22Jun

The HIDDEN ASCVD Study

TITLE: The HIDDEN ASCVD Study

INVESTIGATORS:

Alex Sandhu (1)

Andrew Ambrosy (2)

Fatima Rodriguez (1)

(1) Cardiovascular Medicine, Stanford

(2) Cardiology, Kaiser Medical Center San Francisco

DATE: Wednesday, 3 May 2023

TIME: 1:30–3:00 PM

LOCATION: Conference Room X399, Medical School Office Building, 1265 Welch Road, Stanford, CA

ABSTRACT

The Data Studio Workshop brings together a biomedical investigator with a group of experts for an in-depth session to solicit advice about statistical and study design issues that arise while planning or conducting a research project. This week, the investigator(s) will discuss the following project with the group.

BACKGROUND

Coronary artery calcium (CAC) is the strongest predictor of myocardial infarction (MI). The knowledge of having CAC is a powerful motivator of preventive behavior; this includes the initiation of statin therapy, which has been shown to reduce the risk of atherosclerotic cardiovascular disease (ASCVD) events by 25%. However, <10% of potentially eligible patients receive gated computed tomography (CT) scans to measure CAC. Furthermore, historically marginalized populations undergo even fewer gated CAC scans. CAC can be identified on the 19 million non-gated chest CTs performed annually. In the NOTIFY-1 pilot trial of 176 patients, notification of patients and primary care clinicians of the presence of incidental CAC increased statin prescription rates to 51% compared with 7% in the standard of care arm.

INTRODUCTION

We are submitting an R01 to the NHLBI on a pragmatic adaptive clinical trial in which we are testing different strategies for notifying patients regarding an incidental finding of coronary calcium. We are hoping to adapt the notification strategy to identify the notification approach that most effectively increases medication rates (statin prescription 3 months after notification) and minimizes patient anxiety. Increasing statin initiation among patients with incidental CAC could prevent approximately 500,000 ASCVD events over a decade. However, there are significant knowledge gaps that must be addressed: insights into the incidental CAC notification design that both effectively increases statin rates and is preferable to patients. We propose answering this question using an adaptive RCT conducted within Kaiser Permanente Northern California (KPNC), an integrated health care delivery system providing care to >4.5 million members with broad age, sex, racial, and ethnic diversity. The HIDDEN ASCVD study has the potential to be paradigm shifting by identifying the optimal behavior theory-driven notification strategy for statin therapy. Following this project, effective scaling of opportunistic CAC screening and notification will lead to reduction in ASCVD burden overall, particularly among historically marginalized populations.

HYPOTHESIS-AIM-OBJECTIVE

Our hypothesis is that certain notification strategies (e.g., based on the messenger, the positive/negative framing, etc.) are more effective than others at increasing statin rates and lead to less patient anxiety. Our aim is to compare the effect of multiple behavioral theory-driven incidental CAC notification strategies on rates of statin therapy initiation via an adaptive RCT. We will prospectively identify individuals with incidental CAC without known ASCVD not receiving statin therapy within KPNC. Individuals will be assigned randomly to notification strategies that are modifications of the initial strategy used in the NOTIFY-1 pilot study. Modifications will be based on the MINDSPACE behavioral change framework. Through an adaptive design, the objective will be to design the most effective notification that promotes statin initiation and is acceptable to patients and primary care clinicians.

STUDY POPULATION

The study cohort will be enriched for historically marginalized racial and ethnic groups.

OUTCOMES

The primary outcome will be statin initiation with the key secondary outcome being anxiety related to the notification based on PROMIS Anxiety Short Form.

STRATEGY-TEAM

The HIDDEN-ASCVD study will apply an adaptive RCT grounded in behavioral change theory to evaluate notification strategies and identify the ideal balance between motivating behavioral change and minimizing unnecessary patient anxiety. Our multidisciplinary team brings together early stage investigators (ESIs) and senior scientific leaders from the KPNC Division of Research and Stanford University with expertise in ASCVD prevention, health equity, predictive analytics, delivery science, and RCTs. The clinical coordination will be centered at Kaiser with the analysis planned at Stanford.

SAMPLE SIZE

We are imagining six (6) phases of 6-months each with approximately 800 patients for a total sample of 4800 individuals.

STATISTICAL QUESTIONS

We have a preliminary design and sample size. We need help thinking through the design and need a co-investigator for the award.

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22Jun

Career Trajectory of Academic General Surgery Residency Program Graduates: Do Academic Programs Graduate Academic Surgeons?

TITLE: Career Trajectory of Academic General Surgery Residency Program Graduates: Do Academic Programs Graduate Academic Surgeons?

