Residual Variability Research Articles

A Bayesian Approach to Modeling Variance of Intensive Longitudinal Biomarker Data as a Predictor of Health Outcomes.

Intensive longitudinal biomarker data are increasingly common in scientific studies that seek temporally granular understanding of the role of behavioral and physiological factors in relation to outcomes of interest. Intensive longitudinal biomarker data, such as those obtained from wearable devices, are often obtained at a high frequency typically resulting in several hundred to thousand observations per individual measured over minutes, hours, or days. Often in longitudinal studies, the primary focus is on relating the means of biomarker trajectories to an outcome, and the variances are treated as nuisance parameters, although they may also be informative for the outcomes. In this paper, we propose a Bayesian hierarchical model to jointly model a cross-sectional outcome and the intensive longitudinal biomarkers. To model the variability of biomarkers and deal with the high intensity of data, we develop subject-level cubic B-splines and allow the sharing of information across individuals for both the residual variability and the random effects variability. Then different levels of variability are extracted and incorporated into an outcome submodel for inferential and predictive purposes. We demonstrate the utility of the proposed model via an application involving bio-monitoring of hertz-level heart rate information from a study on social stress.

Using a Bayesian analytic approach to identify county-level ecological factors associated with survival among individuals with early-onset colorectal cancer.

In the United States (US), incidence of early age of onset colorectal cancer (EOCRC, diagnosed <50 years of age) has been increasing. Using a Bayesian analytic approach, we evaluated the association between county-level ecological factors and survival among individuals with EOCRC and identified hotspot and coldspot counties with unexplained low and high survival, respectively. Principal component (PC) analysis was used to reduce dimensionality of 36 county-level social, behavioral, and preventive factors from the Centers for Disease Control and Prevention data. Survival information was derived from the Surveillance, Epidemiology, and End Results Program data from January 1, 2000 to December 31, 2019. The association between the identified PCs and survival was evaluated using multivariable spatial generalized linear mixed models. Counties with residual low and high survival (i.e., unexplained by the PCs) were classified as hotspots and coldspots, respectively. Four PCs were used to explain the spatial variability in 5-year survival among 75,215 individuals with EOCRC: PC1) poverty, chronic disease, health risk behaviors (β = -0.03, 95% credible interval (CrI): -0.04, -0.03); PC2) younger age, chronic disease-free, minority status (β = -0.01, 95% CrI: -0.02, 0.00); PC3) urban environment, preventive services (β = 0.02, 95% CrI: 0.00, 0.03); and PC4) older age (-0.04, 95% CrI: -0.06, -0.02). Among individuals with distant malignancies, the residual spatial variability remained high for two US counties: 1) Salt Lake County, UT residents experiencing 26.5% (95% CrI: 1.5%, 47.8%) lower odds of survival [hotspot], and 2) Riverside County, CA residents experiencing 37% (95% CrI: 7.97%, 78.8%) higher odds survival [coldspot] after adjustment for county-level factors. County-level ecological factors are strongly associated with survival among individuals with EOCRC. Yet there is some evidence of survival disparities among individuals with distant malignancies that remain unexplained by the included factors.

Population pharmacokinetics of tacrolimus whole blood and peripheral blood mononuclear cell concentrations in stable kidney-transplanted patients.

Therapeutic drug monitoring of tacrolimus based on whole blood drug concentrations is routinely performed. The concentration of tacrolimus in peripheral blood mononuclear cells (PMBCs) is likely to better reflect drug exposure at the treatment target site. We aimed to describe the relationship between tacrolimus whole blood and PBMC concentrations, and the influence of patient characteristics on this relationship by developing a population pharmacokinetic model. We prospectively enrolled 63 stable adult kidney-transplanted patients and collected dense (12-h, n = 18) or sparse (4-h, n = 45) pharmacokinetic profiles of tacrolimus. PBMCs were isolated from whole blood (Ficoll density gradient centrifugation), and drug concentrations in whole blood and PBMCs were analysed using liquid chromatography-mass spectrometry. Patient genotype (CYP3A4/5, ABCB1, NR1I2) was assessed with PCR. Population pharmacokinetic modelling and statistical evaluation was performed using NONMEM. Tacrolimus whole blood concentrations were well described using a two-compartment pharmacokinetic model with a lag-time and first-order absorption and elimination. Tacrolimus PBMC concentrations were best estimated from whole blood concentrations with the use of a scaling factor, the ratio of whole blood to PBMC concentrations (RC:PBMC), which was the extent of tacrolimus distribution into PBMC. CYP3A5*1 non-expressors and NR1I2-25 385T allele expressors demonstrated higher RC:PBMC ratios of 42.4% and 60.7%, respectively. Tacrolimus PBMC concentration could not be accurately predicted from whole blood concentrations and covariates because of significant residual unexplained variability in the distribution of tacrolimus into PBMCs and may need to be measured directly if required for future studies.

