A nomogram to predict the risk of surgery-related pressure ulcers in patients undergoing cardiovascular surgery

Abstract

Objective

In this study, we aimed to establish a new nomogram score to predict the occurrence of surgery-related pressure ulcers (SRPU) in patients undergoing cardiovascular surgery.

Methods

We conducted a retrospective study among patients who underwent cardiovascular surgery between February 2016 and November 2020.

Results

We established a prediction model based on a logistic regression model and tested the calibration and discrimination. We included 1163 patients who had undergone cardiovascular surgery. We formulated the logistic regression model, with Logit(P) = −11.745 + 0.024 preoperative hemoglobin value + 0.118 serum sodium value − 0.014 prealbumin value − 0.213 intraoperative mean temperature − 0.058 minimum mean arterial pressure + 0.646 preoperative blood potassium value + 0.264 smoking frequency + 0.760 hypertension history + 0.536 age ≥70 years. In this model ,“+” indicates that the factor is positively related to the occurrence risk of SRPU and “−” indicates that the factor is negatively associated with SRPU risk. The predictive model and nomogram had good accuracy in estimating the risk of SRPU, with a C-index of 0.755 (95% confidence interval: 0.719–0.792).

Conclusions

The present model can be used to effectively screen patients with a high risk of SRPU to devise targeted nursing intervention strategies and ultimately reduce the incidence rate of SRPU.

Keywords

Pressure ulcer surgical procedure cardiovascular nomogram calibration logistic model

Introduction

A pressure ulcer refers to a local lesion of skin and/or soft tissue that forms at the bone prominence or through contact with a medical instrument.¹ These ulcers are often associated with increased morbidity, hospital stay, and health care costs.² In 2009, hospital-acquired pressure ulcers (HAPUs) were responsible for USD 11 billion per year in direct and indirect costs.³ A 2016 study found that 3.5% of patients undergoing major surgery developed HAPUs, which added USD 8200 to the cost of a surgical stay in the hospital, after adjusting for comorbidities, patient characteristics, procedures, and hospital characteristics. These ulcers also increase hospital costs by 44% per hospital stay.^3,4 Additionally, the mean cost of wound care for patients with pressure ulcers in the United Kingdom’s National Health Service ranges from GBP 1400 to over GBP 8500, depending on the ulcer category.⁵ This further emphasizes the need for effective prevention and management strategies for pressure ulcers to alleviate the negative impact on patients and health care systems.

Patients who undergo prolonged surgery are at risk of developing pressure ulcers, also known as surgery-related pressure ulcers (SRPU).⁶ A systematic review revealed that the incidence of SRPU ranged from 0.3% to 57.4%.^7,8 The most common types of surgery associated with SRPU were cardiac procedures (29.3%), followed by general/thoracic, orthopedic, and vascular procedures.⁹ SRPU has become a heavy burden for patients with prolonged surgery, particularly those undergoing cardiovascular surgery.

There are special risk factors during cardiovascular surgery that can easily cause SRPU. One study showed that the duration of cardiopulmonary bypass was closely related to the occurrence of SRPU.¹⁰ The basic methods of cardiopulmonary bypass include hypothermia, pre-flushing and blood dilution, as well as the use of heparin and protamine. These processes cause tissue ischemia and hypoxia, red blood cell destruction, and tissue edema, which may be related to the occurrence of SRPU. Additionally, cardiovascular surgery may involve the use of vasoactive and hormone drugs. Vasoactive drugs can cause peripheral vascular contraction, aggravate ischemia and hypoxia, lead to pressure injury, and can reduce tissue blood flow by 20% to 30%. Vasodilator drugs can cause hypotension and tissue hypoperfusion. In producing the above effects, cardiovascular active drugs can lead to the occurrence of SRPU.^11,12

