Multivariate reference and tolerance regions based on conditional transformation models: Application to glycemic markers.

The reference interval is the most widely used medical decision-making, constituting a central tool in determining whether an individual is healthy or not. When the results of several continuous diagnostic tests are available for the same patient, their clinical interpretation is more reliable if a multivariate reference region (MVR) is available rather than multiple univariate reference intervals. MVRs, defined as regions containing 95% of the results of healthy subjects, extend the concept of the reference interval to the multivariate setting. However, they are rarely used in clinical practice owing to difficulties associated with their interpretability and the restrictions inherent to the assumption of a Gaussian distribution. Further statistical research is thus needed to make MVRs more applicable and easier for physicians to interpret. Since the joint distribution of diagnostic test results may well change with patient characteristics independent of disease status, MVRs adjusted for covariates are desirable. The present work introduces a novel formulation for MVRs based on multivariate conditional transformation models (MCTMs). Additionally, we take into account the estimation uncertainty of such MVRs by means of tolerance regions. These conditional MVRs imply no parametric restriction on the response, and potentially nonlinear continuous covariate effects can be estimated. MCTMs allow the estimation of the effects of covariates on the joint distribution of multivariate response variables and on these variables' marginal distributions, via the use of most likely transformation estimation. Our contributions proved reliable when tested with simulated data and for a real data application with two glycemic markers.

Modeling conditional reference regions: Application to glycemic markers.

Many clinical decisions are taken based on the results of continuous diagnostic tests. Usually, only the results of one single test is taken into consideration, the interpretation of which requires a reference range for the healthy population. However, the use of two different tests, can be necessary in the diagnosis of certain diseases. This obliges a bivariate reference region be available for their interpretation. It should also be remembered that reference regions may depend on patient variables (eg, age and sex) independent of the suspected disease. However, few proposals have been made regarding the statistical modeling of such reference regions, and those put forward have always assumed a Gaussian distribution, which can be rather restrictive. The present work describes a new statistical method that allows such reference regions to be estimated with no insistence on the results being normally distributed. The proposed method is based on a bivariate location-scale model that provides probabilistic regions covering a specific percentage of the bivariate data, dependent on certain covariates. The reference region is estimated nonparametrically and the nonlinear effects of continuous covariates via polynomial kernel smoothers in additive models. The bivariate model is estimated using a backfitting algorithm, and the optimal smoothing parameters of the kernel smoothers selected by cross-validation. The model performed satisfactorily in simulation studies under the assumption of non-Gaussian conditions. Finally, the proposed methodology was found to be useful in estimating a reference region for two continuous diagnostic tests for diabetes (fasting plasma glucose and glycated hemoglobin), taking into account the age of the patient.

Impact of the Covid-19 pandemic on perinatal mental health (Riseup-PPD-COVID-19): protocol for an international prospective cohort study.

BACKGROUND: Corona Virus Disease 19 (COVID-19) is a new pandemic, declared a public health emergency by the World Health Organization, which could have negative consequences for pregnant and postpartum women. The scarce evidence published to date suggests that perinatal mental health has deteriorated since the COVID-19 outbreak. However, the few studies published so far have some limitations, such as a cross-sectional design and the omission of important factors for the understanding of perinatal mental health, including governmental restriction measures and healthcare practices implemented at the maternity hospitals. Within the Riseup-PPD COST Action, a study is underway to assess the impact of COVID-19 in perinatal mental health. The primary objectives are to (1) evaluate changes in perinatal mental health outcomes; and (2) determine the risk and protective factors for perinatal mental health during the COVID-19 pandemic. Additionally, we will compare the results between the countries participating in the study. METHODS: This is an international prospective cohort study, with a baseline and three follow-up assessments over a six-month period. It is being carried out in 11 European countries (Albania, Bulgaria, Cyprus, France, Greece, Israel, Malta, Portugal, Spain, Turkey, and the United Kingdom), Argentina, Brazil and Chile. The sample consists of adult pregnant and postpartum women (with infants up to 6 months of age). The assessment includes measures on COVID-19 epidemiology and public health measures (Oxford COVID-19 Government Response Tracker dataset), Coronavirus Perinatal Experiences (COPE questionnaires), psychological distress (BSI-18), depression (EPDS), anxiety (GAD-7) and post-traumatic stress symptoms (PTSD checklist for DSM-V). DISCUSSION: This study will provide important information for understanding the impact of the COVID-19 pandemic on perinatal mental health and well-being, including the identification of potential risk and protective factors by implementing predictive models using machine learning techniques. The findings will help policymakers develop suitable guidelines and prevention strategies for perinatal mental health and contribute to designing tailored mental health interventions. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04595123 .

