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ARTICLE
Validation of a model to estimate personalised screening
frequency to monitor diabetic retinopathy
Amber A. W. A. van der Heijden &Iris Walraven &Esther van ’tRiet&
Thor Aspelund &Sigrún H. Lund &Petra Elders &Bettine C. P. Polak &
Annette C. Moll &Jan E. E. Keunen &Jacqueline M. Dekker &Giel Nijpels
Received: 14 November 2013 /Accepted: 28 March 2014
#Springer-Verlag Berlin Heidelberg 2014
Abstract
Aims/hypothesis Our study aimed to validate a model to de-
termine a personalised screening frequency for diabetic
retinopathy.
Methods A model calculating a personalised screening inter-
val for monitoring retinopathy based on patients’risk profile
was validated using the data of 3,319 type 2 diabetic patients
in the Diabetes Care System West-Friesland, the Netherlands.
Two-field fundus photographs were graded according to the
EURODIAB coding system. Sight-threatening retinopathy
(STR) was considered to be grades 3–5. Validity of the model
was assessed using calibration and discrimination measures.
We compared model-based time of screening with time of
STR diagnosis and calculated the differences in the number of
fundus photographs using the model compared with those in
annual or biennial screening.
Results During a mean of 53 months of follow-up, 76 patients
(2.3%) developed STR. Using the model, the mean
screening interval was 31 months, leading to a reduced
screening frequency of 61% compared with annual screen-
ing and 23% compared with biennial screening. STR
incidence occurred after a mean of 26 months after the
model-based time of screening in 67 patients (88.2%). In
nine patients (11.8%), STR had developed before the
model-based time of screening. The discriminatory ability
of the model was good (C-statistic 0.83; 95% CI 0.74,
0.92). Calibration showed that the model overestimated
STR risk.
Conclusions/interpretation A large reduction in retinopathy
screening was achieved using the model in this population of
patients with a very low incidence of retinopathy. Considering
the number of potentially missed cases of STR, there is room
for improvement in the model. Use of the model for
personalised screening may eventually help to reduce
healthcare use and costs of diabetes care.
Keywords Diabetic retinopathy .Retinal screening .
Screening intervals
Abbreviations
DCS Diabetes Care System West-Friesland
STR Sight-threatening retinopathy
A. A. W. A. van der Heijden and I. Walraven contributed equally to this
study.
Electronic supplementary material The online version of this article
(doi:10.1007/s00125-014-3246-4) contains peer-reviewed but unedited
supplementary material, which is available to authorised users.
A. A. W. A. van der Heijden (*):I. Walraven :E. van ’tRiet:
P. Elders :B. C. P. Polak :A. C. Moll :J. M. Dekker :G. Nijpels
EMGO Institute for Health and Care Research, VU University
Medical Center, van der Boechorststraat 7, 1081 BT Amsterdam,
The Netherlands
e-mail: a.vanderheijden@vumc.nl
A. A. W. A. van der Heijden:P. Elders :G. Nijpels
Department of General Practice, VU University Medical Center,
Amsterdam, the Netherlands
I. Walraven :A. C. Moll
Department of Ophthalmology, VU University Medical Center,
Amsterdam, the Netherlands
I. Walraven :E. van ’t Riet :B. C. P. Polak :J. M. Dekker
Department of Epidemiology and Biostatistics, VU University
Medical Center, Amsterdam, the Netherlands
T. Aspe l u nd :S. H. Lund
Faculty of Medicine, University of Iceland, Reykjavik, Iceland
J. E. E. Keunen
Department of Ophthalmology, University Medical Center
St Radboud, Nijmegen, the Netherlands
Diabetologia
DOI 10.1007/s00125-014-3246-4
Introduction
With the growing prevalence of type 2 diabetes, diabetic
retinopathy is set to become the leading cause of visual
impairment and blindness [1]. Screening for diabetic retinop-
athy among diabetic patients is an effective method to de-
crease the disease burden by early diagnosis and timely med-
ical treatment [1]. Many countries have therefore adopted an
annual or biennial screening programme which is usually
integrated within regular diabetes care [2–5]. Nevertheless, a
‘one size fits all’approach is costly and time-consuming, as
only a minority of type 2 diabetic patients eventually develop
sight-threatening retinopathy (STR), which is mainly depen-
dent on several well-known risk factors such as diabetes
duration, HbA
1c
, blood pressure and early stages of retinopa-
thy [6–9]. Consequently, patients at low risk of STR may be
over-screened by using a predefined screening interval, while
those at high risk of developing STR may be missed. A
personalised screening interval in which the individual risk
of developing STR is taken into account might therefore be a
more appropriate or cost-effective method to screen for STR
in type 2 diabetic patients [2,3,10–16].
