Risk assessments and structured care interventions for prevention of foot ulceration in diabetes: development and validation of a prognostic model.


Journal

Health technology assessment (Winchester, England)
ISSN: 2046-4924
Titre abrégé: Health Technol Assess
Pays: England
ID NLM: 9706284

Informations de publication

Date de publication:
11 2020
Historique:
entrez: 25 11 2020
pubmed: 26 11 2020
medline: 21 9 2021
Statut: ppublish

Résumé

Diabetes-related foot ulcers give rise to considerable morbidity, generate a high monetary cost for health and social care services and precede the majority of diabetes-related lower extremity amputations. There are many clinical prediction rules in existence to assess risk of foot ulceration but few have been subject to validation. Our objectives were to produce an evidence-based clinical pathway for risk assessment and management of the foot in people with diabetes mellitus to estimate cost-effective monitoring intervals and to perform cost-effectiveness analyses and a value-of-information analysis. We developed and validated a prognostic model using predictive modelling, calibration and discrimination techniques. An overview of systematic reviews already completed was followed by a review of randomised controlled trials of interventions to prevent foot ulceration in diabetes mellitus. A review of the health economic literature was followed by the construction of an economic model, an analysis of the transitional probability of moving from one foot risk state to another, an assessment of cost-effectiveness and a value-of-information analysis. The effects of simple and complex interventions and different monitoring intervals for the clinical prediction rules were evaluated. The main outcome was the incidence of foot ulceration. We compared the new clinical prediction rules in conjunction with the most effective preventative interventions at different monitoring intervals with a 'treat-all' strategy. Data from an electronic health record for 26,154 people with diabetes mellitus in one Scottish health board were used to estimate the monitoring interval. The Prediction Of Diabetic foot UlcerationS (PODUS) data set was used to develop and validate the clinical prediction rule. We searched for eligible randomised controlled trials of interventions using search strategies created for Ovid The clinical prediction rule was found to accurately assess the risk of foot ulceration. Digital infrared thermometry, complex interventions and therapeutic footwear with offloading devices were found to be effective in preventing foot ulcers. The risk of developing a foot ulcer did not change over time for most people. We found that interventions to prevent foot ulceration may be cost-effective but there is uncertainty about this. Digital infrared thermometry and therapeutic footwear with offloading devices may be cost-effective when used to treat all people with diabetes mellitus regardless of their ulcer risk. The threats to the validity of the results in some randomised controlled trials in the review and the large number of missing data in the electronic health record mean that there is uncertainty in our estimates. There is evidence that interventions to prevent foot ulceration are effective but it is not clear who would benefit most from receiving the interventions. The ulceration risk does not change over an 8-year period for most people with diabetes mellitus. A change in the monitoring interval from annually to every 2 years for those at low risk would be acceptable. Improving the completeness of electronic health records and sharing data would help improve our knowledge about the most clinically effective and cost-effective approaches to prevent foot ulceration in diabetes mellitus. This study is registered as PROSPERO CRD42016052324. This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in People with diabetes sometimes have problems with their feet that can become serious and make getting around harder and life less enjoyable. We have developed a test based on a simple score to find out a person’s risk of getting a foot ulcer. We also wanted to know how often the test needs to be done. People who have been tested and learn that they might go on to have foot problems rightly expect to be given treatment that stops the problem happening in the first place. In this project, we read many written reports about the best treatments to prevent foot ulcers. We found that some things can prevent foot ulcers, such as wearing special shoes and insoles, taking the temperature of the skin of the foot and resting when the temperature rises, and receiving specialist care from diabetes foot care teams. However, we also looked at the costs of the test and treatments and found that some treatments are better value for money than others. By using people’s health data from NHS computers, we discovered that very few people with diabetes develop a worse risk score for foot ulcers as time goes on, and it seems that being tested every year is not necessary for everyone. New clinical trials might help to improve foot health for people with diabetes, but if all of the researchers who have collected data from people in clinical trials shared their data it would be possible to find out more about who will gain most from these treatments without spending a lot on new research. It is clear that better input of patients’ health data into NHS computers will benefit diabetes research in the future.