Establishing a Relationship between In Vitro Potency in Cell-Based Assays and Clinical Efficacious Concentrations for Approved GLP-1 Receptor Agonists.

Background: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) play an important role in the treatment of type 2 diabetes (T2D) and obesity. The relationship between efficacy and dosing regimen has been studied extensively for this class of molecules. However, a comprehensive analysis of the translation of in vitro data to in vivo efficacious exposure is still lacking. Methods: We collected clinical pharmacokinetics for five approved GLP-1RAs to enable the simulation of exposure profiles and compared published clinical efficacy endpoints (HbA1c and body weight) with in-house in vitro potency values generated in different cell-based assays. Additionally, we investigated the correlation with target coverage, expressed as a ratio between the steady state drug exposure and unbound potency, body weight, or HbA1c reduction in patients with T2D. Results: We found that the best correlation with in vivo efficacy was seen for in vitro potency data generated in cellular assays performed in the absence of any serum albumin or using ovalbumin. Residual variability was larger using in vitro potency data generated in endogenous cell lines or in the presence of human serum albumin. For the human receptor assay with no albumin, exposures above 100-fold in vitro EC50 resulted in >1.5% point HbA1c reduction, while a 5% BW reduction was related to approximately 3× higher exposures. A similar relationship was seen in the ovalbumin assay. Conclusions: Overall, the relationship established for in vitro potency and in vivo efficacy will help to increase confidence in human dose prediction and trial design for new GLP-1RAs in the discovery and early clinical phases.

A fully automatic tool for development of population pharmacokinetic models.

Population pharmacokinetic (PK) models are widely used to inform drug development by pharmaceutical companies and facilitate drug evaluation by regulatory agencies. Developing a population PK model is a multi-step, challenging, and time-consuming process involving iterative manual model fitting and evaluation. A tool for fully automatic model development (AMD) of common population PK models is presented here. The AMD tool is implemented in Pharmpy, a versatile open-source library for pharmacometrics. It consists of different modules responsible for developing the different components of population PK models, including the structural model, the inter-individual variability (IIV) model, the inter-occasional variability (IOV) model, the residual unexplained variability (RUV) model, the covariate model, and the allometry model. The AMD tool was evaluated using 10 real PK datasets involving the structural, IIV, and RUV modules in three sequences. The different sequences yielded generally consistent structural models; however, there were variations in the results of the IIV and RUV models. The final models of the AMD tool showed lower Bayesian Information Criterion (BIC) values and similar visual predictive check plots compared with the available published models, indicating reasonable quality, in addition to reasonable run time. A similar conclusion was also drawn in a simulation study. The developed AMD tool serves as a promising tool for fast and fully automatic population PK model building with the potential to facilitate the use of modeling and simulation in drug development.

A hybrid SARIMA-Prophet model for predicting historical streamflow time-series of the Sobat River in South Sudan

