A phase Ib/II randomized, open-label drug repurposing trial of glutamate signaling inhibitors in combination with chemoradiotherapy in patients with newly diagnosed glioblastoma: the GLUGLIO trial protocol.

BACKGROUND: Glioblastoma is the most common and most aggressive malignant primary brain tumor in adults. Glioblastoma cells synthesize and secrete large quantities of the excitatory neurotransmitter glutamate, driving epilepsy, neuronal death, tumor growth and invasion. Moreover, neuronal networks interconnect with glioblastoma cell networks through glutamatergic neuroglial synapses, activation of which induces oncogenic calcium oscillations that are propagated via gap junctions between tumor cells. The primary objective of this study is to explore the efficacy of brain-penetrating anti-glutamatergic drugs to standard chemoradiotherapy in patients with glioblastoma. METHODS/DESIGN: GLUGLIO is a 1:1 randomized phase Ib/II, parallel-group, open-label, multicenter trial of gabapentin, sulfasalazine, memantine and chemoradiotherapy (Arm A) versus chemoradiotherapy alone (Arm B) in patients with newly diagnosed glioblastoma. Planned accrual is 120 patients. The primary endpoint is progression-free survival at 6 months. Secondary endpoints include overall and seizure-free survival, quality of life of patients and caregivers, symptom burden and cognitive functioning. Glutamate levels will be assessed longitudinally by magnetic resonance spectroscopy. Other outcomes of interest include imaging response rate, neuronal hyperexcitability determined by longitudinal electroencephalography, Karnofsky performance status as a global measure of overall performance, anticonvulsant drug use and steroid use. Tumor tissue and blood will be collected for translational research. Subgroup survival analyses by baseline parameters include segregation by age, extent of resection, Karnofsky performance status, O6-methylguanine DNA methyltransferase (MGMT) promotor methylation status, steroid intake, presence or absence of seizures, tumor volume and glutamate levels determined by MR spectroscopy. The trial is currently recruiting in seven centers in Switzerland. TRIAL REGISTRATION: NCT05664464. Registered 23 December 2022.

Beyond the two-trials rule.

The two-trials rule for drug approval requires "at least two adequate and well-controlled studies, each convincing on its own, to establish effectiveness." This is usually implemented by requiring two significant pivotal trials and is the standard regulatory requirement to provide evidence for a new drug's efficacy. However, there is need to develop suitable alternatives to this rule for a number of reasons, including the possible availability of data from more than two trials. I consider the case of up to three studies and stress the importance to control the partial Type-I error rate, where only some studies have a true null effect, while maintaining the overall Type-I error rate of the two-trials rule, where all studies have a null effect. Some less-known P $$ P $$ -value combination methods are useful to achieve this: Pearson's method, Edgington's method and the recently proposed harmonic mean χ 2 $$ {\chi}^2 $$ -test. I study their properties and discuss how they can be extended to a sequential assessment of success while still ensuring overall Type-I error control. I compare the different methods in terms of partial Type-I error rate, project power and the expected number of studies required. Edgington's method is eventually recommended as it is easy to implement and communicate, has only moderate partial Type-I error rate inflation but substantially increased project power.

Power priors for replication studies.

The ongoing replication crisis in science has increased interest in the methodology of replication studies. We propose a novel Bayesian analysis approach using power priors: The likelihood of the original study's data is raised to the power of α, and then used as the prior distribution in the analysis of the replication data. Posterior distribution and Bayes factor hypothesis tests related to the power parameter α quantify the degree of compatibility between the original and replication study. Inferences for other parameters, such as effect sizes, dynamically borrow information from the original study. The degree of borrowing depends on the conflict between the two studies. The practical value of the approach is illustrated on data from three replication studies, and the connection to hierarchical modeling approaches explored. We generalize the known connection between normal power priors and normal hierarchical models for fixed parameters and show that normal power prior inferences with a beta prior on the power parameter α align with normal hierarchical model inferences using a generalized beta prior on the relative heterogeneity variance I2. The connection illustrates that power prior modeling is unnatural from the perspective of hierarchical modeling since it corresponds to specifying priors on a relative rather than an absolute heterogeneity scale.

