Warfarin sensitivity is associated with increased hospital mortality in critically Ill patients.

BACKGROUND: Warfarin is a widely used anticoagulant with a narrow therapeutic index and large interpatient variability in the therapeutic dose. Warfarin sensitivity has been reported to be associated with increased incidence of international normalized ratio (INR) > 5. However, whether warfarin sensitivity is a risk factor for adverse outcomes in critically ill patients remains unknown. In the present study, we aimed to evaluate the utility of different machine learning algorithms for the prediction of warfarin sensitivity and to determine the impact of warfarin sensitivity on outcomes in critically ill patients. METHODS: Nine different machine learning algorithms for the prediction of warfarin sensitivity were tested in the International Warfarin Pharmacogenetic Consortium cohort and Easton cohort. Furthermore, a total of 7,647 critically ill patients was analyzed for warfarin sensitivity on in-hospital mortality by multivariable regression. Covariates that potentially confound the association were further adjusted using propensity score matching or inverse probability of treatment weighting. RESULTS: We found that logistic regression (AUC = 0.879, 95% CI: 0.834-0.924) was indistinguishable from support vector machine with a linear kernel, neural network, AdaBoost and light gradient boosting trees, and significantly outperformed all the other machine learning algorithms. Furthermore, we found that warfarin sensitivity predicted by the logistic regression model was significantly associated with worse in-hospital mortality in critically ill patients with an odds ratio (OR) of 1.33 (95% CI, 1.01-1.77). CONCLUSIONS: Our data suggest that the logistic regression model is the best model for the prediction of warfarin sensitivity clinically and that warfarin sensitivity is likely to be a risk factor for adverse outcomes in critically ill patients.

Association of serum 25-Hydroxy Vitamin D level with lipid, lipoprotein, and apolipoprotein level.

Introduction 25-Hydroxy vitamin D (Vit D3) deficiency was found to be associated with vascular dysfunction, arterial stiffening, extent of coronary artery disease and cardiovascular mortality. Previous studies showed positive correlation between serum Vit D3 and HDL-C and negative correlation between Vit D3 and LDL-C. The aim of this study is to investigate more details about the possible association of serum Vit D3 level with lipid, lipoprotein and apolipoprotein level. Methods Totally 101 patients were included in this study and Vit D3, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), High-density lipoprotein cholesterol (HDL-C), total triglyceride (TG), non-high-density lipoprotein cholesterol (Non-HDL-C), low-density lipoprotein particle (LDL-P), small dense low-density lipoprotein particle (sLDL-P), small dense low-density lipoprotein cholesterol (sdLDL-C), High-density lipoprotein cholesterol particles (HDL-P), High-density lipoprotein 2-cholesterol (HDL2-C), Apolipoprotein B(ApoB), Apolipoprotein A1 (Apo A1) and Apolipoprotein B/Apolipoprotein A1 ratio (ApoB/A ratio) were tested. Results Our results show that patients with Vit D3 deficiency (Vit D3 < 30 ng/ml) have significantly higher level of LDL-C, TG, Non-HDL-C, LDL-P, sLDL-P, sdLDL-C, ApoB and ApoB/A ratio compare with patients have normal Vit D3 level (Vit D3 > 30 ng/ml). Patients with normal Vit D3 level have significantly higher level of HDL-C and HDL2-C. Correlation study shows that Vit D3 level is negative correlated with TC, LDL-C, TG, Non-HDL-C, LDL-P, sLDL-P, sdLDL-C, ApoB and ApoB/A ratio and positive correlated with HDL2-C level. Conclusion Our results show that Vit D3 deficiency links to an increased risk for dyslipidemia and that may be the reason that patients with vitamin D deficiency tend to have higher risk of coronary artery disease.

Environmental load of Cryptosporidium parvum oocysts from cattle manure in feedlots from the central and western United States.

The first step in assessing the risk of water contamination by Cryptosporidium parvum oocysts from feedlot cattle (Bos taurus) production systems is to quantify the number of C. parvum oocysts present in the fecal material deposited by feedlot cattle. Our primary objective for this project was to estimate the daily environmental load of C. parvum oocysts in fecal material deposited by feedlot cattle from across the central and western USA. Our secondary goal was to genotype isolates of C. parvum from feedlot cattle to help facilitate proper identification of mammalian sources of waterborne C. parvum. Based on 5274 fecal samples from 22 feedlots in seven states (California, Washington, Colorado, Oklahoma, Texas, Nebraska, and South Dakota), we estimated a point prevalence of C. parvum of 0.99 to 1.08% in fecal material from feedlot pens from a wide range of climates and a diverse range of feedlot management systems. On average, fresh fecal material from throughout feedlot systems (recent arrivals to nearing slaughter) contained about 1.3 to 3.6 oocysts/g feces, which roughly translates to about 2.8 x 10(4) to 1.4 x 10(5) oocysts/animal per day.