- Email: [email protected]
Development and assessment of a pictorial guide to improve accuracy of visual estimation of blood loss of small animals
Journal Pre-proof Development and assessment of a pictorial guide to improve accuracy of visual estimation of blood loss of small animals Scott H. Cumming, BVSc (Hons) MCom MANZCVS, Fernando Martinez-Taboada, LV CertVA PGCert(Biostats) DipECVAA PII:
S1467-2987(20)30008-8
DOI:
https://doi.org/10.1016/j.vaa.2019.10.007
Reference:
VAA 472
To appear in:
Veterinary Anaesthesia and Analgesia
Received Date: 28 May 2019 Revised Date:
23 August 2019
Accepted Date: 6 October 2019
Please cite this article as: Cumming SH, Martinez-Taboada F, Development and assessment of a pictorial guide to improve accuracy of visual estimation of blood loss of small animals, Veterinary Anaesthesia and Analgesia, https://doi.org/10.1016/j.vaa.2019.10.007. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd on behalf of Association of Veterinary Anaesthetists and American College of Veterinary Anesthesia and Analgesia.
RESEARCH STUDY
Development and assessment of a pictorial guide to improve accuracy of visual estimation of blood loss of small animals.
Running title: Estimating blood loss with a pictorial guide
Authors: Scott H. Cumminga* BVSc (Hons) MCom MANZCVS Fernando Martinez-Taboadaa LV CertVA PGCert(Biostats) DipECVAA
Affiliation: a
Anaesthesia Department, The Veterinary Teaching Hospital Sydney, The University of
Sydney, 65 Parramatta Rd, Camperdown, New South Wales, Australia
* Corresponding Author. Email address: [email protected] Postal address: Rm 338, Evelyn Williams Building No. B10, The University of Sydney, New South Wales, 2006, Australia.
Author contributions: SHC: Idea development, study design, data and statistical analysis, manuscript preparation and writing. FMT: Idea development, study design, data and statistical analysis, manuscript preparation and revision, and overall supervision of work.
Conflict of interest statement The authors declare no conflict of interest.
Acknowledgement The Authors would like to thank Alexander Philp for his assistance during the early phases of this project, especially during the production of the images for this study.
1
Abstract
2
Objective To investigate the accuracy of visual blood loss estimation of small animals
3
amongst veterinary staff and final-year veterinary students, and the development and
4
utility of a pictorial guide to improve estimation, in a veterinary hospital.
5
Study Design Online anonymous voluntary survey
6
Methods A two-part online survey was circulated to voluntary participants at the
7
University Veterinary Teaching Hospital Sydney, The University of Sydney, including
8
students, nurses, interns, residents, general practitioners, and specialists. The survey
9
consisted of visual and brief descriptive depictions of blood loss scenarios involving
10
small animals, principally including images of common surgical items and receptacles
11
containing a blood-like substance. Each participant estimated the blood volume (in mL)
12
for each scenario twice, initially [Pre-Guide (PGD)] and then with the aid of a pictorial
13
guide [With-Guide (WGD)]. The pictorial guide used similar images labelled with
14
corresponding volumes. Data were analysed for normality with Shapiro-Wilks test,
15
corrected to absolute error and compared for statistical significance using the Wilcoxon
16
signed-ranks test or the Kruskal-Wallis test as appropriate (p < 0.05).
17
Results A total of 59 participants provided 288 responses. The raw median PGD error
18
was -16 mL (range -105 to 443), indicating a tendency toward underestimation of the
19
actual volume. The WGD median error was 18 mL (range -91 to 191) indicating a
20
tendency toward overestimation when using a pictorial guide (p < 0.0001). Data
21
corrected to absolute error showed a PGD median error of 34 mL (range 0 to 443) and
22
WGD median error of 23 mL (range 0 to 191) (p < 0.0001). There were differences
23
between the participant roles in the PGD phase but not when using the Guide.
24
Conclusion and clinical relevance Participants generally underestimated surgical
25
blood loss, with a wide variation, when visually estimating scenarios involving small
26
animals. A pictorial guide improved estimation by reducing the absolute median error
27
and narrowing the range.
28 29
Keywords: blood loss; blood loss estimation; haemorrhage; pictorial guide
30 31 32 33 34
2
35
Introduction
36
Intraoperative haemorrhage contributes to patient morbidity and mortality (Budair et al.
37
2017). Appropriate management and response to haemorrhage requires both monitoring
38
haemodynamic variables such as mean arterial pressure (MAP) and pulse pressure
39
difference (dPP) as well as accurate measurement of lost blood volume (Pestel et al.
40
2010; Adkins et al. 2014). Acute haemorrhage may necessitate a blood transfusion, and
41
this decision is informed by accurately estimating the volume of blood loss (Weingart et
42
al. 2004; Godinho-Cunha et al. 2011). Inaccuracy of blood loss estimation can have
43
multiple detrimental effects. Overestimation, for example, can lead to unnecessary
44
blood transfusion which wastes finite resources, incurs risk to the recipient and has also
45
been correlated with increased mortality in humans and animals (Hébert et al. 1999;
46
Beattie et al. 2009; Glance et al. 2011; Wang et al. 2014). Underestimation, conversely,
47
can lead to hypovolaemic shock and reduced tissue oxygen delivery, increasing the risk
48
of morbidity and mortality (Beattie et al. 2009; Porter et al. 2013).
49
In the medical literature, estimation of blood loss is well established as a
50
challenging and inherently inaccurate undertaking (Cole 1953; Razvi et al. 1996;
51
Schorn 2010; Ashburn et al. 2012; Adkins et al. 2014; Ali Algadiem et al. 2016;
52
Rothermel & Lipman 2016). A wide variety of methods for assessing blood loss has
53
been published including gravimetry, direct measurement, visual estimation,
54
colorimetry and use of formulae (Lee et al. 2006; Clark et al. 2010; Schorn 2010;
55
Lopez-Picado et al. 2017). Recently, recognising the impracticality of the more accurate
56
methods (such as colorimetry), studies have focused on developing reference guides for
57
assisted estimation of blood loss in the surgical setting (Zuckerwise et al. 2014; Ali
58
Algadiem et al. 2016; Rothermel & Lipman 2016).
3
59
In the veterinary literature, Lee et al (2006) examined the correlation between
60
gravimetric and colorimetric measurement of blood loss. That study demonstrated a
61
positive correlation between the weight of materials and the gold-standard colorimetric
62
method, suggesting gravimetry would be a viable clinical option. Both gravimetric and
63
colorimetric methods have recognised limitations owing to evaporation error,
64
intraoperative utility and timely execution (Lee et al. 2006). A more recent study
65
compared a formulaic method utilising haemoglobin concentrations of suction-collected
66
fluid with a gravimetric and direct measurement method (Clark et al. 2010). These
67
methods, however, are neither timely nor comprehensive.
68
To our knowledge, there have been no studies assessing the accuracy of
69
veterinary practitioners in visual estimation of blood loss in the surgical setting. Nor
70
have there been investigations using pictorial guides to improve accuracy.
71
In this study our primary aim was to investigate the degree of accuracy when
72
estimating blood loss by observation. We hypothesised that there would be a significant
73
difference, or error, when estimating blood volume from images. Secondly, we sought
74
to evaluate the utility of a pictorial guide to improve accuracy. Our hypothesis was that
75
a pictorial guide would assist and therefore reduce the error in the estimation. As a
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tertiary aim, we evaluated the impact of role on the accuracy of measuring blood loss.
77
Role as a variable was selected to encompass professional experience, qualifications,
78
and years of work. We hypothesised that there would have been no difference between
79
roles.
4
80
Materials and Methods
81
This project was approved by the University of Sydney Medical Ethics Research
82
Committee (number 2018/633).
83
A series of images were created simulating blood collected in a variety of
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commonly used surgical blood collection devices including swabs, laparotomy sponge,
85
kidney dish, and suction pot. Several different measured volumes of artificial blood
86
(‘Fake Blood’, Face and Body Paint, Derivan, NSW, Australia) were added to each of
87
these items to create different stages of saturation or filling. Measured volumes of blood
88
were also used to create ‘puddles.’ All puddles, surgical items, and devices were then
89
photographed (Canon EOS-400D, Canon Inc. Japan).
