Four-year clinical update and treatment switching-adjusted outcomes with first-line nivolumab plus ipilimumab with chemotherapy for metastatic non-small cell lung cancer in the CheckMate 9LA randomized trial.

BACKGROUND: In CheckMate 9LA, nivolumab plus ipilimumab with chemotherapy prolonged overall survival (OS) versus chemotherapy regardless of tumor PD-L1 expression or histology. We report updated efficacy and safety in all randomized patients with a minimum 4-year follow-up and an exploratory treatment-switching adjustment analysis in all treated patients who received chemotherapy and subsequent immunotherapy. METHODS: Adults with stage IV/recurrent non-small cell lung cancer (NSCLC), no sensitizing EGFR/ALK alterations, and ECOG performance status ≤1 were randomized 1:1 to nivolumab 360 mg every 3 weeks plus ipilimumab 1 mg/kg every 6 weeks with chemotherapy (two cycles) or chemotherapy (four cycles, with optional maintenance pemetrexed for the nonsquamous population). Assessments included OS, progression-free survival, and objective response rate. Exploratory analyses included efficacy by tumor PD-L1 expression and histology and in patients who discontinued nivolumab plus ipilimumab with chemotherapy due to treatment-related adverse events (TRAEs), and a treatment-switching adjustment analysis using inverse probability of censoring weighting. RESULTS: With a 47.9-month minimum follow-up for OS, nivolumab plus ipilimumab with chemotherapy continued to prolong OS over chemotherapy in all randomized patients (HR 0.74, 95% CI 0.63 to 0.87; 4-year OS rate: 21% versus 16%), regardless of tumor PD-L1 expression (HR (95% CI): PD-L1<1%, 0.66 (0.50 to 0.86) and ≥1%, 0.74 (0.60 to 0.92)) or histology (squamous, 0.64 (0.48 to 0.84) and non-squamous, 0.80 (0.66 to 0.97)). In patients who discontinued all components of nivolumab plus ipilimumab with chemotherapy due to TRAEs (n=61), the 4-year OS rate was 41%. With treatment-switching adjustment for the 36% of patients receiving subsequent immunotherapy in the chemotherapy arm, the estimated HR of nivolumab plus ipilimumab with chemotherapy versus chemotherapy was 0.66 (95% CI 0.55 to 0.80). No new safety signals were observed. CONCLUSIONS: In this 4-year update, patients treated with nivolumab plus ipilimumab with chemotherapy continued to have long-term, durable efficacy benefit over chemotherapy regardless of tumor PD-L1 expression and/or histology. A greater estimated relative OS benefit was observed after adjustment for subsequent immunotherapy use in the chemotherapy arm. These results further support nivolumab plus ipilimumab with chemotherapy as a first-line treatment for patients with metastatic/recurrent NSCLC, including those with tumor PD-L1<1% or squamous histology, populations with high unmet needs.

A cautionary tale: an evaluation of the performance of treatment switching adjustment methods in a real world case study.

BACKGROUND: Treatment switching in randomised controlled trials (RCTs) is a problem for health technology assessment when substantial proportions of patients switch onto effective treatments that would not be available in standard clinical practice. Often statistical methods are used to adjust for switching: these can be applied in different ways, and performance has been assessed in simulation studies, but not in real-world case studies. We assessed the performance of adjustment methods described in National Institute for Health and Care Excellence Decision Support Unit Technical Support Document 16, applying them to an RCT comparing panitumumab to best supportive care (BSC) in colorectal cancer, in which 76% of patients randomised to BSC switched onto panitumumab. The RCT resulted in intention-to-treat hazard ratios (HR) for overall survival (OS) of 1.00 (95% confidence interval [CI] 0.82-1.22) for all patients, and 0.99 (95% CI 0.75-1.29) for patients with wild-type KRAS (Kirsten rat sarcoma virus). METHODS: We tested several applications of inverse probability of censoring weights (IPCW), rank preserving structural failure time models (RPSFTM) and simple and complex two-stage estimation (TSE) to estimate treatment effects that would have been observed if BSC patients had not switched onto panitumumab. To assess the performance of these analyses we ascertained the true effectiveness of panitumumab based on: (i) subsequent RCTs of panitumumab that disallowed treatment switching; (ii) studies of cetuximab that disallowed treatment switching, (iii) analyses demonstrating that only patients with wild-type KRAS benefit from panitumumab. These sources suggest the true OS HR for panitumumab is 0.76-0.77 (95% CI 0.60-0.98) for all patients, and 0.55-0.73 (95% CI 0.41-0.93) for patients with wild-type KRAS. RESULTS: Some applications of IPCW and TSE provided treatment effect estimates that closely matched the point-estimates and CIs of the expected truths. However, other applications produced estimates towards the boundaries of the expected truths, with some TSE applications producing estimates that lay outside the expected true confidence intervals. The RPSFTM performed relatively poorly, with all applications providing treatment effect estimates close to 1, often with extremely wide confidence intervals. CONCLUSIONS: Adjustment analyses may provide unreliable results. How each method is applied must be scrutinised to assess reliability.

