energy utilisation while accounting for non-linearities without specifying a particular functional form. The Bayesian framework was used to incorporate prior knowledge of bioenergetics on tissue retention and heat production and to estimate net and metabolisable energy requirements (NEM and MEM, respectively), and the partial efficiencies of utilising dietary metabolisable energy for maintenance (km) and tissue energy gain (kg). The database used for the study consisted of 719 records of indirect calorimetry on steers and non-pregnant, non-lactating heifers. The NEM was substantially larger in energy retention models (ranged from 0.40 to 0.50 MJ/kg BW0.75.day) than were NEM estimates from heat-production models (ranged from 0.29 to 0.49 MJ/kg BW0.75.day). Similarly, km was also larger in energy retention models than in heat production models. These differences are explained by the nature of y-intercepts (NEM) in these two models. Energy retention models estimate fasting catabolism as the y-intercept, while heat production models estimate fasting heat production. Conversely, MEM was virtually identical in all models and approximately equal to 0.53 MJ/kg BW0.75.day in this database.

Organic compounds in dairy manure undergo a series of reactions producing pollutants such as ammonia and methane. Because various organic compounds have different reaction rates, the emissions could be accurately determined if amounts and concentrations of individual nutrients in manure are known. A set of empirical models were developed for predicting faecal and urinary water, carbon (C), nitrogen (N), acid detergent fibre and neutral detergent fibre output (kg/day) from lactating Holstein cows. Dietary nutrient contents, milk yield and composition, bodyweight, age and days in milk were used with or without dry matter intake (DMI) as potential predictor variables. Multi-collinearity, goodness of fit, model complexity, and random study and animal effects were taken into account during model development, which used 742 measured faecal or urinary nutrient output observations (kg/day). The models were evaluated with an independent dataset (n ≤ 364). When DMI was used as a predictor variable, the models predicted faecal and urinary nutrient outputs successfully with root mean square prediction error as a percentage of average observed values (RMSPE%) ranging from 9.1% to 20.7%. All the predictions except urine output had RMSPE% ranging from 18.3% to 24.6% when DMI was not used. The nutrient output predictions were in reasonable agreement with observed values throughout the data range (systematic bias

Organic compounds in dairy manure undergo a series of reactions producing pollutants such as ammonia and methane. Because various organic compounds have different reaction rates, the emissions could be accurately determined if amounts and concentrations of individual nutrients in manure are known. A set of empirical models were developed for predicting faecal and urinary water, carbon (C), nitrogen (N), acid detergent fibre and neutral detergent fibre output (kg/day) from lactating Holstein cows. Dietary nutrient contents, milk yield and composition, bodyweight, age and days in milk were used with or without dry matter intake (DMI) as potential predictor variables. Multi-collinearity, goodness of fit, model complexity, and random study and animal effects were taken into account during model development, which used 742 measured faecal or urinary nutrient output observations (kg/day). The models were evaluated with an independent dataset (n ≤ 364). When DMI was used as a predictor variable, the models predicted faecal and urinary nutrient outputs successfully with root mean square prediction error as a percentage of average observed values (RMSPE%) ranging from 9.1% to 20.7%. All the predictions except urine output had RMSPE% ranging from 18.3% to 24.6% when DMI was not used. The nutrient output predictions were in reasonable agreement with observed values throughout the data range (systematic bias

