Abstract
Background:
Inflammatory bowel disease (IBD) needs early interventions and an individual specialist–patient relationship. Distance from a tertiary IBD center might affect patient’s disease course and outcome. We investigated whether the patient-to-specialist distance has an impact on the disease course using the well-defined patient collective of the Swiss Inflammatory Bowel Disease Cohort Study (SIBDCS).
Methods:
Patient’s home address at diagnosis (postal zip code) was extracted from the SIBDCS database. Distance between each zip code and the nearest located IBD specialist center was calculated and classified into the following three sections based on proximity: <10 km (group 1); 10–35 km (group 2); >35 km (group 3).
Results:
Our study included in total 408 IBD patients [234 Crohn’s disease (CD), 154 ulcerative colitis (UC), 20 IBD unclassified (IBDU)]. Median age was lowest in group 2 at diagnosis (G1: 28 years; G2: 21 years, G3: 26 years, p < 0.01). The diagnostic delay did not differ between groups. CD patients in group 1 were treated more often with anti-tumor necrosis factor (TNF) agents (72% versus 56%, p = 0.04) and 5-aminosalicylates (44% versus 28%, p = 0.04) than in group 3. UC/IBDU patients in group 1 were treated more often with corticosteroids than patients in group 3 (83% versus 58%, p < 0.01). The occurrence of IBD-related surgeries did not differ between groups.
Conclusions:
Patient-to-specialist distance might affect drug treatment. However, disease course and the need for IBD-related surgery does not seem to be associated with a longer distance to specialist care in Switzerland.
Keywords: diagnostic delay, disease outcome, tertiary care
Introduction
The incidence of inflammatory bowel disease (IBD), comprising Crohn’s disease (CD) and ulcerative colitis (UC), is increasing worldwide in adults as well as in pediatric patients.1–3 Patients are exposed to a tremendous social and personal burden,4 due to an often chronic-relapsing disease course and associated complications, such as fistulas, abscesses, or stenoses.5–7 Shortening the delay between symptom onset and diagnosis (referred to as ‘diagnostic delay’) is key to avoiding long-term complications, hospitalization, and surgery.8 In addition, optimized therapy through a tight and individual physician–patient relationship is necessary. Due to the increasing number of therapeutic options,9,10 a personalized therapeutic approach often requires specialist knowledge from tertiary care centers. Barriers like the distance to such a specialist may prevent effective access to personalized IBD care.
The travel distance to tertiary care centers is relevant for health and disease outcome.11 Especially in oncologic diseases, increasing travel requirements results in worse disease outcome and inappropriate treatment strategies.12 In IBD, the distance between the patient’s home and specialists could therefore influence the risk of delayed diagnosis and the rate of clinical complications by hindering close and specialized therapy monitoring. A recent study from the United States (US) showed an increased need for surgery in IBD patients living at longer distances to an IBD center,13 but did not analyze whether the distance has an impact on the diagnostic delay.
We aimed to investigate whether the distance of the IBD patient to the closest IBD specialist tertiary care center (patient-to-specialist distance; PTSD) is associated with diagnostic delay and disease outcome parameters in a Swiss IBD patient population. We hypothesized that an increase in distance might have an adverse impact on diagnostic delay, treatment, and disease-related complications.
Patients and methods
Study population
Our data was retrieved from the database of the SIBDCS, which has collected data from patients with CD, UC, and IBD unclassified (IBDU) from all over Switzerland since November 2006. Upon consent, pediatric and adult IBD patients are enrolled by their gastroenterologists, either in hospitals or in private practices, by using specific questionnaires for enrollment and for a yearly follow up, thus providing epidemiological, clinical, and psychosocial data, as well as information about health resource consumption.14 In our study, both pediatric and adult patients were included. Tertiary care centers, basically being equivalent to university hospitals, were considered ‘specialized centers’ (Figure 1). When calculating PTSD, we used the home address at time of diagnosis as a reference. Enrollment into the cohort occurred not only at time of diagnosis, but also at later time points, depending on when the treating physician enrolled the patient. To avoid unpredictable effects on PTSD, and study results from patients moving to another area between the diagnosis of IBD and enrollment into the cohort, we excluded all SIBDCS patients with enrollment into the cohort later than 6 months after diagnosis. Thus, moving habits after time of diagnosis were not considered in this study.
Figure 1.
Map of Switzerland with place of residence of the study population and location of specialized health care facilities. Radius in kilometers: 0–10 km yellow, 10–35 km blue, and >35 km orange.
Patient parameters
IBD patients were grouped into CD and UC plus IBDU patients. The following parameters of the study cohort were recorded for analysis: demographic data (such as gender, age at diagnosis, smoking status), type of IBD, time between IBD diagnosis and last medical visit (disease duration), time between onset of symptoms as stated by the patient and diagnosis of IBD (diagnostic delay), disease phenotype, complications (fistulas, stenosis, abscess formation, and colectomy), (EIM), and therapeutic history as recorded in physician reports from the database. Disease phenotype was assessed at initial colonoscopy according to the Montreal classification in CD [location (L1: ileal, L2: colonic, L3: ilieocolonic, L4: isolated upper disease), behavior (B1: nonstricturing and nonpenetrating, B2: stricturing, B3: penetrating, p: accompanied with perianal disease)] and in UC [location E1: ulcerative proctitis, E2: left sided UC, E3: extensive UC (pancolitis)].15 As ‘follow up,’ we analyzed data from the last medical visit available in the SIBDCS data base.
Calculation of distance from home to tertiary center
Distance between every home zip code (at diagnosis) and nearest located IBD specialist center (PTSD) was calculated using the distance calculator of Google maps (https://www.google.com/maps). As a measurement for distance we used a straight line by air. This was done to equalize data for all patients, that is, to account for uncertainty about how patients were travelling (e.g. by car, public transport, or other means), as this is not recorded in the SIBDCS database. Swiss tertiary centers with specialized knowledge in IBD treatment were the following hospitals (Figure 1): University Hospital Zurich, University Hospital Basel, University Hospital Bern (Inselspital), Central University Hospital Lausanne (Centre Hospitalier Universitaire Vaudois), University Hospital Geneva, Cantonal Hospital St. Gallen. Based on proximity, the cohort was divided into three groups: <10 km (group 1); 10–35 km (group 2); >35 km, (group 3). Thresholds were chosen based on Swiss topography. Group 1 should represent an urban area, group 2 should represent a suburban/peripheral area, and group 3 should represent a rural area.
