Clinical Endpoints Research Articles

Comparative Efficacy and Safety of Upadacitinib vs. Vedolizumab, Ustekinumab, and Tofacitinib After Induction and Maintenance for Ulcerative Colitis: Three Matching-Adjusted Indirect Comparisons.

Evidence on the comparative efficacy and safety of approved therapies for ulcerative colitis (UC) during induction and maintenance, including upadacitinib (UPA), vedolizumab (VEDO), ustekinumab (UST), and tofacitinib (TOFA), is limited. Using data from phase 3 trials, three placebo (PBO)-anchored matching-adjusted indirect comparisons of the efficacy and safety of UPA versus VEDO, UST, and TOFA (U-ACHIEVE and U-ACCOMPLISH, GEMINI-1, UNIFI, and OCTAVE induction and maintenance trials) have been conducted. Baseline characteristics from UPA trials were weighted separately to match each comparator trial. Induction responders were re-randomized to oral UPA 15 or 30mg, VEDO 300mg intravenously every 8weeks (Q8W), UST 90mg SC Q8W, or oral TOFA 5mg, or PBO in maintenance. Treat-through efficacy outcomes at weeks 44(UST)/46(VEDO)/52(UPA/TOFA) were adjusted by the likelihood of induction response and included clinical response, clinical remission, and endoscopic improvement. Safety outcomes included adverse events (AEs), serious AEs (SAEs), and AEs leading to discontinuation (except UPA vs. VEDO). Benefit-risk was assessed by numbers needed to treat (NNT)/harm, calculated as the inverse of the difference in proportions of patients achieving each efficacy/safety outcome for UPA versus comparator. The proportions of patients who demonstrated clinical response or endoscopic improvement was greater with UPA 15mg versus VEDO and TOFA (p<0.05). The proportions of patients demonstrating all treat-through efficacy outcomes were significantly greater with UPA 30mg versus VEDO, UST, or TOFA with NNTs 3.2-8.7. No significant differences in proportions of AEs, SAEs, and AEs leading to discontinuation were observed between the two doses of UPA and comparators. In patients with active UC, greater clinical efficacy, and similar safety after 1year of maintenance were observed with UPA versus VEDO, UST, and TOFA, suggesting a favorable benefit-risk profile for UPA. Despite matched baseline characteristics, differences in trial design and endpoints may persist.

De Novo Atherosclerotic Renal Artery Stenosis Covered Stent Treatment for Resistant Hypertension (ARTISAN) Results

BackgroundAtherosclerotic renal artery stenosis (ARAS) may provoke hypertension and/or impaired kidney function. Some patients develop uncontrolled hypertension and deteriorating kidney function despite optimal medical therapy. In these patients, endovascular treatment is an important therapeutic option. ARTISAN was a prospective, open-label, single-arm, multicenter clinical trial to evaluate the safety and effectiveness of the iCast RX covered stent both functionally for reestablishing renal artery flow, and clinically for controlling resistant hypertension. MethodsPatients considered for enrollment had average systolic blood pressure (SBP) ≥155 mm Hg despite taking 3 antihypertensive medications. Prior to enrollment and covered stent placement, angiographic confirmation of ARAS ≥80% with physiologic significance was required. Clinical assessments were performed at 30 days, 9 months, and annually through 36 months. Covered stent safety and efficacy were based on 9-month coprimary end points, including primary vessel patency and SBP improvement at 9 months. Secondary outcomes included target lesion revascularization, major adverse events, and secondary patency. ResultsSixty-eight of the planned 138 subjects were enrolled. Primary patency was seen in 94.3% of subject lesions; the mean SBP reduction was 15.7 mm Hg. The functional and clinical end points met prespecified performance goals of 70% primary patency (P < .0001) and ≥10 mm Hg SBP decrease (P = .0192), respectively, at 9 months. Six subjects (8.8%) experienced 7 major adverse events within 36 months. The clinically driven target lesion revascularization rate was 7.3% at 36 months. ConclusionsThe high primary patency and improvement in SBP, persisting through 36 months, suggest that the iCast RX covered stent is safe and effective for the treatment of appropriately selected patients with ARAS.

