A novel risk-adjusted metric to compare hospitals on their antibiotic-prescribing at hospital discharge.

BACKGROUND: Antibiotic overuse at hospital discharge is common, but there is no metric to evaluate hospital performance at this transition of care. We built a risk-adjusted metric for comparing hospitals on their overall post-discharge antibiotic use. METHODS: This was a retrospective study across all acute-care admissions within the Veterans Health Administration during 2018-2021. For patients discharged to home, we collected data on antibiotics and relevant covariates. We built a zero-inflated negative binomial mixed-model with two random intercepts for each hospital to predict post-discharge antibiotic exposure and length of therapy (LOT). Data were split into training and testing sets to evaluate model performance using absolute error. Hospital performance was determined by the predicted random intercepts. RESULTS: 1,804,300 patient-admissions across 129 hospitals were included. Antibiotics were prescribed to 41.5% while hospitalized and 19.5% at discharge. Median LOT among those prescribed post-discharge antibiotics was 7 (IQR 4-10). The predictive model detected post-discharge antibiotic use with fidelity, including accurate identification of any exposure (area under the precision-recall curve=0.97) and reliable prediction of post-discharge LOT (mean absolute error = 1.48). Based on this model, 39 (30.2%) hospitals prescribed antibiotics less often than expected at discharge and used shorter LOT than expected. Twenty-eight (21.7%) hospitals prescribed antibiotics more often at discharge and used longer LOT. CONCLUSION: A model using electronically-available data was able to predict antibiotic use prescribed at hospital discharge and showed that some hospitals were more successful in reducing antibiotic overuse at this transition of care. This metric may help hospitals identify opportunities for improved antibiotic stewardship at discharge.

Nonparametric predictive model for sparse and irregular longitudinal data.

We propose a kernel-based estimator to predict the mean response trajectory for sparse and irregularly measured longitudinal data. The kernel estimator is constructed by imposing weights based on the subject-wise similarity on L2 metric space between predictor trajectories, where we assume that an analogous fashion in predictor trajectories over time would result in a similar trend in the response trajectory among subjects. In order to deal with the curse of dimensionality caused by the multiple predictors, we propose an appealing multiplicative model with multivariate Gaussian kernels. This model is capable of achieving dimension reduction as well as selecting functional covariates with predictive significance. The asymptotic properties of the proposed nonparametric estimator are investigated under mild regularity conditions. We illustrate the robustness and flexibility of our proposed method via extensive simulation studies and an application to the Framingham Heart Study.

Bispectral EEG (BSEEG) Algorithm Captures High Mortality Risk Among 1,077 Patients: Its Relationship to Delirium Motor Subtype.

OBJECTIVE: Delirium is dangerous and a predictor of poor patient outcomes. We have previously reported the utility of the bispectral EEG (BSEEG) with a novel algorithm for the detection of delirium and prediction of patient outcomes including mortality. The present study employed a normalized BSEEG (nBSEEG) score to integrate the previous cohorts to combine their data to investigate the prediction of patient outcomes. We also aimed to test if the BSEEG method can be applicable regardless of age, and independent of delirium motor subtypes. METHODS: We calculated nBSEEG score from raw BSEEG data in each cohort and classified patients into BSEEG-positive and BSEEG-negative groups. We used log-rank test and Cox proportional hazards models to predict 90-day and 1-year outcomes for the BSEEG-positive and -negative groups in all subjects and motor subgroups. RESULTS: A total of 1,077 subjects, the BSEEG-positive group showed significantly higher 90-day (hazard ratio 1.33 [95% CI 1.16-1.52] and 1-year (hazard ratio 1.22 [95% CI 1.06-1.40] mortality rates than the negative group after adjustment for covariates such as age, sex, CCI, and delirium status. Among patients with different motor subtypes of delirium, the hypoactive group showed significantly higher 90-day (hazard ratio 1.41 [95% CI 1.12-1.76] and 1-year mortality rates (hazard ratio 1.32 [95% CI 1.05-1.67], which remained significant after adjustment for the same covariates. CONCLUSION: We found that the BSEEG method is capable of capturing patients at high mortality risk.

