Single Endpoint Research Articles

Type I Error Control of an Adaptive Endpoint Selection Procedure

In clinical trials, a single primary endpoint or a primary endpoint family is traditionally pre-specified before trial initiation. While in general the primary endpoint(s) of a trial should not be altered thereafter, there may be situations where the pre-specified primary analysis no longer makes sense. For example, when endpoint sensitivity is markedly less than expected. To investigate the operating characteristics of endpoint adaptation, we present two approaches to tackle this problem: primary endpoint adaptation based on (1) unblinded or (2) blinded estimate of the coefficient of variation from the control group. We show that the adaptation method based on blinded data introduced in this manuscript has strong control of the family-wise type I error, while the adaptation based on unblinded data does not. We examine the asymptotic conditions under which the adaptation based on blinded data correctly chooses the most powerful endpoint. We also compare the performance of the blinded adaptation with other potential approaches. We present a case study illustrating how the proposed blinded adaptation could have been used in a real clinical trial. Finally, we provide recommendations on how to implement the adaptation in practice.

The Win Ratio Approach in Bayesian Monitoring for Two-Arm Phase II Clinical Trial Designs With Multiple Time-To-Event Endpoints.

To assess the preliminary therapeutic impact of a novel treatment, futility monitoring is commonly employed in Phase II clinical trials to facilitate informed decisions regarding the early termination of trials. Given the rapid evolution in cancer treatment development, particularly with new agents like immunotherapeutic agents, the focus has often shifted from objective response to time-to-event endpoints. In trials involving multiple time-to-event endpoints, existing monitoring designs typically select one as the primary endpoint or employ a composite endpoint as the time to the first occurrence of any event. However, relying on a single efficacy endpoint may not adequately evaluate an experimental treatment. Additionally, the time-to-first-event endpoint treats all events equally, ignoring their differences in clinical priorities. To tackle these issues, we propose a Bayesian futility monitoring design for a two-arm randomized Phase II trial, which incorporates the win ratio approach to account for the clinical priority of multiple time-to-event endpoints. A joint lognormal distribution was assumed to model the time-to-event variables for the estimation. We conducted simulation studies to assess the operating characteristics of the proposed monitoring design and compared them to those of conventional methods. The proposed design allows for early termination for futility if the endpoint with higher clinical priority (e.g., death) deteriorates in the treatment arm, compared to the time-to-first-event approach. Meanwhile, it prevents an aggressive early termination if the endpoint with lower clinical priority (e.g., cancer recurrence) shows deterioration in the treatment arm, offering a more tailored approach to decision-making in clinical trials with multiple time-to-event endpoints.

PEAR1, PON1, CYP2C19, CYP1A2 and F2R Polymorphisms are Associated with MACE in Clopidogrel-Treated Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention.

The objective of this study was to evaluate the impact of clopidogrel-related gene polymorphisms on the occurrence of recurrent thrombotic events and cardiovascular death in patients with acute coronary syndrome (ACS) following percutaneous coronary intervention (PCI). We conducted genotype testing for 26 specific loci mapped to 18 clopidogrel-associated genes in ACS patients who had undergone PCI and were receiving dual antiplatelet therapy only involving clopidogrel. We documented major adverse cardiovascular events (MACE) and clinical endpoints, analyzing the effect of genetic polymorphisms on treatment outcomes. A total of 200 patients were enrolled in the study, with ischemic events occurring in 21 cases. Carriers of the T-allele for rs41273215 (PEAR1), rs662 (PON1), and the A-allele for rs4244285 (CYP2C19), as well as the C-allele for rs762551 (CYP1A2), exhibited a significant increase in the risk of MACE (OR = 2.76, 95% CI = 1.46-5.22, P = 0.002; OR = 3.72, 95% CI = 1.82-7.64, P = 0.0003; OR = 3.86, 95% CI = 1.89-7.86, P = 0.0002; OR = 2.40, 95% CI = 1.27-4.55, P = 0.007). Notably, the variant T-allele of rs168753 (F2R) was associated with a significant reduction in the risk of such events (OR = 0.29, 95% CI = 0.12-0.67, P = 0.004). No significant associations were found between other single nucleotide polymorphisms (SNPs) and clinical endpoints. Polymorphisms in rs41273215 (PEAR1), rs662 (PON1), rs4244285 (CYP2C19), and rs762551 (CYP1A2) were correlated with an increased risk of MACE in PCI patients. Conversely, the rs168753 (F2R) polymorphism was linked to improved cardiovascular outcomes. Genotyping for these polymorphisms could be instrumental in identifying patients at heightened risk for MACE.