China ARCHES: A Phase 3 Trial of Enzalutamide vs Placebo in Metastatic Hormone-Sensitive Prostate Cancer

Purpose: This study evaluated the efficacy and safety of enzalutamide vs placebo, both combined with androgen deprivation therapy, in Chinese men with metastatic hormone-sensitive prostate cancer. Materials and Methods: Patients were randomized 2:1 to enzalutamide 160 mg/day or placebo, both in combination with androgen deprivation therapy. The primary trial end point was the time to prostate-specific antigen progression. Selected secondary end points included radiographic progression-free survival, time to castration resistance, time to initiation of new antineoplastic therapy, undetectable prostate-specific antigen (&lt;0.2 ng/mL) rate among patients with detectable levels at baseline, and safety. Results: From September 11, 2019, to November 18, 2022, 180 patients were randomized to treatment (enzalutamide: n = 120; placebo: n = 60). Baseline characteristics were balanced between the treatment arms. Median treatment duration in the enzalutamide vs the placebo arm was 25.66 vs 15.11 months. After 63 prostate-specific antigen progression events (enzalutamide: n = 23; placebo: n = 40), enzalutamide significantly reduced the hazard for prostate-specific antigen progression by 87% (hazard ratio: 0.130; 95% confidence interval: 0.076, 0.222; P &lt; .0001) vs placebo. Radiographic progression-free survival, time to castration resistance, and achievement of undetectable prostate-specific antigen levels were increased with enzalutamide vs placebo, while treatment-emergent adverse event rates were similar. Serious and grade 3 to 4 treatment-emergent adverse events occurred in 35.3% and 52.1%, respectively, of enzalutamide recipients vs 20.3% and 39.0%, respectively, of placebo recipients. Conclusions: In Chinese men with metastatic hormone-sensitive prostate cancer, enzalutamide significantly reduced the hazard for prostate-specific antigen progression vs placebo when combined with androgen deprivation therapy. The China ARCHES (NCT04076059) results were consistent with those of the global ARCHES study (NCT02677896).

Major Publications in the Critical Care Pharmacotherapy Literature: 2023.

We aimed to summarize the most significant and impactful publications describing the pharmacotherapeutic care of critically ill patients in 2023. PubMed/MEDLINE and the Clinical Pharmacy and Pharmacology Pharmacotherapy Literature Update. Randomized controlled trials and prospective studies of adult critically ill patients assessing a pharmacotherapeutic intervention and reporting clinical endpoints published between January 1, 2023, and December 31, 2023, were eligible for inclusion in this article. Articles from a systematic search and the Clinical Pharmacy and Pharmacology Pharmacotherapy Literature Update were included. An a priori defined three-round modified Delphi process was employed to achieve consensus on the most impactful publications based on the following considerations: 1) overall contribution to scientific knowledge and 2) novelty to the literature. The systematic search and Clinical Pharmacy and Pharmacology Pharmacotherapy Literature Update returned a total of 1202 articles, of which 1164 were excluded. The remaining 38 articles underwent a three-round modified Delphi process. In each round, articles were independently scored based on overall contribution to scientific knowledge and novelty to the literature. Included articles are summarized and their impact discussed. Article topics included hydrocortisone for severe community-acquired pneumonia, inhaled amikacin for prevention of ventilator-associated pneumonia, methylene blue for septic shock, restrictive vs. liberal fluid management for sepsis-induced hypotension, andexanet alfa for major bleeding associated with factor Xa inhibitors, and early administration of four-factor prothrombin complex concentrate in patients with trauma at risk for massive transfusion. This review provides a summary and perspective on the potential impact of the most relevant articles in 2023 describing advances in the pharmacotherapeutic care of critically ill patients.

Approaches to Early Parkinson's Disease Subtyping.

