Feasibility study of early outpatient review and early cardiac rehabilitation after cardiac surgery: mixed-methods research design-a study protocol.

INTRODUCTION: Following cardiac surgery, patients currently attend an outpatient review 6 weeks after hospital discharge, where recovery is assessed and suitability to commence cardiac rehabilitation (CR) is determined. CR is then started from 8 weeks. Following a median sternotomy, cardiac surgery patients are required to refrain from upper body exercises, lifting of heavy objects and other strenuous activities for 12 weeks. A delay in starting CR can prolong the recovery process, increase dependence on family/carers and can cause frustration. However, current guidelines for activity and exercise after median sternotomy have been described as restrictive, anecdotal and increasingly at odds with modern clinical guidance for CR. This study aims to examine the feasibility of bringing forward outpatient review and starting CR earlier. METHODS AND ANALYSES: This is a multicentre, randomised controlled, open feasibility trial comparing postoperative outpatient review 6 weeks after hospital discharge, followed by CR commencement from 8 weeks (control arm) versus, postoperative outpatient review 3 weeks after hospital discharge, followed by commencement of CR from 4 weeks (intervention arm). The study aims to recruit 100 eligible patients, aged 18-80 years who have undergone elective or urgent cardiac surgery involving a full median sternotomy, over a 7-month period across two centres. Feasibility will be measured by consent, recruitment, retention rates and attendance at appointments and CR sessions. Qualitative interviews with trial participants and staff will explore issues around study processes and acceptability of the intervention and the findings integrated with the feasibility trial outcomes to inform the design of a future full-scale randomised controlled trial. ETHICS AND DISSEMINATION: Ethics approval was granted by East Midlands-Derby Research Ethics Committee on 10 January 2019. The findings will be presented at relevant conferences disseminated via peer-reviewed research publications, and to relevant stakeholders. TRIAL REGISTRATION NUMBER: ISRCTN80441309

A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows

peer-reviewedThe aim of this study was to build and compare predictive models of calving difficulty in dairy heifers and cows for the purpose of decision support and simulation modeling. Models to predict 3 levels of calving difficulty (unassisted, slight assistance, and considerable or veterinary assistance) were created using 4 machine learning techniques: multinomial regression, decision trees, random forests, and neural networks. The data used were sourced from 2,076 calving records in 10 Irish dairy herds. In total, 19.9 and 5.9% of calving events required slight assistance and considerable or veterinary assistance, respectively. Variables related to parity, genetics, BCS, breed, previous calving, and reproductive events and the calf were included in the analysis. Based on a stepwise regression modeling process, the variables included in the models were the dam's direct and maternal calving difficulty predicted transmitting abilities (PTA), BCS at calving, parity; calving assistance or difficulty at the previous calving; proportion of Holstein breed; sire breed; sire direct calving difficulty PTA; twinning; and 2-way interactions between calving BCS and previous calving difficulty and the direct calving difficulty PTA of dam and sire. The models were built using bootstrapping procedures on 70% of the data set. The held-back 30% of the data was used to evaluate the predictive performance of the models in terms of discrimination and calibration. The decision tree and random forest models omitted the effect of twinning and included only subsets of sire breeds. Only multinomial regression and neural networks explicitly included the modeled interactions. Calving BCS, calving difficulty PTA, and previous calving assistance ranked as highly important variables for all 4 models. The area under the receiver operating characteristic curve (ranging from 0.64 to 0.79) indicates that all of the models had good overall discriminatory power. The neural network and multinomial regression models performed best, correctly classifying 75% of calving cases and showing superior calibration, with an average error in predicted probability of 3.7 and 4.5%, respectively. The neural network and multinomial regression models developed are both suitable for use in decision-support and simulation modeling

Optimization of Drug Prescription and Medication Management in Older Adults with Cardiovascular Disease

Cardiovascular disease increases incrementally with age and elderly patients concomitantly sustain multimorbidities, with resultant prescription of multiple medications. Despite conforming with disease-specific cardiovascular clinical practice guidelines, this polypharmacy predisposes many elderly individuals with cardiovascular disease to adverse drug events and non-adherence. Patient-centered care requires that the clinician explore with each patient his or her goals of care and that this shared decision-making constitutes the basis for optimization of medication management. This approach to aligning therapies with patient preferences is likely to promote patient satisfaction, to limit morbidity, and to favorably affect healthcare costs

