Prediction Models for Risk Score and Prognosis of Intraoperatively Acquired Pressure Injury in Surgical Patients: a Multicenter Prospective Cohort Study
Conditions: Pressure Ulcer, Predictive Models, Surgical Patient, Clinical Outcome
Sponsor: Affiliated Hospital of Guangdong Medical University

Study Protocol 
 
Study Title： 
Prediction Models for Risk Score and Prognosis of Intraoperatively Acquired Pressure 
Injury in Surgical Patients: a multicenter cohort study. 
Study Protocol Authors: Yiyue Zhong 
Study Protocol Date: July 23, 2023 

Contents 
Abstract .......................................................................................................................... 4 
1. Introduction ............................................................................................................. 7 
2. Methods................................................................................................................. 11 
2.1 Study Design ................................................................................................... 11 
2.2 Study objective ................................................................................................ 11 
2.3 Setting ............................................................................................................. 11 
2.4 Participants and Eligibility criteria ................................................................ 12 
2.5 Variables ......................................................................................................... 12 
2.5.1 Outcome measure ......................................................................................... 12 
2.5.2 Secondary outcome ...................................................................................... 15 
2.5.3 Predictors ..................................................................................................... 16 
2.6 Follow-up period ............................................................................................ 17 
3. Data Collection ..................................................................................................... 18 
3.1 Data sources and study setting ........................................................................ 18 
3.2 Data collection tools and instruments ............................................................. 18 
3.3 Schedule of Events .......................................................................................... 25 
3.4 Recruitment of subjects................................................................................... 26 
3.5 Data collection ................................................................................................ 26 
3.6 Data management and storage ........................................................................ 26 
4. Data Analysis ........................................................................................................ 30 
4.1 Sample size ...................................................................................................... 30 

4.2 Data analysis .................................................................................................. 31 
4.2.1 Model development ............................................................................... 32 
4.2.2 Model assessment.................................................................................. 34 
4.2.3 Secondary outcome assessment ............................................................ 35 
4.2.4 Methodological considerations ............................................................. 36 
5. Ethical Considerations .......................................................................................... 37 
5.1 Informed consent process. ............................................................................... 37 
5.2 Privacy and confidentiality measures. ............................................................ 37 
5.3 Potential risks and benefits to participants. ................................................... 37 
6. Contributors .......................................................................................................... 39 
7. Funding ................................................................................................................. 39 
8. Competing interests .............................................................................................. 39 
9. Ethics approval...................................................................................................... 39 
10. 
Provenance and peer review .......................................................................... 40 
 
 

Abstract 
Background: 
Intraoperatively acquired pressure injure (IAPI) are a prevalent and important problem 
in the operating room (OR), and risk recognition by a quantifiable risk score is 
considered urgent in patients undergoing surgical procedure. 
Objectives: 
Develop and validate a reliable and simple IAPI risk prediction model for surgical 
patients. 
Design: 
A prospective, multicenter, cohort study. 
Participants and setting: 
All patients who underwent surgery procedure from operating room of more than 5 
tertiary hospitals in China. 
Methods: 
We will use a pre-set observational assessment scale that consists of 24 indicators to 
construct the predictive model in this study. In addition to these pre-set indicators, the 
researchers will also collect other relevant indicators that they believe may contribute 
to the predictive model. Primary outcome was incidence of IAPI of any stage at 7 days 
from operating room admission. Secondary outcomes were in-hospital mortality 
(follow-up endpoint is 3 months after surgical procedure), surgical complication, and 
hospitalization costs. The prospective cohort study dataset is divided randomly into two 

subsets: a training group (80% of the data) and an internal validation group (20% of the 
data). The training group is used for model development, while the internal validation 
group remains untouched until later stages. In model development, researchers can 
explore different variables and test various machine learning algorithms, such as 
logistic regression, artificial neural network (ANN), XGBoost, support vector machine 
(SVM), k-nearest neighbors (k-NN), naive Bayes, gradient boosting machines (GBM), 
AdaBoost, Gaussian mixture model (GMM), and support vector regression (SVR). 
They can train and optimize these models using the training dataset. After training the 
models on the training dataset, researchers can evaluate their performance using 
appropriate evaluation metrics (e.g., accuracy, precision, recall, F1-score, area under 
the receiver operating characteristics curve). Based on the evaluation results, we refine 
the model by tweaking hyperparameters, feature selection, or employing techniques 
like cross-validation or grid search. This iterative process aims to find the best-
performing model configuration. Once the model is optimized using the training dataset, 
it is then evaluated on the internal validation dataset. This evaluation serves as an 
unbiased assessment of the model's performance on unseen data, providing an estimate 
of its ability to generalize. Based on the performance on the internal validation dataset, 
to choose the best-performing model as the final predictive model. 
Results: 
We describe the basic clinical characteristics of the patient underwent surgical 
procedure, and the incidence of primary outcomes (IAPI) and attributable mortality 
were reported. We will evaluate the performance of the model by assessing its accuracy, 

precision, recall, and other relevant metrics. We will compare the predicted outcomes 
with the actual outcomes to determine the model's effectiveness in correctly predicting 
the outcome of interest. Additionally, we may also conduct statistical analyses to 
identify which indicators have the strongest predictive power and contribute the most 
to the model's performance. The results will be presented and interpreted in the study, 
providing valuable information about the model's reliability and potential applicability 
in future scenarios. The secondary outcome of this study is in-hospital mortality, 
surgical complications in patients during the same hospitalization will also be reported. 
Additionally, we evaluated the duration of hospital stay and associated costs incurred 
by patients within this hospitalization period. 
Conclusion: 
A robust predictive model that can accurately predict the desired outcome. 