Early risk assessment of pressure injuries is crucial to reducing their incidence.^13,14 At present, risk assessment of pressure injury relies on the use of assessment scales, such as the Braden, Norton, or Waterlow scales. These traditional scales are mainly applicable in wards, intensive care units (ICUs), and other institutions. However, numerous studies have shown that these scales are not effective in the risk prediction of intraoperatively acquired pressure injury.^15–18 For example, the current most widely used tool for assessing pressure injury risk, the Braden scale, has certain limitations.^19,20 First, it lacks authoritative scientific assessment standards and quantitative criteria for grading all risk factors, resulting in wide deviation of scores as well as low sensitivity and specificity in predicting the risk of pressure injury. Second, this scale has limitations when used in the ICU because nearly all patients admitted to the ICU are considered high-risk, and there are only slight differences between scores for patients with high risk and extremely high risk. A Cochrane review also showed that use of a structured questionnaire to assess pressure injury risk is not directly related to the reduction of pressure injuries.²¹ Therefore, an objective, non-invasive, practical, and consistent scientific method for assessing pressure injuries is needed to reduce their incidence among patients.²²

The nomogram has been widely accepted as a reliable tool used to create a simple intuitive graph of a predictive model, quantifying the risk of a clinical event.^23,24 A nomogram has reportedly better specificity and sensitivity than the Braden score for assessing the risk of pressure injury in critically ill patients admitted to the ICU.²⁵ In this study, we aimed to identify the combination of variables that can be used for highly accurate prediction of SRPU in patients undergoing cardiovascular surgery. Subsequently, a nomogram was constructed to predict the probability of SRPU to support perioperative nurses in clinical nursing practice.

Methods

Patients

We conducted a retrospective review of adult patients who underwent various types of cardiovascular surgery, including aortic valve replacement, mitral valve replacement, coronary artery bypass graft, off-pump coronary artery bypass graft, and vessel replacement surgeries, between February 2016 and November 2020 at a comprehensive teaching hospital in China. The inclusion criteria were age ≥18 years, receiving general anesthesia, and operation time ≥2 hours. The exclusion criteria were patients with pressure ulcer upon admission to the operating room and those with diseases that could affect skin observation.

Data collection

We used a form to collect all relevant data for our study, which was divided into three parts. The first part focused on demographic characteristics, including age, sex, body mass index, and the Barthel self-care ability score. The second part was designed to gather information on SRPU, such as whether SRPU had occurred and the stage of the ulcers. The third part aimed to capture possible risk factors for SRPU, as per Braden and Bergstrom's conceptual model of the etiology of pressure ulcer, which includes factors such as surgery duration, duration of cardiopulmonary bypass, vasoactive agents, and perioperative corticosteroids. Laboratory test results such as prealbumin, hemoglobin, and glycosylated hemoglobin were also included in this part. To ensure unbiased sampling, we obtained the data by working closely with medical workers and cross-referencing the collected information with them. The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.²⁶

Ethical approval

Approval for this study was obtained from the Institutional Review Board of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (No. 201723, 2 March 2017). The requirement for individual informed consent was waived in this retrospective analysis. Throughout the research process, compliance with the regulations set forth by the ethics committee was strictly maintained. Furthermore, all patient details have been de-identified to ensure that no individual information was disclosed in the research.

Statistical analysis

Chi-square tests were used to compare categorical variables, which are reported as frequencies. For continuous variables, differences were tested using either the Student t-test for normally distributed data or the Mann–Whitney U rank-sum test for non-normally distributed data. Mean ± standard deviation was used to report continuous variables with a normal distribution whereas median and interquartile range were used for non-normally distributed continuous variables. The impact of the primary risk factors identified was evaluated using multivariate logistic regression analysis. All statistical analyses were performed using SPSS software version 13.0 (SPSS Inc., Chicago, IL, USA). A logistic regression prediction model for SRPU in patients undergoing cardiovascular surgery was constructed. Subsequently, a nomogram prediction model was built based on the logistic regression model using R version 4.1.0 (The R Foundation for Statistical Computing, Vienna, Austria). Next, calibration and discrimination of the nomogram prediction model were tested. Discrimination was assessed using Harrell’s concordance index (C-index), which ranges from 0.5 to 1.0, with values closer to 1.0 implying better discrimination. Calibration was graphically evaluated with a calibration plot, which illustrates the correlation between the predicted probabilities and the actual outcomes in both the training and validation cohorts. A p-value <0.05 was considered statistically significant for all analyses.

