Inpatient Mortality and 30-Day Readmission Rates Associated with Troponin Testing in Patients without Acute Myocardial Infarction.

OBJECTIVE: Troponin values above the threshold established to diagnose acute myocardial infarction (AMI; >99th percentile) are commonly detected in patients with diagnoses other than AMI. The objective of this study was to compare inpatient mortality and 30-day readmission rate in patients with troponin I (TnI) above and below the 99th percentile in those with type 1 AMI and type 2 myocardial injury. METHODS: Between January 1, 2016 and December 31, 2016, there were 56,895 inpatient hospitalizations; of these 14,326 (25.2%) patients received troponin testing. We evaluated mortality and readmissions in the entire cohort based on the primary discharge International Classification of Diseases, Tenth Edition (ICD-10) diagnosis and grouped into type 1 AMI versus other diagnoses comprising the type 2 AMI group (including ICD-10 codes for congestive heart failure, sepsis, and other). Among those with TnI drawn, we evaluated in-hospital mortality and 30-day readmissions based on troponin values > 99th percentile (≥ 0.1 ng/ml). RESULTS: Among the entire cohort, the inpatient mortality rate was significantly higher in those with TnI testing (5.0%, 95% CI 4.6%-5.3%) compared to those without testing (0.7%, 95% CI 0.6%-0.7%, P < 0.01). In the tested cohort 3,743 (26%) patients had troponin levels above the 99th percentile (> 0.1 ng/ml), and 10,583 (74%) had troponin levels below the 99th percentile (≤ 0.1 ng/ml). Comparing type 2 AMI with type 1 AMI and troponin testing, TnI values ≥ 0.1 ng/ml were associated with higher inpatient mortality (11.6% vs. 3.9%) and 30-day readmission rates (16.9% vs. 10.7%). CONCLUSIONS: A higher inpatient mortality and 30-day readmission rates were found in patients with type 2 AMI compared to type 1 AMI group.

Resistance Training-Induced Elevations in Muscular Strength in Trained Men Are Maintained After 2 Weeks of Detraining and Not Differentially Affected by Whey Protein Supplementation.

Hwang, PS, Andre, TL, McKinley-Barnard, SK, Morales Marroquín, FE, Gann, JJ, Song, JJ, and Willoughby, DS. Resistance training-induced elevations in muscular strength in trained men are maintained after 2 weeks of detraining and not differentially affected by whey protein supplementation. J Strength Cond Res 31(4): 869-881, 2017-Resistance training (RT) with nutritional strategies incorporating whey protein intake postexercise can stimulate muscle protein synthesis and elicit hypertrophy. The early phases of training-induced anabolic responses can be attenuated with longer-term training. It is currently unknown if short-term detraining (DT) can restore these blunted anabolic responses during a subsequent retraining (ReT) period. Twenty resistance-trained men (age 20.95 ± 1.23 years; n = 20) were randomized into one of 2 groups (PRO or CHO; 25 g) in a double-blind manner. Participants followed a 4-day per week RT program (4-week RT; 2-week DT; 4-week ReT) while consuming their respective supplement only on workout days during RT and ReT, but every day during DT. At baseline, 4 weeks after RT (post-RT), 2 weeks after DT (post-2-week DT), and after 4 weeks of ReT after DT (post-ReT), leg press strength (LPS) was assessed and rectus femoris cross-sectional area and lean mass changes were assessed by ultrasonography and dual-energy x-ray absorptiometry, respectively. A factorial 2 × 4 (group by time) analyses of variance with repeated measures were used with a probability level at ≤0.05. LPS was elevated throughout the 10-week training study (p = 0.003) with no decrease in LPS after DT in both groups. Although not statistically significant, both groups retained lean mass after DT. A 2-week period of DT appeared to retain muscular strength in resistance-trained men. Therefore, a short-term period of DT can potentially retain lower-body strength in young resistance-trained men irrespective of supplementing with 25 g of whey protein postexercise.

Eight Weeks of Phosphatidic Acid Supplementation in Conjunction with Resistance Training Does Not Differentially Affect Body Composition and Muscle Strength in Resistance-Trained Men.