1. Introduction 
Pressure injury (PI, also known as pressure sores, pressure ulcers, and pressure-
ulcerative wounds) is a localized injury to the skin or subcutaneous tissue, usually in 
areas with bony prominences, resulting from pressure or pressure combined with shear 
forces.1 It is commonly found in patients who cannot change their body position to 
alleviate the pressure on bony projections. Intraoperative acquired pressure injury (IAPI) 
is a localized skin injury caused by prolonged exposure to pressure, friction, and shear 
forces during surgery. Most occur within 1-3 days after surgery, but they can also 
happen within 6 days after the procedure,2 the incidence rate of IAPI is 4.9%~66.0%.3,4 
The most common anatomical site for developing pressure injuries is the sacrum and 
heels, and most cases are at stage I-II.5-7 After PI occurs, it not only increases the 
physiological and psychological burden 8 on surgical patients but also affects their 
prognosis,9 making it a focus of care for surgical patients.3,4,10-12 Importantly, evidence 
shows that PI is associated with an increased risk of death, although PI is likely to be a 
result of poor health rather than the cause of death.9,13 In particular, in critically ill 
patients, a single-center observational study shows that the occurrence of PI is a 
significant independent predictor of death,14 and data of 1117 ICU 13254 patients from 
90 countries show that patients with PI have higher mortality rates than those without 
PI, and the correlation between pressure injury severity and mortality rate increases 
gradually as the severity of pressure injury increases.9 In addition, a retrospective study 
analyzing the database of inpatients nationwide in the United States found that the 

mortality rate of PI patients was significantly higher than that of patients without 
pressure injuries.15 Therefore, effectively identifying the risk of IAPI (infection-related 
complications) in surgical patients is the primary issue to address in clinical practice. 
Using the PI risk assessment tool or scale is an integral part of the patient 
assessment process to determine if a patient has a risk of developing PI.1 The most 
commonly used risk assessment tools in clinical practice at present include the Norton 
scale,16,17 the Waterlow scale,18-20 the Braden scale,21 the Cubbin & Jackson scale,22-24 
and the Munro scale.25 In the late 1950s and early 1960s, a team including doctors and 
nurses in London was committed to developing a risk assessment scale. Exton-Smith 
and Sherwin have shown that patients who change their body position more than 20 
times per night during sleep have a lower risk of developing PI than those with less 
frequent changes in position.16 Clearly, changing positions is a factor. In addition to that 
in 1962, several other factors such as incontinence, activity, mental state, and physical 
condition were added to create the first risk assessment scale (Norton scale), with a 
lower score indicating a higher risk of PI.17 In the 1980s, the Norton scale played an 
important role in geriatric care units. A new tool, the Waterlow scale, was designed as 
a practical auxiliary tool for prevention and treatment while promoting understanding 
and identifying risk factors for patients experiencing PI, with a higher score indicating 
a higher risk of PI.26 The Braden scale is a scoring system developed at the same time 
as the Waterlow scale, which assesses the risk of pressure injury (PI). The Braden scale 
is designed based on literature reviews and reports of PI etiology. Braden and Bergstrom 
found that key factors were pressure intensity, duration, and tolerance.21 Other risk 

factors such as changes in position, skin moisture, and nutritional status were also 
incorporated into the study. Each variable has three to four levels with operational 
definitions. Similar to the Norton scale, lower scores indicate a higher risk of PI 
occurrence with a "danger" threshold of ≤16 points for patients considered at high risk 
for developing PI. Although the Braden scale, Norton scale, and Waterlow scale all have 
good confidence,27 there is conflicting study evidence on the clinical validity of these 
risk assessment scales, and it is unclear which of these assessments is most important 
for clinical practice or whether combining them will result in better patient care.28,29 An 
early multicenter observational study showed that these risk assessment scales to a 
certain extent predicted the occurrence of PI, but the routine use of these scales would 
lead to inefficient use of preventive measures.28 The latest Cochrane systematic review 
shows that the value of using the Waterlow scale or other scoring scales for risk 
assessment in terms of PI incidence or severity is very small or no value compared with 
clinical judgment.29 The Cubbin & Jackson scale is a risk assessment scale developed 
for ICU patients, which is more suitable for the risk assessment of PI in ICU 
patients.30,31 The Munro scale is a risk assessment scale specifically designed for 
surgical PI risk,25 and recent studies have shown that the Munro scale has good IAPI 
risk assessment ability in surgical patients.32-34 However, the Munro scale includes 15 
indicators of preoperative risks, intraoperative risks, and postoperative factors. 
Conducting the entire scoring system will require a lot of time and effort from staff. 
Implementing it in a busy surgical task may be an important challenge.35 And compared 
to nurses' clinical experience, this may be an inefficient work.28  

Here, the aims of this study to develop and validate a reliable and simple surgical 
patient IAPI risk prediction model by a prospective multicenter cohort data based on 
the combination of the indicators and practical experience of the most used assessment 
tools. 

2. Methods 
2.1 Study Design 
The proposed study design is a prospective, multicenter, cohort study, which aims to 
calculate the risk factors that influence the incidence of IAPI. This design allows 
researchers to observe the natural history of IAPI, and to identify potential risk factors 
and interventions that may affect the incidence of IAPI. 
2.2 Study objective 
We hypothesize that surgical patients with IAPI can be identified. The aims of this study 
are to develop and validate a reliable and simple surgical patient IAPI risk prediction 
model by a prospective multicenter cohort data based on the combination of the 
indicators and practical experience of the most used assessment tools. 
2.3 Setting 
The patients will be recruited from the operating rooms of nine tertiary hospitals in 
China: Affiliated Hospital of Guangdong Medical University (Guangdong), First 
Affiliated Hospital of Sun Yat-sen University (Guangdong), Foshan First People's 
Hospital (Guangdong), First Affiliated Hospital of Zhengzhou University (Henan), and 
The First Affiliated Hospital of Nanchang University (Jiangxi). 