The genome-wide DNA methylation profiles among neurosurgery patients with and without post-operative delirium.

AIMS: There is no previous study demonstrating the differences of genome-wide DNA methylation (DNAm) profiles between patients with and without postoperative delirium (POD). We aimed to discover epigenetic (DNAm) markers that are associated with POD in blood obtained from patients before and after neurosurgery. METHODS: Pre- and post-surgical blood DNA samples from 37 patients, including 10 POD cases, were analyzed using the Illumina EPIC array genome-wide platform. We examined DNAm differences in blood from patients with and without POD. Enrichment analysis with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes terms were also conducted. RESULTS: When POD cases were tested for DNAm change before and after surgery, enrichment analyses showed many relevant signals with statistical significance in immune response related-pathways and inflammatory cytokine related-pathways such as "cellular response to cytokine stimulus", "regulation of immune system process", "regulation of cell activation", and "regulation of cytokine production". Furthermore, after excluding the potential effect of common factors related to surgery and anesthesia between POD cases and non-POD controls, the enrichment analyses showed significant signals such as "immune response" and "T cell activation", which are same pathways previously identified from an independent non-surgical inpatient cohort. CONCLUSIONS: Our first genome-wide DNAm investigation of POD showed promising signals related to immune response, inflammatory response and other relevant signals considered to be associated with delirium pathophysiology. Our data supports the hypothesis that epigenetics play an important role in the pathophysiological mechanism of delirium and suggest the potential usefulness of an epigenetics-based biomarker of POD.

Predictive generalized varying-coefficient longitudinal model.

We propose a nonparametric bivariate varying coefficient generalized linear model to predict a mean response trajectory in the future given an individual's characteristics at present or an earlier time point in a longitudinal study. Given the measurement time of the predictors, the coefficients vary as functions of the future time over which the prediction of the mean response is concerned and illustrate the dynamic association between the future response and the earlier measured predictors. We use a nonparametric approach that takes advantage of features of both the kernel and the spline methods for estimation. The resulting coefficient estimator is asymptotically consistent under mild regularity conditions. We also develop a new bootstrap approach to construct simultaneous confidence bands for statistical inference about the coefficients and the predicted response trajectory based on the coverage rate of bootstrap estimates. We use the Framingham Heart Study to illustrate the methodology. The proposed procedure is applied to predict the probability trajectory of hypertension risk given individuals' health condition in early adulthood and to examine the impact of risk factors in early adulthood on a long-term risk of hypertension over several decades.

Mixed-Methods Evaluation of the Incorporation of Home Specimen Self-Collection Kits for Laboratory Testing in a Telehealth Program for HIV Pre-exposure Prophylaxis.

Home specimen self-collection kits with central laboratory testing may improve persistence with PrEP and enhance telehealth programs. We offered Iowa TelePrEP clients the choice of using a home kit or visiting a laboratory site for routine monitoring. Mixed-methods evaluation determined the proportion of clients who chose a kit, factors influencing choice, associations between kit use and completion of indicated laboratory monitoring, and user experience. About 46% (35/77) chose to use a kit. Compared to laboratory site use, kit use was associated with higher completion of extra-genital swabs (OR 6.33, 95% CI 1.20-33.51, for anorectal swabs), but lower completion of blood tests (OR 0.21, 95% CI 0.06-0.73 for creatinine). Factors influencing choice included self-efficacy to use kits, time/convenience, and privacy/confidentiality. Clients reported kit use was straight-forward but described challenges with finger prick blood collection. Telehealth PrEP programs should offer clients home kits and support clients with blood collection and kit completion.