90
A selection of these images, together with a brief fictional background case
91
description for each scenario including species, weight and procedure, were used to
92
create five ‘Scenarios’. The scenarios consisted of: 1) a suction pot containing 66 mL of
93
artificial blood; 2) a kidney dish containing 105 mL of artificial blood; 3) a puddle of 50
94
mL and swab with 7 mL of artificial blood; 4) a puddle of 17 mL and laparotomy
95
sponge with 40 mL of artificial blood; and 5) a laparotomy sponge with 100 mL, a swab
96
with 6 mL, and a swab with 3 mL of artificial blood (Appendix 1). The background and
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volumes represented in the images were designed to be a realistic reflection of common
98
small animal surgical situations. The scenarios increased in complexity with more
99
components presented in each scenario in comparison with the preceding image.
100
Other images were used to create a pictorial guide (the ‘Guide’) in which items
101
and receptacles containing different levels of filling or saturation were described and
102
labelled. The Guide, consisting of one page, was hosted on the online platform Wix
103
(wix.com, Israel) (Appendix 2).
5
104
A
survey
was
created
and
hosted
online
using
Survey
Monkey
105
(surveymonkey.com, CA, USA). The survey, consisting of four sections, complied with
106
the guidelines detailed in the ‘Checklist for Reporting Results of Internet E-Surveys
107
(CHERRIES)’ (Eysenbach 2004). The first section gathered demographic and
108
professional information including role and years of experience. The second section
109
consisted of the five ‘scenarios’, displaying an image or images and the background
110
information, giving information as to the size of the item or receptacle, but without the
111
volume of blood, and included a text input box. The third section provided a link to the
112
online Guide on a separate browser tab. The fourth section repeated the scenarios from
113
section two in the same order with a similar text input box.
114
The survey was hosted on a local tablet device (Apple iPad, Apple Inc, CA,
115
USA) within the physical premises of the University Veterinary Teaching Hospital
116
Sydney, The University of Sydney. Staff and students were contacted via email and
117
invited to take part in the survey by accessing the tablet. This was therefore a ‘closed’
118
survey (Eysenbach 2004). No incentives were offered for participation. Individually
119
accessing the tablet, all respondents gave digital written consent prior to commencing
120
the survey. In accordance with CHERRIES Guidelines, all information was anonymous
121
and no identifying data were collected (Eysenbach 2004).
122
Respondents progressed through the survey by assessing each scenario and
123
inputting an estimated volume of blood as a numerical value (in mL). Respondents
124
initially assessed each scenario with only the information presented in the survey, and
125
then reassessed each scenario with the aid of the Guide. In this way, each respondent
126
provided one set of responses prior to the Guide [Pre-Guide (PGD)] and one set of
127
responses using the Guide [With-Guide (WGD)]. All respondents completed the survey
128
in a single session. Review and alteration of responses was possible within the phases
6
129
(PGD and WGD), but not between the phases once the participant had progressed to the
130
next phase.
131 132
Statistical methods
133
A minimum sample size of 36 individuals providing 360 observations was calculated in
134
order to detect a 10 mL difference between the PGD and WGD responses using a power
135
of 90% and significance level of 5%.
136
To achieve an equivalent basis of comparison, each given numerical estimation
137
response was converted into an estimate error value by subtracting the actual value from
138
the given response (the ‘raw error’). This allowed assessment of either under- or
139
overestimation of the responses, necessary for practical clinical application. The raw
140
error values were then corrected to absolute values by removing the sign. This
141
eliminated the confounding effects of negative values, and achieved an improved and
142
more comparable representation of error magnitude and the effect of the Guide. Both
143
raw error values and absolute error values were analysed and compared. Initially,
144
descriptive statistics were used to assess the data. Then, the data were assessed for
145
normality using a Shapiro-Wilk test. The statistical significance of the effect of the
146
Guide was assessed using the Wilcoxon signed rank test.
147
The Kruskal-Wallis test was used to assess the effect of the role on the responses
148
for both the PGD and WGD phases using the post hoc Dunn’s Test with Bonferroni
149
post hoc correction.
150
To investigate the effect of the complexity of the scenario on the utility of the
151
Guide, a Wilcoxon signed ranks test was conducted on error values for the PGD and
152
WGD phases, for both the raw error and the absolute error, on each of the scenarios
153
individually. Assessment was then made as to the nature of the error change (better or
7
154
worse), if present. Significance levels were set at p < 0.05. Values are reported as
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median (range), negative raw error values indicate underestimation, while all other
156
values are considered overestimation. All analyses and calculations were undertaken in
157
RStudio Version 1.1.463 for Mac OS 10.14.4 (The R Foundation for Statistical
158
Computing, Austria).
159 160
Results
161
Population Data
162
The initial distribution list consisted of 140 individuals, from which a total of 59
163
individuals responded, giving a participation rate of 41.4%. The participants were
164
spread across a variety of roles including General Practitioner, Advanced General
165
Practitioner, Intern, Nurse, Student, Specialist, and Resident/residency trained. The
166
General Practitioners and Advanced General Practitioners were combined into a single
167
group due to low numbers of each. The survey could only be accessed by one physical
168
device and while it was possible for a participant to abort or abandon the survey during
169
participation, in practice this did not occur. Hence the view rate was 100% of those
170
individuals, who registered to participate in the survey. A total of 588 responses from a
171
theoretical maximum of 590 were provided to the five scenarios, queried twice. Two
172
invalid responses were provided in the With-Guide (WGD) phase and this data was not
173
retained for final analysis (Table 1). Therefore, the completion rate was 99.7%.
174
Pre-Guide Phase.
175
Overall
176
The initial PGD phase median raw error was -16 mL (range -105 to 443 mL). An
177
average of the median deviations across all of the scenarios was - 15.8 mL or 23%
178
underestimation of the actual volume of blood represented in the images. When
8
179
converted to absolute values, the absolute median error was 34 mL (range 0 to 443 mL),
180
and an average of the median error of 39.4 mL or 53%.
181
Role
182
When the PGD raw error responses were grouped according to ‘Role’, there was a
183
difference between the groups (p < 0.0001). The ‘Resident/Residency Trained’ group
184
[median error 8.5 mL (range -49 to 291)] was the only group to overestimate and had a
185
smaller error than both the ‘Specialist’ group [median error -37 mL (range -94 – 34
186
mL), p = 0.0026] and ‘General practitioner’ group [median error -35 mL (range -99 –
187
266 mL), p <0.0001]. The ‘Intern’ group had the smallest median error of -5 mL (range
188
-97 to 443 mL) and was smaller than the ‘General practitioner’ (p = 0.0045).
189
When the raw error values were converted to absolute error, while the General
190
Practitioner group (median error 7 mL, range 0 to 94 mL) had the smallest error, and the
191
‘Specialist’ group (median error 46 mL, range 0 to 266 mL) had the largest error, there
192
was no significant difference between the groups (p = 0.1063). Complete results can be
193
found in Table 1.
194
Scenarios
195
There was a difference between the scenarios for both the raw PGD error (p = 0.0272)
196
and the absolute PGD error (p < 0.0001). For the raw error, Scenario 1 (median -6 mL,
197
range -32 to 64 mL) and Scenario 2 (median -5 mL, range -95 to 115 mL) the median
198
error was less than Scenario 3 (median -32 mL, range -55 to 293 mL). When converted
199
to absolute values, Scenario 1 (median 34 mL, range 0 to 62 mL) was again less than
200
Scenario 3 (median 37, range 3 to 293), and Scenario 5 (median 64, range 9 to 391) was
201
larger than Scenarios 1, 2 (median 25, range 5 to 115), 3 (median 37, range 3 to 293)
202
and 4 (median 37, range 3 to 443).
203
With-Guide Phase
9
204
Overall
205
With use of the Guide, the pooled responses had a median error of 18 mL (range -99 –
206
191 mL), with an average of the median errors of the scenarios 21.8 mL or 29%
207
overestimation. When converted to absolute values, the median error was 23 mL (range
208
0 – 191 mL), with an average of the median errors of the scenarios 26 mL or 35%.
209
Role
210
Using the Guide, there was no difference between the groups according to role for either
211
the raw error (p = 0.2461) or the absolute error (p = 0.4771).
