Use of defibrotide in COVID-19 pneumonia: comparison of a phase II study and a matched real-world cohort control.

The coronavirus disease 2019 (COVID-19) pandemic led to an unprecedented burden on healthcare systems around the world and a severe global socioeconomic crisis, with more than 750 million confirmed cases and at least 7 million deaths reported by 31st December 2023. The DEFI-VID19 study (ClinicalTrials.gov NCT04335201), a phase II, single-arm, multicenter, open-label trial was designed in mid-2020 to assess the safety and efficacy of defibrotide in treating patients with COVID-19 pneumonia. Defibrotide was administered at a dose of 25 mg/kg/d intravenously, divided into four daily doses over a planned 14-day period for patients with COVID-19 pneumonia receiving non-invasive ventilation. The primary endpoint was Respiratory Failure Free Survival (RFFS); Overall Survival (OS), the number of post-recovery days, and adverse events were the secondary endpoints. For comparison, a contemporaneous control cohort receiving standard of care only was retrospectively selected by applying the eligibility criteria of the DEFI-VID19 trial. To adjust for the imbalance between the two cohorts in terms of baseline variable distributions, an outcome regression analysis was conducted. In adjusted analysis, patients receiving defibrotide reported a trend towards higher RFFS (HR=0.71[0.95CI: 0.34 to 1.29, P= .138]) and OS (HR=0.78[0.95CI: 0.33 to 1.53, P= .248]) and showed a significantly increased number of post-recovery days (difference in means: 3.61[ 0.95CI: 0.97 to 6.26, P= .0037]). Despite concomitant thromboprophylaxis with low molecular weight heparin, the safety profile of defibrotide proved to be favorable. Taken together, our findings suggest that defibrotide may represent a valuable addition to the COVID-19 therapeutic options.

Chronic Pain and Pain Management in Older Adults: Protocol and Pilot Results.

BACKGROUND: Chronic pain occurs in 30% of older adults. This prevalence rate is expected to increase, given the growth in the older adult population and the associated growth of chronic conditions contributing to pain. No population-based studies have provided detailed, longitudinal information on the experience of chronic pain in older adults; the pharmacological and nonpharmacological strategies that older adults use to manage their chronic pain; and the effect of chronic pain on patient-reported outcomes. OBJECTIVES: This article aims to describe the protocol for a population-based, longitudinal study focused on understanding the experience of chronic pain in older adults. The objectives are to determine the prevalence and characteristics of chronic pain; identify the pharmacological and nonpharmacological pain treatments used; evaluate for longitudinal differences in biopsychosocial factors; and examine how pain types and pain trajectories affect important patient-reported outcomes. Also included are the results of a pilot study. METHODS: A population-based sample of approximately 1,888 older adults will be recruited from the National Opinion Research Center at the University of Chicago's AmeriSpeak Panel to complete surveys at three waves: enrollment (Wave 1), 6 months (Wave 2), and 12 months (Wave 3). To determine the feasibility, a pilot test of the enrollment survey was conducted among 123 older adults. RESULTS: In the pilot study, older adults with chronic pain reported a range of pain conditions, with osteoarthritis being the most common. Participants reported an array of pharmacological and nonpharmacological pain strategies. Compared to participants without chronic pain, those with chronic pain reported lower physical and cognitive function and poorer quality of life. Data collection for the primary, longitudinal study is ongoing. DISCUSSION: This project will be the first longitudinal population-based study to examine the experience and overall effect of chronic pain in older adults. Pilot study results provide evidence of the feasibility of study methods. Ultimately, this work will inform the development of tailored interventions for older patients targeted to decrease pain and improve function and quality of life.

Intensive Versus Standard Blood Pressure Lowering and Days Free of Cardiovascular Events and Serious Adverse Events: a Post Hoc Analysis of Systolic Blood Pressure Intervention Trial.

