Early Termination Of Trial Research Articles

Genetic Factors Linked to Early Clinical Trial Termination.

Genetic Factors Linked to Early Clinical Trial Termination.

Forecasting Trial Milestones: A Predictive Analysis for Early Termination of the SOUL Study.

Semaglutide, a glucagon-like peptide 1 receptor agonist (GLP1RA), is available in both parenteral and oral preparations. Studies of injectable preparations have convincingly demonstrated its beneficial effect on major adverse cardiac events (MACE). This predictive analysis was undertaken to forecast early termination of the SOUL trial (oral semaglutide) as well as the primary events. SOUL is a multicenter, double-blind, placebo-controlled randomized controlled trial (RCT) evaluating the reduction in MACE associated with oral semaglutide versus placebo in patients with type 2 diabetes (T2D) and cardiovascular (CV) disease. A sample of 9642 participants will be followed for 5years and 5months. A random-effects model meta-analysis, pooling hazard ratios from previous RCTs, was conducted using R software to inform the predictive model. The background CV event rates from the placebo arms of previous RCTs with semaglutide were matched with the pre-adjudicated assumptions of the SOUL trial to create the predictive model. The truncated trial duration, MACE, and its individual components in the intervention and placebo arms were estimated. The predicted difference between the two groups was estimated using the chi-squared test. A pooled analysis of 10,013 patients revealed a significant reduction in the number of MACEs associated with semaglutide (HR 0.79, 95% CI 0.69-0.91). Predictive analysis indicated that 1225 events would be achieved by 3.78years, suggesting premature termination. The mathematical model based on the meta-analysis predicts that the SOUL study on oral semaglutide will be terminated early, with oral semaglutide showing benefits in terms of MACE compared to placebo. If the SOUL study corroborates the findings of this model, it may not only form the basis for the calculation of power but also define the duration of such studies, reducing costs and easing the process of designing cardiovascular outcome trials (CVOTs). INPLASY202460061.

Estimating vaccine efficacy during open-label follow-up of COVID-19 vaccine trials based on population-level surveillance data

While rapid development and roll out of COVID-19 vaccines is necessary in a pandemic, the process limits the ability of clinical trials to assess longer-term vaccine efficacy. We leveraged COVID-19 surveillance data in the U.S. to evaluate vaccine efficacy in U.S. Government-funded COVID-19 vaccine efficacy trials with a three-step estimation process. First, we used a compartmental epidemiological model informed by county-level surveillance data, a “population model”, to estimate SARS-CoV-2 incidence among the unvaccinated. Second, a “cohort model” was used to adjust the population SARS-CoV-2 incidence to the vaccine trial cohort, taking into account individual participant characteristics and the difference between SARS-CoV-2 infection and COVID-19 disease. Third, we fit a regression model estimating the offset between the cohort-model-based COVID-19 incidence in the unvaccinated with the placebo-group COVID-19 incidence in the trial during blinded follow-up. Counterfactual placebo COVID-19 incidence was estimated during open-label follow-up by adjusting the cohort-model-based incidence rate by the estimated offset. Vaccine efficacy during open-label follow-up was estimated by contrasting the vaccine group COVID-19 incidence with the counterfactual placebo COVID-19 incidence. We documented good performance of the methodology in a simulation study. We also applied the methodology to estimate vaccine efficacy for the two-dose AZD1222 COVID-19 vaccine using data from the phase 3 U.S. trial (ClinicalTrials.gov # NCT04516746). We estimated AZD1222 vaccine efficacy of 59.1% (95% uncertainty interval (UI): 40.4%–74.3%) in April, 2021 (mean 106 days post-second dose), which reduced to 35.7% (95% UI: 15.0%–51.7%) in July, 2021 (mean 198 days post-second-dose). We developed and evaluated a methodology for estimating longer-term vaccine efficacy. This methodology could be applied to estimating counterfactual placebo incidence for future placebo-controlled vaccine efficacy trials of emerging pathogens with early termination of blinded follow-up, to active-controlled or uncontrolled COVID-19 vaccine efficacy trials, and to other clinical endpoints influenced by vaccination.