Accurate river streamflow forecasting is pivotal for effective water resource planning, infrastructure design, utilization, optimization, and flood planning and warning. Streamflow prediction remains a difficult task due to several factors such as climate change, topography, and lack of observed data in some cases. This paper investigates and evaluates the individual performances of the seasonal auto-regressive integrated moving average (SARIMA) and Prophet models in forecasting the streamflow of the Sobat River and proposes a hybrid SARIMA-Prophet model to leverage the strengths of both approaches. Using the augmented Dickey-Fuller (ADF) and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests, the flow of the Sobat River was found to be stationary. The performance of the models was then assessed based on their residual errors and predictive accuracy using the mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2). Residual analysis and prediction capabilities revealed that Prophet slightly edged SARIMA in terms of prediction efficacy; however, both models struggled to effectively capture extreme values, resulting in significant overestimations and slight underestimations. The hybrid SARIMA-Prophet model significantly reduced residual variability, achieving a lower MAE of 4.047 m3/s, RMSE of 6.17 m3/s, and a higher R2 of 0.92 than did the SARIMA (MAE: 5.39 m3/s, RMSE: 8.70 m3/s, R2: 0.85) and Prophet (MAE: 5.35 m3/s, RMSE: 8.32 m3/s, and R2: 0.86) models. This indicates that the hybrid model handles both long-term patterns and short-term fluctuations more effectively than the individual models. The findings of the present study highlight the potential of hybrid SARIMA-Prophet models for streamflow forecasting in terms of accuracy and reliability, thus contributing to more effective water resource management and planning, particularly in the Sobat River.

Time series modeling of coastal fishery landings on the Southwestern Atlantic shelf: Influence of environmental drivers

AbstractTime‐series modeling of fisheries provides insights into stock tendencies and enables short‐term forecasting of landings, aiding decision makers in establishing management priorities. The Rio de la Plata Estuary and its maritime front sustain valuable fisheries for Argentina and Uruguay, with striped weakfish (Cynoscion guatucupa), whitemouth croaker (Micropogonias furnieri), and Argentine hake (Merluccius hubbsi) historically representing highest catches. However, their landings have declined in recent decades. To support resource management, we investigated the effectiveness of Autoregressive Integrated Moving Average (ARIMA) models in capturing fishery landing dynamics and providing reliable short‐term predictions. The best models exhibited a good fit and accurately captured the overall trends of landings. Residual variability unaccounted for by the model was analyzed in relation to time‐lagged environmental conditions. A wavelet coherence analysis was employed to examine the effect of the most significant variables on landings. Results revealed that environmental conditions affect differentially landings of each species as a result of their particular ecological traits. Turbidity and salinity affected mainly M. furnieri, which inhabits the innermost part of the estuary. Additionally, C. guatucupa, inhabiting the outer estuary and coastal region, exhibited a stronger association with river runoff compared to M. hubbsi, which inhabits the continental shelf. This study provides the first evidence of ARIMA models' reliability in representing the temporal evolution of catch in these fisheries, offering valuable tools for short‐term landings forecasting and facilitating sustainable management. Wavelet analysis findings will also contribute to enhancing our comprehension of trends in the correlation between environmental conditions and commercial landings.

Hospital variation in adoption of balanced transfusion practices among injured patients requiring blood transfusions

BackgroundThis study sought to measure hospital variability in adoption of balanced transfusion following the Pragmatic, Randomized Optimal Platelet and Plasma Ratios (PROPPR) guidelines. We hypothesized hospital adoption rates of balanced transfusion would be low, and vary significantly among hospitals after controlling for patient, injury and hospital characteristics. Study Design and MethodsThis was an observational cohort study of injured adult patients (≥16 years) in Trauma Quality Improvement Program hospitals 2016–2021. Inclusion criteria were hypotensive patients receiving one transfusion of packed red blood cells, fresh frozen plasma, platelets, or cryoprecipitate. Balanced transfusion was defined as ≥1 ratio of plasma to packed red blood cells or platelets to packed red blood cells or whole blood use at 4 hours. Hierarchical multivariable logistic regression quantified residual hospital-level variability in balanced transfusion rates after adjusting for patient and hospital characteristics. ResultsAmong 172,457 injured patients who received transfusions, 30,386 (17.6%) underwent balanced transfusion. Patient-level balanced transfusion rates were 11% in 2016, rose to 14.0% in 2019, and jumped up once whole blood transfusions were measured to 24.0% in 2020 and to 25.9% in 2021. Approximately 26% of the variability in balanced transfusion rates was attributable to the hospital. Verified level I hospitals had a 2.09 increased adjusted odds of balanced transfusion (95% CI 1.88–2.21) compared to nonverified hospitals. University teaching status had a 1.29 increased adjusted odds of balanced transfusion (95% CI 1.08–1.54) compared with community hospitals. Overall, 150 (23.5%) hospitals were high outliers (high performing) in balanced transfusion adoption and 124 (19.4%) hospitals were low outliers. ConclusionThere was significant variability in hospital adoption of balanced transfusion.

