Design and modeling for drug combination experiments with order effects.

Combinations of drugs are now ubiquitous in treating complex diseases such as cancer and HIV due to their potential for enhanced efficacy and reduced side effects. The traditional combination experiments of drugs focus primarily on the dose effects of the constituent drugs. However, with the doses of drugs remaining unchanged, different sequences of drug administration may also affect the efficacy endpoint. Such drug effects shall be called as order effects. The common order-effect linear models are usually inadequate for analyzing combination experiments due to the nonlinear relationships and complex interactions among drugs. In this article, we propose a random field model for order-effect modeling. This model is flexible, allowing nonlinearities, and interaction effects to be incorporated with a small number of model parameters. Moreover, we propose a subtle experimental design that will collect good quality data for modeling the order effects of drugs with a reasonable run size. A real-data analysis and simulation studies are given to demonstrate that the proposed design and model are effective in predicting the optimal drug sequences in administration.

Interpreting the CPASS Trial: Do Not Shift Motor Therapy to the Subacute Phase.

The Critical Periods After Stroke Study (CPASS, n = 72) showed that, compared to controls, an additional 20 hours of intensive upper limb therapy led to variable gains on the Action Research Arm Test depending on when therapy was started post-stroke: the subacute group (2-3 months) improved beyond the minimal clinically important difference and the acute group (0-1 month) showed smaller but statistically significant improvement, but the chronic group (6-9 months) did not demonstrate improvement that reached significance. Some have misinterpreted CPASS results to indicate that all inpatient motor therapy should be shifted to outpatient therapy delivered 2 to 3 months post-stroke. Instead, however, CPASS argues for a large dose of motor therapy delivered continuously and cumulatively during the acute and subacute phases. When interpreting trials like CPASS, one must consider the substantial dose of early usual customary care (UCC) motor therapy that all participants received. CPASS participants averaged 27.9 hours of UCC occupational therapy (OT) during the first 2 months and 9.8 hours of UCC OT during the third and fourth months post-stroke. Any recovery experienced would therefore result not just from CPASS intensive motor therapy but the combined effects of experimental therapy plus UCC. Statistical limitations also did not allow direct comparisons of the acute and subacute group outcomes in CPASS. Instead of shifting inpatient therapy hours to the subacute phase, CPASS argues for preserving inpatient UCC. We also recommend conducting multi-site dosing trials to determine whether additional intensive motor therapy delivered in the first 2 to 3 months following inpatient rehabilitation can further improve outcomes.

Highly robust causal semiparametric U-statistic with applications in biomedical studies.

With our increased ability to capture large data, causal inference has received renewed attention and is playing an ever-important role in biomedicine and economics. However, one major methodological hurdle is that existing methods rely on many unverifiable model assumptions. Thus robust modeling is a critically important approach complementary to sensitivity analysis, where it compares results under various model assumptions. The more robust a method is with respect to model assumptions, the more worthy it is. The doubly robust estimator (DRE) is a significant advance in this direction. However, in practice, many outcome measures are functionals of multiple distributions, and so are the associated estimands, which can only be estimated via U-statistics. Thus most existing DREs do not apply. This article proposes a broad class of highly robust U-statistic estimators (HREs), which use semiparametric specifications for both the propensity score and outcome models in constructing the U-statistic. Thus, the HRE is more robust than the existing DREs. We derive comprehensive asymptotic properties of the proposed estimators and perform extensive simulation studies to evaluate their finite sample performance and compare them with the corresponding parametric U-statistics and the naive estimators, which show significant advantages. Then we apply the method to analyze a clinical trial from the AIDS Clinical Trials Group.

Robust estimates of regional treatment effects in multiregional randomized clinical trials with ordinal responses.