212
Scenarios
213
There was a difference between the scenarios for both the raw error values (p < 0.0001)
214
and the absolute error value (p = 0.0003). For the raw error values, Scenario 3 (median
215
8, range – 53 – 73 mL) had a smaller error than Scenario 1 (median 34 mL, range -26 –
216
74 mL) and Scenario 2 (median 35 mL, range -25 – 95mL). Similarly, Scenario 4
217
(median 23 mL, range -55 – 73 mL) and Scenario 5 (median 9 mL, range -99 – 191 mL)
218
had a smaller error than Scenario 2. The absolute error values showed that Scenario 2
219
(median 35 mL, range 5 – 95ml) had a greater error than Scenario 3 (median 19 mL,
220
range 0- 133 mL), Scenario 4 (median 23 mL, range 2- 73) and Scenario 5 (median 19
221
mL, range 1 – 191 mL).
222 223
Comparison Pre-Guide phase to With-Guide phase
224
Overall
225
Comparing the error values between the PGD responses and WGD responses, there was
226
a difference for both raw error (p < 0.0001) and absolute error (p < 0.0001) values. For
227
the raw values the median error increased from -16 mL (range -105 – 443 mL) (PGD) to
228
18 mL (range -91 – 191 mL) (WGD). When these data are converted to absolute error
10
229
values, there was a reduction in the median value of 34 mL (range 0- 443 mL) (PGD) to
230
23 mL (range 0- 191 mL) or a 32% reduction in the magnitude of the error compared
231
with the initial response (Fig. 1)
232
Role
233
All roles showed a difference between PGD and WGD raw error values, with both
234
increased and reduced median error values between the phases (see Fig. 2). For the
235
absolute error values, only the Student (p <0.0001), Nurse (p < 0.0354), and Specialist
236
(p = 0.0004) groups showed a difference between PGD and WGD phases (see Fig. 3).
237
The median and range is displayed in Table 2.
238
Scenarios
239
Scenarios 1, 2, and 4 had increased raw median error values (p < 0.0001), and Scenario
240
3 (p < 0.0001) had reduced raw median error WGD compared to PGD. All raw median
241
error changed from underestimation to overestimation. Scenarios 3 (p < 0.0001), 4 (p =
242
0.0110), and 5 (p < 0.0001) had reduced absolute error values WGD phase compared to
243
PGD phase (p < 0.0001). The median and range is displayed in Table 2.
244 245
Discussion
246
Pre-Guide accuracy
247
The results indicate that there was a general underestimation of blood volume compared
248
with the actual volume. Underestimation of blood loss adversely impairs true
249
assessment of an animal’s circulatory requirements during anaesthesia, potentially
250
leading to impaired oxygen delivery and increasing morbidity and mortality (Gutierrez
251
et al. 2004). Our findings agree with those reported in the medical literature, which
252
indicate that underestimation with visual estimation is very common (Bose et al. 2006;
253
Schorn 2010; Rothermel & Lipman 2016). However, our findings differ from those
11
254
described by Razvi et al (1999), who found that larger volumes of blood were
255
associated with a greater magnitude of underestimation error, and smaller volumes
256
tended to be overestimated. In addition, (Yoong et al. 2010) specifically found that
257
overestimation occurred when estimated volumes were low. The volumes used in our
258
study were comparatively small, reflecting this animal population and therefore
259
interpretation and comparisons with medical findings may have limited value. As this is
260
the first study of its kind in the veterinary literature, we cannot compare our findings
261
with previous work in this field.
262
The construction of each scenario had an impact on accuracy, where accuracy
263
decreased with increasing scenario components and difficulty. As the clinical surgical
264
situation is likely to involve multiple items for blood collection, it is logical to conclude
265
that inaccuracy will be even greater in a clinical setting.
266
In the PGD phase of the study role had an impact on estimation accuracy. The
267
‘Resident/Residency Trained’ group and the ‘Intern’ group were more accurate than the
268
‘Specialist’ and ‘General Practitioner’, and the ‘General Practitioner’ groups
269
respectively. This was unexpected since it might be assumed that more senior
270
practitioners would be better at assessing blood loss than more junior colleagues. This
271
contrasts with data reported by (Ashburn et al. 2012), who found that attending
272
physicians had a tendency towards reduced error compared with residents, although this
273
trend was not significant. Within the General Practitioner group, our results may have
274
been influenced by the low sample numbers for this group, which could have
275
contributed to the narrow range of values. Examining the absolute error values and
276
comparing the magnitude of error, there was no significant difference between the
277
groups. These absolute error results are more consistent with the medical literature
12
278
which typically found no difference in role or experience between different groups
279
(Meiser et al. 2001; Dildy et al. 2004; Adkins et al. 2014; Rothermel & Lipman 2016).
280
With-Guide accuracy
281
The use of a pictorial guide reduced the absolute median error and narrowed the range
282
of responses. This suggests that respondents were able to estimate volumes more
283
effectively they used a tool that closely resembled the test images. This finding is
284
similar to medical studies where improvements in accuracy occurred when a pictorial
285
guide was used (Dildy et al. 2004; Zuckerwise et al. 2014; Homcha et al. 2017).
286
Conversely, analysis of the raw error values showed a small increase in WGD median
287
error compared with PGD median error. However, the range of values was again
288
narrower for WGD compared to PGD. While these results appear to contradict the
289
ability of the Guide to reduce the estimation error, the narrowed range supports a
290
finding of improved accuracy.
291
Using the Guide also improved accuracy in more complex scenarios. Without
292
the Guide, the scenarios with fewer items were more accurate and the scenarios with
293
more items were less accurate. With the Guide, Scenarios 3, 4, and 5 showed
294
improvements in accuracy in terms of magnitude of error, while Scenarios 1 and 2
295
showed a decrease in accuracy between the PGD and WGD phases. Given that real-life
296
surgical situations are likely more reflective of the multi-component scenarios, this
297
supports the clinical application of the Guide. The construction of the ‘simple scenarios’
298
may have contributed towards a tendency to overestimation. Scenarios 1 and 2 consisted
299
of one image, each of which was drawn from the pool of created images and therefore
300
similar to images presented in the Guide. This possibly created a shortcut whereby the
301
estimate of the similar image exactly matched one of the Guide images and is a
302
limitation of the study.
13
303
There was no difference between the groups with use of the Guide, suggesting
304
that the impact of role and experience is lessened with assistance from a pictorial guide.
305
This agrees with the medical literature where preintervention differences in groups did
306
not persist postintervention (Zuckerwise et al. 2014). Comparing the PGD and WGD
307
phases of the groups showed mixed results. Only the ‘Specialist’ and ‘Nurse’ groups
308
showed improvements in both raw and absolute error, while the ‘Intern’ and
309
‘Resident/Residency Trained’ groups, with the most accurate Pre-Guide values,
310
deteriorated or did not change. This finding suggests that the Guide is most useful for
311
the more inaccurate estimators. Again, the ranges of WGD responses for most of the
312
groups were much narrower than the PGD ranges, supporting improved estimation
313
accuracy when the Guide is used.
314
There are several limitations to this study. For ease of direct comparison, the
315
same scenarios were used for the Pre-Guide and With-Guide phases of the study, which
316
may have led to a degree of recognition between phases. The images used for the Guide
317
were very similar to those used for scenarios which may limit the broader application
318
and usefulness of the Guide for situations which have different items. The scenarios
319
were presented with images only, and displayed in an online digital format, limiting the
320
interaction and possible assessment of the receptacles which would occur in a real-life
321
situation. The images were created with an artificial blood substitute, which, despite
322
gross physical similarity to real blood, may behave differently when in contact with
323
material substrates and may confound assessment. The relatively small blood volumes
324
we used may have exaggerated small differences in estimation within the scenarios,
325
while making comparison between scenarios more challenging. There were a wide
326
variety of responses to all of the scenarios, which possibly questions the integrity of
327
some of the data. For example, the wide range of responses for Scenario 1 involving the
14
328
suction pot was unexpected given the visible graduation mark depicted in the image.
329
However, these data were retained as the integrity, or otherwise, of these responses is
330
unknown. The signalment of the cases may have biased the estimation based on
331
individual interpretation of each case background. The scenarios only involved cats and
332
dogs therefore no conclusions can be drawn for other species. All of the participants
333
were sourced from the same facility, and despite being from a variety of roles and
334
backgrounds, this may not be representative of the wider population. The study
335
occurred solely at a single institution, and it may be that the utility of this Guide is
336
confined to the facility in which the study took place. Other institutions may find it
337
useful to develop their own pictorial guide customised to their own setting (e.g surgical
338
items, type of containers, etc). The low group sample numbers may have contributed to
339
the lack of significance within all of the groups between phases. Finally, there was no
340
ability to differentiate between departmental role, which prevented an evaluation of any
341
impact that field of work may have had.