Parkinson's disease (PD) unfolds with pathological processes and neurodegeneration well before the emergence of noticeable motor symptoms, providing a window for early identification. The extended prodromal phase allows the use of risk stratification measures and prodromal markers to pinpoint individuals likely to develop PD. Importantly, a growing body of evidence emphasizes the heterogeneity within prodromal and clinically diagnosed PD. The disease likely comprises distinct subtypes exhibiting diverse clinical manifestations, pathophysiological mechanisms, and patterns of α-synuclein progression in the central and peripheral nervous systems. There is a pressing need to refine the definition and early identification of these prodromal subtypes. This requires a comprehensive strategy that integrates genetic, pathological, imaging, and multi-omics markers, alongside careful observation of subtle motor and non-motor symptoms. Such multi-dimensional classification of early PD subtypes will improve our understanding of underlying disease pathophysiology, improve predictions of clinical endpoints, progression trajectory and medication response, contribute to drug discovery and personalized medicine by identifying subtype-specific disease mechanisms, and facilitate drug trials by reducing confounding effects of heterogeneity. Here we explore different subtyping methodologies in prodromal and clinical PD, focusing on clinical, imaging, genetic and molecular subtyping approaches. We also emphasize the need for refined, theoretical a priori disease models. These will be prerequisite to understanding the biological underpinnings of biological subtypes, which have been defined by large scale data-driven approaches and multi-omics fingerprints.

Delta Radiomics — Potential role in Head Neck Cancer

IntroductionDelta radiomics is a tool used to assess the response of oncologic patients undergoing immunotherapy. It extracts high-dimensional quantitative features from medical images, providing information about cancer's phenotype, genotype, and tumoral microenvironment. This analysis could help avoid invasive procedures and help choose the most suitable therapeutic in multiple therapeutic options. Radiomics has gained interest as an imaging biomarker for predicting response to various immunotherapies. Delta radiomics assesses feature variations from one time point to another based on subsequent images, offering higher value for treatment-outcome prediction or patient stratification into risk categories. It has potential benefits for clinical endpoints in oncology, such as differential diagnosis, prognosis, treatment response prediction, and evaluation of side effects. Further research with prospective and multicentre studies is needed for clinical validation of delta radiomics approaches. Statement of clinical significanceIn head and neck oncology, delta radiomics can be used to enhance the precision of diagnosis, assess tumor response, forecast normal tissue toxicity, predict clinical outcome, and pinpoint characteristics for treatment modification. Patients' quality of life may be enhanced by it. It can support post-treatment surveillance. Additionally, it can support the delivery of individualized care based on a patient's reaction to medication and radiation.

Modeling Overall Survival in Patients With Pancreatic Cancer From a Pooled Analysis of Phase II Trials.

We evaluated the validity of surrogacy of progression-free survival (PFS) or time-to-progression (TTP) and overall response rate (ORR) in phase II trials of pancreatic ductal adenocarcinoma (PDAC). In addition, we explored the impact of predictive variables on overall survival (OS) and developed an optimal OS model. We analyzed 1867 clinical endpoint from 619 phase II PDAC trials with a systematic search from PubMed. Endpoint correlations were determined by Spearman's rank correlation. The assessed predictive factors included PFS/TTP, treatment size, therapy type, stage, and previous treatment. The relationship between predictors and OS was explored by a gamma generalized linear model (GLM) with a log-link function and compared with linear models. The Spearman rank correlation coefficient between PFS/TTP and OS was 0.88 (95% confidence interval [CI] 0.85-0.89; p < 0.0001; n = 610) and between ORR and OS was 0.58 (0.52-0.64; p < 0.0001; n = 514). Model comparison favored the GLM model over the linear model, offering more accurate predictions for higher OS values. Consequently, PFS/TTP was the strongest predictor (pseudo-R2 = 0.75), with 1 added median PFS/TTP month associated with 13% (95% CI 13%-14%) increase in median OS. Subgroup analysis revealed that chemotherapy conferred significantly longer OS compared to targeted therapy in 1-Agent and 2-Agent trials, exhibiting a "very large" and "medium" effect size, respectively (rank biserial, rrb = 0.40 [95% CI 0.22-0.56] and rrb = 0.29 [0.16-0.41], both p < 0.0001), although inconsistent efficacy in 3-Agent trials (rrb = 0.12 [-0.07-0.30], p = 0.21). PFS/TTP is a more reliable surrogate than ORR and a strong predictor of OS in phase II trials of pancreatic cancer. Moreover, gamma GLM (log-link function) is a robust tool for modeling positively skewed survival data with non-constant variance, thus can be applied to other cancers' OS data of such nature.