Global clinical trials involving multiple regions are common in current drug development processes. Determining the regional treatment effects of a new therapy over an existing therapy is important to both the sponsors and the regulatory agencies in the regions. Existing methods are mainly for continuous primary endpoints and use subjectively specified models, which may deviate from the true model. Here, we consider trials that have ordinal responses as the primary endpoint. This article extends the recently developed robust semiparametric ordinal regression model to estimate regional treatment effects, in which the regression coefficients and regional effects are modeled parametrically for ease of interpretation, and the regression link function is specified nonparametrically for robustness. The model parameters are estimated by semiparametric maximum likelihood estimation, and the null hypothesis of no regional effect is tested by the Wald test. Simulation studies are conducted to evaluate the performance of the proposed method and compare it with the commonly used parametric model. The results of the former show an improved overall performance over the latter. In particular, the model yields much higher precision in estimation and prediction than the fixed-link model. This result is especially appealing since our interest is to estimate the treatment effect more efficiently and the estimand is of particular interest in multiregional clinical trials. We then apply the method by analyzing real multiregional clinical trials with ordinal responses as their primary endpoint.

Set-regression with applications to subgroup analysis.

Regression is a commonly used statistical model. It is the conditional mean of the response given covariates µ(x)=E(Y|X=x) . However, in some practical problems, the interest is the conditional mean of the response given the covariates belonging to some set A. Notably, in precision medicine and subgroup analysis in clinical trials, the aim is to identify subjects who benefit the most from the treatment, or identify an optimal set in the covariate space which manifests treatment favoritism if a subject's covariates fall in this set and the subject is classified to the favorable treatment subgroup. Existing methods for subgroup analysis achieve this indirectly by using classical regression. This motivates us to develop a new type of regression: set-regression, defined as µ(A)=E(Y|X∈A) which directly addresses the subgroup analysis problem. This extends not only the classical regression model but also improves recursive partitioning and support vector machine approaches, and is particularly suitable for objectives involving optimization of the regression over sets, such as subgroup analysis. We show that the new versatile set-regression identifies the subgroup with increased accuracy. It is easy to use. Simulation studies also show superior performance of the proposed method in finite samples.

Robust estimates of regional treatment effects in multiregional randomized clinical trials with semiparametric logistic model.

In multiregional randomized clinical trials (MRCTs), determining the regional treatment effect of a new treatment over an existing one is important to both the sponsor and related regulatory agencies. Also of particular interest is to test the null hypothesis that the treatment benefit is the same among all the regions. Existing methods are mainly for continuous endpoint and use parametric models, which are not robust. MRCTs are known for facing increased variation and heterogeneity and a robust model for its design and analysis would be desirable. We consider clinical trials with a binary primary endpoint and propose a robust semiparametric logistic model which has a known parametric and an unknown nonparametric component. The parametric component represents our prior knowledge about the model, and the nonparametric part reflects uncertainty. Compared to the classic logistic model for this problem, the proposed model has the following advantages: robust to model assumption, more flexible and accurate to model the relationship between the response and covariates, and possibly more accurate parameter estimates. The model parameters are estimated by profile maximum likelihood approach, and the null hypothesis of regional treatment difference being the same is tested by the profile likelihood ratio statistic. Asymptotic properties of the estimates are derived. Simulation studies are conducted to evaluate the performance of the proposed model, which demonstrated clear advantages over the classic logistic model. The method is then applied to analyzing a real MRCT.

Identification of subgroups via partial linear regression modeling approach.

In clinical trials, treatment effects often vary from subject to subject. Some subjects may benefit more than others from a specific treatment. One of the aims of subgroup analysis is to identify if there are subgroups of subjects with differential treatment effects. As in standard analysis, we first test if subgroups with differential treatment effects exist; if they do, we classify the subjects into different subgroups based on their covariate profiles; otherwise, we conclude no subgroups have differential treatment effects in this population. Existing methods utilize regression models, particularly linear models, for such analysis. However, in practice, not all effects of covariates on responses are linear. To address this issue, the article proposes a more flexible model, the partial linear model with a nonlinear monotone function to describe some specific effects of covariates and with a linear component to describe the effects of other covariates, develops model-fitting algorithm and derives model asymptotics. We then utilize the Wald statistic to test the existence of subgroups and the Neyman-Pearson rule to classify subjects into the subgroups. Simulation studies are conducted to evaluate the finite sample performance of the proposed method by comparing it with the commonly used linear models. Finally, we apply the methods to analyzing a real clinical trial.