A clinical trial termination prediction model based on denoising autoencoder and deep survival regression

AbstractEffective clinical trials are necessary for understanding medical advances but early termination of trials can result in unnecessary waste of resources. Survival models can be used to predict survival probabilities in such trials. However, survival data from clinical trials are sparse, and DeepSurv cannot accurately capture their effective features, making the models weak in generalization and decreasing their prediction accuracy. In this paper, we propose a survival prediction model for clinical trial completion based on the combination of denoising autoencoder (DAE) and DeepSurv models. The DAE is used to obtain a robust representation of features by breaking the loop of raw features after autoencoder training, and then the robust features are provided to DeepSurv as input for training. The clinical trial dataset for training the model was obtained from the ClinicalTrials.gov dataset. A study of clinical trial completion in pregnant women was conducted in response to the fact that many current clinical trials exclude pregnant women. The experimental results showed that the denoising autoencoder and deep survival regression (DAE‐DSR) model was able to extract meaningful and robust features for survival analysis; the C‐index of the training and test datasets were 0.74 and 0.75 respectively. Compared with the Cox proportional hazards model and DeepSurv model, the survival analysis curves obtained by using DAE‐DSR model had more prominent features, and the model was more robust and performed better in actual prediction.

Why are clinical trials of deep brain stimulation terminated? An analysis of clinicaltrials.gov

BackgroundAlthough deep brain stimulation (DBS) has established uses for patients with movement disorders and epilepsy, it is under consideration for a wide range of neurologic and neuropsychiatric conditions. ObjectiveTo review successful and unsuccessful DBS clinical trials and identify factors associated with early trial termination. MethodsThe ClinicalTrials.gov database was screened for all studies related to DBS. Information regarding condition of interest, study aim, trial design, trial success, and, if applicable, reason for failure was collected. Trials were compared and logistic regression was utilized to identify independent factors associated with trial termination. ResultsOf 325 identified trials, 79.7% were successful and 20.3% unsuccessful. Patient recruitment, sponsor decision, and device issues were the most cited reasons for termination. 242 trials (74.5%) were interventional with 78.1% successful. There was a statistically significant difference between successful and unsuccessful trials in number of funding sources (p = 0.0375). NIH funding was associated with successful trials while utilization of other funding sources (academic institutions and community organizations) was associated with unsuccessful trials. 83 trials (25.5%) were observational with 84.0% successful; there were no statistically significant differences between successful and unsuccessful observational trials. ConclusionOne in five clinical trials for DBS were found to be unsuccessful, most commonly due to patient recruitment difficulties. The source of funding was the only factor associated with trial success. As DBS research continues to grow, understanding the current state of clinical trials will help design successful future studies, thereby minimizing futile expenditures of time, cost, and patient engagement.

Celecoxib to improve scar quality following acute burn injury: Lessons learned after premature termination of a randomised trial

BackgroundPre-clinical studies suggest that non-steroidal anti-inflammatory therapy may reduce acute inflammation and modulate scar formation after burn injury. The objective of this trial was to determine if celecoxib administered soon after acute burn improved scar quality. MethodsA single centre, parallel group, placebo-controlled, randomised, superiority trial. Adults with non-major acute burn injury were eligible for recruitment. Participants within 48 h of admission for acute burn injury received either six weeks of celecoxib 200 mg twice daily or identically packaged placebo capsules. The primary outcome was participant-reported Patient Observer Scar Assessment Scale (POSAS) at day 42 following burn injury. The best possible score using this assessment is 7 and the worst 70. Key safety outcomes included study drug side effects. ResultsRestrictions imposed during the COVID-19 pandemic resulted in slow recruitment and early termination after 59 of the 150 planned participants were enrolled. The primary outcome was available for 21/30 (70 %) participants in the celecoxib arm and for 23/29 (79 %) who received placebo. There was no significant difference in the primary outcome between treatment groups with a mean POSAS of 33.4 (standard deviation 12.7) in the celecoxib arm and 36.9 (13.7) in the control arm giving a difference of −3.49 (95 % CI [-11.57, 4.59], p =.39). Gastrointestinal symptoms occurred in 3/30 (10 %) allocated celecoxib and in none allocated placebo. Secondary outcomes including graft loss and delayed healing were similar between groups. ConclusionsAnti-inflammatory therapy did not improve scar quality. Due to early trial termination and significant loss to follow up these findings should be interpreted cautiously. To improve generalisability and to attain recruitment targets, future trials should apply different approaches to improve participant retention as well as including patients with larger burn injuries and an older population.Trial registration: ACTRN12618000732280.

Sotagliflozin and Kidney Outcomes, Kidney Function, and Albuminuria in Type 2 Diabetes and CKD: A Secondary Analysis of the SCORED Trial.

