1. Evaluate the discussion section of the article attached and identify if the following was addressed. (Note, you need to show evidence, do not just say yes or no. Post what the researcher indicated that supports that these elements were addressed in the

Quantitative_Pressureulcerarticle2017T41
 

1. Evaluate the discussion section of the article attached and identify if the following was addressed. (Note, you need to show evidence, do not just say yes or no. Post what the researcher indicated that supports that these elements were addressed in the discussion section. Add the page number where you found them)

a) limitations and strengths of the study variable(s) 

b)hypothesis(es)/research questions 

c) theoretical framework

d) design

e) sample  

f) data collection procedures

g) data analysis 

h)generalizations 

i)conclusions

j)recommendations for future research 

2. After reviewing and evaluating the  “Discussion” section of the article, discuss the strength of the evidence supports a change in current practice (If you think it does, support your answer with evidence based literature. You describe what the article indicated and find another source to support why the strength of evidence support a change in current practice). 

3.  What is your cosmic question? (This should be based on chapter of the week. Pose a research question on discussion section of a research)

Pressure Injury Management

©2020 American Association of Critical-Care Nurses
doi:https://doi.org/10.4037/ajcc2020810

Background Hospital-acquired pressure injuries dispropor-
tionately affect critical care patients. Although risk factors
such as moisture, illness severity, and inadequate perfu-
sion have been recognized, nursing skin assessment data
remain unexamined in relation to the risk for hospital-
acquired pressure injuries.
Objective To identify factors associated with hospital-
acquired pressure injuries among surgical critical care
patients. The specific aim was to analyze data obtained
from routine nursing skin assessments alongside other
potential risk factors identified in the literature.
Methods This retrospective cohort study included 5101
surgical critical care patients at a level I trauma center and
academic medical center. Multivariate logistic regression
using the least absolute shrinkage and selection operator
method identified important predictors with parsimonious
representation. Use of specialty pressure redistribution
beds was included in the model as a known predictive
factor because specialty beds are a common preventive
intervention.
Results Independent risk factors identified by logistic
regression were skin irritation (rash or diffuse, nonlocal-
ized redness) (odds ratio, 1.788; 95% CI, 1.404-2.274; P < .001), minimum Braden Scale score (odds ratio, 0.858; 95% CI, 0.818-0.899; P < .001), and duration of intensive care unit stay before the hospital-acquired pressure injury devel-
oped (odds ratio, 1.003; 95% CI, 1.003-1.004; P < .001). Conclusions The strongest predictor was irritated skin, a potentially modifiable risk factor. Irritated skin should be treated and closely monitored, and the cause should be eliminated to allow the skin to heal.(American Journal of Critical Care. 2020;29:e128-e134)

Risk FactoRs FoR
Hospital- acquiRed
pRessuRe injuRy in
suRgical cRitical
caRe patients
By Jenny Alderden, PhD, APRN, CCRN, CCNS, Linda J. Cowan, PhD, APRN,
RN, FNP-BC, CWS, Jonathan B. Dimas, BSN, RN, CCRN, Danli Chen, MSTAT,
Yue Zhang, PhD, Mollie Cummins, PhD, RN, and Tracey L. Yap, PhD, RN,
WCC, CNE

1.0 HourC E
This article has been designated for CE contact
hour(s). See more CE information at the end of
this article.

e128 AJCC AMERICAN JOURNAL OF CRITICAL CARE, November 2020, Volume 29, No. 6 www.ajcconline.org

www.ajcconline.org AJCC AMERICAN JOURNAL OF CRITICAL CARE, November 2020, Volume 29, No. 6 e129

The relationship between
hospital-acquired pressure
injuries and skin status
remains mostly unexamined
in the critical care

population.

P
atients admitted to the intensive care unit (ICU) are twice as likely as other acute care
patients to have a hospital-acquired pressure injury (HAPI) develop.1 A pressure injury
(PI) is defined as localized damage of the skin or underlying tissue as a result of pres-
sure or pressure in combination with shear.2 Patients who undergo surgery and who
are older than 65 years have a higher risk than younger patients of acquiring a PI in the

hospital.3,4 In the United States, PI costs attributed to patients exceed $26.8 billion annually,5
and having a HAPI develop results in a median 4-day increase in the length of stay.6