342
In conclusion, visual estimation of blood loss is inaccurate and varied. For
343
scenarios involving small animals, the use of a pictorial guide improved the accuracy of
344
estimation of blood loss in both reduction of median error and narrowing of spread of
345
estimation across all groups, while reducing the variation between groups. In particular,
346
the pictorial guide proved most useful in more complex scenarios. In the future, similar
347
investigations, drawn from a broader disciplinary and geographical population, could be
348
considered. Further analysis of the Guide’s utility in the actual surgical setting and
349
comparison with other measurement techniques is also indicated.
350 351 352
15
353
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Budair B, Ahmed U, Hodson J et al. (2017) Are we all guilty of under-estimating intra-
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operative blood loss during hip fracture surgery? Journal of Orthopaedics 14,
368
81-84.
369
Clark L, Corletto F, Garosi LS (2010) Comparison of a method using the HemoCue
370
near patient testing device with a standard method of haemorrhage estimation in
371
dogs undergoing spinal surgery. Veterinary Anaesthesia and Analgesia 37, 44-
372
47.
373 374
Cole F (1953) A new method of estimating blood loss during surgery. The American Journal of Surgery 86, 50-52.
375
Dildy AG, Paine RA, George CN et al. (2004) Estimating Blood Loss: Can Teaching
376
Significantly Improve Visual Estimation? Obstetrics & Gynecology 104, 601-
377
606.
16
378 379
Eysenbach G (2004) Improving the quality of Web surveys: the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). J Med Internet Res 6, e34-e34.
380
Glance LG, Dick AW, Mukamel DB et al. (2011) Association between Intraoperative
381
Blood Transfusion and Mortality and Morbidity in Patients Undergoing
382
Noncardiac Surgery. Anesthesiology 114, 283-292.
383
Godinho-Cunha LF, Ferreira RMRF, Silvestre-Ferreira AC (2011) Whole blood
384
transfusion in small animals: indications and effects. An Acad Bras Cienc 83,
385
611–617.
386 387
Gutierrez G, Reines H, Wulf-Gutierrez M (2004) Clinical review: hemorrhagic shock. Critical Care [NLM - MEDLINE] 8, 373-381.
388
Hébert PC, Wells G, Blajchman MA et al. (1999) A multicenter, randomized, controlled
389
clinical trial of transfusion requirements in critical care. The New England
390
Journal of Medicine 340, 409-417.
391
Homcha EB, Mets JE, Goldenberg DFM et al. (2017) Development and Assessment of
392
Pictorial Guide for Improved Accuracy of Visual Blood Loss Estimation in
393
Cesarean Delivery. Simulation in Healthcare: The Journal of the Society for
394
Simulation in Healthcare 12, 314-318.
395 396
Lee MH, Ingvertsen BT, Kirpensteijn J et al. (2006) Quantification of Surgical Blood Loss. Veterinary Surgery 35, 388-393.
397
Lopez-Picado A, Albinarrate A, Barrachina B (2017) Determination of Perioperative
398
Blood Loss: Accuracy or Approximation? Anesthesia & Analgesia 125, 280-
399
286.
400
Meiser A, Casagranda O, Skipka G et al. (2001) Quantifizierung von Blutverlusten Wie
401
genau ist visuelles Schätzen und wovon hängt die Genauigkeit ab? Der
402
Anaesthesist 50, 13-20.
17
403
Pestel GJ, Fukui K, Kimberger O et al. (2010) Hemodynamic Parameters Change
404
Earlier Than Tissue Oxygen Tension in Hemorrhage. Journal of Surgical
405
Research 160, 288-293.
406
Porter AE, Rozanski EA, Sharp CR et al. (2013) Evaluation of the shock index in dogs
407
presenting as emergencies. Journal of Veterinary Emergency and Critical Care
408
23, 538-544.
409
Razvi K, Chua S, Arulkumaran S et al. (1996) A Comparison Between Visual
410
Estimation and Laboratory Detennination of Blood Loss During the Third Stage
411
of Labour. Australian and New Zealand Journal of Obstetrics and Gynaecology
412
36, 152-154.
413 414 415 416 417
Rothermel LD, Lipman JM (2016) Estimation of blood loss is inaccurate and unreliable. Surgery 160, 946-953. Schorn MN (2010) Measurement of Blood Loss: Review of the Literature. Journal of Midwifery & Women s Health 55, 20-27. Wang D, Cortés‐ Puch I, Sun J et al. (2014) Transfusion of older stored blood worsens
418
outcomes in canines depending on the presence and severity of pneumonia.
419
Transfusion 54, 1712-1724.
420 421 422
Weingart C, Giger U, Kohn B (2004) Whole blood transfusions in 91 cats: a clinical evaluation. Journal of Feline Medicine and Surgery 6, 139-148. Yoong W, Karavolos S, Damodaram M et al. (2010) Observer accuracy and
423
reproducibility of visual estimation of blood loss in obstetrics: how accurate and
424
consistent are health-care professionals? Archives of Gynecology and Obstetrics
425
281, 207-213.
18
426
Zuckerwise CL, Pettker MC, Illuzzi RJ et al. (2014) Use of a Novel Visual Aid to
427
Improve Estimation of Obstetric Blood Loss. Obstetrics & Gynecology 123,
428
982-986.
429 430 431 432
19
Figure 1 All pooled responses displaying error (median and range) pre-Guide (PGD) and with-Guide *WGD), both raw and absolute data. The ‘raw’ values represent the difference between the estimate minus the actual volume. The absolute values represent the raw values with the sign removed to assess magnitude of error. Boxes represent 25th to 75th percentile and whiskers represent 5th to 95th percentile of error responses; the middle line represents the median. The dots indicate outliers beyond the percentile limits.
Figure 2 Raw error responses grouped according to role, displaying pre-Guide (PGD) and with-Guide (WGD) results. The ‘raw’ values represent the difference between the estimate minus the actual volume. The absolute values represent the raw values with the sign removed to assess magnitude of error. Boxes represent 25th to 75th percentile and whiskers represent 5th to 95th percentile of error responses; the middle line represents the median. The dots indicate outliers beyond the percentile limits.
Figure 3. Absolute error responses grouped according to role, displaying pre-Guide (PDG) and with-Guide (WGD) results. The ‘raw’ values represent the difference between the estimate minus the actual volume. The absolute values represent the raw values with the sign removed to assess magnitude of error. Boxes represent 25th to 75th percentile and whiskers represent 5th to 95th percentile of error responses; the middle line represents the median. The dots indicate outliers beyond the percentile limits.
1
Table 1 The number of respondents (n) grouped according to role comparing median
2
(range) error in mL of both raw and absolute values initially for the Pre-Guide (PGD)
3
phase and With-Guide (WGD) phase, with accompanying p – value. Raw WGD Raw PGD Median Median (range) (range) mL mL n
Responses
p - value Absolute WGD Absolute PGD Median Median (range) (range) mL mL
All
General
59 588
6
60
Practitioner Intern
Student
Nurse
Resident/
9
90
15 150
10 100
10 99
Residency trained Specialist
4 5
9
89
-16 (-105 – 443)
18 (-199 – 191)
< 0.0001
34 (0 – 443)
23 (0 – 191)
< 0.0001
-37 (-94 – 34)
15.5 (-41 –141)
< 0.0001
7 (5 – 94)
23 (3- 141)
0.0784
-5 (-97 – 443)
20 (-32 – 93)
0.0036
29 (3 – 443)
23 (1 – 93)
0.3032
-16 (-101 – 291)
17 (-39 – 95)
< 0.0001
46 (3-291)
21 (1 – 133)
< 0.0001
-16 (-105 – 391)
14.5 (-79 – 95)
0.0036
35.5 (3 – 391)
21 (1 – 95)
0.0354
8.5 (-49 – 291)
23 (-89 – 191)
0.0483
27 (3 – 291)
24 (1 – 191)
0.8583
-32 (-99 – 266)
15 (-39 – 95)
< 0.0001
46 (0 – 266)
20 (0 – 95)
0.0004
6
Table 2 Comparison of the different scenarios’ median (range) error in mL, of both raw
7
and absolute values initially for the Pre-Guide (PGD) phase and With-Guide (WGD)
8
phase, with accompanying p – value. Scenario
Raw PGD Median
Raw WGD Median
(range) mL
(range) mL
Absolute PGD
Absolute WGD
Median (range)
Median (range) mL
p-value
mL 1
2
3
4
5
-6 (-62 – 34)
34 (-26 – 74)
< 0.0001
34 (0-62)
34 (4, 74)
0.8195
-5 (-95 – 115)
35 (-25 – 95)
< 0.0001
25 (5 -115)
35 (5 – 95)
0.4714
-32 (-55 – 293)
8 (-53 – 133)
< 0.0001
37 (3 – 293)
19 (0 – 133)
< 0.0001
-17 (-55 – 443)
23 (-55 – 73)
< 0.0001
37 (3 – 443)
23 (2 – 73)
0.01103
-19 (-105 – 391)
9 (-99 – 191)
0.05691
64 (9 – 391)
19 (1 – 191)
< 0.0001
9 10
2
S1467-2987(20)30008-8
DOI:
https://doi.org/10.1016/j.vaa.2019.10.007
Reference:
VAA 472
To appear in:
Veterinary Anaesthesia and Analgesia
Received Date: 28 May 2019 Revised Date:
23 August 2019
Accepted Date: 6 October 2019
Please cite this article as: Cumming SH, Martinez-Taboada F, Development and assessment of a pictorial guide to improve accuracy of visual estimation of blood loss of small animals, Veterinary Anaesthesia and Analgesia, https://doi.org/10.1016/j.vaa.2019.10.007. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd on behalf of Association of Veterinary Anaesthetists and American College of Veterinary Anesthesia and Analgesia.