In the initial analysis of the Effect of Sotagliflozin on Cardiovascular and Renal Events in Patients with Type 2 Diabetes and Moderate Renal Impairment Who Are at Cardiovascular Risk (SCORED) trial, because of early trial termination and suspension of adjudication, reconciliation of eGFR laboratory data and case report forms had not been completed. This resulted in a small number of kidney composite events and a nominal effect of sotagliflozin versus placebo on this outcome. This exploratory analysis uses laboratory eGFR data, regardless of case report form completion, to assess the effects of sotagliflozin on the predefined kidney composite end point in the SCORED trial and additional cardiorenal composite end points. SCORED was a multicenter, randomized trial evaluating cardiorenal outcomes with sotagliflozin versus placebo in 10,584 patients with type 2 diabetes and CKD. This exploratory analysis used laboratory data to derive the eGFR components and case report form data for the non-laboratory-defined components that together made up the kidney and cardiorenal composites. AKI was also assessed in this dataset. Using laboratory data, 223 events were identified, and sotagliflozin reduced the risk of the composite of first event of sustained ≥50% decline in eGFR, eGFR <15 ml/min per 1.73 m 2 , dialysis, or kidney transplant with 87 events (1.6%) in the sotagliflozin group and 136 events (2.6%) in the placebo group (hazard ratio [95% confidence interval], 0.62 [0.48 to 0.82]), P < 0.001). Sotagliflozin reduced the risk of a cardiorenal composite end point defined as the abovementioned composite plus cardiovascular or kidney death with 239 events (4.5%) in the sotagliflozin group and 306 events (5.7%) in the placebo group (hazard ratio [95% confidence interval], 0.77 [0.65 to 0.91], P = 0.0023). The results were consistent when using different eGFR decline thresholds and when only including kidney death in composites (all P < 0.01). The incidence of AKI was similar between treatment groups. In this exploratory analysis using the complete laboratory dataset, sotagliflozin reduced the risk of kidney and cardiorenal composite end points in patients with type 2 diabetes and CKD. ClinicalTrials.gov Identifier: NCT03315143 .

Understanding the Termination of Urologic Cancer Clinical Trials: Insights and Challenges.

Clinical trials are valuable evidence for managing urologic malignancies. Early termination of clinical trials is associated with a waste of resources and may substantially affect patient care. We sought to study the termination rate of urologic cancer clinical trials and identify factors associated with trial termination. A cross-sectional search of ClinicalTrials.gov identified completed and terminated kidney, prostate, and bladder cancer clinical trials started. Trials were assessed for reasons for termination. Multivariable analyses were conducted to determine the significant factors associated with the termination. Between 2000 and 2020, 9,145 oncology clinical trials were conducted, of which 11.30% (n = 1,033) were urologic cancer clinical trials. Of the urologic cancer clinical trials, 25.38% (n = 265) were terminated, with low patient accrual being the most common reason for termination, 52.9% (n = 127). Multivariable analysis showed that only the university funding source odds ratio (OR) of 2.20 (95% CI, 1.45 to 3.32), single-center studies OR of 2.11 (95% CI, 1.59 to 2.81), and sample size of <50 were significant predictors of clinical trial termination OR of 5.26 (95% CI, 3.85 to 7.69); all P values are <.001. The termination rate of urologic cancer clinical trials was 25%, with low accrual being the most frequently reported reason. Trials funded by a university, single-center trials, and small trials (sample size <50) were associated with early termination. A better understanding of these factors might help researchers, funding agencies, and other stakeholders prioritize resource allocations for multicenter trials that aim to recruit a sufficient number of patients.

POLARIS: A phase 2 trial of encorafenib plus binimetinib evaluating high-dose and standard-dose regimens in patients with BRAF V600-mutant melanoma with brain metastasis

Abstract Background POLARIS (phase 2 [ph2]; NCT03911869) evaluated encorafenib (BRAF inhibitor) in combination with binimetinib (MEK1/2 inhibitor) in BRAF/MEK inhibitor-naïve patients with BRAF V600-mutant melanoma with asymptomatic brain metastases. Methods The safety lead-in (SLI) assessed tolerability for high-dose encorafenib 300 mg twice daily (BID) plus binimetinib 45 mg BID. If the high dose was tolerable in ph2, patients would be randomized to receive high or standard dose (encorafenib 450 mg once daily [QD] plus binimetinib 45 mg BID). Otherwise, standard dose was evaluated as the recommended ph2 dose (RP2D). Patients who tolerated standard dosing during Cycle 1 could be dose escalated to encorafenib 600 mg QD plus binimetinib 45 mg BID in Cycle 2. Safety, efficacy, and pharmacokinetics were examined. Results RP2D was standard encorafenib dosing, as &amp;gt;33% of evaluable SLI patients (3/9) had dose-limiting toxicities. Overall, of 13 safety-evaluable patients (10 SLI, 3 ph2), 9 had prior immunotherapy. There were 9 treatment-related adverse events in the SLI and 3 in ph2. Of the SLI efficacy-evaluable patients (n = 10), 1 achieved complete response and 5 achieved partial responses (PR); the brain metastasis response rate (BMRR) was 60% (95% CI: 26.2, 87.8). In ph2, 2 of 3 patients achieved PR (BMRR, 67% [95% CI: 9.4, 99.2]). Repeated encorafenib 300 mg BID dosing did not increase steady-state exposure compared with historical 450 mg QD data. Conclusions Despite small patient numbers due to early trial termination, BMRR appeared similar between the SLI and ph2, and the ph2 safety profile appeared consistent with previous reports of standard-dose encorafenib in combination with binimetinib.