Influence of health insurance on withdrawal of life sustaining treatment for patients with isolated traumatic brain injury: a retrospective multi-center observational cohort study

BackgroundHealthcare inequities for patients with traumatic brain injury (TBI) represent a major priority area for trauma quality improvement. We hypothesized a relationship between health insurance status and timing of withdrawal of life sustaining treatment (WLST) for adults with severe TBI.MethodsThis multicenter retrospective observational cohort study utilized data collected between 2017 and 2020. We identified adult (age ≥ 16) patients with isolated severe TBI admitted participating Trauma Quality Improvement Program centers. We determined the relationship between insurance status (public, private, and uninsured) and the timing of WLST using a competing risk survival analysis framework adjusting for baseline, clinical, injury and trauma center characteristics. Multivariable cause-specific Cox regressions were used to compute adjusted hazard ratios (HR) reflecting timing of WLST, accounting for mortality events. We also quantified the between-center residual variability in WLST using the median odds ratio (MOR) and measured insurance status association with access to rehabilitation at discharge.ResultsWe identified 42,111 adults with isolated severe TBI treated across 509 trauma centers across North America. There were 10,771 (25.6%) WLST events in the cohort and a higher unadjusted incidence of WLST events was evident in public insurance patients compared to private or uninsured groups. After adjustment, WLST occurred earlier for publicly insured (HR 1.07, 95% CI 1.02–1.12) and uninsured patients (HR 1.29, 95% CI 1.18–1.41) compared to privately insured patients. Access to rehabilitation was lower for both publicly insured and uninsured patients compared to patients with private insurance. Accounting for case-mix, the MOR was 1.49 (95% CI 1.43–1.55), reflecting significant residual between-center variation in WLST decision-making.ConclusionsOur findings highlight the presence of disparate WLST practices independently associated with health insurance status. Additionally, these results emphasize between-center variability in WLST, persisting despite adjustments for measurable patient and trauma center characteristics.

External Evaluation of Population Pharmacokinetic Models of Piperacillin in Preterm and Term Patients from Neonatal Intensive Care.

Piperacillin/tazobactam is extensively used off-label to treat late-onset neonatal sepsis, but safety and pharmacokinetic data in this population are limited. Additionally, the organic immaturity of the newborns contributes to a high piperacillin pharmacokinetic variability. This affects the clinical efficacy of the antibiotic treatment and increases the probability of developing drug resistance. This study aimed to evaluate the predictive performance of reported piperacillin population pharmacokinetic models for their application in a model-informed precision dosing strategy in preterm and term Mexican neonatal intensive care patients. Published population pharmacokinetic models for piperacillin which included neonates in their study population were identified. From the reference models, structured models, population pharmacokinetic parameters, and interindividual and residual variability data were extracted to be replicated in pharmacokinetic software (NONMEM® version 7.4). For the clinical study, a sampling schedule was designed, and 2-3 blood samples of 250 µL were taken from neonates who met the inclusion criteria. Piperacillin plasma concentrations were determined by liquid chromatography/tandem mass spectrometry. The clinical treatment data were collected, and piperacillin plasma concentrations were estimated using reference pharmacokinetic models for an a priori or Bayesian approach. Statistical methods were used in terms of bias and precision to evaluate the differences between observed and estimated neonatal piperacillin plasma concentrations with the different approaches and to identify the pharmacokinetic model that best fits the neonatal data. A total of 70 plasma samples were collected from 25 neonatal patients, of which 15 were preterm neonates. The overall median value (range) postnatal age, gestational age, body weight, and serum creatinine at the sampling collecting day were 12 (3-26) days, 34.2 (26-41.1) weeks, 1.78 (0.08-3.90) Kg, 0.47 (0.20-0.90) mg/dL, respectively. Three population pharmacokinetic models for piperacillin in infants up to 2months were identified, and their predictive performance in neonatal data was evaluated. No pharmacokinetic model was suitable for our population using an a priori approach. The model published by Cohen-Wolkowiez et al. in 2014 with a Bayesian approach showed the best performance of the pharmacokinetic models evaluated in our neonatal data. The procedure requires two blood samples (predose and postdose), and, when applied, it predicted 66.6% of the observations with a relative median absolute predicted error of less than 30%. The population pharmacokinetic model developed by Cohen-Wolkowiez et al. in 2014 demonstrated superior performance in predicting the plasma concentration of piperacillin in preterm and term Mexican neonatal intensive care patients. The Bayesian approach, including two different piperacillin plasma concentrations, was clinically acceptable regarding bias and precision. Its application for model-informed precision dosing can be an option to optimize the piperacillin dosage in our population.