Simultaneous Structural Monitoring over Optical Ground Wire and Optical Phase Conductor via Chirped-Pulse Phase-Sensitive Optical Time-Domain Reflectometry.

Optimizing the use of existing high-voltage transmission lines demands real-time condition monitoring to ensure structural integrity and continuous service. Operating these lines at the current capacity is limited by safety margins based on worst-case weather scenarios, as exceeding these margins risks bringing conductors dangerously close to the ground. The integration of optical fibers within modern transmission lines enables the use of Distributed Fiber Optic Sensing (DFOS) technology, with Chirped-Pulse Phase-Sensitive Optical Time-Domain Reflectometry (CP-ΦOTDR) proving especially effective for this purpose. CP-ΦOTDR measures wind-induced vibrations along the conductor, allowing for an analysis of frequency-domain vibration modes that correlate with conductor length and sag across spans. This monitoring system, capable of covering distances up to 40 km from a single endpoint, enables dynamic capacity adjustments for optimized line efficiency. Beyond sag monitoring, CP-ΦOTDR provides robust detection of external threats, including environmental interference and mechanical intrusions, which could compromise cable stability. By simultaneously monitoring the Optical Phase Conductor (OPPC) and Optical Ground Wire (OPGW), this study offers the first comprehensive, real-time evaluation of both structural integrity and potential external aggressions on overhead transmission lines. The findings demonstrate that high-frequency data offer valuable insights for classifying mechanical intrusions and environmental interferences based on spectral content, while low-frequency data reveal the diurnal temperature-induced sag evolution, with distinct amplitude responses for each cable. These results affirm CP-ΦOTDR's unique capacity to enhance line safety, operational efficiency, and proactive maintenance by delivering precise, real-time assessments of both structural integrity and external threats.

Multiple surrogates in the meta-analytic setting for normally distributed endpoints

The identification of good surrogate endpoints is a challenging endeavour. This may, at least partially, be attributable to the fact that most researchers have focused on the identification of a single surrogate endpoint. It is thus implicitly assumed that the treatment effect on the true endpoint (T) can be accurately predicted based on the treatment effect on one surrogate endpoint (S) only. Given the complex nature of many diseases and the different therapeutic pathways in which a treatment can impact T, this assumption may be too optimistic. For example, in oncology, the effect of a treatment often depends on both the treatment’s efficacy and its toxicity. In the present paper, the meta-analytic framework of Buyse et al. (2000) is extended to the setting where multiple S are considered. To cope with potential model convergence issues that often arise in a meta-analytic framework, several simplified model fitting strategies are proposed. Further, simulation studies are conducted to evaluate the properties of the estimated surrogacy metrics, and the new methodology is applied on a case study in schizophrenia. An online Appendix that details how the analyses can be conducted in practice (using the R package Surrogate) is also provided.

Label-free biosensor assay decodes the dynamics of Toll-like receptor signaling

The discovery of Toll-like receptors (TLRs) represented a significant breakthrough that paved the way for the study of host-pathogen interactions in innate immunity. However, there are still major gaps in understanding TLR function, especially regarding the early dynamics of downstream TLR pathways. Here, we present a label-free optical biosensor-based assay as a method for detecting TLR activation in a native and label-free environment and defining the dynamics of TLR pathway activation. This technology is sufficiently sensitive to detect TLR signaling and readily discriminates between different TLR signaling pathways. We define pharmacological modulators of cell surface and endosomal TLRs and downstream signaling molecules and uncover TLR signaling signatures, including potential biased receptor signaling. These findings highlight that optical biosensor assays complement traditional assays that use a single endpoint and have the potential to facilitate the future design of selective drugs targeting TLRs and their downstream effector cascades.