Post-rehabilitation programme to support upper limb recovery in community-dwelling stroke survivors: a mixed methods cluster-feasibility controlled trial

BackgroundLess than 50% of stroke survivors regain their pre-stroke level of upper limb function, compounded with a lack of long-term rehabilitation options available. The Graded Repetitive Arm Supplementary Programme (GRASP)...

Pre-operative prediction of BCR-free survival with mRNA variables in prostate cancer.

Technological innovation yielded opportunities to obtain mRNA expression data for prostate cancer (PCa) patients even prior to biopsy, which can be used in a precision medicine approach to treatment decision-making. This can apply in particular to predict the risk of, and time to biochemical recurrence (BCR). Most mRNA-based models currently proposed to this end are designed for risk classification and post-operative prediction. Effective pre-operative prediction would facilitate early treatment decision-making, in particular by indicating more appropriate therapeutic pathways for patient profiles who would likely not benefit from a systematic prostatectomy regime. The aim of this study is to investigate the possibility to leverage mRNA information pre-operatively for BCR-free survival prediction. To do this, we considered time-to-event machine learning (ML) methodologies, rather than classification models at a specific survival horizon. We retrospectively analysed a cohort of 135 patients with clinical follow-up data and mRNA information comprising over 26,000 features (data accessible at NCBI GEO database, accession GSE21032). The performance of ML models including random survival forest, boosted and regularised Cox models were assessed, in terms of model discrimination, calibration, and predictive accuracy for overall, 3-year and 5-year survival, aligning with common clinical endpoints. Results showed that the inclusion of mRNA information could yield a gain in performance for pre-operative BCR prediction. ML-based time-to-event models significantly outperformed reference nomograms that used only routine clinical information with respect to all metrics considered. We believe this is the first study proposing pre-operative transcriptomics models for BCR prediction in PCa. External validation of these findings, including confirmation of the mRNA variables identified as potential key predictors in this study, could pave the way for pre-operative precision nomograms to facilitate timely personalised clinical decision-making.

Exploring the effect on primary endpoints in trials testing targeted therapy interventions for rheumatoid arthritis: a meta-epidemiological study on the appropriate use of a core outcome set

ObjectivesTo explore which core domain is best associated with the American College of Rheumatology (ACR) 20% response in trials assessing the effect of targeted interventions in rheumatoid arthritis (RA).MethodsA meta-epidemiological...

Validation of necrotising infection clinical composite endpoint in a retrospective cohort of patients with necrotising soft tissue infections.

Rapidly progressive necrotising soft tissue infections (NSTIs) are associated with high mortality and morbidity. Low incidence and disease heterogeneity contribute to low event rates and inadequately powered studies. The Necrotising Infections Clinical Composite Endpoint (NICCE) provides a binary outcome with which to assess interventions for NSTIs. Partly with a view towards studies of hyperbaric oxygen treatment in NSTIs we aimed to validate NICCE in a retrospective cohort of NSTI patients. Eligible patients were admitted between 2012 and 2021 to an adult major referral hospital in Victoria, Australia with surgically confirmed NSTI. The NICCE and its constituents were assessed in the whole cohort (n = 235). The cohort was divided into two groups using the modified sequential organ failure assessment (mSOFA) score, with an admission mSOFA score ≥ 3 defined as high acuity. Baseline characteristics of the whole (n = 235), the high (n = 188) and the low acuity cohorts (n = 47) were similar. Survival rates were high (91.1%). Patients with an admission mSOFA ≥ 3 were less likely to meet NICCE criteria for 'success' compared to the lower acuity cohort (34.1% and 64.7% respectively). Meeting NICCE criteria was significantly associated with lower resource utilisation, measured by intensive care unit days, ventilator days, and hospital length of stay for all patients and for those with high acuity on presentation. The NICCE provides greater discriminative ability than mortality alone. It accurately selects patients at high risk of adverse outcomes, thereby enhancing feasibility of trials. Adaptation of NICCE to include patient-centred outcomes could strengthen its clinical relevance.