Sections du résumé

BACKGROUND
Diabetes-related foot ulcers give rise to considerable morbidity, generate a high monetary cost for health and social care services and precede the majority of diabetes-related lower extremity amputations. There are many clinical prediction rules in existence to assess risk of foot ulceration but few have been subject to validation.
OBJECTIVES
Our objectives were to produce an evidence-based clinical pathway for risk assessment and management of the foot in people with diabetes mellitus to estimate cost-effective monitoring intervals and to perform cost-effectiveness analyses and a value-of-information analysis.
DESIGN
We developed and validated a prognostic model using predictive modelling, calibration and discrimination techniques. An overview of systematic reviews already completed was followed by a review of randomised controlled trials of interventions to prevent foot ulceration in diabetes mellitus. A review of the health economic literature was followed by the construction of an economic model, an analysis of the transitional probability of moving from one foot risk state to another, an assessment of cost-effectiveness and a value-of-information analysis.
INTERVENTIONS
The effects of simple and complex interventions and different monitoring intervals for the clinical prediction rules were evaluated.
MAIN OUTCOME MEASURE
The main outcome was the incidence of foot ulceration. We compared the new clinical prediction rules in conjunction with the most effective preventative interventions at different monitoring intervals with a 'treat-all' strategy.
DATA SOURCES
Data from an electronic health record for 26,154 people with diabetes mellitus in one Scottish health board were used to estimate the monitoring interval. The Prediction Of Diabetic foot UlcerationS (PODUS) data set was used to develop and validate the clinical prediction rule.
REVIEW METHODS
We searched for eligible randomised controlled trials of interventions using search strategies created for Ovid
RESULTS
The clinical prediction rule was found to accurately assess the risk of foot ulceration. Digital infrared thermometry, complex interventions and therapeutic footwear with offloading devices were found to be effective in preventing foot ulcers. The risk of developing a foot ulcer did not change over time for most people. We found that interventions to prevent foot ulceration may be cost-effective but there is uncertainty about this. Digital infrared thermometry and therapeutic footwear with offloading devices may be cost-effective when used to treat all people with diabetes mellitus regardless of their ulcer risk.
LIMITATIONS
The threats to the validity of the results in some randomised controlled trials in the review and the large number of missing data in the electronic health record mean that there is uncertainty in our estimates.
CONCLUSIONS
There is evidence that interventions to prevent foot ulceration are effective but it is not clear who would benefit most from receiving the interventions. The ulceration risk does not change over an 8-year period for most people with diabetes mellitus. A change in the monitoring interval from annually to every 2 years for those at low risk would be acceptable.
FUTURE WORK RECOMMENDATIONS
Improving the completeness of electronic health records and sharing data would help improve our knowledge about the most clinically effective and cost-effective approaches to prevent foot ulceration in diabetes mellitus.
STUDY REGISTRATION
This study is registered as PROSPERO CRD42016052324.
FUNDING
This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in
People with diabetes sometimes have problems with their feet that can become serious and make getting around harder and life less enjoyable. We have developed a test based on a simple score to find out a person’s risk of getting a foot ulcer. We also wanted to know how often the test needs to be done. People who have been tested and learn that they might go on to have foot problems rightly expect to be given treatment that stops the problem happening in the first place. In this project, we read many written reports about the best treatments to prevent foot ulcers. We found that some things can prevent foot ulcers, such as wearing special shoes and insoles, taking the temperature of the skin of the foot and resting when the temperature rises, and receiving specialist care from diabetes foot care teams. However, we also looked at the costs of the test and treatments and found that some treatments are better value for money than others. By using people’s health data from NHS computers, we discovered that very few people with diabetes develop a worse risk score for foot ulcers as time goes on, and it seems that being tested every year is not necessary for everyone. New clinical trials might help to improve foot health for people with diabetes, but if all of the researchers who have collected data from people in clinical trials shared their data it would be possible to find out more about who will gain most from these treatments without spending a lot on new research. It is clear that better input of patients’ health data into NHS computers will benefit diabetes research in the future.

