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.

[Exploration and contemplation of homogenized education of specialists in pulmonary and critical care medicine at member hospitals of a hospital consortium]

Talent construction is the cornerstone to the establishment of a high-quality, homogeneous healthcare system in a healthcare consortium. Pulmonary and critical care medicine (PCCM) as the first pilot specialty, the standardized training of PCCM specialists has started and achieved remarkable results. The consortium member hospitals' physician specialist education is an important complement to PCCM training. The establishment of the consortium provides a new form of the education of physicians in PCCM, with the advantages of high quality teaching, wide coverage of staff and throughout the career development process. This article summarized the current status of physician specialty education in the member hospitals of the consortium, and further proposes the goal of homogenized specialty education for physicians in the member hospitals. And it analyzed in depth the problems that existed in the practice of training for hospital consortium member hospitals specialists, such as non-uniform level of instruction, non-systematic content of training, limited sources of teaching cases, and lack of teaching materials and equipment. For the medical consortium member hospital physician specialty education of in-depth thinking, we put forward the corresponding countermeasures. The aim of this study is to explore the homogenization of the specialty education system of pulmonary and critical care medicine in the member hospitals, in order to comprehensively improve the medical level of respiratory specialists in the member hospitals of the medical consortium.

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.

Balancing Benefit and Harm of Intensive Blood Pressure Lowering Based on Individual Outcome Profile Analysis

BACKGROUND

Intensive blood pressure lowering prevents major adverse cardiovascular events, but some patients experience serious adverse events. Examining benefit-harm profiles may be more informative than analyzing major adverse cardiovascular events and serious adverse events separately.

METHODS

We analyzed data from the Systolic Blood Pressure Intervention Trial (n = 9361), comparing intensive treatment (systolic blood pressure target <120 mm Hg) to standard treatment (<140 mm Hg). A 4-year hierarchical outcome profile was defined for each participant: 1) alive with neither major adverse cardiovascular events nor serious adverse events (most desirable); 2) alive with serious adverse events only; 3) alive with major adverse cardiovascular events only; 4) alive with both events; and 5) deceased (least desirable). We compared 4-year outcome profiles between the treatment groups in the entire population and by frailty subgroups defined using physical frailty phenotype (non-frail, pre-frail, and frail).

RESULTS

The proportion who died were lower with intensive treatment than standard treatment (5% vs 6%). A higher proportion of the intensive treatment group was alive with serious adverse events and no major adverse cardiovascular events (36% vs 33%), and a lower proportion were alive with both events (6% vs 5%) than the standard treatment group. The outcome profiles were more favorable among those with physical frailty phenotype who were treated with intensive treatment vs standard treatment, but outcome profiles were similar between the treatment groups among non-frail or pre-frail participants.

CONCLUSIONS

This post hoc proof-of-concept analysis demonstrates the utility of the outcome profile analysis that simultaneously examines the benefit and harm of the treatment.

Using a Clinically Interpretable End Point Composed of Multiple Outcomes to Evaluate Totality of Treatment Effect in Comparative Oncology Studies

This cohort study demonstrates how to use cumulative event count curves to create a clinically meaningful end point by simultaneously considering recurrence, progression, and survival times from the individual patient.

Effect of Dapagliflozin on Total Heart Failure Events in Patients With Heart Failure With Mildly Reduced or Preserved Ejection Fraction: A Prespecified Analysis of the DELIVER Trial

Importance

In the Dapagliflozin Evaluation to Improve the Lives of Patients With Preserved Ejection Fraction Heart Failure (DELIVER) trial, dapagliflozin reduced the risk of time to first worsening heart failure (HF) event or cardiovascular death in patients with HF with mildly reduced or preserved ejection fraction (EF).

Objective

To evaluate the effect of dapagliflozin on total (ie, first and recurrent) HF events and cardiovascular death in this population.

Design, Setting, and Participants

In this prespecified analysis of the DELIVER trial, the proportional rates approach of Lin, Wei, Yang, and Ying (LWYY) and a joint frailty model were used to examine the effect of dapagliflozin on total HF events and cardiovascular death. Several subgroups were examined to test for heterogeneity in the effect of dapagliflozin, including left ventricular EF. Participants were enrolled from August 2018 to December 2020, and data were analyzed from August to October 2022.

