Effect of closed and permanent stoma on disease course, psychological well-being and working capacity in Swiss IBD cohort study patients

Bianchi, Rahel; Mamadou-Pathé, Barry; Känel, Roland von; Roth, René; Schreiner, Philipp; Rossel, Jean‐Benoît; Burk, Sabine; Dora, Babara; Kloth, Patrizia; Rickenbacher, Andreas; Turina, Matthias; Greuter, Thomas; Misselwitz, Benjamin; Scharl, Michael; Rogler, Gerhard; Biedermann, Luc

doi:10.1371/journal.pone.0274665

Cited by 3 publications

(3 citation statements)

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“…This novel study contributes to the growing psychological research looking at the impact of having an intestinal stoma in adults on well‐being and QoL. It is one of the few known UK studies to compare stoma and non‐stoma participants on a range of psychological measures (Bianchi et al., 2022 ; Silva et al., 2017 ; Temprado Albalat et al., 2018 ). The findings elucidate a better understanding of the psychological needs of those living with a stoma and establish the importance of stoma‐related factors such as the early years following surgery and those experiencing frequent leaks, as being detrimental to psychological well‐being and QoL in some participants.…”

Section: Discussionmentioning

confidence: 97%

“…Polidano et al., 2020 ) and the lived experience assessed by Interpretative Phenomenological Approach (IPA) (Horgan et al., 2020 ; Jordan et al., 2018 ). Reducing psychological distress is part of the UK healthcare policy for stoma patients (Department of Health, 2011 ) and remains a challenge due to a lack of understanding of psychological health needs (Bianchi et al., 2022 ; Wallace, 2016 ), which was addressed in the current study.…”

Section: Introductionmentioning

confidence: 99%

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Living with and without an intestinal stoma: Factors that promote psychological well‐being and self‐care: A cross‐sectional study

Simpson,

Pourshahidi,

Davis

et al. 2023

Nursing Open

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AimsThis study compared those living with and without an intestinal stoma in relation to physical and psychological health, stress and coping, quality of life and resilience. Also, identifying factors that could be used to promote better self‐care in stoma patients in the future.DesignA cross‐sectional and comparative study design was employed.MethodsParticipants were recruited via email and social media (Facebook and Twitter) between August 2018 and March 2019, to complete an online survey. The data were analysed using analysis of variance to examine group difference and a series of hierarchical linear regression analyses determining predictors of psychological well‐being.ResultsOf 278 participants aged 18–68 years who completed the survey, 129 (46%) had a stoma and reported significantly poorer physical health. Approximately one‐fifth experienced problems with stoma management. Psychological well‐being was mediated by the duration of living with a stoma (under 3 years) and frequency of leaks (weekly and monthly).

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Living with and without an intestinal stoma: Factors that promote psychological well‐being and self‐care: A cross‐sectional study

Simpson,

Pourshahidi,

Davis

et al. 2023

Nursing Open

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show abstract

“…However, other patients are unable to retract for various reasons and suffer great physical and psychological damage. Some studies (8,9) have shown that permanent stoma prolongs the life of patients but reduces their quality of life. Therefore, it is crucial to understand high risk factors for permanent stoma so that surgeons can appropriate counsel patients.…”

Section: Introductionmentioning

confidence: 99%

Applying interpretable machine learning algorithms to predict risk factors for permanent stoma in patients after TME

Liu

Zhao

et al. 2023

Front. Surg.

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ObjectiveThe purpose of this study was to develop a machine learning model to identify preoperative and intraoperative high-risk factors and to predict the occurrence of permanent stoma in patients after total mesorectal excision (TME).MethodsA total of 1,163 patients with rectal cancer were included in the study, including 142 patients with permanent stoma. We collected 24 characteristic variables, including patient demographic characteristics, basic medical history, preoperative examination characteristics, type of surgery, and intraoperative information. Four machine learning algorithms including extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM) and k-nearest neighbor algorithm (KNN) were applied to construct the model and evaluate the model using k-fold cross validation method, ROC curve, calibration curve, decision curve analysis (DCA) and external validation.ResultsThe XGBoost algorithm showed the best performance among the four prediction models. The ROC curve results showed that XGBoost had a high predictive accuracy with an AUC value of 0.987 in the training set and 0.963 in the validation set. The k-fold cross-validation method was used for internal validation, and the XGBoost model was stable. The calibration curves showed high predictive power of the XGBoost model. DCA curves showed higher benefit rates for patients who received interventional treatment under the XGBoost model. The AUC value for the external validation set was 0.89, indicating that the XGBoost prediction model has good extrapolation.ConclusionThe prediction model for permanent stoma in patients with rectal cancer derived from the XGBoost machine learning algorithm in this study has high prediction accuracy and clinical utility.

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Lebensqualität von Stomapatienten

Kasparek¹

2023

coloproctology

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Effect of closed and permanent stoma on disease course, psychological well-being and working capacity in Swiss IBD cohort study patients

Abstract: ¶ Membership of the Swiss IBD cohort study is provided in the Acknowledgements.

Cited by 3 publications

References 47 publications

Living with and without an intestinal stoma: Factors that promote psychological well‐being and self‐care: A cross‐sectional study

Living with and without an intestinal stoma: Factors that promote psychological well‐being and self‐care: A cross‐sectional study

Applying interpretable machine learning algorithms to predict risk factors for permanent stoma in patients after TME

Lebensqualität von Stomapatienten

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