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IBM SPSS Predictive Analytics Sales Mastery v1
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Killexams : IBM Predictive test - BingNews Search results Killexams : IBM Predictive test - BingNews Killexams : National Instruments and IBM Testbed Works on Predictive Maintenance

Industrial breakdowns are costly, as are their close cousins: unplanned maintenance to prevent an imminent breakdown. There is a body of evidence that suggests that continuously monitoring industrial equipment to detect early signs of performance degradation or failure is a worthwhile process that will help mitigate lost productivity, expensive repairs, and process inefficiency.

While it’s a great theory, many companies can’t monitor effectively due to aging equipment and a lack of integration of the limited analytics they do have. To try and overcome these challenges, IBM and National Instruments (NI) are working on a test bed project with the goal of developing new predictive maintenance analytics modeling techniques and creating standards for a modern industrial networking environment. Both are members of the Industrial Internet Consortium.

The ultimate goal of the test bed, they say, is to produce a multi-vendor, cloud-based predictive maintenance solution unlike existing platforms. While it operates, the Condition Monitoring and Predictive Maintenance Testbed will engage in continuous online measurements, automated analysis, and balance of plant coverage. IBM will provide the cloud environment and analytics, and NI will supply the monitoring and data acquisition technology.

MORE FROM DESIGN NEWS: Highlights of National Instruments' NIWeek 2015 (Slidehosw)

“To bring industrial equipment up to date by today’s standards, modifications need to be made so that these machines are able to fully harness the potential of the Internet of Things (loT) to provide condition monitoring solutions,” NI’s Stuart Gillen, condition monitoring platform vibration analyst, told Design News. “Combining sensor data from multiple pieces of equipment and/or multiple processes can provide deeper insight into the overall impact of faulty or sub-optimal equipment in advance of catastrophic and costly repairs.”

One of the biggest challenges of the testbed will be to demonstrate the frontiers of analytics as they apply to predictive maintenance. The more data analysis, the better a company can correlate machine data with operational data so maintenance can be optimally scheduled around production requirements. The new solution will be available to new equipment, but is also being geared toward older machinery for the purpose of retrofitting.

“Some of the systems being used in industry for condition monitoring and predictive maintenance today are not open and can’t adapt to all the new sensors being created in the loT,”said Gillen. “NI’s InsightCM software is open to implement new capabilities as needed, and IBM’s Bluemix platform is wide open—it can even handle the interplay of non-traditional data from sources like Twitter and weather forecasts to correlate with production or operations data.”

The sources will ultimately be pulled together by a host of networking technologies, including ZigBee, Bluetooth, Ethernet, and more. Since security is a prime concern in IoT installations, the project will use a multilayer approach, according to Greg Gorman, IBM’s director for Internet of Things.

“The first consideration is just the basic authorization of whether or not the device should be allowed to connect,” Gorman told Design News. “Then, encrypting data to keep it from prying eyes. Also important is ensuring the device isn't being 'faked'; that the device is who it says it is. Finally, end-to-end access control to ensure that people can only access the data from the device (or some of the data) that they are authorized to see,” he said.

IBM, for its part, eventually hopes to go beyond existing data analytics techniques to help the resulting solution gain traction over existing industrial monitoring platforms. While phase one of the test bed will use IBM’s existing analytics technology, PMQ and Maximo, later phases will include advanced technology from Watson, the company’s artificial intelligence system. Gorman says it’s about helping companies do more with less.

“Specialists on machines are expensive to develop, so making the most effective use of them is key,” he said. “Also, just the cost savings from doing predictive maintenance justifies the whole push in this direction -- efficient use of all the resources of the organization -- humans and machines.”

MORE FROM DESIGN NEWS: A Look Inside the Industrial Internet Consortium

Tracey Schelmetic graduated from Fairfield University in Fairfield, Conn. and began her long career as a technology and science writer and editor at Appleton & Lange, the now-defunct medical publishing arm of Simon & Schuster. Later, as the editorial director of telecom trade journal Customer Interaction Solutions (today Customer magazine) she became a well-recognized voice in the contact center industry. Today, she is a freelance writer specializing in manufacturing and technology, telecommunications, and enterprise software.