Interventions

Dapagliflozin, 10 mg, once daily or matching placebo.

Main Outcomes and Measures

The outcome was total episodes of worsening HF (hospitalization for HF or urgent HF visit requiring intravenous HF therapies) and cardiovascular death.

Results

Of 6263 included patients, 2747 (43.9%) were women, and the mean (SD) age was 71.7 (9.6) years. There were 1057 HF events and cardiovascular deaths in the placebo group compared with 815 in the dapagliflozin group. Patients with more HF events had features of more severe HF, such as higher N-terminal pro-B-type natriuretic peptide level, worse kidney function, more prior HF hospitalizations, and longer duration of HF, although EF was similar to those with no HF events. In the LWYY model, the rate ratio for total HF events and cardiovascular death for dapagliflozin compared with placebo was 0.77 (95% CI, 0.67-0.89; P < .001) compared with a hazard ratio of 0.82 (95% CI, 0.73-0.92; P < .001) in a traditional time to first event analysis. In the joint frailty model, the rate ratio was 0.72 (95% CI, 0.65-0.81; P < .001) for total HF events and 0.87 (95% CI, 0.72-1.05; P = .14) for cardiovascular death. The results were similar for total HF hospitalizations (without urgent HF visits) and cardiovascular death and in all subgroups, including those defined by EF.

Conclusions and Relevance

In the DELIVER trial, dapagliflozin reduced the rate of total HF events (first and subsequent HF hospitalizations and urgent HF visits) and cardiovascular death regardless of patient characteristics, including EF.

Trial Registration

ClinicalTrials.gov Identifier: NCT03619213.

Quantifying and Interpreting the Prediction Accuracy of Models for the Time of a Cardiovascular Event-Moving Beyond C Statistic: A Review

Importance

For personalized or stratified medicine, it is critical to establish a reliable and efficient prediction model for a clinical outcome of interest. The goal is to develop a parsimonious model with fewer predictors for broad future application without compromising predictability. A general approach is to construct various empirical models via individual patients' specific baseline characteristics/biomarkers and then evaluate their relative merits. When the outcome of interest is the timing of a cardiovascular event, a commonly used metric to assess the adequacy of the fitted models is based on C statistics. These measures quantify a model's ability to separate those who develop events earlier from those who develop them later or not at all (discrimination), but they do not measure how closely model estimates match observed outcomes (prediction accuracy). Metrics that provide clinically interpretable measures to quantify prediction accuracy are needed.

Observations

C statistics measure the concordance between the risk scores derived from the model and the observed event time observations. However, C statistics do not quantify the model prediction accuracy. The integrated Brier Score, which calculates the mean squared distance between the empirical cumulative event-free curve and its individual patient's counterparts, estimates the prediction accuracy, but it is not clinically intuitive. A simple alternative measure is the average distance between the observed and predicted event times over the entire study population. This metric directly quantifies the model prediction accuracy and has often been used to evaluate the goodness of fit of the assumed models in settings other than survival data. This time-scale measure is easier to interpret than the C statistics or the Brier score.

Conclusions and Relevance

This article enhances our understanding of the model selection/evaluation process with respect to prediction accuracy. A simple, intuitive measure for quantifying such accuracy beyond C statistics can improve the reliability and efficiency of the selected model for personalized and stratified medicine.

[Effectiveness of a whole-process health education model among inpatients with ascites type of advanced schistosomiasis]

OBJECTIVE

To evaluate the effectiveness of a whole-process health education model among inpatients with ascites type of advanced schistosomiasis.

METHODS

A "admission-hospitalization-discharge" whole-process health education model was created, 101 inpatients with ascites type of advanced schistosomiasis were given the whole-process health education. The scores of schistosomiasis control knowledge, attitudes towards schistosomiasis control and healthy behaviors, and awareness of schistosomiasis control knowledge, correct rate of attitudes towards schistosomiasis control and correct rate of healthy behaviors were compared among inpatients with ascites type of advanced schistosomiasis before and after implementation of the whole-process health education.