The net benefit for time-to-event outcome in oncology clinical trials with treatment switching.

BACKGROUND: The net benefit is an effect measure for any type of endpoint, including the time-to-event outcome, and can provide intuitive and clinically meaningful interpretation. It is defined as the probability of a randomly selected subject from the experimental arm surviving by at least a clinically relevant time longer than a randomly selected subject from the control arm. In oncology clinical trials, an intercurrent event such as treatment switching is common, which potentially causes informative censoring; nevertheless, conventional methods for the net benefit are not able to deal with it. In this study, we proposed a new estimator using the inverse probability of censoring weighting (IPCW) method and illustrated an oncology clinical trial with treatment switching (the SHIVA study) to apply the proposed method under the estimand framework. METHODS: The net benefit can be estimated using the survival functions of each treatment group. The proposed estimator was based on the survival functions estimated by the inverse probability of the censoring weighting method that can handle covariate-dependent censoring. The simulation study was undertaken to evaluate the operating characteristics of the proposed estimator under several scenarios; we varied the shapes of the survival curves, treatment effect, covariates effect on censoring, proportion of the censoring, threshold of the net benefit, and sample size. We also applied conventional methods (the scoring rules by Péron or Gehan) and the proposed method to the SHIVA study. RESULTS: Our simulation study showed that the proposed estimator provided less biased results under the covariate-dependent censoring than existing estimators. When applying the proposed method to the SHIVA study, we were able to estimate the net benefit by incorporating the information of the covariates with different estimand strategies to address the intercurrent event of the treatment switching. However, the estimates of the proposed method and those of the aforementioned conventional methods were similar under the hypothetical strategy. CONCLUSIONS: We proposed a new estimator of the net benefit that can include covariates to account for the possibly informative censoring. We also provided an illustrative analysis of the proposed method for the oncology clinical trial with treatment switching using the estimand framework. Our proposed new estimator is suitable for handling the intercurrent events that can potentially cause covariate-dependent censoring.

Antidepressant Augmentation versus Switch in Treatment-Resistant Geriatric Depression.

BACKGROUND: The benefits and risks of augmenting or switching antidepressants in older adults with treatment-resistant depression have not been extensively studied. METHODS: We conducted a two-step, open-label trial involving adults 60 years of age or older with treatment-resistant depression. In step 1, patients were randomly assigned in a 1:1:1 ratio to augmentation of existing antidepressant medication with aripiprazole, augmentation with bupropion, or a switch from existing antidepressant medication to bupropion. Patients who did not benefit from or were ineligible for step 1 were randomly assigned in step 2 in a 1:1 ratio to augmentation with lithium or a switch to nortriptyline. Each step lasted approximately 10 weeks. The primary outcome was the change from baseline in psychological well-being, assessed with the National Institutes of Health Toolbox Positive Affect and General Life Satisfaction subscales (population mean, 50; higher scores indicate greater well-being). A secondary outcome was remission of depression. RESULTS: In step 1, a total of 619 patients were enrolled; 211 were assigned to aripiprazole augmentation, 206 to bupropion augmentation, and 202 to a switch to bupropion. Well-being scores improved by 4.83 points, 4.33 points, and 2.04 points, respectively. The difference between the aripiprazole-augmentation group and the switch-to-bupropion group was 2.79 points (95% CI, 0.56 to 5.02; P = 0.014, with a prespecified threshold P value of 0.017); the between-group differences were not significant for aripiprazole augmentation versus bupropion augmentation or for bupropion augmentation versus a switch to bupropion. Remission occurred in 28.9% of patients in the aripiprazole-augmentation group, 28.2% in the bupropion-augmentation group, and 19.3% in the switch-to-bupropion group. The rate of falls was highest with bupropion augmentation. In step 2, a total of 248 patients were enrolled; 127 were assigned to lithium augmentation and 121 to a switch to nortriptyline. Well-being scores improved by 3.17 points and 2.18 points, respectively (difference, 0.99; 95% CI, -1.92 to 3.91). Remission occurred in 18.9% of patients in the lithium-augmentation group and 21.5% in the switch-to-nortriptyline group; rates of falling were similar in the two groups. CONCLUSIONS: In older adults with treatment-resistant depression, augmentation of existing antidepressants with aripiprazole improved well-being significantly more over 10 weeks than a switch to bupropion and was associated with a numerically higher incidence of remission. Among patients in whom augmentation or a switch to bupropion failed, changes in well-being and the occurrence of remission with lithium augmentation or a switch to nortriptyline were similar. (Funded by the Patient-Centered Outcomes Research Institute; OPTIMUM ClinicalTrials.gov number, NCT02960763.).