Critical Period After Stroke Study (CPASS): A phase II clinical trial testing an optimal time for motor recovery after stroke in humans.

Restoration of human brain function after injury is a signal challenge for translational neuroscience. Rodent stroke recovery studies identify an optimal or sensitive period for intensive motor training after stroke: near-full recovery is attained if task-specific motor training occurs during this sensitive window. We extended these findings to adult humans with stroke in a randomized controlled trial applying the essential elements of rodent motor training paradigms to humans. Stroke patients were adaptively randomized to begin 20 extra hours of self-selected, task-specific motor therapy at ≤30 d (acute), 2 to 3 mo (subacute), or ≥6 mo (chronic) after stroke, compared with controls receiving standard motor rehabilitation. Upper extremity (UE) impairment assessed by the Action Research Arm Test (ARAT) was measured at up to five time points. The primary outcome measure was ARAT recovery over 1 y after stroke. By 1 y we found significantly increased UE motor function in the subacute group compared with controls (ARAT difference = +6.87 ± 2.63, P = 0.009). The acute group compared with controls showed smaller but significant improvement (ARAT difference = +5.25 ± 2.59 points, P = 0.043). The chronic group showed no significant improvement compared with controls (ARAT = +2.41 ± 2.25, P = 0.29). Thus task-specific motor intervention was most effective within the first 2 to 3 mo after stroke. The similarity to rodent model treatment outcomes suggests that other rodent findings may be translatable to human brain recovery. These results provide empirical evidence of a sensitive period for motor recovery in humans.

Effect of Laser Moxibustion for Knee Osteoarthritis: A Multisite, Double-blind Randomized Controlled Trial.

OBJECTIVE: To examine the effects of laser moxibustion on pain and function in patients with knee osteoarthritis (OA). METHODS: A double-blind randomized clinical trial (4-week treatment, 20-week follow-up) was conducted. A total of 392 symptomatic knee OA patients with moderate to severe clinically significant knee pain were randomly assigned to laser treatment or sham laser control group (1:1). Twelve sessions of laser moxibustion or sham laser treatments on the acupuncture points at the affected knee(s) were performed 3 times a week for 4 weeks. The primary outcome measurement was change in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score from baseline to Week 4. RESULTS: Among the 392 randomized participants, 364 (92.86%) completed the trial. The median WOMAC pain score decreased significantly at Week 4 in the active group than in the sham group (2.1, 95% CI 1.6-2.6, P < 0.01). At Week 24, compared to the sham laser, active laser treatment resulted in significant pain reduction and function improvement (3.0, 95% CI 2.5-3.6, P < 0.01, and 14.8, 95% CI 11.9-17.6, P < 0.01, respectively). The physical component of the quality of life significantly improved in the active group vs the sham controls at Week 4 (3.2, 95% CI 1.3-5.0, P = 0.001) up to Week 24 (5.1, 95% CI 3.3-7.0, P < 0.001). No serious adverse effects were reported. CONCLUSION: Laser moxibustion resulted in statistically and clinically significant pain reduction and function improvement following a 4-week treatment in patients with knee OA.

Targeted design for adaptive clinical trials via semiparametric model.

Precision medicine approach that assigns treatment according to an individual's personal (including molecular) profile is revolutionizing health care. Existing statistical methods for clinical trial design typically assume a known model to estimate characteristics of treatment outcomes, which may yield biased results if the true model deviates far from the assumed one. This article aims to achieve model robustness in a phase II multi-stage adaptive clinical trial design. We propose and study a semiparametric regression mixture model in which the mixing proportions are specified according to the subjects' profiles, and each sub-group distribution is only assumed to be unimodal for robustness. The regression parameters and the error density functions are estimated by semiparametric maximum likelihood and isotonic regression estimators. The asymptotic properties of the estimates are studied. Simulation studies are conducted to evaluate the performance of the method after a real data analysis.

Integrative analysis with a system of semiparametric projection non-linear regression models.