Aspelund et al recently developed a model, based on
diabetes-related risk factors, in which they aimed to achieve
a more personalised approach to screening for STR in diabetes
[17]. However, assessment of the accuracy and validity of a
model should be performed before applying it to other popu-
lations. The Diabetes Care System West-Friesland (DCS) is a
large prospective study in which diabetes-related risk factors
and complications are extensively measured in patients with
type 2 diabetes, offering a unique opportunity to validate the
model. The aim of this study was therefore to validate the
personalised screening model of Aspelund et al [17]by
assessing the accuracy of predicting STR.
Methods
Study population The data of type 2 diabetic patients included
in the dynamic cohort of the DCS were used. The DCS
coordinates regional care for type 2 diabetes using a centrally
organised database available to all involved care givers.
Diabetes nurses and dietitians perform annual follow-up
examinations, including glucose control, cardiovascular
risk profiling and identifying the presence of micro- and
macrovascular complications [18].
In the study period between 1998 and 2010, the DCS
cohort consisted of 8,308 type 2 diabetic patients. The cohort
is dynamic, meaning that each year newly diagnosed diabetic
patients as well as patients with a longer duration of diabetes
enter the DCS cohort. For each patient, the year of entry was
considered as baseline. Because current type 2 diabetes guide-
lines recommend annual or biennial screening for retinopathy
[2–5], in most patients diagnostic fundus photography was
performed within 2 years after baseline. We therefore included
patients in whom the results of graded fundus photographs
and variables used to estimate screening frequency were pres-
ent at baseline (n=5,483). In patients with missing fundus
photograph results at baseline but in whom fundus photograph
results were present 1 or 2 years after baseline (n= 1,030), the
measurement taken 1 or 2 years after baseline was considered
to be the baseline measurement. Of the 6,513 patients, we
excluded patients in whom STR was already present at base-
line (EURODIAB grade 3, 4 or 5) (n=61). For 3,133 patients,
no graded fundus photographs within 1 year after model-
based time of screening were available. Patients who had no
graded fundus photographs available within 1 year after
model-based time of screening had a significantly higher age
(+1.4 years), shorter diabetes duration (−0.3 years), lower
HbA
1c
(−0.7%), lower systolic blood pressure (−5.5 mmHg)
and lower diastolic blood pressure (−5.1 mmHg) at baseline.
Furthermore, those patients had a significantly lower preva-
lence of grade 1 retinopathy (3.5%) and a comparable preva-
lence of grade 2 retinopathy (1.4%) at baseline and were
appointed a significantly longer (+9 months) screening inter-
val compared with those included in the current analyses.
Thus, 3,319 type 2 diabetic patients remained for the cur-
rent analyses. Patients who were included but censored due to
incomplete follow-up (n=991) had a significantly higher age
(+6.5 years), longer diabetes duration (+2.4 years), lower
HbA
1c
(−0.21%) and lower diastolic blood pressure
(−3.7 mmHg) compared with patients with complete 60-month
follow-up data. Mortality rates did not significantly differ be-
tween patients with complete (17.5%) or incomplete (16.3%)
follow-up (p=0.2).
Confirmation of diabetes and diabetes duration Type 2 dia -
betes was considered confirmed if at least one of the following
was reported by patients’general practitioner: (1) one or more
classic symptoms (excessive thirst, polyuria, weight loss, hun-
ger or pruritis) and fasting plasma glucose ≥7.0 mmol/l or
random plasma glucose ≥11.1 mmol/l; (2) at least two
elevated plasma glucose levels on different occasions
(fasting glucose ≥7.0 mmol/l or random plasma glucose
≥11.1 mmol/l) in the absence of symptoms [19]. Diabetes
duration was calculated from the date of diabetes diagnosis
until the date of the baseline measurement.