Pan-African evolution of within- and between-country COVID-19 dynamics.

The coronavirus disease 2019 (COVID-19) pandemic is heterogeneous throughout Africa and threatening millions of lives. Surveillance and short-term modeling forecasts are critical to provide timely information for decisions on control strategies. We created a strategy that helps predict the country-level case occurrences based on cases within or external to a country throughout the entire African continent, parameterized by socioeconomic and geoeconomic variations and the lagged effects of social policy and meteorological history. We observed the effect of the Human Development Index, containment policies, testing capacity, specific humidity, temperature, and landlocked status of countries on the local within-country and external between-country transmission. One-week forecasts of case numbers from the model were driven by the quality of the reported data. Seeking equitable behavioral and social interventions, balanced with coordinated country-specific strategies in infection suppression, should be a continental priority to control the COVID-19 pandemic in Africa.

Simulating and reporting frequentist operating characteristics of clinical trials that borrow external information: Towards a fair comparison in case of one-arm and hybrid control two-arm trials.

Borrowing information from historical or external data to inform inference in a current trial is an expanding field in the era of precision medicine, where trials are often performed in small patient cohorts for practical or ethical reasons. Even though methods proposed for borrowing from external data are mainly based on Bayesian approaches that incorporate external information into the prior for the current analysis, frequentist operating characteristics of the analysis strategy are often of interest. In particular, type I error rate and power at a prespecified point alternative are the focus. We propose a procedure to investigate and report the frequentist operating characteristics in this context. The approach evaluates type I error rate of the test with borrowing from external data and calibrates the test without borrowing to this type I error rate. On this basis, a fair comparison of power between the test with and without borrowing is achieved. We show that no power gains are possible in one-sided one-arm and two-arm hybrid control trials with normal endpoint, a finding proven in general before. We prove that in one-arm fixed-borrowing situations, unconditional power (i.e., when external data is random) is reduced. The Empirical Bayes power prior approach that dynamically borrows information according to the similarity of current and external data avoids the exorbitant type I error inflation occurring with fixed borrowing. In the hybrid control two-arm trial we observe power reductions as compared to the test calibrated to borrowing that increase when considering unconditional power.

Combining evidence from clinical trials in conditional or accelerated approval.

Conditional (European Medicines Agency) or accelerated (U.S. Food and Drug Administration) approval of drugs allows earlier access to promising new treatments that address unmet medical needs. Certain post-marketing requirements must typically be met in order to obtain full approval, such as conducting a new post-market clinical trial. We study the applicability of the recently developed harmonic mean χ 2 -test to this conditional or accelerated approval framework. The proposed approach can be used both to support the design of the post-market trial and the analysis of the combined evidence provided by both trials. Other methods considered are the two-trials rule, Fisher's criterion and Stouffer's method. In contrast to some of the traditional methods, the harmonic mean χ 2 -test always requires a post-market clinical trial. If the p -value from the pre-market clinical trial is ≪ 0.025 , a smaller sample size for the post-market clinical trial is needed than with the two-trials rule. For illustration, we apply the harmonic mean χ 2 -test to a drug which received conditional (and later full) market licensing by the EMA. A simulation study is conducted to study the operating characteristics of the harmonic mean χ 2 -test and two-trials rule in more detail. We finally investigate the applicability of these two methods to compute the power at interim of an ongoing post-market trial. These results are expected to aid in the design and assessment of the required post-market studies in terms of the level of evidence required for full approval.

Comment on "Bayesian additional evidence for decision making under small sample uncertainty".

We examine the concept of Bayesian Additional Evidence (BAE) recently proposed by Sondhi et al. We derive simple closed-form expressions for BAE and compare its properties with other methods for assessing findings in the light of new evidence. We find that while BAE is easy to apply, it lacks both a compelling rationale and clarity of use needed for reliable decision-making.