RESULTS

The scores of schistosomiasis control knowledge, schistosomiasis control attitudes and healthy behaviors were all significantly higher among inpatients with ascites type of advanced schistosomiasis after implementation of the whole-process health education than before implementation (Z = -7.688, -3.576 and -4.328, all P values < 0.01). In addition, the awareness of schistosomiasis control knowledge increased from 54.3% to 82.7% (χ² = 188.886, P < 0.01), and the correct rate of attitudes towards schistosomiasis control increased from 88.4% to 98.0% (χ² = 22.001, P < 0.01), while the correct rate of healthy behaviors increased from 48.2% to 59.7% (χ² = 11.767, P < 0.01).

CONCLUSIONS

The whole-process health education model may remarkably improve the awareness of schistosomiasis control knowledge and promote the formation of positive attitudes towards schistosomiasis control and correct behaviors among inpatients with ascites type of advanced schistosomiasis, which is of great significance to facilitate patients' cure.

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.

Quantifying Treatment Effects in Trials with Multiple Event-Time Outcomes

BACKGROUND

Data on the occurrence times of multiple outcomes, reflecting the temporal profile of disease burden/progression, have been used to estimate treatment effects in various recent randomized trials. Most procedures for analyzing these data require specific model assumptions. When the assumptions are not met, the results may be misleading. Robust, model-free procedures for study design and analysis that enable clinically meaningful interpretations are warranted.

METHODS

For each treatment group, we constructed and summarized the estimated mean cumulative count of events over time by the area under the curve (AUC), which can be interpreted as the mean total event-free time lost from multiple undesirable outcomes. A higher curve, and resulting larger AUC, implies a worse treatment. The treatment effect is quantified by the ratio and/or difference of AUCs. The timing and occurrence of recurrent heart failure hospitalizations (HFHs) and cardiovascular (CV) death from Prospective Comparison of ARNI with ARB Global Outcomes in HF with Preserved Ejection Fraction (PARAGON-HF), comparing sacubitril/valsartan with valsartan, are presented for illustration. We also discuss the design of future studies on the basis of the proposed method.

RESULTS

With 48 months of follow-up, estimated AUCs, representing the total event-free time lost to HFHs and CV death, were 11.3 and 13.1 event-months for sacubitril/valsartan and valsartan, respectively. The ratio of these AUCs was 0.86 (95% confidence interval, 0.75 to 1.00; P=0.049), a 14% reduction of disease burden favoring combination therapy. A future study, similar to PARAGON-HF, designed using the new proposal would require fewer patients would than a conventional time-to-first-event analysis.

CONCLUSIONS

The proposed method is robust and model-free and provides a clinically interpretable, time-scale summary of the treatment effect. (Funded by National Institutes of Health.).

Handling Informative Premature Treatment or Study Discontinuation for Assessing Between-Group Differences in a Comparative Oncology Trial

This decision analytical model study examines premature treatment discontinuation in clinical trials for patients with advanced renal cell carcinoma.

[Construction of a nomogram model for predicting 2-year survival rate of small cell lung cancer based on more comprehensive variables]

Objective: To construct a novel prognostic nomogram model based on more comprehensive variables for patients with small-cell lung cancer (SCLC). Methods: The data of 722 patients with SCLC confirmed by pathology in Affiliated Cancer Hospital of Shanxi Medical University from January 2015 to December 2018 were retrospectively analyzed [including 592 males and 130 females, aged from 23 to 82(61±9) years]. A random seed count of 133 was used to divide those patients into training set (n=422) and validation set (n=300). Kaplan-Meier was used for survival curves analysis and univariate Log-rank test was used for evaluating the influence of clinical variables on the prognosis of sclc, variables with P<0.05 in univariate analysis were included in a multivariate Cox regression model. The nomogram was constructed based on the variables which P<0.05 in multivariate analysis. Receiver operating characteristic (ROC) curve, calibration by Integrated Brier score (IBS) and clinical net benefit by decision curve analysis (DCA) were used to evaluate model discriminative power, prediction error value, and clinical net benefit, and compared with the American Joint Committee on Cancer 8th TNM. Results: Male, abnormal monocyte (MON) counts, abnormal neuron specific enolase (NSE), abnormal cytokeratin 19 fragment (Cyfra211), M1a stage, M1b stage, M1c stage, radiotherapy (RT), chemotherapy ≥4 cycles and prophylactic cranial irradiation (PCI) were prognostic factors for SCLC[HR(95%CI)=1.39(1.00-1.92), 1.29(1.02-1.63), 1.41(1.11-1.80), 2.02(1.48-2.76), 1.09(0.77-1.55), 1.44(0.94-2.22), 2.01(1.49-2.71), 0.75(0.57-0.98), 0.40(0.31-0.51)and 0.42(0.26-0.68), respectively, all P<0.05]. The area under ROC curve (AUC) of the nomogram in training set and validation set were 0.814(95%CI: 0.765-0.862)and 0.787 (95%CI: 0.725-0.849), which were higher than TNM [0.616(95%CI: 0.558-0.674) and 0.648(95%CI: 0.581-0.715)].The calibration curve showed a good correlation between the nomogram prediction and actual observation for the 2-year overall survival (OS). IBS indicted a lower prediction error rate (training set: 0.132 vs 0.169; validation set: 0.138 vs 0.169). DCA showed a wider threshold range than TNM (training set: 0.01-0.96 vs 0.01-0.85, validation set: 0.01-0.94 vs 0.01-0.86) and a greater improvement of the clinical net benefit (in training set the nomogram had a greater clinical benefit than TNM in the range of 0.19-0.96, and remained in validation set in the range of 0.19-0.94). Conclusion: The established nomogram model for predicting 2-year OS in patients with SCLC based on 8 variables, including gender, MON, NSE, Cyfra211, M stage, RT, CT cycles and PCI can be used for an more accurately prognosis prediction and reference for therapeutic regimen selection.