Results

Patients’ characteristics

A total of 1163 consecutive patients who had undergone cardiovascular surgery were included in this study, with a mean age of 62.19 ± 12.21 years. Among the total, 66.38% (n = 772) were male patients and 33.62% (n = 391) were female patients.

Incidence and sites of surgery-related pressure ulcers (SRPU)

Of the 1163 patients included in this study, 231 developed SRPU, resulting in an incidence rate of 19.86%. Among patients with SRPU, 203 (87.88%) were classified as stage I, which subsided within 2 hours after the operation; 28 patients (12.12%) were rated as stage II.

Of the 231 patients with SRPU, 205 were found to have two or more pressure ulcers. Among them, the most commonly affected area was the buttocks (n = 169, 73.16%), followed by the elbow (70, 30.30%), heel (68, 29.44%), auricle (29, 12.55%), and back (21, 9.09%).

Univariate analysis of risk factors

We found that patients with SRPU had significantly higher preoperative hemoglobin, serum sodium levels, serum potassium levels, intraoperative erythrocyte consumption, and surgery duration compared with those who did not have SRPU; conversely, preoperative albumin levels, intraoperative mean core temperature, and minimum mean arterial pressure were significantly lower in patients with SRPU (all p < 0.05) (Table 1). Furthermore, we observed that a significantly higher proportion of patients with SRPU had undergone emergency surgery, had a history of hypertension, had undergone coronary artery bypass surgery, smoked, or were ≥70 years old compared with patients who did not have SRPU (p < 0.05) (Table 1).

Table 1.

Baseline characteristics in the groups with and without surgery-related pressure ulcers.

