Accuracy Of Prediction Equations Research Articles

Optimization of Ru/ZSM-5 catalyst for selective d-glucose hydrogenation to sorbitol: A box-behnken design approach

The hydrogenation of D-glucose to sorbitol holds significant importance in biomass biorefineries. With a focus on enhancing the efficiency and selectivity of this reaction, this research investigated various optimization parameters (catalyst dosage, reaction time, and temperature) over 0.5 %Ru/ZSM-5 prepared via impregnation method. The optimization process was carried out using Box-Behnken design (BBD). The results showed accurate predictive equations for all reaction responses, with R2 values exceeding 97 % and no significant errors observed. Additionally, the 2D contour plot revealed the relationship between time and temperature, highlighting that a long reaction time and high temperature could lead to reduced sorbitol yield due to the formation of by-products. Under the optimal catalyst dosage of 0.6 g, reaction time of 100 min, and temperature of 120°C, the D-glucose conversion and sorbitol yield were 99.6 ± 0.35 % and 99.43 ± 0.45 %, respectively. NMR results confirmed the exclusive production of sorbitol under these optimal conditions. Furthermore, the study demonstrated that the catalyst could be reused at least seven times without a significant decrease in D-glucose conversion or sorbitol yield, while ICP-OES analysis confirmed no Ru leaching during the reaction. Therefore, this research provides a biomass valorization strategy by optimizing the production process of sorbitol via D-glucose hydrogenation.

Accuracy of predictive equations against indirect calorimetry during the acute phase of critical illness

Accuracy of predictive equations against indirect calorimetry during the acute phase of critical illness

Whole body sweat rate prediction: indoor treadmill and cycle ergometer exercise.

This article describes the development and validation of accurate whole body sweat rate prediction equations for individuals performing indoor cycle ergometer and treadmill exercise, where power output can be measured or derived from simple inputs. For cycle ergometry, 112 trials (67 participants) were used for model development and another 56 trials (42 participants) for model validation. For treadmill exercise, 171 trials (67 participants) were used for model development and another 95 trials (63 participants) for model validation. Trials were conducted over a range of dry-bulb temperature (20°C to 40°C), relative humidity (14% to 60%), and exercise intensity (∼40% to 85% of peak aerobic power) conditions, which were matched between model development and model validation. Whole body sweat rates were measured, and proprietary prediction models were developed (accounting for all relevant biophysical factors) and then validated. For model validation, mean absolute error for predicted sweating rate was 0.01 and 0.02 L·h-1 for cycle and treadmill trials, respectively. The 95% confidence intervals were modest for cycle ergometer (+0.25 and -0.22 L·h-1) and treadmill exercise (+0.33 and -0.29 L·h-1). The accounted for variance between predicted and measured values was 92% and 78% for cycle and treadmill exercise, respectively. Bland-Altman analysis indicated that zero and one predicted value exceeded the a priori acceptable level of agreement (equivalent to ±2% of total body mass in 3 h) for cycle and treadmill exercise, respectively. There were fewer trials with female subjects, but their values did not differ from those expected for males. This is the foremost study to develop and validate whole body sweat rate prediction equations for indoor treadmill and cycle ergometer exercise of moderate to high intensity. These prediction equations are publicly available for use (https://sweatratecalculator.com).NEW & NOTEWORTHY This study presents the development of new proprietary whole body sweat rate prediction models for people exercising indoors on a cycle ergometer or treadmill using simple input parameters and delivered through a publicly available online calculator: https://sweatratecalculator.com. In an independent validation group, the predictive models for both indoor cycling and treadmill exercise were accurate across moderate to high exercise intensities in temperate to hot conditions. These equations will enable individualized hydration management during physical training and exercise physiology experiments.

Developing Accurate Repetition Prediction Equations for Trained Older Adults with Osteopenia.

