A competing risks model to estimate the risk of graft failure and patient death after kidney transplantation using continuous donor-recipient age combinations.

Graft failure and recipient death with functioning graft are important competing outcomes after kidney transplantation. Risk prediction models typically censor for the competing outcome thereby overestimating the cumulative incidence. The magnitude of this overestimation is not well-described in real-world transplant data. This retrospective cohort study analyzed data from the European Collaborative Transplant Study (CTS; n = 125 250) and from the American Scientific Registry of Transplant Recipients (SRTR; n = 190 258). Separate cause-specific hazard models, using donor and recipient age as continuous predictors, were developed for graft failure and recipient death. The hazard of graft failure increased quadratically with increasing donor age and decreased decaying with increasing recipient age. The hazard of recipient death increased linearly with increasing donor and recipient age. The cumulative incidence overestimation due to competing risk-censoring was largest in high-risk populations for both outcomes (old donors/recipients), sometimes amounting to 8.4 and 18.8 percentage points for graft failure and recipient death, respectively. In our illustrative model for post-transplant risk prediction, the absolute risk of graft failure and death is overestimated when censoring for the competing event, mainly in older donors and recipients. Prediction models for absolute risks should treat graft failure and death as competing events.

Associations of Urine and Plasma Metabolites with Kidney Failure and Death in a CKD Cohort.

RATIONALE & OBJECTIVE: Biomarkers that enable better identification of persons with chronic kidney disease (CKD) who are at higher risk for disease progression and adverse events are needed. This study sought to identify urine and plasma metabolites associated with progression of kidney disease. STUDY DESIGN: Prospective metabolome-wide association study. SETTING & PARTICIPANTS: Persons with CKD enrolled in the German CKD Study (GCKD) with metabolite measurements; with external validation within the Atherosclerosis Risk in Communities Study. EXPOSURES: 1,513 urine and 1,416 plasma metabolites (Metabolon, Inc.) measured at study entry using untargeted mass spectrometry. OUTCOMES: Main endpoints were kidney failure (KF), and a composite endpoint of KF, eGFR <15 mL/min/1.73m2, or 40% decline in eGFR (CKE). Death from any cause was a secondary endpoint. After a median of 6.5 years follow-up, 500 persons experienced KF, 1,083 experienced CKE and 680 died. ANALYTICAL APPROACH: Time-to-event analyses using multivariable proportional hazard regression models in a discovery-replication design, with external validation. RESULTS: 5,088 GCKD participants were included in analyses of urine metabolites and 5,144 in analyses of plasma metabolites. Among 182 unique metabolites, 30 were significantly associated with KF, 49 with CKE, and 163 with death. The strongest association with KF was observed for plasma hydroxyasparagine (hazard ratio: 1.95, 95% confidence interval: 1.68-2.25). An unnamed metabolite measured in plasma and urine was significantly associated with KF, CKE, and death. External validation of the identified associations of metabolites with KF or CKE revealed direction-consistency for 88% of observed associations. Selected associations of 18 metabolites with study outcomes have not been previously reported. LIMITATIONS: Use of observational data and semi-quantitative metabolite measurements at a single time point. CONCLUSIONS: The observed associations between metabolites and KF, CKE or death in persons with CKD confirmed previously reported findings and also revealed several associations not previously described. These findings warrant confirmatory research in other study cohorts.

Bayesian survival analysis with INLA.

This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article "Bayesian survival analysis with BUGS." In addition, we illustrate the implementation of a new joint model for a longitudinal semicontinuous marker, recurrent events, and a terminal event. Our proposal aims to provide the reader with syntax examples for implementing survival models using a fast and accurate approximate Bayesian inferential approach.

An augmented illness-death model for semi-competing risks with clinically immediate terminal events.