RESUMEN: Los kits de pruebas caseras de auto-recolección junto con los ensayos de laboratorio central puedan mejorar la persistencia de PrEP y aumentar los programas de telesalud. Ofrecimos a los clientes de Iowa TelePrEP la opción de o utilizar un kit de pruebas caseras o visitar un sitio de laboratorio para seguimiento rutinario.La evaluación de métodos mixtos determinó la proporción de los clientes que eligieron un kit, los factores que influyen en la elección, las asociaciones entre el uso del kit y la realización del monitoreo de laboratorio indicado y la experiencia de los usuarios. Casi 46 % (35 de 77) eligió utilizar un kit. Comparado con el uso del sitio de laboratorio, el kit fue asociado con mayores tasas de terminación para los hisopos extragenitales (OR 6.33, 95% CI 1.20-33.51, para hisopos anorectales), pero menores tasas de terminación para los análisis de sangre (OR 0.21, 95% CI 0.06-0.73 para creatinina). Los factores que influyeron en la elección incluyeron la autoeficacia para usar los kits, el tiempo / la comodidad y la privacidad/ confidencialidad. Los clientes informaron que el uso del kit fue sencillo pero describieron desafíos con la recolección de sangre por un pinchazo. Los programas de PrEP de telesalud deben ofrecer a los clientes kits para el hogar y apoyarlos con la recolección de sangre y la terminación del kit.

Initial severity-dependent longitudinal model with application to a randomized controlled trial of women with depression.

The assessment of initial severity of a disease is arguably one of the most important factors in identifying appropriate therapies. In this paper, we propose an initial severity-dependent longitudinal model to account for the influence of the initial severity of a disease on the posttreatment severity and the efficacy of medical treatments. The proposed model has the flexibility of nonparametric modeling, as it allows coefficients to vary with the initial severity of the disease. It also provides attractive and practical patient-specific interpretation of initial severity-dependent coefficients. As a result, the proposed model enables patient-specific modeling and treatment recommendations consistent with the assessment of the patient's initial severity, and thus, it can be used as a decision support tool for clinicians. A new empirical likelihood approach is employed for efficient estimation and statistical inference about the initial severity-dependent coefficients. In contrast to the literature on marginal regression models, the proposed estimation procedure allows nuisance parameters associated with the working correlation matrix and the error variances to vary smoothly with the initial severity. The effectiveness of the proposed procedure is demonstrated via simulation studies. We further apply the proposed method by analyzing a data set arising from a randomized controlled trial of women with depression and discover an interesting phenomenon; antidepressant medication intervention is effective for patients with moderate or severe depression, whereas psychotherapy intervention using manual-guided cognitive behavior therapy is effective for patients with a severe case of depression.

Statistical inference in a growth curve quantile regression model for longitudinal data.

This article describes a polynomial growth curve quantile regression model that provides a comprehensive assessment about the treatment effects on the changes of the distribution of outcomes over time. The proposed model has the flexibility, as it allows the degree of a polynomial to vary across quantiles. A high degree polynomial model fits the data adequately, yet it is not desirable due to the complexity of the model. We propose the model selection criterion based on an empirical loglikelihood that consistently identifies the optimal degree of a polynomial at each quantile. After the parsimonious model is fitted to the data, the hypothesis test is further developed to evaluate the treatment effects by comparing the growth curves. It is shown that the proposed empirical loglikelihood ratio test statistic follows a chi-square distribution asymptotically under the null hypothesis. Various simulation studies confirm that the proposed test successfully detects the difference between the curves across quantiles. When the empirical loglikelihood is employed, we incorporate the within-subject correlation commonly existing in longitudinal data and gain estimation efficiency of the quantile regression parameters in the growth curve model. The proposed process is illustrated through the analysis of randomized controlled longitudinal depression data.

Validating effectiveness of subgroup identification for longitudinal data.