Perioperative platelet reactivityover time inpatients undergoing vascular surgery: An observational pilot study.

Despite Antiplatelet therapy (APT), cardiovascular patients undergoing revascularisation remain at high risk for thrombotic events. Individual response to APT varies substantially, resulting in insufficient protection from thrombotic events due to high on-treatment platelet reactivity (HTPR) in ≤40% of patients. Individual variation in platelet response impairs APT guidance on a single patient level. Unfortunately, little is known about individual platelet response to APT over time, timing for accurate residual platelet reactivity measurement, or the optimal test to monitor residual platelet reactivity. To investigate residual platelet reactivity variability over time in individual patients undergoing carotid endarterectomy (CEA) treated with clopidogrel. Platelet reactivity was determined in patients undergoing CEA in a prospective, single-centre, observational study using the VerifyNow (change in turbidity from ADP-induced binding to fibrinogen-coated beads), the VASP assay (quantification of phosphorylation of vasodilator-stimulated phosphoprotein), and a flow-cytometry-based assay (PACT) at four perioperative time points. Genotyping identified slow (CYP2C19*2 and CYP2C19*3) and fast (CYP2C19*17) metabolisers. Between December 2017 and November 2019, 50 patients undergoing CEA were included. Platelet reactivity measured with the VerifyNow (p = < .001) and VASP (p = .029) changed over time, while the PACT did not. The VerifyNow identified patients changing HTRP status after surgery. The VASP identified patients changing HTPR status after eight weeks (p = .018). CYP2C19 genotyping identified 13 slow metabolisers. In patients undergoing CEA, perioperative platelet reactivity measurements fluctuate over time with little agreement between platelet reactivity assays. Consequently, HTPR status of individual patients measured with the VerifyNow and VASP assay changed over time. Therefore, generally used perioperative platelet reactivity measurements seem unreliable for adjusting perioperative APT strategy.

Simulating realistic patient profiles from pharmacokinetic models by a machine learning postprocessing correction of residual variability.

We address the problem of model misspecification in population pharmacokinetics (PopPK), by modeling residual unexplained variability (RUV) by machine learning (ML) methods in a postprocessing step after conventional model building. The practical purpose of the method is the generation of realistic virtual patient profiles and the quantification of the extent of model misspecification, by introducing an appropriate metric, to be used as an additional diagnostic of model quality. The proposed methodology consists of the following steps: After developing a PopPK model, the individual residual errors IRES = DV-IPRED, are computed, where DV are the observations and IPRED the individual predictions and are modeled by ML to obtain IRESML. Correction of the IPREDs can then be carried out as DVML = IPRED + IRESML. The methodology was tested in a PK study of ropinirole, for which a PopPK model was developed while a second deliberately misspecified model was also considered. Various supervised ML algorithms were tested, among which Random Forest gave the best results. The ML model was able to correct individual predictions as inspected in diagnostic plots and most importantly it simulated realistic profiles that resembled the real data and canceled out the artifacts introduced by the elevated RUV, even in the case of the heavily misspecified model. Furthermore, a metric to quantify the extent of model misspecification was introduced based on the R2 between IRES and IRESML, following the rationale that the greater the extent of variability explained by the ML model, the higher the degree of model misspecification present in the original model.

Longitudinal within-person variability around personality trajectories.