Autres résumés

Type: plain-language-summary (eng)
People with diabetes sometimes have problems with their feet that can become serious and make getting around harder and life less enjoyable. We have developed a test based on a simple score to find out a person’s risk of getting a foot ulcer. We also wanted to know how often the test needs to be done. People who have been tested and learn that they might go on to have foot problems rightly expect to be given treatment that stops the problem happening in the first place. In this project, we read many written reports about the best treatments to prevent foot ulcers. We found that some things can prevent foot ulcers, such as wearing special shoes and insoles, taking the temperature of the skin of the foot and resting when the temperature rises, and receiving specialist care from diabetes foot care teams. However, we also looked at the costs of the test and treatments and found that some treatments are better value for money than others. By using people’s health data from NHS computers, we discovered that very few people with diabetes develop a worse risk score for foot ulcers as time goes on, and it seems that being tested every year is not necessary for everyone. New clinical trials might help to improve foot health for people with diabetes, but if all of the researchers who have collected data from people in clinical trials shared their data it would be possible to find out more about who will gain most from these treatments without spending a lot on new research. It is clear that better input of patients’ health data into NHS computers will benefit diabetes research in the future.

Identifiants

pubmed: 33236718
doi: 10.3310/hta24620
pmc: PMC7768791
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

1-198

Subventions

Organisme : Department of Health
ID : 15/171/01
Pays : United Kingdom

Références

Diabetes Metab Res Rev. 2016 Jan;32 Suppl 1:84-98
pubmed: 26340966
JAMA. 1995 Mar 1;273(9):712-20
pubmed: 7853629
Int J Clin Pract. 2006 May;60(5):541-5
pubmed: 16700850
Diabetes Care. 2000 May;23(5):606-11
pubmed: 10834417
Value Health. 2000 Nov-Dec;3 Suppl 1:39-46
pubmed: 16464208
Health Technol Assess. 2015 Jul;19(57):1-210
pubmed: 26211920
Heliyon. 2018 May 02;4(5):e00614
pubmed: 29872752
J Wound Care. 2002 Feb;11(2):70-4
pubmed: 11901743
Diabetologia. 2010 Jul;53(7):1525-33
pubmed: 20369221
Diabet Med. 2002 May;19(5):377-84
pubmed: 12027925
Diabetologia. 2008 Nov;51(11):1954-61
pubmed: 18758747
Diabetes Care. 1989 Jun;12(6):384-8
pubmed: 2659299
Diabetes Metab Res Rev. 2016 Jan;32 Suppl 1:99-118
pubmed: 26342178
Diabetes Educ. 1992 Nov-Dec;18(6):487-90
pubmed: 1296898
Am J Surg. 1989 Dec;158(6):520-3; discussion 523-4
pubmed: 2589581
Diabet Med. 1996 Jun;13(6):561-3
pubmed: 8799661
J Intern Med. 2000 Nov;248(5):397-405
pubmed: 11123504
Diabetes Care. 1995 Nov;18(11):1468-78
pubmed: 8722072
Arch Phys Med Rehabil. 2003 May;84(5):736-46
pubmed: 12736891
Control Clin Trials. 1996 Feb;17(1):1-12
pubmed: 8721797
Diabetes Care. 1995 Oct;18(10):1376-8
pubmed: 8721941
Postgrad Med. 1994 Oct;96(5):177-80, 183-6, 191-2
pubmed: 7937416
Foot Ankle. 1981 Sep;2(2):64-122
pubmed: 7319435
Diabetes Care. 2012 Dec;35(12):2588-90
pubmed: 23011727
JBI Database System Rev Implement Rep. 2016 Jul;14(7):236-65
pubmed: 27532798
Diabetes Care. 2013 Dec;36(12):4109-16
