Comment
Science Photo Library
Use of type 2 diabetes risk scores in clinical practice: a call for action
Published Online January 28, 2015 http://dx.doi.org/10.1016/ S2213-8587(14)70261-X
For more on the Finnish Diabetes Risk Score see http:// www.idf.org/diabetesprevention/questionnaire For more on the Diabetes UK risk score see http://riskscore. diabetes.org.uk/2013
For the QDiabetes score see http://www.qdscore.org/ For NICE guideline recommendations see http:// pathways.nice.org.uk/pathways/ preventing-type-2-diabetes#
166
The global incidence of type 2 diabetes continues to grow, exerting a major effect on years of life lost due to excess cardiovascular morbidity and mortality.1,2 Prediction of a person’s risk of type 2 diabetes is crucial for development of effective, acceptable, and costefficient prevention strategies. Lifestyle modification significantly reduces risk of type 2 diabetes at all levels of genetic susceptibility, rendering targeted advice to at-risk individuals imperative.3 Clinical risk prediction scores form part of every major international guideline for diabetes care and prevention.4,5 Yet, the large number of competing scores, and the often opaque choice of particular instruments promoted by public health bodies, charities, and advocacy groups, continues to pose major unaddressed issues in linking of epidemiological research with translation into everyday practice. Indeed, a survey6 among Australian family doctors showed substantial gaps in physicians’ use of risk assessment methods for type 2 diabetes. A 2011 review7 of 94 type 2 diabetes risk models concluded that only seven scores were adequate for clinical practice—none of which had been investigated in an interventional setting. In 40 of all 43 publications reviewed in detail, the investigators urged adoption of their scores over existing ones, often citing non-specific attributes such as simple, low cost, and convenient. Most models were not independently validated but still claimed wide applicability. The figure shows the cumulative number of publications of risk scores over time. The increased attention given recently to nonEuropean and US contexts is laudable, but research efforts need to be redirected toward replication and validation, rather than drafting of novel scores. A crosssectional comparison of seven models8 showed huge variations in predicted risk; extrapolation to the Irish population resulted in estimated frequencies of atrisk people of 0·26–18·55%, dependent on the score. Similar results were reported in a Swiss study,9 with extrapolated frequencies of 1·21–16·56%. Independent validation is scarce but has been successfully implemented: Abbasi and colleagues10 tested 25 models in a cohort of more than 38 000 Dutch individuals. All scores showed good discrimination for
10-year incident type 2 diabetes; however, calibration (the unadjusted comparison between estimated and reported risks) was poor throughout. A case-cohort study (>27 000 European participants)11 of 12 noninvasive models showed good calibration for nine scores but variable discrimination with substantial differences dependent on age, country, sex, and waist circumference. Although these findings show the value of risk scores, they also underscore the importance of specification of models for population subgroups, countries, and risk categories. The so-called one-scorefits-all approach based on linear combinations of risk indicators seems inappropriate for clinical reality. Recommendations about which risk score to use differ. For example, the International Diabetes Federation recommends the Finnish Diabetes Risk Score12 and Diabetes UK promotes its own score, whereas the National Institute for Heath and Care Excellence (NICE)5 recommends use of any validated computer-based riskassessment. Score selection does make a difference. When two readily available online risk calculators are used for a fictional 54-year-old female of Pakistani ethnicity with height 1·56 m, weight 61 kg, waist circumference 84 cm, light smoker, positive family history, results in 10-year estimates of type 2 diabetes risk differ notably: 14% with the Diabetes UK score versus 24% with the QDiabetes score, suggesting either a moderate (10–20%) or high (>20%) risk. These different results accord with different NICE guideline recommendations: on the basis of the first result, 3-year follow-up only and no active treatment is recommended. On the basis of the second result, intensive lifestyle programmes or metformin or orlistat (as second-line treatment) should be offered. The associated time, cost, and psychosocial implications of treatments suggested by the two different risk estimates are quite different. Despite impressive efforts devoted to development of risk models, little evidence exists about their translation into practical use, be that by public bodies, nongovernmental organisations, clinicians, or the population at large. Who are the target users? What factors affect the likelihood of users adopting a particular risk score? Who www.thelancet.com/diabetes-endocrinology Vol 3 March 2015
Comment