Measurements Annual physical examinations were per-
formed following a standardised protocol. Weight and height
were measured with patients barefoot and wearing light
clothes. HbA
1c
was measured using HPLC. Fasting plasma
glucose was measured by means of a hexokinase method
(Roche Diagnostics, Mannheim, Germany). Levels of total
cholesterol, HDL-cholesterol and triacylglycerol were mea-
sured using enzymatic techniques (Boehringer-Mannheim,
Diabetologia
Mannheim, Germany). Systolic and diastolic blood pressures
were measured on the right arm after 5 min resting in a seated
position using a random-zero sphygmomanometer
(Hawksley-Gelman, Lancing, UK). Information on year of
onset of diabetes and on country of birth of the patient and
the patient’s parents was obtained by self-report [9]. Patients
were categorised into two ethnic groups: Western (European
countries [except for Turkey], Indonesia, USA and Oceania)
and non-Western (Turkey, Morocco, Surinam, Aruba, Neth-
erlands Antilles, Africa, Asia and Latin America).
Diabetic retinopathy From 1998 until 2000, fundus photog-
raphy of both eyes was performed with a Kowa Pro Fundus
camera fitted with a green filter (Kowa Optical Industry,
Torrance, CA, USA). From the beginning of 2000 until
2004, fundus photography of both eyes was performed with
a non-mydriatic Canon CR5 camera (Canon, Tokyo, Japan).
From 2004, fundus photography of both eyes was performed
with a non-mydriatic Topcon TRC NW 100 camera (Topcon,
Tokyo, Japan) [9]. All participants were examined with 45°
fundus photographs. One photograph was centred on the
macula and the other nasally, with the optic disc one disc
diameter from the temporal edge. Mydriasis with 0.5%
tropicamide and 2.5% phenylephrine eye drops was per-
formed when the non-mydriatic photograph was not gradable.
All photographs were graded by an experienced ophthalmol-
ogist who is trained as a retinal specialist. Each patient’sgrade
of retinopathy was based on the grading of the worst eye. All
photographs were graded according to the EURODIAB clas-
sification score, in which grade 0 is ‘no retinopathy’,grade1
is ‘minimal non-proliferative retinopathy’(one or a few
scattered haemorrhages or microaneurysms), grade 2 is
‘moderate non-proliferative retinopathy’,grade3is‘severe
non-proliferative or preproliferative retinopathy’,grade4is
‘photocoagulated retinopathy’and grade 5 is ‘proliferative
retinopathy’[20]. Because patients with grades 3–5retinopa-
thy are usually referred to an ophthalmologist for assessment
and/or treatment, grades 3–5 were included in the outcome
measure and considered as ‘STR’. If abnormalities were seen
near to the macula and maculopathy was suspected, the
EURODIAB grading was minimally set on 3 and the patient
was referred to an ophthalmologist.
Statistical analyses Variables are presented as percentages,
means (±SD) or medians (interquartile range) in case of a
skewed distribution. Differences in baseline characteristics
between patients with and without grades 1–2retinopathyat
baseline were tested by Student’sttest for normal distributed
variables and the Mann–Whitney Utest for variables with a
skewed distribution. Differences in proportions were tested by
the χ
2
test. Using the model of Aspelund et al, STR risk and an
accompanying screening interval ranging from 6 to 60 months
were calculated for each patient based on sex, HbA
1c
,systolic
blood pressure, presence of retinopathy and diabetes
duration [17]. The model is described briefly in the electronic
supplementary material.
In patients with incident STR during follow-up, we
checked whether STR occurred before or after the model-
based time of screening. Outcomes of omitted fundus photo-
graphs according to the model and potentially missed cases of
retinopathy grades 3–5 were checked. For the total population,
we calculated the reduction in screening frequency by com-
paring the mean screening interval of the population assigned
by the model to annual and biennial screening. The predictive
accuracy of the model was estimated using calibration and
discrimination techniques. Calibration is the ability of the
model to predict the number of observed cases of STR during
follow-up and it was visually checked by plotting the predict-
ed risk of developing STR against the observed incidence of
STR. Participants were grouped into quintiles of predicted
STR risk during 60 months of follow-up, which was calculat-
ed in the first step of the model. Using Poisson regression for
survival data, the observed incidence of STR was calculated
within each quintile, taking into account censored data [21].