Determining the factors associated with HAPI
development in critical care patients is necessary to
enable risk-based preventive measures. Although
HAPIs are associated with known risk factors such
as decreased mobility, surgery duration, vasopressor
infusion, excessive moisture, altered perfusion, and
history of a prior PI, the relationship between HAPIs
and skin status remains mostly unexamined in the
critical care population.4,7-18 Assessing skin status
(including turgor, excessive dryness, irritation, skin
tears, and the loss of subcutaneous tissue) to iden-
tify potential HAPI prevention interventions is
particularly essential when caring for older patients
because of age-related changes. Such changes include
thinning skin, decreased subcutaneous tissue, flatten-
ing of the dermal-epidermal junction (decrease in rete
ridges), structural disorganization of collagen fibers
in the dermis, loss of vertical capillary loops, and
loss of elasticity.2

Using informatics to analyze the vast amounts
of electronic health record (EHR) data, such as skin
assessment data, routinely produced during care
delivery is an excellent way to identify risk factors
for HAPI development. Critical care nurses routinely
conduct head-to-toe skin assessments every 12 hours
and document changes in condition in the EHR. How-
ever, these large-scale real-world data have not been
fully examined in relation to HAPIs in the surgical
critical care setting.

The unprecedented quantities and diverse sources
of data collected during care delivery make this an
opportune time to conduct HAPI research. The pur-
pose of our study was to identify factors associated
with HAPI development among surgical critical care
patients. Our specific aim was to examine data
obtained from routine nursing skin assessments along
with other previously reported HAPI risk factors.

Methods
Design and Sample

This was a retrospective cohort study. We included
data from surgical critical care patients admitted con-
secutively to the surgical ICU (SICU) or cardiovascu-
lar surgical ICU (CVICU) at our study site, an urban
level I trauma center and academic medical center,
from 2014 through 2018. We included patients with
a PI present on
admission to the
hospital because
patients with prior
PIs are at increased
risk for subsequent
HAPIs.16 We did not
count community-
acquired PIs as
HAPIs because they
were not acquired in the hospital. However, if patients
with a community-acquired ulcer had a HAPI develop,
that subsequent PI was included in the analysis because
it was hospital acquired. The exclusion criterion was
a stay of less than 24 hours because of inadequate time
for a HAPI to be considered a facility-acquired PI.

Data Collection
Data were obtained via EHR query and retrieved

from our institution’s enterprise data warehouse for
critical care data. For patients with multiple hospital
admissions, we limited data collection to the first
SICU or CVICU admission. A biomedical informat-
ics team performed the query. Query results were
validated by a critical care nurse who verified infor-
mation obtained (including date and time stamps)

About the Authors
Jenny Alderden is an assistant professor and Mollie
Cummins is a professor, University of Utah College of
Nursing, Salt Lake City. Linda J. Cowan is associate
director, VISN 8 Patient Safety Center of Inquiry, James
A. Haley Veterans’ Hospital and Clinics, Tampa, Florida.
Jonathan B. Dimas is a PhD candidate, University of Utah
College of Nursing, and a clinical nurse and analyst,
University of Utah Health, Salt Lake City. Danli Chen is
a biostatistician II and Yue Zhang is an associate profes-
sor, Division of Epidemiology, University of Utah, Salt
Lake City. Tracey L. Yap is an associate professor, Duke
University School of Nursing, Durham, North Carolina.

Corresponding author: Jenny Alderden, PhD, APRN, CCRN, CCNS,
University of Utah College of Nursing, 10 S 2000 E, Salt Lake
City, UT 84112 (email: Jenny.Alderden@Nurs.Utah.Edu).

e130 AJCC AMERICAN JOURNAL OF CRITICAL CARE, November 2020, Volume 29, No. 6 www.ajcconline.org

Data from more than
5000 consecutive sur-

gical critical care
patients were analyzed

retrospectively.

via the human-readable system EHR for 30 patients,
including 15 patients with HAPIs. A practicing criti-
cal care nurse and a certified wound nurse also man-
ually reviewed medical records, including data from
the notes and images, to obtain data that were miss-
ing or unclear in the query.

