Prerace venous blood gases and acid-base values in Standardbred horses: effects of geography, season, prerace furosemide, gender, age, and trainer using big data analytics.

OBJECTIVE: A retrospective study was conducted to establish the prerace venous acid-base and blood gas values of Standardbred horses at rest using big data analytics. SAMPLES: Venous blood samples (73,382) were collected during seven racing seasons from 3 regional tracks in the Commonwealth of Pennsylvania. Horses were detained 2 hours prior to race time. PROCEDURES: A mixed-effects linear regression model was used for estimating the marginal model adjusted mean (marginal mean) for all major outcomes. The interaction between age and gender, track, and the interaction between month, treatment (furosemide), and year were the major confounders included in the model. Random effects were set on individual animal nested within trainer. Partial pressure of venous carbon dioxide (PVCO2), partial pressure of oxygen (PVO2), and pH were measured, and base excess (BE), total carbon dioxide (TCO2), and bicarbonate (HCO3-) were calculated. RESULTS: Significant (P < .001) geographical differences in track locations were seen. Seasonal reductions in acid-base values started in January with significant (P < .001) decreases from adjacent months seen in June, July, and August followed by a gradual return. There were significant increases (P < .001) in BE and TCO2 and decreases in PVO2 with age. Significant differences (P < .001) in acid-base values were seen when comparing genders. A population of trainers were significantly different (P < .001) from the marginal mean and considered outliers. CLINICAL RELEVANCE: In a population of horses, big data analytics was used to confirm the effects of geography, season, prerace furosemide, gender, age, and trainer influence on blood gases and the acid-base profile.

Sleep-Disordered Breathing and Free Fatty Acid Metabolism.

BACKGROUND: Sleep-disordered breathing (SDB) is independently associated with insulin resistance, glucose intolerance, and type 2 diabetes mellitus. However, data on whether SDB alters the metabolism of free fatty acids (FFAs) are lacking. RESEARCH QUESTION: The primary objective of the current study was to characterize alterations in FFA metabolism across the spectrum of SDB severity. STUDY DESIGN AND METHODS: The study sample included 118 participants with and without SDB who underwent full-montage polysomnography, the frequently sampled IV glucose tolerance test (FSIGTT), and body composition measurements including determination of percent body fat. Parameters of lipolysis suppression, time to FFA nadir, and FFA rebound after an IV glucose challenge were derived using a mathematical model. Multivariable regression analyses were used to characterize the independent associations between SDB severity and parameters of FFA metabolism. RESULTS: SDB severity, as assessed by the apnea-hypopnea index, was associated with adipocyte insulin resistance, a decrease in the glucose- and insulin-mediated suppression of lipolysis, a longer duration to reach a nadir in FFA levels during the FSIGTT, and a sluggish rebound in FFA levels after suppression. Severity of SDB-related hypoxemia was independently associated with adipocyte insulin resistance and the time to reach the FFA nadir during the FSIGTT. Finally, a higher percentage of stage N3 sleep was positively associated with greater suppression of lipolysis and a faster rebound in the FFA levels during the FSIGTT. INTERPRETATION: Independent of adiposity, SDB is associated with impairments in FFA metabolism, which may contribute to the development of glucose intolerance and type 2 diabetes in SDB.

Side effects of opioids during short-term administration: effect of age, gender, and race.

OBJECTIVE: Little is known about risk factors that increase the risk of development of opioid side effects. Our objective was to evaluate the effect of the type of opioid, age, gender, and race on the incidence of side effects from short-term opioid use. METHODS: A secondary analysis of a retrospective cohort study in 35 community-based and tertiary hospitals was done. There were 8855 black or white subjects aged 16 years and older. Patients received meperidine (INN, pethidine), morphine, or fentanyl as part of their treatment. Measurements were made to assess the presence of nausea and vomiting and respiratory depression. RESULTS: Of the patients, 26% had nausea and vomiting and 1.5% had respiratory depression after opioid administration. After adjustment for opioid dose, route of administration, age, gender, and race, meperidine produced less nausea and vomiting (odds ratio [OR] = 0.7; 95% confidence interval [CI], 0.5-0.8) and less respiratory depression (OR = 0.6; 95% CI, 0.2-0.9) than morphine. The risk of respiratory depression increased with age. Compared with patients aged between 16 and 45 years, those aged between 61 and 70 years had 2.8 times the risk of development of respiratory depression (95% CI, 1.2-6.6); those aged between 71 and 80 years had 5.4 times the risk (95% CI, 2.4-11.8); and those aged older than 80 years had 8.7 times the risk (95% CI, 3.8-20.0). Men had less nausea and vomiting than women (OR = 0.5; 95% CI, 0.4-0.6). White subjects had more nausea and vomiting than black subjects (OR = 1.4; 95% CI, 1.1-1.7). CONCLUSIONS: Meperidine produced fewer side effects than morphine during short-term use. The risk of respiratory depression increases substantially after 60 years of age. Women have nausea and vomiting more often than men. The effect of race deserves further investigation.

Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders.

The aim of this study was to use Monte Carlo simulations to compare logistic regression with propensity scores in terms of bias, precision, empirical coverage probability, empirical power, and robustness when the number of events is low relative to the number of confounders. The authors simulated a cohort study and performed 252,480 trials. In the logistic regression, the bias decreased as the number of events per confounder increased. In the propensity score, the bias decreased as the strength of the association of the exposure with the outcome increased. Propensity scores produced estimates that were less biased, more robust, and more precise than the logistic regression estimates when there were seven or fewer events per confounder. The logistic regression empirical coverage probability increased as the number of events per confounder increased. The propensity score empirical coverage probability decreased after eight or more events per confounder. Overall, the propensity score exhibited more empirical power than logistic regression. Propensity scores are a good alternative to control for imbalances when there are seven or fewer events per confounder; however, empirical power could range from 35% to 60%. Logistic regression is the technique of choice when there are at least eight events per confounder.