www.thelancet.com/diabetes-endocrinology Vol 3 March 2015
20 Number of publications
Europe USA Australia Asia or South America
15 10 5
1
9
20 1
20 0
20 07
3
20 05
1
20 0
99
20 0
19
97
95
19
19
93
0
19
guides users in score selection? What implications should risk estimates have for individuals? How should these implications be communicated? How often should scores be applied? Many publications are vague about these points. Previously neglected topics include: independent replication, revalidation of established risk factors in population subgroups, post-publication follow-up of implementation and dissemination, and the provision of user support. Risk scores should be developed with a focus on target users to guide variable selection, score extensiveness, and outcome communication. Different intermediaries have different needs and face different issues. Public health bodies require global estimations to inform long-term policy decisions based on geographical and societal variables in addition to individual factors. By contrast, time-pressed primary care providers require quick methods, have to reconcile individualised medicine with public health-care approaches, and might need training and support in efficient use. Finally, individuals using self-assessments can have poor access to support and resources, are guided by educational and social background, and might be falsely reassured and kept from making positive lifestyle changes. Scores might underestimate actual risk, worded communications (eg, “One in eight people like you will develop diabetes in the next years”) are often overly simplistic and crude predictions (eg, “Your risk is X%”) are difficult to interpret. Affected people need well-informed, case-specific guidance to avoid biases such as the ecological fallacy inherent in derivation of predictions for individuals from group-level estimations. Scientists developing risk scores should devote more resources to post-publication follow-up. Potential remedies include an online toolkit for health-care professionals that unites score calculation with case examples, management guidance, context specification, and implementation targets (eg, the American Heart Association and the American College of Cardiology [ACC/AHA] cardiovascular risk calculator); a so-called meta-risk score search engine to select the most applicable estimator by, for example, user type, target population, types of variable, or length of follow-up; guidelines for charities and local providers that make the criteria for selection of purpose-specific and contextspecific methods explicit. In summary, the fruitful growth in research of methods for type 2 diabetes prediction in the past
Year
Figure: Cumulative number of publications of risk scores for diabetes, by geographical region and year of publication6
decades needs to be complemented by a matching degree of attention given to what difference risk models actually make in practice and how implementation can be improved. *Christoph Nowak, Erik Ingelsson, Tove Fall Molecular Epidemiology, Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala 752 37, Sweden [email protected] CN and EI declare no competing interests. TF has received honorarium for lecturing from MSD (Merck). 1 2
3 4 5
6
7 8
9
10
11
12
WHO. Noncommunicable diseases country profile 2014. World Health Organization: Geneva, 2014. Gregg EW, Zhuo X, Cheng YJ, Albright AL, Narayan KM, Thompson TJ. Trends in lifetime risk and years of life lost due to diabetes in the USA, 1985–2011: a modelling study. Lancet Diabetes Endocrinol 2014; 2: 867–74. Hivert MF, Vassy JL, Meigs JB. Susceptibility to type 2 diabetes mellitus— from genes to prevention. Nat Rev Endocrinol 2014; 10: 198–205. American Diabetes Association. Standards of medical care in diabetes—2014. Diabetes Care 2014; 37 (suppl 1): S14–80. National Institute for Health and Care Excellence. Preventing type 2 diabetes: risk identification and interventions for individuals at high risk. PH38. NICE: London, 2012. NPS MedicineWise. Preventive activities in general practice: GP survey results. Sept 23, 2014. http://www.nps.org.au/about-us/what-we-do/ evaluation/gp-survey-results/preventive-health (accessed Dec 4, 2014). Noble D, Mathur R, Dent T, Meads C, Greenhalgh T. Risk models and scores for type 2 diabetes: systematic review. BMJ 2011; 343: d7163. Phillips CM, Kearney PM, McCarthy VJ, Harrington JM, Fitzgerald AP, Perry IJ. Comparison of diabetes risk score estimates and cardiometabolic risk profiles in a middle-aged Irish population. PLoS One 2013; 8: e78950. Schmid R, Vollenweider P, Waeber G, Marques-Vidal P. Estimating the risk of developing type 2 diabetes: a comparison of several risk scores: the Cohorte Lausannoise study. Diabetes Care 2011; 34: 1863–68. Abbasi A, Peelen LM, Corpeleijn E, et al. Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. BMJ; 345: e5900. Kengne AP, Beulens JWJ, Peelen LM, et al. Non-invasive risk score for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. Lancet Diabetes Endocrinol 2014; 2: 19–29. Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 2003; 26: 725–31.