BACKGROUND: Communication of the benefits and harms of blood pressure lowering strategy is crucial for shared decision-making. OBJECTIVES: To quantify the effect of intensive versus standard systolic blood pressure lowering in terms of the number of event-free days DESIGN: Post hoc analysis of the Systolic Blood Pressure Intervention Trial PARTICIPANTS: A total of 9361 adults 50 years or older without diabetes or stroke who had a systolic blood pressure of 130-180 mmHg and elevated cardiovascular risk INTERVENTIONS: Intensive (systolic blood pressure goal <120 mmHg) versus standard blood pressure lowering (<140 mmHg) MAIN MEASURES: Days free of major adverse cardiovascular events (MACE), serious adverse events (SAE), and monitored adverse events (hypotension, syncope, bradycardia, electrolyte abnormalities, injurious falls, or acute kidney injury) over a median follow-up of 3.33 years KEY RESULTS: The intensive treatment group gained 14.7 more MACE-free days over 4 years (difference, 14.7 [95% confidence interval: 5.1, 24.4] days) than the standard treatment group. The benefit of the intensive treatment varied by cognitive function (normal: difference, 40.7 [13.0, 68.4] days; moderate-to-severe impairment: difference, -15.0 [-56.5, 26.4] days; p-for-interaction=0.009) and self-rated health (excellent: difference, -22.7 [-51.5, 6.1] days; poor: difference, 156.1 [31.1, 281.2] days; p-for-interaction=0.001). The mean overall SAE-free days were not significantly different between the treatments (difference, -14.8 [-35.3, 5.7] days). However, the intensive treatment group had 28.5 fewer monitored adverse event-free days than the standard treatment group (difference, -28.5 [-40.3, -16.7] days), with significant variations by frailty status (non-frail: difference, 38.8 [8.4, 69.2] days; frail: difference, -15.5 [-46.6, 15.7] days) and self-rated health (excellent: difference, -12.9 [-45.5, 19.7] days; poor: difference, 180.6 [72.9, 288.4] days; p-for-interaction <0.001). CONCLUSIONS: Over 4 years, intensive systolic blood pressure lowering provides, on average, 14.7 more MACE-free days than standard treatment, without any difference in SAE-free days. Whether this time-based effect summary improves shared decision-making remains to be elucidated. TRIAL REGISTRATION: ClinicalTrials.gov Registration: NCT01206062.

Survival Analysis of Treatment Efficacy in Comparative Coronavirus Disease 2019 Studies.

For survival analysis in comparative coronavirus disease 2019 trials, the routinely used hazard ratio may not provide a meaningful summary of the treatment effect. The mean survival time difference/ratio is an intuitive, assumption-free alternative. However, for short-term studies, landmark mortality rate differences/ratios are more clinically relevant and should be formally analyzed and reported.

Evaluating longitudinal therapy effects via the North Star Ambulatory Assessment.

INTRODUCTION/AIMS: In comparative studies, treatment effects are typically evaluated at a specific time point. When data are collected periodically, an alternative, clinically meaningful approach could be used to assess the totality of treatment effects. We applied a well-developed analytical procedure for evaluating longitudinal treatment effects using North Star Ambulatory Assessment (NSAA) data for illustration. METHODS: The NSAA comprises 17 scorable items/outcomes that measure changes in motor function. Using NSAA data from the published ataluren phase 3, randomized, placebo-controlled trial (NCT01826487), cumulative counts of failures to perform each item (transition from 2/1 [able/impaired] to 0 [unable]) were collected at specified time points for each patient over 48 wk. Treatment group-wise mean cumulative item failure count curves were constructed, comparing ataluren versus placebo and deflazacort versus prednisone/prednisolone among placebo-treated patients. The steeper the curve, the worse the outcome. A clinically meaningful summary of the between-group difference was provided for each comparison. RESULTS: The curve was uniformly steeper for placebo than ataluren after 16 wk and for prednisone/prednisolone than deflazacort after 8 wk. The two curves in each comparison continued to diverge thereafter, indicating sustained treatment benefits over time. Using a unique analytical approach, cumulative failure rates were reduced, on average, by 27% for ataluren versus placebo (rate ratio, 0.73; 95% confidence interval [CI], 0.55-0.97; p = .027) and 28% for deflazacort versus prednisone/prednisolone (rate ratio, 0.72; 95% CI, 0.53-0.96; p = .028). DISCUSSION: Unlike fixed-time analyses, this analytical approach enabled demonstration of cumulative, longitudinal treatment effects over time using repeatedly measured NSAA observations.

How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19.

Clinical trials of treatments for coronavirus disease 2019 (COVID-19) draw intense public attention. More than ever, valid, transparent, and intuitive summaries of the treatment effects, including efficacy and harm, are needed. In recently published and ongoing randomized comparative trials evaluating treatments for COVID-19, time to a positive outcome, such as recovery or improvement, has repeatedly been used as either the primary or key secondary end point. Because patients may die before recovery or improvement, data analysis of this end point faces a competing risk problem. Commonly used survival analysis techniques, such as the Kaplan-Meier method, often are not appropriate for such situations. Moreover, almost all trials have quantified treatment effects by using the hazard ratio, which is difficult to interpret for a positive event, especially in the presence of competing risks. Using 2 recent trials evaluating treatments (remdesivir and convalescent plasma) for COVID-19 as examples, a valid, well-established yet underused procedure is presented for estimating the cumulative recovery or improvement rate curve across the study period. Furthermore, an intuitive and clinically interpretable summary of treatment efficacy based on this curve is also proposed. Clinical investigators are encouraged to consider applying these methods for quantifying treatment effects in future studies of COVID-19.