Preeclampsia is a pregnancy-associated condition posing risks of both fetal and maternal mortality and morbidity that can only resolve following delivery and removal of the placenta. Because in its typical form preeclampsia can arise before delivery, but not after, these two events exemplify the time-to-event setting of "semi-competing risks" in which a non-terminal event of interest is subject to the occurrence of a terminal event of interest. The semi-competing risks framework presents a valuable opportunity to simultaneously address two clinically meaningful risk modeling tasks: (i) characterizing risk of developing preeclampsia, and (ii) characterizing time to delivery after onset of preeclampsia. However, some people with preeclampsia deliver immediately upon diagnosis, while others are admitted and monitored for an extended period before giving birth, resulting in two distinct trajectories following the non-terminal event, which we call "clinically immediate" and "non-immediate" terminal events. Though such phenomena arise in many clinical contexts, to-date there have not been methods developed to acknowledge the complex dependencies between such outcomes, nor leverage these phenomena to gain new insight into individualized risk. We address this gap by proposing a novel augmented frailty-based illness-death model with a binary submodel to distinguish risk of immediate terminal event following the non-terminal event. The model admits direct dependence of the terminal event on the non-terminal event through flexible regression specification, as well as indirect dependence via a shared frailty term linking each submodel. We develop an efficient Bayesian sampler for estimation and corresponding model fit metrics, and derive formulae for dynamic risk prediction. In an extended example using pregnancy outcome data from an electronic health record, we demonstrate the proposed model's direct applicability to address a broad range of clinical questions.

A nonparametric proportional risk model to assess a treatment effect in time-to-event data.

Time-to-event analysis often relies on prior parametric assumptions, or, if a semiparametric approach is chosen, Cox's model. This is inherently tied to the assumption of proportional hazards, with the analysis potentially invalidated if this assumption is not fulfilled. In addition, most interpretations focus on the hazard ratio, that is often misinterpreted as the relative risk (RR), the ratio of the cumulative distribution functions. In this paper, we introduce an alternative to current methodology for assessing a treatment effect in a two-group situation, not relying on the proportional hazards assumption but assuming proportional risks. Precisely, we propose a new nonparametric model to directly estimate the RR of two groups to experience an event under the assumption that the risk ratio is constant over time. In addition to this relative measure, our model allows for calculating the number needed to treat as an absolute measure, providing the possibility of an easy and holistic interpretation of the data. We demonstrate the validity of the approach by means of a simulation study and present an application to data from a large randomized controlled trial investigating the effect of dapagliflozin on all-cause mortality.

Investigating the Shape and Strength of the Relationship Between Maternal Weight Gain and Gestational Age at Delivery in Twin and Singleton Pregnancies.

Both inadequate and excessive maternal weight gain are correlated with preterm delivery in singleton pregnancies, yet this relationship has not been adequately studied in twins. We investigated the relationship between time-varying maternal weight gain and gestational age at delivery in twin pregnancies and compared it with that in singletons delivered in the same study population. We used serial weight measurements abstracted from charts for twin and singleton pregnancies delivered during 1998-2013 in Pittsburgh, Pennsylvania. Our exposure was time-varying weight gain z score, calculated using gestational age-standardized and prepregnancy body mass index-stratified twin- and singleton-specific charts, and our outcome was gestational age at delivery. Our analyses used a flexible extension of the Cox proportional hazards model that allowed for nonlinear and time-dependent effects. We found a U-shaped relationship between weight gain z score and gestational age at delivery among twin pregnancies (lowest hazard of delivery observed at z score = 1.2), which we attributed to increased hazard of early preterm spontaneous delivery among pregnancies with low weight gain and increased hazard of late preterm delivery without labor among pregnancies with high weight gain. Our findings may be useful for updating provisional guidelines for maternal weight gain in twin pregnancies.

The Duke Myasthenia Gravis Clinic Registry: II. Analysis of outcomes.