Corneal Biomechanics in Unilateral Keratoconus and Fellow Eyes with a Scheimpflug-based Tonometer.

SIGNIFICANCE: Before the appearance of evident keratoconus, corneal biomechanical changes may be detectable. Here, these properties are analyzed to detect any difference that could help in the early recognition of keratoconus to allow patients to benefit from early treatments and to avoid refractive procedures in these corneas. PURPOSE: The purpose of this study was to compare corneal biomechanical characteristics as determined by Corvis Scheimpflug Technology tonometry between normal eyes and asymmetric keratoconic eyes. METHODS: Retrospective data from normal eyes (n = 100), keratoconic eyes (n = 18), and their topographically normal fellow eyes (n = 18) were analyzed. Differences in the variables among the groups were determined. For the parameters that showed significant differences, the receiver operating characteristic curve and the area under the curve (AUC) were used to assess the diagnostic accuracy of each variable. The optimal cutoff points were determined when comparing normal and fellow eyes. Also, a new linear combination of variables was performed to obtain better discriminative values. RESULTS: The following variables differed significantly between normal and fellow eyes: length of the flattened cornea in the second applanation, peak distance, curvature radius at highest concavity, and central corneal thickness. When each variable was independently considered, AUCs, sensitivity, and specificity were insufficiently high for good discrimination between the two groups. However, using a linear combination of variables, an optimal cutoff point (0.157) was obtained with an AUC of 0.78, sensitivity of 0.84, and specificity of 0.69. CONCLUSIONS: A best predictive linear combination of corneal biomechanical variables was tested including diameter of the flattened cornea in the second applanation and central corneal thickness. This combination was considered as the best in terms of its prediction capacity, simplicity and clinical application. This formula may be useful in clinical practice to discriminate between normal eyes and incipient keratoconus.

Studying the relationship between a woman's reproductive lifespan and age at menarche using a Bayesian multivariate structured additive distributional regression model.

Studies addressing breast cancer risk factors have been looking at trends relative to age at menarche and menopause. These studies point to a downward trend of age at menarche and an upward trend for age at menopause, meaning an increase of a woman's reproductive lifespan cycle. In addition to studying the effect of the year of birth on the expectation of age at menarche and a woman's reproductive lifespan, it is important to understand how a woman's cohort affects the correlation between these two variables. Since the behavior of age at menarche and menopause may vary with the geographic location of a woman's residence, the spatial effect of the municipality where a woman resides needs to be considered. Thus, a Bayesian multivariate structured additive distributional regression model is proposed in order to analyze how a woman's municipality and year of birth affects a woman's age of menarche, her lifespan cycle, and the correlation of the two. The data consists of 212,517 postmenopausal women, born between 1920 and 1965, who attended the breast cancer screening program in the central region of Portugal.

Two-stage model for multivariate longitudinal and survival data with application to nephrology research.

In many follow-up studies different types of outcomes are collected including longitudinal measurements and time-to-event outcomes. Commonly, it is of interest to study the association between them. Joint modeling approaches of a single longitudinal outcome and survival process have recently gained increasing attention from both frequentist and Bayesian perspective. However, in many studies several longitudinal biomarkers are of interest and instead of selecting one single biomarker, the relationships between all these outcomes and their association with survival needs to be investigated. Our motivating study comes from Peritoneal Dialysis Programme in Nephrology research from Nephrology Unit, CHP (Hospital de Santo António), Porto, Portugal in which the interest relies on the possible association between various biomarkers (calcium, phosphate, parathormone, and creatinine) and the patients' survival. To this aim, we propose a two-stage model-based approach for multivariate longitudinal and survival data that allowed us to study such complex association structure. The multivariate model suggested in this paper provided new insights in the area of nephrology research showing valid results in comparison with those models studying each longitudinal biomarker with survival separately.

Predicting the occurrence of wildfires with binary structured additive regression models.

