Cochlear Implants
by Cochlear Guest Author

Understanding reliability and why it matters

November 29, 2018
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Guest author: Wade Colburn

Wade Colburn

 

 

Of all the different factors in counseling your patient on a cochlear implant, the reliability of the implant is likely one factor that comes to mind. Once a patient commits to a cochlear implant, it is important to know that a reliable and properly functioning implant is the basis for hearing. Furthermore, a reliable implant can reduce the need for a possible revision in the future. All manufacturers claim to have the most reliable 1,2,3 implants, but this can’t be the case.

Let’s take a closer look at what goes in to reliability reporting.  Reliability is measured as Cumulative Survival Percentage (CSP), which is the probability of survival of a device within a given time period, and Cumulative Failure Percentage (CFP), which is the probability of failure of a device within a given time period.

Reliability reporting is done each and every year and should be reported according to the European and Global Consensus Statement on Cochlear Implant Failures and Explantations1. This consensus detailed how device failures and reliability should be reported and established the seven principles of best practice reporting.

In the latest reports of each cochlear implant manufacturer, the CSP was reported as follows: Cochlear TM Profile Series – 99.82% within 4 years 2, Med-El Synchrony Series – 98.16% within 3 years 3, Advanced Bionics HiRes 90K Advantage – 99.29% within 5 years 4. While this may seem like a small percentage difference, the fact is these numbers result in the following likelihood of failure:

  • Cochlear Profile: 1 in 556 devices fail within 4 years.
  • Advanced Bionic HiRes 90K Advantage: 1 in 141 devices fail within 5 years
  • MED-EL Synchrony: 1 in 55 devices fail within 3 years

This way of looking at reliability is powerful and the reason Cochlear stands behind having the best reliability in the industry establishing itself as the most chosen manufacturer in the world.

Reliability Graph

The difference in CSP seems very small (<1%), but it can translate to a big difference with tremendous impact to patients. To establish this evidence supporting Cochlear as #1 in implant reliability, the following calculations were made:

  • Convert failure rate (CFP) based on Cumulative Survival Percentage (CSP)
  1. Cochlear Profile (4 Years)  99.82% (CSP) = 0.18% failure rate
  2. AB HiRes 90K (5 Years) 99.29% (CSP) = 0.71% Failure Rate
  3. MedEl Synchrony (3 Years)  98.16% (CSP) = 1.84% Failure Rate
  • Based on this failure rate, we establish how many will have failed after 1000 implantations (1000 implants x failure rate):
  1. Cochlear Profile (4 Years) = 1000*.0018 = 1.8 Failures in 1000 Implants
  2. AB HiRes 90K (5 Years)= 1000*.0071 = 7.1 Failures in 1000 Implants
  3. MedEl Synchrony (3 Years) = 1000*.0184 = 18.4 Failures in 1000 Implants
  • Last we establish the number of implants for every failure: 1000/ # of Failures per 1000
  1. Cochlear Profile (4 Years) = 1000/ 1.8 = 1 in 556 within 4 years
  2. AB HiRes 90K= 1000/7.1 = 1 in 141 within 5 years
  3. MedEl Synchrony = 1000/18.4 = 1 in 55 within 3 years

Understanding reliability is essential to making the best decision for your patients. To learn more about reliability reporting at Cochlear and see the full Reliability report.

About our guest author:

Wade is the Product Manager for Cochlear Implant product portfolio within Cochlear Americas.  Wade’s responsibilities include product positioning and messaging, life-cycle management, new product launches and surgical support and training. Wade has recently joined Cochlear, but comes from an engineering background with the medical device field, including previous otology experience.

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References:
  1. European Consensus Statement on Cochlear Implant Failures and Explantations. Otol Neurotol. 26: 1097-1099, 2005.
  2. Cochlear Nucleus Implant Reliability Report. Volume 16 | December 2017. D1335113. Cochlear Ltd; 2018
  3. Hearing Implant Reliability Reporting | MED-EL [Internet]. Medel.com 2018 [cited 15 Oct 2018] Available from: https://www.medel.com/us/reliability-reporting/
  4. 2018 Global Implant Reliability Report. 027-N025-02 Rev B. Advanced Bionics AG and affiliates; 2018.
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Cochlear Guest Author

In an effort to keep our content fresh and reflective of a diverse industry, Cochlear Americas features a mix of employees and professional partners to contribute to Cochlear ProNews as Guest Authors.

