Assessing Clinician Utilization of Next-Generation Antibiotics Against Resistant Gram-Negative Infections in U.S. Hospitals : A Retrospective Cohort Study

BACKGROUND

The U.S. antibiotic market failure has threatened future innovation and supply. Understanding when and why clinicians underutilize recently approved gram-negative antibiotics might help prioritize the patient in future antibiotic development and potential market entry rewards.

OBJECTIVE

To determine use patterns of recently U.S. Food and Drug Administration (FDA)-approved gram-negative antibiotics (ceftazidime-avibactam, ceftolozane-tazobactam, meropenem-vaborbactam, plazomicin, eravacycline, imipenem-relebactam-cilastatin, and cefiderocol) and identify factors associated with their preferential use (over traditional generic agents) in patients with gram-negative infections due to pathogens displaying difficult-to-treat resistance (DTR; that is, resistance to all first-line antibiotics).

DESIGN

Retrospective cohort.

SETTING

619 U.S. hospitals.

PARTICIPANTS

Adult inpatients.

MEASUREMENTS

Quarterly percentage change in antibiotic use was calculated using weighted linear regression. Machine learning selected candidate variables, and mixed models identified factors associated with new (vs. traditional) antibiotic use in DTR infections.

RESULTS

Between quarter 1 of 2016 and quarter 2 of 2021, ceftolozane-tazobactam (approved 2014) and ceftazidime-avibactam (2015) predominated new antibiotic usage whereas subsequently approved gram-negative antibiotics saw relatively sluggish uptake. Among gram-negative infection hospitalizations, 0.7% (2551 [2631 episodes] of 362 142) displayed DTR pathogens. Patients were treated exclusively using traditional agents in 1091 of 2631 DTR episodes (41.5%), including "reserve" antibiotics such as polymyxins, aminoglycosides, and tigecycline in 865 of 1091 episodes (79.3%). Patients with bacteremia and chronic diseases had greater adjusted probabilities and those with do-not-resuscitate status, acute liver failure, and Acinetobacter baumannii complex and other nonpseudomonal nonfermenter pathogens had lower adjusted probabilities of receiving newer (vs. traditional) antibiotics for DTR infections, respectively. Availability of susceptibility testing for new antibiotics increased probability of usage.

LIMITATION

Residual confounding.

CONCLUSION

Despite FDA approval of 7 next-generation gram-negative antibiotics between 2014 and 2019, clinicians still frequently treat resistant gram-negative infections with older, generic antibiotics with suboptimal safety-efficacy profiles. Future antibiotics with innovative mechanisms targeting untapped pathogen niches, widely available susceptibility testing, and evidence demonstrating improved outcomes in resistant infections might enhance utilization.

PRIMARY FUNDING SOURCE

U.S. Food and Drug Administration; NIH Intramural Research Program.

Carbapenem use in extended-spectrum cephalosporin-resistant Enterobacterales infections in US hospitals and influence of IDSA guidance: a retrospective cohort study

BACKGROUND

Disparate and rapidly changing practice recommendations from major professional infectious diseases societies for managing non-severe infections caused by extended-spectrum β-lactamase-producing Enterobacterales might hamper carbapenem stewardship. We aimed to understand the real-world management of extended-spectrum cephalosporin-resistant (ECR) Enterobacterales infections in US hospitals and factors influencing preference for carbapenems over alternative treatments.

METHODS

This retrospective cohort study included adults (aged ≥18 years) admitted to hospital with ECR Enterobacterales infections in the PINC AI database. Antibiotic regimens were assessed during empirical and targeted treatment periods and by infection severity and site. Likelihood of receiving targeted carbapenems over time and before or after initial release of the Infectious Diseases Society of America (IDSA) guidance on Sept 8, 2020, was established with generalised estimating equations controlling for patient, hospital, and temporal confounders.

FINDINGS

Between Jan 1, 2018, and Dec 31, 2021, 30 041 inpatient encounters with ECR Enterobacterales infections were identified at 168 US hospitals, of which 16 006 (53·3%) encounters were in women and 14 035 (46·7%) were in men, with a mean age of 67·3 years (SD 15·1). Although few patients received carbapenems empirically (5324 [17·7%] of 30 041), many did so as targeted treatment (17 518 [58·3%] of 30 041), including subgroups of patients without septic shock (3031 [45·6%] of 6651) and patients with urinary tract infections without septic shock (1845 [46·8%] of 3943) in whom specific narrower-spectrum alternatives were active. Transitions from non-carbapenem to carbapenem antibiotics occurred most often on the day that the ECR phenotype was reported, regardless of illness severity. Carbapenems were the predominant choice to treat ECR Enterobacterales infections over time (adjusted odds ratio 1·00 [95% CI 1·00-1·00]), with no additional immediate change (1·07 [0·95-1·20]) or sustained change (0·99 [0·98-1·00]) after IDSA guidance release.

INTERPRETATION

High carbapenem use in targeting non-severe ECR Enterobacterales infections in US hospitals predates 2020 IDSA guidance and has persisted thereafter. Efforts to increase awareness and implementation of recommendations among clinicians to use carbapenem-sparing alternatives in ECR Enterobacterales infections might decrease global carbapenem selective pressure.

FUNDING

US National Institutes of Health Intramural Research Program, National Institute of Allergy and Infectious Diseases, and US Food and Drug Administration.

Safety signal detection with control of latent factors

Postmarket drug safety database like vaccine adverse event reporting system (VAERS) collect thousands of spontaneous reports annually, with each report recording occurrences of any adverse events (AEs) and use of vaccines. We hope to identify signal vaccine-AE pairs, for which certain vaccines are statistically associated with certain adverse events (AE), using such data. Thus, the outcomes of interest are multiple AEs, which are binary outcomes and could be correlated because they might share certain latent factors; and the primary covariates are vaccines. Appropriately accounting for the complex correlation among AEs could improve the sensitivity and specificity of identifying signal vaccine-AE pairs. We propose a two-step approach in which we first estimate the shared latent factors among AEs using a working multivariate logistic regression model, and then use univariate logistic regression model to examine the vaccine-AE associations after controlling for the latent factors. Our simulation studies show that this approach outperforms current approaches in terms of sensitivity and specificity. We apply our approach in analyzing VAERS data and report our findings.

