Brown adipose tissue metabolism in women is dependent on ovarian status.

In rodents, loss of estradiol (E2) reduces brown adipose tissue (BAT) metabolic activity. Whether E2 impacts BAT activity in women is not known. BAT oxidative metabolism was measured in premenopausal (n = 27; 35 ± 9 yr; body mass index = 26.0 ± 5.3 kg/m2) and postmenopausal (n = 25; 51 ± 8 yr; body mass index = 28.0 ± 5.0 kg/m2) women at room temperature and during acute cold exposure using [11C]acetate with positron emission tomography coupled with computed tomograph. BAT glucose uptake was also measured during acute cold exposure using 2-deoxy-2-[18F]fluoro-d-glucose. To isolate the effects of ovarian hormones from biological aging, measurements were repeated in a subset of premenopausal women (n = 8; 40 ± 4 yr; BMI = 28.0 ± 7.2 kg/m2) after 6 mo of gonadotropin-releasing hormone agonist therapy to suppress ovarian hormones. At room temperature, there was no difference in BAT oxidative metabolism between premenopausal (0.56 ± 0.31 min-1) and postmenopausal women (0.63 ± 0.28 min-1). During cold exposure, BAT oxidative metabolism (1.28 ± 0.85 vs. 0.91 ± 0.63 min-1, P = 0.03) and net BAT glucose uptake (84.4 ± 82.5 vs. 29.7 ± 31.4 nmol·g-1·min-1, P < 0.01) were higher in premenopausal than postmenopausal women. In premenopausal women who underwent gonadotropin-releasing hormone agonist, cold-stimulated BAT oxidative metabolism was reduced to a similar level (from 1.36 ± 0.66 min-1 to 0.91 ± 0.41 min-1) to that observed in postmenopausal women (0.91 ± 0.63 min-1). These results provide the first evidence in humans that reproductive hormones are associated with BAT oxidative metabolism and suggest that BAT may be a target to attenuate age-related reduction in energy expenditure and maintain metabolic health in postmenopausal women.NEW & NOTEWORTHY In rodents, loss of estrogen reduces brown adipose tissue (BAT) activity. Whether this is true in humans is not known. We found that BAT oxidative metabolism and glucose uptake were lower in postmenopausal compared to premenopausal women. In premenopausal women who underwent ovarian suppression to reduce circulating estrogen, BAT oxidative metabolism was reduced to postmenopausal levels. Thus the loss of ovarian function in women leads to a reduction in BAT metabolic activity independent of age.

Electrographic screening for infantile epileptic spasms syndrome in a single sleep-wake cycle.

OBJECTIVE: Infantile epileptic spasms syndrome (IESS) is a common and urgent diagnosis with seizure and nonseizure mimics. Evaluation with prolonged video-electroencephalography (EEG) can be time-consuming and costly. This study investigated the use of EEG review of a single sleep-wake cycle to exclude IESS. METHODS: We retrospectively reviewed video-EEG studies to rule out IESS in children between the ages of 2 months and 2 years in the period from January 2019 through June 2020. EEG studies were reviewed from the start of the recording through the first sleep-wake cycle and scored as "normal," "consistent with IESS," or "abnormal but not diagnostic of IESS." Scores were compared to the clinical report created by analysis of the entire video-EEG. RESULTS: Inclusion criteria were met in 238 EEG studies. The mean patient age was 7.6 months. The median duration of the full study was 908 min, compared to 107.5 min for the first sleep-wake cycle only. The median difference in recording time was 801 min, p-value < .01. Scored outcomes were similar. Sixty-eight percent of EEG studies were scored as "normal" on first sleep-wake cycle review as compared to 63% on full study review, 13% scored as "consistent with IESS" compared to 16% and 19% scored as "abnormal but not diagnostic of IESS" compared to 21%. Sensitivity and specificity of the first sleep-wake cycle review for studies "consistent with IESS" was 84% and 100%, respectively. No cases of IESS were scored as normal on first sleep-wake cycle review. SIGNIFICANCE: A single sleep-wake cycle captured on EEG can triage studies when IESS is suspected. A normal first sleep-wake cycle did not miss cases of IESS and could result in reduced EEG recording time. Because most of these cases presented to an emergency department, a normal first sleep-wake cycle may help providers determine the acuity, or necessity, of further testing.

Stepped-care to improve mental health outcomes among underserved patients with lung and head and neck cancer.