OUP accepted manuscript

When designing a comparative oncology trial for an overall or progression-free survival endpoint, investigators often quantify the treatment effect using a difference in median survival times. However, rather than directly designing the study to estimate this difference, it is almost always converted to a hazard ratio (HR) to determine the study size. At the analysis stage, the hazard ratio is utilized for formal analysis, yet because it may be difficult to interpret clinically, especially when the proportional hazards assumption is not met, the observed medians are also reported descriptively. The hazard ratio and median difference contrast different aspects of the survival curves. Whereas the hazard ratio places greater emphasis on late-occurring separation, the median difference focuses locally on the centers of the distributions and cannot capture either short- or long-term differences. Having 2 sets of summaries (a hazard ratio and the medians) may lead to incoherent conclusions regarding the treatment effect. For instance, the hazard ratio may suggest a treatment difference whereas the medians do not, or vice versa. In this commentary, we illustrate these commonly encountered issues using examples from recent oncology trials. We present a coherent alternative strategy that, unlike relying on the hazard ratio, does not require modeling assumptions and always results in clinically interpretable summaries of the treatment effect.

Practical Recommendations on Quantifying and Interpreting Treatment Effects in the Presence of Terminal Competing Risks: A Review

Importance

In a comparative trial, the time to a clinical event is often a key end point. However, the occurrence of a terminal event, such as death or premature study discontinuation, may preclude observation of this outcome. Although various methods for handling competing risks are available, no specific recommendations have been made for scenarios encountered in practice, especially when the terminal event profiles of the study arms are dissimilar. Moreover, appropriate methods for a desirable outcome, such as live hospital discharge, have seldom been discussed.

Observations

Several of the most commonly used methods are reviewed. The first regards the terminal event as censoring and applies standard survival analysis to the event of interest. The between-group difference is usually summarized by the cause-specific hazard ratio. This summary measure is inappropriate when the new therapy markedly prolongs time to the terminal event. Moreover, the corresponding Kaplan-Meier curve for the end point of interest is uninterpretable. The second method is to use the cumulative incidence curve, which is the probability of experiencing the event of interest by each time point, acknowledging that patients who have died will never experience the event. However, the resulting pseudo hazard ratio is difficult to interpret. With a proper alternative summary measure, this approach works well for a desirable outcome but may not for an undesirable outcome. The third method focuses on the event-free survival time by combining information from occurrences of the terminal event and the event of interest simultaneously. This clinically interpretable method naturally accounts for differences in terminal event rates when comparing treatments with respect to the time to an undesirable outcome.

Conclusions and Relevance

This article enhances our understanding of each method's advantages and shortcomings and assists practitioners in choosing appropriate methods for handling competing risk problems in practice.

