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Background Vancomycin dosing strategies targeting trough concentrations of 15–20 mg/L are no longer supported due to lack of efficacy evidence and increased risk of nephrotoxicity. Area-under-the-curve (AUC24) nomograms have demonstrated adequate attainment of AUC24 goals ≥ 400 mg h/L with more conservative troughs (10–15 mg/L). Objective The purpose of this study is to clinically validate a vancomycin AUC24 dosing nomogram compared to conventional dosing methods with regards to therapeutic failure and rates of acute kidney injury. Setting This study was conducted at a tertiary, community, teaching hospital in the United States. Method This retrospective, cohort study compared the rates of therapeutic failures between AUC24-extrapolated dosing and conventional dosing methods. Main outcome measure Primary outcome was treatment failure, defined as all-cause mortality within 30 days, persistent positive methicillin-resistant Staphylococcus aureus blood culture, or clinical failure. Rates of acute kidney injury in non-dialysis patients was a secondary endpoint. Results There were 96 participants in the extrapolated-AUC24 cohort and 60 participants in the conventional cohort. Baseline characteristics were similar between cohorts. Failure rates were 11.5% (11/96) in the extrapolated-AUC24 group compared to 18.3% (11/60) in the conventional group (p = 0.245). Reasons for failure were 6 deaths and 5 clinical failures in the extrapolated-AUC24 cohort and 10 deaths and 1 clinical failure in the conventional group. Acute kidney injury rates were 2.7% (2/73) and 16.4% (9/55) in the extrapolated-AUC24 and conventional cohorts, respectively (p = 0.009). Conclusion Extrapolated-AUC24 dosing was associated with less nephrotoxicity without an increase in treatment failures for bloodstream infections compared to conventional dosing. Further investigation is warranted to determine the relationship between extrapolated-AUC24 dosing and clinical failures. [ABSTRACT FROM AUTHOR]

Objective: Using a large longitudinal sample of adults from the Midlife in the United States (MIDUS) study, the present study extended a recently developed hierarchical model to determine how best to model the accumulation of stressors, and to determine whether the rate of change in stressors or traditional composite scores of stressors are stronger predictors of health outcomes., Method: We used factor analysis to estimate a stress-factor score and then, to operationalize the accumulation of stressors we examined five approaches to aggregating information about repeated exposures to multiple stressors. The predictive validity of these approaches was then assessed in relation to different health outcomes., Results: The prediction of chronic conditions, body mass index, difficulty with activities of daily living, executive function, and episodic memory later in life was strongest when the accumulation of stressors was modeled using total area under the curve (AUC) of estimated factor scores, compared to composite scores that have traditionally been used in studies of cumulative stress, as well as linear rates of change., Conclusions: Like endogenous, biological markers of stress reactivity, AUC for individual trajectories of self-reported stressors shows promise as a data reduction technique to model the accumulation of stressors in longitudinal studies. Overall, our results indicate that considering different quantitative models is critical to understanding the sequelae and predictive power of psychosocial stressors from midlife to late adulthood., (Published by Elsevier Ltd.)

Indoor mold exposures have been qualitatively linked to asthma for more than 25 years. Our goal has been to turn this qualitative link into a quantitative assessment of asthma risk from mold exposures as estimated by the home's environmental relative moldiness index (ERMI) value. The home's ERMI value is derived from the quantitative PCR analysis of 36 molds in a dust sample. Six epidemiological studies of the relationship between ERMI values and asthma, in cities across the U.S., showed that both children and adults with asthma were living in homes with significantly higher ERMI values than the control or comparison homes. Based on these six studies, the accuracy of the ERMI value's link to occupant asthma was analyzed using receiver operating characteristic (ROC) curve and area under the curve (AUC) statistical analysis. The AUC was 0.69 which places the test accuracy in the "fair to good" range for a medical diagnostic test. A logistic regression analysis of the six studies was performed to generate an equation that can be used to predict occupant asthma at specific ERMI values. The ERMI metric may be a useful tool to link the quantification of mold contamination in U.S. homes to some asthma health effects. [ABSTRACT FROM AUTHOR]

