Steno Diabetes Center builds on its strong reputation for research

IBM Business Analytics technologies support ground-breaking scientific publications

Published on 21-Jan-2013

"The ability to handle and combine data sets from different cohorts of patients in SPSS is very valuable... It’s a solid, mature and reliable platform, so we know we can trust it to deliver the correct results – and it gives the wider academic community more confidence in the validity of our studies." - Professor Peter Rossing, Chief Physician and Head of Research, Steno Diabetes Center

Customer:
Steno Diabetes Center

Industry:
Healthcare

Deployment country:
Denmark

Solution:
BA - Business Analytics, BA - Predictive Analytics

Overview

Steno Diabetes Center is a medical research institution that specializes in diabetes. Its campus in Gentofte, Denmark, is home to four centers, which focus on patient care, education, health promotion and research. It treats approximately 6,000 patients and has 220 staff.

Business need:
To break new ground in its research into diabetes and its complications, Steno Diabetes Center needs to be able to analyze complex data sets produced by research projects and clinical trials.

Solution:
IBM® SPSS® Statistics provides data-handling and analysis capabilities that help Steno identify significant factors in the development of the disease and evaluate the effectiveness of treatments.

Benefits:
Makes it easy for medical staff to combine data sets and perform complex analyses, without help from statisticians or IT specialists. Trusted technology builds confidence in Steno’s published research.

Case Study

To read a Danish version of this case study, please click here

Steno Diabetes Center is a medical research institution that specializes in diabetes. Its campus in Gentofte, Denmark, is home to four centers, which focus on patient care, education, health promotion and research. It treats approximately 6,000 patients and has 220 staff.

The Research Center, headed by Professor Peter Rossing, employs 60 academic and laboratory staff, who focus on biomedical research into the long-term complications of type 1 and type 2 diabetes. The research team publishes up to 100 papers each year, as well as journal articles, reviews, and Ph.D. and Masters’ theses.

Professor Rossing comments: “Over time, diabetes can affect the kidneys, the cardiovascular system, the eyes, the nervous system, and many other parts of a patient’s body. To study the way diabetes develops and assess the effectiveness of different types of treatment, we need to organize clinical studies that often involve hundreds of patients and take place over a number of years. This generates relatively large quantities of data, which we need to analyze. For this purpose, our research team uses IBM SPSS Statistics software.”

The biomedical research team at Steno has been using SPSS software for more than 15 years to handle and manipulate its data sets, enable sophisticated analysis techniques, and present results clearly in the form of graphs and tables.

Ease of use

“We started with quite an early version of SPSS, and we have upgraded it regularly over the years,” says Professor Rossing. “We originally chose it because we found it easy to use, and the user-friendliness seems to have increased with each new version. We don’t have any professional statisticians in our research team, so our SPSS users are mainly medical doctors and academics. Even without much training, they are quickly able to learn how to use the software to perform quite sophisticated analyses.”

He adds: “The whole process – from selecting and combining the data sets, through the analysis stage, to the presentation of the results – is handled by a single tool. The graphs and tables produced by SPSS are used directly in the research papers that we submit for publication, so it is really an end-to-end solution.”

Simple survival analysis

Many of the Steno research team’s projects involve survival analyses – a technique that IBM SPSS Statistics supports natively. Options for various types of survival analysis can be selected easily from one of the drop-down menus in the main SPSS interface, making it possible to configure the analysis with just a few mouse-clicks.

In one recent study, Steno took baseline blood samples from 800 patients with type 1 diabetes and measured a number of biomarkers such as Gross Differentiation Factor 15 (GDF 15), Osteoprotegerin (OPG) and Vitamin D. The research team then assessed the patients’ condition over time, with regular check-ups over the course of 12 years, to find out whether they developed complications, and how quickly these complications arose.

Finding the significant factors

“For a commercial organization, the amount of data we collected might not be considered large, but for a small research team, it was a sizeable data set,” comments Professor Rossing. “Without SPSS, it would not have been feasible to complete our analysis. The results enabled us to assess relative risk of each biomarker in the development of complications – for example, we found that low levels of Vitamin D were associated with problems such as cardiovascular diseases, renal complications and all-cause mortality, and that uric acid is related to loss of kidney function.”

These findings have led to publications in journals such as Diabetes and Diabetes Care, as well as citations by other research groups in similar journals.1

Evaluating new treatments

“We use SPSS for both epidemiological studies and clinical studies,” says Professor Rossing. “So, for example, now that we have identified a link between Vitamin D and the early onset of complications, we can plan other studies to assess whether treatments that act on the body’s Vitamin D receptors can improve the long-term prognosis for our patients. Again, the survival analysis tools should be very helpful in this project.”

In the near future, the Steno team will launch an EU-funded project at 15 centers across Europe which will aim to confirm their current findings about the significance of these biomarkers, as well as testing some potential interventions.

Building on a strong reputation

“The ability to handle and combine data sets from different cohorts of patients in SPSS is very valuable in these kinds of large-scale studies,” concludes Professor Rossing. “Equally, the excellent reputation of SPSS among academic and medical researchers is an important factor: we always mention in our publications that we use SPSS, and our choice of tools has never been questioned by any of our peer reviewers. It’s a solid, mature and reliable platform, so we know we can trust it to deliver the correct results – and it gives the wider academic community more confidence in the validity of our studies.”

About IBM Business Analytics

IBM Business Analytics software delivers data-driven insights that help organizations work smarter and outperform their peers. This comprehensive portfolio includes solutions for business intelligence, predictive analytics and decision management, performance management, and risk management.

Business Analytics solutions enable companies to identify and visualize trends and patterns in areas, such as customer analytics, that can have a profound effect on business performance. They can compare scenarios, anticipate potential threats and opportunities, better plan, budget and forecast resources, balance risks against expected returns and work to meet regulatory requirements. By making analytics widely available, organizations can align tactical and strategic decision-making to achieve business goals.

For more information

For further information please visit ibm.com/business-analytics

Products and services used

IBM products and services that were used in this case study.

Software:
SPSS Statistics

Footnotes and legal information

1 For example: “Serum uric acid as a predictor for development of diabetic nephropathy in type 1 diabetes: an inception cohort study”, Diabetes. 2009 Jul;58(7):1668-71; “Plasma growth differentiation factor-15 independently predicts all-cause and cardiovascular mortality as well as deterioration of kidney function in type 1 diabetic patients with nephropathy”, Diabetes Care. 2010 Jul;33(7):1567-72. Epub 2010 Mar 31.

© Copyright IBM Corporation 2013. IBM Danmark ApS, Nymoellevej 91, 2800 Kgs. Lyngby, Denmark. Produced in Denmark. January 2013. IBM, the IBM logo, ibm.com, and SPSS are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the web at: www.ibm.com/legal/copytrade.shtml. This document is current as of the initial date of publication and may be changed by IBM at any time. Not all offerings are available in every country in which IBM operates. The client examples cited are presented for illustrative purposes only. Actual performance results may vary depending on specific configurations and operating conditions. THE INFORMATION IN THIS DOCUMENT IS PROVIDED “AS IS” WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTY OR CONDITION OF NON-INFRINGEMENT. IBM products are warranted according to the terms and conditions of the agreements under which they are provided. The client is responsible for ensuring compliance with laws and regulations applicable to it. IBM does not provide legal advice or represent or warrant that its services or products will ensure that the client is in compliance with any law or regulation.