Non-Invasive PPG-Based System for Continuous Heart Rate Monitoring of Incubated Avian Embryo.

The chicken embryo is a widely used experimental animal model in many studies, including in the field of developmental biology, of the physiological responses and adaptation to altered environments, and for cancer and neurobiology research. The embryonic heart rate is an important physiological variable used as an index reflecting the embryo's natural activity and is considered one of the most difficult parameters to measure. An acceptable measurement technique of embryonic heart rate should provide a reliable cardiac signal quality while maintaining adequate gas exchange through the eggshell during the incubation and embryonic developmental period. In this paper, we present a detailed design and methodology for a non-invasive photoplethysmography (PPG)-based prototype (Egg-PPG) for real-time and continuous monitoring of embryonic heart rate during incubation. An automatic embryonic cardiac wave detection algorithm, based on normalised spectral entropy, is described. The developed algorithm successfully estimated the embryonic heart rate with 98.7% accuracy. We believe that the system presented in this paper is a promising solution for non-invasive, real-time monitoring of the embryonic cardiac signal. The proposed system can be used in both experimental studies (e.g., developmental embryology and cardiovascular research) and in industrial incubation applications.

Is Continuous Heart Rate Monitoring of Livestock a Dream or Is It Realistic? A Review.

For all homoeothermic living organisms, heart rate (HR) is a core variable to control the metabolic energy production in the body, which is crucial to realize essential bodily functions. Consequently, HR monitoring is becoming increasingly important in research of farm animals, not only for production efficiency, but also for animal welfare. Real-time HR monitoring for humans has become feasible though there are still shortcomings for continuously accurate measuring. This paper is an effort to estimate whether it is realistic to get a continuous HR sensor for livestock that can be used for long term monitoring. The review provides the reported techniques to monitor HR of living organisms by emphasizing their principles, advantages, and drawbacks. Various properties and capabilities of these techniques are compared to check the potential to transfer the mostly adequate sensor technology of humans to livestock in term of application. Based upon this review, we conclude that the photoplethysmographic (PPG) technique seems feasible for implementation in livestock. Therefore, we present the contributions to overcome challenges to evolve to better solutions. Our study indicates that it is realistic today to develop a PPG sensor able to be integrated into an ear tag for mid-sized and larger farm animals for continuously and accurately monitoring their HRs.

An Approach towards Motion-Tolerant PPG-Based Algorithm for Real-Time Heart Rate Monitoring of Moving Pigs.

Animal welfare remains a very important issue in the livestock sector, but monitoring animal welfare in an objective and continuous way remains a serious challenge. Monitoring animal welfare, based upon physiological measurements instead of the audio-visual scoring of behaviour, would be a step forward. One of the obvious physiological signals related to welfare and stress is heart rate. The objective of this research was to measure heart rate (beat per minutes) in pigs with technology that soon will be affordable. Affordable heart rate monitoring is done today at large scale on humans using the Photo Plethysmography (PPG) technology. We used PPG sensors on a pig's body to test whether it allows the retrieval of a reliable heart rate signal. A continuous wavelet transform (CWT)-based algorithm is developed to decouple the cardiac pulse waves from the pig. Three different wavelets, namely second, fourth and sixth order Derivative of Gaussian (DOG), are tested. We show the results of the developed PPG-based algorithm, against electrocardiograms (ECG) as a reference measure for heart rate, and this for an anaesthetised versus a non-anaesthetised animal. We tested three different anatomical body positions (ear, leg and tail) and give results for each body position of the sensor. In summary, it can be concluded that the agreement between the PPG-based heart rate technique and the reference sensor is between 91% and 95%. In this paper, we showed the potential of using the PPG-based technology to assess the pig's heart rate.

Heart rate and high frequency heart rate variability during stress as biomarker for clinical depression. A systematic review.

