Chronic nonsurgical skin wounds such as venous stasis and diabetic ulcers have been associated with a number of comorbid conditions; however, the strength of these associations has not been compared. We utilized the Stanford Translational Research Integrated Database Environment (STRIDE) system to identify a cohort of 637 patients with chronic skin ulcers. Preliminary analysis (
Chronic wounds cause a significant morbidity and financial expense in the United States, affecting 6.5 million patients with estimated treatment costs of $25 billion per year [
A number of factors have been documented in the medical literature which predispose patients to poor wound healing. These include underlying diseases such as diabetes mellitus, venous insufficiency, peripheral arterial disease, tobacco smoking, low serum albumin, and inflammatory conditions (such as pyoderma gangrenosum) among others [
Although risk factors for the development of skin ulcers have been identified, clinical indicators of poor wound healing are less well studied. There are no large, well-controlled studies on independent impact of multiple risk factors including demographic, clinical, and laboratory markers to prognosticate outcome. In this study, we seek to stratify the level of risk which comorbidities and laborators values may confer on poor wound healing. If these markers can be identified, at-risk patients can be better identified and treated in a way that more aggressively addresses their comorbid medical condition, thus increasing the likelihood for effective wound healing.
Following Stanford Institutional Review Board approval, we employed a retrospective cohort study design using the Stanford Translational Research Integrated Database Environment (STRIDE) system. STRIDE includes data from Stanford University Hospital and Clinics and the Lucile Packard Children’s Hospital (LPCH). It encompasses 13 years of clinical documents with information on medical diagnoses (including classification by ICD-9 codes), laboratory values, medications, radiology reports, pathology reports, and free text of progress notes, consultations, and discharge summaries. Previously, STRIDE has been successfully utilized to construct cohorts based on ICD-9 codes and laboratory values. In particular, the Stanford Dermatology Department used STRIDE to identify co-morbid medical conditions associated with transaminitis in psoriasis patients taking methotrexate.
The STRIDE Cohort Discovery Tool was used to select a cohort of patients with chronic skin ulcers. The following ICD-9 codes were used to select the cohort: 707.10–19, 785.4, 454.0, 454.2, and 440.23. These pertain to “unspecified ulcer of lower limb,” “ulcer of thigh,” “ulcer of calf, “ulcer of ankle,” “ulcer of heel and midfoot,” “ulcer of other part of foot,” and “ulcer of other part of lower limb”, respectively. The following text restrictions were used to further ensure that all patients included in the cohort had documentation of skin ulcer in physician-authored clinical notes: “ulcer,” “wound,” “erosion,” “breakdown,” and “gangrene.” Patients aged 18 or older who had incident cases of skin ulcers between January 1, 2002 and January 1, 2005 were included. In the case of multiple ICD-9 codes meeting inclusion criteria only data on earliest ulcer within our date restrictions was used for the analysis.
Clinical data on exclusion criteria, outcomes, predictors, and covariates was extracted from STRIDE using its Data Review Tool. The Data Review Tool allows for optimized electronic chart review using string searches and filters based on prespecified criteria.
The following exclusion criteria were used based on manual review of clinical charts using STRIDE’s data review tool: (1) no confirmation of ICD-9 code diagnosis with clinical documentation, (2) pressure ulcer, (3) oral or mucosal ulcer, (4) primary dermatitis rather than skin wound, (5) ulcer is a primary skin infection or cellulitis, (6) ulcer is actually a primary surgical wound, (7) thrombophlebitis without ulceration, (8) ulcer is actually not an ulcer but a deep vein thrombosis without ulceration, (9) ulcer is actually a fistula, and (10) malignancy within wound.
The patients were followed for 1 year out from the date of diagnosis as documented in physician-authored clinical notes. They were assessed on chart review for wound outcomes. A healing wound or good wound-healing outcome was defined as a wound that was documented by a physician to have healed within 1 year of followup. A nonhealing-wound or poor wound-healing outcome was characterized as either (a) a wound that had not healed by 1 year of followup, (b) a wound that required amputation, or (c) a wound that required flap reconstruction over the followup time. Patients whose wound status was unknown after 1 year of followup were characterized as lost to followup. These were subdivided into those who died, those who returned to Stanford subsequently but lacked an update on wound status on clinical chart review, or those who were never again seen at SUH. All patients who were lost to followup were excluded from subsequent data analyses.
Using the STRIDE data review tool, we manually collected data on demographic and clinical variables including laboratory values. In the case of laboratory values with multiple entries over time, only the lab value which was closest to the date of diagnosis of the ulcer within a 3-month time window of date of diagnosis was used. We additionally collected data on age at ulcer diagnosis, sex, race, current smoking, maximum dimension of wound at diagnosis, and preexisting duration of wound at diagnosis. We also collected data on treatments administered for the wound at Stanford University Hospital and Clinics.
We conducted bivariate analyses comparing mean (parametric variables) or median (nonparametric variables) values of continuous predictors among healing and non-healing wounds, using ANOVA or Kruskal-Wallis tests as appropriate. The association of dichotomous variables with the outcome was assessed using the
As illustrated in Figure
Cohort selection and refinement algorithm and results.
Table
Demographics and characteristics of patients with healing and non-healing wounds1.
Healing wound | Nonhealing wound |
| |
---|---|---|---|
Total number of patients | 68 | 67 | |
Age2 (years) |
|
|
0.21 |
Gender (% male) | 32/68 (47.7%) | 34/67 (50.1%) | 0.92 |
Race (% white) | 49/68 (73.1%) | 47/67 (69.1%) | 0.61 |
Smoking3 (% current smokers) | 8/66 (12.1%) | 7/57 (12.2%) | 0.83 |
Size of wound4,5 (cm) | 1.5 (1.0–4.0) | 1.7 (1.0–5.5) | 0.78 |
Preexisting duration 6,7 of wound (months) | 1.0 (0–2) | 1.0 (0–3) | 0.11 |
Location of wound | |||
Lower extremity (%) | 63/68 (94.0%) | 66/67 (98.5%) | |
Upper extremity (%) | 3/68 (4.4%) | 1/67 (1.4%) | 0.52 |
Other location (%) | 1/68 (1.5%) | 0/67 (0%) |
1Healing wounds were those that healed within 1 year of followup. Non-healing wounds were defined as wounds that required amputation or did not heal after 1 year of followup. 2Data expressed as mean
As shown in Table
Clinical risk factors1,2 associated with non-healing wounds3.
Healing wound ( |
Nonhealing wound ( |
|
|
---|---|---|---|
Diabetes mellitus (%) | 28/68 (41.8%) | 55/67 (82.1%) | <0.001* |
Peripheral neuropathy (%) | 28/66 (42.4%) | 51/67 (76.1%) | <0.001* |
Renal insufficiency (%) | 21/67 (31.3%) | 36/67 (53.7%) | 0.008* |
Peripheral arterial disease (%) | 35/67 (52.2%) | 48/65 (73.8%) | 0.02* |
Venous stasis (%) | 32/65 (49.2%) | 21/63 (33.3%) | 0.067 |
Congestive heart failure (%) | 23/65 (35.3%) | 24/61 (39.3%) | 0.65 |
Immunosuppression3 (%) | 15/67 (22.4%) | 13/68 (19.1%) | 0.64 |
Nondermatologic malignancy4 (%) | 6/68 (8.9%) | 3/67 (4.4%) | 0.49 |
1Presence or absence of clinical risk factors was determined coincident with or prior to diagnosis of skin wound. They were assessed by text review of charts for physician documentation and query of specific tests such as echocardiogram. Where presence or absence of risk factors could not be determined the information was recorded as missing. Actual numbers of recorded data for each risk factor are presented in the table. 2Healing wounds were those that healed within 1 year of followup. Non-healing wounds were defined as wounds that required amputation or did not heal after 1
4Only nondermatologic malignancies co-incident with skin wound were ascertained.