An Innovative Interprofessional Simulation: Preparing Students to Tackle the Challenge of Care Transitions

INTRODUCTION Transitions of Care (TOC) are associated with communication breakdowns that contribute to medical errors, medication mistakes, and hospital re-admissions. The purpose of this one-day workshop was to teach interprofessional (IP) skills to healthcare students, focusing on verbal and written communication during a TOC of a standardized patient (SP). METHODS Forty-seven students, representing six healthcare disciplines, worked in IP teams to plan a family meeting for a hospitalized SP who had recently experienced a stroke. Students were to communicate pertinent medical, social, and physical issues to the SP, as well as make discharge recommendations. Discharge summaries were entered into an electronic medical record and transmitted to IP teams simulating either a rehabilitation setting or ambulatory care. IP teams utilized these summaries in their family meeting with the SP. After each scenario, students debriefed, focusing on IP competencies. RESULTS Significant improvements were found in nine of fourteen areas measured by the Attitudes Towards Healthcare Teams Scale. Significant improvements were found for confidence in writing an accurate and concise note as well as gleaning information from a discharge summary. CONCLUSIONS This study demonstrated the effectiveness of a short workshop on improving IP verbal and written communication and confidence in TOC scenarios in acute care, rehabilitation, and ambulatory care

A review of paratuberculosis in dairy herds — Part 1: Epidemiology

Bovine paratuberculosis is a chronic infectious disease of cattle caused by Mycobacterium avium subspecies paratuberculosis (MAP). This is the first in a two-part review of the epidemiology and control of paratuberculosis in dairy herds. Paratuberculosis was originally described in 1895 and is now considered endemic among farmed cattle worldwide. MAP has been isolated from a wide range of non-ruminant wildlife as well as humans and non-human primates. In dairy herds, MAP is assumed to be introduced predominantly through the purchase of infected stock with additional factors modulating the risk of persistence or fade-out once an infected animal is introduced. Faecal shedding may vary widely between individuals and recent modelling work has shed some light on the role of super-shedding animals in the transmission of MAP within herds. Recent experimental work has revisited many of the assumptions around age susceptibility, faecal shedding in calves and calf-to-calf transmission. Further efforts to elucidate the relative contributions of different transmission routes to the dissemination of infection in endemic herds will aid in the prioritisation of efforts for control on farm

A review of paratuberculosis in dairy herds — Part 2: On-farm control

Bovine paratuberculosis is a chronic infectious disease of cattle, caused by Mycobacterium avium subspecies paratuberculosis (MAP). This is the second in a two-part review of the epidemiology and control of paratuberculosis in dairy herds. Several negative production effects associated with MAP infection have been described, but perhaps the most significant concern in relation to the importance of paratuberculosis as a disease of dairy cattle is the potential link with Crohn’s disease in humans. Milk is considered a potential transmission route to humans and it is recognised that pasteurisation does not necessarily eliminate the bacterium. Therefore, control must also include reduction of the levels of MAP in bulk milk supplied from dairy farms. There is little field evidence in support of specific control measures, although several studies seem to show a decreased prevalence associated with the implementation of a combined management and test-and-cull programme. Improvements in vaccination efficacy and reduced tuberculosis (TB) test interference may increase uptake of vaccination as a control option. Farmer adoption of best practice recommendations at farm level for the control of endemic diseases can be challenging. Improved understanding of farmer behaviour and decision making will help in developing improved communication strategies which may be more efficacious in affecting behavioural change on farm

A comparison of machine learning techniques for predicting insemination outcome in Irish dairy cows

Abstract. Reproductive performance has an important effect on economic efficiency in dairy farms with short yearly periods of breeding. The individual factors affecting the outcome of an artificial insemination have been extensively researched in many univariate models. In this study, these factors are analysed in combination to create a comprehensive multivariate model of conception in Irish dairy cows. Logistic regression, Naïve Bayes, Decision Tree learning and Random Forests are trained using 2,723 artificial insemination records from Irish research farms. An additional 4,205 breeding events from commercial dairy farms are used to evaluate and compare the performance of each data mining technique. The models are assessed in terms of both discrimination and calibration ability. The logistic regression model was found to be the most useful model for predicting insemination outcome. This model is proposed as being appropriate for use in decision support and in general simulation of Irish dairy cows