2.4 Participants and Eligibility criteria 
Participants who meet the inclusion criteria: all patients who underwent surgery 
procedure at the participating setting after the study registration. 
Exclusion criteria: Patients who have already been diagnosed with pressure injury 
(stage II and above) before undergoing surgery procedure. Patients/relatives/guardians 
understand the refusal of data to be used for clinically relevant research. Investigators 
believe that certain patient conditions may affect the efficacy and safety assessment of 
this study. 
2.5 Variables 
2.5.1 Outcome measure 
The primary outcome of this study is IAPI in patients who underwent surgery procedure. 
IAPI is a localized skin injury caused by the prolonged action of pressure, friction, and 
(or) shear forces during surgery, most occurring within 1-3 days postoperatively, and 
also within 6 days postoperatively.2 The severity of pressure injuries is classified using 
the National Pressure Ulcer Advisory Panel Pressure Injury Staging System:36 
 
Stage 1 Pressure Injury: The skin is intact with erythema that does not change 
white in local acupressure. 
Intact skin with a localized area of nonblanchable erythema, which may appear 
differently in darkly pigmented skin. Presence of blanchable erythema or changes in 
sensation, temperature, or firmness may precede visual changes. Color changes do not 
include purple or maroon discoloration; these may indicate deep tissue pressure injury. 

 
Stage 2 Pressure Injury: Partial-thickness skin loss with exposed dermis. 
Partial-thickness loss of skin with exposed dermis. The wound bed is viable, pink 
or red, moist, and may also present as an intact or ruptured serum-filled blister. Adipose 
(fat) is not visible and deeper tissue is not visible. Granulation tissue, slough and eschar, 
are not present. These injuries commonly result from adverse microclimate and 
shear in the skin over the pelvis and shear in the heel.  
 
Stage 3 Pressure Injury: Full-Thickness Skin Loss 
Full-thickness skin loss, in which adipose (fat) is visible in the ulcer and 
granulation tissue and epibole (rolled wound edges), is often present. Slough and/or 
eschar may be visible. The depth of tissue damage varies by anatomical location; areas 
of significant adiposity can develop deep wounds. Undermining and tunneling may 
occur. Fascia, muscle, tendon, ligament, cartilage, or bone is not exposed. If slough or 
eschar obscures the extent of tissue loss, this is an unstageable pressure injury. 
 
Stage 4 Pressure Injury: Full-Thickness Skin and Tissue Loss 
Full-thickness skin and tissue loss with exposed or directly palpable fascia, muscle, 
tendon, ligament, cartilage, or bone in the ulcer. Slough and/or eschar may be visible. 
Epibole (rolled edges), undermining, and/or tunneling often occur. Depth varies by 
anatomical location. If slough or eschar obscures the extent of tissue loss, this is an 
unstageable pressure injury. 
 
Unstageable Full-Thickness Pressure Injury: Obscured Full-Thickness Skin 
and Tissue Loss 
Full-thickness skin and tissue loss in which the extent of tissue damage within the 

ulcer cannot be confirmed because it is obscured by slough or eschar. If slough or eschar 
is removed, a Stage 3 or Stage 4 pressure injury will be revealed. Stable eschar (ie, dry, 
adherent, intact without erythema or fluctuance) on ischemic limb or heels 
should not be softened or removed. 
 
Deep Tissue Pressure Injury: Persistent Nonblanchable Deep Red, Maroon or 
Purple Discoloration 
Intact or nonintact skin with localized area of persistent nonblanchable deep red, 
maroon, purple discoloration, or epidermal separation revealing a dark wound bed or 
blood-filled blister. Pain and temperature change often precede skin color changes. 
Discoloration may appear differently in darkly pigmented skin. This injury results from 
intense and/or prolonged pressure and shear forces at the bone-muscle interface. The 
wound may evolve rapidly to reveal the actual extent of tissue injury or may resolve 
without tissue loss. If necrotic tissue, subcutaneous tissue, granulation tissue, fascia, 
muscle, or other underlying structures are visible, this indicates a full-thickness 
pressure injury (unstageable, Stage 3, or Stage 4). Do not use DTPI to describe vascular, 
traumatic, neuropathic, or dermatologic conditions. 
 
Medical Device–Related Pressure Injury 
Medical device–related pressure injuries result from the use of devices designed 
and applied for diagnostic or therapeutic purposes. The resultant pressure injury 
generally conforms to the pattern or shape of the device. The injury should be staged 
using the staging system. 

2.5.2 Secondary outcome 
The secondary outcome of this study is in-hospital mortality,9 with refers to the number 
of patients who any cause die while receiving surgical procedure in a hospital. The data 
from a large sample of 13,254 patients in 1117 ICUs across 90 countries revealed that 
patients with pressure injuries (PI) exhibited a significantly higher mortality rate 
compared to those without PI. Furthermore, there was a gradual increase in the 
correlation between the severity of pressure injury and mortality rate. 
Surgical site infections (SSIs) will also be observed, SSIs are infections of the 
incision or organ or space that occur after surgery.37 Sepsis is defined as life-threatening 
organ dysfunction caused by a dysregulated host response to infection, the subsequent 
statement presents the diagnostic criteria in accordance with the Third International 
Consensus Definition of sepsis:38 
• Organ dysfunction can be identified as an acute change in total Sequential Organ 
Failure Assessment (SOFA) score ≥2 points consequent to the infection. 
• The baseline SOFA score can be assumed to be zero in patients not known to 
have preexisting organ dysfunction.  
• ASOFA score ≥2 reflects an overall mortality risk of approximately 10% in a 
general hospital population with suspected infection. Even patients presenting with 
modest dysfunction can deteriorate further, emphasizing the seriousness of this 
condition and the need for prompt and appropriate intervention, if not already being 
instituted. 
• In lay terms, sepsis is a life-threatening condition that arises when the body’s 