Decades of research have identified average patterns of normative personality development across the lifespan. However, it is unclear how well these correspond to trajectories of individual development. Past work beyond general personality development might suggest these average patterns are oversimplifications, necessitating novel examinations of how personality develops and consideration of new individual difference metrics. This study uses five longitudinal data sets from Germany, Australia, the Netherlands, and the United States (N = 128,345; Mage = 45.42; 53% female) to examine personality development using mixed-effects location scale models. These models quantify individual differences in within-person residual variability, or sigma, around trajectories-thereby testing if models that assume sigma is homogeneous, unsystematic noise are appropriate. We investigate if there are individual differences in longitudinal within-person variability for Big Five trajectories, if there are variables associated with this heterogeneity, and if person-level sigma values can uniquely predict an outcome. Results indicated that, across all models, there was meaningful heterogeneity in sigma-the magnitude of which was comparable to and often even greater than that of intercepts and slopes. Individual differences in sigma were further associated with covariates central to personality development and had robust predictive utility for health status, an outcome with long-established personality associations. Collectively, these findings underscore the presence, degree, validity, and potential utility of heterogeneity in longitudinal within-person variability and indicate the typical linear model does not adequately depict individual development. We suggest it should become the default to consider this individual difference metric in personality development research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Pharmacogenetic Testing or Therapeutic Drug Monitoring: A Quantitative Framework.

Pharmacogenetic profiling and therapeutic drug monitoring (TDM) have both been proposed to manage inter-individual variability (IIV) in drug exposure. However, determining the most effective approach for estimating exposure for a particular drug remains a challenge. This study aimed to quantitatively assess the circumstances in which pharmacogenetic profiling may outperform TDM in estimating drug exposure, under three sources of variability (IIV, inter-occasion variability [IOV], and residual unexplained variability [RUV]). Pharmacokinetic models were selected from the literature corresponding to drugs for which pharmacogenetic profiling and TDM are both clinically considered approaches for dose individualization. The models were used to simulate relevant drug exposures (trough concentration or area under the curve [AUC]) under varying degrees of IIV, IOV, and RUV. Six drug cases were selected from the literature. Model-based simulations demonstrated that the percentage of patients for whom pharmacogenetic exposure prediction is superior to TDM differs for each drug case: tacrolimus (11.0%), tamoxifen (12.7%), efavirenz (49.2%), vincristine (49.6%), risperidone (48.1%), and 5-fluorouracil (5-FU) (100%). Generally, in the presence of higher unexplained IIV in combination with lower RUV and IOV, exposure was best estimated by TDM, whereas, under lower unexplained IIV in combination with higher IOV or RUV, pharmacogenetic profiling was preferred. For the drugs with relatively low RUV and IOV (e.g., tamoxifen and tacrolimus), TDM estimated true exposure the best. Conversely, for drugs with similar or lower unexplained IIV (e.g., efavirenz or 5-FU, respectively) combined with relatively high RUV, pharmacogenetic profiling provided the most accurate estimate for most patients. However, genotype prevalence and the relative influence of genotypes on the PK, as well as the ability of TDM to accurately estimate AUC with a limited number of samples, had an impact. The results could be used to support clinical decision making when considering other factors, such as the probability for severe side effects.

Frequency of CYP2C19, P2RY12, ITGB3, ITGA2, and eNOS3 gene polymorphism in patients with myocardial infarction

The aim of the study was to assess the distribution of polymorphic variants G681A (*2) of the CYP2C19 gene, H1/H2 of the P2RY12 gene, T1565C of the ITGB3 gene, C807T of the ITGA2 gene, and T786C of the eNOS3 gene in the population of the Grodno region, and to study their associations with myocardial infarction (MI). The study of the population consists of 493 people, including 400 patients with MI aged 31 to 74 years and 93 people of the control group aged 32 to 60 years. Research data (clinical and genotyping performed by polymerase chain reaction) were analyzed using STATISTICA 10.0 software. The prevalence of carriage of genotypes associated with high residual platelet reactivity and variability in response to dual antiplatelet therapy among patients with MI was 25.2 % for the G681A polymorphic locus of the CYP2C19 gene (GA + AA), and for the H1/H2 polymorphic locus of the P2RY12 gene (H1/H2 + H2/H2) – 40.0 %, for the C807T polymorphic locus of the ITGA2 gene (CT + TT) – 65.8, for the T1565C polymorphic locus of the ITGB3 gene (TC + CC) – 25.5, for the polymorphic locus T786C of the eNOS gene (TC + CC) – 69.2 %. Among the individuals of the control group, the frequency of occurrence of these genotypes was 18.3, 46.2, 60.2, 37.6, 48.4 %, respectively. In patients with MI, compared to the control group, the TT genotype of the eNOS gene was less common (χ2 = 13.6, p = 0.0002), the CC genotype of the eNOS gene (χ2 = 5.4, p = 0.02) and the allele 786C of the eNOS gene (χ2 = 15.1, p = 0.0001) were more often detected. The carriage of the 786C allele of the eNOS gene increased the risk of MI in the studied population (OR = 2.0, 95 % CI: 1.41‒2.82, p = 0.0001). Gender differences were not found in the distribution of genotypes and alleles within the studied groups. There were no differences in carriage by the number of combinations of minor alleles between the control group and patients with MI. The most common combinations of minor alleles in both groups were comparable.