Post-mortem magnetic resonance imaging with computed tomography-guided biopsy for foetuses and infants: a prospective, multicentre, cross-sectional study.

BACKGROUND: Post-mortem imaging has been suggested as an alternative to conventional autopsy in the prenatal and postnatal periods. Noninvasive autopsies do not provide tissue for histological examination, which may limit their clinical value, especially when infection-related morbidity and mortality are suspected. METHODS: We performed a prospective, multicentre, cross-sectional study to compare the diagnostic performance of post-mortem magnetic resonance imaging with computed tomography-guided biopsy (Virtopsy®) with that of conventional autopsy in foetuses and infants. Cases referred for conventional autopsy were eligible for enrolment. After post-mortem imaging using a computed tomography scanner and a magnetic resonance imaging unit, computed tomography-guided tissue sampling was performed. Virtopsy results were compared with conventional autopsy in determining the likely final cause of death and major pathologies. The primary outcome was the proportion of cases for which the same cause of death was determined by both methods. Secondary outcomes included the proportion of false positive and false negative major pathological lesions detected by virtopsy and the proportion of computed tomography-guided biopsies that were adequate for histological examination. RESULTS: Overall, 101 cases (84 fetuses, 17 infants) were included. Virtopsy and autopsy identified the same cause of death in 91 cases (90.1%, 95% CI 82.7 to 94.5). The sensitivity and specificity of virtopsy for determining the cause of death were 96.6% (95% CI 90.6 to 98.8) and 41.7% (95% CI 19.3 to 68.0), respectively. In 32 cases (31.7%, 95% CI 23.4 to 41.3), major pathological findings remained undetected by virtopsy, and in 45 cases (44.6%, 95% CI 35.2 to 54.3), abnormalities were diagnosed by virtopsy but not confirmed by autopsy. Computed tomography-guided tissue sampling was adequate for pathological comments in 506 of 956 biopsies (52.7%) and added important diagnostic value in five of 30 cases (16.1%) with an unclear cause of death before autopsy compared with postmortem imaging alone. In 19 of 20 infective deaths (95%), biopsies revealed infection-related tissue changes. Infection was confirmed by placental examination in all fetal cases. CONCLUSIONS: Virtopsy demonstrated a high concordance with conventional autopsy for the detection of cause of death but was less accurate for the evaluation of major pathologies. Computed tomography-guided biopsy had limited additional diagnostic value. TRIAL REGISTRATION: ClinicalTrials.gov (NCT01888380).

A marginal moment matching approach for fitting endemic-epidemic models to underreported disease surveillance counts.

Count data are often subject to underreporting, especially in infectious disease surveillance. We propose an approximate maximum likelihood method to fit count time series models from the endemic-epidemic class to underreported data. The approach is based on marginal moment matching where underreported processes are approximated through completely observed processes from the same class. Moreover, the form of the bias when underreporting is ignored or taken into account via multiplication factors is analyzed. Notably, we show that this leads to a downward bias in model-based estimates of the effective reproductive number. A marginal moment matching approach can also be used to account for reporting intervals which are longer than the mean serial interval of a disease. The good performance of the proposed methodology is demonstrated in simulation studies. An extension to time-varying parameters and reporting probabilities is discussed and applied in a case study on weekly rotavirus gastroenteritis counts in Berlin, Germany.

Pitfalls of using IQ short forms in neurodevelopmental disorders: a study in patients with congenital heart disease.