INTRODUCTION/AIMS: The Duke Myasthenia Gravis (MG) Clinic Registry contains comprehensive physician-derived data on patients with MG seen in the Duke MG Clinic since 1980. The aim of this study was to report outcomes in patients seen in the clinic and treated according to the International Consensus Guidance statements. METHODS: This is a retrospective cohort study of patients initially seen after 2000 and followed for at least 2 years in the clinic. Treatment goal (TG) was defined as achieving MGFA post-intervention status of "minimal manifestations" or better; PIS was determined by the treating neurologist. Time-to-event analysis, including Cox proportional hazards modeling, was performed to assess the effect of sex, acetylcholine receptor antibody (AChR-Ab) status, age at disease onset, distribution (ocular vs generalized), thymectomy, and thymoma on the time to achieve TG. RESULTS: Among the 367 cohort patients, 72% achieved TG (median time less than 2 years). A greater proportion of patients with AChR-Abs and thymectomy achieved TG and they did so sooner than patients without these antibodies or thymectomy. Otherwise, there were no significant differences in these findings within the tested subgroups. The disease duration at the first Duke Clinic visit was shorter in patients who achieved TG than in those who did not. DISCUSSION: These results demonstrate outcomes that can be achieved in patients with MG treated according to the current Consensus Guidance statements. Among other things, they can be used to determine the added value and potential role of new treatment modalities developed since 2018.

Penalized estimation of frailty-based illness-death models for semi-competing risks.

Semi-competing risks refer to the time-to-event analysis setting, where the occurrence of a non-terminal event is subject to whether a terminal event has occurred, but not vice versa. Semi-competing risks arise in a broad range of clinical contexts, including studies of preeclampsia, a condition that may arise during pregnancy and for which delivery is a terminal event. Models that acknowledge semi-competing risks enable investigation of relationships between covariates and the joint timing of the outcomes, but methods for model selection and prediction of semi-competing risks in high dimensions are lacking. Moreover, in such settings researchers commonly analyze only a single or composite outcome, losing valuable information and limiting clinical utility-in the obstetric setting, this means ignoring valuable insight into timing of delivery after preeclampsia has onset. To address this gap, we propose a novel penalized estimation framework for frailty-based illness-death multi-state modeling of semi-competing risks. Our approach combines non-convex and structured fusion penalization, inducing global sparsity as well as parsimony across submodels. We perform estimation and model selection via a pathwise routine for non-convex optimization, and prove statistical error rate results in this setting. We present a simulation study investigating estimation error and model selection performance, and a comprehensive application of the method to joint risk modeling of preeclampsia and timing of delivery using pregnancy data from an electronic health record.

Differential impact of missed initial wound clinic visit on 6-month wound healing by race/ethnicity among patients with chronic limb-threatening ischemia.

Chronic limb-threatening ischemia (CLTI) is associated with significant morbidity, including major limb amputation, and mortality. Healing ischemic wounds is necessary to optimise vascular outcomes and can be facilitated by dedicated appointments at a wound clinic. This study aimed to estimate the association between successful wound care initiation and 6-month wound healing, with specific attention to differences by race/ethnicity. This retrospective study included 398 patients with CLTI and at least one ischaemic wound who scheduled an appointment at our wound clinic between January 2015 and July 2020. The exposure was the completion status of patients' first scheduled wound care appointment (complete/not complete) and the primary outcome was 6-month wound healing (healed/not healed). The analysis focused on how this association was modified by race/ethnicity. We used Aalen-Johansen estimators to produce cumulative incidence curves and calculated risk ratios within strata of race/ethnicity. The final adjustment set included age, revascularization, and initial wound size. Patients had a mean age of 67 ± 14 years, were 41% female, 46% non-White and had 517 total wounds. In the overall cohort, 70% of patients completed their first visit and 34% of wounds healed within 6-months. There was no significant difference in 6-month healing based on first visit completion status for White/non-Hispanic individuals (RR [95% CI] = 1.18 [0.91, 1.45]; p-value = 0.130), while non-White individuals were roughly 3 times more likely to heal their wounds if they completed their first appointment (RR [95% CI] = 2.89 [2.66, 3.11]; p-value < 0.001). In conclusion, non-White patients were approximately three times more likely to heal their wound in 6 months if they completed their first scheduled wound care appointment while White/non-Hispanic individuals' risk of healing was similar regardless of first visit completion status. Future efforts should focus on providing additional resources to ensure minority groups with wounds have the support they need to access and successfully initiate wound care.