The aim of this study was to evaluate prediction equations to estimate 1RM in different exercises in older men and women with osteopenia/osteoporosis. Forty well-trained older women and men (73 ± 8 years) with osteopenia/osteoporosis performed 1RM dynamic and isometric maximum strength tests on resistance devices. In addition, each participant performed repetitions-to-fatigue (RTF) in the 5-8RM, 9-12RM, and 13-16RM zones. After evaluating the predictive performance of available 1RM prediction equations from the literature, new prediction equations were developed for all seven exercises. One of the available equations that focus on postmenopausal women already acceptably predicted 1RM from RTF for all but one exercise. Nevertheless, new exercise-specific prediction equations based on a cubic polynomial most accurately predict 1RM from RTF in the 5-8 reps range with mean absolute differences between predicted and actual 1RM of 3.7 ± 3.7% (leg-press) to 6.9 ± 5.5% (leg flexion) that is roughly within the acceptable coefficient of variation. For some exercises, the inclusion of the isometric maximum strength tests slightly increases the prediction performance of the 5-8RM. In conclusion, the present prediction equation accurately estimates 1RM in trained, older women and men with osteopenia/osteoporosis. Further evaluation of this new equation is warranted to determine its applicability to different age groups and populations.

Assessment of Energy Consumption Patterns and Validation of Predictive Equations among ICU Patients at Muhimbili National Hospital in Dar es Salaam, Tanzania

Background: Nutritional support in critically-ill patients is one of the most important parameters guarding the prognosis and influencing morbidity and mortality in these patients, owing to that fact, accurate measurement of the resting energy expenditure using Indirect calorimetry is recommended by guidelines as a gold standard. But due to lack of resources and other technicalities predictive equations are conveniently used as surrogates. Aims: This study was intended to examine the extent of malnutrition, poor nutrition support practices and to validate the common used predictive equations in critically-ill patients in our setting. Methodology: A hospital-based descriptive cross-sectional study was conducted on consecutively sampled 110 mechanically ventilated ICU patients at Muhimbili National Hospital Mloganzila. Anthropometric measurements, duration of stay in ICU, Temperature and Minute Volumes were recorded so as to estimate resting energy expenditure, REE from different predictive equations. Using Indirect Calorimetry Module patient’s REE was measured and recorded then a statistical data analyzed using SPSS software version 23. Results: The prevalence of poor nutritional support was 69%; underfeeding and overfeeding were observed in 41.8% and 27.3% of all participants respectively. Prevalence of malnutrition was 51.8%; underweight and overweight were found to be in 29.1% and 22.7% of all participants respectively. The accuracy of predictive equation in ±10% difference was 30%, 45.5%, 46.4% and 68.2% in HB, MSJ, ESPEN and PENN respectively. Conclusion and Recommendation: Malnutrition and Poor Nutritional support are common problems in ICU. Predictive equations have poor accuracy and validity in comparison to indirect calorimetry. Penn State Equation was the most accurate and with the highest agreement with IC.

Optimizing sorbitol double dehydration: A Box-Behnken design approach with commercial sulfonic acid resin

In this study, double dehydration of sorbitol into isosorbide using commercial sulfonic acid resin as a catalyst was carried out under vacuum conditions generated by water ejection. To improve the efficiency and selectivity of the process, optimum reaction conditions prescribed by temperature, catalyst loading, and reaction time were investigated using the Box-Behnken design (BBD) together with Response Surface Methodology (RSM). The results showed that using the water ejection system could increase reaction activity. Statistically, all the reaction parameters were found to significantly affect the double dehydration reaction response, including sorbitol conversion, 1,4-sorbitant yield, and isosorbide yield. Furthermore, accurate predictive equations for all the reaction responses displayed R2 > 95 %, with no significant errors observed. The optimized conditions resulted in the complete conversion of sorbitol with 6.42 % 1,4-sorbitant yield and 67.55 % isosorbide yield. The equations yielded predicted values of the responses with minor variances being lower than 1 % when compared with the experimental values. However, the efficiency of the catalyst decreased steadily over recycling cycle due to reduced active sites and textural properties, likely caused by structural collapse and by-product accumulation. This work contributes to biomass valorization by optimizing the effective process for the production of isosorbide via commercial catalysts under vacuum conditions.

