| Literature DB >> 31605618 |
H Van Tiggelen1, K LeBlanc2,3, K Campbell2, K Woo4, S Baranoski5, Y Y Chang6, A M Dunk7,8, M Gloeckner9, H Hevia10, S Holloway11, P Idensohn12,13,14, A Karadağ15, E Koren16,17, J Kottner1,18, D Langemo19,20, K Ousey21,22,23, A Pokorná24, M Romanelli25, V L C G Santos26,27, S Smet28, G Tariq29, K Van den Bussche1, A Van Hecke30,31, S Verhaeghe30,32, H Vuagnat33, A Williams34, D Beeckman1,23,35,36,37.
Abstract
BACKGROUND: Skin tears are acute wounds that are frequently misdiagnosed and under-reported. A standardized and globally adopted skin tear classification system with supporting evidence for diagnostic validity and reliability is required to allow assessment and reporting in a consistent way.Entities:
Mesh:
Year: 2019 PMID: 31605618 PMCID: PMC7384145 DOI: 10.1111/bjd.18604
Source DB: PubMed Journal: Br J Dermatol ISSN: 0007-0963 Impact factor: 9.302
Classification of photographs by three experts
| Type | No. of photographs | |||
|---|---|---|---|---|
| Nonpigmented skin ( | Pigmented skin ( | Total ( | ||
| 1 | No skin/flap loss | 8 | 0 | 8 |
| 2 | Partial skin/flap loss | 5 | 3 | 8 |
| 3 | Total skin/flap loss | 8 | 0 | 8 |
The set of 24 photographs used in both survey 1 (test) and survey 2 (retest) was identical.
Figure 1The International Skin Tear Advisory Panel (ISTAP) Classification System.
Participant demographics
| Test ( | Retest ( |
| |
|---|---|---|---|
| Sex | 0·901 | ||
| Female | 1432 (89·4) | 853 (89·6) | |
| Mean ± SD age (y) | 41·2 (12·2) | 42·1 (11·7) | 0·131 |
| Role | 0·329 | ||
| Student nurse | 39 (2·4) | 13 (1·4) | |
| Nurse assistant | 26 (1·6) | 12 (1·3) | |
| Nurse | 745 (46·5) | 416 (43·7) | |
| Head nurse | 61 (3·8) | 44 (4·6) | |
| Nurse specialist | 644 (40·2) | 404 (42·4) | |
| Educator | 45 (2·8) | 34 (3·6) | |
| Researcher | 21 (1·3) | 15 (1·6) | |
| Other | 16 (1·0) | 10 (1·1) | |
| Missing | 4 (0·2) | 4 (0·4) | |
| Education | 0·289 | ||
| Undergraduate | 417 (26·0) | 241 (25·3) | |
| Bachelor degree | 633 (39·5) | 352 (37·0) | |
| Master degree | 475 (29·7) | 310 (32·6) | |
| Doctoral degree | 73 (4·6) | 49 (5·1) | |
| Other /unknown | 3 (0·2) | 0 (0·0) | |
| Expertise in skin tears | 0·272 | ||
| Novice | 219 (13·7) | 112 (11·8) | |
| Advanced beginner | 261 (16·3) | 138 (14·5) | |
| Competent | 389 (24·3) | 229 (24·1) | |
| Proficient | 400 (25·0) | 252 (26·5) | |
| Expert | 332 (20·7) | 221 (23·2) | |
| Wound care module | 0·230 | ||
| Completed | 869 (54·3) | 540 (56·7) | |
| Experience with ISTAP tool | 0·096 | ||
| No previous experience | 1143 (71·4) | 650 (68·3) | |
| Language | 0·065 | ||
| Arabic | 8 (0·5) | 3 (0·3) | |
| Chinese | 146 (9·1) | 72 (7·6) | |
| Czech | 112 (7·0) | 61 (6·4) | |
| Danish | 18 (1·1) | 12 (1·3) | |
| Dutch | 295 (18·4) | 216 (22·7) | |
| English | 381 (23·8) | 195 (20·5) | |
| French | 70 (4·4) | 55 (5·8) | |
| German | 109 (6·8) | 62 (6·5) | |
| Hebrew | 62 (3·9) | 35 (3·7) | |
| Italian | 31 (1·9) | 15 (1·6) | |
| Japanese | 54 (3·4) | 46 (4·8) | |
| Portuguese | 47 (2·9) | 37 (3·9) | |
| Spanish | 70 (4·4) | 45 (4·7) | |
| Swedish | 56 (3·5) | 35 (3·7) | |
| Turkish | 141 (8·8) | 63 (6·6) |
Data are n (%) unless otherwise indicated. aχ2‐test (P < 0·05 considered statistically significant). bExpertise in relation to the assessment and management of skin tears (based on the levels of proficiency defined by Benner).55 cCompletion of a recognized wound care module. dPrevious experience with using the International Skin Tear Advisory Panel (ISTAP) Classification System. eLanguages in which the ISTAP Classification System and the online survey were translated.