BACKGROUND: The comparative effectiveness study (ClinicalTrials.gov, NCT03016403) assessed the effects of a stepped-care intervention versus usual care on mental health outcomes, including anxiety, depression, coping self-efficacy, emotional distress (anxiety and depression combined), health-related quality of life (HRQoL), and perceived stress among underserved patients (i.e., low-income, uninsured, underinsured) with lung cancer (LC) and head-and-neck cancer (HNC). METHODS: In a randomized controlled trial, we investigated if 147 patients who received the stepped-care intervention had better mental health outcomes compared to 139 patients who received usual care. Using an intent-to-treat approach, we analyzed outcomes with linear mixed models. RESULTS: For the primary outcomes estimated mean differences (denoted by "Δ"), depression (Δ = 1.75, 95% CI = 0.52, 2.98, p = 0.01) and coping self-efficacy (Δ = -15.24, 95% CI = -26.12, -4.36, p = 0.01) were better for patients who received the intervention compared to patients who received usual care, but anxiety outcomes were not different. For secondary outcomes, emotional distress (Δ = 1.97, 95% CI: 0.68, 3.54, p =< 0.01) and HRQoL (Δ = -4.16 95% CI: -7.45, -0.87, p = 0.01) were better for patients who received the intervention compared to usual care patients, while perceived stress was not different across groups. CONCLUSIONS: The stepped-care intervention influenced depression and coping self-efficacy, important outcomes for patients with acute illnesses like LC and HNC. Although differences in emotional distress met the minimally important differences (MID) previously reported, depression and HRQoL were not above the MID threshold. Our study is among a few to report differences in mental health outcomes for underserved LC and HNC patients after receiving a psychological intervention. GOV IDENTIFIER: NCT03016403.

Mother's Milk Messaging™: trial evaluation of app and texting for breastfeeding support.

BACKGROUND: New mothers experience BF challenges but have limited evidence-based technology-enabled support. OBJECTIVES: 1) Determine if using the Mother's Milk Messaging™ app improved aspects of breastfeeding and breastfeeding rates and 2) Describe engagement as well as themes from the qualitative feedback on the app. METHOD: Randomized Controlled Trial National sample of primiparous, singleton mothers recruited online and then randomized using stratification by language into three arms: 1) BF text messages plus app; 2) BF text messages, app and physician-moderated private Facebook (FB) group; 3) Attention control group who received injury prevention texts. Exclusive breastfeeding rates as primary outcome and knowledge/attitude, confidence, and social support as secondary outcomes. We determined engagement through analysis of app usage metrics. We conducted and content-coded interviews with participants to learn more about app usage and BF experience. Due to the nature of the intervention participants could not be blinded. RESULTS: There were a total of 346 participants in the trial, with 227 in the Intervention (n = 154 group 1 and n = 156 group 2) and 119 in the control group. Because of minimal Facebook activity, the two intervention groups 1 and 2 were combined. There were no differences in breastfeeding exclusivity and duration. (NS). Women in the intervention arm reported significantly higher confidence with breastfeeding and perceived social support to the control group (p < .05). Greater than 80% registered the app and those that engaged with the app had higher scores with time. Mothers appreciated receiving text messages and videos with reliable information. No harm was reported in this study. CONCLUSION: MMM increased confidence with breastfeeding and with gathering social supports. Exclusively BF was high in all participants. Mothers perceived it as useful and dependable especially the texting.

Application of gap time analysis with flexible hazards to pulmonary exacerbations in the EPIC observational study.

Cystic fibrosis and other chronic lung disease clinical trials often use time to first pulmonary exacerbation (PEx) or total PEx count as endpoints. The use of these outcomes may fail to capture patterns or timing of multiple exacerbations and how covariates influence the risk of future exacerbations. Analysis of gap times between PEx provides a useful framework to understand risks of subsequent events, particularly to assess if there is a temporary increase in a hazard of a subsequent PEx following the occurrence of a PEx. This may be useful for estimating the amount of time needed to follow patients after a PEx and predicting which patients are more likely to have multiple PEx. We propose a smoothed hazard for gap times to account for elevated hazards after exacerbations. A simulation study was conducted to explore model performance and was able to appropriately estimate parameters in all situations with an underlying change point with independent or correlated recurrent events. Models with different change-point structures and trends are compared using Early Pseudomonas Infection Control (EPIC) observational study data, using a quasi-likelihood modification of the Akaike information criterion; a model with a change-point provided a better fit than a model without one. The analysis suggests that the change point may be 1.8 years (SE 0.09) after the end of a PEx. Models including covariates in the hazard function revealed that having one or two copies of the Δ$\Delta$ F508 mutation, female sex, and higher numbers of previous PEx were significantly associated with increased risk of another PEx.