The prognostic impact of intra-procedural mitral valve gradient following transcatheter edge-to-edge repair for primary mitral regurgitation

Abstract Background/Introduction The impact of intra-procedural mitral valve gradient following transcatheter edge-to-edge repair (TEER) in primary mitral regurgitation (MR) is a matter of controversial debates. Purpose The aim of this study was to investigate the prognostic impact of intra-procedural mean mitral valve gradient (MPG) in patients with primary MR undergoing TEER. Methods The PRIME-MR registry included consecutive patients with primary MR undergoing TEER from 2008-2022 at 27 international sites. Clinical outcomes were assessed according to intra-procedural mean MPG measured at the end of the procedure by transesophageal echocardiography. The evolution of mean MPG from intra-procedural to discharge and follow-up was documented. Baseline characteristics and clinical outcomes of patients were investigated according to intra-procedural mean MPG using a cut-off of 5mmHg (low MPG: ≤5mmHg, high MPG: >5mmHg). The prognostic impact of intra-procedural mean MPG was evaluated in multivariable Cox regression analysis. The primary endpoint was 2-year all-cause mortality or heart failure (HF) hospitalization. Results Intra-procedural MPG was available in 1,516 patients (median age 82 years [IQR 76.0, 86.0], 55.1% male). Patients with high intra-procedural MPG presented with less atrial fibrillation and less severe MR but were more symptomatic at baseline than patients with low MPG. Moreover, patients with high intra-procedural MPG had higher baseline MPG and smaller LV dimensions (LVEDV and LVESV). The rate of patients with mean MPG >5mmHg increased from intra-procedural (18.3%) to discharge (31.3%), while remaining stable thereafter (1-year follow-up: 30.9%; last follow-up: 29.6%). At 1-year follow-up the rate of patients presenting with New York Heart Association (NYHA) functional class I or II did not differ between patients with low (82.3%) and high MPG (83.6%, p=0.93). Kaplan-Meier analysis according to mean MPG showed no difference between patients with low and high MPG for the primary endpoint (32.4% vs. 42.1%, p=0.12), as well as for the single endpoints of all-cause mortality (22.3% vs. 27.7%, p=0.47) and HF hospitalization (17.6% vs. 27.5%, p=0.087) through 2 years (Figure 1). Unadjusted spline analyses were provided to depict the prognostic impact of continuous intra-procedural MPG (Figure 2). Following multivariable Cox regression neither MPG >5mmHg (HR 0.91, 95%-CI 0.56-1.49, p=0.72), nor continuous MPG (HR 1.05, 95% CI 0.96-1.15, p=0.28) were independently associated with adverse clinical outcomes. Conclusions Based on a large international cohort of patients with primary MR undergoing TEER, intra-procedural mean MPG did not significantly affect clinical and functional outcomes at 2 years. The long-term impact of elevated post-procedural MPG warrants further investigation.Figure 1Figure 2

Exploring desirability of outcome ranking and generalised pairwise comparisons as endpoints in serious infections: Post hoc application with the MERINO trial