In integrative analysis parametric or nonparametric methods are often used. The former is easier for interpretation but not robust, while the latter is robust but not easy to interpret the relationships among the different types of variables. To combine the advantages of both methods and for flexibility, here a system of semiparametric projection non-linear regression models is proposed for the integrative analysis, to model the innate coordinate structure of these different types of data, and a diagnostic tool is constructed to classify new subjects to the case or control group. Simulation studies are conducted to evaluate the performance of the proposed method, and shows promising results. Then the method is applied to analyze a real omics data from The Cancer Genome Atlas study, compared the results with those from the similarity network fusion, another integrative analysis method, and results from our method are more reasonable.

CO2 Laser Moxibustion for Knee Osteoarthritis: Study Protocol for A Multicenter, Double-blind, Randomized Controlled Trial.

BACKGROUND: Knee osteoarthritis (OA) is a major cause of disability among the older adults. Few treatments are safe and effective. Moxibustion is commonly used in treating knee OA in Chinese medicine (CM). CO2 Laser moxibustion device is a substitute for traditional moxibustion, which mimics the effects of traditional moxibustion. More data are needed to support its application in knee OA. OBJECTIVE: ObjectiveThe trial aims to assess the effect and safety of CO2 laser moxibustion in patients with knee osteoarthritis compared with a sham control. METHODS: This is a protocol for a multicenter, randomized, double-blind, placebo-controlled trial. A total of 392 participants were recruited and assigned to the CO2 laser moxibustion group and sham laser moxibustion group with a 1:1 ratio at 6 outpatient clinics in Shanghai, China. Participants in both groups received treatment at the affected knee(s) at the acupuncture point Dubi (ST 35) and an Ashi point. There were 3 sessions per week for 4 weeks, and an additional 20-week follow-up. Primary outcomes were changes in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain scores at week 4. Secondary outcomes were WOMAC function score, stiffness score and overall score, VAS pain, Short-Form heath survey (SF-36), and patients' global assessment. The serum levels of cytokines involved in progress of knee OA were explored. Safety was assessed during the whole trial. Masking effectiveness was assessed by both participants and treatment providers.This is a protocol for a multicenter, randomized, double-blind, placebo-controlled trial. A total of 392 participants were recruited and assigned to the CO2 laser moxibustion group and sham laser moxibustion group with a 1:1 ratio at 6 outpatient clinics in Shanghai, China. Participants in both groups received treatment at the affected knee(s) at the acupuncture point Dubi (ST 35) and an Ashi point. There were 3 sessions per week for 4 weeks, and an additional 20-week follow-up. Primary outcomes were changes in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain scores at week 4. Secondary outcomes were WOMAC function score, stiffness score and overall score, VAS pain, Short-Form heath survey (SF-36), and patients' global assessment. The serum levels of cytokines involved in progress of knee OA were explored. Safety was assessed during the whole trial. Masking effectiveness was assessed by both participants and treatment providers. DISCUSSION: CO2 laser moxibustion device, designed as a substitute for CM moxibustion, is easy to use and control with no choking smoke and smell, and is a plausible method for double-blind research. This study would provide rigorous evidence for the effect and safety of CO2 laser moxibustion in treating knee OA (Trial registration No.: ISRCTN15030019).

Comparison of the effects of 10.6-µm infrared laser and traditional moxibustion in the treatment of knee osteoarthritis.