BACKGROUND: Short forms of IQ (S-IQ) assessments are time efficient and highly predictive of the full IQ (F-IQ) in healthy individuals. To investigate the validity of S-IQs for patients with neurodevelopmental impairments, this study tested a well-established S-IQ version in patients with congenital heart disease (CHD). METHODS: The Wechsler Intelligence Scale for Children, Fourth Edition was applied in 107 children with complex CHD aged 9-11 years. F-IQ and a well-established S-IQ version were calculated for each patient. The agreement between S-IQ and F-IQ was investigated across the whole spectrum of IQ scores. Finally, we tested a method to adjust IQs to resolve potential bias and validated this method in an independent sample of 55 CHD patients. RESULTS: S-IQ and F-IQ correlated strongly. Nevertheless, the size of the bias correlated with the true IQ, indicating larger error at the tails of the distribution. Estimating a corrected IQ by adjusting the S-IQ with correction parameters substantially improved agreement. CONCLUSION: We here report that substantial bias may underestimate low IQ scores and overestimate high ones. This bias should be considered when at-risk populations are assessed with S-IQs. Importantly, the bias can be minimized by using a correction formula.

Implementation and evaluation of a care bundle for prevention of non-ventilator-associated hospital-acquired pneumonia (nvHAP) - a mixed-methods study protocol for a hybrid type 2 effectiveness-implementation trial.

BACKGROUND: Hospital acquired pneumonia (HAP) is divided in two distinct groups, ventilator-associated pneumonia (VAP) and non-ventilator-associated HAP (nvHAP). Although nvHAP occurs more frequently than VAP and results in similar mortality and costs, prevention guidelines and prevention focus almost exclusively on VAP. Scientific evidence about nvHAP prevention and its implementation is scarce. Therefore, we designed a mixed-methods hybrid type 2 effectiveness-implementation study to investigate both the effectiveness and implementation of a newly developed nvHAP prevention bundle. METHODS: This single-centre project at the 950-bed University Hospital Zurich (UHZ) will engage the wards of nine departments with substantial nvHAP rates. The nvHAP bundle consists of five primary prevention measures: 1) oral care, 2) prevention of dysphagia-related aspiration, 3) mobilization, 4) stopping unnecessary proton pump inhibitors, and, 5) respiratory therapy. Implementation includes the engagement of department-level implementation teams, who sustain the 'core' intervention components of education, training, and environmental restructuring and tailor the implementation strategy to local needs. Both effectiveness and implementation outcomes will be assessed using mixed-methods. As a primary outcome, nvHAP incidence rates will be analysed by Poisson regression models to compare incidence rates before, during, and after the implementation phases (on the hospital and department level). Additionally, the association between process indicators and nvHAP incidence rates will be analysed using longitudinal Poisson regression models. A longitudinal, qualitative study and formative evaluation based on interviews, focus groups, and observations identifies supporting or hindering factors for implementation success in participating departments dynamically over time. This accumulating implementation experience will be constantly fed back to the implementation teams and thus, represents an active implementation element. DISCUSSION: This comprehensive hybrid mixed-methods study is designed to both, measure the effectiveness of a new nvHAP prevention bundle and multifaceted implementation strategy, while also providing insights into how and why it worked or failed. The results of this study may contribute substantially to advancing knowledge and patient safety in the area of a rediscovered healthcare-associated infection - nvHAP. TRIAL REGISTRATION: ClinicalTrials.gov : NCT03361085 . Registered December 2017.

Treatment of opioid withdrawal in neonates with morphine, phenobarbital, or chlorpromazine: a randomized double-blind trial.

Three suitable compounds (morphine, chlorpromazine, and phenobarbital) to treat neonatal abstinence syndrome were compared in a prospective multicenter, double-blind trial. Neonates exposed to opioids in utero were randomly allocated to one of three treatment groups. When a predefined threshold of a modified Finnegan score was reached, treatment started and increased stepwise until symptoms were controlled. If symptoms could not be controlled with the predefined maximal dose of a single drug, a second drug was added. Among 143 infants recruited, 120 needed pharmacological treatment. Median length of treatment for morphine was 22 days (95% CI 18 to 33), for chlorpromazine 25 days (95% CI 21 to 34), and for phenobarbital 32 days (95% CI 27 to 38) (p = ns). In the morphine group, only 3% of infants (1/33) needed a second drug; in the chlorpromazine group, this proportion was 56% (24/43), and in the phenobarbital group 30% (13/44).Conclusion: None of the drugs tested for treating neonatal abstinence syndrome resulted in a significantly shorter treatment length than the others. As morphine alone was able to control symptoms in almost all infants, it may be preferred to the two other drugs but should still be tested against more potent opioids such as buprenorphine.Trial registration: At ClinicalTrials.gov NCT02810782 (registered retrospectively).What is Known:• Neonates exposed to opiates in utero and presenting with withdrawal symptoms should first be treated by non-pharmacological supportive measures.• In those who fail, drugs have to be given, but there is controversy which drug is best.What is New:• Among three candidates, morphine, chlorpromazine and phenobarbital, none resulted in significantly shorter treatment time.• As morphine alone was able to control symptoms in almost all infants, it may be preferred to the two other drugs.