Temporal dynamics of sitting behavior at work.

Sitting for prolonged periods of time impairs people's health. Prior research has mainly investigated sitting behavior on an aggregate level, for example, by analyzing total sitting time per day. By contrast, taking a dynamic approach, here we conceptualize sitting behavior as a continuous chain of sit-to-stand and stand-to-sit transitions. We use multilevel time-to-event analysis to analyze the timing of these transitions. We analyze ∼30,000 objectively measured posture transitions from 156 people during work time. Results indicate that the temporal dynamics of sit-to-stand transitions differ from stand-to-sit transitions, and that people are quicker to switch postures later in the workday, and quicker to stand up after having been more active in the recent hours. We found no evidence for associations with physical fitness. Altogether, these findings provide insights into the origins of people's stand-up and sit-down decisions, show that sitting behavior is fundamentally different from exercise behavior, and provide pointers for the development of interventions.

A parametric model to jointly characterize rate, duration, and severity of exacerbations in episodic diseases.

BACKGROUND: The natural history of many chronic diseases is characterized by periods of increased disease activity, commonly referred to as flare-ups or exacerbations. Accurate characterization of the burden of these exacerbations is an important research objective. METHODS: The purpose of this work was to develop a statistical framework for nuanced characterization of the three main features of exacerbations: their rate, duration, and severity, with interrelationships among these features being a particular focus. We jointly specified a zero-inflated accelerated failure time regression model for the rate, an accelerated failure time regression model for the duration, and a logistic regression model for the severity of exacerbations. Random effects were incorporated into each component to capture heterogeneity beyond the variability attributable to observed characteristics, and to describe the interrelationships among these components. RESULTS: We used pooled data from two clinical trials in asthma as an exemplary application to illustrate the utility of the joint modeling approach. The model fit clearly indicated the presence of heterogeneity in all three components. A novel finding was that the new therapy reduced not just the rate but also the duration of exacerbations, but did not have a significant impact on their severity. After controlling for covariates, exacerbations among more frequent exacerbators tended to be shorter and less likely to be severe. CONCLUSIONS: We conclude that a joint modeling framework, programmable in available software, can provide novel insights about how the rate, duration, and severity of episodic events interrelate, and enables consistent inference on the effect of treatments on different disease outcomes. Trial registration Ethics approval was obtained from the University of British Columbia Human Ethics Board (H17-00938).

DAFT: A universal module to interweave tabular data and 3D images in CNNs.

Prior work on Alzheimer's Disease (AD) has demonstrated that convolutional neural networks (CNNs) can leverage the high-dimensional image information for diagnosing patients. Beside such data-driven approaches, many established biomarkers exist and are typically represented as tabular data, such as demographics, genetic alterations, or laboratory measurements from cerebrospinal fluid. However, little research has focused on the effective integration of tabular data into existing CNN architectures to improve patient diagnosis. We introduce the Dynamic Affine Feature Map Transform (DAFT), a general-purpose module for CNNs that incites or represses high-level concepts learned from a 3D image by conditioning feature maps of a convolutional layer on both a patient's image and tabular clinical information. This is achieved by using an auxiliary neural network that outputs a scaling factor and offset to dynamically apply an affine transformation to the feature maps of a convolutional layer. In our experiments on AD diagnosis and time-to-dementia prediction, we show that the DAFT is highly effective in combining 3D image and tabular information by achieving a mean balanced accuracy of 0.622 for diagnosis, and mean c-index of 0.748 for time-to-dementia prediction, thus outperforming all baseline methods. Finally, our extensive ablation study and empirical experiments reveal that the performance improvement due to the DAFT is robust with respect to many design choices.

Fenfluramine significantly reduces day-to-day seizure burden by increasing number of seizure-free days and time between seizures in patients with Dravet syndrome: A time-to-event analysis.