167
Science Photo Library
Use of type 2 diabetes risk scores in clinical practice: a call for action
Published Online January 28, 2015 http://dx.doi.org/10.1016/ S2213-8587(14)70261-X
For more on the Finnish Diabetes Risk Score see http:// www.idf.org/diabetesprevention/questionnaire For more on the Diabetes UK risk score see http://riskscore. diabetes.org.uk/2013
For the QDiabetes score see http://www.qdscore.org/ For NICE guideline recommendations see http:// pathways.nice.org.uk/pathways/ preventing-type-2-diabetes#
166
The global incidence of type 2 diabetes continues to grow, exerting a major effect on years of life lost due to excess cardiovascular morbidity and mortality.1,2 Prediction of a person’s risk of type 2 diabetes is crucial for development of effective, acceptable, and costefficient prevention strategies. Lifestyle modification significantly reduces risk of type 2 diabetes at all levels of genetic susceptibility, rendering targeted advice to at-risk individuals imperative.3 Clinical risk prediction scores form part of every major international guideline for diabetes care and prevention.4,5 Yet, the large number of competing scores, and the often opaque choice of particular instruments promoted by public health bodies, charities, and advocacy groups, continues to pose major unaddressed issues in linking of epidemiological research with translation into everyday practice. Indeed, a survey6 among Australian family doctors showed substantial gaps in physicians’ use of risk assessment methods for type 2 diabetes. A 2011 review7 of 94 type 2 diabetes risk models concluded that only seven scores were adequate for clinical practice—none of which had been investigated in an interventional setting. In 40 of all 43 publications reviewed in detail, the investigators urged adoption of their scores over existing ones, often citing non-specific attributes such as simple, low cost, and convenient. Most models were not independently validated but still claimed wide applicability. The figure shows the cumulative number of publications of risk scores over time. The increased attention given recently to nonEuropean and US contexts is laudable, but research efforts need to be redirected toward replication and validation, rather than drafting of novel scores. A crosssectional comparison of seven models8 showed huge variations in predicted risk; extrapolation to the Irish population resulted in estimated frequencies of atrisk people of 0·26–18·55%, dependent on the score. Similar results were reported in a Swiss study,9 with extrapolated frequencies of 1·21–16·56%. Independent validation is scarce but has been successfully implemented: Abbasi and colleagues10 tested 25 models in a cohort of more than 38 000 Dutch individuals. All scores showed good discrimination for
10-year incident type 2 diabetes; however, calibration (the unadjusted comparison between estimated and reported risks) was poor throughout. A case-cohort study (>27 000 European participants)11 of 12 noninvasive models showed good calibration for nine scores but variable discrimination with substantial differences dependent on age, country, sex, and waist circumference. Although these findings show the value of risk scores, they also underscore the importance of specification of models for population subgroups, countries, and risk categories. The so-called one-scorefits-all approach based on linear combinations of risk indicators seems inappropriate for clinical reality. Recommendations about which risk score to use differ. For example, the International Diabetes Federation recommends the Finnish Diabetes Risk Score12 and Diabetes UK promotes its own score, whereas the National Institute for Heath and Care Excellence (NICE)5 recommends use of any validated computer-based riskassessment. Score selection does make a difference. When two readily available online risk calculators are used for a fictional 54-year-old female of Pakistani ethnicity with height 1·56 m, weight 61 kg, waist circumference 84 cm, light smoker, positive family history, results in 10-year estimates of type 2 diabetes risk differ notably: 14% with the Diabetes UK score versus 24% with the QDiabetes score, suggesting either a moderate (10–20%) or high (>20%) risk. These different results accord with different NICE guideline recommendations: on the basis of the first result, 3-year follow-up only and no active treatment is recommended. On the basis of the second result, intensive lifestyle programmes or metformin or orlistat (as second-line treatment) should be offered. The associated time, cost, and psychosocial implications of treatments suggested by the two different risk estimates are quite different. Despite impressive efforts devoted to development of risk models, little evidence exists about their translation into practical use, be that by public bodies, nongovernmental organisations, clinicians, or the population at large. Who are the target users? What factors affect the likelihood of users adopting a particular risk score? Who www.thelancet.com/diabetes-endocrinology Vol 3 March 2015
Comment