The interaction of physical and mental vulnerability and environmental constraints is thought to foster the development of psychiatric disorders such as major depressive disorder (MDD). A central factor in the development of psychopathology is mental stress. Despite some evidence for parasympathetic withdrawal and sympathetic overactivity in MDD, the psychophysiological response to stress in depression is not clear-cut. Given the growing interest in heart rate and heart rate variability as indicators for remote monitoring of patients, it is important to understand how patients with MDD react to stress in a laboratory-controlled environment. We conducted a systematic review of studies using electrocardiography to derive heart rate and heart rate variability during stress in patients with clinical depression. We focused on well-validated stress tasks- the mental arithmetic stress task, the Trier social stress task and public speaking task- to minimize confounding effects due to the nature of the stressor. The majority of studies found hypo-reactivity during stress as a hallmark of depression as evidenced by lower fluctuation in heart rate and heart rate variability in the high-frequency band. We address the potential underlying biological mechanisms, the influence of covariates on these measures and briefly discuss the specificity and potential for remote monitoring by using these variables.

An Automatic Head Surface Temperature Extraction Method for Top-View Thermal Image with Individual Broiler.

Surface temperature variation in a broiler's head can be used as an indicator of its health status. Surface temperatures in the existing thermograph based animal health assessment studies were mostly obtained manually. 2185 thermal images, each of which had an individual broiler, were captured from 20 broilers. Where 15 broilers served as the experimental group, they were injected with 0.1mL of pasteurella inoculum. The rest, 5 broilers, served as the control group. An algorithm was developed to extract head surface temperature automatically from the top-view broiler thermal image. Adaptive K-means clustering and ellipse fitting were applied to locate the broiler's head region. The maximum temperature inside the head region was extracted as the head surface temperature. The developed algorithm was tested in Matlab® (R2016a) and the testing results indicated that the head region in 92.77% of the broiler thermal images could be located correctly. The maximum error of the extracted head surface temperatures was not greater than 0.1 °C. Different trend features were observed in the smoothed head surface temperature time series of the broilers in experimental and control groups. Head surface temperature extracted by the presented algorithm lays a foundation for the development of an automatic system for febrile broiler identification.

Towards practical application of sensors for monitoring animal health; design and validation of a model to detect ketosis.

The objective of this study was to design and validate a mathematical model to detect post-calving ketosis. The validation was conducted in four commercial dairy farms in Israel, on a total of 706 multiparous Holstein dairy cows: 203 cows clinically diagnosed with ketosis and 503 healthy cows. A logistic binary regression model was developed, where the dependent variable is categorical (healthy/diseased) and a set of explanatory variables were measured with existing commercial sensors: rumination duration, activity and milk yield of each individual cow. In a first validation step (within-farm), the model was calibrated on the database of each farm separately. Two thirds of the sick cows and an equal number of healthy cows were randomly selected for model validation. The remaining one third of the cows, which did not participate in the model validation, were used for model calibration. In order to overcome the random selection effect, this procedure was repeated 100 times. In a second (between-farms) validation step, the model was calibrated on one farm and validated on another farm. Within-farm accuracy, ranging from 74 to 79%, was higher than between-farm accuracy, ranging from 49 to 72%, in all farms. The within-farm sensitivities ranged from 78 to 90%, and specificities ranged from 71 to 74%. The between-farms sensitivities ranged from 65 to 95%. The developed model can be improved in future research, by employing other variables that can be added; or by exploring other models to achieve greater sensitivity and specificity.

Towards practical application of sensors for monitoring animal health: the effect of post-calving health problems on rumination duration, activity and milk yield.

Three sources of sensory data: cow's individual rumination duration, activity and milk yield were evaluated as possible indicators for clinical diagnosis, focusing on post-calving health problems such as ketosis and metritis. Data were collected from a computerised dairy-management system on a commercial dairy farm with Israeli Holstein cows. In the analysis, 300 healthy and 403 sick multiparous cows were studied during the first 3 weeks after calving. A mixed model with repeated measurements was used to compare healthy cows with sick cows. In the period from 5 d before diagnosis and treatment to 2 d after it, rumination duration and activity were lower in the sick cows compared to healthy cows. The milk yield of sick cows was lower than that of the healthy cows during a period lasting from 5 d before until 5 d after the day of diagnosis and treatment. Differences in the milk yield of sick cows compared with healthy cows became greater from 5 to 1 d before diagnosis and treatment. The greatest significant differences occurred 3 d before diagnosis for rumination duration and 1 d before diagnosis for activity and milk yield. These results indicate that a model can be developed to automatically detect post-calving health problems including ketosis and metritis, based on rumination duration, activity and milk yield.

Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds.

Bovine respiratory disease (BRD) complex in calves impairs health and welfare and causes severe economic losses for the Stockperson. Early recognition of BRD should lead to earlier veterinary (antibiotic/anti-inflammatory) treatment interventions thereby reducing the severity of the disease and associated costs. Coughing is one of the clinical manifestations of BRD. It is believed that by automatically and continuously monitoring the sounds within calf houses, and analysing the coughing frequency, early recognition of BRD in calves is possible. Therefore, the objective of the present study was to develop an automated calf cough monitor and examine its potential as an early warning system for BRD in artificially reared dairy calves. The coughing sounds of 62 calves were continuously recorded by a microphone over a three-month period. A sound analysis algorithm was developed to distinguish calf coughs from other sounds (e.g. mechanical sounds). During the sound recording period the health of the calves was assessed and scored periodically per week by a trained human observer. Calves presenting with BRD received antibiotic and/or anti-inflammatory treatment and the dates of treatment were recorded. This treatment date reference served as a comparison for the investigation of whether an increase in coughing frequency could be related to calves developing BRD. The calf cough detection algorithm achieved 50.3% sensitivity, 99.2% specificity and 87.5% precision. Four out of five periods, where coughing frequency was observed to be increased, coincided with the development of BRD in more than one calf. This period of increased coughing frequency was always observed before the calves were treated. Therefore, the calf cough monitor has the potential to identify early onset of BRD in calves.

Using dynamics of forced expiration to identify COPD where conventional criteria for the FEV1 /FVC ratio do not match.

BACKGROUND AND OBJECTIVE: The definition of chronic obstructive pulmonary disease (COPD) based on a fixed forced expiratory volume in 1 s (FEV1 )/forced vital capacity (FVC) ratio or on the lower limits of FEV1 /FVC of a healthy reference population is the subject of continuous debate. We explored whether dynamics of forced expiratory flow decline on spirometry can identify subjects with and without COPD when the two key diagnostic criteria are discordant. METHODS: Four hundred twenty-three individuals with a history of ≥15 pack-years smoking had pulmonary function measurements conducted. A second-order input-output model was used to describe the dynamics of the forced expiration. The capability of the model parameters to predict presence of disease was explored with a support vector machine classifier. In the discordant individuals, newly classified subjects were validated by other pulmonary function tests. RESULTS: In the non-discordant subjects (n = 370), the second-order model was able to confirm a diagnosis of COPD in 95% of subjects (n = 351). In the discordant individuals (n = 53), the classification by dynamic flow analysis found 28 patients to be healthy whereas 25 patients were still classified as COPD. Hyperinflation, increased airways resistance and reduced dynamic volumes were observed in the newly identified COPD group of discordant subjects. When using non-spirometry-based pulmonary function criteria as a standard for correct diagnoses in the individual discordant subjects, the model allocated 68% (n = 36) of the discordant to a correct diagnosis. CONCLUSIONS: Expiratory flow dynamics can detect airflow limitation and indicate the presence of COPD. In discordant subjects, our methodology allows a better identification of subjects with or without characteristics of COPD.

Relation between observed locomotion traits and locomotion score in dairy cows.

