'Rapid Learning health care in oncology' – An approach towards decision support systems enabling customised radiotherapy' ☆ ☆☆

AbstractPurposeAn overview of the Rapid Learning methodology, its results, and the potential impact on radiotherapy.Material and resultsRapid Learning methodology is divided into four phases. In the data phase, diverse data are collected about past patients, treatments used, and outcomes. Innovative information technologies that support semantic interoperability enable distributed learning and data sharing without additional burden on health care professionals and without the need for data to leave the hospital. In the knowledge phase, prediction models are developed for new data and treatment outcomes by applying machine learning methods to data. In the application phase, this knowledge is applied in clinical practice via novel decision support systems or via extensions of existing models such as Tumour Control Probability models. In the evaluation phase, the predictability of treatment outcomes allows the new knowledge to be evaluated by comparing predicted and actual outcomes.ConclusionPersonalised or tailored cancer therapy ensures not only that patients receive an optimal treatment, but also that the right resources are being used for the right patients. Rapid Learning approaches combined with evidence based medicine are expected to improve the predictability of outcome and radiotherapy is the ideal field to study the value of Rapid Learning. The next step will be to include patient preferences in the decision making

Economic consequences of reproductive performance in dairy cattle

The net economic value of reproductive efficiency in dairy cattle was estimated using a stochastic dynamic simulation model. The objective was to compare the economic consequences of reproductive performance scenarios (“average” and “poor”) of a cow having a good reproductive performance and to explore which reproductive factors have an important impact on economic efficiency. A “good” reproductive performance scenario was defined with 1 ovulation rate (POVUi), 0.7 estrus detection rate (PEst), 0.7 conception rate (PCon), 0.03 incidence rate of postpartum disorders prolonging the ovarian cyclicity (CO), 0.2 incidence rate of postpartum disorders reducing conception (ME), 0.05 embryonic death rate (ED), and voluntary waiting period (VWP) of 9 wks pp (post partum). In the current situation of dairy cows in the Netherlands, an “average” reproductive scenario (0.95 POVUi, 0.5 PEst, 0.5 Pcon, 0.07 CO, 0.27 ME, 0.07 ED and VWP of 12 wks pp) and a “poor” reproductive scenario (0.90 POVUi, 0.3 PEst, 0.3 Pcon, 0.11 CO, 0.33 ME, 0.09 ED and VWP of 15 wks pp) were identified. A sensitivity analysis was performed by comparing changes of single effect of factors in a good and poor scenario with the average scenario. The mean net economic loss (NELi) compared with the good scenario was €34 and €231 per cow per year for the average and poor reproductive performance scenario, respectively. Increasing the calving interval resulted in greater economic loss. The important factors on the cost of reproductive efficiency were the involuntary culling cost and the return of milk production. Variation in PCon, PEst, ME, ED, and VWP had large impacts on economic benefits. Keywords: Dairy cow; Reproductive performance; Simulation model; Economic

Effect of milk yield characteristics, breed, and parity on success of the first insemination in Dutch dairy cows

The objective of this study was to determine the contribution of cow factors to the probability of a successful first insemination (SFI). The investigation was performed with 51,791 lactations from 1,396 herds obtained from the Dutch dairy cow database of the Cattle Improvement Co-operative (CRV). Cows that had the first insemination (AI) between 40 and 150 d postpartum were selected. The first AI was classified as successful when cows were not reinseminated and either calved between 267 and 295 d later or were culled within 135 to 295 d after first AI. The lactation curve characteristics of individual lactations were estimated by Wilmink's curve using the test-day milk records from CRV. The lactation curve characteristics (peak milk yield, milk yield at the first-AI date, time of peak yield (PT), and milk persistency) were calculated. Breed, parity, interval from calving to first AI (CFI), lactation curve characteristics, milk production traits, moment of AI related to PT (before or after PT), calf status, month of AI, and month of calving were selected as independent variables for a model with SFI as a dependent variable. A multivariable logistic regression model was used with farm as a random effect. Overall SFI was 44%. The effect of parity on SFI depended on CFI. The first-parity cows had the greatest SFI (0.43) compared with other parities (0.32–0.39) at the same period of CFI before 60 d in milk (DIM), and cows in parity =5 had the least SFI (0.38–0.40) when AI was after 60 DIM. After 60 DIM, extending CFI did not improve SFI in the first-parity cows, but SFI was improved in multiparous cows. Holstein-Friesian cows had lesser SFI (0.37) compared with cross-breed cows (0.39–0.46). Twin and stillbirth calving reduced SFI (0.39) compared with a single female calf (0.45) or a male calf (0.43) calving. The SFI in different months of AI varied and depended on CFI. Cows that received AI before 60 DIM had a lesser SFI, especially in March, June, and July (0.18, 0.35, and 0.34, respectively). Artificial insemination before PT reduced SFI (0.39) in comparison with AI after PT (0.44). The effect of milk yield at the first-AI date on SFI varied depending on CFI. After 60 DIM at the same period of CFI, a high level of milk yield at the first-AI date reduced SFI. In conclusion, knowledge of the contribution of cow factors on SFI can be applied to support decision making on the moment of insemination of an individual cow in estrus. Key words: milk production; lactation curve; first insemination; successful calvin

Analysis of the economically optimal voluntary waiting period for first insemination

The voluntary waiting period (VWP) is defined as the time between parturition and the time at which the cow is first eligible for insemination. Determining the optimal VWP from field data is difficult and unlikely to happen. Therefore, a Monte-Carlo dynamic-stochastic simulation model was created to calculate the economic effects of different VWP. The model is dynamic and uses time steps of 1 wk to simulate the reproductive cycle (ovulation, estrous detection, and conception), the occurrence of postpartum disorders, and the lactation curve. Inputs of the model were chosen to reflect the situation of Dutch dairy cows. In the model, we initially created a cow of a randomly selected breed, parity, month of calving, calf status of last calving, and expected 305-d milk yield. The randomly varied variables were based upon relevant distributions and adjusted for cow statuses. The lactation curve was modeled by Wood's function. The economic input values in the analysis included: cost of milk production (€0.07 to €0.20 per kg), calf price (€35 to €150 per calf), AI cost (€7 to €24 per AI), calving management cost (€137 to €167 per calving), and culling cost, expressed as the retention pay-off (€118 to €1,117). A partial budget approach was used to calculate the economic effect of varying the VWP from 7 to 15 wk postpartum, using a VWP of 6 wk as reference. Per iteration, the VWP with either the lowest economic loss or the maximum profit was determined as the optimal VWP. The optimal VWP of most cows (90%) was less than 10 wk. On average, every VWP longer than 6 wk gave economic losses. Longer VWP were in particular optimal for the first parity of breeds other than Holstein-Friesian, cows calving in winter with low milk production, high milk persistency, delayed peak milk yield time, a delayed time of first ovulation, or occurrence of a postpartum disorder, and while costs of milk production are low and costs for AI are hig