Outcome Variable
The outcome variable was the development of a

HAPI of any stage (stages 1 through 4, deep tissue
injury, or unstageable) according to the National Pres-
sure Injury Advisory Panel staging guidelines.2 We
included stage 1 HAPIs in our outcome because prior
studies showed that one-third of stage 1 HAPIs detected
among surgical critical care patients worsen to stage

2 or greater.19 A certified
wound nurse verified the PIs
in our sample to differentiate
potential cases of moisture-
related skin breakdown from
true HAPIs. In cases in which a
HAPI might be confused with
another source of injury, the
certified wound nurse made
the final decision as to the

presence or absence of the HAPI. We were able to
differentiate between community-acquired PIs and
HAPIs because each PI in our EHR has a unique
identification number with a date and time stamp.

Predictor Variables
We conducted a systematic review of the litera-

ture to identify predictor variables of interest.4 Possi-
ble predictor variables included vasopressor infusions
and their durations,17 blood gas and laboratory val-
ues,18,19 surgical time,20 levels of sedation and agita-
tion,21 and total score on the Braden Scale (a common
tool used by nursing staff to assess the risk of PI devel-
opment by examining moisture, mobility, sensory
perception, and friction/shear).22

We included comprehensive nursing skin assess-
ment data. At our facility, nurses undergo annual train-
ing in head-to-toe skin assessment and PI staging.
Nurses at our facility conduct a global head-to-toe skin
assessment twice daily and document the following
changes: excessively moist skin, excessively dry skin,
thin epidermis with loss of subcutaneous tissue, and
the presence of irritation (defined as a rash or diffuse,
nonlocalized, blanchable redness). Nurses also doc-
ument the presence of a skin tear. Table 1 lists the
predictor variables included in our analysis.

For patients who had a HAPI develop, we col-
lected data only for events occurring at least 24 hours

before HAPI detection. We chose this time frame to
capture events predictive of a HAPI rather than events
occurring at the same time as a HAPI.

Analysis
Analysis was conducted with R, version 3.6.1

(R Foundation for Statistical Computing).23 We sum-
marized and compared the distributions of potential
prediction factors by HAPI status with a χ2 test for
categorical factors and a 2-sample t test (or its non-
parametric alternative, the Mann-Whitney U test)
for continuous and ordinal variables. We performed
multivariable logistic regression analysis with the least
absolute shrinkage and selection operator (LASSO)24
to identify the subset of potential predictors most
informative for predicting the likelihood of a HAPI
developing. The final model for outcomes was based
on the optimal penalty term using 10-fold cross-
validation criteria.

By imposing some penalty in the regression
model fitting, the LASSO approach can shrink the
coefficients of unimportant predictors to 0 while
retaining prominent predictors. A predictor has
predictability on the outcome only if its coefficient is
nonzero. The final models, therefore, include all
important predictors with parsimonious representa-
tion, enhanced interpretability, and improved pre-
diction precision. In this study, the variable specialty
bed was forced into the model as a known predic-
tion factor (even though our general SICU and
CVICU bed is a low-air-loss mattress) because some
of our patients were placed on other types of specialty
rental beds (eg, bariatric beds or specialty prone
positioning beds) because of body habitus or clini-
cal condition.25

Results
Sample

The initial query produced 5102 patients. We
excluded 1 patient from the analysis because of incom-
plete demographic data, so the final sample size
was 5101. Demographic data are shown in Table 1.

Pressure Injury Outcomes
Of the 5101 patients in our sample, 399 (8%) had

at least 1 HAPI develop. Of the 399 patients with a
HAPI, 110 (28%) had a stage 1 HAPI develop; 182
(46%), stage 2 HAPI; 6 (2%), stage 3 HAPI; 1 (< 1%), stage 4 HAPI; 33 (8%), unstageable HAPI; 62 (16%), deep tissue injury; and 5 (1%), mucosal PI. Of the 110 stage 1 HAPIs, 44 (40%) worsened to a more severe stage during the SICU or CVICU stay. The most common PI location was the coccyx (n = 153

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Pull quote

[38%]), followed by the buttocks (n = 62 [16%]),
sacrum (n = 47 [12%]), extremity excluding heel
(eg, arms or legs; n = 46 [12%]), head or face
(n = 40 [10%]), other location (n = 32 [8%]), back
(n = 10 [3%]), and heel (n = 9 [2%]).

Pressure Injury Predictors
Univariate relationships between potential pre-

dictor variables and HAPI development are presented
in Table 1. From the soft-thresholding property of the

LASSO in linear models, the estimated regression
coefficient is biased toward 0. To mitigate these bias
problems, we report a more unbiased estimation of
regression coefficients from unpenalized multivari-
ate logistic regression using the selected factors in
the LASSO (Table 2).

