Potential prognostic factors for delayed healing of common, non-traumatic skin ulcers: A scoping review.

Healing of non-traumatic skin ulcers is often suboptimal. Prognostic tools that identify people at high risk of delayed healing within the context of routine ulcer assessments may improve this, but robust evidence on which factors to include is lacking. Therefore, we scoped the literature to identify which potentially prognostic factors may warrant future systematic reviews and meta-analyses. We conducted electronic searches in MEDLINE and Embase to identify studies in English published between 1997 and 2017 that tested the association between healing of the three most common non-traumatic skin ulcers encountered by health care professionals (venous leg, diabetic foot, and pressure ulcers) and patient characteristics, ulcer characteristics, and results from clinical investigations. We included 42 studies that investigated factors which may be associated with the healing of venous leg ulcers (n = 17), diabetic foot ulcers (n = 15), and pressure ulcers (n = 10). Across ulcer types, ulcer characteristics were most commonly reported as potential prognostic factors for healing (n = 37), including the size of the ulcer area (n = 29) and ulcer duration at first assessment (n = 16). A total of 35 studies investigated the prognostic value of patient characteristics (n = 35), including age (n = 31), gender (n = 30), diabetes (n = 22), smoking status (n = 15), and history of deep vein thrombosis (DVT) (n = 13). Of these studies, 23 reported results from clinical investigations as potential prognostic factors, with the majority regarding vessel quality. Age, gender, diabetes, smoking status, history of DVT, ulcer area, and ulcer duration at time of first assessment warrant a systematic review and meta-analysis to quantify their prognostic value for delayed ulcer healing.

BACKGROUND

Venous leg ulcers and pressure ulcers are the most common types of complex, non‐traumatic skin ulcers, each with an estimated point prevalence of around 0.3% in the United Kingdom and between 0.05% and 1.52% in the United States.1 Foot ulcers in people with diabetes are also relatively common, with a total UK point prevalence of 0.1%,2 which amounts to a 5.5% prevalence in the UK diabetic population.3 In North America, ulcer prevalence in the diabetic population is estimated to be 13%.3

Skin ulcers can be exceedingly painful and distressing for patients and can impair independence and health‐related quality of life.4 The care of these complex ulcers is costly to health services, largely because of the volume of nursing time required. For example, in 2016, the annual cost for treating and managing pressure ulcers in the United Kingdom and the United States was estimated at approximately £2.6 billion5 and $9.1 to $11.6 billion,6 respectively; for foot ulcer in people with diabetes, estimates were £650 million5, 7 and $9 to $13 billion, respectively.3

Whilst many venous leg ulcers, pressure ulcers, and foot ulcers will heal, for some people, this will be protracted, with some never fully healing. A recent study of 247 people with venous leg ulcers reported that 62% of ulcers had healed within 24 weeks, with the other 38% remaining unhealed at the time of follow up.8 Another study9 on venous leg ulcers reported a median time to healing in three treatment arms of 84, 77, and 91 days. Studies in diabetic foot10 and pressure ulcers11 have reported a median healing time of 10 weeks.

Prognostic tools are used in several disease areas to identify patients at risk of a certain outcome and to aid clinical decisions or manage resources.12 There is scope to use such tools to predict slow healing risk in those with common, non‐traumatic skin ulcers. Yet, there is no overarching intelligence about who is likely to heal and who is not, who may benefit from targeted healing‐oriented intervention and who will not, and whether we can use resources more efficiently by targeting those at highest risk.

Prognosis research is invaluable in providing ways to answer these questions. However, available tools only use ulcer characteristics (eg, ulcer size, tissue type) to predict healing time,13 whereas others require variables that may be difficult to measure as part of regular ulcer assessments in practice (eg, ankle‐brachial pressure index).14 To further improve these tools, we need robust evidence on what factors may have prognostic value for assessing the risk of delayed ulcer healing. Parker et al reviewed the literature up to 2013 in order to identify risk factors for delayed venous leg ulcer healing,15 informing the subsequent development of a prognostic tool.16 However, they applied a limited set of search terms related to prognostic factors, which was not in line with Cochrane guidance (http://methods.cochrane.org/prognosis). Furthermore, it is unclear to what extent Parker's findings generalise to other ulcer types.

Therefore, we conducted a scoping review of the literature to gain insight into which factors may have potential prognostic value for delayed healing of several common non‐traumatic skin ulcer types, with a focus on factors that can be collected as part of routine ulcer assessments. We expect our findings to guide future decisions about what potential prognostic factors should be prioritised for further investigations through systematic reviews and meta‐analyses.