This study attempted to determine the effects of eight weeks of resistance training (RT) combined with phosphatidic acid (PA) supplementation at a dose of either 250 mg or 375 mg on body composition and muscle size and strength. Twenty-eight resistance-trained men were randomly assigned to ingest 375 mg [PA375 (n = 9)] or 250 mg [PA250 (n = 9)] of PA or 375 mg of placebo [PLC (n = 10)] daily for eight weeks with RT. Supplements were ingested 60 minutes prior to RT and in the morning on non-RT days. Participants' body composition, muscle size, and lower-body muscle strength were determined before and after training/supplementation. Separate group x time ANOVAs for each criterion variable were used employing an alpha level of ≤ 0.05. Magnitude- based inferences were utilized to determine the likely or unlikely impact of PA on each criterion variable. A significant main effect for time was observed for improvements in total body mass (p = 0.003), lean mass (p = 0.008), rectus femoris cross-sectional area [RF CSA (p = 0.011)], and lower-body strength (p < 0.001), but no significant interactions were present (p > 0.05). Collectively, magnitude-based inferences determined both doses of PA to have a likely impact of increasing body mass (74.2%), lean mass (71.3%), RF CSA (92.2%), and very likely impact on increasing lower-body strength (98.1% beneficial). When combined with RT, it appears that PA has a more than likely impact on improving lower-body strength, whereas a likely impact exists for increasing muscle size and lean mass.

Bayesian meta-analysis models for microarray data: a comparative study.

BACKGROUND: With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods. RESULTS: Two Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce the gene-specific posterior probability of differential expression, which is the basis for inference. Since the standardized expression integration model includes inter-study variability, it may improve accuracy of results versus the probability integration model. However, due to the small number of studies typical in microarray meta-analyses, the variability between studies is challenging to estimate. The probability integration model eliminates the need to model variability between studies, and thus its implementation is more straightforward. We found in simulations of two and five studies that combining probabilities outperformed combining standardized gene expression measures for three comparison values: the percent of true discovered genes in meta-analysis versus individual studies; the percent of true genes omitted in meta-analysis versus separate studies, and the number of true discovered genes for fixed levels of Bayesian false discovery. We identified similar results when pooling two independent studies of Bacillus subtilis. We assumed that each study was produced from the same microarray platform with only two conditions: a treatment and control, and that the data sets were pre-scaled. CONCLUSION: The Bayesian meta-analysis model that combines probabilities across studies does not aggregate gene expression measures, thus an inter-study variability parameter is not included in the model. This results in a simpler modeling approach than aggregating expression measures, which accounts for variability across studies. The probability integration model identified more true discovered genes and fewer true omitted genes than combining expression measures, for our data sets.

Bayesian models for pooling microarray studies with multiple sources of replications.

BACKGROUND: Biologists often conduct multiple but different cDNA microarray studies that all target the same biological system or pathway. Within each study, replicate slides within repeated identical experiments are often produced. Pooling information across studies can help more accurately identify true target genes. Here, we introduce a method to integrate multiple independent studies efficiently. RESULTS: We introduce a Bayesian hierarchical model to pool cDNA microarray data across multiple independent studies to identify highly expressed genes. Each study has multiple sources of variation, i.e. replicate slides within repeated identical experiments. Our model produces the gene-specific posterior probability of differential expression, which provides a direct method for ranking genes, and provides Bayesian estimates of false discovery rates (FDR). In simulations combining two and five independent studies, with fixed FDR levels, we observed large increases in the number of discovered genes in pooled versus individual analyses. When the number of output genes is fixed (e.g., top 100), the pooled model found appreciably more truly differentially expressed genes than the individual studies. We were also able to identify more differentially expressed genes from pooling two independent studies in Bacillus subtilis than from each individual data set. Finally, we observed that in our simulation studies our Bayesian FDR estimates tracked the true FDRs very well. CONCLUSION: Our method provides a cohesive framework for combining multiple but not identical microarray studies with several sources of replication, with data produced from the same platform. We assume that each study contains only two conditions: an experimental and a control sample. We demonstrated our model's suitability for a small number of studies that have been either pre-scaled or have no outliers.