Within each quintile, observed STR incidence was plotted
against predicted risk. Discrimination is the ability to distin-
guish between those who develop STR during follow-up from
those who do not. Discriminatory ability was estimated by
calculating Harrell’s C-statistic (similar to the area under the
receiver operating characteristic curve), censoring missing
data from patients who did not have complete 60 month
follow-up data [22]. Discriminatory power is graded as poor
for a C-statistic below 0.6, moderate between 0.6 and 0.8, and
good for >0.8.
Finally, we investigated whether updating the model for the
observed risk of developing STR within our study population
improved calibration and discrimination of the model.
Updating the model was performed by calculating the calibra-
tion factor: (1 −the proportion of patients without STR after
60 months)/mean risk calculated by the model. The calibration
factor was then used to update the risk function calculated by
the model: calibration factor × mean risk calculated by
the model.
All statistical analyses were conducted using SPSS version
20 (SPSS, Chicago, IL, USA) and R version 3.0.2 (Vienna,
Austria) for Windows.
Results
A total of 3,319 type 2 diabetic patients were included in the
study, of whom 339 (10.2%) presented grade 1 or 2 retinop-
athy at baseline, mostly mild non-proliferative retinopathy
(grade 1) (9.1%). During a mean of 53 months of follow-up,
76 patients (2.3%) developed STR. Patients with prevalent
Diabetologia
grade 1 or 2 retinopathy at baseline were significantly older,
had longer diabetes duration and higher HbA
1c
levels com-
pared with patients without retinopathy at baseline (Table 1).
Tab le 2shows the model-based screening interval for the
total population (mean [SD] 31.0 [20.0] months), stratified for
patients with different grades of retinopathy at baseline. With
progressing grades of retinopathy at baseline, the model-based
screening interval shortened and the incidence of STR
increased.
Using the model, 2,468 patients (74.4%) were appointed a
screening interval longer than 12 months and 1,755 patients
(52.9%) were assigned a screening interval longer than
24 months. When using the model, screening frequency could
be reduced by 23% compared with biennial screening, and a
reduction of 61% could be achieved compared with annual
screening.
A total of 76 patients developed STR during follow-up. Of
these, 67 patients (88.2%) developed STR after the model-
based time of screening (mean [SD] 25.5 [22.2] months),
meaning that the model-based screening interval was safe in
these patients. Of these, five patients (7.5%) developed STR
within 1 year after baseline; according to the model, screening
should have been performed after 6 months. Of all the patients
who developed STR during a mean of 53 months of follow-
up, nine (11.8%) developed STR before the model-based time
of screening (mean [SD] 24.3 [13.2] months), which is later
than current care. Patients who were potentially missed by the
model had significantly lower systolic blood pressure levels
(127 vs 146 mmHg) at baseline compared with patients
screened in time. HbA
1c
level was not significantly different
between the two patient groups.
The C-statistic of the personalised screening model was
0.83 (95% CI 0.74, 0.92), indicating that the discriminatory
power of the model is a good fit. Calibration of the model is
depicted in Fig. 1a. The figure shows the observed incidence
of STR within each quintile of the predicted risk of developing
STR. The model overestimates the risk of developing grades
3–5 retinopathy. Figure 1b shows the calibration plot of the
observed incidence of STR against the predicted incidence of
STR after updating the model to the observed risk in our study
population. Compared with the initial calibration plot, the
observed incidence within quintiles of the updated predicted
risk lies closer to the diagonal line, meaning that updating the
model improved calibration of the model in our study popu-
lation. When using the updated model, the screening frequen-
cy could be reduced by 29% compared with biennial screen-
ing and a reduction of 65% could be achieved compared with
annual screening. Of all the patients who developed STR
during a mean of 53 months of follow-up, 13 developed
STR before the updated model-based time of screening (mean
[SD] 25.5 [14.3] months), which is later than current care.
Discussion
This study is the first to validate the model for personalised
screening of diabetic retinopathy of Aspelund et al [17].
Compared with conventional annual or biennial screening
intervals, use of the personalised screening model can attain
a substantial reduction in the number of screenings, while
safety and efficacy are barely compromised. Furthermore,
we showed that the model is a good fit and that it overesti-
mates the risk of developing STR.
