Medical Research Papers and Conference Talks

Supported by Sickbay™

When Adenosine is Not Enough

Feb 07, 2022, Santiago O. Valdés, MD. et al.


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Detection of Junctional Ectopic Tachycardia by Central Venous Pressure

Jun 8, 2021, Xin Tan. et al.

National Library of Medicine

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Automated Prediction of Cardiorespiratory Deterioration in Patients With Single Ventricle

Jun 2021, Craig G. Rusin. et al.

Journal of the American College of Cardiology

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RBC Transfusion Induced ST Segment Variability Following the Norwood Procedure

May 2021, Savorgnan, F. et al.

Society of Critical Care Medicine

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Sickbay: A Brief Introduction

April 26, 2021, Ryan L. Melvin

Perioperative Data Science

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Perioperative Physiological Parameters Associated with Severe Acute Kidney Injury after Pediatric Heart Transplant

March 2021, Alexander Alali et al.

AAP National congerence & Exhibition Meeting Abstracts

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A robust Fourier-based method to measure pulse pressure variability

July 2020, Sebastian Acosta et al.

Biomedical Signal Processing and Control

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Hypotensive Response to IV Acetaminophen in Pediatric Cardiac Patients

June 2019, Barbara-Jo Achuff et al.

Pediatric Critical Care Medicine

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Automated Event Detection to Improve Patient Care and Quality

July/Aug 2018, Fauss, E., & Patel, R.

Biomedical Instrumentation & Technology

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Hospital Integrates Remote, Real-Time Monitoring Data from Isolation Unit

Mar/Apr 2018, Fauss, E.

Biomedical Instrumentation & Technology

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An Effective Model of Cerebrovascular Pressure Reactivity and Blood Flow Autoregulation

Jan 2018, Acosta, S., Penny, D., Brady, K., & Rusin, C.

Microvascular Research

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Finding Representative Electrocardiogram Beat Morphologies with CUR

Jan 2018, Hendryx, E., Riviére, B. Sorensen, D., & Rusin, C.

Journal of Biomedical Informatics

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Cardiovascular Mechanics in the Early Stages of Pulmonary Hypertension: A Computational Study

Dec 2017, Acosta, S., Puelz, C., Riviére, B., Penny, D., Brady, K., & Rusin, C.

Biomechanics and Modeling in Mechanobiology

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Predicting Intracranial Pressure and Brain Tissue Oxygen Crises in Patients with Severe Traumatic Brain Injury

Sep 2016, Myers, R., Lazaridis, C., Jermaine, C., Robertson, C., & Rusin, C.

Critical Care Medicine

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Numerical Method of Characteristics for One-Dimensional Blood Flow

Aug 1, 2015, Acosta, S., Puelz, C., Riviére, B., Penny, D., & Rusin, C.

Journal of Computational Physics

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An Effective Model of Blood Flow in Capillary Beds

July 2015, Acosta, S., Penny, D., & Rusin, C.

Microvascular Research

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A New Algorithm for Detecting Central Apnea in Neonates

Jan 2012, Lee, H. et al.

Physiological Measurement

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Emma Fauss, CEO and Craig Rusin, CTO

The groundwork for Medical Informatics Corp. (MIC) was laid when co-founder, Craig Rusin, PhD, was working as a researcher in the cardiology department at the University of Virginia and needed to leverage high-resolution physiological data to identify indicators of disease and predictors of patient conditions. At the time, no tools were available to aggregate, store, and process the volume of data needed to integrate with an analytics program to achieve his goals. Dr. Rusin solved the problem by building his own grid-computing platform, and the precursor for Sickbay™ was born.

Dr. Rusin is now Associate Professor of Medicine in the Department of Pediatric Cardiology at Baylor College of Medicine and Texas Children’s Hospital and Adjunct Professor in the Department of Computational and Applied Mathematics at Rice University. He and his team, as well as other physicians and researchers across the country, now use Sickbay’s™ Medical Research Tools to expedite algorithm development and conduct studies to identify the precursors of disease. Highlights of Sickbay-enabled peer-reviewed medical research papers and studies appear above.

The next step in MIC’s journey is unlocking this research from journals and transforming it into clinical reality by creating new, real-time software based predictive monitors. We believe that by creating these transformations, in conjunction with other researchers, physicians, and hospitals that we will be able to realize our vision: Saving Lives Bit by Bit®.