We propose to utilize the group lasso algorithm for logistic regression to construct a risk scoring system for predicting disease in swine. This work is motivated by the need to develop a risk scoring system from survey data on risk factor for porcine reproductive and respiratory syndrome (PRRS), which is a major health, production and financial problem for swine producers in nearly every country. Group lasso provides an attractive solution to this research question because of its ability to achieve group variable selection and stabilize parameter estimates at the same time. We propose to choose the penalty parameter for group lasso through leave-one-out cross-validation, using the criterion of the area under the receiver operating characteristic curve. Survey data for 896 swine breeding herd sites in the USA and Canada completed between March 2005 and March 2009 are used to construct the risk scoring system for predicting PRRS outbreaks in swine. We show that our scoring system for PRRS significantly improves the current scoring system that is based on an expert opinion. We also show that our proposed scoring system is superior in terms of area under the curve to that developed using multiple logistic regression model selected based on variable significance. [ABSTRACT FROM AUTHOR]

Background: Preterm birth is serious public health worldwide, and early prediction of preterm birth in pregnant women may provide assistance for timely intervention and reduction of preterm birth. This study aimed to develop a preterm birth prediction model that is readily available and convenient for clinical application., Methods: Data used in this case-control study were extracted from the National Vital Statistics System (NVSS) database between 2018 and 2019. Univariate and multivariate logistic regression analyses were utilized to find factors associated with preterm birth. Odds ratio (OR) and 95% confidence interval (CI) were used as effect measures. The area under the curve (AUC), accuracy, sensitivity, and specificity were utilized as model performance evaluation metrics., Results: Data from 3,006,989 pregnant women in 2019 and 3,039,922 pregnant women in 2018 were used for the model establishment and external validation, respectively. Of these 3,006,989 pregnant women, 324,700 (10.8%) had a preterm birth. Higher education level of pregnant women [bachelor (OR = 0.82; 95%CI, 0.81-0.84); master or above (OR = 0.82; 95%CI, 0.81-0.83)], pre-pregnancy overweight (OR = 0.96; 95%CI, 0.95-0.98) and obesity (OR = 0.94; 95%CI, 0.93-0.96), and prenatal care (OR = 0.48; 95%CI, 0.47-0.50) were associated with a reduced risk of preterm birth, while age ≥ 35 years (OR = 1.27; 95%CI, 1.26-1.29), black race (OR = 1.26; 95%CI, 1.23-1.29), pre-pregnancy underweight (OR = 1.26; 95%CI, 1.22-1.30), pregnancy smoking (OR = 1.27; 95%CI, 1.24-1.30), pre-pregnancy diabetes (OR = 2.08; 95%CI, 1.99-2.16), pre-pregnancy hypertension (OR = 2.22; 95%CI, 2.16-2.29), previous preterm birth (OR = 2.95; 95%CI, 2.88-3.01), and plurality (OR = 12.99; 95%CI, 12.73-13.24) were related to an increased risk of preterm birth. The AUC and accuracy of the prediction model in the testing set were 0.688 (95%CI, 0.686-0.689) and 0.762 (95%CI, 0.762-0.763), respectively. In addition, a nomogram based on information on pregnant women and their spouses was established to predict the risk of preterm birth in pregnant women., Conclusions: The nomogram for predicting the risk of preterm birth in pregnant women had a good performance and the relevant predictors are readily available clinically, which may provide a simple tool for the prediction of preterm birth., (© 2022. The Author(s).)