METHODS

We designed and reported our scoping review guided by Arksey and O'Malley's17 framework, further recommendations by Levac et al18 and the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses statement.19

Search strategy

We searched Medline and Embase via Ovid for publications in English between 1997 and 2017. The search syntax consisted of terms related to the three ulcer types (informed by input from the Cochrane Wounds group, http://wounds.cochrane.org), combined with terms for prognosis, prognostic factors, and prediction models, as recommended by the Cochrane Prognosis Methods Group (http://methods.cochrane.org/prognosis); the full search syntax is available in Appendix S1.

Study selection

We assessed the eligibility of studies through a two‐stage screening process. Studies were eligible if they adhered to the following inclusion criteria:

Conducted in an adult population with venous leg ulcers, diabetic foot ulcers, or pressure ulcers. These three non‐traumatic skin ulcers types were selected because of their high prevalence. Other less common ulcer types, such as vasculitic ulcers, were therefore excluded. Traumatic ulcers, such as those arising from surgery or damage to the skin caused by thermal or chemical means, were excluded because of the different aetiology and management of these ulcer types.

Investigating ulcer healing as an outcome. We accepted authors' definitions of ulcer healing and excluded studies that focused on other outcomes, such as ulcer infection or recurrence.

Performed a statistical test on the relationship between individual, potential prognostic factors and ulcer healing as an outcome. We included results of clinical investigations as factors (eg, common blood and urine tests, ankle‐brachial pressure index, transcutaneous oxygenation saturation measurements) but excluded those for which measurement would be impractical in the context of routine ulcer assessments (eg, genetic factors). We also excluded papers that did not report significance levels for individual factors when evaluating a score or model combining multiple factors because this would make them ineligible for inclusion in future meta‐analyses.

Designed as an observational cohort study. We excluded randomised controlled trials (RCTs) because they aim to determine the effect of treatment on ulcer healing while minimising the impact of other influencing factors, whereas these factors are the primary focus of our review. We did include studies that retrospectively used data from a single arm of a trial.

Original studies, thereby excluding review articles and contributions to conference proceedings.

We first screened the titles and abstracts of all articles. Two authors (D.J. and S.M.) independently screened 50% of articles each and a random sample of 10% in duplicate. Any disagreement was solved through discussion. For all studies deemed relevant, the full text was reviewed using the same screening procedure as in the first stage.

Data extraction and synthesis

We developed a structured form to aid extraction of items related to: general study characteristics (year of publication, country, study setting), study population (type of ulcer, sample size), length of follow up, if data collection was retrospective or prospective, and association between potential prognostic factors and the outcome (outcome definition, factors for which an association with the outcome had been tested [ie, potential prognostic factors], statistical method used). Data for all papers were extracted by one author (D.J.); data from a random sample of 20% of included studies were extracted independently by a second author (S.M.), with discrepancies solved through discussion.

Two researchers (S.V. and J.T., who is a clinician with experience of managing skin ulcers) independently reviewed all factors and categorised them as patient characteristics (eg, age, comorbidities, history of ulcers), ulcer characteristics and treatment (eg, size, depth, duration), or results from clinical investigations (eg, ankle‐brachial pressure index, serum albumin). Ulcer characteristics and treatment were defined as any observation, measurement, or treatment specific to the current ulcer site and immediate surrounding area. Clinical investigations included clinical measurements, clinical imaging, biochemical analysis of blood and urine, and microbiology results. Where needed, the same two researchers independently grouped similar factors into further subcategories to aid the synthesis of results, with discrepancies solved through discussion. For example, the “Patient characteristics” category included subcategories, such as “Socio‐economic status” (consisting of, eg, marital status, income, and educational attainment) and “Cardiovascular disease” (covering conditions such as congestive heart failure, peripheral arterial disease, and angina).

In order to identify potential prognostic factors that may warrant a systematic review, we selected factors that had been investigated by at least 10 studies and, for these factors, assessed if they had been defined in ways that were comparable across studies.

RESULTS

The search yielded 6798 unique studies. We reviewed the full text of 72 studies, of which 42 were included. The most common reason for exclusion was that studies did not report ulcer healing as an outcome. Figure 1 shows the flow chart for study selection.

Figure 1

Flow diagram for the identification of relevant studies

Characteristics of included studies

Table 1 displays the characteristics of included studies. Of the 42 studies included, 16 had been conducted in the United States, and for all three ulcer types, this was the most common country of origin. Most studies took place in an outpatient (26/42) or inpatient (8/42) setting; one study included patients from both.32 Studies in community settings (2/42) and nursing homes (2/42) were less common.