N-Acetylcysteine and a Specialized Preventive Intervention for Individuals at High Risk for Psychosis: A Randomized Double-Blind Multicenter Trial.

Clinical high risk for psychosis (CHR-P) offers a window of opportunity for early intervention and recent trials have shown promising results for the use of N-acetylcysteine (NAC) in schizophrenia. Moreover, integrated preventive psychological intervention (IPPI), applies social-cognitive remediation to aid in preventing the transition to the psychosis of CHR-P patients. In this double-blind, randomized, controlled multicenter trial, a 2 × 2 factorial design was applied to investigate the effects of NAC compared to placebo (PLC) and IPPI compared to psychological stress management (PSM). The primary endpoint was the transition to psychosis or deterioration of CHR-P symptoms after 18 months. While insufficient recruitment led to early trial termination, a total of 48 participants were included in the study. Patients receiving NAC showed numerically higher estimates of event-free survival probability (IPPI + NAC: 72.7 ± 13.4%, PSM + NAC: 72.7 ± 13.4%) as compared to patients receiving PLC (IPPI + PLC: 56.1 ± 15.3%, PSM + PLC: 39.0 ± 17.4%). However, a log-rank chi-square test in Kaplan-Meier analysis revealed no significant difference of survival probability for NAC vs control (point hazard ratio: 0.879, 95% CI 0.281-2.756) or IPPI vs control (point hazard ratio: 0.827, 95% CI 0.295-2.314). The number of adverse events (AE) did not differ significantly between the four groups. The superiority of NAC or IPPI in preventing psychosis in patients with CHR-P compared to controls could not be statistically validated in this trial. However, results indicate a consistent pattern that warrants further testing of NAC as a promising and well-tolerated intervention for CHR patients in future trials with adequate statistical power.

Group sequential two-stage preference designs.

The two-stage preference design (TSPD) enables inference for treatment efficacy while allowing for incorporation of patient preference to treatment. It can provide unbiased estimates for selection and preference effects, where a selection effect occurs when patients who prefer one treatment respond differently than those who prefer another, and a preference effect is the difference in response caused by an interaction between the patient's preference and the actual treatment they receive. One potential barrier to adopting TSPD in practice, however, is the relatively large sample size required to estimate selection and preference effects with sufficient power. To address this concern, we propose a group sequential two-stage preference design (GS-TSPD), which combines TSPD with sequential monitoring for early stopping. In the GS-TSPD, pre-planned sequential monitoring allows investigators to conduct repeated hypothesis tests on accumulated data prior to full enrollment to assess study eligibility for early trial termination without inflating type I error rates. Thus, the procedure allows investigators to terminate the study when there is sufficient evidence of treatment, selection, or preference effects during an interim analysis, thereby reducing the design resource in expectation. To formalize such a procedure, we verify the independent increments assumption for testing the selection and preference effects and apply group sequential stopping boundaries from the approximate sequential density functions. Simulations are then conducted to investigate the operating characteristics of our proposed GS-TSPD compared to the traditional TSPD. We demonstrate the applicability of the design using a study of Hepatitis C treatment modality.

Sample size reestimation and Bayesian predictive probability for single-arm clinical trials with a time-to-event endpoint using Weibull distribution with unknown shape parameter

ABSTRACT This manuscript consists of two topics. Firstly, we explore the utility of internal pilot study (IPS) approach for reestimating sample size at an interim stage when a reliable estimate of the nuisance shape parameter of the Weibull distribution for modeling survival data is unavailable during the planning phase of a study. Although IPS approach can help rescue the study power, it is noted that the adjusted sample size can be as much as twice the initially planned sample size, which may put substantial practical constraints to continue the study. Secondly, we discuss Bayesian predictive probability for conducting interim analyses to obtain preliminary evidence of efficacy or futility of an experimental treatment warranting early termination of a clinical trial. In the context of single-arm clinical trials with time-to-event endpoints following Weibull distribution, we present the calculation of the Bayesian predictive probability when the shape parameter of the Weibull distribution is unknown. Based on the data accumulated at the interim, we propose two approaches which rely on the posterior mode or the entire posterior distribution of the shape parameter. To account for uncertainty in the shape parameter, it is recommended to incorporate its entire posterior distribution in our calculation.