785 Variability in Withdrawal of Life Sustaining Treatment Decision for Patients With Severe Traumatic Brain Injury: An Analysis of 510 North American Trauma Centers

INTRODUCTION: Withdrawal of life sustaining treatment (WLST) in severe traumatic brain injury (TBI) is complex with a paucity of standardized guidelines to inform practice patterns. We hypothesized presence of variability in WLST decision-making across centers, which could have important implications for equitable trauma care provision. METHODS: This retrospective study utilized data from adult severe TBI patients treated at trauma centers participating in the Trauma Quality Improvement Program between 2017-2020. Multivariable hierarchical logistic regression was used to adjust for patient, injury and hospital attributes influencing WLST. Residual between-center variability was characterized using the median odds ratio. Disparate WLST practices were further assessed by ranking centers' tendencies for WLST and assessing mortality between quartiles. RESULTS: We identified a total of 85511 subjects with severe TBI treated across 510 trauma centers, of which 20,300 (24%) had WLST. Patient-level factors associated with increased likelihood of WLST were advanced age, white race, treatment in a non-profit trauma center or self-pay/ Medicare insurance status (compared to private insurance). Black race was associated with reduced tendency for WLST. Higher severity intracranial and extracranial injuries also increased likelihood for WLST.After adjustment for patient and hospital attributes, the median odds ratio was 1.44 (1.40-1.48 95%CI), suggesting substantial residual variation in WLST between centers. When centers were grouped into quartiles by their propensity for WLST, there was a 1.37 (1.30-1.42 95%CI) increased adjusted odds of mortality between fourth versus first quartile centers. CONCLUSIONS: We highlighted the presence of disparate WLST practice patterns between trauma centers after adjustment for case-mix and hospital attributes. These findings highlight a strong need for examination of center-level factors contributing to heterogenous WLST practices to ultimately improve equity of care provision for severe TBI patients.

Pharmacokinetics-Based Pediatric Dose Evaluation and Optimization Using Saliva - A Case Study.

Understanding pharmacokinetics (PK) in children is a prerequisite to determine optimal pediatric dosing. As plasma sampling in children is challenging, alternative PK sampling strategies are needed. In this case study we evaluated the suitability of saliva as alternative PK matrix to simplify studies in infants, investigating metamizole, an analgesic used off-label in infants. Six plasma and 6 saliva PK sample collections were scheduled after a single intravenous dose of 10 mg/kg metamizole. Plasma/saliva pharmacometric (PMX) modeling of the active metabolites 4-methylaminoantipyrine (4-MAA) and 4-aminoantipyrine (4-AA) was performed. Various reduced plasma sampling scenarios were evaluated by PMX simulations. Saliva and plasma samples from 25 children were included (age range, 5-70 months; weight range, 8.7-24.8 kg). Distribution of metamizole metabolites between plasma and saliva was without delay. Estimated mean (individual range) saliva/plasma fractions of 4-MAA and 4-AA were 0.32 (0.05-0.57) and 0.57 (0.25-0.70), respectively. Residual variability of 4-MAA (4-AA) in saliva was 47% (28%) versus 17% (11%) in plasma. A simplified sampling scenario with up to 6 saliva samples combined with 1 plasma sample was associated with similar PK parameter estimates as the full plasma sampling scenario. This case study with metamizole shows increased PK variability in saliva compared to plasma, compromising its suitability as single matrix for PK studies in infants. Nonetheless, rich saliva sampling can reduce the number of plasma samples required for PK characterization, thereby facilitating the conduct of PK studies to optimize dosing in pediatric patients.