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Artificial Intelligence – Use in Healthcare by Brian Kaplan, MD

August 31, 2018

Research updates

 

By guest author: Brian Kaplan, M.D. – Chair, Department of Otolaryngology-Head and Neck Surgery at Greater Baltimore Medical Center Director, Cochlear Implant Program at Greater Baltimore Medical Center

Artificial Intelligence (AI) has been around for some time. Every day we see another story about the use of artificial intelligence, machine learning or big data in the news. Advances in medicine have often relied on new discoveries in science and now healthcare is being transformed by the use of these advanced technologies.

In 1959, Arthur Samuel was credited with being the first person to realize that we could not teach a computer everything we know in order to complete given tasks. We needed to teach computers how to learn for themselves1. This discovery, along with the advent of the internet, set into motion the ability for computers to take the next step into current state-of-the-art AI and machine learning. In the 1960s, researchers developed the first problem-solving systems in organic chemistry, which is considered the first application of artificial intelligence. By the 1970s, researchers moved from basic science to applications in medicine with attempts to automate diagnosis2. Today, most AI employed in healthcare revolves around classification using deep learning technologies. It is not that AI systems cannot diagnose certain conditions, but the requirement for machine learning has changed from high cognitive function to classifications tasks. This step in the use of technology requires convincing humans that the classifications are accurate, consistent and repeatable. In a recent study, AI systems were found to be able to identify melanomas (through classification) at a higher rate than expert dermatologists and with greater sensitivity and specificity3. However, the most successful AI solutions are those that augment human action rather than replace it.

There was a time when some experts predicted the use of robots in all areas of healthcare, specifically in surgery. Granted there are some rote aspects of surgery that a robot can certainly handle, but the state-of-the-art techniques employed today are robot-assisted surgery. In LASIK surgery, a skilled surgeon uses a robotic device to complete most of the procedure. The surgeon is still responsible for all of the measurements and calculations required during the surgical procedure and is certainly the crucial asset in the case of an emergency or unexpected anatomy. Machines have an innate advantage during rote tasks of being fast, accurate and operate with a lack of bias, but human discernment provides a successful outcome when unanticipated events occur. Robotic capabilities in surgery may eventually allow remote-robotic surgery. This could change clinical care by providing care to patients who may not otherwise receive advanced care. This increase in access to care is beneficial to remote patients and the clinicians who care for them.

New AI tools help medical professionals change their clinical practice and to standardize care. Allowing AI systems to assist professionals can not only standardize care, it can increase productivity. The key to successful use of AI systems is to ensure the human factor never fades from the healthcare appointment. In the study mentioned previously, once the dermatologists were provided with additional information from the patients, their diagnostic accuracy increased and the article concludes that there was no substitute for a thorough clinical examination to provide a full healthcare picture for patients and their providers.

AI is integrated into many aspects of our lives and we should expect that to continue. Growth in the healthcare sector continues to be strong with one source estimating that artificial intelligence in US healthcare will continue at 35% rate for the next eight years4. Many predictors bemoan the hurdles needed to fully integrate AI into mainstream healthcare. Using AI to standardize care provides assistance to professionals, which allows for enhancing the lives of patients and healthcare professionals.

See the references below for more information.

About our guest author: Brian Kaplan, M.D. – Chair, Department of Otolaryngology-Head and Neck Surgery at Greater Baltimore Medical Center Director, Cochlear Implant Program at Greater Baltimore Medical Center

Dr. Kaplan was born and raised in Boston, Massachusetts. He attended Cornell University before enrolling at the University of Virginia for both medical school and residency training. He currently serves as the Chairman of the Department of Otolaryngology, and Director of the Cochlear Implant Program, at the Greater Baltimore Medical Center.