Table 1

Characteristics of included studies

Reference	Publication year	Country	Setting	Wound type	Sample size	Maximum follow up	Data collection	Outcome	Statistical methodsa
Abbade et al20	2011	Brazil	Outpatient	VLU	90	>10 years	NR	Ulcer not healed after >10 years	t test and regression
Barwell et al21	2000	United Kingdom	Community	VLU	587	24 wk	Prospective	Time to healing	Cox regression
Beckert et al22	2006	Germany	Outpatient	DFU	1000	1 year	Prospective	Time to healing	Cox regression
Berlowitz et al23	1998	United States	Nursing homes	PU	819	6 mo	Retrospective	Healed at follow‐up	t test and regression
Cardinal et al24	2009	United States	NR	VLU	338	12 wk	Retrospective	100% closure	Regression
Chaby et al25	2013	France	Outpatient	VLU	104	24 wk	Prospective	Healed at follow‐up	Regression
Christman et al26	2011	United States	Outpatient	DFU	183	“Patient‐specific”	Retrospective	Wound area change per day	Regression
Gohel et al27	2005	United Kingdom	Outpatient	VLU	1186	24 wk	Prospective	Healed at follow‐up	Cox regression
Hjerppe et al28	2010	Finland	Outpatient	VLU	50	12 wk	Prospective	Healed at follow‐up	χ 2 tests
Horn et al29	2015	United States	Outpatient	PU	NRb	NR	Retrospective	Healed at follow‐up	t test and regression
Ince et al30	2007	United Kingdom	Outpatient	DFU	449	1 year	Prospective	Time to healing	Cox regression
Jemec31	1999	Denmark	Outpatient clinic	VLU	79	18 moc	Prospective	Wound size and healed	χ 2 tests and regression
Jones and Fennie32	2007	United States	Inpatient and outpatient	PU	114	6 mo	Retrospective	Healed at follow up	t test
Kantor and Margolis33	1999	United States	NR	VLU	104	24 wk	Retrospective	Healed at follow up	Wilcoxon rank sum
Kapoor et al34	2008	United States	Nursing homes	PU	2666	90 d	Retrospective	Healed at follow up	Regression
Labropoulos et al35	2011	United States	NR	VLU	127	6 mo	Prospective	Not‐healed by 6 mo	t test
Margolis et al36	2003	United States	Outpatient	DFU	19 280	20 wk	Retrospective	Healed at follow up	Regression
Margolis et al37	1999	United States	Outpatient	VLU	260	24 wk	Retrospective	Healed at follow up	Regression
McGinnis et al38	2013	United Kingdom	Outpatient	PU	140	2 years	Prospective	Time to healing	Cox regression
Meaume et al39	2005	France	Outpatient	VLU	330	6 wk	Prospective	Area reduction of >40%	Regression
Moffatt et al40	2009	United Kingdom	Inpatient and outpatient	VLU	113	48 wk	Prospective	Time to healing	Cox regression
Monami et al41	2008	Italy	Inpatient	DFU	80	6 mo	Prospective	Healed	t test and cox regression
Oyibo et al42	2000	United Kingdom and United States	Outpatient	DFU	194	18 mo	Prospective	Area and time to healed	Correlation and cox regression
Park43	2014	Korea	Inpatient critical care	PU	155	1 year	Retrospective	Healed	t test and regression
Parker et al44	2016	Australia	Outpatient	VLU	247	24 wk	Retrospective	Healed at follow up	t test and regression
Rhou et al45	2015	Australia	Outpatient	DFU	107	12 wk	Retrospective	Healing rate and healed at follow up	t test and regression
Ribu et al46	2008	Oslo, Norway	Outpatient	DFU	99	12 mo	Prospective	Healed at follow up	t test and anova
Scotton et al47	2014	Finland	Outpatient	VLU	94	>1 year	Retrospective	>50% reduction at 6 and 12 mo	Regression
Snyder et al48	2010	United States	Outpatient	DFU	250	12 wk	Retrospective	Wound closure	t test
Sung and Park49	2011	Korea	Inpatient critical care	PU	158	NR	Retrospective	PUSH scale healingd	t test and regression
Takahashi et al50	2009	United States	Wound service in primary care	PU	440	6 mo	Retrospective	Healed at follow up	Regression
Taylor et al51	2002	United Kingdom	Outpatient	VLU	325	104 wk	Retrospective	Time to healed	Cox regression
Vedhara et al52	2010	United Kingdom	Outpatient	DFU	93	24 wk	Prospective	Healed at follow up	Regression
Wallenstein and Brem53	2004	United States	Inpatient	PU	45	12 wk	Prospective	Wound size, time until closure, and % reduction	Gompertz modele
Wang et al54	2014	China	Inpatient	DFU	194	1 year	Retrospective	Healed at follow up	t test and regression
Warriner et al55	2011	United States	NR	DFU	120	12 wk	Retrospective	Healed at follow up	Fisher test
Wielen et al56	2016	Switzerland	Inpatient	PU	55	Hospital length of stay	Prospective	Time until closure	t test
Wipke‐Tevis and Stotts19	1998	United States	Inpatient	VLU	25	4 wk	Prospective	Wound‐healing rate	Correlation and contingency
Yang et al57	2016	Canada	Outpatient	VLU	522	1 year	Retrospective	Healed at follow up	Regression
Yotsu et al58	2014	Japan	Inpatient	DFU	84	NR	Prospective	Healed	t test and anova
Zimny and Pfohl59	2005	Germany	Outpatient	DFU	41	>10 wk	Prospective	Healing time and wound area	Correlation
Zimny et al10	2002	Germany	Outpatient	DFU	31	25 wk	Prospective	Healing time and wound area reduction	Correlation and regression