Discussion
The purpose of our study was to identify risk fac-

tors for HAPI development among SICU and CVICU

Variable

No. (%) of patientsa

P
All

(N = 5101)
With no HAPI

(n = 4702)
With a HAPI

(n = 399)

Table 1
Potential predictor variables and development of
hospital-acquired pressure injury

Abbreviations: HAPI, hospital-acquired pressure injury; ICU,

intensive care unit.

a Unless otherwise indicated in first column.
b Irritated skin is defined as a rash or diffuse, nonlocalized, blanchable redness, not over a bony prominence.
c Riker Sedation-Agitation Scale.
d Calculated as weight in kilograms divided by height in meters squared.

Demographic data
Age, mean (SD), y 58 (17) 59 (16) 58 (16) .24
Sex, male 3302 (65) 3040 (65) 262 (66) .73
Race, White 4256 (83) 3934 (84) 322 (81) .14
Ethnicity, non-Hispanic 4452 (87) 4112 (87) 340 (85) .17
Length of hospital stay, mean (SD), d 12 (11) 11 (9) 28 (20)

<.001

Length of ICU stay before HAPI, mean (SD), d 5 (7) 5 (6) 13 (13) <.001 Laboratory data, mean (SD) Maximum lactate, mg/dL 4.0 (3.7) 3.9 (3.6) 5.6 (4.8) <.001 Maximum serum creatinine, mg/dL 1.9 (1.9) 1.8 (1.9) 2.7 (2.1) <.001 Maximum serum glucose, mg/dL 231 (148) 227 (141) 280 (210) <.001 Minimum hemoglobin, g/dL 8.9 (2.6) 9.1 (2.6) 7.7 (2.2) <.001 Minimum albumin, g/dL 3.1 (0.8) 3.2 (0.8) 2.7 (0.7) <.001 Minimum Pao

2
, mm Hg 54 (40) 55 (41) 47 (32) <.001

Minimum arterial pH 7.27 (0.11) 7.27 (0.10) 7.23 (0.13) <.001 Maximum Paco

2
, mm Hg 52 (14) 52 (13) 55 (16) <.001

Skin status
Thin epidermis/subcutaneous tissue loss 888 (17) 792 (17) 96 (24) <.001 Excessively dry skin 351 (7) 296 (6) 55 (14) <.001 Skin tear 641 (13) 534 (11) 107 (27) <.001 Excessively moist skin 816 (16) 712 (15) 104 (26) <.001 Irritated skinb 1394 (27) 1176 (25) 218 (55) <.001 Community-acquired pressure injury present at

admission
167 (3) 120 (3) 47 (12) <.001

Duration of surgery, mean (SD), h
Longest single surgery 3.0 (2.6) 3.0 (3.2) 3.3 (2.5) .08
Total surgical time 3.7 (3.4) 3.6 (3.3) 4.6 (4.7) <.001 Duration of vasopressor infusion, mean (SD), h Norepinephrine 9 (36) 7 (33) 30 (62) <.001 Epinephrine 8 (35) 7 (31) 23 (61) <.001 Phenylephrine 1 (8) 1 (14) 2 (20) .01 Dopamine 1 (14) 6 (13) 23 (19) .12 Vasopressin 11 (55) 9 (51) 37 (86) <.001 Other potential predictors Minimum Braden Scale score, mean (SD) 13 (3) 13 (3) 12 (3) <.001 Minimum Riker score,c mean (SD) 2.8 (1.2) 2.87 (1.19) 2.15 (1.22) <.001 Admission body mass index,d mean (SD) 30.1 (12.4) 30.1 (12.5) 30.2 (10.7) .89 Nonstandard bed (eg, bariatric bed or other) 1390 (27) 1234 (26) 156 (39) .73 Comorbid diabetes 1756 (34) 1579 (34) 177 (44) <.001

e132 AJCC AMERICAN JOURNAL OF CRITICAL CARE, November 2020, Volume 29, No. 6 www.ajcconline.org

patients. Identifying risk factors is useful to improve
our understanding and care planning for patients
considered high risk and to recognize factors that
are potentially modifiable. In our study, candidate
predictor variables included the duration of vaso-
pressor infusion, blood gas values, surgery duration,
Braden Scale scores, nursing skin assessment data, and
laboratory values. In multivariable LASSO regression,
the most informative predictors for HAPI risk were
length of SICU or CVICU stay, the minimum Braden
Scale score, and skin irritation (defined as a rash or
diffuse, nonlocalized, blanchable redness).

A longer hospital stay is an established risk fac-
tor for HAPI because patients with longer stays gen-
erally experience a higher severity of illness and
longer exposure times than do patients with shorter
stays.9,10,14 Consistent with the results of prior stud-
ies, in our study the duration of ICU stay before HAPI
was an independent predictor for HAPI development,
although the effect was small.7,17,26

The Braden Scale, developed in 1987 for residents
of long-term care facilities,22 was found in a recent
meta-analysis to be a poor predictor of HAPI among
surgical patients.27 In our study, patients with lower

Braden Scale scores (ie, at
greater risk) were 14%
more likely to have a
HAPI develop than were
patients with higher Bra-
den Scale scores. The
clinical relevance of this
finding is uncertain
because the mean (SD)
minimum Braden Scale

score was 13 (3) in patients without a HAPI and
was 12 (3) in patients with a HAPI. On a scale with
possible scores ranging from 6 to 23, this absolute
difference is relatively small and the corresponding

standard deviation is large, so this finding may not
be actionable at a clinical level.28 Black29 specu-
lated that the lack of clinical utility of the Braden
Scale in this population is because of the dynamic
and evolving nature of critical care patients’ physio-
logical status. In the critical care population, a risk
assessment would need to be completed contem-
poraneously with changes in patient condition,
which would be difficult because of time and
workflow constraints.

The strongest predictor of HAPI was skin irrita-
tion, a potentially modifiable risk factor. In our study,
patients with skin irritation were 79% more likely
than those with no skin irritation to have a HAPI
develop. Skin irritation indicates an alteration in
skin integrity and therefore a decrease in tissue toler-
ance to mechanical and shearing forces, such as
those responsible for HAPI development.16,30 Skin
irritation may be caused by excessive skin dryness,
allergic reactions to medications, or prolonged expo-
sure to caustic substances acting as irritants, includ-
ing urine, feces, strong soaps, laundry chemicals,
and latex gloves. In all cases, skin irritation should
be treated and closely monitored and the cause
should be eliminated to allow the skin to heal.

Potential predictor variables not included in our
LASSO model merit consideration as well. Clinically
and statistically significant differences at the univari-
ate level were noted in variables measuring aspects
of perfusion, defined as the delivery of oxygen-rich
blood to tissue. The mean serum lactate level in the
HAPI group was markedly elevated, indicating tissue
hypoperfusion and hypoxia.31 Serum albumin (which
affects perfusion via colloid osmotic pressure) and
hemoglobin (oxygen-carrying capacity) were also
decreased in the HAPI group. In addition, patients
with HAPIs had clinically and statistically signifi-
cantly longer infusion durations for all vasopressors
than did patients without HAPIs.

Consistent with the results of a prior study,32
patients with HAPIs in our study experienced longer
surgical times, highlighting the importance of con-
sidering intraoperative events in HAPI risk. How-
ever, although surgical critical care patients are at
elevated risk for HAPI,3 little is known about intra-
operative factors associated with HAPI risk in the
surgical and cardiovascular surgical critical care pop-
ulation. In a study of patients undergoing urologic
procedures, duration of anesthesia and a diastolic
blood pressure of less than 50 mm Hg were predic-
tive of HAPI development, indicating that perfusion
during surgery may influence HAPI risk.33,34 Research
is urgently needed to identify intraoperative risk

Predictor variable Odds ratio (95% CI) P

Table 2
Results of LASSO logistic regressiona

Abbreviation: LASSO, least absolute shrinkage and selection operator.

a A total of 5019 patients (98%) were included in the logistic regression; 82
patients’ data were excluded from the analysis because of missing data.

b Irritated skin is defined as a rash or diffuse, nonlocalized, blanchable redness,
not over a bony prominence.

c Included in the model as a control factor because specialty beds were used
inconsistently.

Intercept 0.278 (0.147-0.523) <.001 Irritated skinb 1.788 (1.404-2.274) <.001 Minimum Braden Scale score 0.858 (0.818-0.899) <.001 Duration of stay in intensive care unit

before hospital-acquired pressure injury
1.003 (1.003-1.004)

<.001

Specialty bedc 0.816 (0.634-1.044) .11

Of the 110 stage 1 HAPIs,
44 (40%) worsened to a

more severe stage during
the patient’s stay in the

intensive care unit.

www.ajcconline.org AJCC AMERICAN JOURNAL OF CRITICAL CARE, November 2020, Volume 29, No. 6 e133

factors in surgical critical care patients33 and to
identify potentially modifiable risk factors.