Background and Aims: Relative pharmacological effects of e-cigarettes and cigarettes during 24 hours of ad-libitum use have not been described. In this study, 24-hour blood plasma nicotine concentrations and 48-hour subjective effects with use of cigarettes and e-cigarettes were measured among dual users., Design: Two-arm within-subject cross-over design with preferred e-cigarette or cigarette ad-libitum use over 48 hours., Setting: Hospital research ward in San Francisco, California, USA., Participants: Thirty-six healthy dual users of e-cigarettes and cigarettes (n = 8, 25% females)., Measurements: Twenty-four-hour blood plasma nicotine and cotinine concentrations and 48-hour self-reported nicotine withdrawal symptoms and rewarding effects., Findings: Analyses used analysis of variance (ANOVA)-based mixed models with order of product (e-cigarette or cigarette) and product type (combustible cigarette or type of e-cigarette) as fixed effects, and subject as a repeated effect. During a 24-hour period, e-cigarettes produced lower nicotine exposure than cigarettes for the majority of users, although 25% received more nicotine from e-cigarettes, which was predicted by more frequent e-cigarette use or greater dependence. Compared to cigarette smoking, nicotine exposure for variable-power tank users was similar, while cig-a-like (t (30)  = 2.71, P = 0.011, d = 0.745) and fixed-power tank users (t (30)  = 3.37, P = 0.002, d = 0.993) were exposed to less nicotine. Cigarettes were rated higher than e-cigarettes on some desirable subjective effects (e.g. psychological reward, t (322)  = 7.24 P < 0.001, d = 0.432), but withdrawal symptom reduction was comparable. No differences were found between e-cigarette types, but Bayes factors indicate that these measures were insensitive., Conclusions: During a 24-hour period in a hospital setting in the United States, nicotine exposure for dual users of e-cigarettes and cigarettes was similar when using cigarettes or variable-power tank devices only but was lower for those using cig-a-like or fixed-power devices only. Despite lower nicotine levels, all types of e-cigarette were effective in preventing withdrawal symptoms. E-cigarettes were rated less rewarding than cigarettes., (© 2019 Society for the Study of Addiction.)

Background: Cumulative clinical improvement and speed of improvement are important to psoriasis patients., Objective: Compare cumulative benefits of biologics over 12 to 16 weeks in the treatment of moderate to severe psoriasis., Methods: A systematic literature review identified phase III trial data on Psoriasis Area and Severity Index (PASI) responses for biologics during 12 and 16 weeks of treatment. Cumulative clinical benefit, measured by the area under the curve for PASI ≥75% improvement (PASI 75), ≥90% improvement (PASI 90), and 100% improvement (PASI 100), was compared using the network meta-analysis and Bayesian methodology on the relative probability of achieving percentage of maximum area under the curve., Results: Among biologics approved for psoriasis treatment, anti-interleukin-17 biologics demonstrated consistently greater cumulative clinical benefits on PASI 75, PASI 90, and PASI 100 over the 12- or 16-week period than anti-interleukin-23 and other biologics. For biologics with 12-week data, ixekizumab and brodalumab showed greater cumulative benefits for PASI 75, PASI 90, and PASI 100 than secukinumab, followed by guselkumab, infliximab, adalimumab, ustekinumab, and etanercept. Ixekizumab showed greater cumulative benefits than all other biologics reporting 16-week data., Limitations: Recently approved biologics were not included., Conclusion: Ixekizumab (at 12 weeks and 16 weeks) and brodalumab (at 12 weeks) had greater cumulative clinical benefit than all of other biologics studied., (Copyright © 2019 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.)