Data analysis
As the strongest surrogate for adverse disease outcome, need for surgery due to IBD (treatment of fistulas, stenosis, abscess formation, and bowel resections of all extents) as retrieved from surgery reports into the SIBDCS database served as primary endpoint, whereas diagnostic delay, need for therapy with biologicals [anti-TNF agents (infliximab, adalimumab, certolizumab, golimumab) and vedolizumab (other biologics)] or immunomodulators (azathioprine, 6-mercaptopurin, methotrexate) were defined as secondary endpoints. In addition, demographic data were compared between distance groups. All statistical analyses were done using Stata software (version 14.2, StataCorp, College Station, TX, USA). Categorical data were summarized as raw frequencies and relative percentages. Differences in categorical data distributions between independent groups were assessed using the Chi-square test, or the Fisher’s exact test in case of low sample size. Continuous data distribution was assessed using normal QQ-plots; normally distributed data was summarized as mean and standard deviation (SD); non-normally distributed data was summarized as median and interquartile range (IQR). Differences between means were assessed using Student’s t test, or analysis of variance (ANOVA), respectively. Differences between medians were assessed using the Wilcoxon–Mann–Whitney rank-sum test, or the Kruskall–Wallis test. p < 0.05 was considered statistically significant and is given as ‘p’, if all three groups were compared in one test, or ‘P1 versus 3’, if only group 1 was compared with group 3, respectively.
Ethical considerations
The SIBDCS has been approved by the Ethics Committee of the Canton of Zurich (EK-1316). All patients signed informed consent, and the current substudy has been evaluated and approved by the scientific board of the SIBDCS.
Results
Study population
Out of 3326 SIBDCS patients screened between 2006 and 2018, 408 patients were included in the final analysis, with 234 having CD, 154 having UC, and 20 having IBDU (Figure 2). Patient characteristics are shown in Tables 1–3. Based on the defined distance groups, median patient proximity to specialist was 3.6 km (group 1, IQR 1.9–5.8 km), 20.8 km (group 2, IQR 15.4–27.6 km), and 45.8 km (group 3, IQR 40.5–49.2 km). Group 1 (62%) contained more male patients than group 2 (48%) or group 3 (51%) (p = 0.03). Median disease duration was longer in group 1 (6 years), than in group 2 and 3 (both 4 years) (p < 0.01). Median age at diagnosis was lowest in group 2 (21 years) than in group 1 (28 years) and 3 (26 years) (p < 0.01). There were no differences in IBD-types between groups.
Figure 2.
Flow diagram for inclusion criteria.
SIBDCS, Swiss inflammatory bowel disease cohort study.
Table 1.
Patient demographics.
| All patients | 0–10 km (n = 163) |
10–35 km (n = 153) |
>35 km (n = 92) |
p value |
p value 1 versus 3 |
|---|---|---|---|---|---|
| Gender, n (%) | |||||
| Male | 101 (62) | 73 (47.7) | 47 (51.1) | ||
| Female | 62 (38) | 80 (52.3) | 45 (48.9) | 0.03 | 0.09 |
| Age at diagnosis (years) | |||||
| (median, IQR) | 28, 19–39 | 21, 13–36 | 26, 16–41 | <0.01 | 0.44 |
| Disease duration (years) | |||||
| (median, IQR) | 6, 3–9 | 4, 2–6 | 4, 1–7 | <0.01 | <0.01 |
| Diagnosis, n (%) | |||||
| Crohn’s Disease | 99 (60.7) | 81 (52.9) | 54 (58.7) | ||
| Ulcerative Colitis | 57 (35) | 63 (41.2) | 34 (37.0) | ||
| IBDU | 7 (4.3) | 9 (5.9) | 4 (4.4) | 0.70 | 0.94 |
IBDU, IBD unclassified; IQR, interquartile range.
Table 2.
Characteristics of patients with CD.
| CD patients | 0–10 km (n = 99) |
10–35 km (n = 81) |
>35 km (n = 54) |
p value |
p value 1 versus 3 |
|---|---|---|---|---|---|
| Gender, n (%) | |||||
| Male | 55 (55.6) | 35 (43.2) | 30 (55.6) | ||
| Female | 44 (44.4) | 46 (56.8) | 24 (44.4) | 0.19 | 1.00 |
| Age at diagnosis (years) | |||||
| (median, IQR) | 28, 20–39 | 19, 13–32 | 27, 19–41 | <0.01 | 0.90 |
| Smoking status at diagnosis, n (%) | |||||
| Nonsmoker | 64 (64.7) | 62 (76.5) | 34 (63.0) | ||
| Smoker | 33 (33.3) | 18 (22.2) | 19 (35.2) | ||
| Unknown | 2 (2) | 1 (1.2) | 1 (1.8) | 0.34 | 0.93 |
| Smoking status at last follow-up, n (%) | |||||
| Nonsmoker | 69 (69.7) | 68 (84) | 37 (68.5) | ||
| Smoker | 29 (29.3) | 13 (16.1) | 17 (31.5) | ||
| Unknown | 1 (1.0) | 0 (0.0) | 0 (0.0) | 0.06 | 0.90 |
| Disease duration (years) | |||||
| (median, IQR) | 7, 3–9 | 4, 2–7 | 4, 1–7 | <0.01 | <0.01 |
| Diagnostic delay (month) | |||||