Benchmarking against the MOMS Trial: Zurich Results of Open Fetal Surgery for Spina Bifida.

INTRODUCTION: The Management of Myelomeningocele Study, a.k.a. the MOMS trial, was published in 2011 in the New England Journal of Medicine. This prospective randomized controlled trial proved to be a milestone publication that provided definitive evidence that fetal surgery is a novel standard of care for select fetuses with spina bifida aperta (SB). The goal of our study is to assess whether our center can match these benchmark results. MATERIALS AND METHODS: Our study was conducted according to the MOMS protocol using the same inclusion and exclusion criteria and looked at the same outcome parameters that were used in the MOMS trial. Zurich and MOMS results were compared. RESULTS: We enrolled 20 patients between December 2010 and May 2015 all of whom underwent fetal surgery for SB. Among 51 different outcome variables, there were only 3 favorable (multiplicity-adjusted) significant differences (gestational age at birth, hindbrain herniation, and psychomotor development). There were no statistically significant differences regarding any other parameters. CONCLUSION: Our findings confirm that rigorous apprenticeship, training, and comprehensive prospective data collection enable centers like the Zurich Center for Fetal Diagnosis and Therapy to achieve benchmark results for open fetal surgery for myelomeningocele and myeloschisis. These results justify the existence and continuation of our program. Outcome documentation is an essential element of quality management. It is medically and ethically fundamental for fetal medicine and surgery centers offering high-end innovative medical care.

Validation of discrete time-to-event prediction models in the presence of competing risks.

Clinical prediction models play a key role in risk stratification, therapy assignment and many other fields of medical decision making. Before they can enter clinical practice, their usefulness has to be demonstrated using systematic validation. Methods to assess their predictive performance have been proposed for continuous, binary, and time-to-event outcomes, but the literature on validation methods for discrete time-to-event models with competing risks is sparse. The present paper tries to fill this gap and proposes new methodology to quantify discrimination, calibration, and prediction error (PE) for discrete time-to-event outcomes in the presence of competing risks. In our case study, the goal was to predict the risk of ventilator-associated pneumonia (VAP) attributed to Pseudomonas aeruginosa in intensive care units (ICUs). Competing events are extubation, death, and VAP due to other bacteria. The aim of this application is to validate complex prediction models developed in previous work on more recently available validation data.

Extensive Histological Sampling following Focal Therapy of Clinically Significant Prostate Cancer with High Intensity Focused Ultrasound.