Oral Orexin Receptor 2 Agonist in Narcolepsy Type 1

BackgroundNarcolepsy type 1 is caused by severe loss or lack of brain orexin neuropeptides.MethodsWe conducted a phase 2, randomized, placebo-controlled trial of TAK-994, an oral orexin receptor 2–selective agonist, in patients with narcolepsy type 1. Patients with confirmed narcolepsy type 1 according to clinical criteria were randomly assigned to receive twice-daily oral TAK-994 (30 mg, 90 mg, or 180 mg) or placebo. The primary end point was the mean change from baseline to week 8 in average sleep latency (the time it takes to fall asleep) on the Maintenance of Wakefulness Test (range, 0 to 40 minutes; normal ability to stay awake, ≥20 minutes). Secondary end points included the change in the Epworth Sleepiness Scale (ESS) score (range, 0 to 24, with higher scores indicating greater daytime sleepiness; normal, <10) and the weekly cataplexy rate.ResultsOf the 73 patients, 17 received TAK-994 at a dose of 30 mg twice daily, 20 received 90 mg twice daily, 19 received 180 mg twice daily, and 17 received placebo. The phase 2 trial and an extension trial were terminated early owing to hepatic adverse events. Primary end-point data were available for 41 patients (56%); the main reason for missing data was early trial termination. Least-squares mean changes to week 8 in average sleep latency on the MWT were 23.9 minutes in the 30-mg group, 27.4 minutes in the 90-mg group, 32.6 minutes in the 180-mg group, and −2.5 minutes in the placebo group (difference vs. placebo, 26.4 minutes in the 30-mg group, 29.9 minutes in the 90-mg group, and 35.0 minutes the 180-mg group; P<0.001 for all comparisons). Least-squares mean changes to week 8 in the ESS score were −12.2 in the 30-mg group, −13.5 in the 90-mg group, −15.1 in the 180-mg group, and −2.1 in the placebo group (difference vs. placebo, −10.1 in the 30-mg group, −11.4 in the 90-mg group, and −13.0 in the 180-mg group). Weekly incidences of cataplexy at week 8 were 0.27 in the 30-mg group, 1.14 in the 90-mg group, 0.88 in the 180-mg group, and 5.83 in the placebo group (rate ratio vs. placebo, 0.05 in the 30-mg group, 0.20 in the 90-mg group, and 0.15 in the 180-mg group). A total of 44 of 56 patients (79%) receiving TAK-994 had adverse events, most commonly urinary urgency or frequency. Clinically important elevations in liver-enzyme levels occurred in 5 patients, and drug-induced liver injury meeting Hy’s law criteria occurred in 3 patients.ConclusionsIn a phase 2 trial involving patients with narcolepsy type 1, an orexin receptor 2 agonist resulted in greater improvements on measures of sleepiness and cataplexy than placebo over a period of 8 weeks but was associated with hepatotoxic effects. (Funded by Takeda Development Center Americas; TAK-994-1501 and TAK-994-1504 ClinicalTrials.gov numbers, NCT04096560 and NCT04820842.)

Analysis of Terminated Hand and Wrist-Related Clinical Trials

While clinical trials provide high-quality evidence guiding medical decision-making, early trial termination can result in both lost time and resources. Our purpose was to investigate the rate of and reasons for clinical trial termination for hand and wrist-related conditions and identify study characteristics associated with early trial termination. The ClinicalTrials.gov database was queried for all hand and wrist-related clinical trials. All terminated and completed trials were reviewed, with characteristics and reasons for termination recorded. Study characteristics included type, purpose, intervention assessed, enrollment, group allocation, blinding, trial phase, sponsor type, and geographic region. Chi-square test was used to identify associations between trial characteristics and terminated versus completed status. A total of 793 hand and wrist-related clinical trials were identified, with 77 trials (10%) terminated prior to completion. The most common reason for termination was "recruitment/retention difficulty," reported in 37 (48%) terminated trials. In comparing competed versus terminated trials, primary purpose (nonobservational studies), enrollment (<50 patients), and geographic region (North America) were all significantly more likely to be terminated. Terminated trials were more likely to have an intervention type investigating a specific device or drug. Early trial termination for hand and wrist-related conditions is common (10%), with patient recruitment and retention identified as the leading cause of termination. Trials involving potential commercial incentives (those investigating a device or drug) were associated with an increased rate of trial termination. An emphasis on patient enrollment during study design may aid in mitigating the most common cause of early clinical trial termination.