The prevalence and clinical significance of HER2 expression in prostate adenocarcinoma

AIMS

Abnormalities in HER2 are well-established oncogenic drivers and are approved therapeutic targets in various malignancies. Prior studies evaluating HER2 expression in prostate cancer (PCa) have yielded variable results. Most of these studies used immunohistochemistry scoring systems based on breast cancer data. The goal of this study was to determine the prevalence and clinical significance of HER2 expression using a scoring system that better reflects the HER2 staining pattern observed in PCa.

METHODS

We randomly selected similar numbers of localized low risk (AJCC stage I), locally advanced (AJCC stages II & III), and metastatic (AJCC stage IV) PCa patients treated at the DC VA Medical Center between 2000 and 2022. Among them, we included patients who had sufficient PCa tissue samples and clinical information required for this analysis. Two experienced pathologists independently scored HER2 expression (Ventana Pathway anti-HER2) according to a modified gastric cancer HER2 scoring system.

RESULTS

Out of the 231 patients included, 85 % self-identified as Black. 58.9 % of patients expressed HER2 (1+: 35.5 %; 2+: 18.2 %; 3+: 5.2 %). Validity of the results was confirmed using the HercepTest antibody. Higher HER2 expression was associated with a higher Gleason Score/Grade Group and advanced disease.

CONCLUSIONS

Our findings support the adverse prognostic impact on HER2 in PCa. We propose the use of a modified scoring system to evaluate HER2 expression in PCa. The observed high prevalence of HER2 expression supports the consideration of novel HER2-targeted therapies addressing even low levels of HER2 expression in future PCa trials.

Predicting Airflow from Measures Sensitive to Mid-cord Glottal Gap During the COVID-19 Pandemic

OBJECTIVES

To determine if trans-laryngeal airflow, important in assessing vocal function in paresis/paralysis and presbylarynges patients with mid-cord glottal gaps, could be predicted by other measures sensitive to mid-cord glottal gap size but with smaller risks of spreading COVID-19, and if any patient factors need consideration.

METHODS

Four populations were: unilateral vocal fold paresis/paralysis (UVFP, 148), aging and UVFP (UVFP plus aging, 22), bilateral vocal fold paresis/paralysis without airway obstruction (BVFP, 49), and presbylarynges (66). Five measures were selected from the initial clinic visit: mean airflow from repeated /pi/ syllables, longer of 2 /s/ and 2 /z/ productions, higher of 2 cepstral peak prominence smoothed for vowel /a/ (CPPSa), and Glottal Function Index (GFI). S/Z ratios were computed. Stepwise regression models used 3 measures and 5 patient factors (age, sex, etiology, diagnosis, and potentially impaired power source for voicing) to predict airflow.

RESULTS

Log-transformations were required to normalize distributions of airflow and S/Z ratio. The final model revealed age, sex, impaired power source, log-transformed S/Z ratio, and GFI predicted log-transformed airflow (R2 = .275, F[5,278] = 21.1; P < .001).

CONCLUSIONS

The amount of variance explained by the model was not high, suggesting adding other predictive variables to the model might increase the variance explained.

Reliability of Admission Procalcitonin Testing for Capturing Bacteremia Across the Sepsis Spectrum: Real-World Utilization and Performance Characteristics, 65 U.S. Hospitals, 2008-2017

OBJECTIVES

Serum procalcitonin is often ordered at admission for patients with suspected sepsis and bloodstream infections (BSIs), although its performance characteristics in this setting remain contested. This study aimed to evaluate use patterns and performance characteristics of procalcitonin-on-admission in patients with suspected BSI, with or without sepsis.

DESIGN

Retrospective cohort study.

SETTING

Cerner HealthFacts Database (2008-2017).

PATIENTS

Adult inpatients (≥ 18 yr) who had blood cultures and procalcitonin drawn within 24 hours of admission.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Testing frequency of procalcitonin was determined. Sensitivity of procalcitonin-on-admission for detecting BSI due to different pathogens was calculated. Area under the receiver operating characteristic curve (AUC) was calculated to assess discrimination by procalcitonin-on-admission for BSI in patients with and without fever/hypothermia, ICU admission and sepsis defined by Centers for Disease Control and Prevention Adult Sepsis Event criteria. AUCs were compared using Wald test and p values were adjusted for multiple comparisons. At 65 procalcitonin-reporting hospitals, 74,958 of 739,130 patients (10.1%) who had admission blood cultures also had admission procalcitonin testing. Most patients (83%) who had admission day procalcitonin testing did not have a repeat procalcitonin test. Median procalcitonin varied considerably by pathogen, BSI source, and acute illness severity. At a greater than or equal to 0.5 ng/mL cutoff, sensitivity for BSI detection was 68.2% overall, ranging between 58.0% for enterococcal BSI without sepsis and 96.4% for pneumococcal sepsis. Procalcitonin-on-admission displayed moderate discrimination at best for overall BSI (AUC, 0.73; 95% CI, 0.72-0.73) and showed no additional utility in key subgroups. Empiric antibiotic use proportions were not different between blood culture sampled patients with a positive procalcitonin (39.7%) and negative procalcitonin (38.4%) at admission.

CONCLUSIONS

At 65 study hospitals, procalcitonin-on-admission demonstrated poor sensitivity in ruling out BSI, moderate-to-poor discrimination for both bacteremic sepsis and occult BSI and did not appear to meaningfully alter empiric antibiotic usage. Diagnostic stewardship of procalcitonin-on-admission and risk assessment of admission procalcitonin-guided clinical decisions is warranted.