PSI-21 Optimizing sample size for accurate measurements and prediction equations for intestinal morphology in broiler chickens

Abstract Histological measurements of intestinal morphology, including villi height (VH) and crypt depth (CD), are routinely used to evaluate intestinal health and the nutrient absorptive capacity in monogastric animals. These metrics suffer from significant natural variation, and there is no established standard for the number of unique measurements required to robustly estimate these variables for a given animal. Furthermore, the potential utility of additional variables, including villus width (VW), perimeter (VP), or villus area (VA), has not been explored. Therefore, the objective of the current experiment was to estimate the minimum number of measurements required to control the intrasample variance in each variable and determine if more complex or time-consuming metrics, such as VP and/or VA, can be accurately estimated from linear measurements. A set of 1283 intestinal morphology data, including VH, CD, VW, VP, and VA, measured from 68 intestinal tissues (46 ileum and 22 jejunum) of 4 experiments were used in the current study (Figure 1). Retrospective calculation of the CV for all combinations of N measurements per sample was used to determine the impact of measurement number on the quality of data. Data for the jejunum and ileum were pooled together, and the NLIN procedure of SAS was used to estimate the optimal measurement for intestinal morphology measurements using a quadratic broken line. The VA was estimated using geometric calculation with the assumption that VA equals 0.67 × base width × VH. A paired t-test was conducted to compare measured and calculated VA. The optimal number of measurements to minimize within-sample CV for VH, CD, VW, VP, and VA ranged from 10.6 to 10.8. The VH was highly correlated (P < 0.05) with VP (r = 0.95 and 0.87) and VA (r = 0.77 and 0.63) in the jejunum and ileum, respectively (Table 1 and 2). Additionally, there was a significant difference (P < 0.05) between measured (0.110 µm2) and calculated VA (0.086 µm2). In conclusion, this study shows that measuring 11 villi per sample is required for minimizing variations within the jejunal or ileal tissue in broiler chickens. Also, estimating VA based on idealized geometry was found to be inaccurate, necessitating either direct measurement or more complex prediction equations.

Measured and Predicted Resting Metabolic Rate of Dutch and Norwegian Paralympic Athletes

BackgroundAlthough resting metabolic rate (RMR) is crucial for understanding athletes’ energy requirements, limited information is available on the RMR of Paralympic athletes. ObjectiveThe aim of this study was to determine RMR and its predictors in a diverse cohort of Paralympic athletes and evaluate the agreement between measured and predicted RMR from both newly developed and pre-existing equations. DesignThis cross-sectional study, conducted between September 2020 and September 2022 in the Netherlands and Norway, assessed RMR in Paralympic athletes by means of ventilated hood indirect calorimetry and body composition by means of dual-energy x-ray absorptiometry. ParticipantsSixty-seven Paralympic athletes (male: n = 37; female: n = 30) competing in various sports, with a spinal cord disorder (n = 22), neurologic condition (n = 8), limb deficiency (n = 18), visual or hearing impairment (n = 7), or other disability (n = 12) participated. Main outcome measuresRMR, fat-free mass (FFM), body mass, and triiodothyronine (T3) concentrations were assessed. Statistical analysesMultiple regression analyses were conducted with height, FFM, body mass, sex, T3 concentration, and disabilities as potential predictors of RMR. Differences between measured and predicted RMRs were analyzed for individual accuracy, root mean square error, and intraclass correlation. ResultsMean ± SD RMR was 1386 ± 258 kcal/d for females and 1686 ± 302 kcal/d for males. Regression analysis identified FFM, T3 concentrations, and the presence of a spinal cord disorder, as the main predictors of RMR (adjusted R2 = 0.71; F = 50.3; P < .001). The novel prediction equations based on these data, as well as pre-existing equations of Chun and colleagues and Nightingale and Gorgey performed well on accuracy (>60% of participants within 10% of measured RMR), had good reliability (intraclass correlation >0.78), and low root mean square error (≤141 kcal). ConclusionsFFM, total T3 concentrations, and presence of spinal cord disorder are the main predictors of RMR in Paralympic athletes. Both the current study’s prediction equations and those from Chun and colleagues and Nightingale and Gorgey align well with measured RMR, offering accurate prediction equations for the RMR of Paralympic athletes.

Accurate Prediction Equations for Ventilatory Thresholds in Cardiometabolic Disease When Gas Exchange Analysis is Unavailable: Development and Validation.