Background: In a trial of early in-bed cycling in critically ill patients, a video demonstrating use of the cycle in addition to verbal description may improve satisfaction with the informed consent process for all persons involved. Methods: A convenience sample of in-person consent encounters for enrolment in TryCYCLE (NCT01885442), a 33-patient pilot study of in-bed cycling with mechanically ventilated patients in an intensive care unit, were recruited. In this study within a trial, using concealed allocation, we randomized consent encounters to a Video or Standard consent approach. Those in the Video group viewed a 2-minute video of a model using in-bed cycling plus the routine verbal description of the study. The Standard group received the routine verbal description only. Patients and/or substitute decision makers (SDMs) were blinded to the study purpose. After each encounter, patients and/or SDMs and the research coordinator submitted written satisfaction and comfort ratings using 7-point scales (higher scores better). We documented consent outcomes and analyzed between group differences with independent group t-tests. Results: We randomized 14 encounters (6 Video, 8 Standard). Ten completed questionnaires (5 in each group) demonstrated no difference in patient and/or SDM satisfaction or comfort between Video or Standard (mean [standard deviation] Satisfaction: 6.8[0.45] vs. 7.0[0] vs. p=0.37; Comfort: 7.0[0] vs. 7.0[0], p>0.99). The research coordinator evaluated all randomized encounters, with no differences between Video or Standard (Satisfaction: 7.0[0] vs. 6.9[0.35], p=0.41; Comfort: 6.7[0.52] vs. 6.9[0.35], p=0.39). All 14 consent encounters enrolled in TryCYCLE. Conclusions: Patient and/or SDM satisfaction and comfort with consent was very high for both the Video and Standard approaches. Further research, including use of videos to portray different study interventions, is needed, including analysis of patient and/or SDM satisfaction, comfort, comprehension, and consent rates. Registration for host trial: ClinicalTrials.gov, NCT01885442, registered on June 25, 2013

This contribution focuses on applying mathematical models based on systems of ordinary first-order differential equations to synthesize and interpret data from egg production experiments. Models based on linear systems of differential equations are contrasted with those based on nonlinear systems. Regression equations arising from analytical solutions to linear compartmental schemes are considered as candidate functions for describing egg production curves, together with aspects of parameter estimation. Extant candidate functions are reviewed, a role for growth functions such as the Gompertz equation suggested, and a function based on a simple new model outlined. Structurally, the new model comprises a single pool with an inflow and an outflow. Compartmental simulation models based on nonlinear systems of differential equations, and thus requiring numerical solution, are next discussed, and aspects of parameter estimation considered. This type of model is illustrated in relation to development and evaluation of a dynamic model of calcium and phosphorus flows in layers. The model consists of 8 state variables representing calcium and phosphorus pools in the crop, stomachs, plasma, and bone. The flow equations are described by Michaelis-Menten or mass action forms. Experiments that measure Ca and P uptake in layers fed different calcium concentrations during shell-forming days are used to evaluate the model. In addition to providing a useful management tool, such a simulation model also provides a means to evaluate feeding strategies aimed at reducing excretion of potential pollutants in poultry manure to the environment.

Monensin is a widely used feed additive with the potential to minimize methane (CH4) emissions from cattle. Several studies have investigated the effects of monensin on CH4, but findings have been inconsistent. The objective of the present study was to conduct meta-analyses to quantitatively summarize the effect of monensin on CH4 production (g/d) and the percentage of dietary gross energy lost as CH4 (Ym) in dairy cows and beef steers. Data from 22 controlled studies were used. Heterogeneity of the monensin effects were estimated using random effect models. Due to significant heterogeneity (>68%) in both dairy and beef studies, the random effect models were then extended to mixed effect models by including fixed effects of DMI, dietary nutrient contents, monensin dose, and length of monensin treatment period. Monensin reduced Ym from 5.97 to 5.43% and diets with greater neutral detergent fiber contents (g/kg of dry matter) tended to enhance the monensin effect on CH4 in beef steers. When adjusted for the neutral detergent fiber effect, monensin supplementation [average 32 mg/kg of dry matter intake (DMI)] reduced CH4 emissions from beef steers by 19±4 g/d. Dietary ether extract content and DMI had a positive and a negative effect on monensin in dairy cows, respectively. When adjusted for these 2 effects in the final mixed-effect model, monensin feeding (average 21 mg/kg of DMI) was associated with a 6±3 g/d reduction in CH4 emissions in dairy cows. When analyzed across dairy and beef cattle studies, DMI or monensin dose (mg/kg of DMI) tended to decrease or increase the effect of monensin in reducing methane emissions, respectively. Methane mitigation effects of monensin in dairy cows (-12±6 g/d) and beef steers (-14±6 g/d) became similar when adjusted for the monensin dose differences between dairy cow and beef steer studies. When adjusted for DMI differences, monensin reduced Ym in dairy cows (-0.23±0.14) and beef steers (-0.33±0.16). Monensin treatment period length did not significantly modify the monensin effects in dairy cow or beef steer studies. Overall, monensin had stronger antimethanogenic effects in beef steers than dairy cows, but the effects in dairy cows could potentially be improved by dietary composition modifications and increasing the monensin dose.