Variables	Without SRPU(n = 932)	With SRPU(n = 231)	t/Z/χ²	p
Age (y)	61.92 ± 12.280	63.28 ± 11.884	−1.520^a	0.129
Intraoperative plasma consumption (mL)	161.82 ± 325.024	158.87 ± 302.736	0.125^a	0.901
Intraoperative cryoprecipitate consumption (units)	0.91 ± 1.350	1.19 ± 3.194	−0.403^a	0.687
Intraoperative dexamethasone usage (mg)	8.57 ± 3.531	8.94 ± 3.750	−0.613^a	0.540
Intraoperative methylprednisolone usage (mg)	68 ± 37.718	64.68 ± 31.818	0.743^a	0.457
Preoperative hemoglobin value (g/L)	128.34 ± 16.138	131.03 ± 16.375	−2.254 ^a	0.024 *
Preoperative prothrombin time (s)	12.28 ± 4.707	11.95 ± 1.029	0.629^a	0.530
Preoperative serum sodium value (mmol/L)	139.66 ± 3.212	140.16 ± 3.303	−2.130 ^a	0.033 *
Preoperative glycosylated hemoglobin (%)	6.18 ± 1.173	6.31 ± 1.229	0.085^a	0.932
Preoperative albumin level (mg/L)	211.45 ± 46.298	195.89 ± 43.665	4.589 ^a	<0.001 *
Preoperative serum albumin level (g/L)	37.57 ± 4.827	36.98 ± 4.775	1.290^a	0.197
Preoperative Barthel score	94.95 ± 14.610	95.22 ± 16.566	−0.311^a	0.756
Proportion of recent weight loss (%)	0.39 ± 2.954	0.43 ± 2.232	−0.177^b	0.867
Intraoperative erythrocyte consumption (units)	0 (0,0)	0 (0,2)	−3.638^b	<0.001*
Heparin sodium usage (mg)	100 (50,180)	120 (60,200)	−1.677^b	0.093
Preoperative blood glucose level (mmol/L)	4.97 (4.52,5.90)	4.98 (4.63,5.95)	−1.030^b	0.303
Intraoperative mean core temperature (°C)	33.6 (32.36,34.90)	32.60 (30.45,34.60)	−4.932^b	<0.001*
Surgery duration (min)	235 (200,270)	240 (210,320)	−3.065^b	0.002*
Cardiopulmonary bypass duration (min)	10 (0,100)	45 (0,100)	−0.924^b	0.356
Minimum mean arterial pressure (mmHg)	49 (44,55)	45 (36,50.5)	−7.145^b	<0.001*
Preoperative serum potassium value (mmol/L)	3.9 (3.69,4.14)	3.99 (3.71,4.30)	−2.823^b	0.005*
Sex
M	619 (66.42)	153 (66.23)	0.003^c	0.958
F	313 (33.58)	78 (33.77)
Emergency surgery
Y	15 (1.60)	13 (5.62)	12.721 ^c	<0.001 *
N	917 (98.40)	218 (94.38)
Intraoperative position
Supine	882 (94.64)	217 (93.94)	0.172^c	0.678
Lateral	50 (5.36)	14 (6.06)
Hypertension
Y	528 (56.65)	152 (65.80)	6.380 ^c	0.012 *
N	404 (43.35)	79 (34.20)
Disease category
Congenital heart disease	69 (7.40)	10 (4.32)	2.764^c	0.096
Valvular disease of the heart	328 (35.19)	85 (36.80)	0.208^c	0.648
Coronary artery disease	487 (52.25)	147 (63.64)	9.674^c	0.002*
Thoracic aortic aneurysm	55 (5.90)	7 (3.03)	3.023^c	0.082
Other	45 (4.82)	7 (3.03)	1.401^c	0.237
Age (y)
≥70	256 (27.47)	91 (39.40)	12.270 ^c	<0.001 *
<70	676 (72.53)	140 (60.60)
Body mass index
<18.5	115 (12.34)	39 (16.88)	3.591^c	0.166
18.5–22.9	685 (73.50)	158 (68.40)
≥23	132 (14.16)	34 (14.72)
Smoking frequency
Never	799 (85.73)	181 (78.35)	−2.507 ^c	0.012 *
Occasionally	47 (5.04)	28 (12.12)
Often	86 (9.23)	22 (9.53)

t-test; ^brank-sum test; ^cchi-square test.

*p < 0.05.

Values in the table are mean ± standard deviation, P50 (P25, P75), or n (%).

Multivariate logistic regression analysis of risk factors

After conducting univariable analysis, preoperative hemoglobin levels; serum sodium levels; serum potassium levels; intraoperative erythrocyte consumption; surgery duration; preoperative albumin levels; intraoperative mean core temperature; minimum mean arterial pressure; the proportions with emergency surgery, previous history of hypertension, and coronary artery bypass surgery; smoking frequency; and age ≥70 years were identified as significant risk factors and were included in the multivariable logistic regression analysis. The resulting logistic regression model for predicting SRPU was Logit(P) = −11.745 +0.024 preoperative hemoglobin value + 0.118 serum sodium value − 0.014 prealbumin value − 0.213 intraoperative mean temperature − 0.058 minimum mean arterial pressure + 0.646 preoperative blood potassium value + 0.264 smoking frequency + 0.760 hypertension history + 0.536 age ≥70 years. In this model “+” indicates that the factor is positively related to the occurrence risk of SRPU and “−” means that the factor is negatively associated with the risk of SRPU. A detailed list of the logistic regression with these nine significant risk factors can be found in Table 2. Based on this logistic regression model, a nomogram model for SRPU prediction in patients undergoing cardiovascular surgery was created, which can be seen in Figure 1.