Comparative effectiveness of new pharmacotherapy regimens for mild COVID-19 based on symptom resolution dynamics

the treatment of COVID-19 remains a challenging issue worldwide due to the huge economic losses to society as a result of the disease's widespread prevalence, peculiarities of its course and complications of its development. The purpose of the work was comparative analysis of the effectiveness of new treatment regimens for outpatients with mild COVID-19. 64 medical charts of outpatient diseases with a mild course of COVID-19 were studied. Depending on the treatment regimen used, all patients were divided into two groups that received inhaled ACEs or antiviral therapy with the combination drug nortrelvir/ritonavir. The efficacy of each pharmacotherapeutic alternative would be determined on the basis of retrospective data for the timeframe of normalisation of clinical and laboratory parameters. A rule was established that the set of all clinical and laboratory parameters in the normal range is considered as a clinical endpoint. It was found that the use of ACC treats the disease until the rapid disappearance of clinical symptoms, especially in the first treatment, which works on its rapid therapeutic effect in the initial stages. Nirmatrelvir/ritonavir has also been shown to be effective in reducing symptoms, but its effect is incremental, reaching full relief slightly later than in ACC. Modeling of the dynamics of reaching the clinical point of elimination of all clinical symptoms indicates a pharmacoeconomic advantage of the use of ACC over nirmatrelvir/ritonavir, if the additional costs of treatment do not exceed the cost of three days of treatment.

The impact of allocation bias on test decisions in clinical trials with multiple endpoints using multiple testing strategies

BackgroundConsidering multiple endpoints in clinical trials provide a more comprehensive understanding of treatment effects and may lead to increased power or reduced sample size, which may be beneficial in rare diseases. Besides the small sample sizes, allocation bias is an issue that affects the validity of these trials. We investigate the impact of allocation bias on testing decisions in clinical trials with multiple endpoints and offer a tool for selecting an appropriate randomization procedure (RP).MethodsWe derive a model for quantifying the effect of allocation bias depending on the RP in the case of two-arm parallel group trials with continuous multiple endpoints. We focus on two approaches to analyze multiple endpoints, either the Šidák procedure to show efficacy in at least one endpoint and the all-or-none procedure to show efficacy in all endpoints.ResultsTo evaluate the impact of allocation bias on the test decision we propose a biasing policy for multiple endpoints. The impact of allocation on the test decision is measured by the family-wise error rate of the Šidák procedure and the type I error rate of the all-or-none procedure. Using the biasing policy we derive formulas to calculate these error rates. In simulations we show that, for the Šidák procedure as well as for the all-or-none procedure, allocation bias leads to inflation of the mean family-wise error and mean type I error, respectively. The strength of this inflation is affected by the choice of the RP.ConclusionAllocation bias should be considered during the design phase of a trial to increase validity. The developed methodology is useful for selecting an appropriate RP for a clinical trial with multiple endpoints to minimize allocation bias effects.