User Quotes

It is important to look retrospectively at what was done to a patient and look for an inciting event to prevent it from happening in the future. For example, when examining ventilator events like desaturations or loss of EtCO2 that leads to cardiac arrest, the physician examines various signals. Although one can obtain this information from the vent, the timestamps do not align across monitors. When correlating signals from different monitors, you can not get them on the same screen. Sickbay™ Patient Hx allows for all signals at exactly the same time.

- Director of Quality Improvement

In the past if I was worried about a few kids, I would have to call the nurse and ask her for information or walk to the unit and see them. With Sickbay™, I have a new way of observing patients in real-time. This saves me time, allows me to augment the care for the patient that is happening on the floor of the unit and ultimately can provide better patient safety and outcomes. It has revolutionized the way I think about patient monitoring.

- CVICU Physician

At the bedside, the order and trending of patient signals are important. For example, if oxygen drops before blood pressure, it indicates a different issue than blood pressure dropping before oxygen. Patient Hx allows for these patterns to become clearer.

- CVICU Director

I can now remotely view ventilator data for patients. This is something that I previously could only do at the bedside. Now, I can call up a patient of interest on my computer in my office, and observe their recovery or deterioration.

- Director of Anesthesiology

The current patient monitors look for simple patterns and offer limited access to the data they produce. Vendors offer some data recording or data analysis capabilities. However, none offer the scale and power of the Sickbay™ platform.

- Anesthesiologist

I recently had a single-ventricle patient that showed a jump in heart rate from 90 bpm to 160 bpm, with arrhythmia. Unrecognized, this event is life-threatening. Fortunately, while reviewing these events with Patient Hx, I was able to apply medications to the patient when I arrived that morning and monitor the intervention remotely.

- Fellow, CVICU

God bless our nursing staff for having to document in the EMR. But I live and die by waveforms. And not just a snippet. I need to see the entire waveform and what led to an event to determine an intervention or root cause. Patient Hx allows that. It should be mandated in every hospital in the country. We just shouldn’t be practicing medicine without it.

- Director of PICU

We have to document a number to justify a procedure. Now I don’t have to worry about writing something down, or remember, I just focus on what I’m doing for the patient and then after the flurry of intervention is over, I can go to Patient Hx to see exactly when it happened and document more accurately.

- CVICU Nurse

In a single minute I have to process over 300 data points and sometimes I process it wrong. Machines aren’t going to make the decisions for me, but I need them to help me interpret all of that data faster so I can make the best decision and not lose patients because of lack of data. Sickbay™ helps me do that.

- Chief of Staff

I recently had a patient that was being transferred from the ICU to step down. During transport the patient crashed and came to the OR. The problem was all patient data had been deleted when they were discharged from the monitor. I needed to know what the order of events were. What started first? Was it cardiac, was it respiratory? Thankfully our hospital had Sickbay™ so I was able to go into Patient Hx and in under 2 minutes figure out the root cause.

- Surgeon and Cardiologist

Patient Hx allows me to look at actual patient trends. Everyone has rose colored glasses when documenting for the chart but the patient looks how they look and the trends and the waveform don’t lie.

- Charge Nurse, ICU

Patient Hx lets me look minute my minute and ask “are there warning signs?” I was looking at a patient near death’s door. At 3 o’clock their heart rate went up, and never came down. Why didn’t it come down? At 7pm, there was an event and the patient passed away. What was going on before then? I need to know when a patient is about to die, so I can prepare. What does that look like? Patient Hx can help me find those answers.

- Cardiologist

Patient Hx validates what is happening at the bedside. Last week I walked into the room and all the monitors had been shut off.. Looking back on Patient Hx I was able to see the exact time when the monitors were pulled off and also able to see that the shape of the waveform was bad but the numbers were ok. I could tell when the pulse ox stopped picking up and what time we were in the room. The numbers and waveforms made it clear the patient had not had a sentinel event. And I had all the evidence to back this up and even send the complete history to the EMR.

- Nurse Manager, PICU

Strip charting was a constant headache at our hospital. The manual printing and scanning process is of course labor intensive but the bigger issue is trying to get issues to bedside staff and providers faster from our tele techs to expedite intervention. And then there is the reimbursement issue. We had a change in our practices and staffing that was preventing the strips from even being scanned which HIM department quickly realized meant thousands in lost revenue. Automating the process for strip charting with Patient Hx is a game changer. Less manual workload, more money for the hospital, and more important than anything is reduced patient risk.

- Telemetry Charge Nurse