RESEARCH STUDY
Development and assessment of a pictorial guide to improve accuracy of visual estimation of blood loss of small animals.
Running title: Estimating blood loss with a pictorial guide
Authors: Scott H. Cumminga* BVSc (Hons) MCom MANZCVS Fernando Martinez-Taboadaa LV CertVA PGCert(Biostats) DipECVAA
Affiliation: a
Anaesthesia Department, The Veterinary Teaching Hospital Sydney, The University of
Sydney, 65 Parramatta Rd, Camperdown, New South Wales, Australia
* Corresponding Author. Email address: [email protected] Postal address: Rm 338, Evelyn Williams Building No. B10, The University of Sydney, New South Wales, 2006, Australia.
Author contributions: SHC: Idea development, study design, data and statistical analysis, manuscript preparation and writing. FMT: Idea development, study design, data and statistical analysis, manuscript preparation and revision, and overall supervision of work.
Conflict of interest statement The authors declare no conflict of interest.
Acknowledgement The Authors would like to thank Alexander Philp for his assistance during the early phases of this project, especially during the production of the images for this study.
1
Abstract
2
Objective To investigate the accuracy of visual blood loss estimation of small animals
3
amongst veterinary staff and final-year veterinary students, and the development and
4
utility of a pictorial guide to improve estimation, in a veterinary hospital.
5
Study Design Online anonymous voluntary survey
6
Methods A two-part online survey was circulated to voluntary participants at the
7
University Veterinary Teaching Hospital Sydney, The University of Sydney, including
8
students, nurses, interns, residents, general practitioners, and specialists. The survey
9
consisted of visual and brief descriptive depictions of blood loss scenarios involving
10
small animals, principally including images of common surgical items and receptacles
11
containing a blood-like substance. Each participant estimated the blood volume (in mL)
12
for each scenario twice, initially [Pre-Guide (PGD)] and then with the aid of a pictorial
13
guide [With-Guide (WGD)]. The pictorial guide used similar images labelled with
14
corresponding volumes. Data were analysed for normality with Shapiro-Wilks test,
15
corrected to absolute error and compared for statistical significance using the Wilcoxon
16
signed-ranks test or the Kruskal-Wallis test as appropriate (p < 0.05).
17
Results A total of 59 participants provided 288 responses. The raw median PGD error
18
was -16 mL (range -105 to 443), indicating a tendency toward underestimation of the
19
actual volume. The WGD median error was 18 mL (range -91 to 191) indicating a
20
tendency toward overestimation when using a pictorial guide (p < 0.0001). Data
21
corrected to absolute error showed a PGD median error of 34 mL (range 0 to 443) and
22
WGD median error of 23 mL (range 0 to 191) (p < 0.0001). There were differences
23
between the participant roles in the PGD phase but not when using the Guide.
24
Conclusion and clinical relevance Participants generally underestimated surgical
25
blood loss, with a wide variation, when visually estimating scenarios involving small
26
animals. A pictorial guide improved estimation by reducing the absolute median error
27
and narrowing the range.
28 29
Keywords: blood loss; blood loss estimation; haemorrhage; pictorial guide
30 31 32 33 34
2
35
Introduction
36
Intraoperative haemorrhage contributes to patient morbidity and mortality (Budair et al.
37
2017). Appropriate management and response to haemorrhage requires both monitoring
38
haemodynamic variables such as mean arterial pressure (MAP) and pulse pressure
39
difference (dPP) as well as accurate measurement of lost blood volume (Pestel et al.
40
2010; Adkins et al. 2014). Acute haemorrhage may necessitate a blood transfusion, and
41
this decision is informed by accurately estimating the volume of blood loss (Weingart et
42
al. 2004; Godinho-Cunha et al. 2011). Inaccuracy of blood loss estimation can have
43
multiple detrimental effects. Overestimation, for example, can lead to unnecessary
44
blood transfusion which wastes finite resources, incurs risk to the recipient and has also
45
been correlated with increased mortality in humans and animals (Hébert et al. 1999;
46
Beattie et al. 2009; Glance et al. 2011; Wang et al. 2014). Underestimation, conversely,
47
can lead to hypovolaemic shock and reduced tissue oxygen delivery, increasing the risk
48
of morbidity and mortality (Beattie et al. 2009; Porter et al. 2013).
49
In the medical literature, estimation of blood loss is well established as a
50
challenging and inherently inaccurate undertaking (Cole 1953; Razvi et al. 1996;
51
Schorn 2010; Ashburn et al. 2012; Adkins et al. 2014; Ali Algadiem et al. 2016;
52
Rothermel & Lipman 2016). A wide variety of methods for assessing blood loss has
53
been published including gravimetry, direct measurement, visual estimation,
54
colorimetry and use of formulae (Lee et al. 2006; Clark et al. 2010; Schorn 2010;
55
Lopez-Picado et al. 2017). Recently, recognising the impracticality of the more accurate
56
methods (such as colorimetry), studies have focused on developing reference guides for
57
assisted estimation of blood loss in the surgical setting (Zuckerwise et al. 2014; Ali
58
Algadiem et al. 2016; Rothermel & Lipman 2016).
3
59
In the veterinary literature, Lee et al (2006) examined the correlation between
60
gravimetric and colorimetric measurement of blood loss. That study demonstrated a
61
positive correlation between the weight of materials and the gold-standard colorimetric
62
method, suggesting gravimetry would be a viable clinical option. Both gravimetric and
63
colorimetric methods have recognised limitations owing to evaporation error,
64
intraoperative utility and timely execution (Lee et al. 2006). A more recent study
65
compared a formulaic method utilising haemoglobin concentrations of suction-collected
66
fluid with a gravimetric and direct measurement method (Clark et al. 2010). These
67
methods, however, are neither timely nor comprehensive.
68
To our knowledge, there have been no studies assessing the accuracy of
69
veterinary practitioners in visual estimation of blood loss in the surgical setting. Nor
70
have there been investigations using pictorial guides to improve accuracy.
71
In this study our primary aim was to investigate the degree of accuracy when
72
estimating blood loss by observation. We hypothesised that there would be a significant
73
difference, or error, when estimating blood volume from images. Secondly, we sought
74
to evaluate the utility of a pictorial guide to improve accuracy. Our hypothesis was that
75
a pictorial guide would assist and therefore reduce the error in the estimation. As a
76
tertiary aim, we evaluated the impact of role on the accuracy of measuring blood loss.
77
Role as a variable was selected to encompass professional experience, qualifications,
78
and years of work. We hypothesised that there would have been no difference between
79
roles.
4
80
Materials and Methods
81
This project was approved by the University of Sydney Medical Ethics Research
82
Committee (number 2018/633).
83
A series of images were created simulating blood collected in a variety of
84
commonly used surgical blood collection devices including swabs, laparotomy sponge,
85
kidney dish, and suction pot. Several different measured volumes of artificial blood
86
(‘Fake Blood’, Face and Body Paint, Derivan, NSW, Australia) were added to each of
87
these items to create different stages of saturation or filling. Measured volumes of blood
88
were also used to create ‘puddles.’ All puddles, surgical items, and devices were then
89
photographed (Canon EOS-400D, Canon Inc. Japan).