The objective of the current study was to evaluate the utilization of calcium (Ca) and phosphorus (P) in growing sheep consuming increasing amounts of dicalcium phosphate. Eighteen growing sheep, aged 8 months, were fed a basal diet supplemented with 0, 12·5 and 25 g of dicalcium phosphate/day. During the experiment, animals were injected intravenously with 7·4 MBq of 45Ca and 32P and samples of plasma, faeces and urine were subsequently taken daily for 1 week after injection. Rumen fluid was sampled on days 4-7 after injection. Specific radioactivity in plasma and in faeces were used to determine true absorption of Ca and P, whereas plasmatic and ruminal specific radio-activities were used to determine endogenous P flow into the rumen and turnover time of rumen P. Increasing dicalcium phosphate intake led to linear increases in faecal excretion of endogenous Ca and P (P

A meta-analysis investigation based on literature data was conducted to estimate the effect size of nutritional and animal factors on phosphorus (P) excretion in feces and concentrations of P in milk. Two data sets were created for statistical analysis: One to derive prediction equations for P in feces (25 studies; 130 treatments) and another for P in milk (19 studies; 94 treatments). Prediction equations were derived using mixed model regression analysis with a random effect for study, and equations were evaluated based on values for Bayesian information criterion (BIC), root mean square prediction error (RMSPE), and concordance correlation coefficient (CCC) statistics. In terms of RMSPE and CCC values, fecal P excretion was best predicted by P intake, where P in feces (g/d)=-3.8(±3.45) + 0.64(±0.038) × P intake (g/d) (RMSPE: 18.3%, CCC: 0.869). However, significant effects of crude protein [g/kg of dry matter (DM)], neutral detergent fiber (g/kg of DM), and milk yield (kg/d) on fecal P excretion were also found. Despite a lack of improvement in terms of RMSPE and CCC values, these parameters may still explain part of the variation in fecal P excretion. For milk P, expressed as a fraction of P intake, the following equation had the highest CCC and the lowest RMSPE value: P in milk as a fraction of P intake (g/g)=0.42(±0.065) + 0.23(±0.018) × feed efficiency (i.e., fat- and protein-corrected milk yield/dry matter intake) - 0.11(±0.0199) × P in feed (g/kg of DM) (RMSPE: 19.7%; CCC: 0.761). Equations derived to predict fecal P as a fraction of P intake (g/g) or milk P content (g/kg) could not adequately explain the observed variation and did not perform well in terms of RMSPE and CCC values. Examination of the residuals showed that P balance was a seemingly confounding factor in some of the models. The results presented here can be used to estimate P in feces and milk based on commonly measured dietary and milk variables, but could also be used to guide development of mechanistic models on P metabolism in lactating dairy cattle. Factors to consider in future research and modeling efforts regarding efficiency of P use include the effects of dietary neutral detergent fiber, crude protein, starch, variation in P content of milk, and effects of P resorption from bone and body tissues during early lactation.