[image source: National Instruments]

Thu, 07 Jul 2022 12:00:00 -0500 en text/html
Killexams : How to View Output Files in SPSS

C.D. Crowder has been a freelance writer on a variety of syllabus including but not limited to technology, education, music, relationships and pets since 2008. Crowder holds an A.A.S degree in networking and one in software development and continues to develop programs and websites in addition to writing.

Wed, 25 Jul 2018 22:43:00 -0500 en-US text/html
Killexams : Apple and IBM Unveil First Part of Relationship -- What It Looks Like No result found, try new keyword!Apple and IBM unveil ... from powerful predictive analytics -- in the client's kitchen or at the local coffee shop, rather than the adviser's office -- with full ability to test recommendations ... Thu, 04 Aug 2022 12:00:00 -0500 en-us text/html Killexams : IBM's Watson Set To Revolutionize Marketing

When a computer can figure out whether a movie trailer is going to positively affect an audience or not – it makes you wonder how close we are to computer generated predictions on everything else in life. The short answer, according to Michael Karasick, IBM’s VP and Research Director at Almaden Labs, is that IBM ’s Watson is already making them. Since conquering “Jeopardy” and Chess, Watson has been focused on predictive healthcare, customer service, investment advice and culinary pursuits. But they are not stopping there, IBM is allowing select customers to use “Watson as a service” and may soon open it up to developers to build Watson apps.

Yes, the Watson technology is still maturing, but I am convinced that within five years the Watson platform will learn faster and make better predictions with each new field it understands. That’s because, as Karasick told me, “If you train a system like Watson on domain A and domain B, then it knows how to make the equivalence between terminologies in different domains.” That means as Watson solves problems in chemistry; it can generate probable solutions in Physics and Metallurgy too.

Imagine how this might be applied to marketing. By using Watson as a service, a business could train Watson to understand its customers, then use predictive models to recognize new products or services that their customers will buy.

Here’s how Watson can revolutionize marketing

Predict new trends and shifting tastes

Watson is a voracious consumer of data, and it doesn’t forget anything. You can feed it data from credit cards, sales databases, social networks, location data, web pages and it can compile and categorize that information to make high probability predictions.

And most shockingly, Watson is well ahead of its competitors in sentiment analysis. According to Karasick, Watson can recognize irony and sarcasm - and properly apprehend the intended meaning. That means Watson can quickly analyze large trial sizes to determine whether a movie trailer, product offering or clothing line are going to work with consumers.

Analyze social conversations – generate leads

Most social listening solutions on the market today do an adequate job of giving the marketer signals and reports about their industry, competitors, partners and current customers. But it’s up to the marketer to analyze the information and take action.

As Watson has demonstrated in other domains, it can foreseeably predict what information is most important and make recommendations on how to act on it. For example, if it finds a cluster of people discussing problems that the marketer’s solution solves, Watson can automatically notify the sales team or take action on its own to educate the prospective customers.

Determine whether a new innovation will sell or not

Because Watson can learn from one domain of knowledge and make high probability predictions in another, it’s reasonable to assume that if a company wanted to understand whether a new innovation will sell or not, Watson could analyze a company’s current market and customer base to provide success probabilities.

We’re a long way off from a Watson with the taste of a Steve Jobs, but if it has enough understanding of the situation, it can produce insights that can deliver companies a clearer picture of the opportunities and threats.

Computer calculated and automated growth hacking

If you’re a marketer and not familiar with growth hacking, please study up fast. Growth hackers focus on innovative A/B testing techniques to maximize conversions on emails, websites, social media, online content or just about any digital media available to them. It’s a low cost but more effective alternative to traditional media.

I can see how Watson could proactively and intelligently test, measure and optimize digital content, ads, website pages even a company’s product to efficiently maximize customer growth. Andy Johns of Greylock, formerly a growth hacker for Facebook , Twitter and Quora told me that Facebook conducted 6 hacks a day to maximize growth opportunities. I suspect Watson could easily handle 10 times that amount.

This clearly is the digital march of progress. Watson has the potential to eliminate ineffective marketing, Boost good marketing to great marketing, and to predict how to better spend marketing dollars in the future.