Table 2.

Logistic regression analysis of all risk factors for surgery-related pressure ulcers in patients undergoing cardiovascular surgery.

Factor	Coefficient	SE	Wald	OR	p
Preoperative hemoglobin level	0.024	0.006	17.689	1.024	<0.001
Serum sodium level	0.118	0.028	17.863	1.125	<0.001
Preoperative blood potassium level	0.646	0.215	9.057	1.908	0.003
Prealbumin level	−0.014	0.002	39.435	0.986	<0.001
Hypertension history	0.760	0.176	18.589	2.139	<0.001
Smoking frequency	0.264	0.132	4.003	1.302	0.045
Intraoperative mean temperature	−0.213	0.045	22.257	0.808	<0.001
Minimum mean arterial pressure	−0.058	0.009	38.677	0.943	<0.001
Age ≥70 years	0.536	0.180	8.874	1.710	0.003
Constant	−11.745	4.403	7.117	0.000	0.008

SE, standard error; OR, odds ratio.

Figure 1.

Nomogram model for prediction of surgery-related pressure ulcers (SRPU) in patients undergoing cardiovascular surgery.

The predictive model and nomogram displayed good accuracy when estimating the risk of SRPU, with a C-index of 0.755 (95% confidence interval [CI]: 0.719–0.792), showing moderate discrimination ability (Figure 2).

Figure 2.

Calibration plot.

Discussion

According to a national survey conducted in the United States, the prevalence of SRPU was 8.5%, with cardiac procedures being the most common type of surgery associated with SRPU (29.3%), followed by general/thoracic procedures, orthopedic procedures, and vascular procedures.⁹ Similarly, a systematic review revealed that the incidence of SRPU ranged from 0.3% to 57.4%, with a pooled incidence of 15.0% (95% CI: 14.0%–16%) and a pooled SRPU incidence of 18.0% (95% CI: 14.0%–22.0%) for cardiac surgery.⁷ In our study, the incidence of SRPU in adult patients undergoing cardiovascular surgery was 19.86%, which is consistent with the findings of previous studies. As such, patients undergoing cardiovascular surgery represent a key target population for SRPU prevention. The high incidence of SRPU in these patients is chiefly owing to factors such as prolonged operation time, substantial fluctuations in body temperature during surgery, cardiopulmonary bypass, and high use of vasoactive drugs.^27–29 Multiple studies have demonstrated that the occurrence of SRPU not only consumes significant health care resources but also significantly increases patient mortality, postoperative length of stay, and medical expenses. Therefore, the incidence of SRPU has emerged as an important indicator of health care quality for medical institutions.

Studies have demonstrated that the rate of complications related to SRPU range from 16% to 46%, with associated complications significantly increasing patient mortality. It has been reported that if a patient develops sepsis as a complication, the mortality rate can reach 60%.⁹ The results of our study suggest that mortality rates are higher among patients undergoing cardiovascular surgery who develop SRPU than those without SRPU. This may be owing to their poor underlying health status, complicated conditions, and increased risk of complications. SRPU not only has an impact on patients' postoperative recovery but also affects their disease prognosis, which requires special attention.

In recent years, there has been widespread recognition of the importance of preventing SRPU, primarily with growing awareness about the concept of pressure ulcers. Key prevention strategies include positively assessing patients and identifying high-risk groups. Several well-established pressure ulcer risk assessment scales have been extensively applied in clinical practice, such as the Braden, Norton, and Waterlow scales, which have demonstrated good predictive performance. However, these risk assessment scales do not account for specific factors that affect the occurrence of SRPU during surgery. In the case of cardiovascular surgery, the use of intraoperative hypothermia, anesthesia, cardiopulmonary bypass, and large amounts of vasoactive agents render traditional assessment scales less effective. The absence of reliable assessment tools to accurately identify high-risk patients during cardiovascular surgery may lead to increased incidence of SRPU.