Abbreviations: anova, analysis of variance; DFU, diabetic foot ulcer; NR, not reported; PU, pressure ulcer; PUSH, pressure ulcer score for healing; VLU, venous leg ulcer.

Statistical method/test performed to determine if factors were associated with healing.

Patient numbers not reported, but paper reported 7349 body pressure ulcers and 2112 heel pressure ulcers.

Mean follow up.

PUSH which uses a score from 0 to 10 based on ulcer characteristics.

Univariate time series model considering the relationship between an outcome and time (eg, area‐time relationship).

Of the three ulcer types, venous leg ulcers were considered in 17 of 42 of the studies, foot ulcers in 15 of 42, and pressure ulcers in 10 of 42. Sample sizes (ie, participants recruited) ranged from 2519 to 19 280,36 with a median of 155 across studies. The majority of studies included fewer than 1000 patients (38/42). The maximum follow‐up period was reported in 35 studies and ranged from 4 weeks19 to greater than 10 years,20 with a median follow up of 24 weeks. Most studies (38/42) had a follow up of a year or less, with only one study20 reporting a follow up longer than 2 years. In two studies, follow up depended on length of hospital stay26 or time to the next clinic visit.49

Most studies (24/42) defined ulcer healing as a dichotomous outcome indicating whether the ulcer had healed (yes/no); another 10 used time to complete healing. Other definitions were: percentage of ulcer area reduction39, 47; healing rate19; and the pressure ulcer scale for healing score,49 which comprises of sub‐scores for ulcer size, tissue type, and exudate. Four studies considered more than one definition.10, 45, 53, 59 Overall, almost half of the studies (20/42) were prospective, but only three investigated pressure ulcer healing.38, 53, 56 The most common statistical method used to analyse the data was regression analysis (30/42), and this was Cox regression in nine cases. t tests were the second most used statistical analysis performed and was used in 15 studies, some of which also used regression.

Potential prognostic factors

Table 2 shows the result of the categorisation of potential prognostic factors for which studies investigated the association with ulcer healing; see Appendix S2 for the factors as originally reported by the authors of the studies. The number of factors investigated in individual studies ranged from 2 to 45 (median, 15). Factors regarded patient characteristics, ulcer characteristics, or results of clinical investigation in 35 of 42, 37 of 42 and 23 of 42 studies, respectively. Six studies investigated the prognostic value of factors related to only one category, whereas 14 investigated an association with ulcer healing for factors pertaining to two categories, and 22 investigated potential factors related to all three categories.

Table 2

Factors investigated in each included study as potential prognostic factors for ulcer healing; [number] reflects the number of individual factors assigned to that subcategory in case there was more than 1

Reference	Total no. of factors investigated	Potential prognostic factors
		Patient	Ulcer	Clinical investigation
Diabetic foot ulcers (15 studies)
Beckert et al22	4	Local physical manifestations of poor vessel quality	Depth, location, no. of ulcers
Christman et al26	15	Age, blood pressure [2], BMI, body temperature, cardiovascular disease, ethnicity, gender, neuropathy, pulse, smoking status	No. of ulcers	Glycaemic control, inflammatory markers, lipid profile
Ince et al30	12	Age, cardiovascular disease, diabetes [2], gender, non ulcer infection, socio‐economic status [2]	Area, depth, location, time to assessment
Margolis et al36	7	Age, gender	Area, duration, no. of ulcers, response to treatment, severity
Monami et al41	8	Depression, other comorbidities and health issues, smoking status	Area, duration, severity	Glycaemic control, quality of vessels
Oyibo et al42	9	Age, diabetes [2], gender	Area, depth, location, ulcer infection	Quality of vessels
Rhou et al45	26	Age, alcohol consumption, antibiotics use, cardiovascular disease [2], diabetes, gender, kidney disease, medications as proxy for comorbidities/other health issues [5], smoking status	Area, depth, ulcer infection	Alkaline phosphatase, glycaemic control, liver profile [4], renal function, serum albumin/total protein, urate
Ribu et al46	10	Cognitive status and mental health, Cognitive status and mental health [2], Functional status [3], Overall health [3], Pain
Snyder et al48	2		Area, response to treatment
Vedhara et al52	21	Age, BMI, cancer, cardiovascular disease [2], cognitive status and mental health, depression, diabetes, gender, history of previous ulcers, hypertension, musculoskeletal disease, neuropathy, other comorbidities and health issues, smoking status, socio‐economic status [2]	Area, ulcer infection	Glycaemic control, quality of vessels (imaging)
Wang et al54	32	Age, blood pressure [2], cardiovascular disease, diabetes [3], gender, history of previous ulcers, neuropathy, smoking status, socio‐economic status	Area, duration, location, severity	Glycaemic control [2], inflammatory markers [2], lipid profile, positive microbiology, quality of vessels [2], renal function [3], serum albumin/total protein [2], serum haemoglobin, thyroid function, urine albumin
Warriner et al55	3		Area [3]
Yotsu et al58	19	Age, diabetes, gender, history of previous ulcers, kidney disease, other comorbidities and health issues	Area [2], location, necrosis, no. of ulcers, severity, ulcer infection	Quality of vessels [3], renal function, serum albumin/total protein, serum haemoglobin
Zimny and Pfohl59	2		Area [2]
Zimny et al10	2		Area [2]
Pressure ulcers (10 studies)
Berlowitz et al23	19	Age, functional status [2], gender, hospital admission, immobility [5], incontinence, kidney disease, medications as proxy for comorbidities/other health issues, non ulcer infection, other comorbidities and health issues, socio‐economic status, terminal illness	Ulcer type/aetiology
Horn et al29	45	Age, autoimmune disease [3], BMI, Braden score, cardiovascular disease [2], cognitive status and mental health, diabetes [2], history of previous ulcers, gender, hospital admission, immobility [3], incontinence, kidney disease [4], liver dysfunction, medications as proxy for comorbidities/other health issues [2], medications negatively affecting ulcer healing, musculoskeletal disease, non‐traumatic amputation, nutrition [2], other comorbidities and health issues [4], smoking status, socio‐economic status, transplantation, transplantation	Area, duration [2], location, severity, ulcer infection	Quality of vessels (imaging)
Jones and Fennie32	41	Age, BMI, cancer, cardiovascular disease [2], cognitive status and mental health, depression, diabetes, DVT, electrolyte imbalance, ethnicity, gender, history of previous ulcers, hypertension, insurance, Kidney disease, musculoskeletal disease, neuropathy [2], nutrition, other comorbidities and health issues [6], smoking status, socio‐economic status [2], stroke, total number of comorbidities	Area[2], depth, exudate [2], location, necrosis, no. of ulcers [2], severity [2]
Kapoor et al34	11	Age, gender, history of previous ulcers, immobility [3], incontinence [2], terminal illness	Severity [2]
McGinnis et al38	17	Age, Braden score, cardiovascular disease, ethnicity, gender, hospital admission, medications as proxy for comorbidities/other health issues, neuropathy, nutrition, other comorbidities and health issues, pain, smoking status	Area, duration, severity, surrounding skin condition, tissue type
Park43	30	Age, blood pressure, Braden score, cancer, cardiovascular disease [2], diabetes, gender, history of previous ulcers, hypertension, immobility, incontinence, medications as proxy for comorbidities/other health issues, medications negatively affecting ulcer healing, musculoskeletal disease, nutrition [2], other comorbidities and health issues [4], smoking status, stool form	Area, exudate, location, tissue type, ulcer infection	Serum albumin/total protein, serum haemoglobin
Sung and Park49	30	Age, blood pressure, Braden score, cancer, cardiovascular disease, cardiovascular disease, diabetes, gender, history of previous ulcers, hypertension, immobility, incontinence, medications as proxy for comorbidities/other health issues, medications negatively affecting ulcer healing, musculoskeletal disease, nutrition, nutrition, other comorbidities and health issues [4], smoking status, stool form	Area, exudate, location, tissue type, ulcer infection	Serum haemoglobin, serum albumin/total Protein
Takahashi et al50	23	Age, BMI, cancer, cardiovascular disease [3], diabetes, gender, kidney disease, musculoskeletal disease [2], neuropathy, other comorbidities and health issues [4], stroke	Area, no. of ulcers	Glycaemic control, inflammatory markers, quality of vessels, renal function
Wallenstein and Brem53	1		Area
Wielen et al56	10	Age, gender, hospital admission, trauma	Duration, location, severity [2], ulcer status, ulcer type/aetiology
Venous leg ulcers (17 studies)
Abbade et al20	19	Age, BMI, diabetes, DVT, gender, history of previous ulcers, hypertension, local physical manifestations of poor vessel quality [2], multiparity	Area	Quality of vessels [8]
Barwell et al21	12	Age, diabetes, gender, immobility, musculoskeletal disease	Area, duration	Quality of vessels [5]
Chaby et al25	41	Age, BMI, cardiovascular disease [2], cognitive status and mental health [3], depression, DVT, functional status [2], gender, history of previous ulcers, insurance [2], kidney disease, local physical manifestations of poor vessel quality [2], musculoskeletal disease [3], other comorbidities and health issues, pain, prior venous surgery, socio‐economic status [8]	Area [2], duration, response to treatment	Quality of vessels [3], serum albumin/total protein, serum haemoglobin
Cardinal et al24	33	Age, alcohol consumption, BMI [3], cardiovascular disease [2], diabetes, DVT, gender, history of previous ulcers, hypertension, immobility, local physical manifestations of poor vessel quality [4], musculoskeletal disease [2], other comorbidities and health issues, pain, smoking status, stroke	Area [2], duration, exudate, location, necrosis, no. of ulcers, oedema, response to treatment	Quality of vessels
Gohel et al27	12	Age, diabetes, DVT, gender, musculoskeletal disease	Duration, location	Quality of vessels [5]
Hjerppe et al28	14	Age, BMI, diabetes, gender, immobility, medications as proxy for comorbidities/other health issues, smoking status		Quality of vessels [7]
Jemec31	13	Age	Area, duration	Alkaline phosphatase, electrolytes [2], glycaemic control, inflammatory markers, liver profile, quality of vessels, renal function, serum albumin/total protein, serum haemoglobin
Kantor and Margolis33	5		Area [5]
Labropoulos et al35	6	Age, BMI, DVT [3]	Area
Margolis et al37	26	Age, cardiovascular disease [2], diabetes, DVT, ethnicity, gender, hypertension, immobility, insurance, local physical manifestations of poor vessel quality [3], musculoskeletal disease, oedema, other comorbidities and health issues, stroke	Area, duration, no. of ulcers, tissue type [2], ulcer type/aetiology	Quality of vessels [3]
Meaume et al39	16	Age, BMI, cardiovascular disease [2], diabetes, DVT, gender, hypertension, musculoskeletal disease, smoking status, vascular surgery	Area, duration, infection, no. of ulcers, recurrent ulcer
Moffatt et al40	23	Age, diabetes, DVT, gender, immobility, local physical manifestations of poor vessel quality [4], musculoskeletal disease [2]	Area, duration, ulcer type/aetiology	Positive microbiology [6], quality of vessels [3]
Parker et al44	25	Autoimmune disease, depression, DVT, functional status [2], local physical manifestations of poor vessel quality, medications as proxy for comorbidities/other health issues [3], musculoskeletal disease [3], other comorbidities and health issues, pain, socio‐economic status	Area [2], duration, exudate, oedema, pain, pain, PUSH score, response to treatment, tissue type
Scotton et al47	16	Age, antibiotics use [2], diabetes, gender, hypertension, immobility	Area, duration, location, response to treatment [2], ulcer infection [2], ulcer type/aetiology	Quality of vessels
Taylor et al51	31	Age, blood pressure [2], BMI, cardiovascular disease, diabetes, DVT, gender, history of previous ulcers, immobility, local physical manifestations of poor vessel quality, musculoskeletal disease, smoking status, socio‐economic status	Area, duration [2], exudate, location, pain, surrounding skin condition [4], tissue type [4], ulcer status	Quality of vessels [2]
Wipke‐Tevis and Stotts19	8	Gender, nutrition [3]		Inflammatory markers, quality of vessels [2], serum albumin/total protein
Yang et al57	15	Age [2], BMI, cardiovascular disease [3], diabetes, DVT, gender, hypertension, musculoskeletal disease, smoking status, trauma, vascular surgery	Area

Abbreviations: BMI, body mass index; DVT, deep vein thrombosis; PUSH, pressure ulcer score for healing.