Wildfires are one of the main environmental problems facing societies today, and in the case of Galicia (north-west Spain), they are the main cause of forest destruction. This paper used binary structured additive regression (STAR) for modelling the occurrence of wildfires in Galicia. Binary STAR models are a recent contribution to the classical logistic regression and binary generalized additive models. Their main advantage lies in their flexibility for modelling non-linear effects, while simultaneously incorporating spatial and temporal variables directly, thereby making it possible to reveal possible relationships among the variables considered. The results showed that the occurrence of wildfires depends on many covariates which display variable behaviour across space and time, and which largely determine the likelihood of ignition of a fire. The joint possibility of working on spatial scales with a resolution of 1 × 1 km cells and mapping predictions in a colour range makes STAR models a useful tool for plotting and predicting wildfire occurrence. Lastly, it will facilitate the development of fire behaviour models, which can be invaluable when it comes to drawing up fire-prevention and firefighting plans.

Confidence intervals for the symmetry point: an optimal cutpoint in continuous diagnostic tests.

Continuous diagnostic tests are often used for discriminating between healthy and diseased populations. For this reason, it is useful to select an appropriate discrimination threshold. There are several optimality criteria: the North-West corner, the Youden index, the concordance probability and the symmetry point, among others. In this paper, we focus on the symmetry point that maximizes simultaneously the two types of correct classifications. We construct confidence intervals for this optimal cutpoint and its associated specificity and sensitivity indexes using two approaches: one based on the generalized pivotal quantity and the other on empirical likelihood. We perform a simulation study to check the practical behaviour of both methods and illustrate their use by means of three real biomedical datasets on melanoma, prostate cancer and coronary artery disease.

Retinal nerve fiber layer thickness, brain atrophy, and disability in multiple sclerosis patients.

OBJECTIVE: To study the relationship between retinal nerve fiber layer (RNFL) thickness and brain atrophy using magnetic resonance imaging (MRI) with bicaudate ratio (BCR) in patients with multiple sclerosis (MS) with different levels of disease severity. We also assessed whether RNFL thickness correlated with Expanded Disability Status Scale (EDSS) score. METHODS: The participants consisted of 88 patients with MS and 59 age- and sex-matched healthy control subjects. Eleven patients had clinically isolated syndrome (CIS), 68 patients had relapsing-remitting MS (RR-MS), and 9 patients had secondary progressive MS. Patients and controls were evaluated using optical coherence tomography (OCT, Cirrus) and scanning laser polarimetry with variable corneal compensation (GDx VCC). Patients underwent the same brain MRI scanning protocol. Disability was evaluated according to the EDSS. The BCR was calculated by dividing the minimum intercaudate distance by brain width along the same level. RESULTS: The BCR was higher in patients with MS (0.12 ± 0.03) than in controls (0.08 ± 0.009) (P < 0.001). OCT average RNFL thickness in patients with MS was significantly lower (84.51 ± 14.27 µm) than in control subjects (98.44 ± 6.83 µm). BCR was correlated with OCT average RNFL thickness (r = -0.48, P = 0.002) in patients with MS without optic neuritis. Significant correlations were found between average RNFL thickness and EDSS (r = -0.43, P = 0.003). Additionally, there were correlations between BCR with GDx parameters in patients with MS without optic neuritis. CONCLUSIONS: This study shows that RNFL thickness correlates with BCR and with MS subtypes. Additionally, our study indicates that OCT is better suited for MS assessment than GDx. We conclude that the damage of retinal axons appears related to brain damage in patients with MS.

Structured additive regression modeling of age of menarche and menopause in a breast cancer screening program.

Breast cancer risk is believed to be associated with several reproductive factors, such as early menarche and late menopause. This study is based on the registries of the first time a woman enters the screening program, and presents a spatio-temporal analysis of the variables age of menarche and age of menopause along with other reproductive and socioeconomic factors. The database was provided by the Portuguese Cancer League (LPCC), a private nonprofit organization dealing with multiple issues related to oncology of which the Breast Cancer Screening Program is one of its main activities. The registry consists of 259,652 records of women who entered the screening program for the first time between 1990 and 2007 (45-69-year age group). Structured Additive Regression (STAR) models were used to explore spatial and temporal correlations with a wide range of covariates. These models are flexible enough to deal with a variety of complex datasets, allowing us to reveal possible relationships among the variables considered in this study. The analysis shows that early menarche occurs in younger women and in municipalities located in the interior of central Portugal. Women living in inland municipalities register later ages for menopause, and those born in central Portugal after 1933 show a decreasing trend in the age of menopause. Younger ages of menarche and late menopause are observed in municipalities with a higher purchasing power index. The analysis performed in this study portrays the time evolution of the age of menarche and age of menopause and their spatial characterization, adding to the identification of factors that could be of the utmost importance in future breast cancer incidence research.