Lameness is still an important problem in modern dairy farming. Human observation of locomotion, by looking at different traits in one go, is used in practice to assess locomotion. The objectives of this article were to determine which individual locomotion traits are most related to locomotion scores in dairy cows, and whether experienced raters are capable of scoring these individual traits consistently. Locomotion and 5 individual locomotion traits (arched back, asymmetric gait, head bobbing, reluctance to bear weight, and tracking up) were scored independently on a 5-level scale for 58 videos of different cows. Videos were shown to 10 experienced raters in 2 different scoring sessions. Relations between locomotion score and traits were estimated by 3 logistic regression models aiming to calculate the size of the fixed effects on the probability of scoring a cow in 1 of the 5 levels of the scale (model 1) and the probability of classifying a cow as lame (locomotion score ≥3; model 2) or as severely lame (locomotion score ≥4; model 3). Fixed effects were rater, session, traits, and interactions among fixed effects. Odds ratios were calculated to estimate the relative probability to classify a cow as lame when an altered (trait score ≥3) or severely altered trait (trait score ≥4) was present. Overall intrarater and interrater reliability and agreement were calculated as weighted kappa coefficient (κw) and percentage of agreement, respectively. Specific intrarater and interrater agreement for individual levels within a 5-level scale were calculated. All traits were significantly related to the locomotion score when scored with a 5-level scale and when classified as (severely) lame or nonlame. Odds ratios for altered and severely altered traits were 10.8 and 14.5 for reluctance to bear weight, 6.5 and 7.2 for asymmetric gait, and 4.8 and 3.2 for arched back, respectively. Raters showed substantial variation in reliability and agreement values when scoring traits. The acceptance threshold for overall intrarater reliability (κw ≥0.60) was exceeded by locomotion scoring and all traits. Overall interrater reliability values ranged from κw=0.53 for tracking up to κw=0.61 for reluctance to bear weight. Intrarater and interrater agreement were below the acceptance threshold (percentage of agreement <75%). Most traits tended to have lower specific intrarater and interrater agreement in level 3 and 5 of the scale. In conclusion, raters had difficulties in scoring locomotion traits consistently, especially slight alterations were difficult to detect by experienced raters. Yet, the locomotion traits reluctance to bear weight, asymmetric gait, and arched back had the strongest relation with the locomotion score. These traits should have priority in locomotion-scoring-system guidelines and are the best to be used for the development of automated locomotion scoring systems.

Effect of merging levels of locomotion scores for dairy cows on intra- and interrater reliability and agreement.

Locomotion scores are used for lameness detection in dairy cows. In research, locomotion scores with 5 levels are used most often. Analysis of scores, however, is done after transformation of the original 5-level scale into a 4-, 3-, or 2-level scale to improve reliability and agreement. The objective of this study was to evaluate different ways of merging levels to optimize resolution, reliability, and agreement of locomotion scores for dairy cows. Locomotion scoring was done by using a 5-level scale and 10 experienced raters in 2 different scoring sessions from videos from 58 cows. Intra- and interrater reliability and agreement were calculated as weighted kappa coefficient (κw) and percentage of agreement (PA), respectively. Overall intra- and interrater reliability and agreement and specific intra- and interrater agreement were determined for the 5-level scale and after transformation into 4-, 3-, and 2-level scales by merging different combinations of adjacent levels. Intrarater reliability (κw) ranged from 0.63 to 0.86, whereas intrarater agreement (PA) ranged from 60.3 to 82.8% for the 5-level scale. Interrater κw=0.28 to 0.84 and interrater PA=22.6 to 81.8% for the 5-level scale. The specific intrarater agreement was 76.4% for locomotion level 1, 68.5% for level 2, 65% for level 3, 77.2% for level 4, and 80% for level 5. Specific interrater agreement was 64.7% for locomotion level 1, 57.5% for level 2, 50.8% for level 3, 60% for level 4, and 45.2% for level 5. Specific intra- and interrater agreement suggested that levels 2 and 3 were more difficult to score consistently compared with other levels in the 5-level scale. The acceptance threshold for overall intra- and interrater reliability (κw and κ ≥0.6) and agreement (PA ≥75%) and specific intra- and interrater agreement (≥75% for all levels within locomotion score) was exceeded only for the 2-level scale when the 5 levels were merged as (12)(345) or (123)(45). In conclusion, when locomotion scoring is performed by experienced raters without further training together, the lowest specific intra- and interrater agreement was obtained in levels 2 and 3 of the 5-level scale. Acceptance thresholds for overall intra- and interrater reliability and agreement and specific intra- and interrater agreement were exceeded only in the 2-level scale.