To develop and validate equations predicting heart rate (HR) at the first and second ventilatory thresholds (VTs) and an optimized range-adjusted prescription for patients with cardiometabolic disease (CMD). To compare their performance against guideline-based exercise intensity domains. Cross-sectional study involving 2,868 CMD patients from nine countries. HR predictive equations for first and second VTs (VT1, VT2) were developed using multivariate linear regression with 975 cycle-ergometer cardiopulmonary exercise tests (CPET). 'Adjusted' percentages of peak HR (%HRpeak) and HR reserve (%HRR) were derived from this group. External validation with 1,893 CPET (cycle-ergometer or treadmill) assessed accuracy, agreement, and reliability against guideline-based %HRpeak and %HRR prescriptions using mean absolute percentage error (MAPE), Bland-Altman analyses, intraclass correlation coefficients (ICC). HR predictive equations (R²: 0.77 VT1, 0.88 VT2) and adjusted %HRR (VT1: 42%, VT2: 77%) were developed. External validation demonstrated superiority over widely used guideline-directed intensity domains for %HRpeak and %HRR. The new methods showed consistent performance across both VTs with lower MAPE (VT1: 7.1%, VT2: 5.0%), 'good' ICC for VT1 (0.81, 0.82) and 'excellent' for VT2 (0.93). Guideline-based exercise intensity domains had higher MAPE (VT1: 6.8%-21.3%, VT2: 5.1%-16.7%), 'poor' to 'good' ICC for VT1, and 'poor' to 'excellent' for VT2, indicating inconsistencies related to specific VTs across guidelines. Developed and validated HR predictive equations and the optimized %HRR for CMD patients for determining VT1 and VT2 outperformed the guideline-based exercise intensity domains and showed ergometer interchangeability. They offer a superior alternative for prescribing moderate intensity exercise when CPET is unavailable.

Spontaneous Transfer of Droplets across a Microfluidic Liquid-Liquid Interface.

The droplet transfer across an interface in a microchannel is extensively utilized in diverse fields; however, it is challenging to drive droplets to penetrate the interface at such a small scale. In this study, a novel flow pattern of droplet transfer is observed and the mechanism is investigated; accordingly, an accurate prediction equation for determining the critical condition of droplet transfer is proposed. Meanwhile, the liquid film entrainment is also observed, which leads to the formation of an oil-in-water-in-water system. This study serves as a valuable reference for further studies on the mechanism of droplet transfer and provides practical guidance for its industrial application.

Prediction model of asphalt emulsion evaporation rate based on CFD simulation and genetic programming-based symbolic regression

The evaporation rate of asphalt emulsion is critical to the strength development of asphalt emulsion materials. Its value is dependent on environmental factors, i.e. relative humidity, temperature, and wind speed. However, the quantitative relation of evaporation rate with these environmental factors are unclear. To quantificationally study the effect of environmental factors on the water evaporation rate of asphalt emulsion, a computational fluid dynamics (CFD) simulation model of water evaporation is firstly proposed and validated by experiment. Then, wide range of environmental factors are studied in the CFD simulation model, and genetic programming-based symbolic regression is used to train the simulation results of CFD samples. Finally, a relatively accurate prediction equation of the evaporation rate of asphalt emulsion versus environmental factor is proposed, and the sensitivity of the water evaporation rate under the three environmental factors is analyzed. Results show that the evaporation rate can be accurately simulated by CFD. All the three environmental factors can greatly affect the evaporation rate of asphalt emulsion, while their effect on the sensitivity of the water evaporation rate is ranked as: temperature > relative humidity > wind speed. To ensure an acceptable curing period, asphalt emulsion-based materials should be avoided to be paved in the conditions of low temperature and high relative humidity.