ObjectivesRandomised controlled trials in serious infections typically use a single primary endpoint such as mortality. Ordinal or rank-based composite endpoints that evaluate mortality and other measures are a novel approach in clinical trials. Desirability of outcome ranking (DOOR) has been increasingly used in infectious disease trials and generalised pairwise comparisons (GPC) in cardiovascular trials. MethodsWe undertook a post hoc analysis of the MERINO trial, comparing piperacillin-tazobactam (PTZ) with meropenem in bloodstream infections due to ceftriaxone non-susceptible Escherichia coli and Klebsiella spp. to demonstrate and compare the DOOR and GPC approaches. The primary composite endpoint included mortality, microbiological relapse and secondary infection. A further analysis was performed by adding length of stay as a tiebreaker. We applied DOOR to evaluate the DOOR distribution, probability and conducted partial credit analyses. We applied GPC to estimate the win statistics: win ratio, win odds and net benefit. A secondary analysis with both approaches was conducted with trial participants with PTZ susceptible isolates only as recorded by a central laboratory. ResultsThe DOOR probability was 44.3 % (95 % CI: 40.8 %, 47.9 %), indicating superiority of meropenem. Partial credit analyses demonstrated similar conclusions across different patient preferences. The win ratio, win odds and net benefit were 0.40 (95 % CI: 0.23, 0.70; p = 0.001), 0.80 (95 % CI: 0.69, 0.92; p = 0.002) and −11.3 % (95 % CI:18.7 %, −4.0 %; p = 0.003), also indicating superiority of meropenem. With the addition of length of stay, the DOOR probability was 43.7 % (95 % CI: 37.9 %, 49.6 %) and the win ratio, win odds and net benefit were 0.77 (95 % CI: 0.60, 0.99; p = 0.04), 0.78 (95 % CI: 0.62, 0.99; p = 0.04), −12.1 % (95 % CI:23.8 %, −0.3 %; p = 0.04). Superiority of meropenem was not able to be demonstrated in the secondary analysis where only patients with PTZ susceptible isolates were assessed. ConclusionsThough the functionality and interpretation of DOOR and GPC differ, both approaches can enhance the overall understanding of treatment effect in survivors beyond a single endpoint such as mortality.

Statistical analysis of multiple regions-of-interest in multiplexed spatial proteomics data.

Multiplexed spatial proteomics reveals the spatial organization of cells in tumors, which is associated with important clinical outcomes such as survival and treatment response. This spatial organization is often summarized using spatial summary statistics, including Ripley's K and Besag's L. However, if multiple regions of the same tumor are imaged, it is unclear how to synthesize the relationship with a single patient-level endpoint. We evaluate extant approaches for accommodating multiple images within the context of associating summary statistics with outcomes. First, we consider averaging-based approaches wherein multiple summaries for a single sample are combined in a weighted mean. We then propose a novel class of ensemble testing approaches in which we simulate random weights used to aggregate summaries, test for an association with outcomes, and combine the $P$-values. We systematically evaluate the performance of these approaches via simulation and application to data from non-small cell lung cancer, colorectal cancer, and triple negative breast cancer. We find that the optimal strategy varies, but a simple weighted average of the summary statistics based on the number of cells in each image often offers the highest power and controls type I error effectively. When the size of the imaged regions varies, incorporating this variation into the weighted aggregation may yield additional power in cases where the varying size is informative. Ensemble testing (but not resampling) offered high power and type I error control across conditions in our simulated data sets.

Liver-on-a-Chip Integrated with Label-Free Optical Biosensors for Rapid and Continuous Monitoring of Drug-Induced Toxicity.

Drug toxicity assays using conventional 2D static cultures and animal studies have limitations preventing the translation of potential drugs to the clinic. The recent development of organs-on-a-chip platforms provides promising alternatives for drug toxicity/screening assays. However, most studies conducted with these platforms only utilize single endpoint results, which do not provide real-time/ near real-time information. Here, a versatile technology is presented that integrates a 3Dliver-on-a-chip with a label-free photonic crystal-total internal reflection (PC-TIR) biosensor for rapid and continuous monitoring of the status of cells. This technology can detect drug-induced liver toxicity by continuously monitoring the secretion rates and levels of albumin and glutathione S-transferase α (GST-α) of a 3D liver on-a-chip model treated with Doxorubicin. The PC-TIR biosensor is based on a one-step antibody functionalization with high specificity and a detection range of 21.7ngmL-1 to 7.83 x 103ngmL-1 for albumin and 2.20ngmL-1 to 7.94 x 102ngmL-1 for GST-α. This approach provides critical advantages for the early detection of drug toxicity and improved temporal resolution to capture transient drug effects. The proposed proof-of-concept study introduces a scalable and efficient plug-in solution for organ-on-a-chip technologies, advancing drug development and in vitro testing methods by enabling timely and accurate toxicity assessments.