www.thelancet.com/diabetes-endocrinology Vol 3 March 2015
20 Number of publications
Europe USA Australia Asia or South America
15 10 5
1
9
20 1
20 0
20 07
3
20 05
1
20 0
99
20 0
19
97
95
19
19
93
0
19
guides users in score selection? What implications should risk estimates have for individuals? How should these implications be communicated? How often should scores be applied? Many publications are vague about these points. Previously neglected topics include: independent replication, revalidation of established risk factors in population subgroups, post-publication follow-up of implementation and dissemination, and the provision of user support. Risk scores should be developed with a focus on target users to guide variable selection, score extensiveness, and outcome communication. Different intermediaries have different needs and face different issues. Public health bodies require global estimations to inform long-term policy decisions based on geographical and societal variables in addition to individual factors. By contrast, time-pressed primary care providers require quick methods, have to reconcile individualised medicine with public health-care approaches, and might need training and support in efficient use. Finally, individuals using self-assessments can have poor access to support and resources, are guided by educational and social background, and might be falsely reassured and kept from making positive lifestyle changes. Scores might underestimate actual risk, worded communications (eg, “One in eight people like you will develop diabetes in the next years”) are often overly simplistic and crude predictions (eg, “Your risk is X%”) are difficult to interpret. Affected people need well-informed, case-specific guidance to avoid biases such as the ecological fallacy inherent in derivation of predictions for individuals from group-level estimations. Scientists developing risk scores should devote more resources to post-publication follow-up. Potential remedies include an online toolkit for health-care professionals that unites score calculation with case examples, management guidance, context specification, and implementation targets (eg, the American Heart Association and the American College of Cardiology [ACC/AHA] cardiovascular risk calculator); a so-called meta-risk score search engine to select the most applicable estimator by, for example, user type, target population, types of variable, or length of follow-up; guidelines for charities and local providers that make the criteria for selection of purpose-specific and contextspecific methods explicit. In summary, the fruitful growth in research of methods for type 2 diabetes prediction in the past
Year
Figure: Cumulative number of publications of risk scores for diabetes, by geographical region and year of publication6
decades needs to be complemented by a matching degree of attention given to what difference risk models actually make in practice and how implementation can be improved. *Christoph Nowak, Erik Ingelsson, Tove Fall Molecular Epidemiology, Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala 752 37, Sweden [email protected] CN and EI declare no competing interests. TF has received honorarium for lecturing from MSD (Merck). 1 2
3 4 5
6
7 8
9
10
11
12
WHO. Noncommunicable diseases country profile 2014. World Health Organization: Geneva, 2014. Gregg EW, Zhuo X, Cheng YJ, Albright AL, Narayan KM, Thompson TJ. Trends in lifetime risk and years of life lost due to diabetes in the USA, 1985–2011: a modelling study. Lancet Diabetes Endocrinol 2014; 2: 867–74. Hivert MF, Vassy JL, Meigs JB. Susceptibility to type 2 diabetes mellitus— from genes to prevention. Nat Rev Endocrinol 2014; 10: 198–205. American Diabetes Association. Standards of medical care in diabetes—2014. Diabetes Care 2014; 37 (suppl 1): S14–80. National Institute for Health and Care Excellence. Preventing type 2 diabetes: risk identification and interventions for individuals at high risk. PH38. NICE: London, 2012. NPS MedicineWise. Preventive activities in general practice: GP survey results. Sept 23, 2014. http://www.nps.org.au/about-us/what-we-do/ evaluation/gp-survey-results/preventive-health (accessed Dec 4, 2014). Noble D, Mathur R, Dent T, Meads C, Greenhalgh T. Risk models and scores for type 2 diabetes: systematic review. BMJ 2011; 343: d7163. Phillips CM, Kearney PM, McCarthy VJ, Harrington JM, Fitzgerald AP, Perry IJ. Comparison of diabetes risk score estimates and cardiometabolic risk profiles in a middle-aged Irish population. PLoS One 2013; 8: e78950. Schmid R, Vollenweider P, Waeber G, Marques-Vidal P. Estimating the risk of developing type 2 diabetes: a comparison of several risk scores: the Cohorte Lausannoise study. Diabetes Care 2011; 34: 1863–68. Abbasi A, Peelen LM, Corpeleijn E, et al. Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. BMJ; 345: e5900. Kengne AP, Beulens JWJ, Peelen LM, et al. Non-invasive risk score for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. Lancet Diabetes Endocrinol 2014; 2: 19–29. Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 2003; 26: 725–31.
167