90
A selection of these images, together with a brief fictional background case
91
description for each scenario including species, weight and procedure, were used to
92
create five ‘Scenarios’. The scenarios consisted of: 1) a suction pot containing 66 mL of
93
artificial blood; 2) a kidney dish containing 105 mL of artificial blood; 3) a puddle of 50
94
mL and swab with 7 mL of artificial blood; 4) a puddle of 17 mL and laparotomy
95
sponge with 40 mL of artificial blood; and 5) a laparotomy sponge with 100 mL, a swab
96
with 6 mL, and a swab with 3 mL of artificial blood (Appendix 1). The background and
97
volumes represented in the images were designed to be a realistic reflection of common
98
small animal surgical situations. The scenarios increased in complexity with more
99
components presented in each scenario in comparison with the preceding image.
100
Other images were used to create a pictorial guide (the ‘Guide’) in which items
101
and receptacles containing different levels of filling or saturation were described and
102
labelled. The Guide, consisting of one page, was hosted on the online platform Wix
103
(wix.com, Israel) (Appendix 2).
5
104
A
survey
was
created
and
hosted
online
using
Survey
Monkey
105
(surveymonkey.com, CA, USA). The survey, consisting of four sections, complied with
106
the guidelines detailed in the ‘Checklist for Reporting Results of Internet E-Surveys
107
(CHERRIES)’ (Eysenbach 2004). The first section gathered demographic and
108
professional information including role and years of experience. The second section
109
consisted of the five ‘scenarios’, displaying an image or images and the background
110
information, giving information as to the size of the item or receptacle, but without the
111
volume of blood, and included a text input box. The third section provided a link to the
112
online Guide on a separate browser tab. The fourth section repeated the scenarios from
113
section two in the same order with a similar text input box.
114
The survey was hosted on a local tablet device (Apple iPad, Apple Inc, CA,
115
USA) within the physical premises of the University Veterinary Teaching Hospital
116
Sydney, The University of Sydney. Staff and students were contacted via email and
117
invited to take part in the survey by accessing the tablet. This was therefore a ‘closed’
118
survey (Eysenbach 2004). No incentives were offered for participation. Individually
119
accessing the tablet, all respondents gave digital written consent prior to commencing
120
the survey. In accordance with CHERRIES Guidelines, all information was anonymous
121
and no identifying data were collected (Eysenbach 2004).
122
Respondents progressed through the survey by assessing each scenario and
123
inputting an estimated volume of blood as a numerical value (in mL). Respondents
124
initially assessed each scenario with only the information presented in the survey, and
125
then reassessed each scenario with the aid of the Guide. In this way, each respondent
126
provided one set of responses prior to the Guide [Pre-Guide (PGD)] and one set of
127
responses using the Guide [With-Guide (WGD)]. All respondents completed the survey
128
in a single session. Review and alteration of responses was possible within the phases
6
129
(PGD and WGD), but not between the phases once the participant had progressed to the
130
next phase.
131 132
Statistical methods
133
A minimum sample size of 36 individuals providing 360 observations was calculated in
134
order to detect a 10 mL difference between the PGD and WGD responses using a power
135
of 90% and significance level of 5%.
136
To achieve an equivalent basis of comparison, each given numerical estimation
137
response was converted into an estimate error value by subtracting the actual value from
138
the given response (the ‘raw error’). This allowed assessment of either under- or
139
overestimation of the responses, necessary for practical clinical application. The raw
140
error values were then corrected to absolute values by removing the sign. This
141
eliminated the confounding effects of negative values, and achieved an improved and
142
more comparable representation of error magnitude and the effect of the Guide. Both
143
raw error values and absolute error values were analysed and compared. Initially,
144
descriptive statistics were used to assess the data. Then, the data were assessed for
145
normality using a Shapiro-Wilk test. The statistical significance of the effect of the
146
Guide was assessed using the Wilcoxon signed rank test.
147
The Kruskal-Wallis test was used to assess the effect of the role on the responses
148
for both the PGD and WGD phases using the post hoc Dunn’s Test with Bonferroni
149
post hoc correction.
150
To investigate the effect of the complexity of the scenario on the utility of the
151
Guide, a Wilcoxon signed ranks test was conducted on error values for the PGD and
152
WGD phases, for both the raw error and the absolute error, on each of the scenarios
153
individually. Assessment was then made as to the nature of the error change (better or
7
154
worse), if present. Significance levels were set at p < 0.05. Values are reported as
155
median (range), negative raw error values indicate underestimation, while all other
156
values are considered overestimation. All analyses and calculations were undertaken in
157
RStudio Version 1.1.463 for Mac OS 10.14.4 (The R Foundation for Statistical
158
Computing, Austria).
159 160
Results
161
Population Data
162
The initial distribution list consisted of 140 individuals, from which a total of 59
163
individuals responded, giving a participation rate of 41.4%. The participants were
164
spread across a variety of roles including General Practitioner, Advanced General
165
Practitioner, Intern, Nurse, Student, Specialist, and Resident/residency trained. The
166
General Practitioners and Advanced General Practitioners were combined into a single
167
group due to low numbers of each. The survey could only be accessed by one physical
168
device and while it was possible for a participant to abort or abandon the survey during
169
participation, in practice this did not occur. Hence the view rate was 100% of those
170
individuals, who registered to participate in the survey. A total of 588 responses from a
171
theoretical maximum of 590 were provided to the five scenarios, queried twice. Two
172
invalid responses were provided in the With-Guide (WGD) phase and this data was not
173
retained for final analysis (Table 1). Therefore, the completion rate was 99.7%.
174
Pre-Guide Phase.
175
Overall
176
The initial PGD phase median raw error was -16 mL (range -105 to 443 mL). An
177
average of the median deviations across all of the scenarios was - 15.8 mL or 23%
178
underestimation of the actual volume of blood represented in the images. When
8
179
converted to absolute values, the absolute median error was 34 mL (range 0 to 443 mL),
180
and an average of the median error of 39.4 mL or 53%.
181
Role
182
When the PGD raw error responses were grouped according to ‘Role’, there was a
183
difference between the groups (p < 0.0001). The ‘Resident/Residency Trained’ group
184
[median error 8.5 mL (range -49 to 291)] was the only group to overestimate and had a
185
smaller error than both the ‘Specialist’ group [median error -37 mL (range -94 – 34
186
mL), p = 0.0026] and ‘General practitioner’ group [median error -35 mL (range -99 –
187
266 mL), p <0.0001]. The ‘Intern’ group had the smallest median error of -5 mL (range
188
-97 to 443 mL) and was smaller than the ‘General practitioner’ (p = 0.0045).
189
When the raw error values were converted to absolute error, while the General
190
Practitioner group (median error 7 mL, range 0 to 94 mL) had the smallest error, and the
191
‘Specialist’ group (median error 46 mL, range 0 to 266 mL) had the largest error, there
192
was no significant difference between the groups (p = 0.1063). Complete results can be
193
found in Table 1.
194
Scenarios
195
There was a difference between the scenarios for both the raw PGD error (p = 0.0272)
196
and the absolute PGD error (p < 0.0001). For the raw error, Scenario 1 (median -6 mL,
197
range -32 to 64 mL) and Scenario 2 (median -5 mL, range -95 to 115 mL) the median
198
error was less than Scenario 3 (median -32 mL, range -55 to 293 mL). When converted
199
to absolute values, Scenario 1 (median 34 mL, range 0 to 62 mL) was again less than
200
Scenario 3 (median 37, range 3 to 293), and Scenario 5 (median 64, range 9 to 391) was
201
larger than Scenarios 1, 2 (median 25, range 5 to 115), 3 (median 37, range 3 to 293)
202
and 4 (median 37, range 3 to 443).
203
With-Guide Phase
9
204
Overall
205
With use of the Guide, the pooled responses had a median error of 18 mL (range -99 –
206
191 mL), with an average of the median errors of the scenarios 21.8 mL or 29%
207
overestimation. When converted to absolute values, the median error was 23 mL (range
208
0 – 191 mL), with an average of the median errors of the scenarios 26 mL or 35%.
209
Role
210
Using the Guide, there was no difference between the groups according to role for either
211
the raw error (p = 0.2461) or the absolute error (p = 0.4771).