Cardiovascular risk prediction in a New Zealand cohort of patients with known cardiovascular disease - a regional individual patient-level data linkage study

Abstract Background National health databases have been underutilised in the development of cardiovascular disease (CVD) risk prediction equations for people with known CVD, yet can inform population level practice and policy—including identification of health equity gaps, high-risk groups, and high-risk localities. With the recent introduction of novel and expensive preventive cardiovascular drugs, stratifying risk in patients with prior CVD could be required to inform cost-effective use of these medications in the future. Purpose We sought to develop 5-year CVD risk equations in adults with known history of CVD in Aotearoa New Zealand, using data from administrative health databases and characterize distribution of risk according to sex. Methods All people above 18 years of age with a history of CVD hospitalization, living in the Auckland and Northland regions of Aotearoa New Zealand (approximately one-third of the national population) on 1 January 2014 were included. Participants were followed for 5 years (31 December 2018) or until death or the outcome of interest, being any fatal or non-fatal CVD hospitalization (ischemic heart disease, heart failure, ischemic stroke, or peripheral artery disease) during follow-up. Sex-specific 5-year CVD risk prediction equations were developed using multivariable Fine and Gray models taking competing risk of non-cardiovascular death into account. Predictor variables included were age; ethnicity; deprivation level; time since most recent CVD hospitalization, history of smoking, heart failure, atrial fibrillation, chronic renal disease, and diabetes mellitus; use of anti-thrombotic, blood pressure-lowering, and lipid-lowering drugs; and blood levels of glycosylated hemoglobin (HbA1c), non-HDL cholesterol, and creatinine. Results The cohort comprised 42,130 men (median age: 68 years [interquartile range (IQR): 59–76]) and 31,717 women (median age: 70 years [IQR: 60–78]) with 12,875 and 8,734 observed events during follow-up, respectively. Sex-specific 5-year CVD risk prediction equations with excellent calibration (accuracy) were developed (Figure 1). Increasing age; high deprivation level; more recent CVD hospitalization; history of smoking, heart failure, atrial fibrillation, chronic renal disease, diabetes mellitus; use of blood-pressure lowering drugs; and high blood levels of HbA1c, non-HDL cholesterol, or creatinine were predictors of elevated risk. There was a wide range of predicted 5-year risk, with 15% of men and 24% of women having a predicted risk below 15%, and 24% and 20%, respectively, having a predicted risk of 40% or above (Figure 2). Conclusion This study demonstrates that it is possible to develop accurate CVD risk prediction equations from administrative health databases for patients with known CVD. Marked heterogeneity in 5-year risk of a subsequent CVD event emphasise that the current ‘one size fits all’ approach to secondary prevention of CVD requires review.Figure 1Figure 2

Agreement between measured energy expenditure and predictive energy equations in paediatric oncology

Purpose: Optimal nutritional support in childhood cancer relies on the adequate provision of energy. This study investigated the impact of chemotherapy on resting energy expenditure (REE) during the first six months of treatment and the accuracy of predictive equations in calculating said requirements of newly diagnosed children with cancer. Methods: REE was measured at diagnosis utilising a validated bioelectrical impedance analysis (BIA) mobile unit and compared with three predictive equations (Schofield 1985, World Health Organization [WHO] 1985 and the Recommended Dietary Allowance [RDA] 1989). Agreement and accuracy of these equations were tested by determining bias and agreement rates and displayed using the Bland–Altman plot. Baseline values were plotted against monthly follow-up measurements over time. Statistical significance was 5% and a priori limits of agreement set between 90% and 110% of measured REE. Results: Forty-three newly diagnosed children with median age 4 years (IQR 2.0–7.6) were measured prior to chemotherapy initiation. Compared with measured REE (mean ± SD) 719.53 ± 206.29 kcal/day, all predictive equations significantly overestimated REE: WHO 1985 (889.75 ± 323.31 kcal/day; 23% overestimation), Schofield 1985 (899.62 ± 336.10 kcal/day; 25% overestimation) and RDA (1647.67 ± 481.06 kcal/day; 129% overestimation) (p < 0.001). Despite significant proportionate bias in all three equations (p < 0.001), the intra-class consistency coefficient showed good reliability for the Schofield 1985 (0.864) and WHO 1985 (0.849) equations. Though statistically significant (chi-square = 23.11, p < 0.003), the overall 1 kcal/kg (1.3%) increase for all cancer types at six months may not be clinically significant. Conclusion: Existing predictive equations are unable to calculate REE accurately at childhood cancer diagnosis, highlighting the need for future investigations into the development of cancer-specific equations.