Early clinical outcomes of Portico and Edwards Sapien 3 valve prosthesis in transcatheter aortic valve replacement: propensity-matched analysis.

There is a lack of real-world data directly comparing different valve prostheses for transaortic valve replacement (TAVR). We aimed to compare early clinical outcomes at 30-days between the self-expandable Portico valve (Abbott) with the balloon-expandable Edwards Sapien 3 valve (Edwards Lifesciences) (ES3). Out of 1,901 patients undergoing TAVR between January 2018 and December 2021, all patients who received either Portico valve or ES3 valve via transfemoral TAVR were matched using nearest-neighbor (1:1) propensity scoring. Primary endpoints were single safety endpoints and early safety composite endpoints defined by Valve Academic Research Consortium-2 (VARC-2) criteria. The secondary endpoint was to analyze risk predictors for new permanent pacemaker (PPM) implantation in TAVR. Out of 661 complete cases, a total of 434 patients were successfully matched based on age, sex, Euro Score II and STS-score. In the matched cohort, 217 received either a Portico or valve and 217 received an ES3 valve. The VARC-2 early safety composite scores indicated a significantly greater overall 30-day safety risk in the Portico group at 9.2% (n = 20) compared to 3.7% (n = 8) in the ES3 group (p = 0.032). The requirement for new permanent pacemaker (PPM) implantation was also higher in the Portico group, at 21.2% (n = 46) vs. 13.4% (n = 29) in the ES3 group (p = 0.042). 30-day mortality was higher was 3.7% (n = 8) in Portico group compared to 0.9% in ES3 group (p = 0.11). Furthermore, implantation of the Portico valve was identified as a significant risk predictor for new PPM implantation, alongside higher age, preprocedural atrioventricular block (AVB) and longer total procedure duration. This study shows significantly higher rates of early clinical complications for Portico valve prostheses compared to ES3. These findings should be especially taken into consideration when selecting valve prosthesis for high-risk patients.

Chauhan Weighted Trajectory Analysis Reduces Sample Size Requirements and Expedites Time-to-Efficacy Signals in Advanced Cancer Clinical Trials

(1) Background: As Kaplan–Meier (KM) analysis is limited to single unidirectional endpoints, most advanced cancer randomized clinical trials (RCTs) are powered for either progression-free survival (PFS) or overall survival (OS). This discards efficacy information carried by partial responses, complete responses, and stable disease that frequently precede progressive disease and death. Chauhan Weighted Trajectory Analysis (CWTA) is a generalization of KM that simultaneously assesses multiple rank-ordered endpoints. We hypothesized that CWTA could use this efficacy information to reduce sample size requirements and expedite efficacy signals in advanced cancer trials. (2) Methods: We performed 100-fold and 1000-fold simulations of solid tumor systemic therapy RCTs with health statuses rank-ordered from complete response (Stage 0) to death (Stage 4). At increments of the sample size and hazard ratio, we compared KM PFS and OS with CWTA for (i) sample size requirements to achieve a power of 0.8 and (ii) the time to first significant efficacy signal. (3) Results: CWTA consistently demonstrated greater power, and it reduced the sample size requirements by 18% to 35% compared to KM PFS and 14% to 20% compared to KM OS. CWTA also expedited time-to-efficacy signals by 2- to 6-fold. (4) Conclusions: CWTA, by incorporating all efficacy signals in the cancer treatment trajectory, provides a clinically relevant reduction in the required sample size and meaningfully expedites the efficacy signals of cancer treatments compared to KM PFS and KM OS. Using CWTA rather than KM as the primary trial outcome has the potential to meaningfully reduce the numbers of patients, trial duration, and costs to evaluate therapies in advanced cancer.