| (median, IQR) | 5, 2–22 | 7, 3–14 | 2, 2–14 | 0.08 | 0.05 |
| Disease location at diagnosis, n (%) | |||||
| L1 | 25 (25.3) | 19 (23.5) | 19 (35.2) | ||
| L2 | 22 (22.2) | 13 (16.1) | 9 (17.7) | ||
| L3 | 43 (43.4) | 44 (54.3) | 22 (40.7) | ||
| L4 only | 3 (3) | 2 (2.5) | 2 (3.7) | ||
| Unclear/unknown | 6 (6.1) | 3 (3.7) | 2 (3.7) | 0.72 | 0.71 |
| Disease location at last follow up, n (%) | |||||
| L1 | 27 (27.3) | 20 (24.7) | 17 (31.5) | ||
| L2 | 32 (32.3) | 16 (19.8) | 15 (27.8) | ||
| L3 | 33 (33.3) | 36 (44.4) | 21 (38.9) | ||
| L4 only | 4 (4) | 3 (3.7) | 1 (1.8) | ||
| Unclear/unknown | 3 (3) | 6 (7.4) | 0 (0.0) | 0.28 | 0.68 |
| Behavior, n (%) | |||||
| B1 | 47 (47.5) | 48 (59.3) | 29 (53.7) | ||
| B1p | 11 (11.1) | 15 (18.5) | 11 (20.4) | ||
| B2 | 21 (21.2) | 8 (9.9) | 4 (7.4) | ||
| B2p | 6 (6.1) | 2 (2.5) | 4 (7.4) | ||
| B3 | 3 (3) | 6 (7.4) | 5 (9.3) | ||
| B3p | 11 (11.1) | 2 (2.5) | 1 (1.8) | 0.01 | 0.01 |
| CD Complications, n (%) | |||||
| Perianal fistula | 18 (18.2) | 13 (16) | 10 (18.5) | 0.91 | 0.95 |
| Other fistula | 14 (14.1) | 8 (9.9) | 6 (11.1) | 0.66 | 0.59 |
| Any fistula | 23 (23.2) | 19 (23.5) | 15 (27.8) | 0.79 | 0.53 |
| Stenosis | 33 (33.3) | 16 (19.7) | 13 (24.1) | 0.10 | 0.23 |
| Abscess | 13 (13.1) | 12 (14.8) | 7 (13.0) | 0.93 | 0.97 |
| EIM history, n (%) | |||||
| Arthritis | 43 (43.4) | 23 (28.4) | 20 (37.0) | 0.11 | 0.44 |
| Iritis/Uveitis | 9 (9.1) | 5 (6.2) | 4 (7.4) | 0.76 | 0.72 |
| Pyoderma gangraenosum. | 4 (4.0) | 0 (0.0) | 0 (0.0) | 0.06 | 0.29 |
| Erythema nodosum | 5 (5.1) | 8 (9.9) | 1 (1.9) | 0.13 | 0.33 |
| Aphtous/oral ulcers | 23 (23.2) | 10 (12.3) | 5 (9.3) | 0.04 | 0.03 |
| Ankylosing spondylitis | 7 (7.1) | 2 (2.5) | 5 (9.3) | 0.22 | 0.63 |
| Primary sclerosing cholangitis | 1 (1.0) | 1 (1.2) | 0 (0.0) | 0.72 | 1.00 |
| Any of the above | 59 (59.6) | 33 (41.0) | 23 (42.6) | 0.02 | 0.04 |
| Non-CD complications, n (%) | |||||
| Anemia | 31 (31.3) | 30 (37) | 14 (25.9) | 0.39 | 0.48 |
| Malabsorbtion syndrome | 11 (11.1) | 11 (13.6) | 4 (7.4) | 0.53 | 0.46 |
| Venous Thromboses | 2 (2) | 0 (0.0) | 2 (3.7) | 0.25 | 0.53 |
| Osteoporosis | 10 (10.1) | 4 (4.9) | 4 (7.4) | 0.43 | 0.58 |
| Surgery history, n (%) | |||||
| Intestinal resection | 22 (22.2) | 14 (17.3) | 10 (18.5) | 0.68 | 0.59 |
| Fistula/abscess surgery | 16 (16.2) | 10 (12.3) | 6 (11.1) | 0.62 | 0.39 |
| Therapeutic history, n (%) (Ever treated with) | |||||
| 5ASA | 44 (44.4) | 24 (29.6) | 15 (27.8) | 0.04 | 0.04 |
| Antibiotics | 11 (11.1) | 13 (16.0) | 5 (9.3) | 0.44 | 0.72 |
| Steroids | 83 (83.8) | 73 (90.1) | 42 (77.8) | 0.14 | 0.35 |
| Immunomodulators | 75 (75.7) | 62 (76.5) | 41 (75.9) | 0.99 | 0.98 |
| Anti-TNF agent | 71 (71.7) | 50 (61.7) | 30 (55.6) | 0.11 | 0.04 |
| Other biologics | 11 (11.1) | 9 (11.1) | 3 (5.6) | 0.51 | 0.38 |
| Calcineurin inhibitors | 2 (2.0) | 1 (1.2) | 0 (0.0) | 0.79 | 0.54 |
| Therapy at enrollment, n (%) | |||||
| 5-ASA | 25 (25.2) | 7 (8.6) | 6 (11.1) | 0.00 | 0.03 |
| Antibiotics | 5 (5.0) | 5 (6.1) | 2 (3.7) | 0.86 | 1.00 |
| Steroids | 56 (56.5) | 37 (45.7) | 30 (55.6) | 0.30 | 0.90 |
| Immunomodulators | 49 (49.5) | 47 (58.0) | 30 (55.6) | 0.50 | 0.47 |
| Anti-TNF agent | 30 (30.3) | 26 (32.1) | 15 (27.8) | 0.86 | 0.74 |
| Other biologics | 0 (0.0) | 0 (0.0) | 0 (0.0) | – | – |
| Calcineurin inhibitors | 0 (0.0) | 0 (0.0) | 0 (0.0) | – | – |
| Therapy at last follow-up, n (%) | |||||
| 5-ASA | 13 (13.1) | 5 (6.2) | 4 (7.4) | 0.23 | 0.28 |
| Antibiotics | 1 (1.0) | 0 (0.0) | 0 (0.0) | 1.00 | 1.00 |
| Steroids | 22 (22.2) | 15 (22.2) | 8 (14.8) | 0.49 | 0.17 |
| Immunomodulators | 28 (28.2) | 32 (39.5) | 18 (33.3) | 0.28 | 0.51 |
| Anti-TNF agent | 51 (51.5) | 35 (43.2) | 18 (33.3) | 0.09 | 0.03 |
| Other biologics | 5 (5.0) | 7 (8.6) | 3 (5.6) | 0.62 | 1.00 |
| Calcineurin inhibitors | 0 (0.0) | 0 (0.0) | 0 (0.0) | – | – |
| Steroids history, n (%) (ever treated with) | |||||
| Systemic | 64 (65.7) | 62 (76.5) | 35 (64.8) | 0.21 | 1.00 |
| Topical | 10 (10.1) | 11 (13.5) | 3 (5.6) | 0.34 | 0.54 |
| Steroids at enrollment, n (%) | |||||
| Systemic | 40 (40.4) | 26 (32.1) | 20 (37.0) | 0.51 | 0.73 |
| Topical | 2 (2.0) | 1 (1.2) | 1 (1.8) | 1.00 | 1.00 |
| Steroids at last follow-up, n (%) | |||||
| Systemic | 14 (14.1) | 11 (13.5) | 4 (7.4) | 0.46 | 0.29 |
| Topical | 11 (13.5) | 2 (2.5) | 1 (1.8) | 0.33 | 0.35 |
5-ASA, 5-aminosalicylates; CD, Crohn’s disease; IQR, interquartile range; TNF, tumor necrosis factor.