Objective: There is inadequate information about the values of many intraoperative physiological measurements that are associated with improved outcomes after surgery. The purpose of this observational study is to investigate the optimal physiological ranges during major spine surgery., Setting: A teaching hospital in the USA., Participants: A convenience sample of 102 patients receiving major posterior spine surgery with multilevel spinal fusion in a prone position., Methods: Physiological variables, including but not limited to mean arterial pressure (MAP) and cerebral and somatic tissue oxygen saturation (SctO 2 /SstO 2 ), were recorded. The results of these measurements were associated with length of hospital stay and composite complication data and were analysed based on thresholds (ie, a cut-off value for optimal and suboptimal physiology) and the area under the curve (AUC) values. The AUC values were measured as the area enclosed by the actual tracing and the threshold. The outcomes were dichotomised into above-average and below-average (ie, improved) categories., Results: Analyses based on thresholds identified the following variables associated with above-average outcomes: MAP <60 mm Hg, temperature <35°C, heart rate >90 beats per minute (bpm), SctO 2 <60% and SstO 2 >80%. Analyses based on AUC values identified the following as associated with above-average outcomes: MAP <70 and >100 mm Hg, temperature <36°C, heart rate >90 bpm, tidal volume (based on ideal body weight)<6 mL/kg, tidal volume (based on actual body weight) >10 mL/kg and peak airway pressure <15 cmH 2 O., Conclusion: The following physiological ranges are associated with improved outcomes (ie, shorter hospitalisation and fewer complications) during major spine surgery: MAP of 70-100 mm Hg, temperature ≥36°C, heart rate <90 bpm, tidal volume based on ideal body weight >6 mL/kg, SctO 2 >60% and SstO 2 <80%., Competing Interests: Competing interests: LM is a consultant to CAS Medical Systems, Inc. The other authors declare no competing interests., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Aims: Outcome data on ventricular tachycardia (VT) ablation has been limited to few experienced centres. We sought to identify complication rates, predictors, and create a risk score model for predicting complications in patients from real-world data., Methods and Results: A total of 25 451 patients undergoing VT ablation from year 2006 to 2013 were identified from the National Inpatient Sample (NIS) database. The whole cohort was randomly divided into derivation cohort to derive the model and validation cohort to validate the model. Multivariate predictors of any complication were identified using regression model. Each predictor was assigned a risk score and each patient was assigned to one of the four groups (risk score in parenthesis) based on total combined risk score: Group 0 (0), Group 1 (1-5), Group 2 (6-10), and Group 3 (>11). The rate of 'any complication' and 'in-hospital mortality' in whole cohort was 14.7% and 2.8%, respectively. The predictors of any complication include chronic kidney disease, coagulopathy, chronic liver disease, stroke (cerebrovascular accident), emergency procedure, age ≥ 65 years, coronary artery disease, peripheral vascular disease, and female gender. There was a significant increase in complication rate in a linear fashion as the risk score increased. The incidence of any complications increased from 2.7% in Group 0 to 31% in Group 3. The risk score model performed well in predicting complications associated with VT ablation., Conclusion: Patients with higher risk scores have significant increase in any complication and in-hospital mortality from VT ablation. The simple risk score model can help to risk stratify patients prior to VT ablation., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2018. For permissions please email: journals.permissions@oup.com.)

Objective: The metabolic syndrome (MetS) is typically diagnosed based on abnormalities in specific clustered clinical measures that are associated with increased risk for coronary heart disease (CHD) and Type 2 diabetes mellitus (T2DM). However, current MetS criteria result in racial/ethnic discrepancies. Our goals were to use confirmatory factor analysis (CFA) to delineate differential contributions to MetS by sub-group, and if contributions were discovered, develop sex and racial/ethnic-specific equations to calculate MetS severity., Research Design and Methods: Using data on adults from the National Health and Nutrition Examination Survey 1999-2010, we performed a CFA of a single MetS factor that allowed differential loadings across groups, resulting in a sex and race/ethnicity-specific continuous MetS severity score., Results: Loadings to the single MetS factor differed by sub-group for each MetS component (p<0.001), with lower factor loadings among non-Hispanic-blacks for triglycerides and among Hispanics for waist circumference. Systolic blood pressure exhibited low factor loadings among all groups. MetS severity scores were correlated with biomarkers of future disease (high-sensitivity C-reactive-protein, uric acid, insulin resistance). Non-Hispanic-black-males with diabetics had a low prevalence of MetS but high MetS severity scores that were not significantly different from other racial/ethnic groups., Conclusions: This analysis among adults uniquely demonstrated differences between sexes and racial/ethnic groups regarding contributions of traditional MetS components to an assumed single factor. The resulting equations provide a clinically-accessible and interpretable continuous measure of MetS for potential use in identifying adults at higher risk for MetS-related diseases and following changes within individuals over time. These equations hold potential to be a powerful new outcome for use in MetS-focused research and interventions., (© 2013.)