INVESTIGATORS:

Allen Green (1)

Jeff Choi (1)

(1) Department of Surgery

DATE: Wednesday, 10 May 2023

TIME: 1:30–3:00 PM

LOCATION: Conference Room X399, Medical School Office Building, 1265 Welch Road, Stanford, CA

ABSTRACT:

INTRODUCTION:

Many general surgery residency programs emphasize their ability to produce academic surgeons. However, the proportion of academic general surgery residency graduates who become academic surgeons remains unclear.

HYPOTHESIS & AIM:

We aimed to quantify the contemporary prevalence of US academic general surgery residency graduates who become academic surgeons, and elucidate factors associated with pursuing a career in academic surgery.

DATASET:

We identified 2015 and 2018 graduates from 97 Accreditation Council for Graduate Medical Education-accredited general surgery residency programs affiliated with US allopathic medical schools. We extracted program and individual-level data using publicly available Doximity, PubMed, residency program, and faculty profiles. We defined academic surgeons as faculty within university-affiliated surgery departments who published two or more papers as the first or senior author in 2020 and 2021. Using a stepwise likelihood ratio test method to identify covariates, a multivariable logistic regression evaluated associations between program and individual-level factors and a career in academic surgery. The threshold for statistical significance was P <0.05.

STATISTICAL MODELS

We hope to build a logistic regression model that provides us inferences on what residency program-level and resident-level factors are associated with pursuing a career in academic surgery. We plan to use this model to first compare between non-academic and academic surgeons. Secondly, we plan to repeat this analysis comparing a highly productive subset of academic surgeons and the rest of the academic surgeon cohort to identify factors associated with being a highly productive academic surgeon.

STATISTICAL QUESTIONS

(1) Given the complex relationships between our variables, what is the best way to select variables to minimize confounders?

(2) Are there any additional analysis methods outside of logistic regression that we should consider?

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22Jun

Signal Processing and Analysis of Noisy Eye Position Sensor Data

TITLE: Signal Processing and Analysis of Noisy Eye Position Sensor Data

INVESTIGATORS:

Jennifer Raymond (1)

Brian Angeles (1)

Sriram Jayabal (1)

Department of Neurobiology

DATE: Wednesday, 17 May 2023

TIME: 1:30–3:00 PM

LOCATION: Conference Room X399, Medical School Office Building, 1265 Welch Road, Stanford, CA

ABSTRACT

INTRODUCTION

Our lab measures eye velocity responses to visual and vestibular stimuli and their modification by learning, and the neural underpinnings thereof.

BACKGROUND

A key function of the brain is to learn about the statistical relationships between events in the world. A mechanism of this learning is associative neural plasticity, controlled by the timing between neural events. Here, we show that experience can dramatically alter the timing rules governing associative plasticity to match the constraints of a particular circuit and behavior, thereby improving learning. In normal mice, the timing requirements for associative plasticity in the oculomotor cerebellum are precisely matched to the 120 ms delay for visual feedback about behavioral errors. This task-specific specialization of the timing rules for plasticity is acquired through experience; in dark-reared mice that had never experienced visual feedback about oculomotor errors, plasticity defaulted to a coincidence-based rule. Computational modeling suggests two broad strategies for implementing this Adaptive Tuning of the Timing Rules for Associative Plasticity (ATTRAP), which tune plasticity to different features of the statistics of neural activity. The modeling predicts a critical role of this process in optimizing the accuracy of temporal credit assignment during learning; consistent with this, behavioral experiments revealed a delay in the timing of learned eye movements in mice lacking experience-dependent tuning of the timing rules for plasticity. ATTRAP provides a powerful mechanism for matching the timing contingencies for associative plasticity to the functional requirements of a particular circuit and learning task, thereby providing a candidate neural mechanism for meta-learning.

METHODOLOGY

We have previously collected eye position data at a sampling rate of 1 kHz. The data corresponds to the eye movement of an animal either being sinusoidally rotated 180 degrees clockwise and counterclockwise at a rate of 1 Hz. Particular training protocols are conducted to either increase or decrease the magnitude of the eye’s sinusoidal motion. We then differentiate our eye position signals to extract the corresponding velocity traces, and using the stimulus signal data as a reference, we would like to compute the average eye velocity trace over a single 1 Hz period of the stimulus oscillation.

STATISTICAL ISSUES

Is there a better and more principled way to characterize the timing of the eye movement response to sinusoidal stimuli? In much of our previous work, we have fit the eye velocity responses with a sinusoid and reported the amplitude and phase of the fit. But now we are seeing interesting timing effects that are not captured by the sinusoidal fits. In bottom of Fig 2H of the bioRxiv preprint, we just plotted the time (ms) of the absolute peak of the learned eye movement trace for each mouse (calculated by average eye velocity response across ~40 stimulus repetitions post-training minus avg of pre-training eye velocity response).
Unfortunately, our processed and filtered data is still quite noisy, and differentiating the noisy data only makes it worse. We typically apply a lowpass Butterworth filter on our positional data, use a windowed Savitzky-Golay filter to get the corresponding velocity trace, and then apply a custom saccade detection/removal algorithm. We now would like to explore other possible methods to get a cleaner velocity trace from our noisy positional data that has minimal effect on the temporal and amplitude information within the data.
As time allows, we would also appreciate advice about several aspects of the pre-processing steps used to compute eye velocity.
1. methods for digital differentiation and filtering of raw eye position-related signals to obtain eye velocity
2. identification of eye saccades (brief, discrete high velocity/acceleration eye movement events, which we exclude from the analysis) vs. lower frequency continuous “smooth” eye movements and noise
3. elimination of very high frequency noise, which appears in the raw data as a single, occasional wayward 1ms sample in an otherwise smoother raw trace of eye position as a function of time.

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22Jun

Signal Processing and Analysis of Noisy Eye Position Sensor Data

TITLE: Signal Processing and Analysis of Noisy Eye Position Sensor Data

INVESTIGATORS:

Jennifer Raymond (1)

Brian Angeles (1)

Sriram Jayabal (1)

Department of Neurobiology

TIME: 1:30–3:00 PM

LOCATION: Conference Room X399, Medical School Office Building, 1265 Welch Road, Stanford, CA

ABSTRACT

INTRODUCTION

Our lab measures eye velocity responses to visual and vestibular stimuli, their modification via training and learning, and the neural underpinnings thereof. There were great ideas (at the previous workshop) regarding methods to characterize the timing of our eye velocity cycle averages. Unfortunately, time constraints prevented us from presenting our questions and challenges regarding the pre-processing of the eye position data to handle noise and artifacts in the eye position recordings and differentiate to obtain eye velocity.

BACKGROUND

Our lab is interested in understanding the algorithms that the brain uses to learn. To do so, we use oculomotor learning (learned changes in the eye movement responses to visual and vestibular sensory stimuli) as an experimental behavioral model owing to its simplicity, experimental and analytical tractability. We collect eye position data from mice using a magnetic sensing method developed in the lab, as they track a moving visual stimulus or counter-rotate their eyes during head rotation (a vestibular stimulus). We can train the mice to alter the amplitude or timing of the eye movement responses. We would like to optimize the methods we use to pre-process the raw eye position data and the amplitude and timing of the eye movement responses.

METHODOLOGY

Horizontal eye position time-series data is acquired from magnetic sensors at a sampling rate of 1000 Hz, which then undergoes multiple processing steps:

From the raw position data, 1 ms (single sample) transient noise artifact spikes are removed by applying Laplace smoothing (i.e. linearly interpolating the center point of the spike with the average of its nearest neighbors).
A 9th order lowpass (zero-phase) Butterworth filter is applied with a cutoff frequency between 15 and 30 Hz on a mouse-by-mouse basis.
The corresponding eye velocity trace (first derivative) of each block is approximated using a Savitzky-Golay filter over a 30-ms (i.e., 30 sample point) window.
Saccades (brief, discrete high velocity/acceleration eye movement events, which we exclude from the analysis) and other unwanted artifacts in the eye position recordings (caused by electrical noise or body movements/vibrations) are removed by using velocity thresholding; which involves computing the squared differences between the velocity trace and its corresponding 1 Hz sinusoidal fit, and removing the sample points where its corresponding squared difference exceed some set threshold value.
Velocity cycle averages are then computed over a single sinusoidal stimulus cycle.
We typically average the eye velocity responses across stimulus repetitions, and then calculate the differences between velocity averages post- vs pre- behavioral training to calculate the learned change in the eye movement behavior in each session/mouse, and then conduct statistical tests comparing different populations of mice, and/or different kinds of training.

STATISTICAL ISSUES

We would like advice regarding several aspects of the pre-processing steps used to compute eye velocity from noisy positional data.

Recommendations regarding the elimination of the 1ms high frequency transient noise found in our raw position data which we currently remove via interpolation.
Importance of the order of pre-processing steps (e.g., application of a lowpass filter on the raw position signal before or after its differentiation).
Methods for filtering and differentiation of raw eye position-related signals to remove the noise without affecting the eye movement signal.
Best approaches for the detection and removal of eye saccades and unwanted motion artifacts from the eye velocity data.

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Posts classified under: Spring 2023