response to an infection injures its own tissues and organs. 
• Patients with suspected infection who are likely to have a prolonged ICU stay or 
to die in the hospital can be promptly identified at the bedside with quick SOFA, 
ie, alteration in mental status, systolic blood pressure≤100mmHg, or respiratory 
rate ≥22/min. 
• Septic shock is a subset of sepsis in which underlying circulatory and 
cellular/metabolic abnormalities are profound enough to substantially increase 
mortality. 
• Patients with septic shock can be identified with a clinical construct of sepsis with 
persisting hypotension requiring vasopressors to maintain mean arterial pressure 
(MAP) ≥65mmHg and having a serum lactate level >2mmol/L (18mg/dL) despite 
adequate volume resuscitation. With these criteria, hospital mortality is in excess 
of 40%. 
The surgical complications in patients during the same hospitalization will also be 
observed, as defined by the Clavien-Dindo classification.39,40 Additionally, we 
evaluated the duration of hospital stay and associated costs incurred by patients within 
this hospitalization period. 
2.5.3 Predictors 
Physiological observation indicators for all surgical patients were recorded as actual 
observed values within the first 24 hours preoperative, while other intraoperative 
observation indicators (such as anesthesia method, body temperature, blood pressure, 

surgical time, blood loss, etc.) were documented based on actual observations during 
surgical procedures. Data collection was conducted independently using commonly 
employed scales for these observation indicators (Table 1): 
Clearly define all outcomes, exposures, predictors, potential confounders, and effect 
modifiers. Give diagnostic criteria, if applicable 
 
2.6 Follow-up period 
The clinical follow-up will be conducted at 0.5, 2-, 24-, 48-, 72-hours and 7 days post-
surgery, as well as at the three-month mark following the completion of the surgical 
procedure during in-hospital. The investigator has the flexibility to carry out these 
follow-ups either through telephone communication or in-person office visits. 
 

3. Data Collection 
3.1 Data sources and study setting 
The patients will be enrolled from the operating room of nine China tertiary hospitals: 
Affiliated Hospital of Guangdong Medical University (Guangdong), First Affiliated 
Hospital of Sun Yat-sen University (Guangdong), Foshan First People's Hospital 
(Guangdong), First Affiliated Hospital of Zhengzhou University (Henan), The First 
Affiliated Hospital of Nanchang University (Jiangxi). 
3.2 Data collection tools and instruments 
The data collection will be facilitated through a network-based data infrastructure, 
where in the potential predictor variables are comprehensively described in Table 1. 
Further details can be accessed on the website https://www.wjx.cn/. Prior to sample 
inclusion, data collectors at all participation centers underwent mandatory 
homogenization training. The clinical follow-up will be conducted at 0.5, 2, 24, 48, and 
72 hours post-surgical procedure, as well as at the three-month mark following the 
completion of the surgical procedure. The investigator has the flexibility to carry out 
these follow-ups either through ward visit, telephone communication or follow-up 
appointments for outpatient care. 
Table 1: Potential predictor variables 
Braden scale 
1. 
Sensation 
Completely limited: unresponsive (does not moan, flinch or grasp) to painful stimuli because of 
diminished level of consciousness or sedation OR a limited ability to feel pain over most of body 

surface. 
Very limited: responds only to painful stimuli; cannot communicate discomfort except by moaning 
or restlessness OR has a sensory impairment that limits the ability to feel pain or discomfort over half 
of the body. 
Slightly limited: responds to verbal commands but cannot always communicate discomfort or need 
to be turned OR has some sensory impairment that limits ability to feel pain or discomfort in one or 
two extremities. 
No impairment: responds to verbal commands; has no sensory deficit that would limit ability to feel 
or avoid pain or discomfort. 
2. 
Skin moisture 
Constantly moist: skin is kept moist almost constantly by perspiration, urine etc; dampness is 
detected every time patient is moved or turned 
Moist: skin is often, but not always, moist; linen must be changed at least once a shift 
Occasionally moist: skin is occasionally moist, requiring an extra linen change approximately once 
a day 
Rarely moist: skin is usually dry; linen changed only at routine intervals. 
3. 
Activity 
Bedfast: confined to bed 
Chairfast: ability to walk severely limited or nonexistent; cannot bear own weight and/or must be 
assisted into chair or wheelchair. 
Walks occasionally: walks occasionally during day but for very short distances, with or without 
assistance; spends majority of each shift in bed or chair. 
Walks frequently: walks outside the room at least twice a day and inside room at least once every 2h 
during waking hours. 
4. 
Mobility 
Completely immobile: does not make even slight changes in body or extremity position without 
assistance. 
Very limited: makes occasional slight changes in body or extremity position but unable to make 
frequent or significant changes independently. 
Slightly limited: makes frequent, though slight, changes in body or extremity position independently 
No limitations: makes major and frequent changes in position without assistance 
5. 
Nutritional status 
Very poor: never eats a complete meal; rarely eats more than one-third of any food offered; eats two 
servings or less of protein (meat or dairy products) per day; takes fluids poorly; does not take a liquid 
dietary supplement OR is NPO and/or maintained on clear liquids or IV for more than 5 days. 
Probably inadequate: rarely eats a complete meal and generally eats only about one-half of any food 
offered; protein intake includes only three servings of meat or dairy products per day; occasionally 
takes a dietary supplement OR receives less than optimum amount of liquid diet or fed by tube. 
Adequate: eats over one-half of most meals; eats a total of four servings of protein (meat, dairy 
products) each day; occasionally refuses a meal but will usually take a supplement if offered OR is on 
a tube feeding or TPN regimen, which probably meets most of nutritional needs. 
Excellent: eats most of every meal; never refuses a meal; usually eats a total of four or more servings 
of meat and dairy products; occasionally eats between meals; does not require supplementation. 