In clinical trials and biomedical studies, treatments are compared to determine which one is effective against illness; however, individuals can react to the same treatment very differently. We propose a complete process for longitudinal data that identifies subgroups of the population that would benefit from a specific treatment. A random effects linear model is used to evaluate individual treatment effects longitudinally where the random effects identify a positive or negative reaction to the treatment over time. With the individual treatment effects and characteristics of the patients, various classification algorithms are applied to build prediction models for subgrouping. While many subgrouping approaches have been developed recently, most of them do not check its validity. In this paper, we further propose a simple validation approach which not only determines if the subgroups used are appropriate and beneficial but also compares methods to predict individual treatment effects. This entire procedure is readily implemented by existing packages in statistical software. The effectiveness of the proposed method is confirmed with simulation studies and analysis of data from the Women Entering Care study on depression.

Efficient quantile marginal regression for longitudinal data with dropouts.

In many biomedical studies independent variables may affect the conditional distribution of the response differently in the middle as opposed to the upper or lower tail. Quantile regression evaluates diverse covariate effects on the conditional distribution of the response with quantile-specific regression coefficients. In this paper, we develop an empirical likelihood inference procedure for longitudinal data that accommodates both the within-subject correlations and informative dropouts under missing at random mechanisms. We borrow the matrix expansion idea of the quadratic inference function and incorporate the within-subject correlations under an informative working correlation structure. The proposed procedure does not assume the exact knowledge of the true correlation structure nor does it estimate the parameters of the correlation structure. Theoretical results show that the resulting estimator is asymptotically normal and more efficient than one attained under a working independence correlation structure. We expand the proposed approach to account for informative dropouts under missing at random mechanisms. The methodology is illustrated by empirical studies and a real-life example of HIV data analysis.

A Randomized Comparison of Medication and Cognitive Behavioral Therapy for Treating Depression in Low-Income Young Minority Women.

BACKGROUND Longitudinal data arise frequently in biomedical science and health studies where each subject is repeatedly measured over time. We compared the effectiveness of medication and cognitive behavioral therapy on depression in predominantly low-income young minority women. MATERIAL AND METHODS The treatment effects on patients with low-level depression may differ from the treatment effects on patients with high-level depression. We used a quantile regression model for longitudinal data analysis to determine which treatment is most beneficial for patients at different stress levels over time. RESULTS The results confirm that both treatments are effective in reducing the depression score over time, regardless of the depression level. CONCLUSIONS Compared to cognitive behavioral therapy, treatment with medication more often effective, although the size of the effect differs. Thus, no matter how severe a patient's depression symptoms are, antidepressant medication is effective in decreasing depression symptoms.

Epigenetic signals associated with delirium replicated across four independent cohorts.

Delirium is risky and indicates poor outcomes for patients. Therefore, it is crucial to create an effective delirium detection method. However, the epigenetic pathophysiology of delirium remains largely unknown. We aimed to discover reliable and replicable epigenetic (DNA methylation: DNAm) markers that are associated with delirium including post-operative delirium (POD) in blood obtained from patients among four independent cohorts. Blood DNA from four independent cohorts (two inpatient cohorts and two surgery cohorts; 16 to 88 patients each) were analyzed using the Illumina EPIC array platform for genome-wide DNAm analysis. We examined DNAm differences in blood between patients with and without delirium including POD. When we compared top CpG sites previously identified from the initial inpatient cohort with three additional cohorts (one inpatient and two surgery cohorts), 11 of the top 13 CpG sites showed statistically significant differences in DNAm values between the delirium group and non-delirium group in the same directions as found in the initial cohort. This study demonstrated the potential value of epigenetic biomarkers as future diagnostic tools. Furthermore, our findings provide additional evidence of the potential role of epigenetics in the pathophysiology of delirium including POD.

Applying the Worker Well-Being Framework to Identify Factors that Impact Turnover Among Long-Haul Truck Drivers.