Evaluating deep neural networks in optimizing drug discovery and precision medicine: A review

Introduction/Background: Deep neural networks have shown great promise in advancing drug discovery and precision medicine. By leveraging large amounts of complex biomedical and chemical data, deep learning approaches can identify novel targets, predict drug-target and drug-drug interactions, generate new molecular structures, and assist in personalized treatment selection and development. However, fully utilizing deep learning techniques for optimization across the drug development pipeline remains an ongoing challenge. Materials and Methods: A comprehensive literature review was conducted using major bibliographic databases including PubMed, Web of Science, and Scopus. Search terms included combinations of "deep learning", "drug discovery", "precision medicine", "biomedical data", and "neural networks". Over 200 papers published between 2010-2023 related to deep learning applications in pharmacology and genomics were identified and reviewed. Results: Deep learning has been widely applied at various stages of the drug discovery process including target identification/prioritization, lead generation/optimization, and prediction of molecular properties. Convolutional neural networks are commonly used for the representation and classification of biological sequence and image data for tasks such as gene expression analysis and pathogen detection from microscopy images. Graph neural networks effectively model compound structures and interactome networks to predict molecular bindings and disease associations. Multi-modal neural networks integrate diverse data types for personalized treatment response prediction and biomarker discovery. Challenges remain around data and model interpretation, generalization to new targets/diseases, and integration across domains. Discussion: While deep learning has shown promise, rigorous benchmarking and validation on real-world clinical endpoints are still needed to establish usefulness in decision-making. Data and model transparency must be improved to enable scientific insights. Privacy and security risks accompanying "real world" biomedical big data will require ethical practices. Standardization and sharing of resources/protocols could accelerate progress by enabling comparison of techniques. Combining deep learning with other AI paradigms like causal inference may further improve utility in drug discovery and precision healthcare. Conclusion: Deep neural networks demonstrate potential for optimizing drug development and precision medicine applications. Continued advancement relies on addressing challenges around data, models, validation, and ethics. Multi-disciplinary collaborations integrating machine learning, molecular biology, medicine, and other domains are needed to fully realize benefits to patients.

A Kinematic Deviation Index (KDI) for Evaluation of Forelimb Function in Rodents.

Rodent models are widely used to study neurological conditions and assess forelimb movement to measure function performance, deficit, recovery and treatment effectiveness. Traditional assessment methods based on endpoints such as whether the task is accomplished, while easy to implement, provide limited information on movement patterns important to assess different functional strategies. On the other side, detailed kinematic analysis provides granular information on the movement patterns but is difficult to compare across laboratories, and may not translate to clinical metrics of upper limb function. To address these limitations, we developed and validated a kinematic deviation index (KDI) for rodents that mimics current trends in clinical research. The KDI is a unitless summary score that quantifies the difference between an animal movement during a task and its optimal performance derived from spatiotemporal marker sequences without pre-specifying movements. We demonstrate the utility of KDI in assessing reaching and grasping in mice and validate its discrimination between trial endpoints in healthy animals. Furthermore, we show KDI sensitivity to interventions, including acute and chronic spinal cord injury and optogenetic disruption of sensorimotor circuits. The KDI provides a comprehensive measure of motor function that bridges the gap between detailed kinematic analysis and simple success/failure metrics, offering a valuable tool for assessing recovery and compensation in rodent models of neurological disorders.

Frequency-of-seeing curves (psychometric functions) for perimetric stimuli in age-related macular degeneration.

Frequency-of-seeing (FoS) curves (psychometric functions) for perimetric stimuli have been widely used in computer simulations of new visual field test procedures. FoS curves for age-related macular degeneration (AMD) are not available in the literature and are needed for the development of improved microperimetry test procedures, which are of particular interest for use as clinical trial endpoints. Data were refitted from a previous study to generate FoS curves for 20 participants with AMD, each tested at nine locations within the central 10°. Stimulus parameters, background luminance and dB scale were matched to the MAIA-2 microperimeter, and stimuli were presented in a method of constant stimuli to build up FoS curves over multiple runs. FoS curves were fitted with a modified cumulative Gaussian function. The relationship between sensitivity and slope of fitted FoS curves was modelled by robust linear regression, producing models both with and without an eccentricity parameter. FoS curves were satisfactorily fitted to data from 174 visual field locations in 20 participants (age 65-83 years, 11 female). Each curve was made up of a median of 243 (range 177-297) stimulus presentations over a median of 12 (range 9-32) levels. Median sensitivity was 25.5 dB (range 3.8-31.4 dB). The median slope (SD of fitted function) was 1.6 dB (range 0.5-8.5 dB). As in previous studies of other conditions, the slope of fitted FoS curves increased as sensitivity decreased (p < 0.001). FoS are provided for participants with AMD, as well as models of the relationship between sensitivity and slope. These fitted models and data may be useful for computer simulation studies of microperimetry procedures. Full details of the fitted curves are provided as supporting information.