Based on two separate randomized controlled trials (RCTs) on traditional Chinese medicine (TCM) moxibustion and 10.6-µm infrared laser moxibustion in treating knee osteoarthritis (OA), we did an indirect and preliminary comparison of the effects of the 10.6-µm laser moxibustion with the traditional moxibustion for knee osteoarthritis. The objective was to see whether the laser moxibustion is non-inferior to the traditional moxibustion in alleviating symptoms of knee osteoarthritis such as pain, stiffness, and joint dysfunction as well as improving quality of life for the patients with knee osteoarthritis, and whether a further RCT directly comparing the laser and traditional moxibustion is necessary. Pooled data from two RCTs in patients with knee osteoarthritis, trial ISRCTN68475405 and trial ISRCTN26065334, were used. In the two RCTs, the eligibility criteria were almost identical, the treatment procedure (i.e., sessions, duration, and points) were similar, and the outcome measurements (i.e., WOMAC for symptoms and SF-36 for quality of life) were the same. The double robustness method was used for the WOMAC scale and the SF-36 endpoints to detect the difference between traditional and laser moxibustion. The analysis comprised 55 patients from ISRCTN68475405 in real moxibustion arm (moxibustion group) and 88 patients from ISRCTN26065334 in real laser moxibustion arm (laser group). Demographic characteristics and course of disease were similar between the two groups. Causal inference, using the doubly robust estimating approach to correct for bias due to baseline differences, showed that there was no statistically significant difference in the WOMAC pain, stiffness, and physical function between the two treatments at midterm, end of treatment, and 4 weeks after the end of treatment (P > 0.05). The exception was that there was statistically significantly more benefit associated with laser moxibustion compared with traditional moxibustion in physical function at the follow-up of 4 weeks after the end of treatment (P=0.006). There was no statistically significant difference in most SF-36 endpoints (P > 0.05) except that physical functioning (PF), mental health (MH), and bodily pain (BP) were statistically significantly better in the laser group than in the traditional moxibustion group at the follow-up of 4 weeks after the end of treatment (P = 0.005, 0.034, 0.002). The benefits of 10.6-µm infrared laser moxibustion and the traditional moxibustion for knee osteoarthritis were comparable in pain, stiffness, physical dysfunction, and in most of the quality of life subdimensions. The laser moxibustion might be more beneficial in terms of physical function, body pain, and mental health in the long term. RCTs directly comparing 10.6-µm laser moxibustion with traditional moxibustion are warranted.

Subgroup analysis with semiparametric models toward precision medicine.

In analyzing clinical trials, one important objective is to classify the patients into treatment-favorable and nonfavorable subgroups. Existing parametric methods are not robust, and the commonly used classification rules ignore the fact that the implications of treatment-favorable and nonfavorable subgroups can be different. To address these issues, we propose a semiparametric model, incorporating both our knowledge and uncertainty about the true model. The Wald statistics is used to test the existence of subgroups, while the Neyman-Pearson rule to classify each subject. Asymptotic properties are derived, simulation studies are conducted to evaluate the performance of the method, and then method is used to analyze a real-world trial data.

Pembrolizumab in patients with thymic carcinoma: a single-arm, single-centre, phase 2 study.

BACKGROUND: Treatment options are limited for patients with thymic carcinoma. These aggressive tumours are not typically associated with paraneoplastic autoimmune disorders, and strong PD-L1 expression has been reported in thymic epithelial tumours. We aimed to assess the activity of pembrolizumab, a monoclonal antibody that targets PD-1, in patients with advanced thymic carcinoma. METHODS: We completed a single-arm phase 2 study of pembrolizumab in patients with recurrent thymic carcinoma who had progressed after at least one line of chemotherapy. This was a single-centre study performed at Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. Key inclusion criteria were an Eastern Cooperative Oncology Group performance status of 0-2, no history of autoimmune disease or other malignancy requiring treatment or laboratory abnormality, and adequate organ function. Patients received 200 mg of pembrolizumab every 3 weeks for up to 2 years. The primary objective of the study was the proportion of patients who had achieved a response assessed with Response Evaluation Criteria in Solid Tumors version 1.1. Analysis was per protocol, in all eligible patients. The study is registered with ClinicalTrials.gov, number NCT02364076, and is closed to accrual; we report the final analysis. FINDINGS: 41 patients were enrolled from March 12, 2015, to Dec 16, 2016, of whom 40 were eligible and evaluable and one was excluded because of elevated liver enzymes at screening. The median follow-up was 20 months (IQR 14-26). The proportion of patients who achieved a response was 22·5% (95% CI 10·8-38·5); one (3%) patient achieved a complete response, eight (20%) patients achieved partial responses, and 21 (53%) patients achieved stable disease. The most common grade 3 or 4 adverse events were increased aspartate aminotransferase and alanine aminotransferase (five [13%] patients each). Six (15%) patients developed severe autoimmune toxicity, including two (5%) patients with myocarditis. There were 17 deaths at the time of analysis, but no deaths due to toxicity. INTERPRETATION: Pembrolizumab is a promising treatment option in patients with thymic carcinoma. Because severe autoimmune disorders are more frequent in thymic carcinoma than in other tumour types, careful monitoring is essential. FUNDING: Merck & Co.