BACKGROUND: Drivers in the long-haul trucking industry have chronic health conditions, engage in unhealthy behaviors, and leave the industry at high rates. Previous work has not considered the health and safety outcomes resulting from the conditions of work in the trucking industry and their role in turnover. The goal of this study was to understand the expectations of an incoming workforce, explore how work conditions impact their well-being, and identify strategies for retention. METHODS: Semi-structured interviews were conducted among current long-haul drivers and supervisors at trucking companies, and students and instructors at trucking schools (n = 33). Participants were asked about why they decided to enter the industry, their health challenges related to being in the trucking industry and whether these challenges were related to turnover, and strategies for retention. FINDINGS: Health conditions, differences in job expectations, and work demands were associated with leaving the industry. Workplace policies and culture (e.g., lack of supervisor support, schedules that limited home time, company size, and lack of benefits) were associated with workers' intention to leave an organization. Strategies identified to improve retention included integrating health and wellness into onboarding, creating realistic job expectations for those entering the industry, establishing relationships with drivers and dispatchers, and developing policies to limit time away from family. CONCLUSION/APPLICATION TO PRACTICE: Turnover in the trucking industry is a persistent problem and leads to a shortage of skilled workers, increases the workload, and reduces productivity. Understanding the relationship between the conditions of work and well-being provides a more holistic approach to address the health, safety, and well-being of long-haul truck drivers. Health conditions, differences in job expectations, and work demands were associated with leaving the industry. Workplace policies and culture (e.g., supervisor support, schedules that limited home time, lack of benefits) were associated with workers' intention to leave an organization. These conditions provide an opportunity for occupational health interventions to promote the physical as well as psychological health of long-haul truck drivers.

Metformin use history and genome-wide DNA methylation profile: potential molecular mechanism for aging and longevity.

BACKGROUND: Metformin, a commonly prescribed anti-diabetic medication, has repeatedly been shown to hinder aging in pre-clinical models and to be associated with lower mortality for humans. It is, however, not well understood how metformin can potentially prolong lifespan from a biological standpoint. We hypothesized that metformin's potential mechanism of action for longevity is through its epigenetic modifications. METHODS: To test our hypothesis, we conducted a post-hoc analysis of available genome-wide DNA methylation (DNAm) data obtained from whole blood collected from inpatients with and without a history of metformin use. We assessed the methylation profile of 171 patients (first run) and only among 63 diabetic patients (second run) and compared the DNAm rates between metformin users and nonusers. RESULTS: Enrichment analysis from the Kyoto Encyclopedia of Genes and Genome (KEGG) showed pathways relevant to metformin's mechanism of action, such as longevity, AMPK, and inflammatory pathways. We also identified several pathways related to delirium whose risk factor is aging. Moreover, top hits from the Gene Ontology (GO) included HIF-1α pathways. However, no individual CpG site showed genome-wide statistical significance (p < 5E-08). CONCLUSION: This study may elucidate metformin's potential role in longevity through epigenetic modifications and other possible mechanisms of action.

The use of telehealth-supported stewardship activities in acute-care and long-term care settings: An implementation effectiveness trial.

BACKGROUND: We assessed the implementation of telehealth-supported stewardship activities in acute-care units and long-term care (LTC) units in Veterans' Administration medical centers (VAMCs). DESIGN: Before-and-after, quasi-experimental implementation effectiveness study with a baseline period (2019-2020) and an intervention period (2021). SETTING: The study was conducted in 3 VAMCs without onsite infectious disease (ID) support. PARTICIPANTS: The study included inpatient providers at participating sites who prescribe antibiotics. INTERVENTION: During 2021, an ID physician met virtually 3 times per week with the stewardship pharmacist at each participating VAMC to review patients on antibiotics in acute-care units and LTC units. Real-time feedback on prescribing antibiotics was given to providers. Additional implementation strategies included stakeholder engagement, education, and quality monitoring. METHODS: The reach-effectiveness-adoption-implementation-maintenance (RE-AIM) framework was used for program evaluation. The primary outcome of effectiveness was antibiotic days of therapy (DOT) per 1,000 days present aggregated across all 3 sites. An interrupted time-series analysis was performed to compare this rate during the intervention and baseline periods. Electronic surveys, periodic reflections, and semistructured interviews were used to assess other RE-AIM outcomes. RESULTS: The telehealth program reviewed 502 unique patients and made 681 recommendations to 24 providers; 77% of recommendations were accepted. After program initiation, antibiotic DOT immediately decreased in the LTC units (-30%; P < .01) without a significant immediate change in the acute-care units (+16%; P = .22); thereafter DOT remained stable in both settings. Providers generally appreciated feedback and collaborative discussions. CONCLUSIONS: The implementation of our telehealth program was associated with reductions in antibiotic use in the LTC units but not in the smaller acute-care units. Overall, providers perceived the intervention as acceptable. Wider implementation of telehealth-supported stewardship activities may achieve reductions in antibiotic use.