Behavioural ecology meets oncology: quantifying the recovery of animal behaviour to a transient exposure to a cancer risk factor.

Wildlife is increasingly exposed to sublethal transient cancer risk factors, including mutagenic substances, which activates their anti-cancer defences, promotes tumourigenesis, and may negatively impact populations. Little is known about how exposure to cancer risk factors impacts the behaviour of wildlife. Here, we investigated the effects of a sublethal, short-term exposure to a carcinogen at environmentally relevant concentrations on the activity patterns of wild Girardia tigrina planaria during a two-phase experiment, consisting of a 7-day exposure to cadmium period followed by a 7-day recovery period. To comprehensively explore the effects of the exposure on activity patterns, we employed the double hierarchical generalized linear model framework which explicitly models residual intraindividual variability in addition to the mean and variance of the population. We found that exposed planaria were less active compared to unexposed individuals and were able to recover to pre-exposure activity levels albeit with a reduced variance in activity at the start of the recovery phase. Planaria showing high activity levels were less predictable with larger daily activity variations and higher residual variance. Thus, the shift in behavioural variability induced by an exposure to a cancer risk factor can be quantified using advanced tools from the field of behavioural ecology. This is required to understand how tumourous processes affect the ecology of species.

Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data

Abstract. The constellation of Earth-observing satellites has now produced atmospheric greenhouse gas concentration estimates covering a period of several years. Their global coverage is providing additional information on the global carbon cycle. These products can be combined with complex inversion systems to infer the magnitude of carbon sources and sinks around the globe. Multiple factors, including the atmospheric transport model and satellite product aggregation method, can impact such flux estimates. Analysis of variance (ANOVA) is a well-established statistical framework for estimating common signals while partitioning variability across factors in the analysis of experiments. Functional ANOVA extends this approach with a statistical model that incorporates spatiotemporal correlation for each ANOVA component. The approach is illustrated on inversion experiments with different satellite retrieval aggregation methods and identifies consistent significant patterns in flux increments that span large spatial scales. Functional ANOVA identifies these patterns while accounting for the uncertainty at small spatial scales that is attributed to differences in the aggregation method. Functional ANOVA is also applied to a recent flux model intercomparison project (MIP), and the relative magnitudes of inversion system effects and data source (satellite versus in situ) are similar but exhibit slightly different importance for fluxes over different continents. In all examples, the unexplained residual variability is locally sizable in magnitude but with limited spatial and temporal correlation. These common behaviors across flux inversion experiments demonstrate the diagnostic capability for functional ANOVA to simultaneously distinguish the spatiotemporal coherence of carbon cycle processes and algorithmic factors.

Interannual Variability of Zonal Mean Temperature, Water Vapor, and Clouds in the Tropical Tropopause Layer

AbstractWater vapor and cirrus clouds in the tropical tropopause layer (TTL) are important for the climate and are largely controlled by temperature in the TTL. On interannual timescales, both stratospheric and tropospheric modes of the large‐scale variability could affect temperatures in the TTL. Here multiple linear regression (MLR) is used to investigate explained variance in the cold point tropopause temperature (CPT), cold point tropopause height (CPZ), 83 hPa water vapor (WV83), 83 hPa ozone (O383), and total cirrus cloud fraction with cloud base (TTLCCF) and top (ALLCF) above 14.5 km, all averaged over 15°S‐15°N. Predictors of the MLR are a set of stratospheric and tropospheric large‐scale modes of variability. The MLR explains significant variance in CPT (76%), CPZ (78%), WV83 (65%), O383 (62%), TTLCCF (52%), and ALLCF (36%). The interannual variability of CPT and WV83 is dominated by stratospheric processes associated with the Quasi‐Biennial Oscillation (QBO) and Brewer‐Dobson Circulation (BDC), whereas the variability of CPZ, O383, TTLCCF and ALLCF is also controlled by 500 hPa temperature (T500). Residual variability in CPT and CPZ not captured by the MLR are further significantly correlated to stratospheric temperature. It is shown that the portion of the BDC's shallow branch missed by the eddy heat flux based BDC index contributes significant amounts of the explained variances.