1 INTRODUCTION

Patients with chronic leg ulcer, pressure ulcer, or diabetic foot ulcer have reduced quality of life, require high medical and nursing care, and are cost intensive for the healthcare system.1-6 At an annual prevalence of 1.04% in 2012, more than 786 000 persons in Germany had chronic wounds that year, including 326 000 patients who received wound treatment.7 Due to demographic changes and an increase in underlying diseases in the elderly population, further growth in chronic wound cases can be expected.8, 9

Factors that influence the healing process are of great relevance for identifying the causes of prolonged treatment or wound closure disorders. To date, a number of factors that influence wound healing have been reported. Compression therapy has a significant influence on wound healing in patients with leg ulcer.10-13 Wound duration has been shown to be a risk factor.12-17 Wounds that have failed to heal in the previous 3–6 months have a much smaller chance of healing.15 Moreover, a wound size of greater than 10 cm2 is associated with lower probability of healing.15, 16, 18-22 Furthermore, a greater number of wounds is associated with a lower probability of healing.20, 21, 23 Additionally, predictive factors such as vascular interventions, previous medical treatment, severity, patient age, and wound infection have been reported.10, 14, 17, 19-21, 23-25

In the present study, the predictors reported in the literature were used to analyse routine data. Administrative data such as statutory health insurance (SHI) claims data are based on a large population. Claims data are administrative data of German insurance funds. About 85% of the German population is covered by SHI (70 million people) and 11% are covered by complementary private health insurance.26 Apart from size, the absence of recall and selection bias is one of the advantages of this data set. Since wounds primarily affect older people, unhindered access to the study population, regardless of frailty, is a further advantage in comparison to cost-intensive primary studies. In addition to studies of patterns of care, quality of care, resource consumption, and the evaluation of care concepts, the data are increasingly used to develop predictive models. Hence, the aim of this study was to identify possible predictors of wound closure using SHI data.

2 METHODS 2.1 Data source

For the present analysis, data from an SHI operating nationwide, the DAK (DAK-Gesundheit), was used. A random sample of approximately 2.3 million DAK-insured persons in 2010 constituted the data set. The sample of insured persons was anonymised by the DAK and made available to the institute for scientific research. In addition to the sociodemographic details of the insured people, such as age and gender, outpatient and inpatient care data, prescriptions, and medical aids data26 were analysed.

2.2 Case definition

Our study population included insured persons of the DAK sample with a new chronic wound in 2013 (incident cases) who did not have a wound in the previous 2 years. Wound dressings are a basic part of wound treatment. In addition, we used predefined wound medical prescriptions based on a product-specific PZN (Pharmazentralnummer, standard national identification code) to identify patients with active wounds.

A wound was defined as chronic if it did not heal within 8 weeks or if continuous treatment of the underlying disease was not required.27 Therefore, patients with a wound duration of < 8 weeks were excluded from the model.

We examined insured patients suffering from chronic wounds based on the following inclusion criteria:

At least one outpatient (ambulatory primary care) or inpatient (hospital-based) diagnostic code of the International Classification of Diseases (ICD–10 GM) was used to identify: leg ulcer: arterial: I70.23, I70.24; venous: I83.0, I83.2, I87.0; not specified: L97, L98.4; diabetic foot ulcer: E10.–E14.74 and E10.–E14.75; pressure ulcer: L89.1–L89.3 and L89.9. At least one of the following wound products: alginates with/without silver, hydrofiber with/without silver, hydrocolloid dressings with/without silver, hydrogel and hydrogel dressings, super absorbent dressings, fine-pored polyurethane foam dressings or hydropolymer dressings with/without silver, polyhexanide or ibuprofen, film dressings or semipermeable transparent dressings or permeable film dressings, coal dressings with/without silver, hydrophobic dressings, active dressings (eg, silver, hyalurone, oxygen, collagen), honey dressings, moist saline compresses/gauzes with/without zinc oxide, impregnated gauze dressings. 2.3 Definition of time to wound closure: outcome

The main outcome was wound closure, which is the internationally agreed gold standard for effectiveness in the area of wound treatment research. In SHI data, clinical information and hence the start and end points of wound duration and wound closure are not included as independent information.