Put it all together and you’ve revolutionized marketing.

Mon, 18 Jul 2022 05:33:00 -0500 Mark Fidelman en text/html
Killexams : Taking Predictive Maintenance from the IIoT to Big Data Analytics

Predictive maintenance can grow far beyond traditional condition monitoring when the data from equipment is gathered through the Industrial Internet of Things (IIoT) and then stored and processed through Big Data analysis systems, such as IBM’s Watson. “When you gather the regular maintenance data, you can build a history of the data. Then you use algorithms to detect anomalous behavior in the historical data. In time, you learn that when you see this anomaly, you know—based on the history—that this component is likely to fail in the next 10 to 17 days,” Tom Craven, VP of product strategy at RRAMAC Connected Systems, told Design News. “The analysis of the data can predict the very specific failures in a specific timeframe. That’s where IBM Watson comes in.”

Craven will present a session at the Atlantic Design and Manufacturing Show in New York City on June 14 with Kayed Almasarweh, the Watson and cognitive IoT solutions lead at IBM. The program, Leveraging IoT for Predictive Maintenance, will look at the combination of condition monitoring data collection and the analysis of that data via Big Data processing in IBM’s Watson.

Grabbing the Quick ROI

Before customers make the major jump into Big Data processing, they can enjoy an early return on investment (ROI) from predictive maintenance basics—the stream of equipment data that comes from sensors and is delivered to a condition monitoring system via the IIoT. ROI can be achieved more quickly if the company doesn’t have to set up the servers and configure the software. Service companies like RRAMAC can grab the sensor data over the internet and process it on remote servers. “When you’re not installing a bunch of software and spending time learning how to configure it in-house, it shortens the timeline to the ROI,” said Craven. “The initial investment is less when you don’t have to invest in all the development hours to get it going.”

The reduced investment allows companies that couldn’t otherwise afford to develop a condition monitoring system to reap the benefits of predictive maintenance. “The IIoT brings predictive maintenance to a whole new set of customers where it wouldn’t have made sense before,” said Craven. “A lot of companies can benefit from predictive maintenance even if it doesn’t make sense for them to do it on their own.”

Giving the OEMs Their Own Equipment Data

Not all predictive maintenance data needs to go directly to the end user. In some cases, the end customer is using a piece of equipment that’s not connected to a factory line. Examples can include a recycling machine or a rock crusher. The user doesn’t have the network to gather equipment data, so the equipment OEM can track the machine data and monitor the equipment’s health. “Sometimes, our customer is the OEM. The OEM gets the information. The customer may get the information as well, including the alerts,” said Craven. “We provide data to the OEM and if the OEM chooses, the OEM can provide the data to the customer.”

OEMs often sell extended warranties. But the OEM can only sell the extended warranty if the health of the machine can be monitored. “If you have a machine that requires maintenance, those machines can wear out quickly if they’re not maintained,” said Craven. “If the OEM is monitoring the equipment regularly and making sure the customer is doing regular maintenance, the OEM can extend the warranty knowing that it’s enforced. It’s just like Ford not supporting the warranty on a car that hasn’t had regular oil changes.”

Rob Spiegel has covered automation and control for 17 years, 15 of them for Design News. Other syllabus he has covered include supply chain technology, alternative energy, and cyber security. For 10 years, he was owner and publisher of the food magazine Chile Pepper.

Wed, 20 Jul 2022 12:00:00 -0500 en text/html
Killexams : Prescriptive and Predictive Analytics Market Business overview 2022, and Forecast to 2030 | By -Accenture, Oracle, IBM, Microsoft

The MarketWatch News Department was not involved in the creation of this content.

Aug 01, 2022 (Market Insight Reports) -- New Jersey, United States- IBI’s most accurate assessment on the Prescriptive and Predictive Analytics Market assesses market size, example, and projection to 2030. The market study integrates basic assessment data and affirmations, making it a significant resource report for bosses, examiners, industry-trained professionals, and other key people who need a self-analyzed study to all more promptly fathom market designs, improvement drivers, open entryways, and looming challenges, as well as about competitors.