Expert opinion on design and endpoints for studies on catheter ablation of atrial fibrillation.

Catheter ablation of atrial fibrillation (AF) is frequently studied in randomized trials, observational and registry studies. The aim of this expert opinion is to provide guidance for clinicians and industry regarding the development of future clinical studies on catheter ablation of AF, implement lessons learned from previous studies, and promote a higher degree of consistency across studies. Studies on catheter ablation of AF may benefit from well-described definitions of endpoints and consistent methodology and documentation of outcomes related to efficacy, safety and cost-effectiveness. The availably of new, innovative technologies warrants further consideration about their application and impact on study design and the choice of endpoints. Moreover, recent insights gained from AF ablation studies suggest a reconsideration of some methodological aspects. A panel of clinical experts on catheter ablation of AF and designing and conducting clinical studies developed an expert opinion on the design and endpoints for studies on catheter ablation of AF. Discussions within the expert panel with the aim to reach consensus on predefined topics were based on outcomes reported in the literature and experiences from recent clinical trials. A comprehensive set of recommendations is presented. Key elements include the documentation of clinical AF, medication during the study, repeated ablations and their effect on endpoint assessments, postablation blanking and the choice of rhythm-related and other endpoints. This expert opinion provides guidance and promotes consistency regarding design of AF catheter ablation studies and identified aspects requiring further research to optimize study design and methodology. Recent insights from studies on catheter ablation of atrial fibrillation (AF) and the availability of new innovative technologies warrant reconsideration of methodological aspects related to study design and the choice and assessment of endpoints. This expert opinion, developed by clinical experts on catheter ablation of AF provides a comprehensive set of recommendations related to these methodological aspects. The aim of this expert opinion is to provide guidance for clinicians and industry regarding the development of clinical studies, implement lessons learned from previous studies, and promote a higher degree of consistency across studies.

Breast cancer drugs: FDA approval, development time, efficacy, clinical benefits, innovation, trials, endpoints, quality of life, value, and price

ObjectiveThis study analyzes the development, benefits, trial evidence, and price of new breast cancer drugs with US Food and Drug Administration (FDA) approval.MethodsWe identified 26 drugs with 42 FDA-approved indications for early and metastatic breast cancer (2000–2023). Data were collected from FDA labels, clinicaltrials.gov, and Medicare and Medicaid. Overall survival (OS) and progression-free survival (PFS) hazard ratios (HRs) and tumor response’s relative risk (RR) alongside objective response rate (ORR) were meta-analyzed.ResultsThe median development time for breast cancer drugs was 7.8 years (95% CI 6.2–10.8). 26% of treatments were considered innovative (“first-in-indication”) with 88% acting via a targeted mechanism. 64% were small molecules, 19% antibodies, and 18% antibody-drug conjugates. 38% were approved for HR + and 31% for HER2 + breast cancer. 6 indications were for early and 36 for metastatic breast cancer. Indications utilized FDA’s special programs: orphan (2%), fast track (24%), accelerated approval (19%), priority review (74%), breakthrough therapy (44%). Approval was predominantly supported by phase 3 trials (88%) of randomized controlled design (66%), enrolling a median of 585 patients (IQR 417–752) at 181 centers (IQR 142–223) across 19 countries (IQR 17–20). New drugs’ HR were 0.78 for OS (95% CI 0.74–0.82) and 0.59 for PFS (95% CI 0.54–0.64) with a RR for tumor response of 1.61 (95% CI 1.46–1.76). Median improvements of OS were 2.8 months (IQR 1.8–5.8) and PFS were 4.4 months (IQR 2.2–7.1). In single-arm trials, the average ORR was 31% (95% CI 10–53). In meta-regressions, the correlation between OS/PFS was 0.34 (p = 0.031) and OS/response was 0.01 (p = 0.435). 60% of treatments had a ‘high-value’ ESMO-MCBS score with 14% demonstrating improvements in quality of life. The median price was $16,013 per month (95% CI 13,097–17,617). There was no association between prices and patient benefit. The median value per life year gained was $62,419 (IQR 25,840–86,062).ConclusionsOver the past two decades, the development of innovative and effective drugs transformed the treatment landscape for breast cancer patients. Yet, investigators and regulators must safeguard that highly-priced new drugs demonstrate improvements in patient-centered clinical endpoints: overall survival and quality of life.