Wound dressings have already been shown to be a valid criterion for identifying florid (active) chronic wounds using SHI data.27, 28 Also against the background that every florid wound is treated with a dressing, as every insured person in Germany (99% of the population) has free access to the healthcare system and also to dressings. Furthermore, dressings are used exclusively for the treatment of a wound.

Therefore, active wound duration and the main outcome, wound closure, was modelled on the basis of the first and last prescription (PZN) of predefined specific wound dressings (see section case definition) in combination with a wound diagnosis. The follow-up observation period was 1 year (2014) in order to identify the outcome (12 months without prescription). Follow-up ended during the defined period depending on when the last prescription was issued. The year 2015 was only studied to monitor prescriptions.

Patients were not identified as healed if they (a) had a prescription within the second follow-up period of 12 months, (b) died, or (c) were no longer insured within the predefined follow-up observation period. Patients were censored when they died or were no longer insured within the predefined follow-up observation period.

2.4 Definition of healing predictors

The following healing predictors were analysed, depending on wound aetiology:

wound-specific characteristics (infection and diagnostic procedures: vascular diagnosis, smear test, biopsy, and allergy diagnosis), causal therapy (compression, pressure distribution such as shoe provision, pressure relief such as mattress systems, vascular surgery), local therapy (wound debridement, negative pressure therapy, skin grafts), wound-relevant comorbidities (cardiovascular, pulmonary, infection, liver, dermatological, allergological, renal, rheumatological, immunological, neurological, and vascular diseases as well as metabolic disorders, malignant tumours, and lip- and lymphoedema),29 further characteristics (medical wound specialist [dermatologist or surgeon in outpatient care] and hospitalisation due to the wound diagnosis) and sociodemographic characteristics (age, sex).

To obtain these predictors, we used special codes for the utilisation of outpatient (ambulatory primary care) and inpatient (hospital-based) care.30, 31 Given that different treatments are recommended for the indications, a model was constructed for each indication.

2.5 Statistical analysis

Time to closure was assessed by means of Kaplan-Meier analysis stratified for patients with leg, pressure, and diabetic foot ulcers. We identified factors associated with time to wound closure in patients with chronic wounds by estimating hazard ratios (HR) with 95% confidence intervals (95% CI) using Cox proportional hazards models. Kaplan-Meier survival curves and time-dependent explanations were used to assess the proportional hazards assumption.32 The likelihood-ratio test (significance level α ≤ 0.05) was used to test the significance of the overall model and transferability of the results to the population. To assess the discrimination, which is a mathematical measure of the representation of model performance, the c-statistic of the model was taken into account.33, 34 A value of 0.5 shows a random effect and 1 a maximum of discrimination.35 Values between 0.7 and 0.8 are regarded as acceptable and between 0.8 and 0.9 as excellent. Values above 0.8 are rarely reached.36 The final prediction model was obtained using backward selection (level of significance < 0.01).

The characteristics, such as wound aetiology and sociodemographic characteristics, were collected at baseline (first initial treatment of the wound). The other predictors, such as comorbidities or therapies, were considered over time (for a maximum of 1 year after “wound beginning”). This period was chosen to ensure a temporal dependency between the outcome and the predictors and to avoid an inflation of the effects by cumulative effect modifiers (confounders). Cardinally scaled influence variables were not recoded into a binary dummy variable but were included in the regression as continuous variables. Dummy coding is often practiced and discussed because it is associated with a loss of information.37 In order to better interpret the cardinally scaled result, in particular mean age, this variable was centred on the mean age.38

All analyses were performed using Statistical Analysis System SAS Version 9.4 (SAS for Windows 2000, SAS Institute Inc., Cary, North Carolina).

The study was conducted in accordance with national guidelines for the use of administrative data sets.39, 40 Based on the guidelines, the approval of an ethics committee was not required.

3 RESULTS 3.1 Baseline characteristics

The study population for wound closure with incident chronic wounds in 2013 comprised 3745 patients with leg ulcer, 3342 with pressure ulcer, and 791 with diabetic foot ulcer (Figure 1).

Sampling cohort

The baseline characteristics are shown in Table 1. More than half of wounds healed within the observation period of 1 year. The mean age of patients with leg ulcers was about 78 years, with pressure ulcers 81 years and with diabetic foot ulcers 74 years. The average number of comorbidities was 3.7 in patients with leg ulcers, 3.3 in patients with pressure ulcers, and 4.6 in patients with diabetic foot ulcers. The majority of patients with a leg ulcer had at least one inpatient stay during the first year. On average, patients with leg ulcer had fewer stays compared to the other indications.

TABLE 1. Baseline characteristics of patients with a new chronic wound Leg ulcers (n = 3745) Pressure ulcers (n = 3342) Diabetic foot ulcers (n = 791) Wound healing Number of wounds closed, n (%) 1375 (36.7) 740 (22.1) 299 (37.8) Sociodemographic data Sex (female), n (%) 2367 (63.2) 2228 (66.7) 361 (45.6) Age (years), mean (SD) 77.9 (12.6) 81.0 (12.5) 73.5 (11.7) ≤54, n (%) 204 (5.5) 148 (4.4) 57 (7.2) 55-64, n (%) 328 (8.8) 197 (5.9) 111 (14.0) 65-74, n (%) 697 (18.6) 417 (12.5) 219 (27.7) 75-84, n (%) 1184 (31.6) 956 (28.6) 254 (32.1) 85-94, n (%) 1207 (32.2) 1410 (42.2) 144 (18.2) ≥95, n (%) 125 (3.3) 214 (6.4) 6 (0.8) Comorbidity, mean (SD) 3.7 (1.7) 3.3 (1.7) 4.6 (1.5) 0, n (%) 82 (2.2) 137 (4.1) 0 1, n (%) 238 (6.4) 340 (10.2) 12 (1.5) 2, n (%) 576 (15.4) 660 (19.8) 58 (7.3) 3, n (%) 857 (22.9) 798 (23.9) 120 (15.2) 4, n (%) 855 (22.9) 648 (19.4) 193 (24.4) 5, n (%) 647 (17.3) 444 (13.3) 203 (25.7) ≥6, n (%) 490 (13.1) 315 (9.5) 205 (25.9) Diagnostic and infection Vascular diagnosis, n (%) 1330 (35.5) 640 (19.2) 389 (49.2) Smear test, n (%) 341 (9.1) 339 (10.1) 79 (10.0) Biopsy, n (%) 255 (6.8) 120 (3.6) 53 (6.7) Allergy diagnosis, n (%) 15 (0.4) 4 (0.1) 0 Infection, n (%) 935 (25.0) 401 (12.0) 251 (31.7) Causal therapy Compression therapy, n (%) 1813 (48.4) n.a. n.a. Pressure distribution (eg, shoe provision), n (%) 893 (23.9) 554 (16.6) 441 (55.8) Pressure relief (eg, mattress systems), n (%) n.a. 1853 (55.5) n.a. Vascular surgery, n (%) 549 (14.7) n.a. 201 (25.4) Local therapy Negative pressure therapy, n (%) 337 (9.0) 205 (6.1) 121 (15.3) Skin grafts, n (%) 635 (17.0) 388 (11.6) 220 (27.8) Debridement, n (%) 396 (10.6) 220 (6.6) 133 (16.8) Further characteristics Medical wound specialist, n (%) 2154 (57.5) 1250 (37.4) 441 (55.8) Hospital stays, mean (SD) 0.5 (1.0) 0.4 (0.9) 0.9 (1.3) 0, n (%) 2602 (69.5) 2434 (72.8) 439 (55.5) 1, n (%) 690 (18.4) 572 (17.1) 184 (23.3) 2, n (%) 269 (7.2) 203 (6.1) 93 (11.8) ≥3, n (%) 184 (4.9) 133 (4.0) 75 (9.5) Abbreviations: n.a., not assessed; SD, standard deviation. 3.2 Time to wound closure

The Kaplan-Meier survival curves (Figure 2) show, for each aetiology, the time to wound closure (first prescription of a wound dressing until last issuance of a wound dressing—maximum 365 days follow-up). The modelled healing probability after 365 days was 36.7% for patients with leg ulcer, 37.8% for patients with diabetic foot ulcer, and 22.1% for patients with decubitus. Furthermore, the curves show that almost 20% of wounds in patients with leg ulcers or diabetic foot ulcers healed after 6 months.

Survival (wound healing) in incident chronic wound

To identify predictors of wound closure, we performed several Cox regression models for each wound aetiology:

3.3 Patients with leg ulcers

In the first crude model for patients with leg ulcers, the variables age, number of comorbidities, vascular diagnostics, smear test, vascular surgery, skin grafts, and the number of inpatient stays were found to be negative predictors of wound closure (Table 2). In the adjusted model, controlled for all influencing variables, the significant negative predictors of wound closure were age, number of comorbidities, smear test, and number of inpatient stays, and a positive predictor was health care by at least one medical wound specialist. Thus, many wound treatments in the adjusted model were no longer significant, whereas treatment by a wound specialist became a significant positive predictor of wound closure (Table 3). Therefore, insured persons who were treated by a medical wound specialist at least once show a 18% higher chance of wound closure (HR = 1.18, CI = 1.05–1.31, P = .004) than those not treated by a wound specialist. Insured persons also showed a 26% lower chance of recovery with each further inpatient stay (HR = 0.74, CI = 0.67–0.80, P ≤ .001). Furthermore, the chance of wound closure decreased by 6% with each further comorbidity (HR = 0.94, CI = 0.91–0.97, P ≤ .001).

TABLE 2. Crude Cox regression model Leg ulcers Pressure ulcers Diabetic foot ulcers HR (95% CI) P HR (95% CI) P HR (95% CI) P Sociodemographic data Femalea 1.01 (0.91–1.13) .795 1.22 (1.04–1.43) .013 1.25 (1.00–1.57) .055 Centred ageb 0.99 (0.98–0.99) ≤.001 0.99 (0.99–1.00) .003 0.99 (0.98–1.00) .009 Number of comorbidities 0.90 (0.88–0.93) ≤.001 0.95 (0.91–0.99) .021 0.86 (0.80–0.93) ≤.001 Diagnostic and infection Vascular diagnosisc 0.80 (0.72–0.90) ≤.001 0.99 (0.83–1.18) .935 0.66 (0.52–0.83) ≤.001 Smear testc 0.62 (0.50–0.76) ≤.001 0.85 (0.66–1.07) .171 0.61 (0.40–0.93) .022 Biopsyc 0.78 (0.62–0.97) .026 0.61 (0.39–0.96) .031 0.73 (0.44–1.20) .216 Allergy diagnosisc 0.97 (0.44–2.17) .944 0.00 (0.00–0.00) .941 0.00 (0.00–0.00) .941 Infectionc 1.10 (0.98–1.25) .114 1.09 (0.88–1.36) .427 0.96 (0.76–1.22) .752 Causal therapy Compression therapyc 0.89 (0.80–0.98) .024 n.a. n.a. n.a. n.a. Pressure distribution (eg, shoe provision)c 0.89 (0.79–1.01) .067 n.a. n.a. 0.98 (0.78–1.23) .847 Pressure relief (eg, mattress systems)c n.a. n.a. 0.61 (0.52–0.70) ≤.001 n.a. n.a. Vascular surgeryc 0.66 (0.56–0.77) ≤.001 n.a. n.a. 0.62 (0.46–0.83) .001 Local therapy Negative pressure therapyc 0.78 (0.64–0.95) .013 0.76 (0.55–1.05) .099 0.92 (0.67–1.25) .582 Skin graftsc 0.75 (0.64–0.87) ≤.001 0.61 (0.47–0.80) ≤.001 0.89 (0.69–1.14) .353 Debridementc 0.79 (0.66–0.94) .009 0.63 (0.46–0.88) .007 0.84 (0.62–1.15) .284 Further characteristics Medical wound specialistc 1.13 (1.01–1.26) .030 1.07 (0.92–1.23) .392 1.01 (0.81–1.28) .903 Number of hospital stays 0.73 (0.68–0.78) ≤.001 0.76 (0.69–0.85) ≤.001 0.81 (0.73–0.90) ≤.001 Note: Likelihood-ratio test < 0.001. c-statistic: leg ulcer (0.6206), pressure ulcer (0.6059), diabetic foot ulcer (0.6025). Abbreviation: n.a., not assessed. TABLE 3. Adjusted Cox regression model Leg ulcers Pressure ulcers Diabetic foot ulcers HR (95% CI) P HR (95% CI) P HR (95% CI) P Sociodemographic data