pubmed: 24130357
J Intern Med. 1994 May;235(5):463-71
pubmed: 8182403
Ann Intern Med. 1994 Aug 1;121(3):200-6
pubmed: 8017747
J Epidemiol Community Health. 1998 Jun;52(6):377-84
pubmed: 9764259
Open Med. 2009;3(3):e123-30
pubmed: 21603045
Diabetes Care. 2007 Jan;30(1):14-20
pubmed: 17192326
Diabetes Care. 1987 May-Jun;10(3):263-72
pubmed: 3297575
N Engl J Med. 1993 Jul 29;329(5):304-9
pubmed: 8147960
Ann Intern Med. 2019 Jan 1;170(1):W1-W33
pubmed: 30596876
Med Decis Making. 2006 Nov-Dec;26(6):565-74
pubmed: 17099194
J Am Podiatr Med Assoc. 2009 Jan-Feb;99(1):28-34
pubmed: 19141719
Diabet Med. 1998 May;15(5):412-7
pubmed: 9609364
Diabetes Care. 1997 Dec;20(12):1833-7
pubmed: 9405902
JAMA. 2002 May 15;287(19):2552-8
pubmed: 12020336
Diabetes Care. 2011 Apr;34(4):1041-6
pubmed: 21447666
West J Nurs Res. 2008 Apr;30(3):325-41; discussion 342-9
pubmed: 17607055
Diabetes Care. 1997 May;20(5):725-34
pubmed: 9135934
Diabet Med. 1991 Feb-Mar;8(2):111-7
pubmed: 1827394
JAMA. 1993 Oct 13;270(14):1714-8
pubmed: 8411502
Dermatol Surg. 2001 Apr;27(4):347-51
pubmed: 11298704
Cochrane Database Syst Rev. 2012 Oct 17;10:CD001488
pubmed: 23076893
Diabetes Care. 1994 Jun;17(6):557-60
pubmed: 8082524
Am Fam Physician. 2002 Nov 1;66(9):1655-62
pubmed: 12449264
Diabetologia. 2011 May;54(5):1190-9
pubmed: 21249490
BMC Med Res Methodol. 2011 Feb 03;11(1):15
pubmed: 21291558
Diabetes Care. 2004 Jul;27(7):1774-82
pubmed: 15220265
Diabetes Metab Res Rev. 2019 Feb;35(2):e3105
pubmed: 30513132
Am J Public Health. 1996 Jul;86(7):935-8
pubmed: 8669516
Diabetes Care. 1986 Mar-Apr;9(2):173-8
pubmed: 3698783
Health Care Manag Sci. 2005 Nov;8(4):253-65
pubmed: 16379409
J Am Coll Surg. 2011 Oct;213(4):552-66.e5
pubmed: 21943802
Diabetes Metab Res Rev. 2000 Sep-Oct;16 Suppl 1:S75-83
pubmed: 11054894
Cochrane Database Syst Rev. 2000;(3):CD002302
pubmed: 10908550
J Clin Epidemiol. 1998 Dec;51(12):1235-41
pubmed: 10086815
Am Heart J. 1946 Jul;32:82-7
pubmed: 20990921
Diabetes Care. 1994 Aug;17(8):909-17
pubmed: 7956643
Diabetes Care. 2006 Jun;29(6):1202-7
pubmed: 16731996
Lancet. 2005 Nov 12;366(9498):1719-24
pubmed: 16291066
Diabet Med. 1998 Jan;15(1):80-4
pubmed: 9472868
Ostomy Wound Manage. 2003 Nov;49(11):76-84
pubmed: 14652415
Diabetes Educ. 2003 Mar-Apr;29(2):273-82
pubmed: 12728754
Eval Health Prof. 2016 Jun;39(2):131-84
pubmed: 26130465
Int J Older People Nurs. 2016 Sep;11(3):214-39
pubmed: 26916809
Ann Intern Med. 2015 Jan 6;162(1):W1-73
pubmed: 25560730
Rev Bras Fisioter. 2010 Jan-Feb;14(1):31-7
pubmed: 20414559
Diabetes Care. 2004 Apr;27(4):901-7
pubmed: 15047646
Am J Med. 2007 Dec;120(12):1042-6
pubmed: 18060924
J Clin Epidemiol. 2016 Jan;69:225-34
pubmed: 26092286
Int Wound J. 2005 Jun;2(2):166-70
pubmed: 16722866
J Diabetes Complications. 2011 Jan-Feb;25(1):52-62
pubmed: 19854075
Curr Diabetes Rev. 2014;10(4):215-30
pubmed: 25245020
PLoS One. 2013 May 08;8(5):e62597
pubmed: 23667497
Foot Ankle Int. 1995 Jul;16(7):388-94
pubmed: 7550950
J Vasc Surg. 2012 Oct;56(4):1015-24.e1
pubmed: 22854267
Cochrane Database Syst Rev. 2019 Oct 3;10:ED000142
pubmed: 31643080
BMC Bioinformatics. 2011 Mar 17;12:77
pubmed: 21414208
QJM. 2013 Dec;106(12):1103-10
pubmed: 24072752
Diabetes Care. 2006 Nov;29(11):2562-3; author reply 2563
pubmed: 17065710
Diabet Med. 2016 Nov;33(11):1493-1498
pubmed: 26666583
Diabet Med. 1998 Jun;15(6):508-14