Strengths of the present study include the large sample size
and its long-term follow-up with measurement of type 2
diabetes-related risk factors and complications. Furthermore,
fundus photographs were graded using the internationally
Tabl e 1 Baseline characteristics
of the type 2 diabetic patient
population stratified by
retinopathy grade (EURODIAB)
at baseline
Data are presented as proportions,
means (SD) or median
(interquartile range) in case of a
skewed distribution
*Significantly (p<0.05) different
from reference category
(no retinopathy)
Characteristic No retinopathy Grade 1 retinopathy Grade 2 retinopathy
n, % 2,980 (89.8) 302 (9.1) 37 (1.1)
Age, years 60.3 (11.3) 62.6 (12.0)* 62.4 (10.2)
Men, % 53.7 57.6 67.6
Ethnicity, % Western 91.4 87.6 85.7
BMI, kg/m
2
30.2 (5.4) 29.3 (5.1)* 30.1 (5.0)
Diabetes duration, years 1.1 (0.3–3.7) 2.8 (0.9–8.5)* 9.6 (2.7–14.4)*
HbA
1c
, % 7.6 (2.0) 7.8 (1.8) 8.4 (1.7)*
HbA
1c
, mmol/mol 60 (21) 61 (20) 68 (18)*
Systolic blood pressure, mmHg 145.3 (22.0) 146.5 (22.5) 148.8 (18.3)
Diastolic blood pressure, mmHg 83.2 (10.8) 83.6 (11.7) 82.3 (10.8)
Total cholesterol, mmol/l 5.3 (1.2) 5.3 (1.0) 5.7 (1.1)
LDL-cholesterol, mmol/l 3.2 (1.3) 3.3 (0.9) 3.5 (0.9)
HDL-cholesterol, mmol/l 1.2 (0.3) 1.2 (0.3) 1.1 (0.3)
Triacylglycerol, mmol/l 2.1 (1.7) 1.9 (1.0) 2.7 (2.3)
Diabetologia
accepted EURODIAB classification system [20], which en-
hances generalisability of our results to other populations.
Using the model, a reduction in screening frequency rang-
ing from 23% to 61% could be achieved compared with
biennial or annual screening, respectively. These reductions
are comparable to those of Aspelund et al, who showed in a
Danish population used to test the fit of the model that 59% of
the screenings could be safely omitted. Other studies have
already shown that annual screening intervals can be safely
prolonged for individuals at low risk of developing STR [2,3,
10–16]. The model of Aspelund et al also integrated a shorter
(<12 months) screening interval for patients at high risk of
STR, perhaps diminishing the time to diagnosis of STR.
We showed that the model overestimated the risk of devel-
oping retinopathy in the present study cohort, which was also
true in the Danish cohort used to test the fit of the model [17].
Overestimation of the model may be due to several reasons.
First, the present study cohort consists of a well-treated group
of type 2 diabetic patients, in whom glucose and blood pres-
sure control are very well maintained. This may result in less
precise estimates of the relative contribution of the indepen-
dent risk factors in the model. Second, baseline prevalence of
grades 1–2 retinopathy in our cohort is lower than in the
cohorts used by Aspelund et al. Third, estimation of the risk
could have been less precise due to the relatively low inci-
dence of grades 3–5 retinopathy (2.3%). Updating the model
to the observed risk of STR in our population improved the
calibration of the model but did not lead to increased predic-
tive accuracy. Updating the model before using it in popula-
tions with a different prevalence of STR is therefore not
recommended.
Patients who developed STR during follow-up and were
missed by the model had significantly lower baseline systolic
blood pressure levels, which led to an underestimation of STR
risk. Taking into account previous trends in risk factors, med-
ication use and more specific information on the presence of
early stages of retinopathy (grades 1–2) might increase the
accuracy of the model in estimating patients’risk of STR.
In the present analyses, the development of STR was
determined using the follow-up data until the first fundus
photograph after the model-based screening interval. This
might have led to a less precise estimation of the predictive
accuracy of the model. However, the time between model-
based time of screening and actual fundus photograph results
was restricted to a maximum of 1 year.