PurposeFew models have been developed specifically for the epidemiology of diabetes. Diabetes incidence is critical in predicting diabetes prevalence. However, reliable estimates of disease incidence rates are difficult to obtain. The aim of this study was to propose a mathematical framework for predicting diabetes prevalence using incidence rates estimated within the model using body mass index (BMI) data.MethodsA generic mechanistic model was proposed considering birth, death, migration, aging, and diabetes incidence dynamics. Diabetes incidence rates were determined within the model using their relationships with BMI represented by the Hill equation. The Hill equation parameters were estimated by fitting the model to National Health and Nutrition Examination Survey (NHANES) 1999-2010 data and used to predict diabetes prevalence pertaining to each NHANES survey year. The prevalences were also predicted using diabetes incidence rates calculated from the NHANES data themselves. The model was used to estimate death rate parameters and to quantify sensitivities of prevalence to each population dynamic.ResultsThe model using incidence rate estimates from the Hill equations successfully predicted diabetes prevalence of younger, middle-aged, and older adults (prediction error, 20.0%, 9.64%, and 7.58% respectively). Diabetes prevalence was positively associated with diabetes incidence in every age group, but the associations among younger adults were stronger. In contrast, diabetes prevalence was more sensitive to death rates in older adults than younger adults. Both diabetes incidence and prevalence were strongly sensitive to BMI at younger ages, but sensitivity gradually declined as age progressed. Younger and middle aged adults diagnosed with diabetes had at least a two-fold greater risk of death than their nondiabetic counterparts. Nondiabetic older adults were found to be under slightly higher death risk (0.079) than those diagnosed with diabetes (0.073).ConclusionsThe proposed model predicts diagnosed diabetes incidence and prevalence reasonably well using the link between BMI and diabetes development risk. Ethnic group and gender-specific model parameter estimates could further improve predictions. Model prediction accuracy and applicability need to be comprehensively evaluated with independent data sets.

Experiments designed to investigate the effect of dietary nutrient concentrations on the growth and development of pullets are relatively long term and expensive to conduct. As the cost of research increases, mathematical models become valuable tools to answer research and development questions. Modeling growth curves allows nutritionists and poultry researchers to predict dynamic or daily nutrient needs more adequately than using fixed requirements. The potential and validity of a specially reparameterized monomolecular model to partition nutrient intakes between requirements for maintenance and growth was previously demonstrated in relation to ruminants, pigs, chickens, turkeys, and broiler breeder pullets. In the current study, the model was evaluated for its ability to estimate ME and CP requirements for maintenance and growth in egg-type pullets. On the basis of the results of this study, along with those previously reported for chickens, turkeys, and broiler breeder pullets, this model is advantageous because it can predict the magnitude and direction of responses of growing poultry to dietary ME and CP intakes without requiring initial assumptions. The model also has the advantage of biological interpretability of the parameter estimates. One of the main consequences of this interpretability is that the results from several experiments can be pooled to obtain the best estimates of the response coefficients. ©2012 Poultry Science Association, Inc.

Experiments designed to investigate the effect of dietary nutrient concentrations on the growth and development of pullets are relatively long term and expensive to conduct. As the cost of research increases, mathematical models become valuable tools to answer research and development questions. Modeling growth curves allows nutritionists and poultry researchers to predict dynamic or daily nutrient needs more adequately than using fixed requirements. The potential and validity of a specially reparameterized monomolecular model to partition nutrient intakes between requirements for maintenance and growth was previously demonstrated in relation to ruminants, pigs, chickens, turkeys, and broiler breeder pullets. In the current study, the model was evaluated for its ability to estimate ME and CP requirements for maintenance and growth in egg-type pullets. On the basis of the results of this study, along with those previously reported for chickens, turkeys, and broiler breeder pullets, this model is advantageous because it can predict the magnitude and direction of responses of growing poultry to dietary ME and CP intakes without requiring initial assumptions. The model also has the advantage of biological interpretability of the parameter estimates. One of the main consequences of this interpretability is that the results from several experiments can be pooled to obtain the best estimates of the response coefficients. ©2012 Poultry Science Association, Inc.

The use of repeated measures analysis of variance (ANOVA) options for the analysis of in vitro ruminal fermentation gas production profiles is illustrated. Because of the different variances and covariance structures among profile observations, ordinary ANOVA for more than two-time points is not recommended. To mitigate this problem, the Greenhouse–Geisser epsilon correction can be applied to reduce the degrees of freedom, inflated by violation of the sphericity assumption, for F ratio probability calculations. After this correction, the Box–Greenhouse–Geisser ANOVA (modified ANOVA) layout appears similar to the layout of a split-plot design ANOVA with whole plots divided into subplots (incubation time). Any F tests in the main plot part are valid but F tests involving the time factor from the subplot part need modification because time factor, by its very nature, cannot be allocated at random. Application of multivariate ANOVA, distance multivariate ANOVA, ante-dependence and mixed model analysis are also considered. All these options lend themselves to wide application in the applied biological sciences. [ABSTRACT FROM AUTHOR]