212
Scenarios
213
There was a difference between the scenarios for both the raw error values (p < 0.0001)
214
and the absolute error value (p = 0.0003). For the raw error values, Scenario 3 (median
215
8, range – 53 – 73 mL) had a smaller error than Scenario 1 (median 34 mL, range -26 –
216
74 mL) and Scenario 2 (median 35 mL, range -25 – 95mL). Similarly, Scenario 4
217
(median 23 mL, range -55 – 73 mL) and Scenario 5 (median 9 mL, range -99 – 191 mL)
218
had a smaller error than Scenario 2. The absolute error values showed that Scenario 2
219
(median 35 mL, range 5 – 95ml) had a greater error than Scenario 3 (median 19 mL,
220
range 0- 133 mL), Scenario 4 (median 23 mL, range 2- 73) and Scenario 5 (median 19
221
mL, range 1 – 191 mL).
222 223
Comparison Pre-Guide phase to With-Guide phase
224
Overall
225
Comparing the error values between the PGD responses and WGD responses, there was
226
a difference for both raw error (p < 0.0001) and absolute error (p < 0.0001) values. For
227
the raw values the median error increased from -16 mL (range -105 – 443 mL) (PGD) to
228
18 mL (range -91 – 191 mL) (WGD). When these data are converted to absolute error
10
229
values, there was a reduction in the median value of 34 mL (range 0- 443 mL) (PGD) to
230
23 mL (range 0- 191 mL) or a 32% reduction in the magnitude of the error compared
231
with the initial response (Fig. 1)
232
Role
233
All roles showed a difference between PGD and WGD raw error values, with both
234
increased and reduced median error values between the phases (see Fig. 2). For the
235
absolute error values, only the Student (p <0.0001), Nurse (p < 0.0354), and Specialist
236
(p = 0.0004) groups showed a difference between PGD and WGD phases (see Fig. 3).
237
The median and range is displayed in Table 2.
238
Scenarios
239
Scenarios 1, 2, and 4 had increased raw median error values (p < 0.0001), and Scenario
240
3 (p < 0.0001) had reduced raw median error WGD compared to PGD. All raw median
241
error changed from underestimation to overestimation. Scenarios 3 (p < 0.0001), 4 (p =
242
0.0110), and 5 (p < 0.0001) had reduced absolute error values WGD phase compared to
243
PGD phase (p < 0.0001). The median and range is displayed in Table 2.
244 245
Discussion
246
Pre-Guide accuracy
247
The results indicate that there was a general underestimation of blood volume compared
248
with the actual volume. Underestimation of blood loss adversely impairs true
249
assessment of an animal’s circulatory requirements during anaesthesia, potentially
250
leading to impaired oxygen delivery and increasing morbidity and mortality (Gutierrez
251
et al. 2004). Our findings agree with those reported in the medical literature, which
252
indicate that underestimation with visual estimation is very common (Bose et al. 2006;
253
Schorn 2010; Rothermel & Lipman 2016). However, our findings differ from those
11
254
described by Razvi et al (1999), who found that larger volumes of blood were
255
associated with a greater magnitude of underestimation error, and smaller volumes
256
tended to be overestimated. In addition, (Yoong et al. 2010) specifically found that
257
overestimation occurred when estimated volumes were low. The volumes used in our
258
study were comparatively small, reflecting this animal population and therefore
259
interpretation and comparisons with medical findings may have limited value. As this is
260
the first study of its kind in the veterinary literature, we cannot compare our findings
261
with previous work in this field.
262
The construction of each scenario had an impact on accuracy, where accuracy
263
decreased with increasing scenario components and difficulty. As the clinical surgical
264
situation is likely to involve multiple items for blood collection, it is logical to conclude
265
that inaccuracy will be even greater in a clinical setting.
266
In the PGD phase of the study role had an impact on estimation accuracy. The
267
‘Resident/Residency Trained’ group and the ‘Intern’ group were more accurate than the
268
‘Specialist’ and ‘General Practitioner’, and the ‘General Practitioner’ groups
269
respectively. This was unexpected since it might be assumed that more senior
270
practitioners would be better at assessing blood loss than more junior colleagues. This
271
contrasts with data reported by (Ashburn et al. 2012), who found that attending
272
physicians had a tendency towards reduced error compared with residents, although this
273
trend was not significant. Within the General Practitioner group, our results may have
274
been influenced by the low sample numbers for this group, which could have
275
contributed to the narrow range of values. Examining the absolute error values and
276
comparing the magnitude of error, there was no significant difference between the
277
groups. These absolute error results are more consistent with the medical literature
12
278
which typically found no difference in role or experience between different groups
279
(Meiser et al. 2001; Dildy et al. 2004; Adkins et al. 2014; Rothermel & Lipman 2016).
280
With-Guide accuracy
281
The use of a pictorial guide reduced the absolute median error and narrowed the range
282
of responses. This suggests that respondents were able to estimate volumes more
283
effectively they used a tool that closely resembled the test images. This finding is
284
similar to medical studies where improvements in accuracy occurred when a pictorial
285
guide was used (Dildy et al. 2004; Zuckerwise et al. 2014; Homcha et al. 2017).
286
Conversely, analysis of the raw error values showed a small increase in WGD median
287
error compared with PGD median error. However, the range of values was again
288
narrower for WGD compared to PGD. While these results appear to contradict the
289
ability of the Guide to reduce the estimation error, the narrowed range supports a
290
finding of improved accuracy.
291
Using the Guide also improved accuracy in more complex scenarios. Without
292
the Guide, the scenarios with fewer items were more accurate and the scenarios with
293
more items were less accurate. With the Guide, Scenarios 3, 4, and 5 showed
294
improvements in accuracy in terms of magnitude of error, while Scenarios 1 and 2
295
showed a decrease in accuracy between the PGD and WGD phases. Given that real-life
296
surgical situations are likely more reflective of the multi-component scenarios, this
297
supports the clinical application of the Guide. The construction of the ‘simple scenarios’
298
may have contributed towards a tendency to overestimation. Scenarios 1 and 2 consisted
299
of one image, each of which was drawn from the pool of created images and therefore
300
similar to images presented in the Guide. This possibly created a shortcut whereby the
301
estimate of the similar image exactly matched one of the Guide images and is a
302
limitation of the study.
13
303
There was no difference between the groups with use of the Guide, suggesting
304
that the impact of role and experience is lessened with assistance from a pictorial guide.
305
This agrees with the medical literature where preintervention differences in groups did
306
not persist postintervention (Zuckerwise et al. 2014). Comparing the PGD and WGD
307
phases of the groups showed mixed results. Only the ‘Specialist’ and ‘Nurse’ groups
308
showed improvements in both raw and absolute error, while the ‘Intern’ and
309
‘Resident/Residency Trained’ groups, with the most accurate Pre-Guide values,
310
deteriorated or did not change. This finding suggests that the Guide is most useful for
311
the more inaccurate estimators. Again, the ranges of WGD responses for most of the
312
groups were much narrower than the PGD ranges, supporting improved estimation
313
accuracy when the Guide is used.
314
There are several limitations to this study. For ease of direct comparison, the
315
same scenarios were used for the Pre-Guide and With-Guide phases of the study, which
316
may have led to a degree of recognition between phases. The images used for the Guide
317
were very similar to those used for scenarios which may limit the broader application
318
and usefulness of the Guide for situations which have different items. The scenarios
319
were presented with images only, and displayed in an online digital format, limiting the
320
interaction and possible assessment of the receptacles which would occur in a real-life
321
situation. The images were created with an artificial blood substitute, which, despite
322
gross physical similarity to real blood, may behave differently when in contact with
323
material substrates and may confound assessment. The relatively small blood volumes
324
we used may have exaggerated small differences in estimation within the scenarios,
325
while making comparison between scenarios more challenging. There were a wide
326
variety of responses to all of the scenarios, which possibly questions the integrity of
327
some of the data. For example, the wide range of responses for Scenario 1 involving the
14
328
suction pot was unexpected given the visible graduation mark depicted in the image.
329
However, these data were retained as the integrity, or otherwise, of these responses is
330
unknown. The signalment of the cases may have biased the estimation based on
331
individual interpretation of each case background. The scenarios only involved cats and
332
dogs therefore no conclusions can be drawn for other species. All of the participants
333
were sourced from the same facility, and despite being from a variety of roles and
334
backgrounds, this may not be representative of the wider population. The study
335
occurred solely at a single institution, and it may be that the utility of this Guide is
336
confined to the facility in which the study took place. Other institutions may find it
337
useful to develop their own pictorial guide customised to their own setting (e.g surgical
338
items, type of containers, etc). The low group sample numbers may have contributed to
339
the lack of significance within all of the groups between phases. Finally, there was no
340
ability to differentiate between departmental role, which prevented an evaluation of any
341
impact that field of work may have had.