6. 
Shear force 
Problem: requires moderate to maximum assistance in moving; complete lifting with- out sliding 
against sheets is impossible; frequently slides down in bed or chair, requiring frequent repositioning 
with maximum assistance; spasticity, contractures or agitation leads to almost constant friction. 
Potential problem: moves feebly or requires minimum assistance; during a move, skin probably 
slides to some extent against sheets, chair, restraints or other devices; maintains relatively good 
position in chair or bed most of the time but occasionally slides down. 
No apparent problem: moves in bed and chair independently and has sufficient muscle strength to 
lift up completely during move; maintains good position in bed or chair at all times. 
Waterlow scale 
7. 
Gender 
Male 
Female 
8. 
Age 
14-49 
50-64 
65-74 
75-80 
81+ 
9. 
Skin type 
Healthy 
Tissue paper (thin/fragile) 
Dry (appears flaky) 
Oedematous (puffy) 
Clammy (moist to touch)/pyrexia 
Discoloured (bruising/mottled) 
Broken (established ulcer) 
10. Body Mass Index（BMI）Build/weight for height 
Average – BMI 20-24.9 
Above average – BMI 25-29.9 
Obese – BMI > 30 
Below average – BMI < 20 
11. Nutritional Element 
Unplanned weight loss in past 3-6 months 
< 5% score 0, 5-10% score 1, >10% score 2 
BMI >20 score 0, BMI 18.5-20 score 1, BMI < 18.5 score 2 
Patient/ client acutely ill or no nutritional intake > 5 days 
12. Tissue Malnutrition 
Multiple organ failure/terminal cachexia 
Single organ failure e.g., cardiac, renal, respiratory 
Peripheral vascular disease 
Anaemia (Hb < 80 g/L) 
Smoking 

13. Continence 
Complete/catheterised 
Incontinent urine 
Incontinent faeces 
Doubly incontinent (urine & faeces) 
14. Mobility 
Fully mobile 
Restless/fidgety 
Apathetic (sedated/depressed/reluctant to move) 
Restricted (restricted by severe pain or disease) 
Bedbound (unconscious/unable to change position/traction) 
Chair bound (unable to leave chair without assistance) 
15. Major surgery 
On table > 6 hours 
Orthopaedic/ below waist/spinal (up to 48 hours post op) 
On table > 2 hours (up to 48 hours post op) 
16. Neurological deficit 
Motor/sensory 
Diabetes 
Paraplegia 
Cardiovascular and cerebrovascular diseases/ multiple sclerosis (MS)/ cerebrovascular accident 
(CVA) 
17. Medication 
Long term/high dose steroid 
Cytotoxic 
Anti-inflammatory 
Norton scale 
18. Physical condition 
Very bad 
Poor 
Fair 
Good 
19. Mental condition 
Stupor 
Confused 
Apathetic 
Alert 
20. Activity 
Bed 
Chairbound 
Walks with help 
Ambulant 
21. Mobility 

Mobility 
Slightly limited 
Very limited 
Immobile 
22. Incontinent 
Doubly 
Usually (urine) 
Occasionally 
No 
Cubbin & Jackson scale 
23. Age 
<40 
40-55 
56-70 
>70 
24. Weight 
Average weight 
Obese 
Cachectic 
Any of above and oedema 
25. General skin condition 
Intact 
Red skin 
Grazed/excoriated/skin 
Necrosis/exuding 
26. Mental condition 
Awake and alert 
Agitated/restless/confused 
Apathetic/sedated but responsive 
Coma/unresponsive/unpurposeful movements 
27. Mobility 
Fully ambulant 
Walks with slight help 
Very limited/chairbound 
Immobile/bedrest 
28. Haemodynamic status 
Stable without inotropic support 
Stable with inotropic support 
Unstable with inotropic support 
Critical with inotropic support 
29. Respiration 
Spontaneous 
Continuous positive airway pressure (CPAP)/T-piece 

Mechanical ventilation 
Breathless at rest/on exertion 
30. Nutrition 
Full diet + fluids 
Light diet/oral fluids/enteral 
Parenteral feeding 
Clear IV fluids only 
31. Incontinence 
None/anuric/catheterised 
Urine 
Faeces 
Urine + faeces 
32. Hygiene 
Competent in maintaining own hygiene 
Maintaining own hygiene with slight help 
Requires much assistance 
Fully dependent 
Munro scale 
Preoperative Risk Factor Score 
33. Nutritional state (Length of NPO status) 
12 hours or < 
>12 hours but<24 hours 
>24 hours 
34. Body Mass Index or weight 
<30 kg/m² (normal) 
30 kg/m²-35 kg/m² (underweight/obese) 
>35kg/m² (morbidly obese) 
35. Weight Loss (Weight loss in 30–180 days) 
Up to 7.4% weight loss, no change or unknown 
Between 7.5% and 9.9% weight loss 
≥10% weight loss 
36. Age 
39 or < 
40-59 
60 or > 
37. Mobility 
Not limited, or slightly limited, moves independently 
Very limited, requires transfer assistance 
Completely immobile, requires full assistance 
38. Comorbidity (Identify and add 1 point for each condition) 
Smoking/Asthma/Hypertension/Diabetes/Vascular disease/respiratory disease 
Intraoperative Risk Factor Score 
39. Physical status/American Society of Anesthesiologists pre-anesthesia evaluation score 

Healthy and mild systemic disease, no functional limitations 
Moderate to severe systemic disease, some function limitation 
Moderate to severe systemic disease, constant threat to life and functionally incapacitating or 
ASA >3 
40. Anesthesia 
Minimum alveolar concentration (MAC), local 
Regional 
General 
41. Body temperature (98.6°F=37°C) 
36.1°-37.8° (body temperature maintained) 
>37.8° or <36.1° (+ or -2°) (fluctuated + or ->2°) 
>37.8° or <36.1° (+ or - >2°) (maintained + or ->2°) 
42. Hypotension 
Absent or <10% change in blood pressure 
Fluctuating or 11%–20% change in blood pressure 
Persistent or 21%–50% change in blood pressure 
43. Moisture (Skin under patient) 
Remains dry 
Some moisture 
Pooled or heavy fluid 
44. Support surface (Positioning aids, warming blanket, position change) 
None/use of blanket over/stationary 
Use of aids/blanket under/stationary 
Shearing force/added pressure/variable position 
45. Position (for surgical procedure) 
Lithotomy 
Lateral 
Supine/Prone 
Postoperative Risk Factor Score 
46. Length of surgery (Total time from arrival to preoperative and departure from postoperative 
units) 
Up to 2 hours 
>2 hours ＆ <4 hours 
>4 hours 
47. Blood loss 
Up to 200 cc 
201–400 cc 
> 400 cc 
Relevance of clinical nursing judgement as a contributing factor 
48. Nurses are trained in surgical positions. 
49. The nurse assumes the role of leading the surgical position. 
50. The nurse executed the surgical positioning in accordance with the predetermined 
plan. 