Joint semiparametric models for case-cohort designs

Two-phase studies such as case-cohort and nested case-control studies are widely used cost-effective sampling strategies. In the first phase, the observed failure/censoring time and inexpensive exposures are collected. In the second phase, a subgroup of subjects is selected for measurements of expensive exposures based on the information from the first phase. One challenging issue is how to utilize all the available information to conduct efficient regression analyses of the two-phase study data. This paper proposes a joint semiparametric modeling of the survival outcome and the expensive exposures. Specifically, we assume a class of semiparametric transformation models and a semiparametric density ratio model for the survival outcome and the expensive exposures, respectively. The class of semiparametric transformation models includes the proportional hazards model and the proportional odds model as special cases. The density ratio model is flexible in modeling multivariate mixed-type data. We develop efficient likelihood-based estimation and inference procedures and establish the large sample properties of the nonparametric maximum likelihood estimators. Extensive numerical studies reveal that the proposed methods perform well under practical settings. The proposed methods also appear to be reasonably robust under various model mis-specifications. An application to the National Wilms Tumor Study is provided.

Generalized fiducial inference for the restricted mean survival time

The standard modeling approach for time-to-event outcomes subject to censoring is based on the hazard function, with hazard ratios capturing the effect of exposures on the risk of outcome. The restricted mean survival time, defined as the expected time to event up to a pre-specified time horizon, provides an alternative useful summary of time-to-event outcomes. Restricted mean survival time can be estimated nonparametrically and can be used to compare groups or interventions when the proportional hazards (PHs) assumption does not hold. Moreover, even when the proportional hazards assumption holds, the restricted mean survival time, an additive measure of risk, provides additional information to the hazard ratio, which is a measure of relative risk that can be difficult to interpret in absence of an estimate of the reference risk. Herein, a generalized fiducial approach is proposed for restricted mean survival time, and its asymptotic properties are investigated. Numerical simulations show the proposed approach provides one- and two-sided confidence intervals with coverage probabilities close to nominal values and controls the type-I error for two-group comparisons even for small sample sizes with a low number of events. Data from a type 1 diabetes study is used for illustration.

The prevalence and clinical significance of HER2 overexpression in prostate adenocarcinoma

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Background: The HER2 oncogene serves as a prognostic marker in breast cancer and is a therapeutic target in breast, lung, and GI malignancies, including cancers with low Her2 overexpression. A prior study by Minner et al ( Clin Cancer Res, 2010) evaluating Her2 overexpression in prostate cancer (PCa) showed a prevalence ~20%. The goal of this study is to evaluate the prevalence and clinical significance of Her2 overexpression in PCa in a predominantly African American (AA) cohort. Methods: 124 PCa patients managed at the Washington DC VA Medical Center between 2000-2021 were randomly selected: 35 indolent (AJCC stage I), 46 locally advanced (AJCC stages II&III), 19 locally advanced at diagnosis but progressed to metastatic (AJCC stage IV), and 24 de novo metastatic (AJCC stage IV). Immunohistochemistry (IHC) for Her2 was performed on one representative tissue section from core biopsies or a radical prostatectomy for each case. IHC intensity was scored independently by two experienced pathologists as negative (0, no staining), low positive (1+, faint membranous staining), moderate positive (2+, weak to moderate complete, basolateral or lateral membranous staining) or strong positive (3+, strong complete, basolateral or lateral membranous staining). Fisher’s exact test was used to test the association between Her2 expression and categorical variables. Results: 108 patients (87%) self-identified as AA. The mean age at diagnosis was 62.9 years. The prevalence of positive Her2 expression (1+, 2+,3+) was 48% (59/124) in the entire cohort, 29% (10/35) in the indolent group, 48% (22/46) in the locally advanced group, 68% (13/19) in the locally advanced group that progressed to metastatic disease, and 58% (14/24) in the de novo metastatic group ( p=0.0014). Prevalence of Her2 positive expression was 28% (10/36) in patients with a Gleason Score (GS) of 6, whereas in patients with a GS of 9 it was 78% (9/11) ( p<0.0001). Mean (non-zero) Her2 expression was highest in the de novo metastatic group (Her2 2.0, SD 0.78), followed by the locally advanced group that progressed to metastatic disease (Her2 1.54, SD 0.66), the locally advanced group without metastatic disease (Her2 1.36, SD 0.58), and lowest in the indolent group (Her2 1.0, SD 0.0) ( p=0.0016). Next generation sequencing data was available for 20 metastatic patients (12 positive for Her2 expression) and did not show the presence of HER2 mutations or amplifications. Conclusions: In this predominantly AA cohort, expression of Her2 was identified in almost 50% of patients with PCa. Her2 scores ≥2 were associated with higher GS and advanced disease. Moreover, a Her2 score of 3 was seen in 6 patients in our cohort. Historic studies on targeting Her2 in advanced PCa have failed to demonstrate clinical benefit. However, in the era of targeted therapy, Her2 inhibition addressing low-levels of Her2 overexpression should be considered in future trials.