The validity of hospital diagnostic and procedure codes reflecting morbidity in preterm neonates born <32 weeks gestation.

OBJECTIVE: To determine the validity of diagnostic hospital billing codes for complications of prematurity in neonates <32 weeks gestation. STUDY DESIGN: Retrospective cohort data from discharge summaries and clinical notes (n = 160) were reviewed by trained, blinded abstractors for the presence of intraventricular hemorrhage (IVH) grades 3 or 4, periventricular leukomalacia (PVL), necrotizing enterocolitis (NEC), stage 3 or higher, retinopathy of prematurity (ROP), and surgery for NEC or ROP. Data were compared to diagnostic billing codes from the neonatal electronic health record. RESULTS: IVH, PVL, ROP and ROP surgery had strong positive predictive values (PPV > 75%) and excellent negative predictive values (NPV > 95%). The PPVs for NEC (66.7%) and NEC surgery (37.1%) were low. CONCLUSION: Diagnostic hospital billing codes were observed to be a valid metric to evaluate preterm neonatal morbidities and surgeries except in the instance of more ambiguous diagnoses such as NEC and NEC surgery.

Anti-inflammatory medication use associated with reduced delirium risk and all-cause mortality: A retrospective cohort study.

OBJECTIVE: To investigate the relationship between history of anti-inflammatory medication use and delirium risk, as well as long-term mortality. METHODS: In this retrospective cohort study, subjects recruited between January 2016 and March 2020 were analyzed. Information about anti-inflammatory medication use history including aspirin, NSAIDs, glucosamine, and other anti-inflammatory drugs, was collected. Logistic regression analysis investigated the relationship between anti-inflammatory medications and delirium. Log-rank analysis and cox proportional hazards model investigated the relationship between anti-inflammatory medications and one-year mortality. RESULTS: The data from 1274 subjects were analyzed. The prevalence of delirium was significantly lower in subjects with NSAIDs usage (23.0%) than in those without NSAIDs usage (35.0%) (p < 0.001). Logistic regression analysis controlling for age, sex, dementia status, and hospitalization department showed that the risk of delirium tended to be reduced by a history of NSAIDs use (OR, 0.76 [95% CI, 0.55 to 1.03]). The one-year mortality in the subjects with NSAIDs (survival rate, 0.879 [95% CI, 0.845 to 0.906]) was significantly lower than in the subjects without NSAIDs (survival rate, 0.776 [95% CI, 0.746 to 0.803]) (p < 0.001). A history of NSAIDs use associated with the decreased risk of one-year mortality even after adjustment for age, sex, Charlson Comorbidity Index, delirium status, and hospitalization department (HR, 0.70 [95% CI, 0.51 to 0.96]). CONCLUSION: This study suggested that NSAIDs usage was associated with decreased delirium prevalence and lower one-year mortality. The potential benefit of NSAIDs on delirium risk and mortality were shown.

Impact of the Dopamine System on Long-Term Cognitive Impairment in Parkinson Disease: An Exploratory Study.