pubmed: 9632127
Diabetes Care. 1999 Jul;22(7):1125-36
pubmed: 10388978
Diabetes Care. 2003 Jun;26(6):1691-5
pubmed: 12766095
Diabetes Care. 1994 Jun;17(6):541-7
pubmed: 8082522
Cochrane Database Syst Rev. 2015 Aug 24;(8):CD007610
pubmed: 26299991
Diabetes Care. 1999 Jul;22(7):1036-42
pubmed: 10388963
J Clin Epidemiol. 2003 Nov;56(11):1092-9
pubmed: 14615000
J Fam Pract. 2000 Nov;49(11 Suppl):S17-29
pubmed: 11093555
Diabetes Metab. 2004 Dec;30(6):549-56
pubmed: 15671925
Diabetes Care. 2014 Jul;37(7):1982-9
pubmed: 24760263
Diabet Med. 1999 Oct;16(10):801-12
pubmed: 10547206
Diabet Med. 2006 Sep;23(9):944-54
pubmed: 16922700
Ann Intern Med. 1993 Jul 1;119(1):36-41
pubmed: 8498761
J Diabetes Complications. 2017 Apr;31(4):700-707
pubmed: 28153676
Clin Epidemiol. 2017 Mar 15;9:157-166
pubmed: 28352203
Res Synth Methods. 2017 Mar;8(1):92-108
pubmed: 28074553
Diabetologia. 2011 May;54(5):991-3
pubmed: 21331469
Psychosom Med. 2004 May-Jun;66(3):411-21
pubmed: 15184705
Stat Med. 2016 Jan 30;35(2):214-26
pubmed: 26553135
J Stat Softw. 2016 May 12;70:
pubmed: 29593450
Health Technol Assess. 2015 Feb;19(14):1-503, v-vi
pubmed: 25692211
BMJ. 2005 Apr 2;330(7494):765
pubmed: 15767266
BMJ. 2018 Sep 5;362:k3359
pubmed: 30185425
Lancet Diabetes Endocrinol. 2016 Sep;4(9):781-788
pubmed: 27177729
Diabet Med. 1995 Apr;12(4):349-54
pubmed: 7600753
BMJ. 2014 Jan 24;348:g264
pubmed: 24464281
Diabetes Care. 2004 Nov;27(11):2642-7
pubmed: 15504999
J Am Podiatr Med Assoc. 2001 Jul-Aug;91(7):343-50
pubmed: 11466459
Vasa. 1992;21(2):193-7
pubmed: 1621441
Stat Med. 1990 Nov;9(11):1303-25
pubmed: 2277880
Diabetologia. 2006 Dec;49(12):2819-23
pubmed: 17021919
Diabetes Res Clin Pract. 2017 Feb;124:84-92
pubmed: 28119194
Cochrane Database Syst Rev. 2016 Sep 14;9:CD010680
pubmed: 27623758
Health Econ. 1994 May-Jun;3(3):201-3
pubmed: 7921062
Diabetologia. 2012 Jul;55(7):1919-25
pubmed: 22398645
Diabetes Care. 1990 May;13(5):513-21
pubmed: 2351029
Biom J. 2010 Apr;52(2):271-87
pubmed: 20349448
J Bone Joint Surg Am. 2003 Aug;85(8):1436-45
pubmed: 12925622
Diabetes Care. 1986 Jan-Feb;9(1):1-10
pubmed: 3948638
BMC Med Res Methodol. 2015 Aug 05;15:59
pubmed: 26242875
BMC Endocr Disord. 2015 Oct 09;15:55
pubmed: 26452544
BMJ Open. 2013 May 08;3(5):
pubmed: 23657467
Diabet Med. 2000 Aug;17(8):581-7
pubmed: 11073179
Int J Endocrinol. 2015;2015:615680
pubmed: 26448748
Diabet Med. 2011 Jun;28(6):747-54
pubmed: 21418097
Diabetes Res Clin Pract. 1995 Apr;28(1):29-34
pubmed: 7587909
Stat Med. 2013 Dec 10;32(28):4890-905
pubmed: 23857554
Scott Med J. 2011 Aug;56(3):151-5
pubmed: 21873720
Am J Kidney Dis. 2002 Sep;40(3):566-75
pubmed: 12200809
Diabetologia. 2001 Nov;44(11):2077-87
pubmed: 11719840
Int J Low Extrem Wounds. 2012 Mar;11(1):59-64
pubmed: 22336901
Health Technol Assess. 2000;4(21):1-237
pubmed: 11074391
Med Decis Making. 2013 Aug;33(6):743-54
pubmed: 23341049
Diabetes Care. 2009 May;32(5):897-9
pubmed: 19196880
QJM. 2011 May;104(5):403-10
pubmed: 21186178
Adv Skin Wound Care. 2012 Nov;25(11):519-24; quiz 525-6
pubmed: 23080240
Med Decis Making. 2014 Apr;34(3):311-26
pubmed: 24246566
J Clin Epidemiol. 1996 Dec;49(12):1373-9
pubmed: 8970487
Diabetes Metab Res Rev. 2008 May-Jun;24 Suppl 1:S162-80
pubmed: 18442178
Phys Ther. 2008 Nov;88(11):1385-98
pubmed: 18801859
Diabetes Care. 1992 Oct;15(10):1386-9
pubmed: 1425105