A novel rank-based non-parametric method for longitudinal ordinal data.

Longitudinal ordinal data are common in biomedical research. Although various methods for the analysis of such data have been proposed in the past few decades, they are limited in several ways. For instance, the constraints on parameters in the proportional odds model may result in convergence problems; the rank-based aligned rank transform method imposes constraints on other parameters and the distributional assumptions with parametric model. We propose a novel rank-based non-parametric method that models the profile rather than the distribution of the data to make an effective statistical inference without the constraint conditions. We construct the test statistic of the interaction first, and then construct the test statistics of the main effects separately with or without the interaction, while "adjusted coefficient" for the case of ties is derived. A simulation study is conducted for comparison between rank-based non-parametric and rank-transformed analysis of variance. The results show that type I errors of the two methods are both maintained closer to the priori level, but the statistical power of rank-based non-parametric is greater than that of rank-transformed analysis of variance, suggesting higher efficiency of the former. We then apply rank-based non-parametric to two real studies on acne and osteoporosis, and the results also illustrate the effectiveness of rank-based non-parametric, particularly when the distribution is skewed.

A novel nonparametric confidence interval for differences of proportions for correlated binary data.

Various confidence interval estimators have been developed for differences in proportions resulted from correlated binary data. However, the width of the mostly recommended Tango's score confidence interval tends to be wide, and the computing burden of exact methods recommended for small-sample data is intensive. The recently proposed rank-based nonparametric method by treating proportion as special areas under receiver operating characteristic provided a new way to construct the confidence interval for proportion difference on paired data, while the complex computation limits its application in practice. In this article, we develop a new nonparametric method utilizing the U-statistics approach for comparing two or more correlated areas under receiver operating characteristics. The new confidence interval has a simple analytic form with a new estimate of the degrees of freedom of n - 1. It demonstrates good coverage properties and has shorter confidence interval widths than that of Tango. This new confidence interval with the new estimate of degrees of freedom also leads to coverage probabilities that are an improvement on the rank-based nonparametric confidence interval. Comparing with the approximate exact unconditional method, the nonparametric confidence interval demonstrates good coverage properties even in small samples, and yet they are very easy to implement computationally. This nonparametric procedure is evaluated using simulation studies and illustrated with three real examples. The simplified nonparametric confidence interval is an appealing choice in practice for its ease of use and good performance.

Experimental design for multi-drug combination studies using signaling networks.

Combinations of multiple drugs are an important approach to maximize the chance for therapeutic success by inhibiting multiple pathways/targets. Analytic methods for studying drug combinations have received increasing attention because major advances in biomedical research have made available large number of potential agents for testing. The preclinical experiment on multi-drug combinations plays a key role in (especially cancer) drug development because of the complex nature of the disease, the need to reduce development time and costs. Despite recent progresses in statistical methods for assessing drug interaction, there is an acute lack of methods for designing experiments on multi-drug combinations. The number of combinations grows exponentially with the number of drugs and dose-levels and it quickly precludes laboratory testing. Utilizing experimental dose-response data of single drugs and a few combinations along with pathway/network information to obtain an estimate of the functional structure of the dose-response relationship in silico, we propose an optimal design that allows exploration of the dose-effect surface with the smallest possible sample size in this article. The simulation studies show our proposed methods perform well.

A New Preprocedure Risk Score for Predicting Contrast-Induced Acute Kidney Injury.