Diagnostic accuracy and agreement with reference standard (n = 1601 raters)
| Mean (95% CI) | Median (IQR) | 2·5th–97·5th percentile | |
|---|---|---|---|
| Po
| 0·79 (0·79–0·80) | 0·83 (0·75–0·88) | 0·42–0·96 |
| Ptype 1
| 0·86 (0·85–0·86) | 0·89 (0·80–0·94) | 0·43–1·00 |
| Ptype 2
| 0·75 (0·74–0·75) | 0·78 (0·67–0·88) | 0·31–0·94 |
| Ptype 3
| 0·76 (0·76–0·77) | 0·80 (0·71–0·88) | 0·32–1·00 |
| Type 1 vs. 2+3 | |||
| Sensitivity | 0·88 (0·87–0·88) | 0·88 (0·88–1·00) | 0·38–1·00 |
| Specificity | 0·92 (0·92–0·93) | 0·94 (0·88–1·00) | 0·69–1·00 |
| Type 2 vs. 1+3 | |||
| Sensitivity | 0·77 (0·76–0·77) | 0·75 (0·62–0·88) | 0·25–1·00 |
| Specificity | 0·86 (0·86–0·87) | 0·88 (0·81–0·94) | 0·56–1·00 |
| Type 3 vs. 1+2 | |||
| Sensitivity | 0·74 (0·73–0·75) | 0·75 (0·62–0·88) | 0·25–1·00 |
| Specificity | 0·91 (0·90–0·91) | 0·94 (0·88–1·00) | 0·62–1·00 |
CI, confidence interval; IQR, interquartile range; type 1, no skin/flap loss; type 2, partial skin/flap loss; type 3, total skin/flap loss. aOverall proportion of agreement; bproportion of specific agreement.
Inter‐rater reliability (n = 1601 raters)
| Fleiss Kappa coefficient (95% CI) | |
|---|---|
| Total sample ( | 0·57 (0·57–0·57) |
| Expertise in skin tears | |
| Novice ( | 0·43 (0·42–0·43) |
| Advanced beginner ( | 0·56 (0·56–0·56) |
| Competent ( | 0·57 (0·57–0·57) |
| Proficient ( | 0·62 (0·62–0·62) |
| Expert ( | 0·64 (0·64–0·64) |
| Education | |
| Undergraduate ( | 0·55 (0·55–0·55) |
| Bachelor's degree ( | 0·58 (0·57–0·58) |
| Master's degree ( | 0·59 (0·59–0·59) |
| Doctoral degree ( | 0·53 (0·52–0·53) |
| Experience with ISTAP tool | |
| Previous experience ( | 0·64 (0·64–0·64) |
| No previous experience ( | 0·55 (0·55–0·55) |
CI, confidence interval; ISTAP, International Skin Tear Advisory Panel.
Intrarater reliability and agreement (n = 952 raters)
| Mean (95% CI) | Median (IQR) | 2·5th–97·5th percentile | |
|---|---|---|---|
| Cohen's Kappa coefficient | 0·74 (0·73–0·75) | 0·75 (0·68–0·87) | 0·31–0·94 |
| Po
| 0·83 (0·82–0·84) | 0·83 (0·79–0·92) | 0·54–0·96 |
| Ptype 1
| 0·86 (0·85–0·87) | 0·89 (0·82–0·94) | 0·54–1·00 |
| Ptype 2
| 0·78 (0·77–0·79) | 0·82 (0·71–0·89) | 0·39–0·95 |
| Ptype 3
| 0·83 (0·82–0·84) | 0·86 (0·78–0·92) | 0·50–1·00 |
CI, confidence interval; IQR, interquartile range; type 1, no skin/flap loss; type 2, partial skin/flap loss; type 3, total skin/flap loss. aOverall proportion of agreement; bproportion of specific agreement.