Past research interests have included cartilage and inner ear hair cell regeneration. Clinical studies have involved various ototopical agents, along with hybrid and traditional cochlear implantation.

Dr. Kaplan spent over ten years consulting in the medical device and services space, working with venture, public and private equity. In 2013 he cofounded the technology company Everseat, providing a novel, mobile platform optimizing scheduling efficiency for medical practices, hospitals and health systems. He was a consultant for several years for Cochlear before becoming the Global Head of Clinical Innovation, focusing on modernizing the delivery mechanism and creating the standard of care for cochlear implantation.

He currently resides in Baltimore, Maryland, USA, with his wife and three children.

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  1. https://en.wikipedia.org/wiki/Arthur_Samuel
  2. Uchan, S. February 8th, 2018, blogpost “AI in Medicine – A Historical Perspective” https://rowanalytics.com/blog-post/ai-in-medicine-a-historical-perspective/
  3. Welch, A. May, 29, 2018, CBS NEWS “AI better than dermatologists at detecting skin cancer, study finds” https://www.cbsnews.com/news/ai-better-than-dermatologists-at-detecting-skin-cancer-study-finds/
  4. Taylor, Phil April 13th, 2018, PMLive “Artificial intelligence in healthcare ‘set for 40% growth to 2024’” https://www.pmlive.com/pharma_news/artificial_intelligence_in_healthcare_set_for_40_growth_to_2024_1231723?es_p=6544595
Patient sitting with audiologist

Understanding reliability reporting and why it matters: setting a new standard with ANSI/AAMI CI86:2017

February 20, 2020

By Wade Colburn, Product Manager – Cochlear Implants

As a company that is committed to quality both in our products and reporting, we are excited about the compliance to a new standard for cochlear implants.

What is ANSI/AAMI CI86:2017?

AAMI standards are common in the medical field with the goal of advancing safety in health technology.  Cochlear Implants are one of the latest technologies to have a standard created, AANSI/AAMI CI86: 2017 Cochlear implant systems: requirements for safety, functional verification, labeling and reliability reporting.

ANSI/AAMI CI86: 2017 was developed by the AAMI Cochlear Implant Committee and establishes minimum safety and performance requirements for cochlear implants based on industry-accepted test methods. The intent of the standard is to assist manufacturers in testing and labeling the devices and in reporting device system reliability to the professional health care community and public. 

While this standard specifies specific requirements, test procedures and labeling that should be followed, it also provides greater specificity on how both implant and processor reliability should be reported. Therefore, we will soon be publishing a reliability report in compliance with ANSI/AAMI CI86: 2017. This means we will have two different reliability reports available: European Consensus Statement reliability report and the ANSI/AAMI CI86:2017 reliability report.

How do these reports differ?

There are a few key differences between the two reports:

What is the difference in what is counted for implant reliability?

The European Consensus Statement report considers all device failures when reporting implant reliability. In contrast, ANSI/AAMI CI86 report considers device removals for any medical or device related reason.  As an example, if an implant is removed because of an infection even if the device is still functioning, it will be reported as a failure with the new ANSI/AAMI CI86 standard, but it would not have been counted as part of the European Consensus Statement.

What should I expect regarding the sound processor reliability report?

Sound processor reporting will use Failed Component Return Rate (FCRR) as the metric to be reported. The FCRR is a percentage calculated by comparing the number of failed sound processors returned within a month to the cumulative sales of the same sound processor by the end of that month. The FCRR is reported as a monthly figure over a period of 24 months.

Check out this example (not real numbers):

When comparing sound processor reliability, consider these factors:

  • Compare the FCRR over time: Monthly processor return volumes are variable and can be impacted by factors such as seasonality or how long the device has been available in the market. By considering the full 24 months of FCRR data, rather than individual months, you will gain a better view of overall processor reliability.
  • Consider product generations: Predictors of manufacturer reliability would include both a consistent record of sound processor reliability and improving FCRR data for each new generation of sound processors.

To learn more about reliability reporting and download our latest reliability report in compliance with ANSI/AAMI CI86.  For more information on the difference between the European Consensus Report and ANSI/AAMI CI86, click here .

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