Performance of an image analysis processing system for hen tracking in an environmental preference chamber.

Image processing systems have been widely used in monitoring livestock for many applications, including identification, tracking, behavior analysis, occupancy rates, and activity calculations. The primary goal of this work was to quantify image processing performance when monitoring laying hens by comparing length of stay in each compartment as detected by the image processing system with the actual occurrences registered by human observations. In this work, an image processing system was implemented and evaluated for use in an environmental animal preference chamber to detect hen navigation between 4 compartments of the chamber. One camera was installed above each compartment to produce top-view images of the whole compartment. An ellipse-fitting model was applied to captured images to detect whether the hen was present in a compartment. During a choice-test study, mean ± SD success detection rates of 95.9 ± 2.6% were achieved when considering total duration of compartment occupancy. These results suggest that the image processing system is currently suitable for determining the response measures for assessing environmental choices. Moreover, the image processing system offered a comprehensive analysis of occupancy while substantially reducing data processing time compared with the time-intensive alternative of manual video analysis. The above technique was used to monitor ammonia aversion in the chamber. As a preliminary pilot study, different levels of ammonia were applied to different compartments while hens were allowed to navigate between compartments. Using the automated monitor tool to assess occupancy, a negative trend of compartment occupancy with ammonia level was revealed, though further examination is needed.

Modelling and quantification of the thermoregulatory responses of the developing avian embryo: electrical analogies of a physiological system.

Homeothermic animals, including birds, try to keep their body temperature at a constant level within certain boundaries by using thermoregulatory mechanisms. However, during incubation, the thermoregulatory system of the chicken embryo evolves through different stages from a poikilothermic to a homeothermic system. Hence, the thermal response of the fertile egg to changes in ambient temperature is different from one day to another during the embryonic development. The incubated egg can be considered as a physical (thermal) system, which transfers energy (heat) down a potential gradient (temperature difference). The heat flow between the micro-environment and the eggshell under a thermal driving force (temperature difference) has been studied in the past by using the analogy to the flow of electric charge under an electromotive-force. In this work, the thermal-response of incubated eggs to a step-increase in ambient-air temperature is studied and modelled. It is shown that the incubated egg is reacting as a first-order system between embryonic days ED01 and ED13, while, starting from ED14, the egg is reacting as a second-order system. This extends the existing RC (resistor-capacitor) circuit analogue to an RLC (resistor-inductor-capacitor) circuit analogue at the later stage of incubation. The concept of considering the fertile egg and its surrounding environment as an energy-handling device is introduced in this paper. It is suggested that the thermoregulation of the embryo has a thermal induction-like effect starting from ED14 and increasing gradually till hatching.

Field Implementation of Precision Livestock Farming: Selected Proceedings from the 2nd U.S. Precision Livestock Farming Conference.

Precision Livestock Farming (PLF) involves the real-time monitoring of images, sounds, and other biological, physiological, and environmental parameters to assess and improve animal health and welfare within intensive and extensive production systems [...].

Computer quantification of airway collapse on forced expiration to predict the presence of emphysema.