Bias and Type I error Control in Correcting Treatment Effect for Treatment Switching Using Marginal Structural Models in Phase III Oncology Trials.

This research focuses on the bias and type I error control issues when the marginal structural models (MSMs) are applied to evaluate the causal survival benefits of active intervention versus control in randomized clinical trials (RCTs) with treatment switching after disease progression. When MSMs are applied in the RCT setting, the question of interest, model specifications, strategies for type I error control, bias reduction, etc. differ somewhat from those for observational studies. This manuscript discusses the approaches used to accommodate these differences. Through Monte Carlo simulations and a case study, our research demonstrates that, with sufficient attention paid to issues applicable to RCTs in particular, MSMs may perform better than the inverse probability of censoring weighting (IPCW) method in analyzing the survival endpoint in RCTs with treatment switching because more information is used by the MSM.

An Analysis of 5-Level Version of EQ-5D Adjusting for Treatment Switching: The Case of Patients With Epidermal Growth Factor Receptor T790M-Positive Nonsmall Cell Lung Cancer Treated With Osimertinib.

OBJECTIVES: Treatment switching from control to treatment after disease progression is common in oncology trials. Analyses of survival data typically adjust for this bias, but such adjustments are rarely performed in analyses of patient-reported outcomes. This analysis aimed to examine the impact of adjusting for treatment switching on estimated treatment effects on 5-level version of EQ-5D (EQ-5D-5L) utilities and quality-adjusted life-years (QALYs). The AURA3 trial (NCT02151981) was a randomized controlled trial comparing osimertinib with platinum-based doublet chemotherapy (standard care) in patients with locally advanced or metastatic epidermal growth factor receptor mutant- and T790M-positive nonsmall cell lung cancer whose disease has progressed with previous epidermal growth factor receptor tyrosine kinase inhibitor therapy. METHODS: Descriptive analyses were used to compare treatment arms. The primary analysis used a 2-stage least squares instrumental variable regression to estimate treatment effect adjusting for treatment crossover. Time to deterioration, defined from baseline to minimally important deterioration in EQ-5D-5L utility, was assessed using a rank preserving structural failure time model. RESULTS: Intention-to-treat analysis of imputed data showed incremental QALYs for osimertinib of 0.23 at 60 weeks. Accounting for treatment switching increased this to 0.52 in the primary analysis and to 0.63 QALYs in sensitivity analysis at 150 weeks. Time to deterioration analysis showed longer health-related quality of life maintenance with osimertinib, of 12.76 weeks, although this was at the borderline of statistical significance (acceleration factor, ψ = -0.275; 95% confidence interval -0.50 to 0.00). CONCLUSIONS: This analysis demonstrates methods to adjust for treatment switching in the analysis of EQ-5D-5L from clinical trials. Failure to account for crossover substantially underestimated the QALY gain for osimertinib.

Estimands for overall survival in clinical trials with treatment switching in oncology.

An addendum of the ICH E9 guideline on Statistical Principles for Clinical Trials was released in November 2019 introducing the estimand framework. This new framework aims to align trial objectives and statistical analyses by requiring a precise definition of the inferential quantity of interest, that is, the estimand. This definition explicitly accounts for intercurrent events, such as switching to new anticancer therapies for the analysis of overall survival (OS), the gold standard in oncology. Traditionally, OS in confirmatory studies is analyzed using the intention-to-treat (ITT) approach comparing treatment groups as they were initially randomized regardless of whether treatment switching occurred and regardless of any subsequent therapy (treatment-policy strategy). Regulatory authorities and other stakeholders often consider ITT results as most relevant. However, the respective estimand only yields a clinically meaningful comparison of two treatment arms if subsequent therapies are already approved and reflect clinical practice. We illustrate different scenarios where subsequent therapies are not yet approved drugs and thus do not reflect clinical practice. In such situations the hypothetical strategy could be more meaningful from patient's and prescriber's perspective. The cross-industry Oncology Estimand Working Group (www.oncoestimand.org) was initiated to foster a common understanding and consistent implementation of the estimand framework in oncology clinical trials. This paper summarizes the group's recommendations for appropriate estimands in the presence of treatment switching, one of the key intercurrent events in oncology clinical trials. We also discuss how different choices of estimands may impact study design, data collection, trial conduct, analysis, and interpretation.