RIPK2 as a prognostic biomarker and predictor of aggressive prostate cancer

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Background: Receptor-Interacting Protein Kinase 2 (RIPK2) has been known to play a significant role in inflammation and anti-microbial response via the NF-KB pathway. Recently, RIPK2 was also shown to stabilize and activate c-Myc, a molecule implicated in the progression of various malignancies including prostate cancer (PCa). Our study aims to determine the prognostic value of RIPK2 expression in PCa using immunohistochemistry (IHC) analysis of biopsy specimens with special focus on a predominantly African American (AA) cohort. Methods: Eligible patients aged ≥18 years diagnosed with PCa at the Washington DC VA Medical Center were randomly selected and retrospectively grouped into indolent, locally advanced, and metastatic disease categories and stratified by age, ethnicity, AJCC stage, PSA and Gleason score at diagnosis. Representative tissue from prostate core biopsy, transurethral resection, or radical prostatectomy was analyzed for each patient. RIPK2 expression and intensity were assessed by IHC and graded by two independent pathologists by the following scale: 0 (no cytoplasmic staining), 1+ (weak cytoplasmic staining similar to benign prostatic tissue), 2+ (moderate cytoplasmic staining), and 3+ (strong cytoplasmic staining). The primary endpoint was RIPK2 expression by disease group, and key secondary outcomes were RIPK2 association with Gleason score, PSA at diagnosis, age and race. Fisher’s exact test was used to evaluate statistical significance of the association between RIPK2 expression level and disease category. Results: A total of 89 patients were selected for analysis, of which 75 (84.3%) were AA. All tumors had at least 1+ level RIPK2 expression and there was a highly significant difference in RIPK2 expression according to disease severity (p < 0.001). Stratified RIPK2 expression was 1.53, 1.83, and 2.28 in the indolent, locally advanced, and metastatic groups respectively. We observed a significant directly proportional correlation between RIPK2 expression and a higher PSA level (p= 0.0021), Gleason score (p< 0.001), higher age (p= 0.0276), and AJCC stage (p< 0.001) at the time of diagnosis. Mean RIPK2 expression was 1.81 in AA patients and 2.15 in White patients (p= 0.0332). Conclusions: PCa is the most prevalent cancer in US males and there is an urgent need for more accurate biomarkers that can predict disease severity and suggest actionable targeted therapy. In this retrospective analysis, we confirm that higher RIPK2 expression is associated with more advanced PCa. We extend these findings to a predominantly AA cohort, which may promote improvements in personalized medicine for this population at higher risk for PCa if RIPK2-targeting drugs such as Ponatinib can be utilized.

Evaluating p53 nuclear expression and prostate cancer progression in a predominantly African American cohort

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Background: Increased utilization of next-generation sequencing has revealed evidence of early TP53 alterations in localized prostate cancer (PCa), suggesting a growing clinical potential for p53 immunohistochemistry (IHC). Prior research by Harmon and Gesztes et al. (Radiology, 2021 & Scientific Reports, 2022) support the clinical utility of detecting p53 overexpression in core biopsies and radical prostatectomies (RP) to predict underlying TP53 alterations, biochemical recurrence (BCR), and metastasis. However, to date, no established consensus exists evaluating p53 “overexpression” in PCa. This study aimed to compare two fundamental approaches to assess p53 expression across various specimens with regard to PCa progression. A secondary study aim explored whether ethnic differences in p53 expression exist. Methods: This retrospective cohort study comprised 84 randomly selected PCa patients (46 metastatic; 38 non-metastatic) enrolled between 1996 – 2021 at the DC VA Medical Center. Representative sections of core biopsies (60%), RP (17%), prostate chips (7%), and metastatic deposits (16%) were examined, where (%) equals a share of the total cohort. IHC was performed on selected sections with a p53 monoclonal antibody (Biocare Medical; p53 Tumor Suppressor Protein; clone DO-7). p53 nuclear expression was manually scored according to the highest intensity (0 absent, 1+ weak, 2+ moderate, or 3+ marked) and percentage (0%, <1%, 1-5%, and >5%) of tumor cell nuclei observed in one index block per patient. All slides were reviewed by two pathologists who were blinded to clinical data. Fisher’s exact test was used to test the association between p53 positivity and categorical variables. Results: Of 84 men included, 63 (75%) self-identified as African American (AA). The median follow-up time was 6.2 years. Thirty-four patients (40%) exhibited p53 nuclear expression, of which 18 patients (21%) showed marked (3+) nuclear intensity. Notably, 11 of 18 (61%) 3+ patients displayed <5% p53 expression. All patients with >5% p53 positivity showed marked (3+) p53 staining. The presence of marked p53 staining, regardless of percentage, was found to be significantly associated with a higher Gleason score ( p=0.0002), higher PSA at the time of biopsy ( p<0.0001), BCR ( p=0.0007) and metastasis ( p<0.0001). Importantly, marked p53 staining was identified only in patients who developed metastatic disease. No significant differences were observed in marked p53 expression between AA and Caucasian men. Conclusions: In prostate cancer, marked (3+) p53 nuclear intensity (regardless of the percentage) is associated with disease progression with no observable ethnic predilection. Therefore, careful assessment of intensity is essential to avoid under-detection (when percent criteria are applied) and maximize the number of patients who may benefit from enhanced clinical interventions.

The COVID-19 Community Research Partnership: a multistate surveillance platform for characterizing the epidemiology of the SARS-CoV-2 pandemic

The COVID-19 Community Research Partnership (CCRP) is a multisite surveillance platform designed to characterize the epidemiology of the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-COV-2) pandemic. This article describes the CCRP study design and methodology. The CCRP includes two prospective cohorts, one with six health systems in the mid-Atlantic and southern USA, and the other with six health systems in North Carolina. With enrollment beginning in April 2020, sites invited persons within their healthcare systems as well as community members to participate in daily surveillance for symptoms of COVID-like illnesses, testing, and risk behaviors. Participants with electronic health records (EHRs) were also asked to volunteer data access. Subsets of participants, representative of the general population and including oversampling of populations of interest, were selected for repeated at-home serology testing. By October 2021, 65 739 participants (62 261 adult and 3478 pediatric) were enrolled, with 89% providing syndromic data, 74% providing EHR data, and 70% participating in one of the two serology sub-studies. An average of 62% of the participants completed a daily survey at least once a week, and 55% of the serology kits were returned. The CCRP provides rich regional epidemiologic data and the opportunity to more fully characterize the risks and sequelae of SARS-CoV-2 infection.

Synthesizing studies for comparing different treatment sequences in clinical trials

With advances in cancer treatments and improved patient survival, more patients may go through multiple lines of treatment. It is of clinical importance to choose a sequence of effective treatments (eg, lines of treatment) for individual patients with the goal of optimizing their long-term clinical outcome (eg, survival). Several important issues arise in cancer studies. First, cancer clinical trials are usually conducted by each line of treatment. For a treatment sequence, we may have first line and second line treatment data from two different studies. Second, there is typically a treatment initiation period varying from patient to patient between progression of disease and the start of the second line treatment due to administrative reasons. Additionally, the choice of the second line treatment for patients with progression of disease may depend on their characteristics. We address all these issues and develop semiparametric methods under the potential outcome framework for the estimation of the overall survival probability for a treatment sequence and for comparing different treatment sequences. We establish the large sample properties of the proposed inferential procedures. Simulation studies and an application to a colorectal clinical trial are provided.