Moving beyond conventional stratified analysis to assess the treatment effect in a comparative oncology study

In a comparative oncology study with progression-free or overall survival as the endpoint, the primary or key secondary analysis is routinely stratified by patients' baseline characteristics when evaluating the treatment difference. The validity of a conventional strategy such as a stratified HR analysis depends on stringent model assumptions that are unlikely to be met in practice, especially in immunotherapy studies. Thus, the resulting summary is generally neither valid nor interpretable. This article discusses issues with conventional stratified analyses and presents alternatives using data from KEYNOTE-189, a recent immunotherapy trial for treating patients with metastatic, non-squamous, non-small-cell lung cancer.

Intensive versus standard blood pressure control in type 2 diabetes: a restricted mean survival time analysis of a randomised controlled trial

BACKGROUND

Restricted mean survival time analysis offers an intuitive and robust summary of treatment effect compared with HRs.

OBJECTIVE

To examine the effect of intensive versus standard blood pressure (BP) control on death or cardiovascular events in type 2 diabetes.

DESIGN

Secondary analysis of the Action to Control Cardiovascular Risk in Diabetes Blood Pressure trial.

SETTING

77 sites in the USA and Canada.

PARTICIPANTS

4733 adults with type 2 diabetes at high risk for cardiovascular events.

INTERVENTIONS

Systolic BP target <120 mm Hg (n=2371) versus <140 mm Hg (n=2362).

MEASUREMENTS

Composite endpoint of death, non-fatal myocardial infarction or non-fatal stroke.

RESULTS

The mean event-free survival time over 5 years (1825 days) was similar between intensive and standard BP control (1716 vs 1714 days; mean difference, 1.3 (95% CI -18.1 to 20.7) days). However, intensive BP treatment was more beneficial for those assigned to standard glycaemic control (1725 vs 1697 days; mean difference, 28.1 (95% CI 0.4 to 55.9) days), but not for those assigned to intensive glycaemic control (1706 vs 1731 days; mean difference, -25.2 (95% CI -52.3 to 1.9) days) (p=0.008 for interaction). In subgroup analysis, the mean event-free survival time difference between intensive and standard BP treatment was -76.0 (95% CI -131.8 to -20.3) days for those with cognitive impairment and 21.8 (95% CI -24.0 to 67.5) days for those with normal cognitive function (p=0.008 for interaction). The effect was not different by age, sex and baseline cardiovascular disease status.

CONCLUSIONS

Intensive BP treatment may reduce death and cardiovascular events among patients with type 2 diabetes receiving standard glycaemic treatment and without cognitive impairment.

TRIAL REGISTRATION NUMBER

NCT00000620; Post-results.

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.

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.

Restricted mean survival time versus conventional measures for treatment decision-making

BACKGROUND/OBJECTIVES

Restricted mean survival time (RMST) summarizes treatment effect in terms of a gain or loss in the event-free days. It remains uncertain whether communicating treatment benefit and harm using RMST-based summary is more effective than conventional summary based on absolute and relative risk reduction. We compared the effect of RMST-based approach and conventional approach on decisional conflict using an example of intensive versus standard blood pressure-lowering strategies.

DESIGN

On-line survey.

SETTING

A convenience sample of patients in the United States.

PARTICIPANTS

Two hundred adults aged 65 and older with hypertension requiring anti-hypertensive treatment (response rate 85.5%).

INTERVENTIONS

Participants were randomly assigned to either RMST-based summary or conventional summary about the benefit and harm of blood pressure-lowering strategies.

MEASUREMENTS

Decisional Conflict Scale (DCS), ranging from 0 (no conflict) to 100 (high conflict), and preference for intensive blood pressure-lowering strategy.

RESULTS

Participants assigned to RMST-based approach (n = 100) and conventional approach (n = 100) had similar age (mean [standard deviation, SD]: 72.3 [5.6] vs 72.8 [5.5] years) and proportions of female (50 [50.0%] vs 61 [61.0%]) and white race (92 [92.0%] vs 92 [92.0%]). The mean (SD) DCS score was 25.2 (15.0) for RMST-based approach and 25.6 (14.1) for conventional approach (p = 0.84). The number (%) of participants who preferred intensive strategy was 10 (10.0%) for RMST-based approach and 14 (14.0%) for conventional approach (p = 0.52). The results were consistent in subgroups defined by age, sex, education level, cardiovascular disease status, and predicted mortality risk categories.

