Abstract
Background
The focus of research and management of Dupuytren’s disease (DD) is shifting from relieving symptoms in the later stages of disease towards the prevention of contractures. Treatment services might likewise shift towards primary care. Studying characteristics of DD patients who seek medical care for the first time, may identify a symptomatic target group for early DD treatments. We present the first study that estimates the incidence and prevalence of DD in primary care by applying a text-mining algorithm to registration data.
Methods
This is a population-based cohort study using electronic health records from Dutch general practices involved in a regional research network. Descriptive statistics were used to describe sex, age, comorbidities and lifestyle factors, the latter two were identified via International Classification of Primary Care (ICPC) codes. Incidence rate was calculated as number of patients with a first contact for DD/1000 person years for the years 2017–2021, point prevalence as the percentage of patients with a contact for DD in 2021. DD contacts were identified using a text-mining algorithm.
Results
The incidence ranged between 1.41 and 1.72/1000 person years and the overall prevalence was 1.99%. Incidence and prevalence are higher among males and increase with age, peaking between 61 and 80 years.
Conclusions
Our results of prevalence and incidence of DD in primary care give an insight into the relevant population of patients with symptomatic DD that might be the future target group for potential disease controlling treatments.
Keywords: Dupuytren contracture, epidemiology, general practice, incidence, prevalence
Introduction
Dupuytren’s disease (DD) is a fibrotic disorder of the hand and is characterized in the early-stages of the disease by formation of nodules or pits in the palm of the hand, which eventually may develop into cords that can bend the fingers. These contractures often occur in a later stage of disease and may lead to functional limitations that can interfere with daily activities [1,2]. The course of disease varies. A retrospective study showed that 50% of patients developed progressive flexion deformities during a ten year follow up [3]. Other prospective studies reported progression from any severity grade in 21–50% of patients within 18–18 years [4,5]. The main treatment in patients having pronounced impairment because of flexion deformities is surgical division or excision of the cords. Surgical treatment is symptomatic and unfortunately does not cure the disease and recurrences may develop, necessitating revision treatment [6,7]. Clostridial Collagenase Histiolyticum (CCH) injections, followed by passive joint extension to induce cord rupture, are also a suitable treatment for DD [8]. However, CCH has been withdrawn from non-USA markets and is therefore not available in Europe.
The focus of DD research and management is shifting from relieving symptoms in the later stages of disease towards the prevention of contractures. Interventions such as corticosteroid or adalumimab injections, or radiotherapy, which aim to slow down or stop progression of early-stage DD, have been studied [9]. A recent randomized controlled trial reported that repeated intranodular adalimumab injections resulted in softening and size reduction of early-stage DD nodules, which continued to decrease further after the final injection [10]. The ideal target group for this disease controlling treatment consists of patients who present with early DD symptoms, but are prone to progress. Therefore it is important to map patients who seek medical care for DD for the first time. Usually, these patients first present in primary care. In many European countries and part of the health plans in the United States, a general practitioner (GP) or family doctor acts as a gatekeeper and determines whether or not patients require secondary care [11].
Since primary care is less expensive and more accessible than secondary, specialized health care, the decision for referral is a vital component of demand management and health care costs [12,13]. Therefore, if possible, treatment services should shift towards primary care in the future.
There have been many studies on the prevalence of DD in the general population [14–19]. However, the majority of people with DD in the general population have asymptomatic nodules without limitations [14] and are probably less likely to seek medical attention. Studying the frequency of symptomatic DD patients who seek medical care will identify the number of patients in the target population that could be eligible for early DD treatment. Patients registries provide unique opportunities to study the incidence and prevalence of conditions. So far, one study reported the prevalence and incidence of DD patients presenting in primary care using registration data, based on codes registered by GPs [19]. However, this may lead to misclassification or underestimation, because of coding errors [20].
This study therefore aims to assess the incidence and prevalence of DD in Dutch general practices by analyzing registry data using codes and an additional text-mining algorithm.
Methods
Study design
This is a registry study in a dynamic population of patients from general practices in the northern part of the Netherlands. In the Netherlands, GP’s act as gatekeepers in the health care system, and thus present the point of entry of all DD patients into the health care system.