Characteristics of Scientific Success in Phase 1 Pediatric Oncology Clinical Trials: Establishing a Need for Integrated Patient-Reported Outcomes Even in Early Phase Trials

Background: Pediatric clinical trials are particularly vulnerable to challenges in participant accrual, making them more likely to encounter early termination. Objective: To describe clinical trial characteristics of phase 1 pediatric oncology clinical trials registered in ClinicalTrials.gov through 2017. Method: We identified a retrospective cohort and abstracted key clinical trial characteristics from this data source. We calculated a bivariate logistic regression to explore factors related to early trial termination. Results: Over 1400 phase 1 trials were included in this analysis. Only 3.2% of trials included any type of patient-reported outcome (PRO) as a secondary outcome and 21.6% of phase 1 trials did not meet scientific success due to early termination. National Institutes of Health (NIH)-funded phase 1 trials had the lowest percentage of early termination. Conclusions: Phase 1 clinical trials for pediatric patients with cancer are at risk of early termination due to lack of enrollment. A deeper understanding of clinical trial design elements, characteristics, and eligibility determination may contribute to a higher proportion of trials meeting successful enrollment. Implications for Practice: Oncology nurses and nurse scientists uniquely contribute to an understanding of family-centered approaches and should take an active role in trial design for pediatric cancers that could help capture meaningful PROs during a vulnerable clinical trajectory. What Is Foundational: Very little attention has been given to understanding characteristics of phase 1 clinical trials for pediatric cancers that may impact the state of the science and care delivery for patients with advanced pediatric cancer diagnoses.

Piloting an automated clinical trial eligibility surveillance and provider alert system based on artificial intelligence and standard data models

BackgroundTo advance new therapies into clinical care, clinical trials must recruit enough participants. Yet, many trials fail to do so, leading to delays, early trial termination, and wasted resources. Under-enrolling trials make it impossible to draw conclusions about the efficacy of new therapies. An oft-cited reason for insufficient enrollment is lack of study team and provider awareness about patient eligibility. Automating clinical trial eligibility surveillance and study team and provider notification could offer a solution.MethodsTo address this need for an automated solution, we conducted an observational pilot study of our TAES (TriAl Eligibility Surveillance) system. We tested the hypothesis that an automated system based on natural language processing and machine learning algorithms could detect patients eligible for specific clinical trials by linking the information extracted from trial descriptions to the corresponding clinical information in the electronic health record (EHR). To evaluate the TAES information extraction and matching prototype (i.e., TAES prototype), we selected five open cardiovascular and cancer trials at the Medical University of South Carolina and created a new reference standard of 21,974 clinical text notes from a random selection of 400 patients (including at least 100 enrolled in the selected trials), with a small subset of 20 notes annotated in detail. We also developed a simple web interface for a new database that stores all trial eligibility criteria, corresponding clinical information, and trial-patient match characteristics using the Observational Medical Outcomes Partnership (OMOP) common data model. Finally, we investigated options for integrating an automated clinical trial eligibility system into the EHR and for notifying health care providers promptly of potential patient eligibility without interrupting their clinical workflow.ResultsAlthough the rapidly implemented TAES prototype achieved only moderate accuracy (recall up to 0.778; precision up to 1.000), it enabled us to assess options for integrating an automated system successfully into the clinical workflow at a healthcare system.ConclusionsOnce optimized, the TAES system could exponentially enhance identification of patients potentially eligible for clinical trials, while simultaneously decreasing the burden on research teams of manual EHR review. Through timely notifications, it could also raise physician awareness of patient eligibility for clinical trials.

Abstract P3-03-24: The WISDOM Study: Early Ascertainment of Breast Cancer Diagnoses by Utilizing Self-Reported Questionnaires and Data Warehouse Electronic Health Records