BACKGROUND: Most of the risk models for predicting contrast-induced acute kidney injury (CI-AKI) are available for only postcontrast exposure prediction; however, prediction before the procedure is more valuable in practice. This study aimed to develop a risk scoring system based on preprocedural characteristics for early prediction of CI-AKI in patients after coronary angiography or percutaneous coronary intervention (PCI). METHODS: We prospectively recruited 1777 consecutive patients who were randomized in an approximate 3:2 ratio to create a development data set (n = 1076) and a validation data set (n = 701). A risk score model based on preprocedural risk factors was developed using stepwise logistic regression. Validation was performed by bootstrap and split-sample methods. RESULTS: The occurrence of CI-AKI was 5.97% (106 of 1777), 5.95% (64 of 1076), and 5.99% (42 of 701) in the overall, developmental, and validation data sets, respectively. The risk score was developed with 5 prognostic factors (age, serum creatinine levels, N-terminal pro b-type natriuretic peptide levels, high-sensitivity C-reactive protein, and primary PCI), ranged from 0-36, and was well calibrated (Hosmer-Lemeshow χ2 = 4.162; P = 0.842). Good discrimination was obtained both in the developmental and validation data sets (C-statistic, 0.809 and 0.798, respectively). The risk score was highly and positively associated with CI-AKI (P for trend < 0.001) in-hospital and long-term outcomes. CONCLUSIONS: The novel risk score model we developed is a simple and accurate tool for early/preprocedural prediction of CI-AKI in patients undergoing coronary angiography or PCI. This tool allows assessment of the risk of CI-AKI before contrast exposure, allowing for timely initiation of appropriate preventive measures.

SAFE-HEaRt: Rationale and Design of a Pilot Study Investigating Cardiac Safety of HER2 Targeted Therapy in Patients with HER2-Positive Breast Cancer and Reduced Left Ventricular Function.

BACKGROUND: Human epidermal growth receptor 2 (HER2) targeted therapies have survival benefit in adjuvant and metastatic HER2 positive breast cancer but are associated with cardiac dysfunction. Current U.S. Food and Drug Administration recommendations limit the use of HER2 targeted agents to patients with normal left ventricular (LV) systolic function. METHODS: The objective of the SAFE-HEaRt study is to evaluate the cardiac safety of HER2 targeted therapy in patients with HER2 positive breast cancer and mildly reduced left ventricular ejection fraction (LVEF) with optimized cardiac therapy. Thirty patients with histologically confirmed HER2 positive breast cancer (stage I-IV) and reduced LVEF (40% to 49%) who plan to receive HER2 targeted therapy for ≥3 months will be enrolled. Prior to initiation on study, optimization of heart function with beta-blockers and angiotensin converting enzyme inhibitors will be initiated. Patients will be followed by serial echocardiograms and cardiac visits during and 6 months after completion of HER2 targeted therapy. Myocardial strain and blood biomarkers, including cardiac troponin I and high-sensitivity cardiac troponin T, will be examined at baseline and during the study. DISCUSSION: LV dysfunction in patients with breast cancer poses cardiac and oncological challenges and limits the use of HER2 targeted therapies and its oncological benefits. Strategies to prevent cardiac dysfunction associated with HER2 targeted therapy have been limited to patients with normal LVEF, thus excluding patients who may receive the highest benefit from those strategies. SAFE-HEaRt is the first prospective pilot study of HER2 targeted therapies in patients with reduced LV function while on optimized cardiac treatment that can provide the basis for clinical practice changes. The Oncologist 2017;22:518-525 IMPLICATIONS FOR PRACTICE: Human epidermal growth receptor 2 (HER2) targeted therapies have survival benefit in adjuvant and metastatic HER2 positive breast cancer but are associated with cardiac dysfunction. To our knowledge, SAFE-HEaRt is the first clinical trial that prospectively tests the hypothesis that HER2 targeted therapies may be safely administered in patients with mildly reduced cardiac function in the setting of ongoing cardiac treatment and monitoring. The results of this study will provide cardiac safety data and inform consideration of clinical practice changes in patients with HER2 positive breast cancer and reduced cardiac function, as well as provide information regarding cardiovascular monitoring and treatment in this population.