51. Other variables (at the discretion of the nurse/doctor) 
 
3.3 Schedule of Events 
The events schedule comprised three sequential stages: recruitment and screening, 
baseline assessment, and outcome follow-up. The primary endpoint of this study is to 
evaluate the clinical outcomes three months post-surgery, its follow-ups should be 
office visits, but telephone contact will be allowed. Data collected during all follow-up 
visits will include IAPI, SSI, surgical complication, death. Original source documents 
must be submitted for any clinical events (death, IAPI, SSI, surgical complication, 
within 3 months). 
Table 2 Schedule of Events in the observational cohort for this study. 
 
Recruitment 
& screening 
Baseline 
assessme
nts 
Follow up 
0.5 
hours 
2 
hours 
24 
hours 
48 
hours 
72 
hours 
7 
days 
3 
months 
Informed consent1 
X 
X 
 
 
 
 
 
 
 
Inclusion/exclusion 
criteria 
X 
X 
 
 
 
 
 
 
 
Demography/ 
Surgical procedures 
X 
X 
 
 
 
 
 
 
 
Potential predictor 
variables 
 
X 
 
 
 
 
 
 
 
IAPI 
 
 
X 
X 
X 
X 
X 
X 
X 
SSI 
 
 
 
 
 
X 
X 
X 
X 
Surgical 
complication2 
 
 
X 
X 
X 
X 
X 
X 
X 
Death 
 
 
X 
X 
X 
X 
X 
X 
X 
Abbreviation: IAPI: intraoperatively acquired pressure injure, SSI: surgical site infections. 
1 The informed consent should be signed prior to the surgical procedures, but is can be signed after the surgical 
procedures in the urgent situation. 
2 The surgical complications in patients during the same hospitalization will also be observed, as defined by the 
Clavien-Dindo classification. 
 

3.4 Recruitment of subjects 
One day prior to the surgical procedure, the perioperative nurse/investigator informed 
the prospective surgical participant about their potential enrollment in the observational 
cohort for this study. In exceptional circumstances, assessment of the patient before 
admission into the operating room on the day of surgery procedure remains permissible. 
Prior informed consent was duly obtained from either the patients themselves or their 
legal guardians. 
3.5 Data collection 
The clinical data of each surgical patient were collected by itinerant nurses who actively 
participated in the surgical procedure, and the data they collected were promptly 
submitted post-operation. Endpoint follow-up was conducted by dedicated personnel 
through telephone interviews, web-based data completion, or other appropriate 
methods. 
3.6 Data management and storage 
The study incorporates three pre-determined key components. The network-based 
data will be programmed to monitoring: adherence to inclusion and exclusion criteria, 
homogeneous training of evaluators, and timely completion of all required data 
collection (without any missed visits, studies or specimens). 
The itinerant nurses will determine the form content by identifying the minimal set 
of measurements for specified variables, selecting documentably valid and reliable 

measurements (if more than one candidate), and developing, testing, and assessing 
reliability of new measures as required. Experienced data coordinating center (setting 
in participating institutions of principal investigator) staff will then order and format 
items to ensure clarity, smooth flow, and minimize missing information using clear skip 
patterns, consistent coding for all close-ended items, and standard footers to identify 
form name, version date, and page number. Standard modular data forms will be 
identified and developed for use in both the trial and registry as needed. 
The Case Report Forms (CRF) will be developed as an online electronic form, 
enabling investigators from individual sites to access and input data via the internet. 
The data coordinating center will assume responsibility for providing training to the 
investigator and clinical site personnel as required. Designated monitors will receive 
appropriate training to effectively monitor study progress, encompassing various 
aspects such as adherence to protocol and accurate completion of electronic Case 
Report Forms (eCRFs). To ensure consistent implementation of the standard protocol, 
data collection, and management across sites, the data coordinating center will facilitate 
comprehensive on-site or web conference-based training sessions over a period of one 
week. These sessions aim to cover essential components of clinical staff training 
including: (1) Familiarization with Registry Protocols; (2) Proficiency in database 
systems and eCRF for local web-based data entry; (3) Competence in medical record 
abstraction; (4) Skillful specimen/media collection and handling; (5) Effective data 
handling techniques; (6) Interview techniques refinement; and finally, (7) Clear 
understanding of quality control expectations. 

The data coordinating center will ensure that all study data is protected and 
maintained securely during the observation period. All observation data, including 
investigational data and medical records, will be stored in a secure location accessible 
only by authorized personnel. 
The study monitor will conduct periodic site visits to review the progress of the 
study, verify data quality, and ensure compliance with the protocol and applicable 
regulations. The study monitor will also conduct consecutively enrolled data audits to 
ensure the accuracy and completeness of the data. 
During the site visits, the study monitor will review the Investigator's study files, 
including subject consent forms, screening records, and observation-related 
correspondence. The study monitor will also examine the medical records of study 
subjects to confirm their eligibility, the conduct of procedures, and the treatment 
provided. 
The study monitor will communicate any observations or concerns to the 
Investigator/site and data coordinating center in a timely manner. The Investigator/site 
will have the opportunity to respond to these observations or concerns and provide 
explanations or clarifications. 
In the event of non-compliance with the protocol or applicable regulations, the trial 
monitor will work with the Investigator/site and data coordinating center to develop 
and implement corrective actions to address the issue. The trial monitor will continue 
to monitor the trial until the issue is resolved and will report back to the data 
coordinating center on the status of the corrective actions taken. 

The study monitor's responsibilities do not end with the completion of the trial. 
After the study is complete, the trial monitor will assist in closing out the trial, including 
data verification, final report preparation, and auditing of study records. 

4. Data Analysis 
4.1 Sample size 
The primary outcome of this study is IAPI in patients who underwent surgery procedure. 
The fixed position of surgical patients in the surgical procedure, and the occurrence of 
IAPI risk is time-dependent,41 which it is positively correlated with the advancement of 
time and the occurrence of IAPI events in surgical patients.42 For time-to-event 
outcomes,43 ensure the sample size is enough to: Estimate the overall outcome 
proportion with sufficient precision at one or more key time-points in follow-up; Target 
a shrinkage factor of 0.9; Target small optimism of 0.05 in the apparent R2 Nagelkerke. The 
formula provided by Riley et al. is as follows: 
Time-event-outcome, the sample size (number of participants, n) required to 
achieve the expected uniform contraction coefficient S can be expressed as: 
𝑛=
𝑃
(S −1)In (1 −𝑅𝑐𝑠
2
𝑆)
 
To calculate the sample size, to ensure that the expected optimistic value of R2 (i.e., 
R𝑐𝑠
2 /max(R𝑐𝑠
2 )) has a smaller expected optimism. For time-event-outcome, the first step 
is to calculate the contraction coefficient corresponding to the expected optimistic value 
in R2Nagelkerke: 
𝑆=
R𝑐𝑠
2
R𝑐𝑠
2 + δmax(R𝑐𝑠
2 ) 
The recent randomized controlled trial results showed an incidence of IAPI (stage 
I and above) of 13.9%,44 assuming 0.05 acceptable difference in apparent & adjusted 

R-squared. The study used the R4.1.2 software (pmsampsize package) to estimate the 
minimum sample size required to develop the new model. Minimum sample size 
required for new model development is 4115, with 572 events (assuming an outcome 
prevalence = 0.139) and an Events per Predictor Parameter (EPP) is 23.83. In addition, 
according to the pre-model design requirements, the total sample was divided into 80% 
samples for the development of the new model and 20% samples for the validation of 
the model. This means that a total of 5144 cases are required. Considering a possible 
10% loss of follow-up, the minimum sample size required for the prediction model was 
5658 cases. 
 
4.2 Data analysis 
This study is divided into two distinct sections. The aim of the first part is to create and 
validate a IAPI risk score for individuals from the whole cohort, predicting their 
individual IAPI values using a combination of potential predictive variables. The aim 
of the second part is to evaluate whether the IAPI score is helpful in predicting 90-day 
mortality in individuals from the whole cohort. We also evaluated the incidence and 
attributable mortality to IAPI, SSI, and surgical complications in patients admitted for 

surgical procedures during the same study period. 
All statistical analyses were performed with R (the latest version; R Foundation for 
Statistical Computing) and Python (the latest version; Python Software Foundation). 
All tests conducted in this study were 2-tailed, and P<0.05 was deemed statistically 
significant. 
4.2.1 Model development 
The entire cohort is randomly divided into two datasets: a training cohort (80%), which 
will potentially be utilized for training twelve machine learning models and optimizing 
their parameters, and an internal validation cohort (20%), which was employed to 
evaluate the developed models on unseen data and refine the hyperparameters. 
The variable selection process in the development sample will be conducted using 
a recursive feature elimination (RFE) algorithm.45 RFE ranks the most relevant 
predictors in a dataset by training models with and without all potential predictor 
combinations. To identify the optimal number of variables, we will assess the 
incremental gain in predictive performance associated with each variable and stop at 
the inflexion point. RFE is an iterative process that starts with all possible features in 
the dataset and progressively removes the least relevant features, building a model after 
each iteration. The goal is to find the optimal subset of features that maximizes the 
predictive power of the model. In each iteration, RFE evaluates the performance of the 
model with and without a specific feature and removes the feature if its removal results 
in a significant improvement in model performance. This process is repeated until the 

desired number of features is reached or a stopping criterion is met. The inflexion point 
is the point at which the incremental gain in predictive performance begins to decrease 
significantly. Identifying the inflexion point helps to ensure that the final model does 
not overfit the data and can generalize well to new data.46 In conclusion, by using the 
RFE algorithm and identifying the inflexion point, the development sample will 
undergo a thorough variable selection process, ensuring that the final model is as 
predictive as possible while avoiding overfitting. 
After variable selection with RFE, we will be trained potential seven statistical 
models with centred and scaled selected predictors because of the different scales of the 
predictors and to ease the intercept of models to the IAPI, including a logistic regression, 
decision tree, random forest,47 artificial neural network (ANN),48 eXtreme Gradient 
Boosting algorithm (XGBoost),49 support vector machine (SVM), k-nearest neighbors 
(k-NN). Logistic regression is suitable for binary classification problems, while 
decision trees can handle both binary and multi-class problems. Random forests and 
XGBoost are based on decision trees and are known for their good performance in 
predicting both continuous and categorical variables. ANNs can be used for nonlinear 
problems, but they require more time for training. SVM and k-NN are also popular for 
classification problems, but they require different preprocessing techniques. 
To evaluate the performance of each model, we will be using the cross-validation. 
With k-fold cross-validation, the data is randomly divided into k subsets, and each 
model is trained on k-1 subsets and tested on one subset. This process is repeated k 
times, with each of the k subsets being used once as the test set. The average 