Abstract BACKGROUND WISDOM (Women Informed to Screen Depending on Measures of risk) is a pragmatic trial comparing the safety, efficacy, cost, and patient acceptability of personalized versus annual breast cancer screening in women ages 40-74 years with no personal history of breast cancer. Cancer registry reporting is considered the gold standard for ascertaining cancer diagnosis data; however, the time lag between diagnosis and reporting (1-2 years or more) by the California Cancer Registry (CCR) presents challenges. The WISDOM study must quickly ascertain and report accurate cancer diagnoses to the Data Safety Monitoring Board; delays could lead to preventable harm, unnecessary costs, and unwarranted early trial termination. To ascertain cancer diagnoses in a timely manner, we used multiple data collection methods and compared self-reported with Electronic Health Records (EHR) information, and in some cases verified with chart review. Specifically, we conducted a procedural study with WISDOM participants who sought care at a University of California (UC) health system by extracting their data from the University of California Data Warehouse (UCDW) to 1) verify self-reported breast cancer diagnoses in WISDOM; 2) ensure that all on-study breast cancer diagnoses have been captured within the WISDOM study cohort; and 3) assess the accuracy and reliability of self-reported data. The UCDW provides harmonized EHR data from the six University of California (UC) health systems and constitutes ~120 billion data points from 7.8 million patient records. METHODS We provided the UCDW with a list of WISDOM participants who indicated they had sought care from a UC health system. Participants with recorded breast cancer diagnostic code (ICD-10CM C50.) were matched with the WISDOM self-reported cancer dataset to uncover participants who had no recorded breast cancer diagnosis in the study system. For those individuals with no recorded self-report diagnosis, coordinators conducted manual chart review to determine whether the participants had been diagnosed with breast cancer. RESULTS This cohort included 11,314 enrolled WISDOM participants who self-reported care at a UC and had at least one diagnostic or procedural breast care code. Among these participants, 160 had a ICD-10-CM C50 breast cancer diagnostic code of which 132 breast cancer cases were confirmed: 111 were already self-reported through WISDOM and 21 additional confirmed breast cancer cases were identified through the UCDW. As a standard process, only WISDOM self-reported cancer cases that were confirmed by chart review are entered into the study database. The percentage of confirmed UCDW-identified cases that were not also self-reported was greater for recent diagnoses (16% overall and 6% for period prior to June 2020). Among the 11,314 participants in our cohort, the CCR identified 61 pre-June 2020 cancer diagnoses. Of these 61 diagnoses, 92% (56) were identified by the UCDW procedural process. If the UCDW had been used as the single source for cancer diagnosis, 21 participants without cancer and 7 participants with unclear status (unlocated or inaccessible records) would have been classified as cancer cases. DISCUSSION/CONCLUSION Self-reported data provides quick ascertainment with relative accuracy compared to cancer registry. Cancer ascertainment can be further improved by combining self-reported data with EHR data from a health system data warehouse registry, particularly for self-reported questionnaire issues such as timing and lack of response. Accuracy of self-reported cancer diagnosis from annually distributed questionnaires improves over time. Identifying cancer diagnosis discordance between data sources can inform processes to improve self-reported study accuracy. Citation Format: Katherine Leggat-Barr, Rita H. Ryu, Allison Stover Fiscalini, Tomiyuri Lewis, Rohini S. Bulusu, Samrrah A. Raouf, Hannah Lui Park, Alyssa N. Rocha, Liliana Johansen, Laura Van ’t Veer, Laura J. Esserman, Michael A. Hogarth. The WISDOM Study: Early Ascertainment of Breast Cancer Diagnoses by Utilizing Self-Reported Questionnaires and Data Warehouse Electronic Health Records [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-24.

Improving clinical trial design using interpretable machine learning based prediction of early trial termination

This study proposes using a machine learning pipeline to optimise clinical trial design. The goal is to predict early termination probability of clinical trials using machine learning modelling, and to understand feature contributions driving early termination. This will inform further suggestions to the study protocol to reduce the risk of wasted resources. A dataset containing 420,268 clinical trial records and 24 fields was extracted from the ct.gov registry. In addition to study characteristics features, 12,864 eligibility criteria search features are used, generated using a public annotated eligibility criteria dataset, CHIA. Furthermore, disease categorization features are used allowing a study to belong more than one category specified by clinicaltrials.gov. Ensemble models including random forest and extreme gradient boosting classifiers were used to train and evaluate predictive performance. We achieved a Receiver Operator Characteristic Area under the Curve score of 0.80, and balanced accuracy of 0.70 on the test set using gradient boosting classification. We used Shapley Additive Explanations to interpret the termination predictions to flag feature contributions. The proposed pipeline will lead to an optimised clinical trial design and consequently help potentially life-saving treatments reach patients faster.

The impact of the COVID-19 pandemic on early termination of ophthalmology clinical trials: A cross-sectional analysis of ClinicalTrials.gov

Objective: To study the effect of the COVID-19 pandemic on the early termination of ophthalmology clinical trials. Methods: On June 10, 2022, we searched ClinicalTrials.gov and identified clinical trials pertaining to eye diseases. We included trials last updated between January 1, 2020 and June 8, 2022, as ones possibly impacted by the pandemic. We selected all interventional trials in any stage and country that were “recruiting,” “active, not recruiting,” “enrolling by invitation,” “suspended,” “terminated,” “completed,” or “withdrawn” and excluded trials that had been completed or discontinued before 2020, had incomplete data, trials in which the eye was not the primary focus of the trial (e.g., Chediak-Higashi syndrome, myasthenia gravis). The following trial-level characteristics were collected: location, trial status, enrollment count, ocular condition, sponsors, intervention purpose, trial phase (I–IV), randomization, number of arms, and reasons for discontinuation. In addition to calculating descriptive statistics, we assessed whether trial characteristics differed between ophthalmology clinical trials canceled due to COVID-19 and those canceled for other reasons. Results: Following the screening, 2280/12,679 (18%) ophthalmology clinical trials were retained. Of these, 142 (6.2%) were discontinued between January 1, 2020 and June 8, 2022. Moreover, 34 out of 142 (23.9%) ophthalmology clinical trials were discontinued due to COVID-19. These trials were more likely to be sponsored by academic medical centers (26/34, 76.5% vs 57/108, 52.8%, p = 0.03) and were not assigned to a specific study phase, indicating they were not investigational new drugs (22/34, 64.7% vs 46/108 42.6%, p = 0.003). Conclusions: COVID-19-related trial discontinuations were more likely to be reported by academic medical centers and associated with trials investigating fully approved drugs, medical devices, procedures, diagnostic imaging, and behavioral changes. Further investigation of these characteristics may lead to a more robust and resilient understanding of the causes of early termination of these clinical trials.

Phase I Trial of Brentuximab Vedotin Plus Cyclosporine in Relapsed/Refractory Hodgkin Lymphoma

Introduction Treatment of relapsed/refractory (R/R) Hodgkin lymphoma (HL) following failure of brentuximab vedotin (BV) and PD-1 blockade is a major unmet need. Resistance to BV is associated with multidrug resistance gene 1 (MDR1) overexpression. There are pre-clinical data showing that MDR1 inhibitors such as cyclosporine (CsA) or verapamil (VRP) are synergistic with BV in BV-resistant HL cell lines. Preliminary results of a phase I study evaluating the safety and efficacy of combination of BV with CsA and VRP in R/R HL were previously reported. Here we report the final results of the completed study. Methods This is a single center, open-label phase I investigator-initiated study. Patients (pts) with HL whose disease had relapsed or was refractory to ≥1 prior lines of therapy were enrolled. The dose finding portion followed a 3+3 design with 4 planned dose levels (DL). BV was given at 1.2 mg/kg (DL1) or 1.8 mg/kg (DL2-4) intravenously every 3 weeks. Planned dose of CsA was 5 mg/kg (DL1-2) or 7.5 mg/kg (DL3-4) orally twice daily on days 1-5. Planned dose of VRP was 120 mg orally four times daily on days 1-5 (DL4). Each cycle was 3 weeks. Once the maximum tolerated dose (MTD) was determined, BV-refractory pts were enrolled in an expansion cohort at the MTD. Primary objective was to determine the MTD and safety of the combination. Secondary objectives were overall response rate (ORR), complete response (CR) rate, response duration (DOR), overall survival (OS), and progression-free survival (PFS). Results Twenty-nine pts were enrolled onto the study, 14 in the dose-finding portion and 15 in the dose expansion BV-refractory cohort. With respect to baseline characteristics, 38% of patients were female, 69% had advanced stage disease, 45% had extranodal disease, and 24% had B symptoms. Median age was 36 (20-69) and pts had a median of 5 (3-12) prior lines of therapy. All had prior BV (only 2 BV-sensitive) and 27 had prior PD-1 blockade. Four pts were treated on DL1, 22 on DL2, and 3 on DL3. MTD was DL2. DLTs observed were: DL1 10-day CsA (n=1) grade (Gr) 3 hyperbilirubinemia, abdominal pain, and hypertension, DL2 (n=1) Gr3 abdominal pain and Gr3-4 neutropenia, DL3 (n=2) Gr3 bone pain/constipation/Gr4 lymphopenia (n=1) and Gr3 hyperglycemia (n=1). Median duration of treatment was 3 (1-16) cycles. Most frequent adverse events (AEs) were nausea (90%), hypertension (90%), anemia (86%), fatigue (76%), neutropenia (76%), and leukopenia (76%). All pts had Gr3+ AEs with most frequent being neutropenia (62%). Reasons for treatment discontinuation included death (n=4), disease progression (n=10), toxicity (n=2), patient refusal (n=8), loss to follow-up (n=2), MD decision (n=1), proceeding to HCT (n=1), and study completion (n=1). Treatment-related death occurred in 3 pts (pneumonitis at DL1, respiratory failure and hypotension at DL2). The ORR/CR was 62%/24%. The median DOR and PFS was 5 months and median OS was not reached. Discussion The combination of BV and CsA was effective in BV-refractory R/R HL but was also associated with toxicity, including treatment-related deaths that led to early termination of the clinical trial. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal