Decreasing Opioid Addiction and Diversion Using Behavioral Economics Applied Through a Digital Engagement Solution: Protocol for a Randomized Controlled Trial.

BACKGROUND: Despite strong and growing interest in ending the ongoing opioid health crisis, there has been limited success in reducing the prevalence of opioid addiction and the number of deaths associated with opioid overdoses. Further, 1 explanation for this is that existing interventions target those who are opiate-dependent but do not prevent opioid-naïve patients from becoming addicted. OBJECTIVE: Leveraging behavioral economics at the patient level could help patients successfully use, discontinue, and dispose of their opioid medications in an acute pain setting. The primary goal of this project is to evaluate the effect of the 3 versions of the Opioid Management for You (OPY) tool on measures of opioid use relative to the standard of care by leveraging a pragmatic randomized controlled trial (RCT). METHODS: A team of researchers from the Center for Learning Health System Sciences (CLHSS) at the University of Minnesota partnered with M Health Fairview to design, build, and test the 3 versions of the OPY tool: social influence, precommitment, and testimonial version. The tool is being built using the Epic Care Companion (Epic Inc) platform and interacts with the patient through their existing MyChart (Epic Systems Corporation) personal health record account, and Epic patient portal, accessed through a phone app or the MyChart website. We have demonstrated feasibility with pilot data of the social influence version of the OPY app by targeting our pilot to a specific cohort of patients undergoing upper-extremity procedures. This study will use a group sequential RCT design to test the impact of this important health system initiative. Patients who meet OPY inclusion criteria will be stratified into low, intermediate, and high risk of opiate use based on their type of surgery. RESULTS: This study is being funded and supported by the CLHSS Rapid Prospective Evaluation and Digital Technology Innovation Programs, and M Health Fairview. Support and coordination provided by CLHSS include the structure of engagement, survey development, data collection, statistical analysis, and dissemination. The project was initially started in August 2022. The pilot was launched in February 2023 and is still running, with the data last counted in August 2023. The actual RCT is planned to start by early 2024. CONCLUSIONS: Through this RCT, we will test our hypothesis that patient opioid use and diverted prescription opioid availability can both be improved by information delivery applied through a behavioral economics lens via sending nudges directly to the opioid users through their personal health record. TRIAL REGISTRATION: ClinicalTrials.gov NCT06124079; https://clinicaltrials.gov/study/NCT06124079. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/52882.

Uncertainty directed factorial clinical trials.

The development and evaluation of novel treatment combinations is a key component of modern clinical research. The primary goals of factorial clinical trials of treatment combinations range from the estimation of intervention-specific effects, or the discovery of potential synergies, to the identification of combinations with the highest response probabilities. Most factorial studies use balanced or block randomization, with an equal number of patients assigned to each treatment combination, irrespective of the specific goals of the trial. Here, we introduce a class of Bayesian response-adaptive designs for factorial clinical trials with binary outcomes. The study design was developed using Bayesian decision-theoretic arguments and adapts the randomization probabilities to treatment combinations during the enrollment period based on the available data. Our approach enables the investigator to specify a utility function representative of the aims of the trial, and the Bayesian response-adaptive randomization algorithm aims to maximize this utility function. We considered several utility functions and factorial designs tailored to them. Then, we conducted a comparative simulation study to illustrate relevant differences of key operating characteristics across the resulting designs. We also investigated the asymptotic behavior of the proposed adaptive designs. We also used data summaries from three recent factorial trials in perioperative care, smoking cessation, and infectious disease prevention to define realistic simulation scenarios and illustrate advantages of the introduced trial designs compared to other study designs.

Looking ahead in early-phase trial design to improve the drug development process: examples in oncology.

BACKGROUND: Clinical trial design must consider the specific resource constraints and overall goals of the drug development process (DDP); for example, in designing a phase I trial to evaluate the safety of a drug and recommend a dose for a subsequent phase II trial. Here, we focus on design considerations that involve the sequence of clinical trials, from early phase I to late phase III, that constitute the DDP. METHODS: We discuss how stylized simulation models of clinical trials in an oncology DDP can quantify important relationships between early-phase trial designs and their consequences for the remaining phases of development. Simulations for three illustrative settings are presented, using stylized models of the DDP that mimic trial designs and decisions, such as the potential discontinuation of the DDP. RESULTS: We describe: (1) the relationship between a phase II single-arm trial sample size and the likelihood of a positive result in a subsequent phase III confirmatory trial; (2) the impact of a phase I dose-finding design on the likelihood that the DDP will produce evidence of a safe and effective therapy; and (3) the impact of a phase II enrichment trial design on the operating characteristics of a subsequent phase III confirmatory trial. CONCLUSIONS: Stylized models of the DDP can support key decisions, such as the sample size, in the design of early-phase trials. Simulation models can be used to estimate performance metrics of the DDP under realistic scenarios; for example, the duration and the total number of patients enrolled. These estimates complement the evaluation of the operating characteristics of early-phase trial design, such as power or accuracy in selecting safe and effective dose levels.

Accessible Data Collections for Improved Decision Making in Neuro-Oncology Clinical Trials.

Drug development can be associated with slow timelines, particularly for rare or difficult-to-treat solid tumors such as glioblastoma. The use of external data in the design and analysis of trials has attracted significant interest because it has the potential to improve the efficiency and precision of drug development. A recurring challenge in the use of external data for clinical trials, however, is the difficulty in accessing high-quality patient-level data. Academic research groups generally do not have access to suitable datasets to effectively leverage external data for planning and analyses of new clinical trials. Given the need for resources to enable investigators to benefit from existing data assets, we have developed the Glioblastoma External (GBM-X) Data Platform which will allow investigators in neuro-oncology to leverage our data collection and obtain analyses. GBM-X strives to provide an uncomplicated process to use external data, contextualize single-arm trials, and improve inference on treatment effects early in drug development. The platform is designed to welcome interested collaborators and integrate new data into the platform, with the expectation that the data collection can continue to grow and remain updated. With such features, GBM-X is designed to help to accelerate evaluation of therapies, to grow with collaborations, and to serve as a model to improve drug discovery for rare and difficult-to-treat tumors in oncology.

Validation of Predictive Analyses for Interim Decisions in Clinical Trials.

PURPOSE: Adaptive clinical trials use algorithms to predict, during the study, patient outcomes and final study results. These predictions trigger interim decisions, such as early discontinuation of the trial, and can change the course of the study. Poor selection of the Prediction Analyses and Interim Decisions (PAID) plan in an adaptive clinical trial can have negative consequences, including the risk of exposing patients to ineffective or toxic treatments. METHODS: We present an approach that leverages data sets from completed trials to evaluate and compare candidate PAIDs using interpretable validation metrics. The goal is to determine whether and how to incorporate predictions into major interim decisions in a clinical trial. Candidate PAIDs can differ in several aspects, such as the prediction models used, timing of interim analyses, and potential use of external data sets. To illustrate our approach, we considered a randomized clinical trial in glioblastoma. The study design includes interim futility analyses on the basis of the predictive probability that the final analysis, at the completion of the study, will provide significant evidence of treatment effects. We examined various PAIDs with different levels of complexity to investigate if the use of biomarkers, external data, or novel algorithms improved interim decisions in the glioblastoma clinical trial. RESULTS: Validation analyses on the basis of completed trials and electronic health records support the selection of algorithms, predictive models, and other aspects of PAIDs for use in adaptive clinical trials. By contrast, PAID evaluations on the basis of arbitrarily defined ad hoc simulation scenarios, which are not tailored to previous clinical data and experience, tend to overvalue complex prediction procedures and produce poor estimates of trial operating characteristics such as power and the number of enrolled patients. CONCLUSION: Validation analyses on the basis of completed trials and real world data support the selection of predictive models, interim analysis rules, and other aspects of PAIDs in future clinical trials.

Prospectively shared control data across concurrent randomised clinical trials.

Sharing data from control groups across concurrent randomised clinical trials with identical enrolment criteria and the same control therapy can translate into efficiencies for the drug development process. We discuss potential benefits and risks of prospective data-sharing plans for concurrent randomised trials.

Inference in response-adaptive clinical trials when the enrolled population varies over time.

A common assumption of data analysis in clinical trials is that the patient population, as well as treatment effects, do not vary during the course of the study. However, when trials enroll patients over several years, this hypothesis may be violated. Ignoring variations of the outcome distributions over time, under the control and experimental treatments, can lead to biased treatment effect estimates and poor control of false positive results. We propose and compare two procedures that account for possible variations of the outcome distributions over time, to correct treatment effect estimates, and to control type-I error rates. The first procedure models trends of patient outcomes with splines. The second leverages conditional inference principles, which have been introduced to analyze randomized trials when patient prognostic profiles are unbalanced across arms. These two procedures are applicable in response-adaptive clinical trials. We illustrate the consequences of trends in the outcome distributions in response-adaptive designs and in platform trials, and investigate the proposed methods in the analysis of a glioblastoma study.

The design and evaluation of hybrid controlled trials that leverage external data and randomization.

Patient-level data from completed clinical studies or electronic health records can be used in the design and analysis of clinical trials. However, these external data can bias the evaluation of the experimental treatment when the statistical design does not appropriately account for potential confounders. In this work, we introduce a hybrid clinical trial design that combines the use of external control datasets and randomization to experimental and control arms, with the aim of producing efficient inference on the experimental treatment effects. Our analysis of the hybrid trial design includes scenarios where the distributions of measured and unmeasured prognostic patient characteristics differ across studies. Using simulations and datasets from clinical studies in extensive-stage small cell lung cancer and glioblastoma, we illustrate the potential advantages of hybrid trial designs compared to externally controlled trials and randomized trial designs.

The use of external control data for predictions and futility interim analyses in clinical trials.

BACKGROUND: External control (EC) data from completed clinical trials and electronic health records can be valuable for the design and analysis of future clinical trials. We discuss the use of EC data for early stopping decisions in randomized clinical trials (RCTs). METHODS: We specify interim analyses (IAs) approaches for RCTs, which allow investigators to integrate external data into early futility stopping decisions. IAs utilize predictions based on early data from the RCT, possibly combined with external data. These predictions at IAs express the probability that the trial will generate significant evidence of positive treatment effects. The trial is discontinued if this predictive probability becomes smaller than a prespecified threshold. We quantify efficiency gains and risks associated with the integration of external data into interim decisions. We then analyze a collection of glioblastoma (GBM) data sets, to investigate if the balance of efficiency gains and risks justify the integration of external data into the IAs of future GBM RCTs. RESULTS: Our analyses illustrate the importance of accounting for potential differences between the distributions of prognostic variables in the RCT and in the external data to effectively leverage external data for interim decisions. Using GBM data sets, we estimate that the integration of external data increases the probability of early stopping of ineffective experimental treatments by up to 25% compared to IAs that do not leverage external data. Additionally, we observe a reduction of the probability of early discontinuation for effective experimental treatments, which improves the RCT power. CONCLUSION: Leveraging external data for IAs in RCTs can support early stopping decisions and reduce the number of enrolled patients when the experimental treatment is ineffective.

Integration of survival data from multiple studies.

We introduce a statistical procedure that integrates datasets from multiple biomedical studies to predict patients' survival, based on individual clinical and genomic profiles. The proposed procedure accounts for potential differences in the relation between predictors and outcomes across studies, due to distinct patient populations, treatments and technologies to measure outcomes and biomarkers. These differences are modeled explicitly with study-specific parameters. We use hierarchical regularization to shrink the study-specific parameters towards each other and to borrow information across studies. The estimation of the study-specific parameters utilizes a similarity matrix, which summarizes differences and similarities of the relations between covariates and outcomes across studies. We illustrate the method in a simulation study and using a collection of gene expression datasets in ovarian cancer. We show that the proposed model increases the accuracy of survival predictions compared to alternative meta-analytic methods.

Leveraging external data in the design and analysis of clinical trials in neuro-oncology.

Integration of external control data, with patient-level information, in clinical trials has the potential to accelerate the development of new treatments in neuro-oncology by contextualising single-arm studies and improving decision making (eg, early stopping decisions). Based on a series of presentations at the 2020 Clinical Trials Think Tank hosted by the Society of Neuro-Oncology, we provide an overview on the use of external control data representative of the standard of care in the design and analysis of clinical trials. High-quality patient-level records, rigorous methods, and validation analyses are necessary to effectively leverage external data. We review study designs, statistical methods, risks, and potential distortions in using external data from completed trials and real-world data, as well as data sources, data sharing models, ongoing work, and applications in glioblastoma.

KMDATA: a curated database of reconstructed individual patient-level data from 153 oncology clinical trials.

We created a database of reconstructed patient-level data from published clinical trials that includes multiple time-to-event outcomes such as overall survival and progression-free survival. Outcomes were extracted from Kaplan-Meier (KM) curves reported in 153 oncology Phase III clinical trial publications identified through a PubMed search of clinical trials in breast, lung, prostate and colorectal cancer, published between 2014 and 2016. For each trial that met our search criteria, we curated study-level information and digitized all reported KM curves with the software Digitizelt. We then used the digitized KM survival curves to estimate (possibly censored) patient-level time-to-event outcomes. Collections of time-to-event datasets from completed trials can be used to support the choice of appropriate trial designs for future clinical studies. Patient-level data allow investigators to tailor clinical trial designs to diseases and classes of treatments. Patient-level data also allow investigators to estimate the operating characteristics (e.g. power and type I error rate) of candidate statistical designs and methods. Database URL: https://10.6084/m9.figshare.14642247.v1.

Assessment of Simulated SARS-CoV-2 Infection and Mortality Risk Associated With Radiation Therapy Among Patients in 8 Randomized Clinical Trials.

Importance: During the COVID-19 pandemic, cancer therapy may put patients at risk of SARS-CoV-2 infection and mortality. The impacts of proposed alternatives on reducing infection risk are unknown. Objective: To investigate how the COVID-19 pandemic is associated with the risks and benefits of standard radiation therapy (RT). Design, Setting, and Participants: This comparative effectiveness study used estimated individual patient-level data extracted from published Kaplan-Meier survival figures from 8 randomized clinical trials across oncology from 1993 to 2014 that evaluated the inclusion of RT or compared different RT fractionation regimens. Included trials were Dutch TME and TROG 01.04 examining rectal cancer; CALGB 9343, OCOG hypofractionation trial, FAST-Forward, and NSABP B-39 examining early stage breast cancer, and CHHiP and HYPO-RT-PC examining prostate cancer. Risk of SARS-CoV-2 infection and mortality associated with receipt of RT in the treatment arms were simulated and trials were reanalyzed. Data were analyzed between April 1, 2020, and June 30, 2020. Exposures: COVID-19 risk associated with treatment was simulated across different pandemic scenarios, varying infection risk per fractions (IRFs) and case fatality rates (CFRs). Main Outcomes and Measures: Overall survival was evaluated using Cox proportional hazards modeling under different pandemic scenarios. Results: Estimated IPLD from a total of 14 170 patients were included in the simulations. In scenarios with low COVID-19-associated risks (IRF, 0.5%; CFR, 5%), fractionation was not significantly associated with outcomes. In locally advanced rectal cancer, short-course RT was associated with better outcomes than long-course chemoradiation (TROG 01.04) and was associated with similar outcomes as RT omission (Dutch TME) in most settings (eg, TROG 01.04 median HR, 0.66 [95% CI, 0.46-0.96]; Dutch TME median HR, 0.91 [95% CI, 0.80-1.03] in a scenario with IRF 5% and CFR 20%). Moderate hypofractionation in early stage breast cancer (OCOG hypofractionation trial) and prostate cancer (CHHiP) was not associated with survival benefits in the setting of COVID-19 (eg, OCOG hypofractionation trial median HR, 0.89 [95% CI, 0.74-1.06]; CHHiP median HR, 0.87 [95% CI, 0.75-1.01] under high-risk scenario with IRF 10% and CFR 30%). More aggressive hypofractionation (FAST-Forward, HYPO-RT-PC) and accelerated partial breast irradiation (NSABP B-39) were associated with improved survival in higher risk scenarios (eg, FAST-Forward median HR, 0.58 [95% CI, 0.49-0.68]; HYPO-RT-PC median HR, 0.60 [95% CI, 0.48-0.75] under scenario with IRF 10% and CFR 30%). Conclusions and Relevance: In this comparative effectiveness study of data from 8 clinical trials of patients receiving radiation therapy to simulate COVID-19 risk and mortality rates, treatment modification was not associated with altered risk from COVID-19 in lower-risk scenarios and was only associated with decreased mortality in very high COVID-19-risk scenarios. This model, which can be adapted to dynamic changes in COVID-19 risk, provides a flexible, quantitative approach to assess the potential impact of treatment modifications and supports the continued delivery of standard evidence-based care with appropriate precautions against COVID-19.

The effects of releasing early results from ongoing clinical trials.

Most trials do not release interim summaries on efficacy and toxicity of the experimental treatments being tested, with this information only released to the public after the trial has ended. While early release of clinical trial data to physicians and patients can inform enrollment decision making, it may also affect key operating characteristics of the trial, statistical validity and trial duration. We investigate the public release of early efficacy and toxicity results, during ongoing clinical studies, to better inform patients about their enrollment options. We use simulation models of phase II glioblastoma (GBM) clinical trials in which early efficacy and toxicity estimates are periodically released accordingly to a pre-specified protocol. Patients can use the reported interim efficacy and toxicity information, with the support of physicians, to decide which trial to enroll in. We describe potential effects on various operating characteristics, including the study duration, selection bias and power.

TBCRC 048: Phase II Study of Olaparib for Metastatic Breast Cancer and Mutations in Homologous Recombination-Related Genes.

PURPOSE: Olaparib, a poly (ADP-ribose) polymerase (PARP) inhibitor (PARPi), is approved for the treatment of human epidermal growth factor receptor 2 (HER2)-negative metastatic breast cancer (MBC) in germline (g)BRCA1/2 mutation carriers. Olaparib Expanded, an investigator-initiated, phase II study, assessed olaparib response in patients with MBC with somatic (s)BRCA1/2 mutations or g/s mutations in homologous recombination (HR)-related genes other than BRCA1/2. METHODS: Eligible patients had MBC with measurable disease and germline mutations in non-BRCA1/2 HR-related genes (cohort 1) or somatic mutations in these genes or BRCA1/2 (cohort 2). Prior PARPi, platinum-refractory disease, or progression on more than two chemotherapy regimens (metastatic setting) was not allowed. Patients received olaparib 300 mg orally twice a day until progression. A single-arm, two-stage design was used. The primary endpoint was objective response rate (ORR); the null hypothesis (≤ 5% ORR) would be rejected within each cohort if there were four or more responses in 27 patients. Secondary endpoints included clinical benefit rate and progression-free survival (PFS). RESULTS: Fifty-four patients enrolled. Seventy-six percent had estrogen receptor-positive HER2-negative disease. Eighty-seven percent had mutations in PALB2, sBRCA1/2, ATM, or CHEK2. In cohort 1, ORR was 33% (90% CI, 19% to 51%) and in cohort 2, 31% (90% CI, 15% to 49%). Confirmed responses were seen only with gPALB2 (ORR, 82%) and sBRCA1/2 (ORR, 50%) mutations. Median PFS was 13.3 months (90% CI, 12 months to not available/computable [NA]) for gPALB2 and 6.3 months (90% CI, 4.4 months to NA) for sBRCA1/2 mutation carriers. No responses were observed with ATM or CHEK2 mutations alone. CONCLUSION: PARP inhibition is an effective treatment for patients with MBC and gPALB2 or sBRCA1/2 mutations, significantly expanding the population of patients with breast cancer likely to benefit from PARPi beyond gBRCA1/2 mutation carriers. These results emphasize the value of molecular characterization for treatment decisions in MBC.

A Quantitative Framework for Modeling COVID-19 Risk During Adjuvant Therapy Using Published Randomized Trials of Glioblastoma in the Elderly.

BACKGROUND: During the ongoing COVID-19 pandemic, contact with the healthcare system for cancer treatment can increase risk of infection and associated mortality. Treatment recommendations must consider this risk for elderly and vulnerable cancer patients. We re-analyzed trials in elderly glioblastoma (GBM) patients, incorporating COVID-19 risk, in order to provide a quantitative framework for comparing different radiation (RT) fractionation schedules on patient outcomes. METHODS: We extracted individual patient-level data (IPLD) for 1,321 patients from Kaplan-Meier curves from five randomized trials on treatment of elderly GBM patients including available subanalyses based on MGMT methylation status. We simulated trial data with incorporation of COVID-19 associated mortality risk in several scenarios (low, medium, and high infection and mortality risks). Median overall survival and hazard ratios were calculated for each simulation replicate. RESULTS: Our simulations reveal how COVID-19-associated risks affect survival under different treatment regimens. Hypofractionated RT with concurrent and adjuvant temozolomide (TMZ) demonstrated the best outcomes in low and medium risk scenarios. In frail elderly patients, shorter courses of RT are preferable. In patients with methylated MGMT receiving single modality treatment, TMZ-alone treatment approaches may be an option in settings with high COVID-19-associated risk. CONCLUSIONS: Incorporation of COVID-19-associated risk models into analysis of randomized trials can help guide clinical decisions during this pandemic. In elderly GBM patients, our results support prioritization of hypofractionated RT and highlight the utility of MGMT methylation status in decision-making in pandemic scenarios. Our quantitative framework can serve as a model for assessing COVID-19 risk associated with treatment across neuro-oncology.

Lessons Learned from Deescalation Trials in Favorable Risk HPV-Associated Squamous Cell Head and Neck Cancer-A Perspective on Future Trial Designs.

In recent years several clinical studies have investigated deintensified treatments in human papillomavirus (HPV)-associated head and neck squamous cell carcinoma. Two large phase III trials, RTOG 1016 and De-ESCALaTE, which attempted to reduce toxicity by replacing radiotherapy in combination with cisplatin with the use of cetuximab in combination with radiotherapy, recently suggested that radiotherapy + cetuximab leads to inferior survival compared with standard therapy (observed HRs of 1.45 and 5 in RTOG 1016 and De-ESCALaTE), as well as increased rates of locoregional failure. These unexpected results should prompt a careful examination of deintensification trials, both in HPV-associated oropharyngeal cancer and in other contexts. Statistical designs for deintensification studies should be consistent with the study aims of reducing toxicities while maintaining survival nearly identical to the standard of care. We suggest criteria to design future deintensification trials and discuss important operating characteristics, including tradeoffs between power and stringent early stopping rules to reduce the number of patients exposed to inferior treatments. Using retrospective analyses of previous clinical studies, we compared designs with different operating characteristics. As an example, using outcomes data from RTOG 1016 and De-ESCALaTE, we conducted analyses to determine advantages of (i) stringent futility early-stopping rules and of (ii) study designs that leverage both toxicity and efficacy endpoints for interim analyses. We show that increasing the frequency of interim-futility analyses has little impact on power, but the average study duration and number of subjects enrolled before the trial is closed for inferiority can decrease substantially (from 57.8 to 18 months, and from 764 to 645 subjects). Moreover, the number of observed deaths during the study can be reduced by up to 68%.

Deviation from the Proportional Hazards Assumption in Randomized Phase 3 Clinical Trials in Oncology: Prevalence, Associated Factors, and Implications.

PURPOSE: Deviations from proportional hazards (DPHs), which may be more prevalent in the era of precision medicine and immunotherapy, can lead to underpowered trials or misleading conclusions. We used a meta-analytic approach to estimate DPHs across cancer trials, investigate associated factors, and evaluate data-analysis approaches for future trials.Experimental Design: We searched PubMed for phase III trials in breast, lung, prostate, and colorectal cancer published in a preselected list of journals between 2014 and 2016 and extracted individual patient-level data (IPLD) from Kaplan-Meier curves. We re-analyzed IPLD to identify DPHs. Potential efficiency gains, when DPHs were present, of alternative statistical methods relative to standard log-rank based analysis were expressed as sample-size requirements for a fixed power level. RESULTS: From 152 trials, we obtained IPLD on 129,401 patients. Among 304 Kaplan-Meier figures, 75 (24.7%) exhibited evidence of DPHs, including eight of 14 (57%) KM pairs from immunotherapy trials. Trial type [immunotherapy, odds ratio (OR), 4.29; 95% confidence interval (CI), 1.11-16.6], metastatic patient population (OR, 3.18; 95% CI, 1.26-8.05), and non-OS endpoints (OR, 3.23; 95% CI, 1.79-5.88) were associated with DPHs. In immunotherapy trials, alternative statistical approaches allowed for more efficient clinical trials with fewer patients (up to 74% reduction) relative to log-rank testing. CONCLUSIONS: DPHs were found in a notable proportion of time-to-event outcomes in published clinical trials in oncology and was more common for immunotherapy trials and non-OS endpoints. Alternative statistical methods, without proportional hazards assumptions, should be considered in the design and analysis of clinical trials when the likelihood of DPHs is high.