*indicates statistical significance at
Clinical lab data was obtained for a limited subset of the patients as detailed in Table
Laboratory biomarkers1 in patients with healing and non-healing wounds.
Healing wound | Non-healing wound |
| |
---|---|---|---|
Albumin2 (g/dL) |
|
|
<0.01* |
Hb3 (g/dL) |
|
|
0.01* |
WBC4 (cells/mm3) |
|
|
<0.01* |
Random glucose5 (mg/dL) |
|
|
0.13 |
HbA1c6 (%) |
|
|
0.17 |
1Laboratory measurements closest to diagnosis of ulcer but no more than 3 months prior to diagnosis were recorded.
2Based on
Table
Therapeutic interventions in patients with healing and non-healing wounds.
Healing wound ( |
Nonhealing wound ( |
|
|
---|---|---|---|
Topical treatments (%) | 66/68 (97.1%) | 63/67 (94.0%) | 0.44 |
Systemic antibiotics (%) | 38/68 (55.9%) | 55/67 (82.1%) | 0.001* |
Wound debridement (%) | 15/68 (25.9%) | 29/67 (43.3%) | 0.008* |
Peripheral revascularization (%) | 16/68 (23.5%) | 18/67 (26.9%) | 0.65 |
Compression stockings (%) | 4/68 (5.9%) | 6/67 (8.9%) | 0.53 |
Venous ablation or stripping (%) | 5/68 (7.4%) | 0/67 (0%) | 0.058 |
Skin graft (%) | 4/68 (5.9%) | 6/67 (8.9%) | 0.53 |
Wound vacuum (%) | 4/68 (5.9%) | 3/67 (4.5%) | 0.99 |
Hyperbaric oxygen (%) | 1/68 (1.4%) | 1/67 (1.5%) | 0.99 |
1Healing wounds were those that healed within 1 year of followup. Non-healing wounds were defined as wounds that required amputation or did not heal after 1 year of followup. 2Topical treatments included but were not limited to wet-dry dressings, papain ointment, topical antibiotics, silvadene ointment, whirlpool treatment, accuzyme application, and Dakin's soaks. 3Systemic antibiotics refer to antibiotics administered orally or intravenously. 4Wound debridement refers to surgical debridement or debridement in physician's office. 5Peripheral revascularization refers to angioplasty or bypass graft of peripheral arteries. 6Skin graft referred to human skin autograft or synthetic skin graft/Dermagraft. 7Hyperbaric oxygen refers to treatment in hyperbaric oxygen chamber.
*indicates statistical significance at
Table
Adjusted and unadjusted odds of having a poor wound-healing outcome1 (
Predictors | Unadjusted | Adjusted | ||
---|---|---|---|---|
OR2 | 95% CI3 | OR2 | 95% CI3 | |
Diabetes mellitus | 6.38* | 2.89–14.1 | 5.87* | 1.36–25.3 |
Peripheral neuropathy | 4.17* | 1.99–8.76 | 0.97 | 0.24–3.91 |
Renal insufficiency | 2.54* | 1.26–5.15 | 1.32 | 0.42–4.1 |
Need for systemic antibiotics4 | 3.62* | 1.65–7.95 | 3.88* | 1.06–14.2 |
Peripheral arterial disease | 2.31* | 1.13–4.72 | 1.43 | 0.45–4.52 |
Albumin (per unit) | 0.21* | 0.09–0.48 | 0.20* | 0.07–0.60 |
Hemoglobin (per unit) | 0.79 | 0.65–0.95 | 1.05 | 0.73–1.4 |
1Poor wound-healing outcome was defined as wounds that required amputation or did not heal after 1 year of followup. 2OR refers to odds ratio. 395% CI refers to 95% confidence interval. 4All cases of systemic antibiotic administration were reviewed and found to reflect secondary infection of skin wound with the exception of 2 cases which were excluded from this analysis.
*indicates statistical significance at
To our knowledge this is the first large study to quantify the independent impact of multiple clinical risk factors and lab biomarkers on wound outcomes in chronic skin ulcers.
Based on our preliminary analysis of 135 patients, we found that a high number of patients with chronic skin ulcers (49.7%) seen at a tertiary care center suffered extremely poor wound-healing outcomes (amputations or non-healing wound after 1 year of followup). 36.3% of these chronic wounds resulted in amputations. Among patients who had ulcers that ultimately healed within 1 year, median time to healing was 2 months with an interquartile range of 1–5 months. Demographic and behavioral factors such as age, sex, and current smoking status did not vary significantly for healing versus non-healing wounds. In addition, clinical features of the wound such as wound size and preexisting wound duration also did not significantly predict the outcome.
Of all the known clinical co-morbidities for poor wound outcomes such as diabetes, peripheral neuropathy, renal insufficiency, and peripheral arterial disease, the strongest association for poor outcome was diabetes. This was confirmed on multivariate adjustment indicating that diabetes confers additional risk for poor wound healing independent of peripheral neuropathy, macrovascular disease, or renal disease. This suggests that microvascular disease is very important and may indeed be the most critical factor in the pathogenesis of poor wound healing among diabetics, although it is difficult to draw causal conclusions based on this observational data. If validated by other clinical studies, this finding would indicate the need for further research to develop therapies targeted to diabetic microvascular disease.
Several lab biomarkers were significantly predictive of poor wound healing on the univariate analysis including hemoglobin, WBC count, and albumin levels. The association of WBC levels likely reflects underlying inflammation or infection while that of hemoglobin levels with wound healing suggests that local oxygen supply to a wound site through erythrocytes may be an important factor in wound healing [
Only low albumin levels remained significantly associated with poor wound healing upon multivariate adjustment. Hypoalbuminemia could be secondary to underlying malnutrition which would also cause poor wound healing [
In addition to lab biomarkers and clinical comorbidities coexistent with skin ulcers, the secondary infection of these wounds and need for systemic antibiotics were a significant prognosticator of poor outcome. This strongly emphasizes that the prevention of wound infection is critical to avoiding poor outcome such as amputation in skin ulcers. In this context, early or perhaps even prophylactic antibiotic use among high-risk patients (for instance those with diabetes) may be warranted.
There were several limitations of the present study including its observational design and use of ICD-9 codes to designate skin ulcers. However, rigorous chart review to confirm clinical documentation of a skin ulcer by a physician was done in order to ensure that all those included in the study had actual skin ulcers that met our rigorous inclusion and exclusion criteria. Regarding the observational design, while an attempt was made to control known demographic, clinical, and lab risk factors, there may still be unmeasured confounding. Additionally, there was a high loss to followup in our data largely due to the nature of retrospective review of patient charts. While some patients were true losses to followup in that they did not return to Stanford or died, there were several for whom no followup data on their skin ulcers was noted. The large losses to followup from these sources introduce the possibility of selection bias in our results. Future work may include a sensitivity analysis to examine the impact of that loss to followup on our study results. Another problem associated with electronic chart review is a high rate of missing data on lab and other covariates. Since we limited the selection of lab values to those within a clinically meaningful time window in relation to the diagnosis of skin ulcers, our analyses on lab biomarkers of wound healing were limited by small sample sizes.
In the future, this analysis will be extended to additional patients discovered through the cohort discovery tool. This will increase the power for a prognostication model that will incorporate a wide range of both clinical and lab biomarkers. This will allow us to comprehensively risk stratify patients to identify those who would most benefit from early and aggressive wound care therapies such as hyperbaric oxygen, wound vacuum, or skin grafting [
The authors would like to thank Dr. Gomathi Krishnan for all her help retrieving data from the STRIDE system and Olena Mykhaylichenko for administrative assistance.