performance across all k tests is then used as the final performance measure. To assess 
the degree of potential over-fitting of each algorithm, we will be trained them using a 
10-fold 10-repeat cross-validation procedure. 
4.2.2 Model assessment 
To evaluate the predictive performance of the developed model, we used the area 
under the receiver operating characteristics curve (AUC-ROC) to assess discrimination 
accuracy in the internal validation cohort. The AUC-ROC is an index that measures the 
classification ability of a model, with a value close to 1 indicating high accuracy and a 
value close to 0.5 indicating low accuracy. We used three different cutoff points for the 
IAPI risk score to estimate the levels of progressive PI severity in population-based 
studies. These cutoff points were used to categorise participants into different risk 
groups based on their predicted risk of IAPI. We compared all models with Braden scale, 
Waterlow scale, Norton scale, Cubbin & Jackson scale, and Munro scale. 95% 
confidence intervals (CIs) were computed using bootstrapping with 2000 random 
draws.50 A bootstrap 95% CI was calculated for the AUC-ROC. This allowed us to 
assess the stability and reliability of the model's predictions. To inspect the calibration 
of the predictive models, we estimated linear regression models between predicted and 
observed liver stiffness values using calibration intercepts and slopes. Calibration 
intercepts represent the average difference between the predicted and observed values, 
while calibration slopes represent the magnitude of the relationship between the two 
variables. We also plotted graphical representations, such as calibration plots and 

Bland-Altman plots, to visually assess the agreement between the predicted and 
observed values. These plots help us to identify any biases or trends in the data that may 
affect the model's performance. 
4.2.3 Secondary outcome assessment 
The secondary outcome of this study is in-hospital mortality,9 survival analysis, 
including Kaplan-Meier analysis and log-rank testing, was used to evaluate the 
association between IAPI and various patient characteristics. Generalized linear mixed-
effects regression analysis with a logit link function and a random effect for country 
was used to account for possible confounding factors. Odds ratios and 95% confidence 
intervals were used to present the results. We assumed that the occurrence of in-hospital 
mortality would be rare, ensuring that odds ratio estimates derived from a case-control 
design could serve as reliable estimators of relative risk for calculating the IAPI 
mortality attributable fraction. The following approach was used for attributable 
fraction calculation for each admission type: the inverted probability weights (IPW) for 
each patient were calculated, representing the cumulative risk of the patient acquiring 
IAPI during hospitalization, under a multivariable logistic regression analysis including 
baseline age, Charlson Comorbidity Index,51 surgical complications,39,40 and a time-
dependent variable for the occurrence of clinically relevant events. We estimated the 
association between IAPI occurrence on hospital mortality post operation three months 
through a mixed logistic regression model weighted by IPW. The delta method was 
employed to compute the 95% CIs for both the odds ratio and attributable mortality.52 

We also assessed the incidence and attributable mortality of surgical complications 
in patients during the same hospitalization by . Additionally, we evaluated the duration 
of hospital stay and associated costs incurred by patients within this hospitalization 
period. 
4.2.4 Methodological considerations 
4.2.4.1 Alternate definition of potential predictor variables case considered. 
Potential predictor variables could be inconsistent in definitions of Norton scale,16,17 
Waterlow scale,18-20 Braden scale,21 Cubbin & Jackson scale,22-24 and Munro scale.25 
The quartiles or dichotomous categories of predictor variables will be used as covariates 
for a specific patient population, while employing classification homogenization to 
improve clarity for the assessor. Our objective is to attain increased simplicity and 
validity in assessment scales. 
4.2.4.2 Alternate analysis considered. 
The process of variable selection involves using a recursive feature elimination (RFE) 
algorithm to identify the most important features in the data.45 This algorithm has been 
shown to be effective in developing highly sensitive predictive models, and logistic 
regression can provide better predictive accuracy.53 A fitted logistic regression model 
allows for the derivation of a risk score using a points system.54 This risk score can be 
used to predict the likelihood of an event occurring. The flexibility and predictive power 
of logistic regression make it an attractive choice for this analysis. 
 

5. Ethical Considerations 
5.1 Informed consent process. 
This study was approved by the relevant Institutional Review Boards and informed 
consent will be obtained (No. PJKT2023-007). All patients and families will provide 
written informed consent/assent and will have the ability to withdraw at any time 
without explanation. 
5.2 Privacy and confidentiality measures. 
The participation of patients and their families in this study poses minimal to negligible 
risks. Their involvement will not have any negative impact or limitations on receiving 
additional assessments, investigations, consultations, or management as determined by 
treating surgeons. The only potential risks lie in the security and privacy of their data, 
including responses to various questionnaires used for identifying prognosticators for 
IAPI. Our research personnel will take all necessary and customary measures to ensure 
data security while maintaining patient privacy and confidentiality. 
5.3 Potential risks and benefits to participants. 
Participants need to provide personal information, such as name, address, phone 
number, etc. If this information is not processed and protected, it may be leaked by 
hackers or other means. As previously mentioned, the only potential risks lie in the 
security and privacy of their data, including responses to various questionnaires used 
for identifying prognosticators for IAPI. Research results will be disseminated at 

international conferences and in manuscripts to peer-reviewed journals. 
 

6. Contributors 
Yiyue Zhong, and Fengqiu Gong led the study concept and design, selected outcome 
measures and involved in writing of the manuscript. Yiyue Zhong wrote the first draft 
of the manuscript. All authors read, critically revised and approved the final version of 
the manuscript. 
 
7. Funding 
This study was funded by the Science and Technology Fund Project of Zhanjiang (No. 
2017A01023, and 2021A05073) and the Clinical Research Fund of Affiliated Hospital 
of Guangdong Medical University ((No. LCYJ2019B008, and LCYJ2022B008). In 
addition, data collection and storage were supported by the National Natural Science 
Foundation of China ((No. 82072208, and 82372174). 
8. Competing interests 
None. 
9. Ethics approval 
The study was approved by the institutional review board (IRB No. PJKT2023-007) 
and conformed to the Official Regulation of Medical Records Management in Medical 
Institutions with regard to patient data integrity and the principles of the Declaration of 

Helsinki. 
10. Provenance and peer review 
Not commissioned; internally peer reviewed. 
 

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