Empty cells mean that no potential factors were identified.

Medications negatively affecting ulcer healing include steroids, immunomodulating drugs, and anti‐coagulants known to prolong bleeding time.

Local physical manifestations of poor vessel quality include clinical findings such as lipodermatosclerosis, varicose eczema, hyperpigmentation, etc.

PUSH score which uses ulcer characteristics to provide a score between 0 and 10.

Patient characteristics

In the 36 studies investigating patient characteristics, age was investigated as a possible prognostic factor in 31 studies (11 in diabetic foot ulcers, 14 in venous leg ulcers, and 9 in pressure ulcers). This was the most commonly investigated factor, followed by gender, which was considered in 30 studies. Most studies considered age a continuous variable, whereas one used categories.20 Socio‐economic status (eg, marital status, educational level, home ownership) was considered in 10 studies. The three comorbidities most commonly investigated as a potential prognostic factor were diabetes (23/42: 7 diabetic foot, 10 venous leg, and 5 pressure ulcer studies); cardiovascular disease (17/42); and musculoskeletal disease (16/42). The subcategory of diabetes was defined homogeneously as most studies discussed diabetes as a dichotomous variable, labelling patients as diabetic or not. However, cardiovascular disease contained angina (n = 2), peripheral arterial disease (n = 6), and several other conditions grouped under this subcategory. Musculoskeletal disease was also very heterogeneous, with rheumatoid arthritis (n = 6) being the most common factor in this subcategory. Other patient characteristics investigated as possible prognostic factors included: smoking status (15/42), history of deep vein thrombosis (DVT; 13/42), body mass index (BMI; 13/42), and immobility (12/42). While smoking status and history of DVT were defined relatively homogeneously across studies (mostly as current smoker yes/no and prior DVT yes/no), immobility contained many definitions, from walking aid use to paralysis; BMI was considered either a continuous or categorical variable with different categories across studies.

Ulcer characteristics

Ulcer characteristics were the most commonly investigated potential prognostic factor (39/42). Overall, 14, 15, and 10 studies investigated the association of at least one ulcer characteristic with the healing of diabetic foot ulcers, venous leg ulcers, and pressure ulcers, respectively. Across ulcer types, the size of the ulcer area was most frequently reported (33/42). Most studies (26/42) defined ulcer areas as the complete area of the ulcer in centimetres squared at baseline (ie, time of the first assessment), with only some studies using a different definition (eg, 90% area reduction in area at 4 weeks48 or area change in first 2 weeks33). Duration of ulcer at baseline (mostly reported in days and used as a continuous variable in the models) was the next most reported factor (17/42 studies), followed by ulcer location (14/42). However, ulcer location had a number of definitions, and no single definition was used across 10 or more studies; for example, 1 study29 considered which side of the body the ulcer was, while others22 used the categories of toe ulcer or foot ulcer.

Results of clinical investigations

A total of 23 studies considered the results of clinical investigations a possible prognostic factor for ulcer healing, with 13 of these concerned the assessments of vessel quality (eg, through imaging tests). However, this was very homogeneous and included a variety of investigations for vessel quality (eg, transcutaneous oxygen pressure, skin perfusion pressure, and ankle‐brachial index). Abbade et al20 considered results from seven imaging tests possible prognostic factors. Other results from clinical investigations included serum albumin (8/42), serum haemoglobin (7/42), glycaemic control (eg, HAb1C) (7/42), and renal function (eg, estimated glomerular filtration rate) (5/42). Only two studies investigated microbiology clinical investigations.40, 54

DISCUSSION

Summary of findings

We conducted a scoping review and identified a body of literature investigating potential prognostic factors for healing of venous leg ulcers, diabetic foot ulcers, and pressure ulcers that could be assessed in routine care settings. We included 42 papers, from which we identified age, gender, smoking status, diabetes, musculoskeletal disease, cardiovascular disease, immobility, ulcer area, ulcer location, ulcer duration at time of first assessment, and vessel quality as the factors that were most often investigated as a potential prognostic factor for ulcer healing. Of these, we selected those that were defined sufficiently homogeneously across studies in order to be investigated in a future systematic review and meta‐analysis; all selected factors are listed under “Recommendations for future research” below.