BACKGROUND: Spirometric parameters are the mainstay for diagnosis of COPD, but cannot distinguish airway obstruction from emphysema. We aimed to develop a computer model that quantifies airway collapse on forced expiratory flow-volume loops. We then explored and validated the relationship of airway collapse with computed tomography (CT) diagnosed emphysema in two large independent cohorts. METHODS: A computer model was developed in 513 Caucasian individuals with ≥15 pack-years who performed spirometry, diffusion capacity and CT scans to quantify emphysema presence. The model computed the two best fitting regression lines on the expiratory phase of the flow-volume loop and calculated the angle between them. The collapse was expressed as an Angle of collapse (AC) which was then correlated with the presence of emphysema. Findings were validated in an independent group of 340 individuals. RESULTS: AC in emphysema subjects (N = 251) was significantly lower (131° ± 14°) compared to AC in subjects without emphysema (N = 223), (152° ± 10°) (p < 0.0001). Multivariate regression analysis revealed AC as best indicator of visually scored emphysema (R2 = 0.505, p < 0.0001) with little significant contribution of KCO, %predicted and FEV1, %predicted to the total model (total R2 = 0.626, p < 0.0001). Similar associations were obtained when using CT-automated density scores for emphysema assessment. Receiver operating characteristic (ROC) curves pointed to 131° as the best cut-off for emphysema (95.5% positive predictive value, 97% specificity and 51% sensitivity). Validation in a second group confirmed the significant difference in mean AC between emphysema and non-emphysema subjects. When applying the 131° cut-off, a positive predictive value of 95.6%, a specificity of 96% and a sensitivity of 59% were demonstrated. CONCLUSIONS: Airway collapse on forced expiration quantified by a computer model correlates with emphysema. An AC below 131° can be considered as a specific cut-off for predicting the presence of emphysema in heavy smokers.

System identification of mGluR-dependent long-term depression.

Recent advances have started to uncover the underlying mechanisms of metabotropic glutamate receptor (mGluR)-dependent long-term depression (LTD). However, it is not completely clear how these mechanisms are linked, and it is believed that several crucial mechanisms remain to be revealed. In this study, we investigated whether system identification (SI) methods can be used to gain insight into the mechanisms of synaptic plasticity. SI methods have been shown to be an objective and powerful approach for describing how sensory neurons encode information about stimuli. However, to our knowledge, it is the first time that SI methods have been applied to electrophysiological brain slice recordings of synaptic plasticity responses. The results indicate that the SI approach is a valuable tool for reverse-engineering of mGluR-LTD responses. We suggest that such SI methods can aid in unraveling the complexities of synaptic function.

"Yes, we can!": Perceptions of collective efficacy sources in volleyball.

Collective efficacy can be defined as a group's shared confidence that they will successfully achieve their goal. We examined which behaviours and events are perceived as sources of collective efficacy beliefs in a volleyball context. In study 1, volleyball coaches from the highest volleyball leagues (n = 33) in Belgium indicated the most important sources of collective efficacy. This list was then adapted based on the literature and on feedback given by an expert focus group, resulting in a 40-item questionnaire. In Study 2, coaches and players from all levels of volleyball in Belgium (n = 2365) rated each of these sources on their predictive value for collective efficacy. A principal component analysis revealed that the 40 sources could be divided into eight internally consistent factors. Positive supportive communication (e.g., enthusiasm after making a point) was identified as the factor most predictive for positive collective efficacy beliefs. The factor referring to the negative emotional reactions of players (e.g., discouraging body language) was the most predictive for negative collective efficacy beliefs. These findings offer a starting point for the design of continuous measurements of collective efficacy through observation.

Monitoring Internal and External Load During Volleyball Competition.

PURPOSE: The aim of this study was to identify the relationships between continuously measured internal and external load variables during volleyball competition. METHODS: A total of 8 male elite volleyball athletes (Belgian Liga A and Liga B) were monitored during official competition matches. In total, 63 individual measurements are included in this study. The authors used heart-rate (HR) data as internal load and accelerometer-based activity as external load. Data were recorded at a sampling frequency of 1 Hz using wearable technology during official competition. Workload during continuous game play and individual jumps performed while serving and spiking (selected by means of video analysis) were studied using correlation analysis and dynamic time-series modeling. RESULTS: Significant linear correlations were observed between peak acceleration and maximal HR of individual serves (ρ = .62; P = 1.6e-5) and spikes (ρ = .49; P = 1.2e-3) that were performed during the warm-up. These same actions performed during the match did not show significant correlations. The correlation between the mean HR and mean activity throughout the entire match was also found to be significant (ρ = .67; P = 2.0e-9). With respect to the time-series models, the mean value for the goodness of fit (RT2) between HR and activity was equal to .83 and .67 for the individual actions and the entire matches, respectively. CONCLUSIONS: The results show that there are strong relationships between internal and external load during volleyball competition. Second-order transfer function models are capable of explaining the main dynamics of HR (internal load) in response to accelerometer-based activity (external load). Time-series analysis of continuously measured workload is proposed for use in practice.