Study population and setting
We used primary care data from the general practitioner-registration database (‘Academisch Huisarts Ontwikkel Netwerk’, AHON) of the northern region of the Netherlands, a research registry using Dutch electronic health records from general practices. It comprises care data of 58 participating general practices including data from around 500,000 patient records. The registry does not contain all primary care practices in the northern part of the Netherlands, it currently accounts for 10.5% of the population in the North. Age, gender and urbanisation distribution of the registry population are comparable to regional norms in the north [21]. General practices provide patient data from 5 years prior to the start of the registration at AHON. Diagnoses are recorded according to the International Classification of Primary Care (ICPC); symptoms, treatment policies and referrals are recorded in free text [22,23].
The data is stored and a trusted third party ensures pseudonymisation of patient-identifying data. Patients have the possibility to object to having their data included in the database.
For the current study, data up to and including the 31st of December 2021 was extracted. A patient’s registration period started from registration, and ended either by the patient’s death, registration termination, the end of data collection from their practice or the end of the study period (31 December 2021).
Studies using this database do not fall within the scope of the Medical Research Involving Human Subjects Act (WMO) according to the medical ethical committee of the UMCG (research register number 202100077), and therefore require no further ethical approval. We did obtain approval of the scientific committee of the AHON (ID number 74).
Patients
Patients over 18 years and up to 105 years were selected. Patients with missing registration dates, registration dates prior to date of birth or deregistration dates past 2022 were seen as coding errors and were therefore excluded. The patient-identifying data for pseudonymisation included date of birth, gender and postal codes. If patients moved, or moved from one practice to another during the study period, a duplicate patient could occur (i.e. different ID numbers belonging to the same patient). Duplicate patient were identified via two patient identification pseudonyms and listed registration fees. Subsequently part of the data belonging to the same patient was either deleted or data was merged to avoid duplicates (Supplementary data: Appendix 1 and Figure 1).
Figure 1.
Number of registered person IDs and final patients from the AHON registry up to and including 31st of December 2021.
Data extraction
Firstly, we extracted all contacts with ICPC code L99.03 (‘Dupuytren’s contracture’) or with the letters ‘Dup’ in the free text. Secondly, we extracted three variables based on these contacts: DD diagnosis, the date of first contact for DD and the number of contacts for DD per patient. Comorbidities such as diabetes, excessive smoking, hypercholesterolemia, obesity, epilepsy and the use of alcohol were extracted using their respective ICPC code (Supplementary data: appendix 3).
DD diagnosis was defined as a contact with ICPC code L99.03 or as a contact with DD mentioned by the GP in free text of the contact. Due to the large number of contacts, we created a text-mining algorithm to determine the diagnosis DD from the free text fields (Supplementary data: Appendix 2). The validation of this algorithm was done in three steps:
Authors RvS and DB manually evaluated a random sample of 5% of all extracted contacts independent from each other and decided on the DD diagnosis based on the free text. Their scores were compared and an agreement of 0.95 (Cohen’s Kappa) was found.
The text-mining algorithm was run on the same sample and adjusted until the agreement between the algorithm scores and the scores from the joined judgment from both authors was 0.95 for DD diagnosis.
Finally, the text-mining algorithm was run on a new random sample of 2.5% of the total cohort for validation. The scores were compared to the scores of the first author and had an agreement of 0.93 for DD diagnosis.
Newly diagnosed patients were identified as those having their first contact for DD, without prior DD consultations. The date of first DD contact was established as the earliest contact where DD was diagnosed.
We extracted the sex and age of all patients registered. The following Dupuytren correlates were also extracted based on their ICPC codes: diabetes mellitus, excessive smoking, alcohol abuse, hypercholesterolemia, obesity and epilepsy.
Statistical analysis
All statistical analyses were executed using R (version 4.0.5). Descriptive statistics of the total cohort and of patients with DD and their comorbidities were presented by numbers, percentages, and median and interquartile range (IQR). The incidence and prevalence was defined as ‘the rates of patients with DD who present in primary care in the northern part of the Netherlands’.
We determined the incidences for the years 2017 to 2021, stratified by gender and age categories, by creating sub-cohorts for each year in which patients who were not at risk were excluded. Patients were not at risk when they (1) entered the AHON database later then the year of analysis, (2) left the AHON database prior to the year of analysis, and (3) had a DD diagnosis prior to the year of analysis.
We calculated the incidence for each created subcohort by dividing the number of newly diagnosed DD patients that year by the person-years at risk.
We calculated the 2021 mid-year point prevalence stratified by sex and age categories (age <40, 40–50, 50–60, 60–70, 80–90 and 90+) of patients registered in 2021. To minimize underestimation we decided to maintain a run-in period of at least 10 years. The number of DD patients in 2021 was divided by the number of patients registered at the 1st of July 2021 (mid-year population). Confidence intervals were calculated using the package tidyverse and function ‘binom.exact’ [24]. We also calculated an overall age standardized prevalence using the method of direct standardization with function ‘ageadjust.direct’ in package epitools [25,26].
Results
Baseline characteristics
Within our cohort of 399,319 patients we identified 3361 patients with DD of which 60.7% was male. The median age at diagnosis was 64.5 years (IQR 56.9 − 72.1). Table 1 shows the baseline characteristics of the DD population and the occurrence of relevant comorbidities.
Table 1.
Mid-year characteristics of the DD patients from the AHON registry in 2021.
| DD patients (n = 2826,%) | |
|---|---|
| Age (median, IQR) | 68.8 (60.8 − 75.9) |
| Males (n, %) | 1,700 (60.2) |
| Diabetes Mellitus, yes (n, %) | 550 (19.5) |
| Excessive smoking, yes (n, %) | 288 (10.2) |
| Alcohol abuse, yes (n, %) | 76 (2.7) |
| Hypercholesterolemia, yes (n, %) | 533 (18.9) |
| Overweight/Obesity, yes (n, %) | 212 (7.5) |
| Epilepsy, yes (n, %) | 55 (2) |
Incidence
Figure 2 shows that the incidence rates were fairly stable over time ranging from 1.65 (95%CI 1.41 − 1.91) to 2.08 (95%CI 1.82 − 2.39)/1000 person years for males and from 1.12 (95%CI 0.93 − 1.33) to 1.36 (95%CI 1.15 − 1.60)/1000 person years for females. The overall incidence rates ranged from 1.41 (95%CI 1.26 − 1.58)/1000 person years in 2020 to 1.72 (95%CI 1.55 − 1.91)/1000 person years in 2019
Figure 2.
The incidence of Dupuytren’s disease in the AHON registry specified for sex and year of observation.
Overall, the incidence was higher for males than for females and the highest incidence was seen between those aged 61-80 years. Figure 3 shows that the incidence increased with age but declined above the age of 80 years.
Figure 3.
The incidence of Dupuytren’s disease in the AHON registry specified per year of observation and age category.
Prevalence
The point prevalence of DD in 2021 was 1.99% (95% CI: 1.89–2.10). Figure 3 shows that the prevalence was the lowest under the age of 40, increasing with every age group to peak at 71–80 years for males and 81–90 years for females to decrease again after 90 years of age. The prevalence was higher for males in every age group: the overall prevalence for males was 2.50% (95%CI 2.33 − 2.67) compared to 1.50% (95%CI 1.38 − 1.64) for females (Figure 4).
Figure 4.
2021 mid-year prevalence of Dupuytren’s disease in the AHON registry per age category, stratified by sex.
Discussion
Our study reports the incidence and prevalence of patients presenting with DD in primary care. The results show an incidence ranging between 1.41 and 1.72/1000 person years and an overall prevalence of 1.99% in primary care in the northern part of the Netherlands. Both incidence and prevalence are higher among males and increase with age, peaking between 61 and 80 years.
Our results show that DD is not a rare disease in primary care. In comparison, in 2019 the DD incidence was higher than the incidence of appendicitis or diverticulosis (both 1.6/1000 persons) [27].
Our reported incidence somewhat exceeds the incidence from a Swedish cohort study that described the number of people seeking medical care for DD in primary, secondary and tertiary care in 2013 [16]. They reported annual incidences of ‘health-seeking patients with DD’ of 2.7/1000 person years for males and 0.9/1000 person years for females ≥ 50 years. Their results of higher incidence rates in males peaking between ages 70 and 80 years, agrees with our results. They also reported a lower overall point-prevalence compared to our results of 0.92% in patients ≥20 years (1.35% in males and 0.50% in females). In line with our results, the highest prevalence was among those aged >70 for both males and females [16].
A recent cohort study, originating from a primary care registration database in the United Kingdom, described an increasing DD incidence trend from 2000 to 2013, of 0.303/1000 person-years to 0.587/1000 person-years. They reported a point prevalence of 0.67% in 2013 which is lower compared to our findings [28]. The fact that we found higher rates, might be explained by the different time window in which the studies were conducted and are in line with the finding of an increasing incidence of symptomatic DD [29]. Another explanation for the discrepancy might be the difference in ethnicity between the study populations. DD is particularly common in areas where people of northern European descent live [30]. Our study was conducted in the northern region of the Netherlands, which has a lower percentage of people with migration backgrounds compared to the United Kingdom [31,32]. A final reason might be related to the different methodology of DD assessment. In the UK study, the diagnosis DD was only assessed via registrational coding, whereas we additionally employed a text-mining algorithm. In our study, using only registration codes for case retrieval would have led to a prevalence of 1.38% (instead of 1.99%), confirming one of the pitfalls in relying on registration based data coding [20].
Two studies reported a much higher prevalence [14,33]. These studies screened for signs of DD in the general population aged >50 years and reported point prevalences of 22.1% and 32%. In concordance with our results, males were more often affected and the prevalence peaked between the ages of 70–80 years. One of these studies was also conducted in the northern region of the Netherlands [14]. Despite this, our study showed a much lower prevalence (22% vs. 2%). This difference can be explained by the fact that they looked for DD signs in a stratified random sample of the general population by visiting people at home, while our population consisted of people who sought medical care and then were diagnosed with DD by the GP. This illustrates that many people with DD are asymptomatic and that only a minority finds their symptoms serious enough to seek medical consultation. This may also account for the lower prevalence in the oldest age groups, as they might have a decreased tendency to seek medical help for DD.
The slight decrease in incidence in 2020 and 2021 in our study might be explained by the COVID-19 pandemic. During the pandemic, access to primary health care was restricted by national and local regulations. People were discouraged to visit their GP for not directly life-threatening symptoms or were afraid to catch a COVID-19 infection [34].
A strength of our study is the use of a large primary care registration-based dataset, that consisted data of almost 300,000 GP registered persons. However, the primary goal of registration data is to monitor patient care and not to collect data for research purposes. This might have led to under registration of some of the descriptive information such as lifestyle factors.
A limitation of our study is that we could not differentiate between patients seeking GP care specifically for Dupuytren’s disease (DD) or if DD was noted incidentally during a visit for another issue. The data available did not allow us to determine whether DD was the primary reason for the consultation or simply one of several diagnoses documented during the visit. Because a large part of the participating practices registered in 2017 and provided patient information from 5 years prior, we chose to calculate the point prevalence in 2021. We decided not to calculate the prevalence rates of prior years, because we wanted to maintain a run-in period of at least 10 years to minimize underestimation. However, we cannot exclude that the ‘first DD contact’ was preceded by a previous contact many years earlier, which might result in an overestimation of the incidence rate.
Another strength is that selection bias is unlikely, because the Netherlands uses a primary healthcare system in which all people are registered with a GP. However, the Northern region of the Netherlands is not representative of the Netherlands [32], which may make our results somewhat less generalisable for the whole country. For instance, it is known that the northern part of the Netherlands has fewer people with a migration background compared to the western part. Similar differences likely exist in other European countries with gatekeeping healthcare systems. We expect that differences between these countries will be relatively small. However, generalizability to countries with different demographics or healthcare systems is likely limited. A final strength of our study is the use of a text-mining algorithm to extract DD diagnoses, compensating for the possible pitfalls in the use of ICPC codes (ICPC L99.03) among GPs and addressing the impracticality and potential errors associated with manually scoring large datasets. Our algorithm was validated through a three-step procedure and excellent agreement was achieved with manually scored data in a validation subset of the data.
Conclusion
Based on GP codes and text mining of GP notes, we observed an incidence of 1.72/1000 person years and an overall prevalence of 1.99% of people with DD seeking medical help in a typical gatekeeping setting. Our results provide an insight into the relevant population of patients with symptomatic DD that seek medical care for the first time, which might – at least in part – be the future target group for potential disease controlling treatments.
Supplementary Material
Acknowledgements
This research was conducted using the AHON database. We are grateful for the support work provided by Feikje Groenhof and Ronald Wilmink.
Authors Contributors
All authors contributed to the conceptualization and methodology of the study. The statistical analysis and data interpretation were performed by RvS, MB and DB. The manuscript was written by RvS and all authors provided feedback and commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Ethical approval
This study was approved by the local medical ethics committee (METc UMCG 202100077).
Disclosure statement
PW is member of a Data Monitoring Committee of Fidia ltd, Milan, Italy. PW is member of the scientific advisory board of the International Dupuytren Society, and PW is and DB was member of the scientific advisory board of the Dutch Dupuytren Society. These interests are not related to the submitted work. All other authors declare no potential conflicts of interests with respect to the research, authorship, and/or publication of this article.
Data availability statement
The datasets used and/or analysed during the current study are available from the corresponding author and AHON commission on reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author and AHON commission on reasonable request.