Non-alcoholic fatty liver disease: A new predictor of recurrent ischemic stroke and transient ischemic attack in patients with carotid atherosclerosis

PurposeNon-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. This study focused on assessing the predictive significance of NAFLD for recurrent stroke and transient ischemic attack (TIA) risk to determine the value of NAFLD. MethodThis study included 742 participants (mean age: 64.26 ± 9.42 years, 497 males) with carotid atherosclerosis who underwent carotid CT angiography (CTA) between January 2013 and December 2021 in this retrospective study. NAFLD was diagnosed by non-enhanced abdominal CT. The clinical endpoint was a recurrent ischemic stroke or TIA. Cox proportional hazards and Kaplan-Meier analysis assessed whether NAFLD was associated with the endpoint. We accessed the predictive values of NAFLD, clinical, plaque characteristics, and combined model by the C statistics. The predictive performance of the combined model was assessed by receiver operating characteristic curve (ROC) analysis. ResultsA total of 742 participants (mean age: 64.26 ± 9.42 years, 497 males) were included. During 2.9 years of follow-up (interquartile range, 2.1–3.9), 166 patients reached the clinical endpoint. Multivariable cox analyses showed NAFLD was associated with recurrent stroke or TIA in all groups (all P<0.05). Patients with NAFLD had a lower event-free survival (EFS) rate than those without NAFLD (P<0.05). The combined model, including NAFLD, clinical data and plaque features, showed the best performance in predicting the clinical endpoint (AUC=0.79). ConclusionsNAFLD contributes to the prediction of recurrent ischemic stroke or TIA. NAFLD may be a novel imaging marker that offers a new perspective on preventing cardiovascular disease in the clinic.

Shifting the Paradigm: The Transformative Role of Neoadjuvant Therapy in Early Breast Cancer.

The use of neoadjuvant systemic therapy (NST) has become increasingly important in the treatment of breast cancer because of its various advantages. These include the ability to downstage tumors without compromising locoregional control and the potential to obtain valuable information about clinical and biological response to therapy with implications for individual prognoses. Surgical response assessment paves the way for response-adapted therapy, and pathological complete response (pCR; defined as ypT0/is ypN0) serves as an additional endpoint for drug development trials. Recommended NST regimens commonly consist of anthracyclines and taxane, with dose-dense anthracyclines and weekly paclitaxel often preferred, whenever feasible. For patients with human epidermal growth factor receptor-2 (HER2)-positive tumors, dual anti-HER2 therapy (trastuzumab and pertuzumab) is indicated together with NST in case of elevated risk of recurrence. For patients with triple-negative breast cancer (TNBC), adding carboplatin to NST correlates with improved pCR and survival rates, as does the addition of immune checkpoint inhibitors. For hormone receptor (HR)-positive/HER2-negative cancers, emerging data on NST including immune checkpoint inhibitors may elevate the significance of NST in high-risk luminal breast cancer. Here, we present a synthesis of the results from neoadjuvant clinical trials that aim at optimizing treatment options for patients with high-risk breast cancer.