Estimation of Energy Value and Digestibility and Prediction Equations for Sheep Fed with Diets Containing Leymus chinensis Hay

The objective of this study was to investigate the feeding value of sheepgrass, including its chemical composition, dry matter intake, nutrient digestibility, and available energy, as well as the prediction equations of dry matter intake (DMI), neutral detergent fiber digestibility (NDFD), dry matter digestibility (DMD), digestible energy (DE), and metabolizable energy (ME). Two independent experiments based on a completely randomized experimental design were performed to evaluate the feeding value. The results showed that there were significant relationships between chemical composition and DMI, digestibility, and available energy. The best-fit equations were as follows: DMI (g/d·W0.75) = 121.75 + 0.06CP (%) − 0.24EE (%) − 0.10ADF (%) − 0.60NDF (%) − 0.15OM (%) (R2 = 0.85, p &lt; 0.01), DMD (%) = −1.37 + 0.23CP (%) + 2.96EE (%) + 0.32ADF (%) − 0.82NDF (%) + 1.27OM (%) (R2 = 0.83, p &lt; 0.01), NDFD (%) = 225.58 − 0.59CP (%) + 0.04EE (%) + 0.09ADF (%) − 2.46NDF (%) + 0.12OM (%) (R2 = 0.67, p &lt; 0.01), DE (MJ/kg) = −5.19 + 0.38OM (%) − 0.26NDF (%) − 0.03ADF (%) + 0.16CP (%) (R2 = 0.91, p &lt; 0.01), and ME (MJ/kg) = 5.55 + 0.67DE (MJ/kg) + 0.01CP (%) − 0.01ADF (%) − 0.08NDF (%) + 0.02OM (%) (R2 = 0.98, p &lt; 0.01). This study found the energy value of sheepgrass to be 11 MJ/kg, which is similar to that of millet grass silage. The NDF was the main component that affected DMI and digestibility. Using a hay replacement ratio of 28.5% to determine the forage value of sheepgrass allowed accurate prediction equations to be established. The NDF demonstrated the strongest correlation with DMI, NDFD, OMD, DE, and ME. DE was estimated to be the best single predictor of ME.

Diagnostic value of neutrophil gelatinase-associated lipocalin (NGAL) as an early biomarker for detection of renal failure in hypertensives: a case–control study in a regional hospital in Ghana

BackgroundRenal failure is one of the most serious vascular effects of hypertension. For better therapy and prevention of complications, early kidney disease identification in these patients is absolutely essential. However, current studies have proposed plasma Neutrophil Gelatinase Associated Lipocalin (pNGAL) to be a better biomarker comparative to serum creatinine (SCr). This study assessed the diagnostic utility of plasma neutrophil gelatinase-associated lipocalin (pNGAL) as a biomarker for early nephropathy diagnosis in hypertensive individuals.MethodsThis hospital-based case–control study comprised 140 hypertensives and 70 healthy participants. A well-structured questionnaire and patient case notes were used to document relevant demographic and clinical information. 5 ml of venous blood sample was taken to measure fasting blood sugar levels, creatinine, and plasma NGAL levels. All data were analyzed using the Statistical Package for Social Sciences (SPSS release 20.0, copyrite©SPSS Inc.) and a p-value < 0.05 was considered statistically significant.ResultsIn this study the plasma neutrophil gelatinase-associated lipocalin (NGAL) levels were significantly higher in cases compared to controls. Hypertensive cases also had significantly higher waist-circumference compared to the control group. The median fasting blood sugar level was significantly higher in cases compared to controls. This study established the use of Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), and Cockcroft and Gault formula (CG) as the most accurate predictive equations for assessing renal dysfunction. The threshold for NGAL above which renal impairment can be assessed was found to be 109.4 ng/ml (sen-91%, spec. – 68%), 120 ng/ml (sen- 100%, spec- 72%) and 118.6 ng/ml (sen- 83%, spec- 72%) for MDRD, CKD-EPI and CG equations respectively. The prevalence of CKD was 16.4%, 13.6% and 20.7% respectively using the MDRD, CKD-EPI and CG.ConclusionFrom this study, pNGAL is a better indicator of kidney impairment in the early stages of CKD as compared with sCr in general hypertensive population.