Modelling lethality and teratogenicity of zebrafish (Danio rerio) due to β-lactam antibiotics employing the QSTR approach

ABSTRACT Nowadays, β-lactam antibiotics are one of the most consumed OTC (over-the-counter) medicines in the world. Its frequent use against several infectious diseases leads to the development of antibiotic resistance. Another unavoidable risk factor of β-lactam antibiotics is environmental toxicity. Numerous terrestrial as well as aquatic species have suffered due to the excessive use of these pharmaceuticals. In this present study, we have performed a toxicity assessment employing a novel in silico technique like quantitative structure–toxicity relationships (QSTRs) to explore toxicity against zebrafish (Danio rerio). We have developed single as well as inter-endpoint QSTR models for the β-lactam compounds to explore important structural attributes responsible for their toxicity, employing median lethal (LC50) and median teratogenic concentration (TC50) as the endpoints. We have shown how an inter-endpoint model can extrapolate unavailable endpoint values with the help of other available endpoint values. To verify the models’ robustness, predictivity, and goodness-of-fit, several universally popular metrics for both internal and external validation were extensively employed in model validation (single endpoint models: r 2 = 0.631 − 0.75, Q 2 F1 = 0.607 − 0.684; inter-endpoint models: r 2 = 0.768 − 0.84, Q 2 F1 = 0.678 − 0.76). Again, these models were engaged in the prediction of these two responses for a true external set of β-lactam molecules without response values to prove the reproducibility of these models.

ToxMPNN: A deep learning model for small molecule toxicity prediction.

Machine learning (ML) has shown a great promise in predicting toxicity of small molecules. However, the availability of data for such predictions is often limited. Because of the unsatisfactory performance of models trained on a single toxicity endpoint, we collected toxic small molecules with multiple toxicity endpoints from previous study. The dataset comprises 27 toxic endpoints categorized into seven toxicity classes, namely, carcinogenicity and mutagenicity, acute oral toxicity, respiratory toxicity, irritation and corrosion, cardiotoxicity, CYP450, and endocrine disruption. In addition, a binary classification Common-Toxicity task was added based on the aforementioned dataset. To improve the performance of the models, we added marketed drugs as negative samples. This study presents a toxicity predictive model, ToxMPNN, based on the message passing neural network (MPNN) architecture, aiming to predict the toxicity of small molecules. The results demonstrate that ToxMPNN outperforms other models in capturing toxic features within the molecular structure, resulting in more precise predictions with the ROC_AUC testing score of 0.886 for the Toxicity_drug dataset. Furthermore, it was observed that adding marketed drugs as negative samples not only improves the predictive performance of the binary classification Common-Toxicity task but also enhances the stability of the model prediction. It shows that the graph-based deep learning (DL) algorithms in this study can be used as a trustworthy and effective tool to assess small molecule toxicity in the development of new drugs.

Calorimetric investigation on heat release during the disintegration process of pharmaceutical tablets

The compendial USP〈701〉 disintegration test method offers a crucial pass/fail assessment for immediate release tablet disintegration. However, its single end-point approach provides limited insight into underlying mechanisms. This study introduces a novel calorimetric approach, aimed at providing comprehensive process profiles beyond binary outcomes. We developed a novel disintegration reaction calorimeter to monitor the heat release throughout the disintegration process and successfully obtained enthalpy change profiles of placebo tablets with various porosities. The formulation comprised microcrystalline cellulose (MCC), anhydrous lactose, croscarmellose sodium (CCS), and magnesium stearate (MgSt). An abrupt temperature rise was observed after introducing the disintegration medium to tablets, and the relationship between the heat rise time and the tablet’s porosity was investigated. The calorimeter’s sensitivity was sufficient to discern distinct heat changes among individual tablets, and the analysis revealed a direct correlation between the two. Higher porosity corresponded to shorter heat rise time, indicating faster disintegration rates. Additionally, the analysis identified a concurrent endothermic process alongside the anticipated exothermic phenomenon, potentially associated with the dissolution of anhydrous lactose. Since lactose is the only soluble excipient within the blend composition, the endothermic process can be attributed to the absorption of heat as lactose molecules dissolve in water. The findings from this study underscore the potential of utilising calorimetric methods to quantify the wettability of complex compounds and, ultimately, optimise tablet formulations.