Mixture survival trees for cancer risk classification

In oncology studies, it is important to understand and characterize disease heterogeneity among patients so that patients can be classified into different risk groups and one can identify high-risk patients at the right time. This information can then be used to identify a more homogeneous patient population for developing precision medicine. In this paper, we propose a mixture survival tree approach for direct risk classification. We assume that the patients can be classified into a pre-specified number of risk groups, where each group has distinct survival profile. Our proposed tree-based methods are devised to estimate latent group membership using an EM algorithm. The observed data log-likelihood function is used as the splitting criterion in recursive partitioning. The finite sample performance is evaluated by extensive simulation studies and the proposed method is illustrated by a case study in breast cancer.

Classification Performance and Feature Space Characteristics in Individuals With Upper Limb Loss Using Sonomyography

Objective: Sonomyography, or ultrasound-based sensing of muscle deformation, is an emerging modality for upper limb prosthesis control. Although prior studies have shown that individuals with upper limb loss can achieve successful motion classification with sonomyography, it is important to better understand the time-course over which proficiency develops. In this study, we characterized user performance during their initial and subsequent exposures to sonomyography. Method: Ultrasound images corresponding to a series of hand gestures were collected from individuals with transradial limb loss under three scenarios: during their initial exposure to sonomyography (Experiment 1), during a subsequent exposure to sonomyography where they were provided biofeedback as part of a training protocol (Experiment 2), and during testing sessions held on different days (Experiment 3). User performance was characterized by offline classification accuracy, as well as metrics describing the consistency and separability of the sonomyography signal patterns in feature space. Results: Classification accuracy was high during initial exposure to sonomyography (96.2 ± 5.9%) and did not systematically change with the provision of biofeedback or on different days. Despite this stable classification performance, some of the feature space metrics changed. Conclusions: User performance was strong upon their initial exposure to sonomyography and did not improve with subsequent exposure. Clinical Impact: Prosthetists may be able to quickly assess if a patient will be successful with sonomyography without submitting them to an extensive training protocol, leading to earlier socket fabrication and delivery.

Efficient Multiple Imputation for Sensitivity Analysis of Recurrent Events Data with Informative Censoring

Missing data are commonly encountered in clinical trials due to dropout or nonadherence to study procedures. In trials in which recurrent events are of interest, the observed count can be an undercount of the events if a patient drops out before the end of the study. In many applications, the data are not necessarily missing at random and it is often not possible to test the missing at random assumption. Consequently, it is critical to conduct sensitivity analysis. We develop a control-based multiple imputation method for recurrent events data, where patients who drop out of the study are assumed to have a similar response profile to those in the control group after dropping out. Specifically, we consider the copy reference approach and the jump to reference approach. We model the recurrent event data using a semiparametric proportional intensity frailty model with the baseline hazard function completely unspecified. We develop nonparametric maximum likelihood estimation and inference procedures. We then impute the missing data based on the large sample distribution of the resulting estimators. The variance estimation is corrected by a bootstrap procedure. Simulation studies demonstrate the proposed method performs well in practical settings. We provide applications to two clinical trials.

Framework to Classify Reverse Cardiac Remodeling With Mechanical Circulatory Support: The Utah-Inova Stages

BACKGROUND

Variable definitions and an incomplete understanding of the gradient of reverse cardiac remodeling following continuous flow left ventricular assist device (LVAD) implantation has limited the field of myocardial plasticity. We evaluated the continuum of LV remodeling by serial echocardiographic imaging to define 3 stages of reverse cardiac remodeling following LVAD.

METHODS

The study enrolled consecutive LVAD patients across 4 study sites. A blinded echocardiographer evaluated the degree of structural (LV internal dimension at end-diastole [LVIDd]) and functional (LV ejection fraction [LVEF]) change after LVAD. Patients experiencing an improvement in LVEF ≥40% and LVIDd ≤6.0 cm were termed responders, absolute change in LVEF of ≥5% and LVEF <40% were termed partial responders, and the remaining patients with no significant improvement in LVEF were termed nonresponders.

RESULTS

Among 358 LVAD patients, 34 (10%) were responders, 112 (31%) partial responders, and the remaining 212 (59%) were nonresponders. The use of guideline-directed medical therapy for heart failure was higher in partial responders and responders. Structural changes (LVIDd) followed a different pattern with significant improvements even in patients who had minimal LVEF improvement. With mechanical unloading, the median reduction in LVIDd was -0.6 cm (interquartile range [IQR], -1.1 to -0.1 cm; nonresponders), -1.1 cm (IQR, -1.8 to -0.4 cm; partial responders), and -1.9 cm (IQR, -2.9 to -1.1 cm; responders). Similarly, the median change in LVEF was -2% (IQR, -6% to 1%), 9% (IQR, 6%-14%), and 27% (IQR, 23%-33%), respectively.

CONCLUSIONS

Reverse cardiac remodeling associated with durable LVAD support is not an all-or-none phenomenon and manifests in a continuous spectrum. Defining 3 stages across this continuum can inform clinical management, facilitate the field of myocardial plasticity, and improve the design of future investigations.

Inferring latent heterogeneity using many feature variables supervised by survival outcome

In cancer studies, it is important to understand disease heterogeneity among patients so that precision medicine can particularly target high-risk patients at the right time. Many feature variables such as demographic variables and biomarkers, combined with a patient's survival outcome, can be used to infer such latent heterogeneity. In this work, we propose a mixture model to model each patient's latent survival pattern, where the mixing probabilities for latent groups are modeled through a multinomial distribution. The Bayesian information criterion is used for selecting the number of latent groups. Furthermore, we incorporate variable selection with the adaptive lasso into inference so that only a few feature variables will be selected to characterize the latent heterogeneity. We show that our adaptive lasso estimator has oracle properties when the number of parameters diverges with the sample size. The finite sample performance is evaluated by the simulation study, and the proposed method is illustrated by two datasets.