In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle

Precision livestock farming technologies are used to monitor animal health and welfare parameters continuously and in real time in order to optimize nutrition and productivity and to detect health issues at an early stage. The possibility of predicting blood metabolites from milk samples obtained during routine milking by means of infrared spectroscopy has become increasingly attractive. We developed, for the first time, prediction equations for a set of blood metabolites using diverse machine learning methods and milk near-infrared spectra collected by the AfiLab instrument. Our dataset was obtained from 385 Holstein Friesian dairy cows. Stacking ensemble and multi-layer feedforward artificial neural network outperformed the other machine learning methods tested, with a reduction in the root mean square error of between 3 and 6% in most blood parameters. We obtained moderate correlations (r) between the observed and predicted phenotypes for γ-glutamyl transferase (r = 0.58), alkaline phosphatase (0.54), haptoglobin (0.66), globulins (0.61), total reactive oxygen metabolites (0.60) and thiol groups (0.57). The AfiLab instrument has strong potential but may not yet be ready to predict the metabolic stress of dairy cows in practice. Further research is needed to find out methods that allow an improvement in accuracy of prediction equations.

Effects of environmental condition, size, coat type, and body condition score on rectal temperature prediction in dogs using infrared auricular and surface temperature.

Background and Aim:Generally, rectal body temperature (BTrectum) is used to prefer as core body temperature in dogs. However, this procedure is time- and labor-consuming with stress induction. Therefore, infrared auricular temperature (BTear) and surface temperature (ST) could be applied to estimate BTrectum. This study aimed to estimate BTrectum from BTear or ST in various areas and determined the factors that influenced the accuracy of prediction equations.Materials and Methods:Under controlled temperature (n=197) and ambient temperature (n=183), the parameters BTrectum, BTear, and ST at internal pinna, auricular canal, lateral aspect of shoulder, hip, axillary area, inguinal area, footpad, and anal area (STrectum) were measured. In addition, temperature and humidity levels of the surrounding environment were recorded. The correlation between each measurement technique was calculated. The BTrectum prediction equation was created using all measured data and several influencing factors (environmental condition, size, coat type, and body condition score [BCS]).Results:The highest correlation with BTrectum was observed for BTear (r=0.61, p<0.01), which was similar to STrectum (r=0.61, p<0.01). Based on multiple linear regression model results using BTrectum as the dependent variable, BTear or STrectum were first selected as independent variables in all estimation equations. Ambient temperatures (R2=0.397), small breed (R2=0.582), long hair (R2=0.418), and/or a BCS of 2 (R2=0.557) provided the highest coefficients of determination of the prediction equation.Conclusion:The most appropriate predictors for estimating BTrectum were STrectum and BTear, which were impacted by the dog’s signalments and the environment. To obtain satisfactory outcomes, the equation must be selected depending on the dog’s signalments and the environmental conditions. However, based on the findings of this investigation, the accuracy remains low in several equations, and further studies are needed to improve the accuracy of the equation, mainly by increasing the sample size and developing a specific equation for each dog’s signaling and environmental condition.

New Predictive Resting Metabolic Rate Equations for High-Level Athletes: A Cross-Validation Study.

The present study aims a) to assess the agreement between the measured resting metabolic rate (RMR) using indirect calorimetry and different predictive equations (predicted RMR), and b) to propose and cross-validate two new predictive equations for estimating the RMR in high-level athletes. The RMR of 102 athletes (44 women) was assessed using indirect calorimetry, whereas the body composition was assessed using skinfolds. Comparisons between measured and predicted RMR values were performed using one-way ANOVA. Mean difference, root mean square error (RMSE), simple linear regression, and Bland-Altman plots were used to evaluate the agreement between measured and predicted RMR. The accuracy of predictive equations was analyzed using narrower and wider accuracy limits (±5% and ±10%, respectively) of measured RMR. Multiple linear regression models were employed to develop the new predictive equations based on traditional predictors (equation 1) and the stepwise method (equation 2). The new equations 1 and 2 presented good agreement based on the mean difference (3 and -15 kcal·d -1 ), RMSE (200 and 192 kcal·d -1 ), and R2 (0.71 and 0.74), respectively, and accuracy (61% of subjects between the limit of ±10% of measured RMR). Cunningham's equation provided the best performance for males and females among the existing equations, whereas Jagim's equation showed the worst performance for males (mean difference = -335 kcal·d -1 ; RMSE = 386 kcal·d -1 ). Compared with measured RMR, most predictive equations showed heteroscedastic distribution (linear regression's intercept and slope significantly different from zero; P ≤ 0.05), mainly in males. The new proposed equations can estimate the RMR in high-level athletes accurately. Cunningham's equation is a good option from existing equations, and Jagim's equation should not be used in high-level male athletes.