Reduced time to efficacy signals in advanced cancer trials using Chauhan weighted trajectory analysis.

e23028 Background: As Kaplan-Meier (KM) analyzes single unidirectional endpoints, most advanced cancer randomized clinical trials (RCTs) are powered for a 10 endpoint of either progression free survival (PFS) or overall survival (OS). This disregards efficacy information carried by stable disease (SD), partial response (PR), and complete response (CR) that may precede progressive disease (PD) and death. Chauhan Weighted Trajectory Analysis (CWTA), a generalization of KM, permits simultaneous evaluation of multiple rank ordered endpoints. We hypothesized that using CWTA with all efficacy inputs could expedite RCT efficacy signals. Methods: We performed 100-fold simulations of RCTs in advanced cancer and determined the time to first significant efficacy signal (p < 0.05) for KM PFS and OS (logrank test) and CWTA (weighted logrank test). RCTs were stochastically generated at defined sample size (SS) allocated 1:1 to control or intervention and run for 60 months. Rank ordered health statuses were CR = 0, PR = 1, SD = 2, PD = 3, and Death = 4. All patients we assigned SD at time 0 and, each month, were capable of response (PR then CR), maintained SD, or irreversible exacerbation to PD or Death. Event probabilities were modified between groups as defined by a hazard ratio (HR). We modeled a control group CR rate of ~10% and a PR rate of ~50% to reflect first-line advanced cancer RCTs with a dropout rate of 10% over 60 months. At increments of SS and HR, we determined the mean and standard deviation of time-to-efficacy signals. Results: CWTA markedly reduced the time-to-efficacy signals when compared to KM PFS (23% to 67%) and KM OS (43% to 82%) (Table 1). Conclusions: CWTA, by incorporating the entirety of the cancer trajectory including disease response, progression, and death, meaningfully expedites the efficacy signals of cancer treatments compared to KM PFS and OS. We have previously reported CWTA increases trial power and reduces sample size requirements (Chauhan U, Zhao K, Walker J, Mackey JR; 2023. DOI: 10.3390/biomedinformatics3040052). Using CWTA rather than KM reduces trial duration, cost, and numbers of patients needed to evaluate therapies in advanced cancer, while reducing the regulatory risk inherent in powering a trial for a single KM primary endpoint. [Table: see text]

Evaluation of an automated clinical decision system with deep learning dose prediction and NTCP model for prostate cancer proton therapy

Objective. To evaluate the feasibility of using a deep learning dose prediction approach to identify patients who could benefit most from proton therapy based on the normal tissue complication probability (NTCP) model. Approach. Two 3D UNets were established to predict photon and proton doses. A dataset of 95 patients with localized prostate cancer was randomly partitioned into 55, 10, and 30 for training, validation, and testing, respectively. We selected NTCP models for late rectum bleeding and acute urinary urgency of grade 2 or higher to quantify the benefit of proton therapy. Propagated uncertainties of predicted ΔNTCPs resulting from the dose prediction errors were calculated. Patient selection accuracies for a single endpoint and a composite evaluation were assessed under different ΔNTCP thresholds. Main results. Our deep learning-based dose prediction technique can reduce the time spent on plan comparison from approximately 2 days to as little as 5 seconds. The expanded uncertainty of predicted ΔNTCPs for rectum and bladder endpoints propagated from the dose prediction error were 0.0042 and 0.0016, respectively, which is less than one-third of the acceptable tolerance. The averaged selection accuracies for rectum bleeding, urinary urgency, and composite evaluation were 90%, 93.5%, and 93.5%, respectively. Significance. Our study demonstrates that deep learning dose prediction and NTCP evaluation scheme could distinguish the NTCP differences between photon and proton treatment modalities. In addition, the dose prediction uncertainty does not significantly influence the decision accuracy of NTCP-based patient selection for proton therapy. Therefore, automated deep learning dose prediction and NTCP evaluation schemes can potentially be used to screen large patient populations and to avoid unnecessary delays in the start of prostate cancer radiotherapy in the future.