The Prescriptive and Predictive Analytics Market Report’s Objectives

• -To study and sort out the Prescriptive and Predictive Analytics market’s size concerning both worth and volume.
• -Measure a slice of the pie of huge Prescriptive and Predictive Analytics market parts.
• -To show how the Prescriptive and Predictive Analytics market is made in a different region of the planet.
• -To investigate and look at smaller than usual business areas concerning their Prescriptive and Predictive Analytics market responsibilities, potential outcomes, and individual advancement designs.
• -To deliver a quick and dirty assessment of key business procedures utilized by top firms checking out the post, as creative work, facilitated endeavors, plans, affiliations, acquisitions, unions, new developments, and thing dispatches.

Receive the trial Report of Prescriptive and Predictive Analytics Market 2022 to 2030:

The worldwide Prescriptive and Predictive Analytics market is expected to grow at a booming CAGR of 2022-2030, rising from USD billion in 2021 to USD billion in 2030. It also shows the importance of the Prescriptive and Predictive Analytics market main players in the sector, including their business overviews, financial summaries, and SWOT assessments.

Prescriptive and Predictive Analytics Market Segmentation & Coverage:

Prescriptive and Predictive Analytics Market segment by Type:
Collection Analytics, Marketing Analytics, Supply-Chain Analytics, Behavioral Analytics, Talent Analytics

Prescriptive and Predictive Analytics Market segment by Application:
Finance & Credit, Banking & Investment, Retail, Healthcare & Pharmaceutical, Insurance, Others

The years examined in this study are the following to estimate the Prescriptive and Predictive Analytics market size:

History Year: 2015-2019
Base Year: 2021
Estimated Year: 2022
Forecast Year: 2022 to 2030

Cumulative Impact of COVID-19 on Market:

Various enterprises have faced issues due to COVID-19. This is legitimate in the business as well. In light of the COVID-19 plague, a couple of countries’ watchman monetary plans have been cut. Most assessment projects are expected to momentarily stand by in this way. Results of onboard PC stages to various Middle Eastern, African, and Latin American countries have furthermore reduced. These possible results influence the PC stage’s progression.

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Regional Analysis:

Bargains procedures, adventure, and cost structures are among the critical syllabus covered here, as well as a highlight on the Prescriptive and Predictive Analytics market in a key locales like Asia Pacific, North America, Latin America, Europe, and the Middle East and Africa. This market examination, which unites the two figures and real factors, moreover covers the money-related parts of affiliations.

The Key companies profiled in the Prescriptive and Predictive Analytics Market:

The study examines the Prescriptive and Predictive Analytics market’s competitive landscape and includes data on important suppliers, including Accenture, Oracle, IBM, Microsoft, QlikTech, SAP, SAS Institute, Alteryx, Angoss, Ayata, FICO, Information Builders, Inkiru, KXEN, Megaputer, Revolution Analytics, StatSoft, Splunk Anlytics, Tableau, Teradata, TIBCO, Versium, Pegasystems, Pitney Bowes, Zemantis,& Others

Table of Contents:

List of Data Sources:
Chapter 2. Executive Summary
Chapter 3. Industry Outlook
3.1. Prescriptive and Predictive Analytics Global Market segmentation
3.2. Prescriptive and Predictive Analytics Global Market size and growth prospects, 2015 – 2026
3.3. Prescriptive and Predictive Analytics Global Market Value Chain Analysis
3.3.1. Vendor landscape
3.4. Regulatory Framework
3.5. Market Dynamics
3.5.1. Market Driver Analysis
3.5.2. Market Restraint Analysis
3.6. Porter’s Analysis
3.6.1. Threat of New Entrants
3.6.2. Bargaining Power of Buyers
3.6.3. Bargaining Power of Buyers
3.6.4. Threat of Substitutes
3.6.5. Internal Rivalry
3.7. PESTEL Analysis
Chapter 4. Prescriptive and Predictive Analytics Global Market Product Outlook
Chapter 5. Prescriptive and Predictive Analytics Global Market Application Outlook
Chapter 6. Prescriptive and Predictive Analytics Global Market Geography Outlook
6.1. Prescriptive and Predictive Analytics Industry Share, by Geography, 2022 & 2030
6.2. North America
6.2.1. Market 2022 -2030 estimates and forecast, by product
6.2.2. Market 2022 -2030, estimates and forecast, by application
6.2.3. The U.S. Market 2022 -2030 estimates and forecast, by product Market 2022 -2030, estimates and forecast, by application
6.2.4. Canada Market 2022 -2030 estimates and forecast, by product Market 2022 -2030, estimates and forecast, by application
6.3. Europe
6.3.1. Market 2022 -2030 estimates and forecast, by product
6.3.2. Market 2022 -2030, estimates and forecast, by application
6.3.3. Germany Market 2022 -2030 estimates and forecast, by product Market 2022 -2030, estimates and forecast, by application
6.3.4. the UK Market 2022 -2030 estimates and forecast, by product Market 2022 -2030, estimates and forecast, by application
6.3.5. France Market 2022 -2030 estimates and forecast, by product Market 2022 -2030, estimates and forecast, by application
Chapter 7. Competitive Landscape
Chapter 8. Appendix