Analysis of Response Data for Assessing Treatment Effects in Comparative Clinical Studies.

In comparative studies, treatment effect is often assessed using a binary outcome that indicates response to the therapy. Commonly used summary measures for response include the cumulative and current response rates at a specific time point. The current response rate is sometimes called the probability of being in response (PBIR), which regards a patient as a responder only if they have achieved and remain in response at present. The methods used in practice for estimating these rates, however, may not be appropriate. Moreover, whereas an effective treatment is expected to achieve a rapid and sustained response, the response at a fixed time point does not provide information about the duration of response (DOR). As an alternative, a curve constructed from the current response rates over the entire study period may be considered, which can be used for visualizing how rapidly patients responded to therapy and how long responses were sustained. The area under the PBIR curve is the mean DOR. This connection between response and DOR makes this curve attractive for assessing the treatment effect. In contrast to the conventional method for analyzing the DOR data, which uses responders only, the above procedure includes all patients in the study. Although discussed extensively in the statistical literature, estimation of the current response rate curve has garnered little attention in the medical literature. This article illustrates how to construct and analyze such a curve using data from a recent study for treating renal cell carcinoma. Clinical trialists are encouraged to consider this robust and clinically interpretable procedure as an additional tool for evaluating treatment effects in clinical studies.

Restricted mean survival time for the analysis of cardiovascular outcome trials assessing non-inferiority: Case studies from antihyperglycemic drug development.

Cardiovascular outcome trials (CVOTs) have been employed in multiple therapeutic areas to explore whether a noncardiovascular drug increases the risk for cardiovascular events. These studies are now a central part of drug development programs for antihyperglycemic drugs. These programs are expected to demonstrate that new antihyperglycemic drugs for patients with Type 2 diabetes do not have unacceptable cardiovascular risk. The hazard ratio, which is usually provided as evidence that patients receiving the investigational treatment are not at statistically significantly greater cardiovascular risk than patients on the control treatment, can be difficult to interpret for various reasons. Therefore, an alternative approach known as the Restricted Mean Survival Time (RMST) or τ-year mean survival time is presented, and its ability to overcome interpretation challenges with the hazard ratio discussed. The RMST approach is applied to five completed CVOTs and is compared with the corresponding hazard ratios. Additionally, detailed considerations are given on how to design a non-inferiority CVOT using the RMST approach. The RMST methodology is shown to be a practical alternative to the hazard ratio methodology for designing a non-inferiority CVOT.

Efficiency of two sample tests via the restricted mean survival time for analyzing event time observations.

In comparing two treatments with the event time observations, the hazard ratio (HR) estimate is routinely used to quantify the treatment difference. However, this model dependent estimate may be difficult to interpret clinically especially when the proportional hazards (PH) assumption is violated. An alternative estimation procedure for treatment efficacy based on the restricted means survival time or t-year mean survival time (t-MST) has been discussed extensively in the statistical and clinical literature. On the other hand, a statistical test via the HR or its asymptotically equivalent counterpart, the logrank test, is asymptotically distribution-free. In this article, we assess the relative efficiency of the hazard ratio and t-MST tests with respect to the statistical power under various PH and non-PH models theoretically and empirically. When the PH assumption is valid, the t-MST test performs almost as well as the HR test. For non-PH models, the t-MST test can substantially outperform its HR counterpart. On the other hand, the HR test can be powerful when the true difference of two survival functions is quite large at end but not the beginning of the study. Unfortunately, for this case, the HR estimate may not have a simple clinical interpretation for the treatment effect due to the violation of the PH assumption.

Quantifying the totality of treatment effect with multiple event-time observations in the presence of a terminal event from a comparative clinical study.

To evaluate the totality of one treatment's benefit/risk profile relative to an alternative treatment via a longitudinal comparative clinical study, the timing and occurrence of multiple clinical events are typically collected during the patient's follow-up. These multiple observations reflect the patient's disease progression/burden over time. The standard practice is to create a composite endpoint from the multiple outcomes, the timing of the occurrence of the first clinical event, to evaluate the treatment via the standard survival analysis techniques. By ignoring all events after the composite outcome, this type of assessment may not be ideal. Various parametric or semiparametric procedures have been extensively discussed in the literature for the purposes of analyzing multiple event-time data. Many existing methods were developed based on extensive model assumptions. When the model assumptions are not plausible, the resulting inferences for the treatment effect may be misleading. In this article, we propose a simple, nonparametric inference procedure to quantify the treatment effect, which has an intuitive clinically meaningful interpretation. We use the data from a cardiovascular clinical trial for heart failure to illustrate the procedure. A simulation study is also conducted to evaluate the performance of the new proposal.