Table 3.
Characteristics of patients with UC and unclassified IBDU.
| UC/IBDU patients | 0–10 km (n = 64) |
10–35 km (n = 72) |
>35 km (n = 38) |
p value |
p value 1 versus 3 |
|---|---|---|---|---|---|
| Gender, n (%) | |||||
| Male | 46 (71.9) | 38 (52.8) | 17 (44.7) | ||
| Female | 18 (28.1) | 34 (47.2) | 21 (55.3) | 0.01 | <0.01 |
| Age at diagnosis (years) | |||||
| (median, IQR) | 29, 18–41 | 24, 14–39 | 22, 15–40 | 0.22 | 0.21 |
| Smoking status at diagnosis, n (%) | |||||
| Nonsmoker | 58 (90.6) | 59 (81.9) | 32 (84.2) | ||
| Smoker | 4 (6.2) | 12 (16.7) | 6 (15.8) | ||
| Unknown | 2 (3.1) | 1 (1.4) | 0 (0.0) | 0.23 | 0.21 |
| Smoking status at last follow up, n (%) | |||||
| Nonsmoker | 53 (82.8) | 63 (87.5) | 31 (81.6) | ||
| Smoker | 10 (15.6) | 9 (12.5) | 7 (18.4) | ||
| Unknown | 1 (1.6) | 0 (0.0) | 0 (0.0) | 0.69 | 0.86 |
| Disease duration (years) | |||||
| (median, IQR) | 5, 2–9 | 4, 2–5 | 3, 1–7 | 0.02 | 0.03 |
| Diagnostic delay (month) | |||||
| (median, IQR) | 3, 1–8 | 4, 1–8 | 3, 1–5 | 0.71 | 0.68 |
| Disease location at diagnosis, n (%) | |||||
| Pancolitis | 34 (53.1) | 33 (45.8) | 18 (47.4) | ||
| Left-sided colitis | 16 (25.0) | 31 (43.1) | 5 (13.2) | ||
| Proctitis | 12 (18.7) | 6 (8.3) | 12 (31.6) | ||
| Unknown/unclear | 2 (3.1) | 2 (2.7) | 3 (7.9) | <0.01 | 0.20 |
| Disease location at last follow up, n (%) | |||||
| Pancolitis | 31 (48.4) | 35 (48.6) | 19 (50.0) | ||
| Left-sided colitis | 15 (23.4) | 25 (34.7) | 12 (31.6) | ||
| Proctitis | 13 (20.3) | 9 (12.5) | 7 (18.4) | ||
| Unknown/unclear | 5 (7.8) | 3 (4.1) | 0 (0.0) | 0.44 | 0.36 |
| EIM history, n (%) | |||||
| Arthritis | 16 (25.0) | 17 (23.6) | 4 (10.5) | 0.17 | 0.12 |
| Iritis/uveitis | 3 (4.7) | 1 (1.4) | 0 (0.0) | 0.35 | 0.29 |
| Pyoderma gangraenosum | 0 (0.0) | 2 (2.7) | 0 (0.0) | 0.51 | – |
| Erythema nodosum | 2 (3.1) | 1 (1.4) | 0 (0.0) | 0.60 | 0.52 |
| Aphthous/oral ulcers | 5 (7.8) | 5 (6.9) | 2 (5.3) | 1.00 | 1.00 |
| Ankylosing spondylitis | 2 (3.1) | 1 (1.4) | 2 (5.3) | 0.36 | 0.62 |
| Primary sclerosing cholangitis | 2 (3.1) | 3 (4.1) | 2 (5.3) | 0.88 | 0.62 |
| Any of the above | 23 (36.5) | 25 (34.7) | 8 (21.1) | 0.24 | 0.11 |
| Non-UC complications, n (%) | |||||
| Anemia | 22 (34.4) | 22 (30.6) | 9 (23.6) | 0.52 | 0.25 |
| Malabsorbtion syndrome | 5 (7.8) | 4 (5.5) | 1 (2.6) | 0.59 | 0.40 |
| Venous thromboses | 0 (0.0) | 0 (0.0) | 0 (0.0) | – | – |
| Osteoporosis | 8 (12.5) | 6 (8.3) | 1 (2.6) | 0.24 | 0.14 |
| Surgery history, n (%) | |||||
| Colectomy | 4 (6.3) | 3 (4.1) | 0 (0.0) | 0.37 | 0.29 |
| Therapy history (%) (ever treated with) | |||||
| 5-ASA | 61 (95.3) | 68 (94.4) | 38 (100.0) | 0.34 | 0.17 |
| Antibiotics | 8 (12.5) | 4 (5.5) | 1 (2.6) | 0.17 | 0.14 |
| Steroids | 53 (82.8) | 55 (76.4) | 22 (57.9) | 0.01 | <0.01 |
| Immunomodulators | 34 (53.1) | 40 (55.5) | 21 (55.3) | 0.95 | 0.83 |
| Anti-TNF agent | 22 (34.4) | 23 (31.9) | 8 (21.1) | 0.34 | 0.15 |
| Other biologics | 8 (12.5) | 5 (6.9) | 1 (2.6) | 0.22 | 0.14 |
| Calcineurin inhibitor | 2 (3.1) | 2 (2.8) | 0 (0.0) | 0.68 | 0.52 |
| Therapy at enrollment, n (%) | |||||
| 5-ASA | 51 (79.7) | 56 (77.8) | 35 (92.1) | 0.16 | 0.09 |
| Antibiotics | 1 (1.6) | 1 (1.4) | 0 (0.0) | 1.00 | 1.00 |
| Steroids | 26 (40.6) | 28 (38.9) | 6 (15.8) | 0.02 | <0.01 |
| Immunomodulators | 21 (32.8) | 21 (29.2) | 12 (31.6) | 0.89 | 0.89 |
| Anti-TNF agent | 4 (6.2) | 9 (1.2) | 2 (5.3) | 0.37 | 1.00 |
| Other biologics | 0 (0.0) | 0 (0.0) | 0 (0.0) | – | – |
| Calcineurin inhibitor | 0 (0.0) | 2 (2.7) | 0 (0.0) | 0.51 | – |
| Therapy at last follow up, n (%) | |||||
| 5-ASA | 42 (65.6) | 47 (65.2) | 30 (78.9) | 0.28 | 0.15 |
| Antibiotics | 2 (3.1) | 0 (0.0) | 0 (0.0) | 0.18 | 0.52 |
| Steroids | 16 (25.0) | 15 (20.8) | 2 (5.3) | 0.03 | 0.01 |
| Immunomodulators | 20 (31.2) | 19 (26.4) | 18 (47.4) | 0.07 | 0.10 |
| Anti-TNF agent | 9 (14.1) | 16 (22.2) | 6 (15.8) | 0.46 | 1.00 |
| Other biologics | 6 (9.4) | 3 (4.2) | 0 (0.0) | 0.11 | 0.08 |
| Calcineurin inhibitor | 0 (0.0) | 1 (1.4) | 0 (0.0) | 1.00 | – |
| Steroids history, n (%) (ever treated with) | |||||
| Systemic | 47 (73.4) | 52 (72.2) | 21 (55.3) | 0.11 | 0.05 |
| Topical | 17 (26.6) | 10 (13.9) | 2 (5.3) | 0.01 | <0.01 |
| Steroids at enrollment, n (%) | |||||
| Systemic | 22 (34.4) | 24 (33.3) | 6 (15.8) | 0.96 | 0.04 |
| Topical | 3 (4.7) | 0 (0) | 0 (0) | 0.05 | 0.29 |
| Steroids at last follow up, n (%) | |||||
| Systemic | 9 (14.1) | 9 (12.5) | 2 (5.3) | 0.36 | 0.20 |
| Topical | 8 (12.5) | 2 (2.8) | 2 (5.3) | 0.01 | 0.02 |
5-ASA, 5-aminosalicylates; CD, Crohn’s disease; EIM, extraintestinal manifestations; IBDU, inflammatory bowel disease unclassified; IQR, interquartile range, TNF, tumor necrosis factor; UC, ulcerative colitis.
The detailed clinical characteristics of CD patients according to PTSD are listed in Table 2. Differences were found in the following characteristics: patients in group 1 had a longer median disease duration (7 years, IQR 3–9 years), than patients in groups 2 and 3 (both 4 years, both p < 0.01). Median age at diagnosis was lowest in group 2 (19 years) than in groups 1 (28 years) and 3 (27 years) (p < 0.01). Patients in group 3 had the shortest diagnostic delay (median 2 months, IQR: 2–14 months), while the diagnostic delay was longer in group 1 (5 months, IQR: 2–22 months) and group 2 (7 months, IQR: 3–14 months) (P1 versus 3 = 0.05).
The disease behavior differed between groups (p = 0.01, P1 versus 3 = 0.01): patients in group 1 had more often a stricturing behavior (B2 + B2p, 27.3%) than a nonstricturing and nonpenetrating behavior (B1 + B1p, 58.6%) compared with groups 2 (B2 + B2p, 12.4%; B1 + B1p, 77.8%) and 3 (B2 + B2p, 14.8%; B1 + B1p, 74.1%), respectively. Local complications (fistulas, stenoses, abscesses) were not different between groups. Out of all extraintestinal complications triggered by CD, only aphthous and oral ulcers occurred more often in patients of group 1 (p = 0.04, P1 versus 3 = 0.03).
Detailed clinical characteristics of UC and IBDU patients according to PTSD are shown in Table 3. Differences were found in the following characteristics: group 1 (72%) contained more male patients than group 2 (53%) or group 3 (45%) (p = 0.01). Median disease duration was longer in group 1 (5 years), than in group 2 (4 years) and group 3 (3 years) (p = 0.02). Disease location at diagnosis differed between groups (p < 0.01): patients in group 2 more often had left-sided colitis (43.1%), while group 3 more often had proctitis (31.6%). At the last follow up, disease localization no longer differed between groups.
Treatment of CD patients
Detailed treatment information is summarized in Table 2. At the time of enrollment, patients in group 1 (25%) were treated more often with 5-aminosalicylates (5-ASA) than patients in group 2 (8%) and group 3 (11%) (p < 0.01). There was no difference for the use of anti-TNF agents, steroids, antibiotics, calcineurin inhibitors, immunomodulators, and other biologicals between groups.
At the time of last follow up, anti-TNF agents were used more often in group 1 (51.5%) than in group 2 (43.3%) and group 3 (33.3%, p = 0.09, P1 versus 3 = 0.03). There was no difference in the use of 5-ASA, steroids, antibiotics, calcineurin inhibitors, immunomodulators, and other biologicals between groups at follow up.
Analyzing the drug treatment at any time during the entire study period, 71.7% of patients in group 1 were treated with anti-TNF agents compared with 61.7% in group 2 and 55.6% in group 3 (P1 versus 3 = 0.04). In addition, during the entire study period CD patients of group 1 had a higher use of 5-ASA (44.4%) than patients of group 2 (29.6%) and group 3 (27.8%) (p = 0.04). Intestinal resection, fistula, and abscess surgery were not different between groups.
Treatment of UC/IBDU patients
Detailed treatment information is summarized in Table 3. At the time of enrollment, UC and IBDU patients in group 1 (40.6%) and in group 2 (38.9%) were more often treated with steroids than in group 3 (15.8%) (p = 0.02). Especially, systemic steroids were used more often at enrollment in group 1 (34%) than in group 3 (16%) (P1 versus 3 = 0.04). Of note, topical steroids were only used in group 1 at enrollment (see Table 3). At the last follow up, group 1 (25%) still used more steroids than group 2 (20%) and group 3 (5%) (p = 0.03). Also, topical steroids were used more often at follow up in group 1 (12%) than in group 2 (2%) and group 3 (5%) (p = 0.01). Over the entire study period, the use of topical (group 1: 26%, group 2: 13%, group 3: 5%, p = 0.01) and systemic (group 1: 73%, group 2: 72%, group 3: 55%, P1 versus 3 = 0.05) steroids decreased with increasing distance from the tertiary referral centers. The application of 5-ASA, antibiotics, immunomodulators, anti-TNF agents, or other biologicals, as well as calcineurin inhibitors, was not different between groups and did not change over time. The frequency of surgery was not different between groups.
Discussion
To optimize therapy of IBD, patients need access to specialized care and close monitoring of therapy and development of complications, as well as periodical endoscopies. Distance and travel time could negatively influence the implementation of these measures and affect disease outcome.
In this multicenter cohort study of Swiss IBD patients, a shorter distance to an IBD specialist was related to increased use of 5-ASA and anti-TNF agents in CD, as well as an increased use of, particularly, topical steroids in UC patients. In CD, diagnostic delay was shortest in patients living at a larger distance to the specialist. The incidence of a more severe disease course, as represented by the number of surgical interventions, was not related to a longer PTSD, either in CD or in UC/IBDU.
Many studies have shown that an early initiation of medical treatment results in a better outcome in IBD patients.5,16,17 Due to unspecific or mild early symptoms, diagnosing IBD can be demanding. Minimizing the diagnostic delay is, besides early immunomodulator/biological therapy, an important parameter in avoiding complications and surgery.16,18,19 To the best of our knowledge, the influence of distance to center on diagnostic delay has not yet been investigated. Vavricka and colleagues showed that patients from the Swiss IBD Cohort Study are diagnosed with a delay of 9 months in CD and 4 months in UC.20 In our study, the diagnostic delay was even shorter than previously reported,20 and we have seen a surprisingly low diagnostic delay of only 2 months in CD patients living over 35 km away from a tertiary IBD center. This observation might indicate a decreasing diagnostic delay within the last years, or also be related to the different sample size out of the same SIBDCS database in this study (408 IBD patients) compared with the above-mentioned Swiss study (1591 IBD patients).
Early treatment with immunomodulators has proven to reduce the risk of intestinal surgery, perianal surgery, and other complications.21 Other studies suggest that therapies with anti-TNF antibodies even appear to influence fistula healing in CD in a positive manner.17,22 The timing of such therapy is of great importance.23 Another Swiss study showed that treatment with anti-TNF agents started within the first 2 years after diagnosis reduces the risk of developing intestinal strictures in CD.21 In our study, the use of anti-TNF and immunomodulators at enrollment did not differ between PTSD groups, which could explain the nonexistent difference in occurrence of CD-related complications and perianal fistulizing disease.
Our results are in contrast to a single-center study from Massachusetts that investigated the influence of distance between IBD patients and the Massachusetts General Hospital in Boston on disease outcome.13 They found an increased need for IBD-related surgery, biologicals, and immunomodulators in patients living at the largest distance from the specialist. Differences between the two studies might be related to different distances to the specialist. Our largest mean distance to specialist was 48.7 km, while it was 81.8 km in Massachusetts. It might be possible that our largest distance was not large enough to detect a difference in treatment and outcome. This is also supported by a study investigating the relationship between distance and outcome after cardiac operations.24 The outcome was significantly worse in patients living beyond 100 km from the hospital, which is a much larger distance than analyzed in our study.
On the other hand, it has been demonstrated that biological therapy is more often prescribed at specialized centers.25 From our database, we were not able to determine whether patients were treated at a private practice or in an outpatient setting at the tertiary center. When the cohort study was initiated, it first began recruiting patients at the centers, and later in the periphery. It therefore might be possible that patients living closest to the specialist were actually treated at a center, while the patients living at larger distances were treated at private practices. This might explain our finding that more patients living closest to the specialist were treated with anti-TNF agents (over 70% in group 1 were treated with anti-TNF agents), if analyzing the medication received during the entire study period. This might also apply to our finding that more systemic steroids were used in patients living closest to the specialist in UC/IBDU. Corticosteroids are usually used when therapy with 5-ASA is insufficient,26,27 or for treatment of an acute moderate-to-severe flare.27,28 Since we did not detect differences in disease outcome, it is likely that treatment differences between distance groups can be explained by the setting (private practice versus center) in which patients were treated. Telemedicine approaches might be an option to improve treatment outcomes for patients living at longer distances to the specialist.
Increasing disease duration in CD is, besides the above-mentioned delay in immunomodulator/biological therapy and increased diagnostic delay, a risk factor for repetitive CD-related intestinal surgery and complications.16 We found a higher median disease duration in patients living closest to the specialist. The cumulative probability for intestinal surgery 10 years after diagnosis is 38% in CD and 25% in UC, respectively.8,29 In addition, previous studies found that specialized gastroenterologist in-hospital care results in lower in-hospital mortality risk,6,30,31 and in earlier surgical treatment.25 However, although we expected a worse disease outcome with increasing PTSD, we did not detect a difference in the occurrence of surgical interventions between groups.
Our study bears several limitations. We excluded the majority of patients from the analysis, as they were enrolled into the cohort later than 6 months after the first presentation to a gastroenterologist. In contrast to the study from Boston,13 which was monocentric, we had to find a way to ensure stable distances, that is, a stable address, between patients and IBD center from our cohort data, as address data might not have been constantly updated in the database. Compared with the study from Boston, we also had a lower difference between the distance groups: Groups 1 and 3 differed by 25 km. Given the fact, that the IBD centers were 70–100 km apart, we had to choose the distance groups accordingly. However, the rather small difference between the groups might have hampered the identification of more distinct differences. In addition, in the statistical analysis, we were not able to adjust for confounding factors, such as treatment duration (i.e. time on therapy, or if a therapy had to be stopped because of intolerance), socioeconomic status or insurance type, as this was not recorded in the database. These factors might have influenced treatment decisions.
Conclusion
In conclusion, our study shows that, although differences in treatment with PTSD exist, this does not seem to influence the diagnostic delay and disease outcome. This might be related to the small area, and therefore overall short distances, in Switzerland. We had hypothesized that an increase in distance between patient’s home and IBD specialist might have an adverse impact on diagnosis and disease outcome. While we detected a difference in treatment, disease complications did not differ.
Acknowledgments
Authors Lorenz Grob and Sena Bluemel contributed equally. The members of the SIBDCS study group are as follows: Karim Abdelrahman, Gentiana Ademi, Patrick Aepli, Amman Thomas, Claudia Anderegg, Anca-Teodora Antonino, Eva Archanioti, Eviano Arrigoni, Diana Bakker de Jong, Bruno Balsiger, Polat Bastürk, Peter Bauerfeind, Andrea Becocci, Dominique Belli, José M. Bengoa, Luc Biedermann, Janek Binek, Mirjam Blattmann, Stephan Boehm, Tujana Boldanova, Jan Borovicka, Christian P. Braegger, Stephan Brand, Lukas Brügger, Simon Brunner, Patrick Bühr, Bernard Burnand, Sabine Burk, Emanuel Burri, Sophie Buyse, Dahlia-Thao Cao, Ove Carstens, Dominique H. Criblez, Sophie Cunningham, Fabrizia D’Angelo, Philippe de Saussure, Lukas Degen, Joakim Delarive, Christopher Doerig, Barbara Dora, Susan Drerup, Mara Egger, Ali El-Wafa, Matthias Engelmann, Jessica Ezri, Christian Felley, Markus Fliegner, Nicolas Fournier, Montserrat Fraga, Yannick Franc, Pascal Frei, Remus Frei, Michael Fried, Florian Froehlich, Raoul Ivano Furlano, Luca Garzoni, Martin Geyer, Laurent Girard, Marc Girardin, Delphine Golay, Ignaz Good, Ulrike Graf Bigler, Beat Gysi, Johannes Haarer, Marcel Halama, Janine Haldemann, Pius Heer, Benjamin Heimgartner, Beat Helbling, Peter Hengstler, Denise Herzog, Cyrill Hess, Roxane Hessler, Klaas Heyland, Thomas Hinterleitner, Claudia Hirschi, Petr Hruz, Pascal Juillerat, Carolina Khalid-de Bakker, Stephan Kayser, Céline Keller, [Christina Knellwolf (-Grieger)], Christoph Knoblauch, Henrik Köhler, Rebekka Koller, Claudia Krieger(-Grübel), Patrizia Künzler, Rachel Kusche, Frank Serge Lehmann, Andrew Macpherson, Michel H. Maillard, Michael Manz, Astrid Marot, Rémy Meier, Christa Meyenberger, Pamela Meyer, Pierre Michetti, Benjamin Misselwitz, Patrick Mosler, Christian Mottet, Christoph Müller, Beat Müllhaupt, Leilla Musso, Michaela Neagu, Cristina Nichita, Jan Niess, Andreas Nydegger, Nicole Obialo, Diana Ollo, Cassandra Oropesa, Ulrich Peter, Daniel Peternac, Laetitia Marie Petit, Valérie Pittet, Daniel Pohl, Marc Porzner, Claudia Preissler, Nadia Raschle, Ronald Rentsch, Alexandre Restellini, Sophie Restellini, Jean-Pierre Richterich, Frederic Ris, Branislav Risti, Marc Alain Ritz, Gerhard Rogler, Nina Röhrich, Jean-Benoît Rossel, Vanessa Rueger, Monica Rusticeanu, Markus Sagmeister, Gaby Saner, Bernhard Sauter, Mikael Sawatzki, Michael Scharl, Martin Schelling, Susanne Schibli, Hugo Schlauri, Dominique Schluckebier, Daniela Schmid, Sybille Schmid (-Uebelhart), Jean-François Schnegg, Alain Schoepfer, Vivianne Seematter, Frank Seibold, Mariam Seirafi, Gian-Marco Semadeni, Arne Senning, Christiane Sokollik, Joachim Sommer, Johannes Spalinger, Holger Spangenberger, Philippe Stadler, Peter Staub, Dominic Staudenmann, Volker Stenz, Michael Steuerwald, Alex Straumann, Bruno Strebel, Andreas Stulz, Michael Sulz, Aurora Tatu, Michela Tempia-Caliera, Joël Thorens, Kaspar Truninger, Radu Tutuian, Patrick Urfer, Stephan Vavricka, Francesco Viani, Jürg Vögtlin, Roland Von Känel, Dominique Vouillamoz, Rachel Vulliamy, Paul Wiesel, Reiner Wiest, Stefanie Wöhrle, Samuel Zamora, Silvan Zander, Tina Wylie, Jonas Zeitz, and Dorothee Zimmermann.
Footnotes
Author contributions: MS and AS conceived and designed the project. MS supervised the project. MS and GR obtained funding. LG and SB analyzed the data and wrote the manuscript. NF and JBR performed statistical analyses. SRV, LB, JZ discussed the results and reviewed the manuscript. All authors approved the final version of the manuscript.
Conflict of interest: The authors declare that there is no conflict of interest.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and publication of this article: This work was supported by grants from the Stiftung Experimentelle Biomedizin to MS, Swiss National Science Foundation (Grant No. 314730_166381 and Grant No. 320030_184753) to MS and to GR for the Swiss IBD Cohort (Grant No. 3347CO-108792).
ORCID iDs: Sena Bluemel 
https://orcid.org/0000-0002-0518-5505
Michael Scharl
https://orcid.org/0000-0002-6729-1469
Contributor Information
Lorenz Grob, Division of Gastroenterology and Hepatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Sena Bluemel, Division of Gastroenterology and Hepatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Luc Biedermann, Division of Gastroenterology and Hepatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Nicolas Fournier, Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland.
Jean-Benoit Rossel, Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland.
Stephan R. Vavricka, Division of Gastroenterology and Hepatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
Jonas Zeitz, Division of Gastroenterology and Hepatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland Center of Gastroenterology, Clinic Hirslanden Zurich, Zurich, Switzerland.
Gerhard Rogler, Division of Gastroenterology and Hepatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Andreas Stallmach, Department of Internal Medicine IV, University Hospital Jena, Jena, Germany.
Michael Scharl, Department of Gastroenterology and Hepatology, University Hospital Zurich, University of Zurich, Rämistrasse 100, Zurich, 8091, Switzerland.
References
- 1. Burisch J, Munkholm P. Inflammatory bowel disease epidemiology. Curr Opin Gastroenterol 2013; 29: 357–362. [DOI] [PubMed] [Google Scholar]
- 2. Molodecky NA, Soon IS, Rabi DM, et al. Increasing incidence and prevalence of the inflammatory bowel diseases with time, based on systematic review. Gastroenterology 2012; 142: 46–54.e42; quiz e30. [DOI] [PubMed] [Google Scholar]
- 3. Benchimol EI, Fortinsky KJ, Gozdyra P, et al. Epidemiology of pediatric inflammatory bowel disease: a systematic review of international trends. Inflamm Bowel Dis 2011; 17: 423–439. [DOI] [PubMed] [Google Scholar]
- 4. Boonen A, Dagnelie PC, Feleus A, et al. The impact of inflammatory bowel disease on labor force participation: results of a population sampled case-control study. Inflamm Bowel Dis 2002; 8: 382–389. [DOI] [PubMed] [Google Scholar]
- 5. Etchevers MJ, Aceituno M, Sans M. Are we giving azathioprine too late? The case for early immunomodulation in inflammatory bowel disease. World J Gastroenterol 2008; 14: 5512–5518. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Ananthakrishnan AN, McGinley EL, Binion DG. Does it matter where you are hospitalized for inflammatory bowel disease? A nationwide analysis of hospital volume. Am J Gastroenterol 2008; 103: 2789–2798. [DOI] [PubMed] [Google Scholar]
- 7. Pariente B, Mary JY, Danese S, et al. Development of the Lémann index to assess digestive tract damage in patients with Crohn’s disease. Gastroenterology 2015; 148: 52–63.e53. [DOI] [PubMed] [Google Scholar]
- 8. Bewtra M, Su C, Lewis JD. Trends in hospitalization rates for inflammatory bowel disease in the United States. Clin Gastroenterol Hepatol 2007; 5: 597–601. [DOI] [PubMed] [Google Scholar]
- 9. Rutgeerts P, Sandborn WJ, Feagan BG, et al. Infliximab for induction and maintenance therapy for ulcerative colitis. N Engl J Med 2005; 353: 2462–2476. [DOI] [PubMed] [Google Scholar]
- 10. Duijvestein M, Battat R, Vande Casteele N, et al. Novel therapies and treatment strategies for patients with inflammatory bowel disease. Curr Treat Options Gastroenterol 2018; 16: 129–146. [DOI] [PubMed] [Google Scholar]
- 11. Kelly C, Hulme C, Farragher T, et al. Are differences in travel time or distance to healthcare for adults in global north countries associated with an impact on health outcomes? A systematic review. BMJ Open 2016; 6: e013059. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Ambroggi M, Biasini C, Del Giovane C, et al. Distance as a barrier to cancer diagnosis and treatment: review of the literature. Oncologist 2015; 20: 1378–1385. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Borren NZ, Conway G, Tan W, et al. Distance to specialist care and disease outcomes in inflammatory bowel disease. Inflamm Bowel Dis 2017; 23: 1234–1239. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Pittet V, Juillerat P, Mottet C, et al. Cohort profile: the Swiss inflammatory bowel disease cohort study (SIBDCS). Int J Epidemiol 2009; 38: 922–931. [DOI] [PubMed] [Google Scholar]
- 15. Satsangi J, Silverberg MS, Vermeire S, et al. The Montreal classification of inflammatory bowel disease: controversies, consensus, and implications. Gut 2006; 55: 749–753. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Schoepfer AM, Dehlavi MA, Fournier N, et al. Diagnostic delay in Crohn’s disease is associated with a complicated disease course and increased operation rate. Am J Gastroenterol 2013; 108: 1744–1753; quiz 1754. [DOI] [PubMed] [Google Scholar]
- 17. D’Haens G. Anti-TNF-alpha treatment strategies: results and clinical perspectives. Gastroenterol Clin Biol 2009; 33(Suppl. 3): S209–S216. [DOI] [PubMed] [Google Scholar]
- 18. Lee DW, Koo JS, Choe JW, et al. Diagnostic delay in inflammatory bowel disease increases the risk of intestinal surgery. World J Gastroenterol 2017; 23: 6474–6481. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Nahon S, Lahmek P, Paupard T, et al. Diagnostic delay is associated with a greater risk of early surgery in a French cohort of Crohn’s disease patients. Dig Dis Sci 2016; 61: 3278–3284. [DOI] [PubMed] [Google Scholar]
- 20. Vavricka SR, Spigaglia SM, Rogler G, et al. Systematic evaluation of risk factors for diagnostic delay in inflammatory bowel disease. Inflamm Bowel Dis 2012; 18: 496–505. [DOI] [PubMed] [Google Scholar]
- 21. Safroneeva E, Vavricka SR, Fournier N, et al. Impact of the early use of immunomodulators or TNF antagonists on bowel damage and surgery in Crohn’s disease. Aliment Pharmacol Ther 2015; 42: 977–989. [DOI] [PubMed] [Google Scholar]
- 22. Sands BE, Anderson FH, Bernstein CN, et al. Infliximab maintenance therapy for fistulizing Crohn’s disease. N Engl J Med 2004; 350: 876–885. [DOI] [PubMed] [Google Scholar]
- 23. González-Lama Y, Suárez C, González-Partida I, et al. Timing of thiopurine or anti-TNF initiation is associated with the risk of major abdominal surgery in Crohn’s disease: a retrospective cohort study. J Crohns Colitis 2016; 10: 55–60. [DOI] [PubMed] [Google Scholar]
- 24. Cote CL, Singh S, Yip AM, et al. Increased distance from the tertiary cardiac center is associated with worse 30-day outcomes after cardiac operations. Ann Thorac Surg 2015; 100: 2213–2218. [DOI] [PubMed] [Google Scholar]
- 25. Law CC, Sasidharan S, Rodrigues R, et al. Impact of specialized inpatient IBD care on outcomes of IBD hospitalizations: a cohort study. Inflamm Bowel Dis 2016; 22: 2149–2157. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Rogler G, Beglinger C, Mottet C, et al. Topical therapy of ulcerative colitis. Praxis (Bern 1994) 2011; 100: 1413–1424. [DOI] [PubMed] [Google Scholar]
- 27. Kucharzik T, Dignass AU, Atreya R, et al. Updated S3-guideline colitis ulcerosa. German society for digestive and metabolic diseases (DGVS) - AWMF registry 021/009. Z Gastroenterol 2018; 56: 1087–1169. [DOI] [PubMed] [Google Scholar]
- 28. Dulai PS, Jairath V. Acute severe ulcerative colitis: latest evidence and therapeutic implications. Ther Adv Chronic Dis 2018; 9: 65–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Solberg IC, Vatn MH, Høie O, et al. Clinical course in Crohn’s disease: results of a Norwegian population-based ten-year follow-up study. Clin Gastroenterol Hepatol 2007; 5: 1430–1438. [DOI] [PubMed] [Google Scholar]
- 30. Murthy SK, Steinhart AH, Tinmouth J, et al. Impact of gastroenterologist care on health outcomes of hospitalised ulcerative colitis patients. Gut 2012; 61: 1410–1416. [DOI] [PubMed] [Google Scholar]
- 31. Kaplan GG, McCarthy EP, Ayanian JZ, et al. Impact of hospital volume on postoperative morbidity and mortality following a colectomy for ulcerative colitis. Gastroenterology 2008; 134: 680–687. [DOI] [PubMed] [Google Scholar]