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In 2030, what will the Prescriptive and Predictive Analytics market’s improvement rate be?
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Sun, 31 Jul 2022 16:36:00 -0500 en-US text/html
Killexams : Healthcare Predictive Analytics Market Size Worth US$ 22.59 Billion by 2027 | CAGR 22.40%

 IMARC Group’s latest research report, titled “Healthcare Predictive Analytics Market: Global Industry Trends, Share, Size, Growth, Opportunity, and Forecast 2022-2027,”  The global healthcare predictive analytics market reached a value of US$ 6.32 Billion in 2021. Looking forward, IMARC Group expects the market to reach a value of US$ 22.59 Billion by 2027, exhibiting a CAGR of 22.40% during 2022-2027.

What is Predictive Analytics in Healthcare?

Healthcare predictive analytics refers to the advanced software solutions that automatically analyze data to Boost the overall patient outcomes and provide effective care. It is widely utilized for remote monitoring, diagnosis, treatment course design, clinical decision support, care quality improvement, care cost reduction, and prognosis purposes. Healthcare predictive analytics can store large amounts of data, predict the patient response, provide a more personalized healthcare experience, etc. Furthermore, these software tools are based on the environment, risk factors, genetics, and medical history of the individual patient.  It is widely used for remote monitoring, diagnosis, treatment course design, prognosis, clinical decision support, care quality improvement, and care cost reduction purposes. It can store large amounts of data, predict a patient’s response and provide a more personalized healthcare experience. Healthcare predictive analytics aids in improving patient healthcare, easing patient diagnosis, detecting patients at higher risks, gaining better insights and enabling healthcare practitioners to make well-informed decisions. 

Request Free trial Report (Exclusive Offer on this report):

Important Attribute and highlights of the Report:

  • Detailed analysis of the global market share
  • Market Segmentation by component, analytics type, delivery model, application, and end-user.
  • Historical, current, and projected size of the market in terms of volume and value
  • Latest industry trends and developments
  • Competitive Landscape for Healthcare Predictive Analytics Market
  • Strategies of major players and product offerings

The elevating digitalization levels across the healthcare sector are primarily driving the healthcare predictive analytics market. In addition to this, these tools are widely employed to Boost efficiency in clinical operations and augment health outcomes via enhanced care and patient engagement, which is further catalyzing the market growth. Moreover, the increasing adoption of electronic health record (EHR) systems is acting as another significant growth-inducing factor. Apart from this, the integration of the internet of things (IoT) and Big Data solutions to assist in collecting patient records and test results and analyzing disease patterns is also positively influencing the global market. Furthermore, the extensive utilization of these software solutions by healthcare payers, owing to its cost-effectiveness, reduced number of unnecessary tests, fraud detection, improved decision-making, etc., is anticipated to fuel the healthcare predictive analytics market over the forecasted period.

Key Players Included in Global Healthcare Predictive Analytics Market Research Report:

  • Allscripts Healthcare Solutions
  • Cerner
  • IBM
  • Cotiviti
  • Oracle
  • Health Catalyst
  • Inovalon, Optum
  • Citiustech
  • Mckesson
  • Medeanalytics
  • SAS Institute
  • SCIO Health Analytics
  • Vitreoshealth
  • Wipro
  • Cognizant
  • Siemens Healthcare
  • Hewlett-Packard
  • Koninklijke Philips
  • GE Healthcare etc.