OBJECTIVE: The number, unpredictability, and severity of seizures experienced by patients with Dravet syndrome (DS) negatively impact quality of life (QOL) for patients, caregivers, and families. Metrics are needed to assess whether patients with residual seizures have moved meaningfully toward seizure freedom after treatment with new antiseizure medications. METHODS: We evaluated the time required postrandomization for each patient to experience the same number of seizures experienced during baseline (i.e., time-to-nth seizure), using a post hoc time-to-event (TTE) analysis of data from two Phase 3 placebo-controlled trials of adjunctive fenfluramine for DS (Study 1, N = 119; Study 2, N = 87). Patients aged 2-19 years were randomized to placebo or adjunctive fenfluramine (Study 1: .7 mg/kg/day or .2 mg/kg/day; Study 2: .4 mg/kg/day with stiripentol). Data were analyzed by Kaplan-Meier TTE curves and waterfall plots. RESULTS: The proportion of patients who never reached baseline seizure frequency was greater with fenfluramine than with placebo (Study 1: fenfluramine .7 mg/kg/day, 60%; fenfluramine .2 mg/kg/day, 31%; placebo, 13%; Study 2: fenfluramine .4 mg/kg/day, 58%; placebo, 2%). Median time-to-nth seizure was longer after fenfluramine than after placebo (Study 1: fenfluramine .7 mg/kg/day, 13 weeks; .2 mg/kg/day, 10 weeks; placebo, 7 weeks; Study 2: fenfluramine .4 mg/kg/day, 13 weeks; placebo, 5 weeks; p < .001). Longest duration of convulsive seizure-free days was increased in active groups versus the placebo group (Study 1: fenfluramine .7 and .2 mg/kg/day, 25.0 and 15.0 days; placebo, 9.5 days [p = .0001; p = .0352]; Study 2: fenfluramine .4 mg/kg/day, 22.0 days; placebo, 13.0 days [p = .004]). The most common adverse events included decreased appetite, pyrexia, upper respiratory tract infection, diarrhea, and fatigue. SIGNIFICANCE: These data demonstrate that fenfluramine can significantly reduce day-to-day seizure burden in patients with DS, providing prolonged periods of convulsive seizure-free days, which may help reduce the physical and emotional disease toll while improving health-related QOL for patients and caregivers.

Sitting patterns in cardiovascular disease patients compared with healthy controls and impact of cardiac rehabilitation.

PURPOSE: To identify how and when to intervene in cardiovascular disease (CVD) patients' sedentary behavior, we moved beyond studying total volume of sitting and examined sitting patterns. By analyzing the timing of stand-to-sit and sit-to-stand transitions, we compared sitting patterns (a) between CVD patients and healthy controls, and (b) before and after cardiac rehabilitation (CR). METHODS: One hundered twenty nine CVD patients and 117 age-matched healthy controls continuously wore a tri-axial thigh-worn accelerometer for 8 days (>120 000 posture transitions). CVD patients additionally wore the accelerometer directly and 2 months after CR. RESULTS: With later time of the day, both CVD patients and healthy controls sat down sooner (i.e., shorter standing episode before sitting down; HR = 1.01, 95% CI [1.011, 1.015]) and remained seated longer (HR = 0.97, CI [0.966, 0.970]). After more previous physical activity, both groups sat down later (HR = 0.97, CI [0.959, 0.977]), and patients remained seated longer (HR = 0.96; CI [0.950, 0.974]). Immediately and 2-months following CR, patients sat down later (HRpost-CR  = 0.96, CI [0.945, 0.974]; HRfollow-up  = 0.96, CI [0.948, 0.977]) and stood up sooner (HRpost-CR  = 1.04, CI [1.020, 1.051]; HRfollow-up  = 1.03, CI [1.018, 1.050]). These effects were less pronounced with older age, higher BMI, lower sedentary behavior levels, and/or higher physical activity levels at baseline. CONCLUSION: Cardiac rehabilitation programs could be optimized by targeting CVD patients' sit-to-stand transitions, by focusing on high-risk moments for prolonged sitting (i.e., in evenings and after higher-than-usual physical activity) and attending to the needs of specific patient subgroups.