On symmetric semiparametric two-sample problem

We consider a two-sample problem where data come from symmetric distributions. Usual two-sample data with only magnitudes recorded, arising from case-control studies or logistic discriminant analyses, may constitute a symmetric two-sample problem. We propose a semiparametric model such that, in addition to symmetry, the log ratio of two unknown density functions is modeled in a known parametric form. The new semiparametric model, tailor-made for symmetric two-sample data, can also be viewed as a biased sampling model subject to symmetric constraint. A maximum empirical likelihood estimation approach is adopted to estimate the unknown model parameters, and the corresponding profile empirical likelihood ratio test is utilized to perform hypothesis testing regarding the two population distributions. Symmetry, however, comes with irregularity. It is shown that, under the null hypothesis of equal symmetric distributions, the maximum empirical likelihood estimator has degenerate Fisher information, and the test statistic has a mixture of χ² -type asymptotic distribution. Extensive simulation studies have been conducted to demonstrate promising statistical powers under correct and misspecified models. We apply the proposed methods to two real examples.

Semiparametric frailty models for zero-inflated event count data in the presence of informative dropout

Recurrent events data are commonly encountered in medical studies. In many applications, only the number of events during the follow-up period rather than the recurrent event times is available. Two important challenges arise in such studies: (a) a substantial portion of subjects may not experience the event, and (b) we may not observe the event count for the entire study period due to informative dropout. To address the first challenge, we assume that underlying population consists of two subpopulations: a subpopulation nonsusceptible to the event of interest and a subpopulation susceptible to the event of interest. In the susceptible subpopulation, the event count is assumed to follow a Poisson distribution given the follow-up time and the subject-specific characteristics. We then introduce a frailty to account for informative dropout. The proposed semiparametric frailty models consist of three submodels: (a) a logistic regression model for the probability such that a subject belongs to the nonsusceptible subpopulation; (b) a nonhomogeneous Poisson process model with an unspecified baseline rate function; and (c) a Cox model for the informative dropout time. We develop likelihood-based estimation and inference procedures. The maximum likelihood estimators are shown to be consistent. Additionally, the proposed estimators of the finite-dimensional parameters are asymptotically normal and the covariance matrix attains the semiparametric efficiency bound. Simulation studies demonstrate that the proposed methodologies perform well in practical situations. We apply the proposed methods to a clinical trial on patients with myelodysplastic syndromes.

Controlling false discovery proportion in identification of drug-related adverse events from multiple system organ classes

Analyzing safety data from clinical trials to detect safety signals worth further examination involves testing multiple hypotheses, one for each observed adverse event (AE) type. There exists certain hierarchical structure for these hypotheses due to the classification of the AEs into system organ classes, and these AEs are also likely correlated. Many approaches have been proposed to identify safety signals under the multiple testing framework and tried to achieve control of false discovery rate (FDR). The FDR control concerns the expectation of the false discovery proportion (FDP). In practice, the control of the actual random variable FDP could be more relevant and has recently drawn much attention. In this paper, we proposed a two-stage procedure for safety signal detection with direct control of FDP, through a permutation-based approach for screening groups of AEs and a permutation-based approach of constructing simultaneous upper bounds for false discovery proportion. Our simulation studies showed that this new approach has controlled FDP. We demonstrate our approach using data sets derived from a drug clinical trial.

Quantifying time-varying cause-specific hazard and subdistribution hazard ratios with competing risks data

Various non-proportional hazard models have been developed in the literature for competing risks data. The regression coefficients under these models, however, typically cannot be compared directly. We propose new methods to quantify the average of the time-varying cause-specific hazard ratios and subdistribution hazard ratios through two general classes of transformations and weight functions that are chosen to reflect the relative importance of the hazard ratios in different time periods. We further propose an L∞ -norm type of test statistic that incorporates the test statistics for all possible pairs of the transformation function and weight function under consideration. Extensive simulations are conducted under various settings of the hazards and demonstrate that the proposed test performs well under all settings. An application to a clinical trial in follicular lymphoma is examined in detail.

Sparsity Analysis of a Sonomyographic Muscle-Computer Interface

OBJECTIVE

Sonomyography has been shown to be a promising method for decoding volitional motor intent from analysis of ultrasound images of the forearm musculature. The objectives of this paper are to determine the optimal location for ultrasound transducer placement on the anterior forearm for imaging maximum muscle deformations during different hand motions, and to investigate the effect of using a sparse set of ultrasound scanlines for motion classification for ultrasound-based muscle-computer interfaces (MCIs).

METHODS

The optimal placement of the ultrasound transducer along the forearm was identified using freehand three-dimensional reconstructions of the muscle thickness during rest and motion completion. Based on the ultrasound images acquired from the optimally placed transducer, classification accuracy with equally spaced scanlines across the cross-sectional field of view was determined. Furthermore, the unique contribution of each scanline to class discrimination using Fisher criterion (FC) and mutual information (MI) with respect to motion discriminability was determined.

RESULTS

Experiments with five able-bodied subjects show that the maximum muscle deformation occurred between 40%-50% of the forearm length for multiple degrees-of-freedom. The average classification accuracy was 94% ± 6% with the entire 128-scanline image and 94% ± 5% with four equally spaced scanlines. However, no significant improvement in classification accuracy was observed with optimal scanline selection using FC and MI.

CONCLUSION

For an optimally placed transducer, a small subset of ultrasound scanlines can be used instead of a full imaging array without sacrificing performance in terms of classification accuracy for multiple degrees-of-freedom.

SIGNIFICANCE

The selection of a small subset of transducer elements can enable the reduction of computation, and simplification of the instrumentation and power consumption of wearable sonomyographic MCIs, particularly for rehabilitation and gesture recognition applications.