An Alternative Prediction Equation for Evaluation of Six-Minute Walk Distance in Stable Coronary Artery Disease Patients.

Background: As cardio-vascular diseases are the leading cause of death worldwide, establishing measures to improve cardiovascular health is of crucial importance. Exercise plays an essential role in cardiac rehabilitation of patients with coronary artery disease (CAD), in whom an evaluation of the cardiorespiratory fitness (CRF) is necessary. CRF of CAD patients could be assessed using 6-min walk test (6MWT), and the results interpreted by using Enright-Sherill prediction equation which has mainly been designed and evaluated for a healthy population. Hypothesizing that the Enright-Sherill prediction equation might not be best suited for CAD patients, our aim was to reevaluate this equation in CAD patients, and potentially establish a more accurate 6MWD prediction equation to be applied in these patients. Methods: 6MWD was measured in a cross-sectional study in 67 CAD patients (44 women) who were members of the Coronary club Ljubljana, Slovenia. In addition, the predicted 6MWD was calculated for men and women using Enright-Sherill gender specific regression equation. Multivariate regression analysis was used to obtain a new prediction equation, and the agreement between the measured and the predicted 6MWD analyzed using the repeated measures ANOVA. Results: Men achieved 451 ± 122 m and women 485 ± 69 m without significant differences between sexes (F = 0.022, p = 0.882) when adjusted for age, height, body mass, and waist circumference. When comparing the measured (473 ± 91 m) and the predicted (422 ± 57 m) values of 6MWD in CAD patients we found that the Enright-Sherill prediction equation significantly (F = 27.734, p < 0.001) underestimated the 6MWD by 52 ± 81 m. A significant regression equation was established [F (3,63) = 44.663, p < 0.001], with a R 2 of 0.680 where 6MWD equals 1,057 m—4.966 x age (years)—0.614 x WC (cm)–68.629 x NYHA class. Conclusion: The results of this study stress the importance of regular and actual walking ability testing in patients with stable CAD to obtain their CRF, rather than simply predicting it from regression equations obtained from non-representative or non-comparable samples. Our developed prediction equation warrants additional validation and may represent a good substitute for currently used predictions obtained from a healthy population.

Energy requirements for critically ill patients with COVID-19.

Early reports suggested that predictive equations significantly underestimate the energy requirements of critically ill patients with coronavirus disease 2019 (COVID‐19) based on the results of indirect calorimetry (IC) measurements. IC is the gold standard for measuring energy expenditure in critically ill patients. However, IC is not available in many institutions. If predictive equations significantly underestimate energy requirements in severe COVID‐19, this increases the risk of underfeeding and malnutrition, which is associated with poorer clinical outcomes. As such, the purpose of this narrative review is to summarize and synthesize evidence comparing measured resting energy expenditure via IC with predicted resting energy expenditure determined via commonly used predictive equations in adult critically ill patients with COVID‐19. Five articles met the inclusion criteria for this review. Their results suggest that many critically ill patients with COVID‐19 are in a hypermetabolic state, which is underestimated by commonly used predictive equations in the intensive care unit (ICU) setting. In nonobese patients, energy expenditure appears to progressively increase over the course of ICU admission, peaking at week 3. The metabolic response pattern in patients with obesity is unclear because of conflicting findings. Based on limited evidence published thus far, the most accurate predictive equations appear to be the Penn State equations; however, they still had poor individual accuracy overall, which increases the risk of underfeeding or overfeeding and, as such, renders the equations an unsuitable alternative to IC.