Efficacy of the Zero Suicide framework in reducing recurrent suicide attempts: cross-sectional and time-to-recurrent-event analyses.

BACKGROUND: The Zero Suicide framework is a system-wide approach to prevent suicides in health services. It has been implemented worldwide but has a poor evidence-base of effectiveness. AIMS: To evaluate the effectiveness of the Zero Suicide framework, implemented in a clinical suicide prevention pathway (SPP) by a large public mental health service in Australia, in reducing repeated suicide attempts after an index attempt. METHOD: A total of 604 persons with 737 suicide attempt presentations were identified between 1 July and 31 December 2017. Relative risk for a subsequent suicide attempt within various time periods was calculated using cross-sectional analysis. Subsequently, a 10-year suicide attempt history (2009-2018) for the cohort was used in time-to-recurrent-event analyses. RESULTS: Placement on the SPP reduced risk for a repeated suicide attempt within 7 days (RR = 0.29; 95% CI 0.11-0.75), 14 days (RR = 0.38; 95% CI 0.18-0.78), 30 days (RR = 0.55; 95% CI 0.33-0.94) and 90 days (RR = 0.62; 95% CI 0.41-0.95). Time-to-recurrent event analysis showed that SPP placement extended time to re-presentation (HR = 0.65; 95% CI 0.57-0.67). A diagnosis of personality disorder (HR = 2.70; 95% CI 2.03-3.58), previous suicide attempt (HR = 1.78; 95% CI 1.46-2.17) and Indigenous status (HR = 1.46; 95% CI 0.98-2.25) increased the hazard for re-presentation, whereas older age decreased it (HR = 0.92; 95% CI 0.86-0.98). The effect of the SPP was similar across all groups, reducing the risk of re-presentation to about 65% of that seen in those not placed on the SPP. CONCLUSIONS: This paper demonstrates a reduction in repeated suicide attempts after an index attempt and a longer time to a subsequent attempt for those receiving multilevel care based on the Zero Suicide framework.

Bayesian survival analysis with BUGS.

Survival analysis is one of the most important fields of statistics in medicine and biological sciences. In addition, the computational advances in the last decades have favored the use of Bayesian methods in this context, providing a flexible and powerful alternative to the traditional frequentist approach. The objective of this article is to summarize some of the most popular Bayesian survival models, such as accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data. Moreover, an implementation of each presented model is provided using a BUGS syntax that can be run with JAGS from the R programming language. Reference to other Bayesian R-packages is also discussed.

Novel application of a discrete time-to-event model for randomized oral immunotherapy clinical trials with repeat food challenges.

The evaluation of double-blind, placebo-controlled food challenges (DBPCFC) generally focuses on a participant passing a challenge at a predetermined dose, and does not consider the dose of reaction for those who fail or are censored due to study discontinuation. Further, a number of food allergy trials have incorporated multiple DBPCFCs throughout the duration of the study in order to evaluate changes in reaction over time including sustained unresponsiveness from treatment. Outcomes arising from these trials are commonly modeled using Chi-squared or Fisher's exact tests at each time point. We propose applying time-to-event methodology to food allergy trials in order to exploit the inherent granularity of challenge outcomes that additionally accommodates repeated DBPCFCs. Specifically, we consider dose-to-failure for each study challenge and extend the cumulative tolerated dose across challenges to result in a dose-time axis. A discrete time-to-event framework is applied to the dose-time outcome to assess the efficacy of treatment across the entire study period. We illustrate ideas with data from the Peanut Oral Immunotherapy Study: Safety, Efficacy and Discovery (POISED) trial, conducted at Stanford University, which evaluated the efficacy of oral immunotherapy on desensitization and sustained unresponsiveness in peanut allergic children and adults. We demonstrate the advantages of time-to-event approaches for assessing the efficacy of treatment over time and incorporating information for those who failed or were lost to follow up. Further, we introduce a dose-time outcome that is interpretable to clinicians and allows for examination of such outcomes over time.