Ask Analyst for Instant Discount and get Full Report with TOC & List of Figure:

Report Coverage:

Report Features Details
Base Year of the Analysis 2021
    Historical Period 2016-2021
Forecast Period 2022-2027
Units US$ Billion
Segment Coverage Product, Deployment Mode, Application, End User, Region
Region Covered  Asia Pacific, Europe, North America, Latin America, Middle East and Africa
Countries Covered United States, Canada, Germany, France, United Kingdom, Italy, Spain, Russia, China, Japan, India, South Korea, Australia, Indonesia, Brazil, Mexico
Companies Covered Allscripts Healthcare Solutions Inc., Cerner Corporation, IBM Corporation, McKesson Corporation, MedeAnalytics Inc., Microsoft Corporation, Oracle Corporation, SAS Institute Inc., UnitedHealth Group Incorporated and Verisk Analytics.
Customization Scope 10% Free Customization
Report Price and Purchase Option Single User License: US$ 2499
Five User License: US$ 3499
Corporate License: US$ 4499
Post-Sale Analyst Support 10-12 Weeks
Delivery Format PDF and Excel through Email (We can also provide the editable version of the report in PPT/Word format on special request)

COVID-19 Impact Overview:

We are regularly tracking the direct effect of COVID-19 on the market, along with the indirect influence of associated industries. These observations will be integrated into the report.

Key Market Segmentation:

Breakup by Component:

  • Services
  • Software
    • Electronic Health Record Software
    • Practice Management
    • Workforce Management
  • Hardware
    • Data Storage
    • Routers
    • Firewalls
    • Virtual Private Networks
    • E-Mail Servers
    • Others

Breakup by Analytics Type:

  • Descriptive Analytics
  • Predictive Analytics
  • Prescriptive Analytics
  • Cognitive Analytics

Breakup by Delivery Model:

  • On-Premise Delivery Model
  • On-Demand Delivery Model

Breakup by Application:

  • Financial Analytics
  • Clinical Analytics
  • Operational Analytics
  • Others

Breakup by End-User:

  • Hospitals and Clinics
  • Finance and Insurance Agencies
  • Research Organizations

Breakup by Region:

  • North America
  • Europe
  • Asia Pacific
  • Middle East and Africa
  • Latin America

TOC for the Healthcare Predictive Analytics Market Research Report:

  • Preface
  • Scope and Methodology
  • Executive Summary
  • Introduction
  • Global Healthcare Predictive Analytics Market
  • SWOT Analysis
  • Value Chain Analysis
  • Price Analysis
  • Competitive Landscape

Who we are:

IMARC Group is a leading market research company that offers management strategy and market research worldwide. We partner with clients in all sectors and regions to identify their highest-value opportunities, address their most critical challenges, and transform their businesses.

IMARC’s information products include major market, scientific, economic and technological developments for business leaders in pharmaceutical, industrial, and high technology organizations. Market forecasts and industry analysis for biotechnology, advanced materials, pharmaceuticals, food and beverage, travel and tourism, nanotechnology and novel processing methods are at the top of the company’s expertise.

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Mon, 18 Jul 2022 20:02:00 -0500 IMARC en-US text/html
Killexams : Childhood Type 1 Diabetes Tests Suggested at Ages 2 and 6

Screening children for type 1 diabetes-associated islet autoantibodies at ages 2 years and 6 years would identify most of those who go on to develop the condition by mid-adolescence, new data suggest.

Both genetic screening and islet-cell autoantibody screening for type 1 diabetes risk have become less expensive in accurate years. Nonetheless, as of now, most children who receive such screening do so through programs that screen relatives of people who already have the condition, such as the global TrialNet program.

Some in the type 1 diabetes field have urged wider screening, with the rationale that knowledge of increased risk can prepare families to recognize the early signs of hyperglycemia and seek medical help to prevent onset of diabetic ketoacidosis.