CONCLUSION

In a sample of relatively healthy older adults with hypertension, RMST-based approach was as effective as conventional approach on decisional conflict about choosing a blood pressure-lowering strategy. This study provides proof-of-concept evidence that RMST-based approach can be used in conjunction with absolute and relative risk reduction for communicating treatment benefit and harm in a decision aid.

Utility of Restricted Mean Survival Time for Analyzing Time to Nursing Home Placement Among Patients With Dementia

This cohort study assesses the utility of restricted mean survival time as a method for quantifying time to nursing home placement among patients with dementia.

Restricted Mean Survival Time in Action to Control Cardiovascular Risk in Diabetes (ACCORD) Blood Pressure

Background: Restricted mean survival time (RMST) provides mean time lost or gained by an intervention. This may be a more intuitive way to understand treatment effect. Objective: To determine overall and subgroup treatment effects from blood pressure targets in older adults with diabetes. Methods: We analyzed the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial data. Our outcome was cardiovascular disease free survival. We measured 5-year RMST (days) between standard and intensive blood pressure control, and compared the difference by age (≥70 or &lt;70 years old) and glycemic control (standard vs. intensive). Results: Over 5 years, those with intensive treatment lived, on average, 1716 days compared to 1714 days with standard treatment, with a RMST difference of 1.3 days (95% confidence interval, -22.1, 12.4). Among adults ≥70 years old, compared to standard treatment, intensive treatment resulted in an additional 13.6 (95% CI, -43.2, 70.3) days, where the difference was -0.5 (-20.8, 19.7) days for those aged &lt;70 years old (p-for-interaction=0.673). Compared to standard treatment, intensive treatment resulted in 28.1 (0.4, 55.9) more days for those assigned to standard glycemic control, but it appeared to result in 25.2 fewer days (-52.3, 1.9) for those assigned to intensive glycemic control (p-for-interaction=0.007). Discussion: The benefit of intensive treatment over standard treatment varies by age and glycemic control. RMST difference may allow for more intuitive and personalized weighing of benefits and risks of intensive blood pressure control.

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.

Meta-analyses of ataluren randomized controlled trials in nonsense mutation Duchenne muscular dystrophy

Aim: Assess the totality of efficacy evidence for ataluren in patients with nonsense mutation Duchenne muscular dystrophy (nmDMD). Materials & methods: Data from the two completed randomized controlled trials (ClinicalTrials.gov: NCT00592553; NCT01826487) of ataluren in nmDMD were combined to examine the intent-to-treat (ITT) populations and two patient subgroups (baseline 6-min walk distance [6MWD] ≥300–<400 or <400 m). Meta-analyses examined 6MWD change from baseline to week 48. Results: Statistically significant differences in 6MWD change with ataluren versus placebo were observed across all three meta-analyses. Least-squares mean difference (95% CI): ITT (n = 342), +17.2 (0.2–34.1) m, p = 0.0473; ≥300–<400 m (n = 143), +43.9 (18.2–69.6) m, p = 0.0008; <400 m (n = 216), +27.7 (6.4–49.0) m, p = 0.0109. Conclusion: These meta-analyses support previous evidence for ataluren in slowing disease progression versus placebo in patients with nmDMD over 48 weeks. Treatment benefit was most evident in patients with a baseline 6MWD ≥300–<400 m (the ambulatory transition phase), thereby informing future trial design.

Selecting appropriate endpoints for assessing treatment effects in comparative clinical studies for COVID-19

To evaluate the efficacy and safety of a new treatment for COVID-19 vs. standard care, certain key endpoints are related to the duration of a specific event, such as hospitalization, ICU stay, or receipt of supplemental oxygen. However, since patients may die in the hospital during study follow-up, using, for example, the duration of hospitalization to assess treatment efficacy can be misleading. If the treatment tends to prolong patients' survival compared with standard care, patients in the new treatment group may spend more time in hospital. This can lead to a "survival bias" issue, where a treatment that is effective for preventing death appears to prolong an undesirable outcome. On the other hand, by using hospital-free survival time as the endpoint, we can circumvent the survival bias issue. In this article, we use reconstructed data from a recent, large clinical trial for COVID-19 to illustrate the advantages of this approach. For the analysis of ICU stay or oxygen usage, where the initiating event is potentially an outcome of treatment, standard survival analysis techniques may not be appropriate. We also discuss issues with analyzing the durations of such events.