Living With Survival Analysis in Orthopedics.

Time to event data occur commonly in orthopedics research and require special methods that are often called "survival analysis." These data are complex because both a follow-up time and an event indicator are needed to correctly describe the occurrence of the outcome of interest. Common pitfalls in analyzing time to event data include using methods designed for binary outcomes, failing to check proportional hazards, ignoring competing risks, and introducing immortal time bias by using future information. This article describes the concepts involved in time to event analyses as well as how to avoid common statistical pitfalls. Please visit the followinghttps://youtu.be/QNETrx8B6IUandhttps://youtu.be/8SBoTr9Jy1Qfor videos that explain the highlights of the paper in practical terms.

Rotational thromboelastometry can predict the probability of bleeding events in a translational rat model of haemophilia A following gene-based FVIIa prophylaxis.

INTRODUCTION: Monitoring of clinical effectiveness of bypassing agents in haemophilia patients is hampered by the lack of validated laboratory assays. Thromboelastography (TEG) and rotational thromboelastometry (ROTEM) have been evaluated for predicting clinical effectiveness of bypassing agents, however, with limited success. AIM: Application of a longitudinal model-based approach may allow for a quantitative characterization of the link between ROTEM parameters and the probability of bleeding events. METHODS: We analyse longitudinal data from haemophilia A rats receiving gene-based FVIIa prophylaxis in terms of total circulatory levels of FVII/FVIIa, clotting time (CT) measured using ROTEM and the probability of bleeding events. RESULTS: Using population pharmacokinetic-pharmacodynamic (PKPD) modelling, a PK-CT-repeated time-to-event (RTTE) model was developed composed of three submodels (a) a FVII/FVIIa PK model, (b) a PK-CT model describing the relationship between predicted FVIIa expression and CT and (c) a RTTE model describing the probability of bleeding events as a function of CT. The developed PK-CT-RTTE model accurately described the vector dose-dependent plasma concentration-time profile of total FVII/FVIIa and the exposure-response relationship between AAV-derived FVIIa expression and CT. Importantly, the developed model accurately described the occurrence of bleeding events over time in a quantitative manner, revealing a linear relationship between predicted change from baseline CT and the probability of bleeding events. CONCLUSION: Using PK-CT-RTTE modelling, we demonstrated that ROTEM parameters can accurately predict the probability of bleeding events in a translational animal model of haemophilia A.

A time-to-event analysis of the exposure-response relationship for bezlotoxumab concentrations and CDI recurrence.

Bezlotoxumab is a monoclonal antibody approved for the prevention of recurrent Clostridium difficile infection (rCDI). In a previous exposure-response (E-R) analysis of bezlotoxumab exposure and rCDI, based on data from two phase 3 trials in participants who received placebo or bezlotoxumab 10 mg/kg, rCDI was treated as a binary endpoint and discontinued subjects were imputed as not having rCDI, resulting in an apparent positive E-R trend between rCDI rates and bezlotoxumab exposure. Therefore, a time-to-event (TTE) analysis was applied to investigate the E-R relationship, accounting for the time to rCDI occurrence and participant discontinuation. A TTE model, applying a time-dependent hazard function and right-censoring of data based on rCDI, discontinuation, or study end was developed. Exposure effects and covariates effects were evaluated as predictors affecting the hazard. The TTE model consisted of a Gompertz function with age, endogenous immunoglobulin G to C. difficile toxin B (IgG-B), history of CDI, hospitalization, sex, Charlson Comorbidity Index, and concomitant use of systemic antibiotics affecting the hazard. Exposure effects were characterized with a maximum effect (Emax) E-R relationship on the baseline parameter, and bezlotoxumab exposures achieved at the 10 mg/kg dose were found to be on the plateau of the E-R curve. Endogenous IgG-B significantly impacted the Emax, indicating that low-titer participants derive a greater benefit from bezlotoxumab treatment compared with high-titer participants. The results support the conclusions of the previous E-R analysis, where exposures achieved at the 10 mg/kg dose are on the plateau of the E-R curve.