Moreover, potential therapies to prevent or delay type 1 diabetes are currently in development, including the anti-CD3 monoclonal antibody teplizumab (Tzield, Provention Bio).

However, given that the incidence of type 1 diabetes is about 1 in 300 children, any population-wide screening program would need to be implemented in the most efficient and cost-effective way possible with limited numbers of tests, say Mohamed Ghalwash, PhD, of the Center for Computational Health, IBM Research, Yorktown Heights, New York, and colleagues.

Results from their analysis of nearly 25,000 children from five prospective cohorts in Europe and the United States were published online July 5 in Lancet Diabetes & Endocrinology.

Screening in Kids Feasible, but May Need Geographic Tweaking

"Our results show that initial screening for islet autoantibodies at two ages (2 years and 6 years) is sensitive and efficient for public health translation but might require adjustment by country on the basis of population-specific disease characteristics," Ghalwash and colleagues write.

In an accompanying editorial, pediatric endocrinologist Maria J. Redondo, MD, PhD, writes: "This study is timely because accurate successes in preventing type 1 diabetes highlight the need to identify the best candidates for intervention...This paper constitutes an important contribution to the literature."

However, Redondo, of Baylor College of Medicine and Texas Children's Hospital, Houston, also cautioned: "It remains to be seen whether Ghalwash and colleagues' strategy could work in the general population because all the participants in the combined dataset had genetic risk factors for the disease or a relative with type 1 diabetes, in whom performance is expected to be higher."

She also noted that most participants were of northern European ancestry and that it is unknown whether the same or a similar screening strategy could be applied to individuals older than 15 years, in whom preclinical type 1 diabetes progresses more slowly.

Two-Time Childhood Screening Yielded High Sensitivity, Specificity

The data from a total of 24,662 participants were pooled from five prospective cohorts from Finland (DIPP), Germany (BABYDIAB), Sweden (DiPiS), and the United States (DAISY and DEW-IT).

All were at elevated risk for type 1 diabetes based on human leukocyte antigen (HLA) genotyping, and some had first-degree relatives with the condition. Participants were screened annually for three type 1 diabetes-associated autoantibodies up to age 15 years or the onset of type 1 diabetes.

During follow-up, 672 children developed type 1 diabetes by age 15 years and 6050 did not. (The rest hadn't yet reached age 15 years or type 1 diabetes onset.) The median age at first appearance of islet autoantibodies was 4.5 years.

A two-age screening strategy at 2 years and 6 years was more sensitive than screening at just one age, with a sensitivity of 82% and a positive predictive value of 79% for the development of type 1 diabetes by age 15 years.

The predictive value increased with the number of autoantibodies tested. For example, a single islet autoantibody at age 2 years indicated a 4-year risk of developing type 1 diabetes by age 5.99 years of 31%, while multiple antibody positivity at age 2 years carried a 4-year risk of 55%.

By age 6 years, the risk over the next 9 years was 39% if the test had been negative at age 2 years and 70% if the test had been positive at 2 years. But overall, a 6-year-old with multiple autoantibodies had an overall 83% risk of type 1 diabetes regardless of the test result at 2 years.

The predictive performance of sensitivity by age differed by country, suggesting that the optimal ages for autoantibody testing might differ by geographic region, Ghalwash and colleagues say.

Redondo commented, "The model might require adaptation to local factors that affect the progression and prevalence of type 1 diabetes." And, she added, "important aspects, such as screening cost, global access, acceptability, and follow-up support will need to be addressed for this strategy to be a viable public health option."

The study was funded by JDRF. Ghalwash and another author are employees of IBM. A third author was a JDRF employee when the research was done and is now an employee of Janssen Research and Development. Redondo has reported no relevant financial relationships.

Lancet Diabetes Endocrinol. Published online July 5, 2022. Article, Editorial

Miriam E. Tucker is a freelance journalist based in the Washington, DC, area. She is a regular contributor to Medscape, with other work appearing in The Washington Post, NPR's Shots blog, and Diabetes Forecast magazine. She is on Twitter: @MiriamETucker.

For more diabetes and endocrinology news, follow us on Twitter and Facebook.

Mon, 11 Jul 2022 12:01:00 -0500 en text/html
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