Background: Little is known about the impact of the dopamine system on development of cognitive impairment (CI) in Parkinson disease (PD). Objectives: We used data from a multi-site, international, prospective cohort study to explore the impact of dopamine system-related biomarkers on CI in PD. Methods: PD participants were assessed annually from disease onset out to 7 years, and CI determined by applying cut-offs to four measures: (1) Montreal Cognitive Assessment; (2) detailed neuropsychological test battery; (3) Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) cognition score; and (4) site investigator diagnosis of CI (mild cognitive impairment or dementia). The dopamine system was assessed by serial Iodine-123 Ioflupane dopamine transporter (DAT) imaging, genotyping, and levodopa equivalent daily dose (LEDD) recorded at each assessment. Multivariate longitudinal analyses, with adjustment for multiple comparisons, determined the association between dopamine system-related biomarkers and CI, including persistent impairment. Results: Demographic and clinical variables associated with CI were higher age, male sex, lower education, non-White race, higher depression and anxiety scores and higher MDS-UPDRS motor score. For the dopamine system, lower baseline mean striatum dopamine transporter values (P range 0.003-0.005) and higher LEDD over time (P range <0.001-0.01) were significantly associated with increased risk for CI. Conclusions: Our results provide preliminary evidence that alterations in the dopamine system predict development of clinically-relevant, cognitive impairment in Parkinson's disease. If replicated and determined to be causative, they demonstrate that the dopamine system is instrumental to cognitive health status throughout the disease course. TRIAL REGISTRATION: Parkinson's Progression Markers Initiative is registered with ClinicalTrials.gov (NCT01141023).

Evaluation of heparinized syringes for measuring newborn metabolites in neonates with a central arterial line.

Newborn metabolic screening is emerging as a novel method for predicting neonatal morbidity and mortality in neonates born very preterm (<32 weeks gestation). The purpose of our study was to determine if blood collected by an electrolyte-balanced dry lithium heparin syringe, as is routine for blood gas measurements, affects targeted metabolite and biomarker levels. Two blood samples (one collected with a heparinized syringe and the other with a non-heparinized syringe) were obtained at the same time from 20 infants with a central arterial line and tested for 49 metabolites and biomarkers using standard procedures for newborn screening. Overall, the median metabolite levels did not significantly differ by syringe type. However, there was wide variability, particularly for amino acids and immunoreactive trypsinogen, for individual paired samples and therefore, consideration should be given to sample collection when using these metabolites in prediction models of neonatal morbidity and mortality.

Genome-wide DNA methylation analysis of post-operative delirium with brain, blood, saliva, and buccal samples from neurosurgery patients.

OBJECTIVE: No previous study demonstrates the difference in the genome-wide DNA methylation status of post-operative delirium (POD) using human brain tissue obtained from neurosurgery and multiple peripheral tissues such as blood, saliva, and buccal samples from the same individuals. We aimed to identify epigenetic marks of DNA methylation in the brain and peripheral tissues to elucidate the potential pathophysiological mechanism of POD. METHODS: The four tissue types (brain, blood, saliva, buccal) of DNA samples from up to 40 patients, including 11 POD cases, were analyzed using Illumina EPIC array. DNAm differences between patients with and without POD were examined. We also conducted enrichment analysis based on the top DNAm signals. RESULTS: The most different CpG site between control and POD was found at cg16526133 near the ADAMTS9 gene from the brain tissue(p = 8.66E-08). However, there are no CpG sites to reach the genome-wide significant level. The enrichment analysis based on the 1000 top hit CpG site (p < 0.05) on the four tissues showed several intriguing pathways. In the brain, there are pathways including "positive regulation of glial cell differentiation". Blood samples showed also pathways related to immune function. Besides, both saliva and the buccal sample showed pathways related to circadian rhythm, although these findings were not FDR significant. CONCLUSION: Enrichment analysis found several intriguing pathways related to potential delirium pathophysiology. Present data may further support the role of epigenetics, especially DNA methylation, in the molecular mechanisms of delirium pathogenesis.