Efficient methods for signal detection from correlated adverse events in clinical trials

It is an important and yet challenging task to identify true signals from many adverse events that may be reported during the course of a clinical trial. One unique feature of drug safety data from clinical trials, unlike data from post-marketing spontaneous reporting, is that many types of adverse events are reported by only very few patients leading to rare events. Due to the limited study size, the p-values of testing whether the rate is higher in the treatment group across all types of adverse events are in general not uniformly distributed under the null hypothesis that there is no difference between the treatment group and the placebo group. A consequence is that typically fewer than 100 α percent of the hypotheses are rejected under the null at the nominal significance level of α . The other challenge is multiplicity control. Adverse events from the same body system may be correlated. There may also be correlations between adverse events from different body systems. To tackle these challenging issues, we develop Monte-Carlo-based methods for the signal identification from patient-reported adverse events in clinical trials. The proposed methodologies account for the rare events and arbitrary correlation structures among adverse events within and/or between body systems. Extensive simulation studies demonstrate that the proposed method can accurately control the family-wise error rate and is more powerful than existing methods under many practical situations. Application to two real examples is provided.

Robust Inference after Random Projections via Hellinger Distance for Location-Scale Family

Big data and streaming data are encountered in a variety of contemporary applications in business and industry. In such cases, it is common to use random projections to reduce the dimension of the data yielding compressed data. These data however possess various anomalies such as heterogeneity, outliers, and round-off errors which are hard to detect due to volume and processing challenges. This paper describes a new robust and efficient methodology, using Hellinger distance, to analyze the compressed data. Using large sample methods and numerical experiments, it is demonstrated that a routine use of robust estimation procedure is feasible. The role of double limits in understanding the efficiency and robustness is brought out, which is of independent interest.

A class of semiparametric cure models with current status data

Current status data occur in many biomedical studies where we only know whether the event of interest occurs before or after a particular time point. In practice, some subjects may never experience the event of interest, i.e., a certain fraction of the population is cured or is not susceptible to the event of interest. We consider a class of semiparametric transformation cure models for current status data with a survival fraction. This class includes both the proportional hazards and the proportional odds cure models as two special cases. We develop efficient likelihood-based estimation and inference procedures. We show that the maximum likelihood estimators for the regression coefficients are consistent, asymptotically normal, and asymptotically efficient. Simulation studies demonstrate that the proposed methods perform well in finite samples. For illustration, we provide an application of the models to a study on the calcification of the hydrogel intraocular lenses.

Quantification of Muscle Tissue Properties by Modeling the Statistics of Ultrasound Image Intensities Using a Mixture of Gamma Distributions in Children With and Without Cerebral Palsy

OBJECTIVES

To investigate whether quantitative ultrasound (US) imaging, based on the envelope statistics of the backscattered US signal, can describe muscle properties in typically developing children and those with cerebral palsy (CP).

METHODS

Radiofrequency US data were acquired from the rectus femoris muscle of children with CP (n = 22) and an age-matched cohort without CP (n = 14) at rest and during maximal voluntary isometric contraction. A mixture of gamma distributions was used to model the histogram of the echo intensities within a region of interest in the muscle.

RESULTS

Muscle in CP had a heterogeneous echo texture that was significantly different from that in healthy controls (P < .001), with larger deviations from Rayleigh scattering. A mixture of 2 gamma distributions showed an excellent fit to the US intensity, and the shape and rate parameters were significantly different between CP and control groups (P < .05). The rate parameters for both the single gamma distribution and mixture of gamma distributions were significantly higher for contracted muscles compared to resting muscles, but there was no significant interaction between these factors (CP and muscle contraction) for a mixed-model analysis of variance.

CONCLUSIONS

Ultrasound tissue characterization indicates a more disorganized architecture and increased echogenicity in muscles in CP, consistent with previously documented increases in fibrous infiltration and connective tissue changes in this population. Our results indicate that quantitative US can be used to objectively differentiate muscle architecture and tissue properties.

Biomarker threshold adaptive designs for survival endpoints

Due to the importance of precision medicine, it is essential to identify the right patients for the right treatment. Biomarkers, which have been commonly used in clinical research as well as in clinical practice, can facilitate selection of patients with a good response to the treatment. In this paper, we describe a biomarker threshold adaptive design with survival endpoints. In the first stage, we determine subgroups for one or more biomarkers such that patients in these subgroups benefit the most from the new treatment. The analysis in this stage can be based on historical or pilot studies. In the second stage, we sample subjects from the subgroups determined in the first stage and randomly allocate them to the treatment or control group. Extensive simulation studies are conducted to examine the performance of the proposed design. Application to a real data example is provided for implementation of the first-stage algorithms.

A semiparametric method for comparing the discriminatory ability of biomarkers subject to limit of detection

Receiver operating characteristic curves and the area under the curves (AUC) are often used to compare the discriminatory ability of potentially correlated biomarkers. Many biomarkers are subject to limit of detection due to the instrumental limitation in measurements and may not be normally distributed. Standard parametric methods assuming normality can lead to biased results when the normality assumption is violated. We propose new estimation and inference procedures for the AUCs of biomarkers subject to limit of detection by using the semiparametric transformation model allowing for heteroscedasticity. We obtain the nonparametric maximum likelihood estimators by maximizing the likelihood for the observed data with limit of detection. The proposed estimators are shown to be consistent, asymptotically normal, and asymptotically efficient. Additionally, we propose a Wald type test statistic to compare the AUCs of 2 potentially correlated biomarkers with limit of detection. Extensive simulation studies demonstrate that the proposed method is robust to nonnormality while performing as well as its parametric counterpart when the normality assumption is true. An application to an autism study is provided.