We performed logistic regression analysis to identify risk factors associated with SRPU in adult patients undergoing cardiovascular surgery; these were preoperative hemoglobin, prealbumin, serum sodium, serum potassium, intraoperative mean body temperature, minimum mean arterial pressure, smoking frequency, hypertension history, and age ≥70 years. Most of these risk factors have been identified in previous studies. For example, a meta-analysis of 12 studies including 1217 patients with pressure ulcers and 5538 patients without pressure ulcers showed that advanced age, smoking, fever, mean arterial pressure, edema, history of diabetes, prolonged ICU stay, sedation, glucocorticoids, vasopressin, mechanical ventilation, prolonged mechanical ventilation, low albumin, and hemoglobin levels were risk factors for pressure ulcers in ICU patients. Several studies have also established that low levels of albumin and hemoglobin are risk factors for pressure ulcers. In our study, high hemoglobin level was a risk factor for SRPU, which was inconsistent with other studies. However, intraoperative erythrocyte consumption was also associated with the occurrence of SRPU, which means transfusing many units of red blood cells. Additionally, one study found that for every 1.8°C decrease in body temperature, the likelihood of pressure injury increased by 20.2%. However, there is a paucity of evidence regarding the possible correlation between electrolyte levels, such as blood sodium and blood potassium, and SRPU development; this requires further investigation.

There have been a few advances in the assessment of pressure injury using new assessment tools. The first has been the use of models constructed via machine learning or deep learning. Far more sophisticated methods and larger amounts of data than previously can now be leveraged, resulting in more accurate and precise predictive models than ever before.³⁰ In this study, we developed a nomogram model to predict SRPU in patients undergoing cardiovascular surgery. A nomogram, also known as an abacus or alignment chart, is a two-dimensional diagram used to roughly calculate a function. Over the past two decades, nomograms have been used for risk assessment in clinical medicine. These provide a more practical explanation of the impact of each predictor variable on the results, showing dynamic vigor in today’s digital era. Our study findings indicated that the nomogram model developed herein has acceptable goodness-of-fit and calibration, as well as moderate discrimination. Calibration refers to the consistency between the estimated probability generated by the model and the actual observed probability. The C-index of 0.755 (95% CI: 0.719–0.792) indicates that the model has moderate discrimination. With the assistance of the nomogram, a simple SRPU assessment tool suitable for clinical use can be obtained, which will enable health care teams to can make more rational clinical decisions and improve the quality of patient care.

Limitations

The current study has some limitations that must be taken into account. First, the recruitment of patients was limited to a single center, which may restrict the applicability of our findings to other health care settings. Second, it was not feasible to perform calibration and discrimination testing of the nomogram model in other medical centers; hence, the external validity and reliability of this model require confirmation. Furthermore, owing to time constraints, we were unable to expand the sample size for our model, which necessitates further investigation. Third, the study participants were older patients. Aging is a major risk factor for pressure ulcers, with considerable physiological changes owing to aging. The average age of our study population may also have increased the incidence of SRPU, which is another limitation of this study.

Conclusion

Our newly developed nomogram can provide individualized prediction of SRPU in patients undergoing cardiovascular surgery. Considering the abovementioned limitations, the study results can be considered a baseline, and the present model may not be completely accurate in predicting SRPU. Further studies are needed to validate our findings. Ultimately, the establishment of a nomogram will improve the quality of nursing care provided to surgical patients during the perioperative period.

Footnotes

Acknowledgements

We thank all those who participated in this study.

Author contributions

Study conception and design: YC

Data collection: XQX

Drafting of the article: XW

Critical revision of the article: YC

Declaration of conflict interest

The authors declare that there is no conflict of interest.

Funding

This study was supported by the Nursing Research Fund of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (no. RJHK2023-11).

ORCID iD

Yuan Chen

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