Association of Urine Findings with Metabolic Syndrome Traits in a Population of Patients with Nephrolithiasis

Background The odds of nephrolithiasis increase with more metabolic syndrome (MetS) traits. We evaluated associations of metabolic and dietary factors from urine studies and stone composition with MetS traits in a large cohort of stone-forming patients. Methods Patients .18 years old who were evaluated for stones with 24-hour urine collections between July 2009 and December 2018 had their records reviewed retrospectively. Patient factors, laboratory values, and diagnoses were identified within 6 months of urine collection and stone composition within 1 year. Four groups with none, one, two, and three or four MetS traits (hypertension, obesity, dyslipidemia, and diabetes) were evaluated. Trends across groups were tested using linear contrasts in analysis of variance and analysis of covariance. Results A total of 1473 patients met the inclusion criteria (835 with stone composition). MetS groups were 684 with no traits, 425 with one trait, 211 with two traits, and 153 with three or four traits. There were no differences among groups for urine volume, calcium, or ammonium excretion. There was a significant trend (P,0.001) for more MetS traits being associated with decreasing urine pH, increasing age, calculated dietary protein, urine uric acid (UA), oxalate, citrate, titratable acid phosphate, net acid excretion, and UA supersaturation. The ratio of ammonium to net acid excretion did not differ among the groups. After adjustment for protein intake, the fall in urine pH remained strong, while the upward trend in acid excretion was lost. Calcium oxalate stones were most common, but there was a trend for more UA (P,0.001) and fewer calcium phosphate (P50.09) and calcium oxalate stones (P50.01) with more MetS traits. Conclusions Stone-forming patients with MetS have a defined pattern of metabolic and dietary risk factors that contribute to an increased risk of stone formation, including higher acid excretion, largely the result of greater protein intake, and lower urine pH

N-terminal pro-B-type natriuretic peptide and risk of future cognitive impairment in the REGARDS cohort

Background: Improved understanding of the etiology of cognitive impairment is needed to develop effective preventive interventions. Higher amino-terminal pro-B-type natriuretic peptide (NT-proBNP) is a biomarker of cardiac dysfunction associated with risk of cardiovascular diseases and stroke in apparently healthy people. Objective: To study the association of NT-proBNP with risk of incident cognitive impairment. Methods: The Reasons for Geographic and Racial Differences in Stroke is a national cohort study of 30,239 black and white Americans age 45 and older at baseline, enrolled in 2003-7. Among participants without prebaseline stroke or cognitive impairment, baseline NT-proBNP was measured in 470 cases of incident cognitive impairment and 557 controls. Cases were participants scoring below the 6th percentile of demographically-adjusted means on at least 2 of 3 serially administered tests (word list learning, word list recall and semantic fluency) over 3.5 years follow-up. Results: Adjusting for age, gender, race, region of residence, education, and income, there was an increased odds ratio of incident cognitive impairment with increasing NT-proBNP; participants in the 4th versus 1st quartile (>127 versus ≤33 pg/ml) had a 1.69-fold increased odds (95% CI 1.11–2.58). Adjustment for cardiovascular risk factors and presence of an apolipoprotein E4 allele had no substantial impact on the odds ratio. Results did not differ by age, race, gender, or presence of an apolipoprotein E4 allele. Conclusion: Higher NT-pro-BNP was associated with incident cognitive impairment in this prospective study, independent of atherogenic and Alzheimer’s disease risk factors. Future work should clarify pathophysiologic connections of NT-proBNP and cognitive dysfunction

Recalibrating single-study effect sizes using hierarchical Bayesian models

INTRODUCTION: There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.METHODS: We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.RESULTS: The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p &lt; 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p &lt; 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments. DISCUSSION: Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.</p

Recalibrating single-study effect sizes using hierarchical Bayesian models

INTRODUCTION: There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.METHODS: We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.RESULTS: The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p &lt; 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p &lt; 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments. DISCUSSION: Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.</p

Empirical comparisons of logistic regression, Poisson regression, and Cox proportional hazards modeling in analysis of occupational cohort data

Three multiplicative models commonly used in the analysis of occupational cohort studies are logistic, Poisson, and Cox proportional hazards regression. Although the underlying theories behind these are well known, this has not always led to clear decisions for selecting which to use in practice. This research was conducted to examine the effect model choice has on the epidemiologic interpretation of occupational cohort data. The three models were applied to a National Cancer Institute historical cohort of formaldehyde-exposed workers. Samples were taken from this dataset to create scenarios for model comparisons, varying the study size (n = 600, 3000, 6000), proportion of subjects experiencing the outcome (2.5%, 10%, 50%), strength of association between exposure and outcome (weak, moderate, strong), follow-up length (5, 15, 30 years), and proportion of subjects lost to follow-up (0%, 10%, 17.5%). Other factors investigated included how to handle subjects lost to follow-up in logistic regression. Models were compared on risk estimates, confidence intervals, and practical issues such as ease of use. The Poisson and Cox models yielded nearly identical relative risks and confidence intervals in all situations except when confounding by age could not be closely controlled in the Poisson analysis, which occurred when the sample size was small or outcome was rare. Logistic regression findings were more variable, with risk estimates differing most from the Cox results when there was a common outcome or strong relative risk. Logistic was also less precise than the others. Thus, although logistic was the easiest model to implement, it should only be used in occupational cohort studies when the outcome is rare (5% or less), and the relative risk is less than about 2. Even then, since it does not account for follow-up time differences between subjects or changes in risk factors values over time, the Cox or Poisson models are better choices. Selecting between these can usually be based on convenience, except when confounding cannot be closely controlled in the Poisson model but can in the Cox model, or when the Poisson assumption of exponential baseline survival is not met. In these cases Cox should be used

Cost Analysis and Supply Utilization of Laparoscopic Cholecystectomy

Laparoscopic cholecystectomy (LC) is one of the highest volume surgeries performed annually. We hypothesized that there is a statistically significant intradepartmental cost variance with supply utilization variability amongst surgeons of different subspecialty. This study sought to describe laparoscopic cholecystectomy cost of care among three subspecialties of surgeons. This retrospective observational cohort study captured 372 laparoscopic cholecystectomy cases performed between June 2015 and June 2016 by 12 surgeons divided into three subspecialties: 2 in bariatric surgery (BS), 5 in acute care surgery (ACS), and 5 in general surgery (GS). The study utilized a third-party software, Surgical Profitability Compass Procedure Cost Manager and Crimson System (SPCMCS) (The Advisory Board Company, Washington, DC), to stratify case volume, supply cost, case duration, case severity level, and patient length of stay intradepartmentally. Statistical methods included the Kruskal-Wallis test. Average composite supply cost per case was $569 and median supply cost per case was$554. The case volume was 133 (BS), 109 (ACS), and 130 (GS). The median intradepartmental total supply cost was $674.5 (BS),$534 (ACS), and $564 (GS) (P<0.005). ACS and GS presented with a higher standard deviation of cost,$98 (ACS) and $110 (GS) versus$26 (BS). The median case duration was 70 min (BS), 107 min (ACS), and 78 min (GS) (P<0.02). The average patient length of stay was 1.15 (BS), 3.10 (ACS), and 1.17 (GS) (P<0.005). Overall, there was a statistically significant difference in median supply cost (highest in BS; lowest in ACS and GS). However, the higher supply costs may be attenuated by decreased operative time and patient length of stay. Strategies to reduce total supply cost per case include mandating exchange of expensive items, standardization of supply sets, increased price transparency, and education to surgeons