Abstract
This study aims to analyse the risk factors of Peristomal Moisture‐Associated Skin Damage (PMASD) in colorectal cancer patients, construct a prediction model, and verify its effect. A total of 375 patients who underwent rectal cancer stoma surgery at the Liaoning Cancer Hospital between January and December 2020 were selected according to the inclusion and exclusion criteria. The clinical data were retrospectively analysed for modelling and internal validation (modelling group). According to the same criteria, the clinical data of 242 patients from January and June 2021 were retrospectively analysed for external validation (validation group). Baseline patient data were recorded. Patients in the modelling group were divided into those with and without PMASD based on the occurrence of PMASD during hospitalisation. Logistic regression analysis was used to examine the factors of PMASD and the PMASD nomogram model of colorectal cancer. Internal model validation was performed with the Bootstrap method, using the ROC and H‐L goodness of fit test to evaluate the differentiation and calibration of the model. Last, external validation of the model was performed. In the modelling group, 212 patients with colorectal cancer developed PMASD. According to the results of the logistic regression analysis, high fasting plasma glucose and fasting blood glucose (FPG), a history of radiotherapy, the height of the stoma opening (i.e., flat or lower than the skin surface), and skin folds around the stoma are risk factors for PMASD (OR > 1, P < 0.05). The stool shaping and colostomy are protective factors for PMASD in patients with colorectal cancer (OR < 1, P < 0.05). To establish the prediction of colorectal cancer, patient development of PMASD line, graph model, and internal verification was carried out using the Bootstrap method: H‐L test P = 0.846, area under curve, area under the ROC curve (0 > 0.75, 95% CI: 0.778–0, AUC = 0.820). The external validation included the H‐L test (P = 0.137, AUC [0.862] > 0.75, 95% CI: 0.815–0.909), with the maximum value of the Youden index as the best cut‐off value for the model. The ROC curve had a Youden index of 0.559, a sensitivity of 0.877, and a specificity of 0.657. The prompt model area showed good calibration and discrimination. The PMASD in patients with colorectal cancer is affected by defecation traits, the stoma opening height, stoma type, FPG, skin folds around the stoma, and previous radiotherapy history. The nomogram model can provide an effective means to reasonably predict the risk of PMASD in patients with colorectal cancer.
Keywords: colorectal cancer, enterostomy, predictive model, skin injury
1. BACKGROUND
The incidence and number of deaths caused by colorectal cancer rank third worldwide in cancer death. 1 Enterostomy is one of the most common surgical treatments used for colorectal cancer. 2 , 3 Peristomal Moisture‐Associated Skin Damage (PMASD) refers to the moisture‐related skin reaction and corrosion that occur at the junction of the stoma and surrounding skin and is considered the most common type of stoma skin injury. Because of the particularity of the disease, such as widespread malnutrition, more surgery, radiotherapy, chemotherapy, biological treatment, and other comprehensive treatment methods, the influencing factors of PMASD have the characteristics of the disease. We evaluate patients with colorectal cancer tumours using the medical big data platform (YIDUCLOUD) to extract the clinical disease‐related factors, clarify the independent risk factors of PMASD, and build the PMASD risk prediction model to provide the basis for effective measures in clinical practice.
2. METHODS
2.1. Study population
The modelling group used the retrospective analysis method to select the patients with colorectal cancer enterostomy admitted to the Liaoning Cancer Hospital between January and December 2020 as the model fitting sample. The inclusion criteria were as follows: (1) diagnosis of colorectal cancer by pathological examination 4 ; (2) aged ≥18 years old; (3) fixed or ready to contact caregivers; (4) clear awareness and normal communication skills. The exclusion criteria were as follows: (1) multi‐site stoma; (2) preoperative skin metamorphosis or damage; (3) serious illness or death; (4) no response to follow‐up or other reasons; and (5) absence of one or more predictors. This clinical research protocol complies with relevant provisions of Helsinki Declaration on the protection of the rights and interests of subjects.
For the validation group, we used the retrospective analysis method to select the patients admitted to the hospital between January and June 2021 as the external validation samples of the model. The inclusion and exclusion criteria were the same as described above.
2.2. Statistical analysis
2.2.1. Baseline data: predictors
According to PMASD‐related diagnosis and treatment standards for adult enterostomy, 5 industry standards, 6 expert consensus 7 and literature reports, 8 , 9 Combined with the opinions of clinical and medical information technology experts, the alternative predictors include three categories and 27 items: (1) demographic data (4 items), including gender, age, height, and weight; (2) disease‐related data (12 items), including fasting blood glucose (FPG), Haemoglobin (HB), Albumin (Alb), smoking history, drinking alcohol history, allergy history, radiation therapy history, chemotherapy history, previous bowel cancer surgery history, diabetes mellitus history, hypertension history, and diet; (3) stoma information (11 items), 10 including stomy opening position, stomy opening height, stomy support rod, stomy shape, stomy surrounding skin folds, presence of incision, chassis replacement frequency, defecation characteristics, defecation rule, stomy retention time, and stomy type. The predictor evaluation criteria were as follows: FPG‐venous blood (reference range: 3–6.11 mmol/L); HB‐venous blood (reference range: 130–175 g/L); Alb‐venous blood (reference range: 37.0–53.0 g/L); “stoma opening height” higher than skin surface, flat skin surface; “defecation rule” means fixed defecation times per day; the “defecation trait” evaluation criteria was the Bristol stool classification. 11 The stool was classified as formed stool (Bristol: 1–4), slightly formed (Bristol: 5), paste (Bristol: 6), and water sample (Bristol: 7).
2.2.2. Data collection
Two uniformly trained nurses were responsible for reviewing the data. First, the academic research department and information technology department of the hospital provided access to the clinical big data (medical crossing cloud) platform. After “data cleaning” and “standardised conversion” according to the agreed data format loaded into the data warehouse, we exported the research objects into the medical information system outcome index and predictor data to produce a final extraction data in Excel. The screening conditions were as follows: (1) diagnosis, including rectal malignancy tumour, colon malignancy tumour, and rectosigmoid junction malignancy tumour; (2) surgical type, including combined abdominal‐perineal‐rectal resection, colostomy, temporary ileostomy, colostomy, small enterostomy, sigmoid colostomy, Hartmann's surgery, and Laparoscopic Hartmann's surgery; (3) admission to the department, including the colorectal surgery ward; (4) time, From January 1, 2020 to June 31, 2021; (5) extraction factors, including age, gender, chief complaint, diagnosis name (original field), operation name, height, weight, FPG‐venous blood, HB‐venous blood, Alb‐venous blood, current medical history, and previous history. The remaining data were obtained from the disease and follow‐up records.
2.2.3. Data processing
Excel 16.0 software was used to input the data; the analysis was carried out with SPSS25.0 statistical software. The count data were expressed as frequency, and percentage, using χ2. Inspection; the measurement data were represented as x ± s using the t‐test. Based on the results of the univariate analysis, the variables with a P < 0.05 were included in the multivariate analysis and binary logistic regression, and used the likelihood ratio progression method to screen the predictive factors with P < 0.05 to establish the model. The inspection level took a bilateral α = 0.05; the risk probability was calculated using the equation, and the nomogram was drawn using the R (R 4.1.0) software package to visually evaluate the risk. The effects of the predictors in the model were evaluated by a regression coefficient (βvalue), odds ratio (OR value), and 95% (CI). The prediction performance was evaluated with the model sensitivity and specificity of the ROC curve (H‐L test).
3. RESULTS
3.1. General data
375 patients were included in the modelling group, with 212 PMASD cases (56.5%); 242 patients were included in the validation group, with 143 PMASD 143 cases (59.1%). No differences in general data between the two groups were observed (P > 0.05).
3.2. Univariate analysis of PMASD risk factors in patients with colorectal cancer
The difference between the two groups regarding age, FPG, allergy history, radiotherapy history, stoma opening location, stoma opening height, skin folds around the stoma, chassis and replacement frequency was statistically significant (P < 0.05), as summarised in Table 1.
TABLE 1.
Univariate analysis of PMASD in CRC patients (n = 375, %)
| Predictor | PMASD | χ 2 /t | P | |
|---|---|---|---|---|
| NO | Yes | |||
| Age (year) | 60.24 ± 10.99 | 62.92 ± 8.29 | 4.314 | 0.038 |
| FPG | ||||
| Normal | 67 (40.6) | 98 (59.4) | 18.489 | 0.000 |
| Above normal levels | 42 (20.1) | 167 (79.9) | ||
| Allergic history | ||||
| No | 157 (45.5) | 273 (54.5) | 9.963 | 0.007 |
| Yes | 6 (20.0) | 24 (80.0) | ||
| History of radiotherapy | ||||
| No | 104 (49.5) | 106 (50.5) | 7.126 | 0.005 |
| Yes | 59 (35.8) | 106 (28.3) | ||
| The position of the stoma opening | ||||
| Middle | 11 (73.3) | 4 (26.7) | 5.672 | 0.017 |
| Border | 152 (42.2) | 208 (57.8) | ||
| The height of the stoma opening | ||||
| Higher than the skin surface | 134 (47.9) | 146 (52.1) | 16.810 | 0.000 |
| Flat skin surface | 26 (40.6) | 38 (59.4) | ||
| Lower than the skin surface | 3 (9.7) | 28 (90.3) | ||
| Skin folds around the stoma | ||||
| No | 148 (48.5) | 157 (51.5) | 17.010 | 0.000 |
| Yes | 15 (21.4) | 55 (78.6) | ||
| Chassis replacement frequency | ||||
| 1–2 days | 24 (40.7) | 35 (59.3) | 9.111 | 0.028 |
| 3–4 days | 55 (36.2) | 97 (63.8) | ||
| 5–6 days | 52 (47.7) | 57 (52.3) | ||
| Adventitia 7 days | 32 (58.2) | 23 (41.8) | ||
| Receptional traits | ||||
| Watery stool | 72 (28.8) | 178 (71.2) | 68.850 | 0.000 |
| Pasty stool | 52 (66.7) | 26 (33.3) | ||
| Slightly formed stool | 23 (82.1) | 5 (17.9) | ||
| Formed stool | 16 (84.2) | 3 (15.8) | ||
| The type of stoma | ||||
| Ileostomy | 111 (37.8) | 183 (62.2) | 18.069 | 0.000 |
| Colon stomy | 52 (64.2) | 29 (35.8) | ||
3.3. Multivariate logistic regression analysis of PMASD in patients with colorectal cancer
The values of the variables included in the logistic regression analysis are illustrated in Table 2. The results showed that the FPG was above normal, history of radiotherapy, highly flat or below the skin surface, and skin folds around the stoma were risk factors for PMASD (OR > 1, P < 0.05); exclusive defecation and colostomy were the protective factors for PMASD (OR < 1, P < 0.05), as summarised in Table 3.
TABLE 2.
Variable assignment table
| Project | Assignment |
|---|---|
| Y: PMASD | No = 0, Yes = 1 |
| X1:FPG | Normal = 0, Above the normal level = 1 |
| X2: History of radiotherapy | No = 0, Yes = 1 |
| X3: Manufacture opening height | Using “higher than skin surface” as the reference, set the “flat skin surface” and “lower than skin surface”2 dumb variables |
| X4: Skin folds around the ostomy | No = 0, Yes = 1 |
| X5: defecation traits | With the “water sample toilet” as the reference, set the “Pasty stool”, “Slightly formed stool”, “formed stool “3 dumb variables |
| X6: stoma type | Ileostomy = 0, Colostomy = 1 |
TABLE 3.
Multivariate analysis of PMASD (n = 375)
| Project | B price | Standard error | Wald χ2 price | P price | OR price | 95% CI | |
|---|---|---|---|---|---|---|---|
| Constant | 2.062 | 0.764 | 7.286 | 0.007 | 7.860 | ||
| FPG | 1.147 | 0.287 | 15.997 | 0.000 | 3.150 | 1.795 | 5.527 |
| History of radiotherapy | 0.651 | 0.262 | 6.165 | 0.013 | 1.917 | 1.147 | 3.205 |
| The height of the stoma opening | 2.307 | 0.737 | 9.790 | 0.002 | 10.047 | 2.368 | 42.628 |
| Skin folds around the stoma | 1.100 | 0.380 | 8.379 | 0.004 | 3.004 | 1.427 | 6.328 |
| Receptional traits | −2.858 | 0.721 | 15.716 | 0.000 | 0.057 | 0.014 | 0.236 |
| The type of stoma | −1.193 | 0.310 | 14.815 | 0.000 | 0.303 | 0.165 | 0.557 |
3.4. Model building and predictive analysis
The build equations were based on the logistic regression model. Z = 1.147 × X1(FPG) + 0.651 × X2(radiotherapy history) +2.307 × X3(stoma opening height) + 0.838 × X4(Skin folds around the stoma)‐2.858 × X5(defecation trait)‐1.193 × X6(stoma type) + 2.062. The predicted probability was: P = 1/1 + exp(−Z). A nomogram drawn by the prediction model is displayed in Figure 1. Each risk factor of the model can recieve the corresponding score according to the score scale in line 1. All the risk factors of the patients are added up to obtain the total score, and then the corresponding risk probability can be found through the total score. 12 , 13
FIGURE 1.
A nomographic chart of PMASD risk prediction in colorectal cancer
The nomogram model for predicting colorectal cancer and patients developing PMASD was verified using the Bootstrap internal validation method and the prediction model (H‐L test; χ2 = 4.118, P = 0.846, AUC = 0.820, 95% CI: 0.778–0.863). Using the Youden index maximum as the best cut‐off of the model, the ROC curve has a Youden index of 0.525, a sensitivity of 0.860, and a specificity of 0.665, as summarised in Figure 2.
FIGURE 2.
The ROC curves predicting the occurrence of PMASD for the modelling group
3.5. Model external validation using the time‐period validation method
242 cases were included. The predicted positive rate was 49.1%, the actual positive rate was 59.1%, the sensitivity was 83.2%; the predicted negative rate was 27.6%, the actual negative rate was 40.9%, and the specificity was 67.7%, indicating that the clinical prediction effect of this model was good. The AUC was 0.862 (95% CI: 0.815–0.909; H‐L test χ2 = 11.048, P = 0.137). Using the Youden index maximum as the best cut‐off of the model, the ROC curve had a Youden index of 0.559, a sensitivity of 0.877, and a specificity of 0.657, as summarised in Figure 3.
FIGURE 3.
The ROC curves of the model predicting PMASD occurrence for the validation group
4. DISCUSSION
The incidence of PMASD in ostomy patients remains at relatively high levels, 8 , 14 , 15 estimated at 33.3% and 42.9% by Tao Yan and Liu Yingge, respectively. In the survey of 796 ostomy nurses and practice participants related to peristomal skin problems in North America, approximately 77.7% of the patients presented skin problems around the stoma, with PMASD as the most common problem. The incidence of PMASD in this study was 56.5% to 59.5%, a high level compared with those reported in the literature. PMASD not only brings physiological pain to patients and increases the economic burden but also easily separates stoma chassis from the skin because of the wet material corrosion, resulting in the failure of the stoma pocket paste or excrement leakage on the skin or clothing, which seriously reduces the quality of life of patients. Currently, PMASD‐related studies have mainly considered stoma‐related factors such as stoma opening height and stoma site as the analysis variables; while model prediction has adopted the PMASD occurrence risk probability calculation, 8 hence clinical application needs the evaluation of the personnel involved to have a statistical basis. This study combined the characteristics of patients with colorectal cancer, the choice of risk factors based on colorectal cancer‐related domestic and foreign literature, and expert opinions with six risk factors to extract the clinical application based on the visual nomogram model for high‐risk patients for the simple, early identification and probability measurement of PMASD risk implementation targeted intervention.
The relationship between the factors and the occurrence of PMASD was analysed. The FPG automatically extracted preoperative FPG‐venous blood from the Yidu Cloud data system. The results showed that the incidence of postoperative PMASD in patients with higher than normal preoperative fasting blood glucose (>6.11 mmol/L) was significantly higher (79.9%) than that in those with normoglycemia (59.4%). However, PMASD incidence has shown no statistical significance among patients with a history of diabetes and those without such clinical history; communication with patients and their family members and found that the preoperative blood sugar levels of some patients without the knowledge of whether they had diabetes was high compared with normal values, which may indicate diabetes. Arumagam 16 found that diabetes is one of the risk factors for peristomal skin injury. The Chinese Expert Consensus and Pathway Management Guidelines for Accelerating Rehabilitation Surgery (2018 edition) also proposed that persistent hyperglycemia can lead to an increased risk of postoperative infection, wound healing, and endothelial dysfunction. 7 Our study also supports the above views.
Regarding the history of radiotherapy, the risk of PMASD in this study was 1.917 times higher for PMASD than that for those who had not experienced radiotherapy [OR = 1.917, P = 0.013]. The skin in the radiation area of radiotherapy patients often shows redness, itching, blisters, and rupture. If the skin area around the ostomy is within the irradiation field range, the skin in the original radiation damage combined with excrement stimulation is more likely to lead to PMASD. Patients have also presented gastrointestinal reactions because of radiotherapy. Irregular excretions such as diarrhoea or constipation can also cause pocket leakage, pollution, and swelling bags, increasing the occurrence probability of PMASD. 17 Liu Na 18 found that the risk of perioral dermatitis was 23.159 times higher than that of patients without radiotherapy [OR = 23.159, P < 0.000], which was significantly higher than that in this study. This may be because this study only evaluated PMASD, while the type of dermatitis studied by other scholars also included fungal infection, folliculitis, and external stimulus injury.
Concerning the height of the stoma opening, patients with low or flat opening height to the skin surface have a high incidence of PMASD. Although some stomas have more exposed mucosa than the skin, PMASD also appears because the location of the opening is located at the edge of the stoma, and the levelling is lower than the skin.
As for the skin folds around the ostomy, when the patient changes his position after surgery, the skin around the enterostomy is depressed, the chassis cannot completely stick to the skin, and faecal leakage leads to the occurrence of PMASD. 19 Recent studies on postoperative stomal complications have confirmed that the location of the stoma is closely related to whether postoperative complications occur. 20 , 21 The Chinese Code for the Diagnosis and Treatment of Colorectal Cancer (2020 edition) also recommends that doctors, ostomy therapists, patients, and their family members should jointly choose the stoma site before surgery. 5
The Group Standard of the Chinese Nursing Society used Adult Enterostomy Care (2020) to suggest that the ideal height of the ostomy is higher than that of the skin surface by 1.0–2.0 cm. 6 The skin around the stoma should be smooth, and the opening position should be higher than the skin surface to avoid affecting the chassis adhesion and prevent the occurrence of PMASD.
This study found that the incidence of PMASD (71.2%) was much higher than that for other defecation traits (33.3%, 17.9%, and 15.8%). The Study by Zhang Jun 22 also suggested that patients are prone to leakage and erosion of the stomy chassis because of dilute water samples, resulting in frequent replacement of stomy instruments and stripping of the chassis, which will increase the mechanical damage to the stomy skin, coupled with moisture and faeces stimulation and increased risk of PMASD.
Regarding the type of stoma, the incidence of PMASD in ileostomy (62.2%) was higher than that of colostomy (35.8%). Our results are consistent with the study by Pittman J and Nybaek H. 23 , 24 This may be related to the rich blood supply of the ileum, with a thin intestinal lumen and large in content, the secretions are mainly small, and its pH value is significantly higher than that of normal skin, which has a strong stimulating effect on ostomy skin and is more likely to induce PMASD.
The prediction model performs well, and the nomogram realised that visual evaluation is convenient for clinical operation. The predictive performance of the model was assessed by discrimination and calibration. The differentiation degree is tested with the ROC curve, where an AUC <0.6 indicates that the model discrimination ability is poor, 0.6 < AUC <0.75 indicates that the model has a certain discrimination ability, and AUC > 0.75 indicates that the calibration degree can be evaluated by the H‐L goodness‐of‐fit test. A P > 0.05 from the H‐L test suggested a better model prediction accuracy. 13 Through the internal and external verification of the model, good differentiation and calibration showed ideal prediction performance. Among the six predictors included in the model, discharge watery stool had the greatest impact on the occurrence of PMASD, while the remaining predictors were the height of the stoma opening (i.e., flat or lower than the skin level), ileostomy, FPG above normal, perioral skin fold, and a history of radiotherapy. The clinical application of the PMASD nomogram model in high‐risk patients with colorectal cancer patients can adjust perioperative blood sugar, active preoperative stoma positioning, guide patients with the effective application of leakage cream, stomy care products, reasonable diets to reduce diarrhoea in patients with stoma, and prevent the occurrence of PMASD.
5. CONCLUSIONS
In this study, the PMASD risk warning model includes six independent risk factors, including discharge of watery stool, the height of stoma opening (i.e., flat or lower than the skin level), ileostomy, FPG above normal, perioral skin fold, and a history of radiotherapy. A high early warning effect has been verified for PMASD in patients with colostomy of colorectal cancer. Altogether, the PMASD risk warning model has clinical application value.
FUNDING INFORMATION
Nursing Research Project of the Journal of Chinese Medical Association (CMAPH‐NRD2021043).
Wang H, Jiang G, Wang Y, et al. Construction and validation of the perioral moisture‐related skin damage (PMASD) risk prediction model in patients with colorectal cancer. Int Wound J. 2023;20(6):2207‐2214. doi: 10.1111/iwj.14098
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.