Mainly due to large model-based screening intervals and
the dynamic nature of the cohort, a considerable number of
patients had incomplete follow-up. The estimated risk of STR
in patients with incomplete follow-up was lower than in
patients with 5 years of follow-up. The predictive accuracy
of the model was determined by estimating calibration and
Tabl e 2 Model-based screening interval and cases of STR for the total population, stratified by grade of retinopathy (EURODIAB) at baseline
Variable Total population Retinopathy grade at baseline
012
N(%) 3,319 2,980 (89.8) 302 (9.1) 37 (1.1)
Incidence of STR during a mean of 53 months of follow-up, n(%) 76 (2.3) 31 (1.0) 27 (8.9) 18 (48.6)
Screening interval, months, mean (SD) 31.0 (20.0) 32.8 (19.8) 15.9 (14.4) 10.9 (10.2)
Screening interval, months, median (interquartile range) 26 (12–53) 29 (15–56) 10 (6–19) 6 (6–11)
Cases of STR missed if the model was applied
a
, mean (SD) 9 (0.3) 9 (0.3) 0 0
a
STR incidence before model-based time of screening
0
0.05
0.10
0.15
0.20
0 0.05 0.10 0.15 0.20
Observed incidence of sight
threatening retinopathy
Predicted risk of sight threatening retinopathy
a
0
0.05
0.10
0.15
0.20
0 0.05 0.10 0.15 0.20
Observed incidence of sight
threatening retinopathy
Predicted risk of si
g
ht threatenin
g
retino
p
ath
y
b
Fig. 1 Observed incidence of STR (EURODIAB grades 3–5) within
quintiles of predicted risk according to the model (a) and according to the
updated model based on STR incidence in the study population (b)
Diabetologia
discrimination of the model while censoring patients with
incomplete follow-up. Taking into account patients with in-
complete follow-up might have led to a more precise estimate
of the accuracy of the model.
We validated the model in a mainly white population.
Before extrapolation of the results to patients of other ethnic
origins, the model should first be validated in these groups.
The current study was performed in a real-life setting and
the fundus photographs were graded by only one grader. We
therefore have no information on inter- and intra-rater reliabil-
ity. The fundus photographs were graded by an experienced
ophthalmologist who was trained as a retinal specialist, en-
hancing reliable grading in a real-life setting [23]. Still, some
cases of STR might have been misclassified by the grader, due
to the possibility of over-grading background retinopathy as
preproliferative retinopathy and due to the enhanced possibility
of missing fine exudates by retinal specialists [24]. Differences
in grading protocols may also affect the validity of the
personalised screening model. Validation before implementing
the model in populations where other grading protocols are
used is therefore recommended.
A limitation of personalised screening is that it is dependent
on the accuracy and punctuality of the care system and care
professionals. The use of personalised screening intervals
makes the system more prone to errors: for example, missing
a fundus screening. It is therefore recommended to use a
computerised system when applying the model in clinical care
so that reminders for screening are automatically generated to
ensure safe follow-up procedures.
To conclude, we have validated a new model for
personalised diabetic retinopathy screening and demonstrated
that a large reduction in screenings for retinopathy was
achieved in this well-managed population with a relatively
short duration of diabetes and an extremely low incidence of
STR. Further improvement of the model might decrease the
number of missed cases of STR. Use of the model for
personalised screening may eventually help to reduce
healthcare use and costs of diabetes care.
Acknowledgements We gratefully acknowledge M. S. A. Suttorp-
Shulten (Department of Ophthalmology, OLVG, Amsterdam, the
Netherlands) for grading of the fundus photographs.
Funding This study was funded byZonMw, the Netherlands organisation
for health research.
Duality of interest The authors declare that there is no duality of
interest associated with this manuscript.
Contribution statement AAWAvdH was involved in the conception
and design of the study, analysis and interpretation of the data and drafting
of the manuscript. IW was involved in the analysis and interpretation of
the data and drafting of the manuscript. EvtR, TA, SHL, PE, BCPP,
ACM, JEEK, JMD and GN were involved in the interpretation of the
data and revision of the manuscript. GN was involved in the conception
of the study, interpretation of the data and revision of the manuscript. All
authors approved the final version of the manuscript. AAWAvdH and IW
are the guarantors of this work, had full access to the data in the study and
take responsibility for the integrity of the data and the accuracy of the data
analysis.
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