Predicting COVID-19 mortality risk in Toronto, Canada: a comparison of tree-based and regression-based machine learning methods.

BACKGROUND: Coronavirus disease (COVID-19) presents an unprecedented threat to global health worldwide. Accurately predicting the mortality risk among the infected individuals is crucial for prioritizing medical care and mitigating the healthcare system's burden. The present study aimed to assess the predictive accuracy of machine learning methods to predict the COVID-19 mortality risk. METHODS: We compared the performance of classification tree, random forest (RF), extreme gradient boosting (XGBoost), logistic regression, generalized additive model (GAM) and linear discriminant analysis (LDA) to predict the mortality risk among 49,216 COVID-19 positive cases in Toronto, Canada, reported from March 1 to December 10, 2020. We used repeated split-sample validation and k-steps-ahead forecasting validation. Predictive models were estimated using training samples, and predictive accuracy of the methods for the testing samples was assessed using the area under the receiver operating characteristic curve, Brier's score, calibration intercept and calibration slope. RESULTS: We found XGBoost is highly discriminative, with an AUC of 0.9669 and has superior performance over conventional tree-based methods, i.e., classification tree or RF methods for predicting COVID-19 mortality risk. Regression-based methods (logistic, GAM and LASSO) had comparable performance to the XGBoost with slightly lower AUCs and higher Brier's scores. CONCLUSIONS: XGBoost offers superior performance over conventional tree-based methods and minor improvement over regression-based methods for predicting COVID-19 mortality risk in the study population.

Dynamic prediction based on variability of a longitudinal biomarker.

BACKGROUND: Tacrolimus is given post-kidney transplant to suppress the immune system, and the amount of drug in the body is measured frequently. Higher variability over time may be indicative of poor drug adherence, leading to more adverse events. It is important to account for the variation in Tacrolimus, not just the average change over time. METHODS: Using data from the University of Colorado, we compare methods of assessing how the variability in Tacrolimus influences the hazard of de novo Donor Specific Antibodies (dnDSA), an early warning sign of graft failure. We compare multiple joint models in terms of fit and predictive ability. We explain that the models that account for the individual-specific variability over time have the best predictive performance. These models allowed each patient to have an individual-specific random error term in the longitudinal Tacrolimus model, and linked this to the hazard of dnDSA model. RESULTS: The hazard for the variance and coefficient of variation (CV) loading parameter were greater than 1, indicating that higher variability of Tacrolimus had a higher hazard of dnDSA. Introducing the individual-specific variability improved the fit, leading to more accurate predictions about the individual-specific time-to-dnDSA. CONCLUSIONS: We showed that the individual's variability in Tacrolimus is an important metric in predicting long-term adverse events in kidney transplantation. This is an important step in personalizing the dosage of TAC post-transplant to improve outcomes post-transplant.

Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker.

BACKGROUND: Risk prediction models for time-to-event outcomes play a vital role in personalized decision-making. A patient's biomarker values, such as medical lab results, are often measured over time but traditional prediction models ignore their longitudinal nature, using only baseline information. Dynamic prediction incorporates longitudinal information to produce updated survival predictions during follow-up. Existing methods for dynamic prediction include joint modeling, which often suffers from computational complexity and poor performance under misspecification, and landmarking, which has a straightforward implementation but typically relies on a proportional hazards model. Random survival forests (RSF), a machine learning algorithm for time-to-event outcomes, can capture complex relationships between the predictors and survival without requiring prior specification and has been shown to have superior predictive performance. METHODS: We propose an alternative approach for dynamic prediction using random survival forests in a landmarking framework. With a simulation study, we compared the predictive performance of our proposed method with Cox landmarking and joint modeling in situations where the proportional hazards assumption does not hold and the longitudinal marker(s) have a complex relationship with the survival outcome. We illustrated the use of the RSF landmark approach in two clinical applications to assess the performance of various RSF model building decisions and to demonstrate its use in obtaining dynamic predictions. RESULTS: In simulation studies, RSF landmarking outperformed joint modeling and Cox landmarking when a complex relationship between the survival and longitudinal marker processes was present. It was also useful in application when there were several predictors for which the clinical relevance was unknown and multiple longitudinal biomarkers were present. Individualized dynamic predictions can be obtained from this method and the variable importance metric is useful for examining the changing predictive power of variables over time. In addition, RSF landmarking is easily implementable in standard software and using suggested specifications requires less computation time than joint modeling. CONCLUSIONS: RSF landmarking is a nonparametric, machine learning alternative to current methods for obtaining dynamic predictions when there are complex or unknown relationships present. It requires little upfront decision-making and has comparable predictive performance and has preferable computational speed.

An Interactive Web-Based Lethal Means Safety Decision Aid for Suicidal Adults (Lock to Live): Pilot Randomized Controlled Trial.

BACKGROUND: Counseling to reduce access to lethal means such as firearms and medications is recommended for suicidal adults but does not routinely occur. We developed the Web-based Lock to Live (L2L) decision aid to help suicidal adults and their families choose options for safer home storage. OBJECTIVE: This study aimed to test the feasibility and acceptability of L2L among suicidal adults in emergency departments (EDs). METHODS: At 4 EDs, we enrolled participants (English-speaking, community-dwelling, suicidal adults) in a pilot randomized controlled trial. Participants were randomized in a 13:7 ratio to L2L or control (website with general suicide prevention information) groups and received a 1-week follow-up telephone call. RESULTS: Baseline characteristics were similar between the intervention (n=33) and control (n=16) groups. At baseline, many participants reported having access to firearms (33/49, 67%), medications (46/49, 94%), or both (29/49, 59%). Participants viewed L2L for a median of 6 min (IQR 4-10 min). L2L also had very high acceptability; almost all participants reported that they would recommend it to someone in the same situation, that the options felt realistic, and that L2L was respectful of values about firearms. In an exploratory analysis of this pilot trial, more participants in the L2L group reported reduced firearm access at follow-up, although the differences were not statistically significant. CONCLUSIONS: The L2L decision aid appears feasible and acceptable for use among adults with suicide risk and may be a useful adjunct to lethal means counseling and other suicide prevention interventions. Future large-scale studies are needed to determine the effect on home access to lethal means. TRIAL REGISTRATION: ClinicalTrials.gov NCT03478501; https://clinicaltrials.gov/ct2/show/NCT03478501.

A Toolbox Approach to Obesity Treatment in Urban Safety-Net Primary Care Clinics: a Pragmatic Clinical Trial.

BACKGROUND: There is a need for new strategies to improve the success of obesity treatment within the primary care setting. OBJECTIVE: To determine if patients offered low out-of-pocket cost weight management tools achieved more weight loss compared to usual care. DESIGN: Twelve-month pragmatic clinical weight loss trial with a registry-based comparator group performed in primary care clinics of an urban safety-net hospital. PARTICIPANTS: From a large clinical registry, we randomly selected 428 patients to have the opportunity to receive the intervention. INTERVENTIONS: Medical weight management tools-partial meal replacements, recreation center vouchers, pharmacotherapy, commercial weight loss program vouchers, and a group behavioral weight loss program-for $5 or $10 monthly. Patients chose their tools, could switch tools, and could add a second tool at 6 months. MAIN MEASURES: The primary outcome was the proportion of intervention-eligible patients who achieved ≥ 5% weight loss. The main secondary outcome was the proportion of on-treatment patients who achieved ≥ 5% weight loss. KEY RESULTS: Overall, 71.3% (305 of 428) had available weight measurement data/PCP visit data to observe the primary outcome. At 12 months, 23.3% (71 of 305) of intervention-eligible participants and 15.7% (415 of 2640) of registry-based comparators had achieved 5% weight loss (p < 0.001). Of the on-treatment participants, 34.5% (39 of 113) achieved 5% weight loss. Mean percentage weight loss was - 3.15% ± 6.41% for on-treatment participants and - 0.30% ± 6.10% for comparators (p < 0.001). The initially preferred tools were meal replacements, pharmacotherapy, and recreation center passes. CONCLUSIONS: Access to a variety of low out-of-pocket cost weight management tools within primary care resulted in ≥ 5% body weight loss in approximately one quarter of low-income patients with obesity. TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT01922934.

CDKL5 deficiency disorder: Relationship between genotype, epilepsy, cortical visual impairment, and development.

OBJECTIVE: The cyclin-dependent kinase like 5 (CDKL5) gene is a known cause of early onset developmental and epileptic encephalopathy, also known as CDKL5 deficiency disorder (CDD). We sought to (1) provide a description of seizure types in patients with CDD, (2) provide an assessment of the frequency of seizure-free periods and cortical visual impairment (CVI), (3) correlate these features with genotype and gender, and (4) correlate these features with developmental milestones. METHODS: This is a cohort study of patients with CDD. Phenotypic features were explored and correlated with gene variant grouping and gender. A developmental score was created based on achieving seven primary milestones. Phenotypic variables were correlated with the developmental score to explore markers of better developmental outcomes. Multivariate linear regression was used to account for age at last visit. RESULTS: Ninety-two patients with CDD were seen during the enrollment period. Eighteen were male (19%); median age at last visit was 5 years (interquartile range = 2.0-11.0). Eighty-one percent of patients developed epileptic spasms, but only 47% of those also had hypsarrhythmia. Previously described hypermotor-tonic-spasms sequence was seen in only 24% of patients, but 56% of patients had seizures with multiple phases (often tonic and spasms). Forty-three percent of patients experienced a seizure-free period ranging from 1 to >12 months, but only 6% were still seizure-free at the last visit. CVI was present in 75% of all CDD patients. None of these features was associated with genotype group or gender. CVI was correlated with reduced milestone achievement after adjusting for age at last visit and a history of hypsarrhythmia. SIGNIFICANCE: The most common seizure types in CDD are epileptic spasms (often without hypsarrhythmia) and tonic seizures that may cluster together. CVI is a common feature in CDD and is correlated with achieving fewer milestones.

Comparison of a time-varying covariate model and a joint model of time-to-event outcomes in the presence of measurement error and interval censoring: application to kidney transplantation.

BACKGROUND: Tacrolimus (TAC) is an immunosuppressant drug given to kidney transplant recipients post-transplant to prevent antibody formation and kidney rejection. The optimal therapeutic dose for TAC is poorly defined and therapy requires frequent monitoring of drug trough levels. Analyzing the association between TAC levels over time and the development of potentially harmful de novo donor specific antibodies (dnDSA) is complex because TAC levels are subject to measurement error and dnDSA is assessed at discrete times, so it is an interval censored time-to-event outcome. METHODS: Using data from the University of Colorado Transplant Center, we investigated the association between TAC and dnDSA using a shared random effects (intercept and slope) model with longitudinal and interval censored survival sub-models (JM) and compared it with the more traditional interval censored survival model with a time-varying covariate (TVC). We carried out simulations to compare bias, level and power for the association parameter in the TVC and JM under varying conditions of measurement error and interval censoring. In addition, using Markov Chain Monte Carlo (MCMC) methods allowed us to calculate clinically relevant quantities along with credible intervals (CrI). RESULTS: The shared random effects model was a better fit and showed both the average TAC and the slope of TAC were associated with risk of dnDSA. The simulation studies demonstrated that, in the presence of heavy interval censoring and high measurement error, the TVC survival model underestimates the association between the survival and longitudinal measurement and has inflated type I error and considerably less power to detect associations. CONCLUSIONS: To avoid underestimating associations, shared random effects models should be used in analyses of data with interval censoring and measurement error.

Trends in the Mode of Delivery of Pregnant Women in Rural Guatemala from a Quality Improvement Database.

Purpose To evaluate trends and factors associated with mode of delivery in the rural Southwest Trifinio region of Guatemala. Description We conducted a retrospective analysis of self-reported antepartum factors and postpartum outcomes recorded in a quality improvement database among 430 women enrolled in a home-based maternal healthcare program between June 1, 2015 and August 1, 2017. Assessment Over the study period, the rates of cesarean delivery (CD) increased (from 30 to 45%) and rates of vaginal delivery (VD) decreased (70-55%) while facility-based delivery attendance remained stable around 70%. Younger age (23.5 years for VD vs. 21.6 years for CD, p < 0.001), nulliparity (25.1% for VD vs. 45.0% for CD, p < 0.001), prolonged/obstructed labor (2.4% for VD vs. 55.6% for CD, p < 0.001), and fetal malpresentation (0% for VD vs. 16.3% CD, p < 0.001) significantly influenced mode of delivery in univariate analysis. The leading indications for CD were labor dysfunction (47.5%), malpresentation (14.5%), and prior cesarean delivery (19.8%). The CD rate among the subpopulation of term, nulliparous women with singleton pregnancies in vertex presentation also increased from 20% of all CD in 2015, to 38% in 2017. Conclusion Among low-income women from rural Guatemala, the CD rate has increased above the World Health Organization (WHO) recommendations in a period of 3 years. Additional research on the factors affecting this trend are essential to guide interventions that might improve the appropriateness of CD, and to determine if reducing or stabilizing rates is necessary.

Preoperatively Screened Obstructive Sleep Apnea Is Associated With Worse Postoperative Outcomes Than Previously Diagnosed Obstructive Sleep Apnea.

BACKGROUND: Obstructive sleep apnea (OSA) affects up to 26% of US adults, is often undiagnosed, and increases perioperative morbidity. We hypothesized that patients screened on the day of surgery as moderate/high risk for OSA (S-OSA) present similar perioperative respiratory complications, hospital use, and mortality than patients with previously diagnosed OSA (D-OSA). Second, we hypothesized that both OSA groups have more respiratory complications than No-OSA patients. METHODS: The electronic medical database from 1 academic and 2 community hospitals was retrospectively queried to identify adults undergoing nonemergent inpatient surgery (January 1, 2012, to December 31, 2014). Based on the day-of-surgery preoperative assessment and STOP-BANG (Snoring, Tiredness, Observed apnea during sleep, high blood Pressure, Body mass index >35, Age >50 years, thick Neck, Gender male) score, they were classified as D-OSA, S-OSA, or No-OSA. Perioperative respiratory events and interventions, hospital use, and mortality were measured. The primary outcome composite (adverse respiratory events [AREs]) included perioperative hypoxemic events and difficult airway management. Hypoxemic event was defined as peripheral saturation of oxygen (SpO2) <90% by continuous pulse oximetry for ≥3 minutes, or if validated and/or manually entered into the medical chart. Hypoxemia was classified as mild (lowest SpO2 86%-89%) or moderate/severe (lowest SpO2 ≤85%). Secondary outcomes included postoperative respiratory interventions, intensive care unit admission, hospital length of stay, and 30-day and 1-year all-cause mortality. Outcomes were compared using linear and logistic regression analyses. RESULTS: A total of 28,912 patients were assessed: 3432 (11.9%) D-OSA; 1546 (5.3%) S-OSA; and 23,934 (82.8%) No-OSA patients. At least 1 ARE was present in 68.0% of S-OSA; 71.0% of D-OSA; and 52.1% of No-OSA patients (unadjusted P < .001), primarily ≥1 moderate/severe hypoxemic event after discharge from the postanesthesia care unit (PACU; 39.9% in S-OSA; 39.5% in D-OSA; and 27.1% in No-OSA patients). S-OSA patients compared to D-OSA patients presented lower rates of moderate/severe hypoxemia in the PACU but similar intraoperatively and postoperatively, higher difficult mask ventilation rates, and similar difficult intubation reports. After adjusting for demographic, health, and surgical differences and hospital type, the likelihood of ≥1 ARE was not different in S-OSA and D-OSA patients (adjusted odds ratio 0.90 [99% confidence interval, 0.75-1.09]; P = .15). S-OSA patients compared to D-OSA patients had significantly increased postoperative reintubation, mechanical ventilation, direct intensive care unit admission after surgery, hospital length of stay, and 30-day all-cause mortality. CONCLUSIONS: Patients classified as S-OSA have similar rates of AREs to D-OSA patients, but increased postoperative respiratory interventions, hospital use, and 30-day all-cause mortality. These worse postoperative outcomes in S-OSA patients than D-OSA patients could reflect the lack of awareness and appropriate management of this bedside S-OSA diagnosis after PACU discharge. Multidisciplinary interventions are needed for these high-risk patients.

Surgical Risk Preoperative Assessment System (SURPAS): I. Parsimonious, Clinically Meaningful Groups of Postoperative Complications by Factor Analysis.

OBJECTIVE: To use factor analysis to cluster the 18 American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) perioperative complications into a reproducible, smaller number of clinically meaningful groups of postoperative complications, facilitating and streamlining future study and application in live clinical settings. BACKGROUND: The ACS NSQIP collects and reports on eighteen 30-day postoperative complications (excluding mortality), which are variably grouped in published analyses using ACS NSQIP data. This hinders comparison between studies of this widely used quality improvement dataset. METHODS: Factor analysis was used to develop a series of complication clusters, which were then analyzed to identify a parsimonious, clinically meaningful grouping, using 2,275,240 surgical cases in the ACS NSQIP Participant Use File (PUF), 2005 to 2012. The main outcome measures are reproducible, data-driven, clinically meaningful clusters of complications derived from factor solutions. RESULTS: Factor analysis solutions for 5 to 9 latent factors were examined for their percent of total variance, parsimony, and clinical interpretability. Applying the first 2 of these criteria, we identified the 7-factor solution, which included clusters of pulmonary, infectious, wound disruption, cardiac/transfusion, venous thromboembolic, renal, and neurological complications, as the best solution for parsimony and clinical meaningfulness. Applying the last (clinical interpretability), we combined the wound disruption with the infectious clusters resulting in 6 clusters for future clinical applications. CONCLUSIONS: Factor analysis of ACS NSQIP postoperative complication data provides 6 clinically meaningful complication clusters in lieu of 18 postoperative morbidities, which will facilitate comparisons and clinical implementation of studies of postoperative morbidities.

Surgical Risk Preoperative Assessment System (SURPAS): III. Accurate Preoperative Prediction of 8 Adverse Outcomes Using 8 Predictor Variables.

OBJECTIVE: To develop accurate preoperative risk prediction models for multiple adverse postoperative outcomes applicable to a broad surgical population using a parsimonious common set of risk variables and outcomes. SUMMARY BACKGROUND DATA: Currently, preoperative assessment of surgical risk is largely based on subjective clinician experience. We propose a paradigm shift from the current postoperative risk adjustment for cross-hospital comparison to patient-centered quantitative risk assessment during the preoperative evaluation. METHODS: We identify the most common and important predictor variables of postoperative mortality, overall morbidity, and 6 complication clusters from previously published prediction analyses that used forward selection stepwise logistic regression. We then refit the prediction models using only the 8 most common and important predictor variables, and compare the discrimination and calibration of these models to the original full-variable models using the c-index, Hosmer-Lemeshow analysis, and Brier scores. RESULTS: Accurate risk models for 30-day outcomes of mortality, overall morbidity, and 6 clusters of complications were developed using a set of 8 preoperative risk variables. C-indexes of the 8 variable models are between 97.9% and 99.2% of those of the full models containing up to 28 variables, indicating excellent discrimination using fewer predictor variables. Hosmer-Lemeshow analyses showed observed to expected event rates to be nearly identical between parsimonious models and full models, both showing good calibration. CONCLUSIONS: Accurate preoperative risk assessment of postoperative mortality, overall morbidity, and 6 complication clusters in a broad surgical population can be achieved with as few as 8 preoperative predictor variables, improving feasibility of routine preoperative risk assessment for surgical patients.

Surgical Risk Preoperative Assessment System (SURPAS): II. Parsimonious Risk Models for Postoperative Adverse Outcomes Addressing Need for Laboratory Variables and Surgeon Specialty-specific Models.

OBJECTIVE: To develop parsimonious prediction models for postoperative mortality, overall morbidity, and 6 complication clusters applicable to a broad range of surgical operations in adult patients. SUMMARY BACKGROUND DATA: Quantitative risk assessment tools are not routinely used for preoperative patient assessment, shared decision making, informed consent, and preoperative patient optimization, likely due in part to the burden of data collection and the complexity of incorporation into routine surgical practice. METHODS: Multivariable forward selection stepwise logistic regression analyses were used to develop predictive models for 30-day mortality, overall morbidity, and 6 postoperative complication clusters, using 40 preoperative variables from 2,275,240 surgical cases in the American College of Surgeons National Surgical Quality Improvement Program data set, 2005 to 2012. For the mortality and overall morbidity outcomes, prediction models were compared with and without preoperative laboratory variables, and generic models (based on all of the data from 9 surgical specialties) were compared with specialty-specific models. In each model, the cumulative c-index was used to examine the contribution of each added predictor variable. C-indexes, Hosmer-Lemeshow analyses, and Brier scores were used to compare discrimination and calibration between models. RESULTS: For the mortality and overall morbidity outcomes, the prediction models without the preoperative laboratory variables performed as well as the models with the laboratory variables, and the generic models performed as well as the specialty-specific models. The c-indexes were 0.938 for mortality, 0.810 for overall morbidity, and for the 6 complication clusters ranged from 0.757 for infectious to 0.897 for pulmonary complications. Across the 8 prediction models, the first 7 to 11 variables entered accounted for at least 99% of the c-index of the full model (using up to 28 nonlaboratory predictor variables). CONCLUSIONS: Our results suggest that it will be possible to develop parsimonious models to predict 8 important postoperative outcomes for a broad surgical population, without the need for surgeon specialty-specific models or inclusion of laboratory variables.

Psychological Factors Associated with Delayed Symptom Resolution in Children with Concussion.

