Risk of myeloproliferative neoplasms among U.S. Veterans from Korean, Vietnam, and Persian Gulf War eras.

The Promise to Address Comprehensive Toxics (PACT) Act expanded U.S. Veterans' health care and benefits for conditions linked to service-connected exposures (e.g., Burn Pits, Agent Orange). However, myeloproliferative neoplasms (MPN) are not recognized as presumptive conditions for Veterans exposed to these toxic substances. This study evaluated the development of MPN among U.S. Veterans from the Korean, Vietnam, and Persian Gulf War eras. This retrospective cohort study included 65 425 Korean War era Veterans; 211 927 Vietnam War era Veterans; and 214 007 Persian Gulf War era Veterans from January 1, 2006, to January 26, 2023. Veterans with MPN, thrombosis, bleeding, and cardiovascular risk factors were identified through ICD-9 and -10 codes. Veterans from the Persian Gulf War era had the highest risk of developing MPN compared with Veterans from the Korean and Vietnam War eras, hazard ratio (HR) 4.92, 95% confidence interval (CI) 4.20-5.75 and HR 2.49, 95% CI 2.20-2.82, both p < .0001, respectively. Vietnam War era Veterans also had a higher risk of MPN development compared with Korean War era Veterans, HR 1.97, 95% CI 1.77-2.21, p < .0001. Persian Gulf War era Veterans were diagnosed with MPN at an earlier age, had higher risks of thrombosis and bleeding, and had lower survival rates compared with Korean War and Vietnam War era Veterans. This study reinforces evidence that environmental and occupational hazards increase the risk of clonal myeloid disorders and related complications, impacting overall survival with MPN. Limitations include the inability to confirm clonality and fully verify deployment and exposure status.

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 ß-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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Statistically efficient association analysis of quantitative traits with haplotypes and untyped SNPs in family studies.

BACKGROUND: Associations between haplotypes and quantitative traits provide valuable information about the genetic basis of complex human diseases. Haplotypes also provide an effective way to deal with untyped SNPs. Two major challenges arise in haplotype-based association analysis of family data. First, haplotypes may not be inferred with certainty from genotype data. Second, the trait values within a family tend to be correlated because of common genetic and environmental factors. RESULTS: To address these challenges, we present an efficient likelihood-based approach to analyzing associations of quantitative traits with haplotypes or untyped SNPs. This approach properly accounts for within-family trait correlations and can handle general pedigrees with arbitrary patterns of missing genotypes. We characterize the genetic effects on the quantitative trait by a linear regression model with random effects and develop efficient likelihood-based inference procedures. Extensive simulation studies are conducted to examine the performance of the proposed methods. An application to family data from the Childhood Asthma Management Program Ancillary Genetic Study is provided. A computer program is freely available. CONCLUSIONS: Results from extensive simulation studies show that the proposed methods for testing the haplotype effects on quantitative traits have correct type I error rates and are more powerful than some existing methods.

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.

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.