Extending induced ROC methodology to the functional context.

The receiver operating characteristic (ROC) curve is the most widely used measure for evaluating the discriminatory performance of a continuous marker. Often, covariate information is also available and several regression methods have been proposed to incorporate covariate information in the ROC framework. Until now, these methods are only developed for the case where the covariate is univariate or multivariate. We extend ROC regression methodology for the case where the covariate is functional rather than univariate or multivariate. To this end, semiparametric- and nonparametric-induced ROC regression estimators are proposed. A simulation study is performed to assess the performance of the proposed estimators. The methods are applied to and motivated by a metabolic syndrome study in Galicia (NW Spain).

Model building in nonproportional hazard regression.

Recent developments of statistical methods allow for a very flexible modeling of covariates affecting survival times via the hazard rate, including also the inspection of possible time-dependent associations. Despite their immediate appeal in terms of flexibility, these models typically introduce additional difficulties when a subset of covariates and the corresponding modeling alternatives have to be chosen, that is, for building the most suitable model for given data. This is particularly true when potentially time-varying associations are given. We propose to conduct a piecewise exponential representation of the original survival data to link hazard regression with estimation schemes based on of the Poisson likelihood to make recent advances for model building in exponential family regression accessible also in the nonproportional hazard regression context. A two-stage stepwise selection approach, an approach based on doubly penalized likelihood, and a componentwise functional gradient descent approach are adapted to the piecewise exponential regression problem. These three techniques were compared via an intensive simulation study. An application to prognosis after discharge for patients who suffered a myocardial infarction supplements the simulation to demonstrate the pros and cons of the approaches in real data analyses.

A cross-country study on the impact of governmental responses to the COVID-19 pandemic on perinatal mental health.

This study aimed to analyse the role of governmental responses to the coronavirus disease 2019 (COVID-19) outbreak, measured by the Containment and Health Index (CHI), on symptoms of anxiety and depression during pregnancy and postpartum, while considering the countries' Inequality-adjusted Human Development Index (IHDI) and individual factors such as age, gravidity, and exposure to COVID-19. A cross-sectional study using baseline data from the Riseup-PPD-COVID-19 observational prospective international study (ClinicalTrials.gov: NCT04595123) was carried out between June and October 2020 in 12 countries (Albania, Brazil, Bulgaria, Chile, Cyprus, Greece, Israel, Malta, Portugal, Spain, Turkey, and the United Kingdom). Participants were 7645 pregnant women or mothers in the postpartum period-with an infant aged up to 6 months-who completed the Edinburgh Postnatal Depression Scale (EPDS) or the Generalised Anxiety Disorder Assessment (GAD-7) during pregnancy or the postpartum period. The overall prevalence of clinically significant depression symptoms (EPDS ≥ 13) was 30%, ranging from 20,5% in Cyprus to 44,3% in Brazil. The prevalence of clinically significant anxiety symptoms (GAD-7 ≥ 10) was 23,6% (ranging from 14,2% in Israel and Turkey to 39,5% in Brazil). Higher symptoms of anxiety or depression were observed in multigravida exposed to COVID-19 or living in countries with a higher number of deaths due to COVID-19. Furthermore, multigravida from countries with lower IHDI or CHI had higher symptoms of anxiety and depression. Perinatal mental health is context-dependent, with women from more disadvantaged countries at higher risk for poor mental health. Implementing more restrictive measures seems to be a protective factor for mental health, at least in the initial phase of the COVID-19.

Longitudinal relationship of liver injury with inflammation biomarkers in COVID-19 hospitalized patients using a joint modeling approach.

The mechanisms underlying liver disease in patients with COVID-19 are not entirely known. The aim is to investigate, by means of novel statistical techniques, the changes over time in the relationship between inflammation markers and liver damage markers in relation to survival in COVID-19. The study included 221 consecutive patients admitted to the hospital during the first COVID-19 wave in Spain. Generalized additive mixed models were used to investigate the influence of time and inflammation markers on liver damage markers in relation to survival. Joint modeling regression was used to evaluate the temporal correlations between inflammation markers (serum C-reactive protein [CRP], interleukin-6, plasma D-dimer, and blood lymphocyte count) and liver damage markers, after adjusting for age, sex, and therapy. The patients who died showed a significant elevation in serum aspartate transaminase (AST) and alkaline phosphatase levels over time. Conversely, a decrease in serum AST levels was observed in the survivors, who showed a negative correlation between inflammation markers and liver damage markers (CRP with serum AST, alanine transaminase [ALT], and gamma-glutamyl transferase [GGT]; and D-dimer with AST and ALT) after a week of hospitalization. Conversely, most correlations were positive in the patients who died, except lymphocyte count, which was negatively correlated with AST, GGT, and alkaline phosphatase. These correlations were attenuated with age. The patients who died during COVID-19 infection displayed a significant elevation of liver damage markers, which is correlated with inflammation markers over time. These results are consistent with the role of systemic inflammation in liver damage during COVID-19.