The PAncreatic Surgery Composite Endpoint PACE – Development and Validation of a Clinically Relevant Endpoint Requiring Lower Sample Sizes

Abstract Background Single endpoints in prospective and randomized studies have become impractical due to their low frequency and the marginal benefit of new interventions. Aims To provide a composite endpoint in pancreatic surgery. Methods Data from prospective studies were used to develop (n=1273) and validate (n=544) a composite endpoint based on postoperative pancreatic fistula, post-pancreatectomy hemorrhage as well as reoperation and reinterventions. All patients had pancreatectomies of different extents. The association of the developed PAncreatic surgery Composite Endpoint (PACE) with prolonged length of hospital stay (LOS) >75th percentile and mortality were assessed. A single-institution database was used for external validation (n = 2666). Sample size calculations were made for single outcomes and the composite endpoint. Results In the internal validation cohort, the PACE demonstrated an AUC of 78.0%, a sensitivity of 90.4% and a specificity of 67.6% in predicting a prolonged LOS. In the external cohort, the AUC was 76.9%, the sensitivity 73.8% and the specificity 80.1%. The 90-day mortality rate was significantly different for patients with a positive versus a negative PACE both in the development and internal validation cohort (5.1% vs 0.9%; P< 0.001), as well as in the external validation cohort (8.5% vs 1.2%, P< 0.001). The PACE enabled sample size reductions of up to 80.5% compared to single outcomes. Conclusion The PACE performed well in predicting prolonged hospital stays and can be used as a standardized and clinically relevant endpoint for future prospective trials enabling lower sample sizes and therefore improved feasibility compared to single outcome parameters.

Deep Learning Bridged Bioactivity, Structure, and GC-HRMS-Readable Evidence to Decipher Nontarget Toxicants in Sediments.

Identifying causative toxicants in mixtures is critical, but this task is challenging when mixtures contain multiple chemical classes. Effect-based methods are used to complement chemical analyses to identify toxicants, yet conventional bioassays typically rely on an apical and/or single endpoint, providing limited diagnostic potential to guide chemical prioritization. We proposed an event-driven taxonomy framework for mixture risk assessment that relied on high-throughput screening bioassays and toxicant identification integrated by deep learning. In this work, the framework was evaluated using chemical mixtures in sediments eliciting aryl-hydrocarbon receptor activation and oxidative stress response. Mixture prediction using target analysis explained <10% of observed sediment bioactivity. To identify additional contaminants, two deep learning models were developed to predict fingerprints of a pool of bioactive substances (event driver fingerprint, EDFP) and convert these candidates to MS-readable information (event driver ion, EDION) for nontarget analysis. Two libraries with 121 and 118 fingerprints were established, and 247 bioactive compounds were identified at confidence level 2 or 3 in sediment extract using GC-qToF-MS. Among them, 12 toxicants were analytically confirmed using reference standards. Collectively, we present a "bioactivity-signature-toxicant" strategy to deconvolute mixtures and to connect patchy data sets and guide nontarget analysis for diverse chemicals that elicit the same bioactivity.

Resuscitation Endpoints in Polytrauma Patients: Fixed or Dynamic

Abstract Shock results from insufficient oxygen delivery, leading to metabolic acidosis, inflammation, and coagulopathy. Resuscitation aims to restore normal physiology by addressing various haemodynamic, metabolic, and regional endpoints that gauge the severity of shock. While numerous endpoints exist, no universal standard applies. This review critically examines the importance of endpoints in resuscitation in the management of shock and the improvement of trauma patients’ outcomes. A thorough literature search and analysis highlighting effective endpoints in clinical practice and areas necessitating further investigation was carried out. Established markers such as serum lactate, base deficit, and pH offer valuable insights over time, although a single value may not suffice. Early controlled resuscitation in haemorrhagic shock and tailored haemostatic approaches enhance survival rates. Damage control resuscitation strategies have been proven to improve outcomes by prioritizing haemorrhage management as an additional endpoint. As trauma is a dynamic condition that changes from minute to minute based on the patient’s condition during the acute phase, it is imperative that resuscitation strategies and endpoints remain dynamic during this phase to effectively accommodate changes in the patient’s condition. This belief is supported by the wide variation in endpoints without a consensus on a single endpoint or value.