A modified weighted log-rank test for confirmatory trials with a high proportion of treatment switching.

In confirmatory cancer clinical trials, overall survival (OS) is normally a primary endpoint in the intention-to-treat (ITT) analysis under regulatory standards. After the tumor progresses, it is common that patients allocated to the control group switch to the experimental treatment, or another drug in the same class. Such treatment switching may dilute the relative efficacy of the new drug compared to the control group, leading to lower statistical power. It would be possible to decrease the estimation bias by shortening the follow-up period but this may lead to a loss of information and power. Instead we propose a modified weighted log-rank test (mWLR) that aims at balancing these factors by down-weighting events occurring when many patients have switched treatment. As the weighting should be pre-specified and the impact of treatment switching is unknown, we predict the hazard ratio function and use it to compute the weights of the mWLR. The method may incorporate information from previous trials regarding the potential hazard ratio function over time. We are motivated by the RECORD-1 trial of everolimus against placebo in patients with metastatic renal-cell carcinoma where almost 80% of the patients in the placebo group received everolimus after disease progression. Extensive simulations show that the new test gives considerably higher efficiency than the standard log-rank test in realistic scenarios.

Exploring the Impact of Treatment Switching on Overall Survival from the PROfound Study in Homologous Recombination Repair (HRR)-Mutated Metastatic Castration-Resistant Prostate Cancer (mCRPC).

BACKGROUND: In oncology trials, treatment switching from the comparator to the experimental regimen is often allowed but may lead to underestimating overall survival (OS) of an experimental therapy. OBJECTIVE: This study evaluates the impact of treatment switching from control to olaparib on OS using the final survival data from the PROfound study and compares validated adjustment methods to estimate the magnitude of OS benefit with olaparib. PATIENTS AND METHODS: The primary population from PROfound (Cohort A) was included, alongside two populations approved for treatment with olaparib by the European Medicines Agency and US Food and Drug Administration: BRCAm and Cohort A+B (excluding the PPP2R2A gene). Five methods were explored to adjust for switching: excluding or censoring patients in the control arm who receive subsequent olaparib, Rank Preserving Structural Failure Time Model (RPSFTM), Inverse Probability of Censoring Weights, and Two-Stage Estimation. RESULTS: The RPSFTM was considered the most appropriate approach for PROfound as the results were robust to sensitivity analysis testing of the common treatment effect assumption. For Cohort A, the final OS hazard ratio reduced from 0.69 (95% CI 0.5-0.97) to between 0.42 (0.18-0.90) and 0.52 (0.31-1.00) for olaparib versus control, depending on the RPSFTM selected. Median OS reduced from 14.7 months to between 11.73 and 12.63 months for control. CONCLUSIONS: The magnitude of the statistically significant (P < 0.05) survival benefit of olaparib versus control observed in Cohort A of PROfound is likely to be underestimated if adjustment for treatment switching from control to olaparib is not conducted. The RPSFTM was considered the most plausible method, although further development and validation of robust methods to estimate the magnitude of impact of treatment switching is needed.

Delayed treatment effects, treatment switching and heterogeneous patient populations: How to design and analyze RCTs in oncology.

In the analysis of survival times, the logrank test and the Cox model have been established as key tools, which do not require specific distributional assumptions. Under the assumption of proportional hazards, they are efficient and their results can be interpreted unambiguously. However, delayed treatment effects, disease progression, treatment switchers or the presence of subgroups with differential treatment effects may challenge the assumption of proportional hazards. In practice, weighted logrank tests emphasizing either early, intermediate or late event times via an appropriate weighting function may be used to accommodate for an expected pattern of non-proportionality. We model these sources of non-proportional hazards via a mixture of survival functions with piecewise constant hazard. The model is then applied to study the power of unweighted and weighted log-rank tests, as well as maximum tests allowing different time dependent weights. Simulation results suggest a robust performance of maximum tests across different scenarios, with little loss in power compared to the most powerful among the considered weighting schemes and huge power gain compared to unfavorable weights. The actual sources of non-proportional hazards are not obvious from resulting populationwise survival functions, highlighting the importance of detailed simulations in the planning phase of a trial when assuming non-proportional hazards.We provide the required tools in a software package, allowing to model data generating processes under complex non-proportional hazard scenarios, to simulate data from these models and to perform the weighted logrank tests.

Semiparametric copula-based analysis for treatment effects in the presence of treatment switching.

In controlled trials, "treatment switching" occurs when patients in one treatment group switch to alternative treatments during the trial, and poses challenges to treatment effect evaluation owing to crossover of the treatments groups. In this work, we assume that treatment switching can occur after some disease progression event and view the progression and death events as two semicompeting risks. The proposed model consists of a copula model for the joint distribution of time-to-progression (TTP) and overall survival (OS) up to the earlier of the two events, as well as a conditional hazard model for OS subsequent to progression. The copula model facilitates assessing the marginal distributions of TTP and OS separately from the association between the two events, and, in particular, the treatment effect on OS in the absence of treatment switching. The proposed conditional hazard model for death subsequent to progression allows us to assess the treatment switching (crossover) effect on OS given occurrence of progression and covariates. Semiparametric proportional hazards models are employed in the marginal models for TTP and OS. A nonparametric maximum likelihood procedure is developed for model inference, which is verified through asymptotic theory and simulation studies. The proposed analysis is applied to a lung cancer dataset to illustrate its real utility.

Improved two-stage estimation to adjust for treatment switching in randomised trials: g-estimation to address time-dependent confounding.

In oncology trials, control group patients often switch onto the experimental treatment during follow-up, usually after disease progression. In this case, an intention-to-treat analysis will not address the policy question of interest - that of whether the new treatment represents an effective and cost-effective use of health care resources, compared to the standard treatment. Rank preserving structural failure time models (RPSFTM), inverse probability of censoring weights (IPCW) and two-stage estimation (TSE) have often been used to adjust for switching to inform treatment reimbursement policy decisions. TSE has been applied using a simple approach (TSEsimp), assuming no time-dependent confounding between the time of disease progression and the time of switch. This is problematic if there is a delay between progression and switch. In this paper we introduce TSEgest, which uses structural nested models and g-estimation to account for time-dependent confounding, and compare it to TSEsimp, RPSFTM and IPCW. We simulated scenarios where control group patients could switch onto the experimental treatment with and without time-dependent confounding being present. We varied switching proportions, treatment effects and censoring proportions. We assessed adjustment methods according to their estimation of control group restricted mean survival times that would have been observed in the absence of switching. All methods performed well in scenarios with no time-dependent confounding. TSEgest and RPSFTM continued to perform well in scenarios with time-dependent confounding, but TSEsimp resulted in substantial bias. IPCW also performed well in scenarios with time-dependent confounding, except when inverse probability weights were high in relation to the size of the group being subjected to weighting, which occurred when there was a combination of modest sample size and high switching proportions. TSEgest represents a useful addition to the collection of methods that may be used to adjust for treatment switching in trials in order to address policy-relevant questions.

Estimation of treatment effects and model diagnostics with two-way time-varying treatment switching: an application to a head and neck study.

Treatment switching frequently occurs in clinical trials due to ethical reasons. Intent-to-treat analysis without adjusting for switching yields biased and inefficient estimates of the treatment effects. In this paper, we propose a class of semiparametric semi-competing risks transition survival models to accommodate two-way time-varying switching. Theoretical properties of the proposed method are examined. An efficient expectation-maximization algorithm is derived to obtain maximum likelihood estimates and model diagnostic tools. Existing software is used to implement the algorithm. Simulation studies are conducted to demonstrate the validity of the model. The proposed method is further applied to data from a clinical trial with patients having recurrent or metastatic squamous-cell carcinoma of head and neck.

Plasma Thymidine Kinase Activity as a Biomarker in Patients with Luminal Metastatic Breast Cancer Treated with Palbociclib within the TREnd Trial.

PURPOSE: Thymidine kinase 1 (TK1) is downstream to the CDK4/6 pathway, and TK activity (TKa) measured in blood is a dynamic marker of outcome in patients with advanced breast cancer (ABC). This study explores TK1 as a biomarker of palbociclib response, both in vitro and in patients with ABC. EXPERIMENTAL DESIGN: Modulation of TK1 levels and activity by palbociclib were studied in seven estrogen receptor-positive breast cancer cell lines: sensitive (PDS) and with palbociclib acquired resistance (PDR). TKa was assayed in plasma obtained at baseline (T0), after one cycle (T1), and at disease progression on palbociclib (T2) in patients enrolled in the "To Reverse ENDocrine Resistance" (TREnd) trial (n = 46). RESULTS: Among E2F-dependent genes, TK1 was significantly downregulated after short-term palbociclib. Early TKa reduction by palbociclib occurred in PDS but not in PDR cells. In patients, median TKa (mTKa) at T0 was 75 DiviTum units per liter (Du/L), with baseline TKa not proving prognostic. At T1, mTKa decreased to 35 Du/L, with a minority of patients (n = 8) showing an increase-correlating with a worse outcome than those with decreased/stable TKa (n = 33; mPFS 3.0 vs 9.0 months; P = 0.002). At T2, mTKa was 251 Du/L; patients with TKa above the median had worse outcomes on post-study treatment compared with those with lower TKa (2.9 vs 8.7 months; P = 0.05). CONCLUSIONS: TK is a dynamic marker of resistance to palbociclib which may lead to early identification of patients in whom treatment escalation may be feasible. In addition, TKa may stratify prognosis in patients with acquired resistance to palbociclib.

Switching to systemic therapy after locoregional treatment failure: Definition and best timing.

In patients with unresectable hepatocellular carcinoma (HCC) without both macrovascular invasion and extrahepatic metastasis, the initial treatment choice recommended is transarterial chemoembolization (TACE). Before sorafenib came into wide use, TACE had been pointlessly carried out repeatedly. It was in the early 2010s that the concept of TACE refractory was advocated. Two retrospective studies from Japan indicated that conversion from TACE to sorafenib the day after patients were deemed as TACE refractory improved overall survival compared with continued TACE, according to the definition by the Japan Society of Hepatology. Nowadays, phase 3 trials have shown clinical benefits of several novel molecular target agents. Compared with the era of sorafenib, sequential treatments with these molecular target agents have gradually prolonged patients' survival and have become major strategies in patients with HCC. Taking these together, conversion from TACE to systemic therapies at the time of TACE refractory, compared with before, may have a greater impact on survival and may be considered deeper in the decisions-making process in patients with unresectable HCC who are candidate for TACE. Up-to-date information on the concept of TACE refractory is summarized in this review. We believe that the survival of patients with unresectable HCC without both macrovascular invasion and extrahepatic metastasis may be dramatically improved by optimal timing of TACE refractory and switching to systemic therapies.

Angiotensin-Converting Enzyme Inhibitor-Associated Angioedema: From Bed to Bench.

BACKGROUND AND OBJECTIVE: Angiotensin-converting enzyme inhibitor-associated angioedema (ACEI-AAE) affects 0.1%-0.7% of patients treated with ACEIs. While previous research suggests that angioedema attacks result from increased vascular permeability, the pathogenesis is not completely understood. Objective: This study aimed to describe the clinical, genetic, and laboratory parameters of ACEI-AAE patients and to investigate the role of vascular endothelial growth factors A and C (VEGF-A and VEGF-C), angiopoietins 1 and 2 (Ang1/Ang2), and secretory phospholipase A2 (sPLA2) in the pathogenesis of ACEI-AAE. METHODS: The clinical and laboratory data of ACEI-AAE patients were collected from 2 angioedema reference centers. Healthy volunteers and ACEI-treated patients without angioedema were enrolled to compare laboratory parameters. Genetic analyses to detect mutations in the genes SERPING1, ANGPT1, PLG, and F12 were performed in a subset of patients. RESULTS: A total of 51 patients (57% male) were diagnosed with ACEI-AAE. The average time to onset of symptoms from the start of ACEI therapy was 3 years (range, 30 days-20 years). The most commonly affected sites were the lips (74.5%), tongue (51.9%), and face (41.2%). Switching from ACEIs to sartans was not associated with an increased risk of angioedema in patients with a history of ACEIAAE. VEGF-A, VEGF-C, and sPLA2 plasma levels were higher in ACEI-AAE patients than in the controls. Ang1/2 concentrations remained unchanged. No mutations were detected in the genes analyzed. CONCLUSIONS: Our data suggest that sartans are a safe therapeutic alternative in ACEI-AAE patients. Increased concentrations of VEGF-A, VEGF-C, and sPLA2 in ACEI-AAE patients suggest a possible role of these mediators in the pathogenesis of ACEI-AAE.