Assessing neural activity related to decision-making through flexible odds ratio curves and their derivatives.

It is well established that neural activity is stochastically modulated over time. Therefore, direct comparisons across experimental conditions and determination of change points or maximum firing rates are not straightforward. This study sought to compare temporal firing probability curves that may vary across groups defined by different experimental conditions. Odds-ratio (OR) curves were used as a measure of comparison, and the main goal was to provide a global test to detect significant differences of such curves through the study of their derivatives. An algorithm is proposed that enables ORs based on generalized additive models, including factor-by-curve-type interactions to be flexibly estimated. Bootstrap methods were used to draw inferences from the derivatives curves, and binning techniques were applied to speed up computation in the estimation and testing processes. A simulation study was conducted to assess the validity of these bootstrap-based tests. This methodology was applied to study premotor ventral cortex neural activity associated with decision-making. The proposed statistical procedures proved very useful in revealing the neural activity correlates of decision-making in a visual discrimination task.

Offering, participation and adherence to cardiac rehabilitation programmes in the elderly: a European comparison based on the EU-CaRE multicentre observational study.

AIMS: Cardiac rehabilitation (CR) is strongly recommended but participation of elderly patients has not been well characterized. This study aims to analyse current rates and determinants of CR referral, participation, adherence, and compliance in a contemporary European cohort of elderly patients. METHODS AND RESULTS: The EU-CaRE observational study included data from consecutive patients aged ≥ 65 with acute coronary syndrome, revascularization, stable coronary artery disease, or heart valve replacement, recruited in eight European centres. Rates and factors determining offering, participation, and adherence to CR programmes and compliance with training sessions were studied across centres, under consideration of extensive-outpatient vs. intensive-inpatient programmes. Three thousand, four hundred, and seventy-one patients were included in the offering and participation analysis. Cardiac rehabilitation was offered to 80.8% of eligible patients, formal contraindications being the main reason for not offering CR. Mean participation was 68.0%, with perceived lack of usefulness and transport issues being principal barriers. Mean adherence to CR programmes of participants in the EU-CaRE study (n = 1663) was 90.3%, with hospitalization/physical impairment as principal causes of dropout. Mean compliance with training sessions was 86.1%. Older age was related to lower offering and participation, and comorbidity was associated with lower offering, participation, adherence, and compliance. Intensive-inpatient programmes displayed higher adherence (97.1% vs. 85.9%, P < 0.001) and compliance (full compliance: 66.0% vs. 38.8%, P < 0.001) than extensive-outpatient programmes. CONCLUSION: In this European cohort of elderly patients, older age and comorbidity tackled patients' referral and uptake of CR programmes. Intensive-inpatient CR programmes showed higher completion than extensive-outpatient CR programmes, suggesting this formula could suit some elderly patients.

Patterns of monogenean abundance in native and invasive populations of Planiliza haematocheila (Teleostei: Mugilidae): interactions between climate and host defence mechanisms explain parasite release.

One of the most intriguing questions in invasive biology is how an introduced species changes its population parameters in a new environment. Translocation of free-living species commonly results in co-introduction of their parasites. The current study focuses on the abundance pattern of the monogenean parasite Ligophorus llewellyni of the pacific so-iuy mullet, Planiliza haematocheila, across the native and introduced distribution ranges. We evaluated parasite release by the so-iuy mullet by comparing abundance patterns of L. llewellyni under effects of the host length, water temperature and month of the year in the Sea of Japan and the Sea of Azov. Generalised additive models applied to analysis of parasite abundance data showed that relationships between the mean number of L. llewellyni and the three tested independent variables were not linear. Our results suggest that the introduced host lost a large amount of parasite abundance due to the effect of warm climate in a new region, which is mediated by host defence mechanisms. The abundance of L. llewellyni rapidly rose in autumn, as fish activity and immune response decrease, reached the maximum in winter and began to fall in spring as a warm temperature facilitates the fish immune defence. The abundance of L. llewellyni showed an initial increase in response to fish growth and reached an asymptote. The response curves built for native and introduced regions reached an asymptote at different fish body lengths, reflecting the fish growth rate, which is higher in the introduced range of P. haematocheila. We found that the carried parasite species holds the same trend in relationships compared with its native area, between the mean number of monogeneans per host and independent variables increasing abundance with fish length, low temperature and cold months. Our results open new perspectives for future research on statistical modelling of parasite abundance across native and introduced distribution ranges in order to provide deeper insight into host-parasite interactions of invasive populations.