Limitations
Our study was limited by its retrospective design

because we accessed only data available in the EHR.
The subjectivity of clinician interpretation is also
a limitation; individual nurses’ definitions of skin
irritation may not exactly coincide. Furthermore, we
did not differentiate medical device–related HAPIs
from other HAPIs. Other predictor variables that
have been associated with HAPI in this population
were not selected because these variables could not
be obtained from the EHR. We did not include com-
pliance with PI prevention protocols (eg, repositioning
schedules) because the EHR is not a reliable source
of information about preventive interventions. For
instance, every 2 hours our EHR prompts nursing staff
to document a position change. However, the changes
might be faithfully documented every 2 hours but not
always performed.35 Finally, our sample was from a sin-
gle site with a predominantly White population, which
may also affect the generalizability of our results.35,36

Conclusions
Our results indicate that nursing staff should

consider changes in the epidermal layer, especially
skin irritation, to be influential risk factors for HAPI.
Skin irritation should be promptly treated by elimi-
nating the cause. The SICU and CVICU patients who
had HAPI develop in our study also exhibited poor
perfusion and longer surgical times. Future research
is needed to elucidate the relationship between per-
fusion, intraoperative events, and HAPI risk.

FINANCIAL DISCLOSURES
This research was funded by an American Association of
Critical-Care Nurses–Sigma Theta Tau Critical Care Grant.
This study was also supported by the University of Utah
Population Health Research Foundation, with funding in
part from the National Center for Research Resources and
the National Center for Advancing Translational Sciences,
National Institutes of Health (grant UL1TR002538).

SEE ALSO
For more about hospital-acquired pressure injuries,
visit the Critical Care Nurse website, www.ccnonline.org,
and read the article by Schroeder and Sitzer, “Nursing Care
Guidelines for Reducing Hospital-Acquired Nasogastric
Tube–Related Pressure Injuries” (December 2019).

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e134 AJCC AMERICAN JOURNAL OF CRITICAL CARE, November 2020, Volume 29, No. 6 www.ajcconline.org

1.0 Hour Category AC E
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This article has been designated for CE contact hour(s). The evaluation demonstrates your knowledge of the
following objectives:

1. Identify independent risk factors for hospital-acquired pressure injuries.
2. Describe potential treatments for skin irritation.
3. Determine the clinical relevance of stage 1 pressure injuries in the surgical and cardiovascular surgical

population.

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tered Nursing (CA BRN), CA Provider Number CEP1036, for 1.0 contact hour.

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in intensive care unit patients at risk according to the Water-
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2702.2008.02598.x

27. He W, Liu P, Chen HL. The Braden Scale cannot be used alone
for assessing pressure ulcer risk for surgical patients: a
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28. Anthony D, Papanikolaou P, Parboteeah S, Saleh M. Do risk
assessment scales for pressure ulcers work? J Tissue Viabil-
ity. 2010;19(4):132-136.

29. Black J. Pressure ulcer prevention and management: a dire
need for good science. Ann Intern Med. 2015;162(5):387-388.

30. Yap TL, Rapp MP, Kennerly S, Cron SG, Bergstrom N. Com-
parison study of Braden Scale and time-to-erythema mea-
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31. Antinone R, Kress T. Measuring serum lactate. Nurs Crit Care.
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32. Lu CX, Chen HL, Shen WQ, Feng LP. A new nomogram score
for predicting surgery-related pressure ulcers in cardiovas-
cular surgical patients. Int Wound J. 2017;14(1):226-232.

33. Chello C, Lusini M, Schilirò D, Greco SM, Barbato R, Nenna
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difficult risk assessment, and profound clinical implications.
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34. Connor T, Sledge JA, Bryant-Wiersema L, Stamm L, Potter P.
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35. Yap TL, Kennerly SM, Simmons MR, et al. Multidimensional
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36. Girardeau-Hubert S, Deneuville C, Pageon H, et al. Recon-
structed skin models revealed unexpected differences in
epidermal African and Caucasian skin. Sci Rep. 2019;9(1):
7456. doi:10.1038/s41598-019-43128-3

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