Relation to other studies

Parker et al15 conducted a literature review in 2015 on risk factors for delayed healing in venous leg ulcers, including 27 papers published between 2000 and 2013. Our review updates their work while extending search terms for prognostic factors in line with Cochrane guidance (http://handbook-5-1.cochrane.org/) and broadening the scope to include two additional prevalent ulcer types: diabetic foot and pressure ulcers. There were several studies in Parker's review that were captured in our search but did not meet our inclusion criteria. The main reasons for excluding these papers were: ineligible population (ie, also including people with arterial ulcers), ineligible study design (randomised control trial), ineligible publication type (literature review), or analysing effectiveness of an intervention. Parker's review identified ulcer area, ulcer duration, DVT, and history of previous ulcers as consistently reported risk factors for healing. The former three factors were also identified by our review, which suggests that they might have prognostic value across ulcer types.

Recommendations for future research

Age, gender, diabetes, smoking status, history of DVT, ulcer area, and ulcer duration at time of first assessment were investigated in a substantial number of studies, with definitions being sufficiently homogeneous. These potential prognostic factors therefore each warrant a dedicated systematic review and meta‐analysis. Effect sizes of each factor may vary between ulcer types; applying meta‐regression60 would adjust for this. Another option would be to address each ulcer type in a separate meta‐analysis, but this would reduce statistical power. However, there is probably sufficient similarity between ulcer types to warrant a meta‐analysis that pools the results across types, thus increasing statistical power. Individual patient data meta‐analyses61 are recommended as the gold standard for reducing heterogeneity and standardisation of definitions, but this will require access to individual patient data of all included studies, which is known to be difficult. Access to individual patient data would also allow access to raw data and not rely on categories used in the studies, which would open up possibilities to investigate additional factors in a meta‐analysis, such as BMI.

Many studies included in our review had short follow‐up times and small sample sizes, which will negatively affect the precision of the individual study effect sizes for the potential prognostic factors. Future meta‐analyses will increase this precision by combining evidence across studies into a single combined estimate of prognostic effect size, which will contribute to a better understanding of prognostic factors for ulcer healing. However, the robustness of findings from meta‐analyses investigating prognostic factors depends on the quality and risk bias of included studies,17 and future systematic reviews and meta‐analyses would need to assess this, for example, using the Quality in Prognostic studies (QUIPS) tool.62

Results from clinical investigations may have prognostic value for ulcer healing, but the current evidence in observational studies is too scarce to warrant systematic reviews in this area. Therefore, future research may focus on conducting larger cohort studies investigating the association between results from clinical investigation and ulcer healing. While we excluded comparative effectiveness studies, a recent Cochrane review63 included RCTs to investigate the prognostic value of protease for the healing of venous leg ulcers, resulting in the inclusion of 11 studies for meta‐analysis. A hierarchical approach or down‐weighting of observational data through the use of a power prior would be advised64 in this circumstance.

Strengths and limitations of this review

To our knowledge, this scoping review is the first to investigate potential prognostic factors across the most common ulcer types. We used rigorous and transparent methods, including a reproducible search strategy; duplicate assessment of articles for relevance independently by two authors, and use of a pre‐determined data extraction template. A recent study65 assessing the methodological quality of 494 scoping reviews found that only 22% reported a search strategy, 36% had used two reviewers, and 43% had a data extraction template.

Our scoping review also has limitations. We did not consult stakeholders as the last phase of the review process.66 This may have resulted in additional relevant studies being missed. In addition, we excluded studies on the potential prognostic value of genetic factors because they are currently difficult to include in routine ulcer assessments. Future scoping reviews should investigate this further as soon as simple genetic tests become available.

CONCLUSION

Age, gender, diabetes, smoking status, ulcer area, ulcer location, and ulcer duration at time of first assessment are potential prognostic factors that warrant a systematic review and meta‐analysis to quantify their value for predicting delayed healing of common non‐traumatic skin ulcers during routine assessment. This will contribute to optimising our understanding of ulcer healing and provide valuable information for clinical practice and guidelines.

Appendix S1. Search strategy.

Click here for additional data file.

Appendix S2.

Click here for additional data file.