Improvement in VO2peak predicts readmissions for cardiovascular disease and mortality in patients undergoing cardiac rehabilitation.

BACKGROUND: Improvement in exercise capacity is a main goal of cardiac rehabilitation but the effects are often lost at long-term follow-up and thus also the benefits on prognosis. We assessed whether improvement in VO2peak during a cardiac rehabilitation programme predicts long-term prognosis. METHODS AND RESULTS: We performed a retrospective analysis of 1561 cardiac patients completing cardiac rehabilitation in 2011-2017 in Copenhagen. Mean age was 63.6 (11) years, 74% were male and 84% had coronary artery disease, 6% chronic heart failure and 10% heart valve replacement. The association between baseline VO2peak and improvement after cardiac rehabilitation and being readmitted for cardiovascular disease and/or all-cause mortality was assessed with three different analyses: Cox regression for the combined outcome, for all-cause mortality and a multi-state model. During a median follow-up of 2.3 years, 167 readmissions for cardiovascular disease and 77 deaths occurred. In adjusted Cox regression there was a non-linear decreasing risk of the combined outcome with higher baseline VO2peak and with improvement of VO2peak after cardiac rehabilitation. A similar linear association was seen for all-cause mortality. Applying the multi-state model, baseline VO2peak and change in VO2peak were associated with risk of a cardiovascular disease readmission and with all-cause mortality but not with mortality in those having an intermediate readmission for cardiovascular disease. CONCLUSION: VO2peak as well as change in VO2peak were highly predictive of future risk of readmissions for cardiovascular disease and all-cause mortality. The predictive value did not extend beyond the next admission for a cardiovascular event.

Volumetric Absorptive Microsampling (VAMS) for assaying immunosuppressants from venous whole blood by LC-MS/MS using a novel atmospheric pressure ionization probe (UniSpray™).

Therapeutic drug monitoring (TDM) of immunosuppressants (IMS) is crucial to prevent rejection or toxicity after solid organ transplantation. Microsampling techniques (sampling <50 µL of blood) can be a good alternative to conventional venous sampling for TDM, due to their numerous advantages, including its easy and low-invasive sampling, enabling self-collection, and cost-saving shipment and storage. Furthermore, volumetric absorptive microsampling (VAMS) enables the collection of precise and accurate blood volumes, overcoming the hematocrit (Hct) effect related to dried blood spots, while offering the same benefits. In this work, an LC-MS/MS method for the determination of the 5 most common IMS (mycophenolic acid -MPA-, tacrolimus -TAC-, sirolimus -SIR-, everolimus -EVE- and cyclosporin A -CsA-) in venous blood collected with Mitra™ VAMS devices was developed and validated, employing a novel LC-MS/MS interface, Unispray™. The method was fully validated including linearity, limits of detection (LOD) and quantification (LLOQ), accuracy, precision, selectivity, carry-over, matrix effect, recovery, impact of Hct on recovery and autosampler and short-/long-term stability, satisfying acceptance criteria in all cases. LLOQs were 0.5 ng/mL for TAC, SIR and EVE, 20 ng/mL for CsA and 75 ng/mL for MPA. No impact of the Hct (range: 0.2 to 0.62 L/L) on recovery was found for any analyte. All compounds were stable in VAMS for at least 8 months at -20 °C. In addition, as part of the VAMS analytical method validation, we performed for the first time a broad statistical study to compare liquid venous blood concentrations from patients under TAC (n = 53) and MPA (n = 20) treatment to those observed when the same specimens were absorbed into VAMS. Our results showed that venous blood VAMS concentrations were correlated to those found in the original liquid venous blood, proving that the VAMS material itself will not bias blood drug concentrations. Therefore, the present method could be applied to evaluate possible correlations between venous blood and capillary blood collected with VAMS.