Canine-Assisted Interventions and the Relevance of Welfare Assessments for Human Health, and Transmission of Zoonosis: A Literature Review.

CAIs (canine-assisted interventions) include "canine-assisted therapy" in which a therapist sets client-oriented goals, 'canine-assisted activities' with recreational goals for clients, and 'canine-assisted education/learning' in which teachers or coaches create learning goals for students or clients. CAIs vary in nearly every way; their only common trait is the involvement of dogs to respond to human need. However, the benefits of involving dogs are highly dependent on the animal's health and behavior. A dog exhibiting negative behavior or an unwell dog might pose a risk, especially for CAI target groups, specifically individuals with immunosuppression, chronic illness, children, elderly, etc. Therefore, positive animal welfare as preventative medicine to avoid incidents or transmission of zoonosis is an attractive hypothesis, with implications for human and animal, health and well-being. This review aims to summarize the current published knowledge regarding different aspects of welfare in CAIs and to discuss their relevance in the light of health and safety in CAI participants. As method for this study, a literature search was conducted (2001-2022) using the Prisma method, describing issues of dog welfare as defined in the Welfare Quality® approach. This welfare assessment tool includes 4 categories related to behavior, health, management, and environment; it was, therefore, applicable to CAIs. Results indicate that dogs working in CAIs are required to cope with diverse variables that can jeopardize their welfare. In conclusion, we propose regular welfare assessments for dogs in CAIs, which would also protect the quality of the CAI sessions and the clients' safety and well-being.

Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model.

BACKGROUND: The intensive care unit (ICU) length of stay (LOS) of patients undergoing cardiac surgery may vary considerably, and is often difficult to predict within the first hours after admission. The early clinical evolution of a cardiac surgery patient might be predictive for his LOS. The purpose of the present study was to develop a predictive model for ICU discharge after non-emergency cardiac surgery, by analyzing the first 4 hours of data in the computerized medical record of these patients with Gaussian processes (GP), a machine learning technique. METHODS: Non-interventional study. Predictive modeling, separate development (n = 461) and validation (n = 499) cohort. GP models were developed to predict the probability of ICU discharge the day after surgery (classification task), and to predict the day of ICU discharge as a discrete variable (regression task). GP predictions were compared with predictions by EuroSCORE, nurses and physicians. The classification task was evaluated using aROC for discrimination, and Brier Score, Brier Score Scaled, and Hosmer-Lemeshow test for calibration. The regression task was evaluated by comparing median actual and predicted discharge, loss penalty function (LPF) ((actual-predicted)/actual) and calculating root mean squared relative errors (RMSRE). RESULTS: Median (P25-P75) ICU length of stay was 3 (2-5) days. For classification, the GP model showed an aROC of 0.758 which was significantly higher than the predictions by nurses, but not better than EuroSCORE and physicians. The GP had the best calibration, with a Brier Score of 0.179 and Hosmer-Lemeshow p-value of 0.382. For regression, GP had the highest proportion of patients with a correctly predicted day of discharge (40%), which was significantly better than the EuroSCORE (p < 0.001) and nurses (p = 0.044) but equivalent to physicians. GP had the lowest RMSRE (0.408) of all predictive models. CONCLUSIONS: A GP model that uses PDMS data of the first 4 hours after admission in the ICU of scheduled adult cardiac surgery patients was able to predict discharge from the ICU as a classification as well as a regression task. The GP model demonstrated a significantly better discriminative power than the EuroSCORE and the ICU nurses, and at least as good as predictions done by ICU physicians. The GP model was the only well calibrated model.