OBJECTIVE: To characterize the psychological factors associated with persistent symptoms after pediatric concussion. STUDY DESIGN: Longitudinal cohort study of 179 children with concussion 8-18 years old evaluated in a pediatric emergency department. Participants were followed for 1 month for delayed symptom resolution, defined as ≥3 symptoms that were new/worse than preinjury symptoms measured by the use of graded symptom inventory. Preinjury psychological traits were measured by parental report on subscales of the Personality Inventory for Children-2 (maladjustment, cognitive abilities, somatization). Child report of postinjury anxiety and injury perception were measured with the State-Trait Anxiety Inventory for Children and Children's Illness Perception Questionnaire. Psychological instrument scores were compared between those with and without delayed symptom resolution via a Kruskal-Wallis test. Associations between psychological traits and delayed symptom resolution were investigated by the use of logistic regression. RESULTS: Delayed symptom resolution occurred in 21% of participants. Score distributions were significantly worse on the State-Trait Anxiety Inventory for Children (38 [IQR 33-40] vs 35 [IQR 31-39]; P = .04) and somatization subscale (1 [IQR 0-3] vs 1 [IQR 0-1]; P = .01) among children with delayed symptom resolution compared with children with early symptom resolution. Somatization was associated with delayed symptom resolution (aOR 1.35, 95% CI 1.08-1.69). The proportion of children with abnormal somatization scores was significantly greater in the delayed symptom resolution group (34.2%) than the early symptom resolution group (12.8%; P < .01). Other psychological measures were not different between groups. CONCLUSION: Somatization is associated with delayed symptom resolution in this cohort of children with concussion. Postconcussive symptoms lasting at least 1 month may warrant referral to a neuropsychologist familiar with postconcussion care.

Response to second treatment after initial failed treatment in a multicenter prospective infantile spasms cohort.

OBJECTIVE: Infantile spasms (IS) represent a severe epileptic encephalopathy presenting in the first 2 years of life. Recommended first-line therapies (hormonal therapy or vigabatrin) often fail. We evaluated response to second treatment for IS in children in whom the initial therapy failed to produce both clinical remission and electrographic resolution of hypsarhythmia and whether time to treatment was related to outcome. METHODS: The National Infantile Spasms Consortium established a multicenter, prospective database enrolling infants with new diagnosis of IS. Children were considered nonresponders to first treatment if there was no clinical remission or persistence of hypsarhythmia. Treatment was evaluated as hormonal therapy (adrenocorticotropic hormone [ACTH] or oral corticosteroids), vigabatrin, or "other." Standard treatments (hormonal and vigabatrin) were compared to all other nonstandard treatments. We compared response rates using chi-square tests and multivariable logistic regression models. RESULTS: One hundred eighteen infants were included from 19 centers. Overall response rate to a second treatment was 37% (n = 44). Children who received standard medications with differing mechanisms for first and second treatment had higher response rates than other sequences (27/49 [55%] vs. 17/69 [25%], p < 0.001). Children receiving first treatment within 4 weeks of IS onset had a higher response rate to second treatment than those initially treated later (36/82 [44%] vs. 8/34 [24%], p = 0.040). SIGNIFICANCE: Greater than one third of children with IS will respond to a second medication. Choosing a standard medication (ACTH, oral corticosteroids, or vigabatrin) that has a different mechanism of action appears to be more effective. Rapid initial treatment increases the likelihood of response to the second treatment.

Short-term markers of DNA damage among roofers who work with hot asphalt.

BACKGROUND: Roofers are at increased risk for various malignancies and their occupational exposures to polycyclic aromatic hydrocarbons (PAHs) have been considered as important risk factors. The overall goal of this project was to investigate the usefulness of phosphorylated histone H2AX (γH2AX) as a short-term biomarker of DNA damage among roofers. METHODS: Blood, urine, and dermal wipe samples were collected from 20 roofers who work with hot asphalt before and after 6 h of work on Monday and Thursday of the same week (4 sampling periods). Particle-bound and gas-phase PAHs were collected using personal monitors during work hours. γH2AX was quantified in peripheral lymphocytes using flow cytometry and 8-hydroxy-2-deoxyguanosine (8-OHdG) was assessed in urine using ELISA. General linear mixed models were used to evaluate associations between DNA damage and possible predictors (such as sampling period, exposure levels, work- and life-style factors). Differences in mean biomarker and DNA damage levels were tested via ANOVA contrasts. RESULTS: Exposure measurements did not show an association with any of the urinary biomarkers or the measures of DNA damage. Naphthalene was the most abundant PAH in gas-phase, while benzo(e)pyrene was the most abundant particle-bound PAH. Post-shift levels of γH2AX and 8-OHdG were higher on both study days, when compared to pre-shift levels. Cigarette smoking was a predictor of γH2AX and urinary creatinine was a predictor of urinary 8-OHdG. Between-subject variance to total variance ratio was 35.3 % for γH2ax and 4.8 % for 8-OHdG. CONCLUSION: γH2AX is a promising biomarker of DNA damage in occupational epidemiology studies. It has a lower within-subject variation than urinary 8-OHdG and can easily be detected in large scale groups. Future studies that explore the kinetics of H2AX phosphorylation in relation to chemical exposures may reveal the transient and persistent nature of this sensitive biomarker of early DNA damage.