Modeling event count data in the presence of informative dropout with application to bleeding and transfusion events in myelodysplastic syndrome

In many biomedical studies, it is often of interest to model event count data over the study period. For some patients, we may not follow up them for the entire study period owing to informative dropout. The dropout time can potentially provide valuable insight on the rate of the events. We propose a joint semiparametric model for event count data and informative dropout time that allows for correlation through a Gamma frailty. We develop efficient likelihood-based estimation and inference procedures. The proposed nonparametric maximum likelihood estimators are shown to be consistent and asymptotically normal. Furthermore, the asymptotic covariances of the finite-dimensional parameter estimates attain the semiparametric efficiency bound. Extensive simulation studies demonstrate that the proposed methods perform well in practice. We illustrate the proposed methods through an application to a clinical trial for bleeding and transfusion events in myelodysplastic syndrome. Copyright © 2017 John Wiley & Sons, Ltd.

Statistical design of noninferiority multiple region clinical trials to assess global and consistent treatment effects

Noninferiority multiregional clinical trials (MRCTs) have recently received increasing attention in drug development. While a major goal in an MRCT is to estimate the global treatment effect, it is also important to assess the consistency of treatment effects across multiple regions. In this paper, we propose an intuitive definition of consistency of noninferior treatment effects across regions under the random-effects modeling framework. Specifically, we quantify the consistency of treatment effects by the percentage of regions that meet a predefined treatment margin. This new approach enables us to achieve both goals in one modeling framework. We propose to use a signed likelihood ratio test for testing the global treatment effect and the consistency of noninferior treatment effects. In addition, we provide guidelines for the allocation rule to achieve optimal power for testing consistency among multiple regions. Extensive simulation studies are conducted to examine the performance of the proposed methodology. An application to a real data example is provided.

On inference of control-based imputation for analysis of repeated binary outcomes with missing data

Missing data are common in longitudinal clinical trials. How to handle missing data is critical for both sponsors and regulatory agencies to assess treatment effect from the trials. Recently, a control-based imputation has been proposed, where the missing data are imputed based on the assumption that patients who discontinued the test drug will have a similar response profile to the patients in the control group. Under control-based imputation, the variance estimation may be biased using Rubin's formula which could produce biased statistical inferences. We evaluate several statistical methods for obtaining appropriate variances under control-based imputation for analysis of repeated binary outcomes with monotone missing data and show that both the analytical method developed by Robins & Wang and the nonparametric bootstrap method provide more appropriate variance estimates under various simulation settings. We use the methods in an application of an antidepressant Phase III clinical trial and give discussion and recommendations on method performance and preference.

Genome-wide association of trajectories of systolic blood pressure change

BACKGROUND

There is great interindividual variation in systolic blood pressure (SBP) as a result of the influences of several factors, including sex, ancestry, smoking status, medication use, and, especially, age. The majority of genetic studies have examined SBP measured cross-sectionally; however, SBP changes over time, and not necessarily in a linear fashion. Therefore, this study conducted a genome-wide association (GWA) study of SBP change trajectories using data available through the Genetic Analysis Workshop 19 (GAW19) of 959 individuals from 20 extended Mexican American families from the San Antonio Family Studies with up to 4 measures of SBP. We performed structural equation modeling (SEM) while taking into account potential genetic effects to identify how, if at all, to include covariates in estimating the SBP change trajectories using a mixture model based latent class growth modeling (LCGM) approach for use in the GWA analyses.

RESULTS

The semiparametric LCGM approach identified 5 trajectory classes that captured SBP changes across age. Each LCGM identified trajectory group was ranked based on the average number of cumulative years as hypertensive. Using a pairwise comparison of these classes the heritability estimates range from 12 to 94 % (SE = 17 to 40 %).

CONCLUSION

These identified trajectories are significantly heritable, and we identified a total of 8 promising loci that influence one's trajectory in SBP change across age. Our results demonstrate the potential utility of capitalizing on extant genetic data and longitudinal SBP assessments available through GAW19 to explore novel analytical methods with promising results.

Anthropometrics, Physical Performance, and Injury Characteristics of Youth American Football

BACKGROUND

Prior research has described the anthropometric and physical performance characteristics of professional, collegiate, and high school American football players. Yet, little research has described these factors in American youth football and their potential relationship with injury.

PURPOSE

To characterize anthropometric and physical performance measures, describe the epidemiology of injury, and examine the association of physical performance measures with injury among children participating within age-based divisions of a large metropolitan American youth football league.

STUDY DESIGN

Case-control study; Level of evidence, 3.

METHODS

Demographic, anthropometric, and physical performance characteristics and injuries of 819 male children were collected over a 2-year period (2011-2012). Injury data were collected by the league athletic trainer (AT) and coaches. Descriptive analysis of demographic, anthropometric, and physical performance measures (40-yard sprint, pro-agility, push-ups, and vertical jump) were conducted. Incidence rates were computed for all reported injuries; rates were calculated as the number of injuries per 1000 athlete-exposures (AEs). Multinomial logistic regression was used to identify whether the categories of no injury, no-time-loss (NTL) injury, and time-loss (TL) injury were associated with physical performance measures.

RESULTS

Of the 819 original participants, 760 (92.8%) completed preseason anthropometric measures (mean ± SD: age, 11.8 ± 1.2 years; height, 157.4 ± 10.7 cm; weight, 48.7 ± 13.3 kg; experience, 2.0 ± 1.8 years); 640 (78.1%) players completed physical performance measures. The mean (±SD) 40-yard sprint and pro-agility measures of the players were 6.5 ± 0.6 and 5.7 ± 0.5 seconds, respectively; the number of push-ups and maximal vertical jump height were 16.5 ± 9.3 repetitions and 42.3 ± 8.4 cm, respectively. Players assigned to different teams within age divisions demonstrated no differences in anthropometric measures; 40-yard dash and pro-agility times differed significantly (P < .05) between players assigned to different teams. A total of 261 NTL and TL injuries were reported during 35,957 AEs (games: 22%, n = 7982 AEs; practices: 78%, n = 27,975 AEs). The overall incidence rate was 7.26 per 1000 AEs (95% CI, 6.37-8.14). Physical performance measures did not predict NTL or TL injuries (P > .05).

CONCLUSION

No practically meaningful differences existed in anthropometric or physical performance measures between teams within age-based levels of play. Findings suggest that age-only criterion for player groupings can evenly match in terms of physical performance.