342
In conclusion, visual estimation of blood loss is inaccurate and varied. For
343
scenarios involving small animals, the use of a pictorial guide improved the accuracy of
344
estimation of blood loss in both reduction of median error and narrowing of spread of
345
estimation across all groups, while reducing the variation between groups. In particular,
346
the pictorial guide proved most useful in more complex scenarios. In the future, similar
347
investigations, drawn from a broader disciplinary and geographical population, could be
348
considered. Further analysis of the Guide’s utility in the actual surgical setting and
349
comparison with other measurement techniques is also indicated.
350 351 352
15
353
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354
Adkins AR, Lee D, Woody DJ et al. (2014) Accuracy of blood loss estimations among
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Preoperative Anemia in Noncardiac Surgery: A Single-center Cohort Study.
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Anesthesiology 110, 574-581.
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Bose P, Regan F, Paterson Brown S (2006) Improving the accuracy of estimated blood
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International Journal of Obstetrics & Gynaecology 113, 919-924.
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Budair B, Ahmed U, Hodson J et al. (2017) Are we all guilty of under-estimating intra-
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Clark L, Corletto F, Garosi LS (2010) Comparison of a method using the HemoCue
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dogs undergoing spinal surgery. Veterinary Anaesthesia and Analgesia 37, 44-
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Cole F (1953) A new method of estimating blood loss during surgery. The American Journal of Surgery 86, 50-52.
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Dildy AG, Paine RA, George CN et al. (2004) Estimating Blood Loss: Can Teaching
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Eysenbach G (2004) Improving the quality of Web surveys: the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). J Med Internet Res 6, e34-e34.
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Glance LG, Dick AW, Mukamel DB et al. (2011) Association between Intraoperative
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Blood Transfusion and Mortality and Morbidity in Patients Undergoing
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Godinho-Cunha LF, Ferreira RMRF, Silvestre-Ferreira AC (2011) Whole blood
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transfusion in small animals: indications and effects. An Acad Bras Cienc 83,
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Gutierrez G, Reines H, Wulf-Gutierrez M (2004) Clinical review: hemorrhagic shock. Critical Care [NLM - MEDLINE] 8, 373-381.
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Hébert PC, Wells G, Blajchman MA et al. (1999) A multicenter, randomized, controlled
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clinical trial of transfusion requirements in critical care. The New England
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Journal of Medicine 340, 409-417.
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Homcha EB, Mets JE, Goldenberg DFM et al. (2017) Development and Assessment of
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Pictorial Guide for Improved Accuracy of Visual Blood Loss Estimation in
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Cesarean Delivery. Simulation in Healthcare: The Journal of the Society for
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Simulation in Healthcare 12, 314-318.
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Lee MH, Ingvertsen BT, Kirpensteijn J et al. (2006) Quantification of Surgical Blood Loss. Veterinary Surgery 35, 388-393.
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Lopez-Picado A, Albinarrate A, Barrachina B (2017) Determination of Perioperative
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Blood Loss: Accuracy or Approximation? Anesthesia & Analgesia 125, 280-
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Meiser A, Casagranda O, Skipka G et al. (2001) Quantifizierung von Blutverlusten Wie
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genau ist visuelles Schätzen und wovon hängt die Genauigkeit ab? Der
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Anaesthesist 50, 13-20.
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Pestel GJ, Fukui K, Kimberger O et al. (2010) Hemodynamic Parameters Change
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Earlier Than Tissue Oxygen Tension in Hemorrhage. Journal of Surgical
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Research 160, 288-293.
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Porter AE, Rozanski EA, Sharp CR et al. (2013) Evaluation of the shock index in dogs
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presenting as emergencies. Journal of Veterinary Emergency and Critical Care
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23, 538-544.
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Razvi K, Chua S, Arulkumaran S et al. (1996) A Comparison Between Visual
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Estimation and Laboratory Detennination of Blood Loss During the Third Stage
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Rothermel LD, Lipman JM (2016) Estimation of blood loss is inaccurate and unreliable. Surgery 160, 946-953. Schorn MN (2010) Measurement of Blood Loss: Review of the Literature. Journal of Midwifery & Women s Health 55, 20-27. Wang D, Cortés‐ Puch I, Sun J et al. (2014) Transfusion of older stored blood worsens
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Zuckerwise CL, Pettker MC, Illuzzi RJ et al. (2014) Use of a Novel Visual Aid to
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429 430 431 432
19
Figure 1 All pooled responses displaying error (median and range) pre-Guide (PGD) and with-Guide *WGD), both raw and absolute data. The ‘raw’ values represent the difference between the estimate minus the actual volume. The absolute values represent the raw values with the sign removed to assess magnitude of error. Boxes represent 25th to 75th percentile and whiskers represent 5th to 95th percentile of error responses; the middle line represents the median. The dots indicate outliers beyond the percentile limits.
Figure 2 Raw error responses grouped according to role, displaying pre-Guide (PGD) and with-Guide (WGD) results. The ‘raw’ values represent the difference between the estimate minus the actual volume. The absolute values represent the raw values with the sign removed to assess magnitude of error. Boxes represent 25th to 75th percentile and whiskers represent 5th to 95th percentile of error responses; the middle line represents the median. The dots indicate outliers beyond the percentile limits.
Figure 3. Absolute error responses grouped according to role, displaying pre-Guide (PDG) and with-Guide (WGD) results. The ‘raw’ values represent the difference between the estimate minus the actual volume. The absolute values represent the raw values with the sign removed to assess magnitude of error. Boxes represent 25th to 75th percentile and whiskers represent 5th to 95th percentile of error responses; the middle line represents the median. The dots indicate outliers beyond the percentile limits.
1
Table 1 The number of respondents (n) grouped according to role comparing median
2
(range) error in mL of both raw and absolute values initially for the Pre-Guide (PGD)
3
phase and With-Guide (WGD) phase, with accompanying p – value. Raw WGD Raw PGD Median Median (range) (range) mL mL n
Responses
p - value Absolute WGD Absolute PGD Median Median (range) (range) mL mL
All
General
59 588
6
60
Practitioner Intern
Student
Nurse
Resident/
9
90
15 150
10 100
10 99
Residency trained Specialist
4 5
9
89
-16 (-105 – 443)
18 (-199 – 191)
< 0.0001
34 (0 – 443)
23 (0 – 191)
< 0.0001
-37 (-94 – 34)
15.5 (-41 –141)
< 0.0001
7 (5 – 94)
23 (3- 141)
0.0784
-5 (-97 – 443)
20 (-32 – 93)
0.0036
29 (3 – 443)
23 (1 – 93)
0.3032
-16 (-101 – 291)
17 (-39 – 95)
< 0.0001
46 (3-291)
21 (1 – 133)
< 0.0001
-16 (-105 – 391)
14.5 (-79 – 95)
0.0036
35.5 (3 – 391)
21 (1 – 95)
0.0354
8.5 (-49 – 291)
23 (-89 – 191)
0.0483
27 (3 – 291)
24 (1 – 191)
0.8583
-32 (-99 – 266)
15 (-39 – 95)
< 0.0001
46 (0 – 266)
20 (0 – 95)
0.0004
6
Table 2 Comparison of the different scenarios’ median (range) error in mL, of both raw
7
and absolute values initially for the Pre-Guide (PGD) phase and With-Guide (WGD)
8
phase, with accompanying p – value. Scenario
Raw PGD Median
Raw WGD Median
(range) mL
(range) mL
Absolute PGD
Absolute WGD
Median (range)
Median (range) mL
p-value
mL 1
2
3
4
5
-6 (-62 – 34)
34 (-26 – 74)
< 0.0001
34 (0-62)
34 (4, 74)
0.8195
-5 (-95 – 115)
35 (-25 – 95)
< 0.0001
25 (5 -115)
35 (5 – 95)
0.4714
-32 (-55 – 293)
8 (-53 – 133)
< 0.0001
37 (3 – 293)
19 (0 – 133)
< 0.0001
-17 (-55 – 443)
23 (-55 – 73)
< 0.0001
37 (3 – 443)
23 (2 – 73)
0.01103
-19 (-105 – 391)
9 (-99 – 191)
0.05691
64 (9 – 391)
19 (1 – 191)
< 0.0001
9 10
2