Auteurs

Fay Crawford (F)

NHS Fife, R&D Department, Queen Margaret Hospital, Dunfermline, UK.
The Sir James Mackenzie Institute for Early Diagnosis, The School of Medicine, University of St Andrews, St Andrews, UK.

Francesca M Chappell (FM)

Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.

James Lewsey (J)

Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.

Richard Riley (R)

Research Institute for Primary Care and Health Sciences, Keele University, Keele, UK.

Neil Hawkins (N)

Health Economics and Health Technology Assessment (HEHTA), Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK.

Donald Nicolson (D)

NHS Fife, R&D Department, Queen Margaret Hospital, Dunfermline, UK.

Robert Heggie (R)

Health Economics and Health Technology Assessment (HEHTA), Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK.

Marie Smith (M)

Library & Knowledge Service, Victoria Hospital, NHS Fife, Kirkcaldy, UK.

Margaret Horne (M)

Usher Institute, University of Edinburgh, Edinburgh, UK.

Aparna Amanna (A)

NHS Fife, R&D Department, Queen Margaret Hospital, Dunfermline, UK.

Angela Martin (A)

Diabetes Centre, Victoria Hospital, NHS Fife, Kirkcaldy, UK.

Saket Gupta (S)

Diabetes Centre, Victoria Hospital, NHS Fife, Kirkcaldy, UK.

Karen Gray (K)

NHS Fife, R&D Department, Queen Margaret Hospital, Dunfermline, UK.

David Weller (D)

Usher Institute, University of Edinburgh, Edinburgh, UK.

Julie Brittenden (J)

Institute of Cardiovascular & Medical Sciences, University of Glasgow, Glasgow, UK.

Graham Leese (G)

Diabetes and Endocrinology, Ninewells Hospital, NHS Tayside, Dundee, UK.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH