Introduction
Methods
Data source and processing
CEA | CAS | Totala | ||||
---|---|---|---|---|---|---|
Total (%) | 73,042 | (100) | 15,367 | (100) | 88,182 | (100) |
Males (%) | 49,727 | (68) | 10,711 | (70) | 60,282 | (68) |
Age (years, median, Q1–3) | 72 | [65–77] | 71 | [63–76] | 72 | [65–77] |
Elixhauser score (median, Q1–3) | 3 | [0–8] | 4 | [0–9] | 3 | [0–8] |
As principal hospital diagnosis | 57,632 | (79) | 11,305 | (74) | 68,789 | (78) |
Documented secondary diseases
| ||||||
CHD (I25) | 20,767 | (28) | 5066 | (33) | 25,764 | (29) |
Otherb heart diseases | 18,406 | (25) | 3957 | (26) | 22,295 | (25) |
Peripheral arterial disease | 16,312 | (22) | 3988 | (26) | 20,241 | (23) |
Arterial hypertension | 60,226 | (82) | 12,086 | (79) | 72,135 | (82) |
Chronic lung disease | 6779 | (9.3) | 1195 | (7.8) | 7954 | (9.0) |
Diabetes mellitus | 22,045 | (30) | 4521 | (29) | 26,497 | (30) |
Kidney failure | 11,058 | (15) | 2563 | (17) | 13,590 | (15) |
Cancer | 984 | (1.3) | 262 | (1.7) | 1241 | (1.4) |
Coagulopathy | 2952 | (4.0) | 493 | (3.2) | 3434 | (3.9) |
Obesity | 5934 | (8.1) | 997 | (6.5) | 6914 | (7.8) |
District type: patient place of residence
| ||||||
City | 19,263 | (26) | 4354 | (28) | 23,543 | (27) |
Urban district | 28,634 | (39) | 5977 | (39) | 34,531 | (39) |
Rural district | 13,180 | (18) | 2581 | (17) | 15,723 | (18) |
Sparsely populated district | 11,965 | (16) | 2455 | (16) | 14,385 | (16) |
Admission type
| ||||||
Planned admission | 49,850 | (68) | 10,517 | (68) | 60,236 | (68) |
Emergency admission | 17,442 | (24) | 3889 | (25) | 21,262 | (24) |
Transfer | 5750 | (7.9) | 961 | (6.3) | 6684 | (7.6) |
District type: hospital
| ||||||
City | 34,254 | (47) | 8404 | (55) | 42,658 | (48) |
Urban district | 20,453 | (28) | 3856 | (25) | 24,309 | (27) |
Rural district | 8709 | (12) | 1488 | (10) | 10,197 | (12) |
Sparsely populated district | 9626 | (13) | 1619 | (11) | 11,245 | (13) |
Distance: place of residence to hospital
| ||||||
Linear distance (km, median, Q1–3) | 11.0 | [5.3–22] | 12.3 | [5.6–26] | 11.1 | [5.3–22.2] |
CEA n = 73,042 | CAS n = 15,367 | Totala n = 88,182 | ||||
---|---|---|---|---|---|---|
Revascularization procedure
| ||||||
CEA only | 72,815 | (100) | – | – | 72,815 | (83) |
CAS only | – | – | 15,140 | (98.5) | 15,140 | (17) |
Combined CEA/CAS |
227
|
(0.3)
|
227
|
(1.5)
| 227 | (0.3) |
Annual case numbers
b
| ||||||
All (CEA, CAS) | 82 | [50–129] | 81 | [48–133] | 82 | [49–130] |
Diagnosis and treatment
| ||||||
CT angiography (head/neck) | 12,662 | (17) | 2574 | (17) | 15,137 | (17) |
MR angiography (head/neck) | 10,416 | (14) | 2149 | (14) | 12,520 | (14) |
DSA (neck vessels) | 23,237 | (32) | 11,901 | (77) | 34,960 | (40) |
Treatment on a stroke unitc | 1607 | (2.2) | 790 | (5.1) | 2387 | (2.7) |
Intensive cared | 16,540 | (23) | 1478 | (9.6) | 17,941 | (20) |
Artificial respiration (yes/no) | 2615 | (3.6) | 807 | (5.3) | 3385 | (3.8) |
Complications (coded)
| ||||||
Hospital mortality | 632 | (0.9) | 222 | (1.4) | 840 | (1.0) |
Acute MI (I21, I22) | 625 | (0.9) | 136 | (0.9) | 753 | (0.9) |
Resuscitation (8–77) | 509 | (0.7) | 80 | (0.5) | 584 | (0.7) |
Hospital stay/DRG
| ||||||
Patient hospital stay | 7 | [5–10] | 4 | [3–9] | 6 | [5–10] |
Case mix index | 1.51 | [1.47–2.00] | 1.65 | [1.62–2.71] | 1.51 | [1.47–2.25] |
Type of discharge (survivors)
| ||||||
Regular discharge home | 66,000 | (91) | 13,376 | (88) | 79,215 | (90) |
Discharge against medical advice | 368 | (0.5) | 144 | (1.0) | 510 | (0.6) |
Transfer to rehabilitation center (099) | 3184 | (4.4) | 889 | (5.9) | 4039 | (4.6) |
Transfer to another hospital (079, 089) | 2340 | (3.2) | 620 | (4.1) | 2945 | (3.3) |
Other type of dischargee | 518 | (0.7) | 116 | (0.8) | 633 | (0.7) |
Regional characteristics (n = 402 districts) | Correlation Spearman’s r | Procedure frequency (CEA + CAS) | ||||
---|---|---|---|---|---|---|
In the lower decile | In the upper decile | p-Value | ||||
Prevalence risk factors (in %) | ||||||
Type 2 diabetes |
0.227
b
| 8.34 | (7.62–9.24) | 9.35 | (8.30–10.41) |
0.005
|
Rate of smokers |
0.128
b
| 27.9 | (26.8–30.4) | 31.3 | (27.9–32.8) |
0.008
|
Obesity rate |
0.260
b
| 14.9 | (14.2–16.4) | 18.0 | (16.00–20.4) |
<0.001
|
Health system infrastructure
| ||||||
Driving time to next hospital (min) | 0.039 | 10 | (4–13) | 10 | (5–13) | 0.885 |
Number of hospitalsa | −0.089 | 2.13 | (1.46–3.40) | 1.78 | (1.25–2.41) | 0.080 |
Total number of bedsa | 0.053 | 384 | (281–750) | 454 | (338–653) | 0.402 |
Surgical bedsa | 0.079 | 91 | (68–178) | 125 | (79–146) | 0.361 |
Internal medicine bedsa |
0.105
b
| 123 | (96–217) | 148 | (126–180) | 0.441 |
Neurological bedsa | −0.007 | 13 | (0–39) | 15 | (0–32) | 0.941 |
Physiciansa | −0.046 | 149 | (128–234) | 148 | (132–164) | 0.482 |
General practitionersa | −0.076 | 64.3 | (59.9–66.9) | 62.8 | (57.1–67.5) | 0.384 |
Vascular surgeons (SHI physicians)a | 0.014 | 0.797 | (0.32–1.40) | 0.606 | (0.00–1.11) | 0.569 |
Angiologists (SHI physicians)a | 0.001 | 0.136 | (0.00–0.70) | 0.0 | (0.00–0.61) | 0.444 |
Economic factors
| ||||||
Gross domestic product in T€ per wage earner |
−0.116
b
| 61.4 | (59.0–66.2) | 58.4 | (54.2–63.6) |
0.022
|
Household income in € per inhabitant |
−0.220
b
| 1807 | (1682–1926) | 1668 | (1458–1780) |
0.002
|
Size of household (persons) | −0.014 | 2.17 | (1.98–2.25) | 2.15 | (2.00–2.23) | 0.886 |
Old age poverty > 65 years (%) | −0.020 | 16.0 | (12.3–27.1) | 19.5 | (10.6–28.2) | 0.452 |
Debtors per 100 inhabitants |
0.156
b
| 7.95 | (6.55–9.40) | 9.70 | (8.30–11.50) |
0.003
|
Public debt in € per inhabitant |
0.150
b
| 801 | (588–1513) | 1475 | (1027–2212) |
0.003
|
Unemployment rate in % |
0.218
b
| 4.30 | (3.15–5.95) | 6.30 | (4.65–9.65) |
<0.001
|
Other factors
| ||||||
Population density (inhabitants per km2) | −0.063 | 221 | (124–423) | 176 | (106–764) | 0.518 |
Inward migration (per 1000 inhabitants) |
−0.239
b
| 47.9 | (40.8–66.8) | 40.0 | (29.0–45.4) |
0.001
|
Outward migration (per 1000 inhabitants) |
−0.231
b
| 43.3 | (37.2–58.1) | 36.2 | (29.8–41.0) |
0.002
|
Proportion of foreigners in % | −0.151 | 7.00 | (5.15–10.25) | 5.25 | (2.95–10.35) | 0.117 |
School leavers with university entrance qualification in % | 0.043 | 29.9 | (22.0–39.6) | 31.2 | (24.2–39.7) | 0.615 |
Self-employed per 1000 wage earners |
−0.100
b
| 114 | (92–137) | 110 | (90–123) | 0.174 |
Care recipients (per 10,000 inhabitants) |
0.287
b
| 285 | (262–340) | 370 | (313–400) |
<0.001
|
Type of district: place of residence
| ||||||
City | – | 6 | (15%) | 7 | (18%) | 0.404c 0.388d |
Urban district | – | 17 | (43%) | 13 | (33%) | |
Rural district with approaches at population densification | – | 10 | (25%) | 7 | (18%) | |
Sparsely populated rural districts | – | 7 | (18%) | 13 | (33%) | |
Type of region: place of residence
| ||||||
Urban region | – | 9 | (23%) | 12 | (30%) | 0.207c 0.768d |
Region with approaches at population densification | – | 21 | (53%) | 13 | (33%) | |
Rural region | – | 10 | (25%) | 15 | (38%) |
Case definition
Primary study variable and spatial resolution
Statistical analysis
Hypothesis-generating exploratory analyses
Results
Characteristics of patients included
Regional variation
Exploratory analysis (to generate hypotheses)
Discussion
Limitations
-
The data are not findings made in the clinical setting, but administrative claims data for the purposes of hospital reimbursement.
-
Since clinical details, e. g., the degree of stenosis or initial neurological symptoms, are not coded in the DRG data, no conclusions can be drawn on the quality of the indications, choice of procedure or guideline conformity.
-
The StBA’s DRG statistics do not document which diagnoses were already present on admission, making it impossible to reliably differentiate between comorbidity and complication; similarly, it was not possible to measure neurological outcome.
-
The follow-up period covered only inpatient stays.
-
All analyses refer to patients’ place of residence; an analysis of the place of treatment on the level of NUTS3 or regional policy region was not possible for data protection reasons.
-
Exploratory analyses were performed to generate hypotheses and could only be carried out on an aggregated level. Therefore, it is not possible to rule out an ecological fallacy.
Practical conclusion
-
The total number of CEA and CAS procedures in the DRG statistics showed good agreement with eQA data. The fact that, although the two reporting channels are subject to different control processes yet are not independent of each other, points to complete data collection by eQA.
-
In relation to districts and towns, the overall age and gender-standardized incidence of CEA and CAS in carotid stenosis varied between 13 and 89 per 100,000 inhabitants.
-
The regional frequency of all CEA and CAS procedures demonstrated a positive spatial autocorrelation and, thus a clustered spatial pattern of distribution. The CEA and CAS were frequently performed in northern Bavaria, Mecklenburg-Western Pomerania, and North Rhine-Westphalia (44–89 per 100,000 inhabitants), whereas they were carried out less frequently in Baden-Württemberg, eastern Lower Saxony, and Schleswig-Holstein (13–27 per 100,000 inhabitants).
-
Only patients from the western Rhineland-Palatinate and central and south-west Schleswig-Holstein showed high percentages of CAS (41–57%), while other regions showed CAS percentages of between 4% and 33%.
-
This study shows the level of heterogeneity in both the indication and the choice of method to treat carotid stenosis in Germany. Although natural variation and differences in actual incidence are likely, they are unable to fully account for the variation observed. This raises the question of the extent to which the German S3 guidelines on carotid stenosis have been adopted, as well as the social question of nationwide, demand-oriented treatment for everyone.
-
Particularly in view of the German government’s quality offensive and the IQTiG’s efforts to improve eQA this article, which captures the quintessential, practice-relevant points, raises the question as to whether non-consideration of regional aspects and separating eQS analyses according to CEA and CAS represent a relevant weakness in the quality assurance procedure. Therefore, the inclusion of regional parameters and relevant risk factors in the evaluation of eQA data should be examined in order to assess outcome quality from the perspective of the individual service providers, and not separately from the quality of patient selection, indication, and choice of procedure from the point of view of the regional patient population.