PURPOSE: Clinically significant, localized prostate cancer is currently treated with whole gland therapy. This approach is effective but associated with genitourinary and rectal side effects. Focal therapy of prostate cancer has been proposed as an alternative. The aim of this study was to determine the oncologic and functional outcomes of focal high intensity focused ultrasound therapy of prostate cancer. MATERIALS AND METHODS: In this single center, prospective study 75 men were treated between April 2014 and April 2018. Multiparametric magnetic resonance imaging and transperineal template saturation prostate biopsy were performed to localize prostate cancer, followed by focal ablation with high intensity focused ultrasound. The study primary end point was the detection of clinically significant prostate cancer, defined as Gleason score 7 or greater, at 6-month followup transperineal template saturation prostate biopsy. Genitourinary side effects were of secondary interest. RESULTS: Median patient age was 67 years (IQR 60-71) and median prostate specific antigen was 5.87 ng/ml (IQR 4.65-7.44). There were 5 low risk (6.7%) and 70 intermediate risk (93.3%) cancers. Clinically significant prostate cancer was detected in 41% of the men (95% CI 30.3-53.0) who underwent biopsy at 6 months and the median number of sampled cores was 44 (IQR 36-44). Prostate specific antigen (OR 1.17, IQR 0.49-2.85, p=0.71) and multiparametric magnetic resonance imaging (14.3% sensitivity, IQR 6.7-31.5) performed poorly to predict positive biopsies. Pad-free continence and erection sufficient for penetration were preserved in 63 of 64 (98.4%) and 31 of 45 patients (68.9%), respectively. CONCLUSIONS: Focal therapy with high intensity focused ultrasound leads to a low rate of genitourinary side effects. Followup biopsy of treated and untreated prostates remains the only modality to adequately select men in need of early salvage treatment.

Power priors based on multiple historical studies for binary outcomes.

Incorporating historical information into the design and analysis of a new clinical trial has been the subject of much recent discussion. For example, in the context of clinical trials of antibiotics for drug resistant infections, where patients with specific infections can be difficult to recruit, there is often only limited and heterogeneous information available from the historical trials. To make the best use of the combined information at hand, we consider an approach based on the multiple power prior that allows the prior weight of each historical study to be chosen adaptively by empirical Bayes. This choice of weight has advantages in that it varies commensurably with differences in the historical and current data and can choose weights near 1 if the data from the corresponding historical study are similar enough to the data from the current study. Fully Bayesian approaches are also considered. The methods are applied to data from antibiotics trials. An analysis of the operating characteristics in a binomial setting shows that the proposed empirical Bayes adaptive method works well, compared to several alternative approaches, including the meta-analytic prior.

Dynamic clinical prediction models for discrete time-to-event data with competing risks-A case study on the OUTCOMEREA database.

The development of clinical prediction models requires the selection of suitable predictor variables. Techniques to perform objective Bayesian variable selection in the linear model are well developed and have been extended to the generalized linear model setting as well as to the Cox proportional hazards model. Here, we consider discrete time-to-event data with competing risks and propose methodology to develop a clinical prediction model for the daily risk of acquiring a ventilator-associated pneumonia (VAP) attributed to P. aeruginosa (PA) in intensive care units. The competing events for a PA VAP are extubation, death, and VAP due to other bacteria. Baseline variables are potentially important to predict the outcome at the start of ventilation, but may lose some of their predictive power after a certain time. Therefore, we use a landmark approach for dynamic Bayesian variable selection where the set of relevant predictors depends on the time already spent at risk. We finally determine the direct impact of a variable on each competing event through cause-specific variable selection.

Optimizing the Design and Analysis of Clinical Trials for Antibacterials Against Multidrug-resistant Organisms: A White Paper From COMBACTE's STAT-Net.

Innovations are urgently required for clinical development of antibacterials against multidrug-resistant organisms. Therefore, a European, public-private working group (STAT-Net; part of Combatting Bacterial Resistance in Europe [COMBACTE]), has reviewed and tested several innovative trials designs and analytical methods for randomized clinical trials, which has resulted in 8 recommendations. The first 3 focus on pharmacokinetic and pharmacodynamic modeling, emphasizing the pertinence of population-based pharmacokinetic models, regulatory procedures for the reassessment of old antibiotics, and rigorous quality improvement. Recommendations 4 and 5 address the need for more sensitive primary end points through the use of rank-based or time-dependent composite end points. Recommendation 6 relates to the applicability of hierarchical nested-trial designs, and the last 2 recommendations propose the incorporation of historical or concomitant trial data through Bayesian methods and/or platform trials. Although not all of these recommendations are directly applicable, they provide a solid, evidence-based approach to develop new, and established, antibacterials and address this public health challenge.