Objective To develop a risk prediction model for moderate-to-severe catheter-related bladder discomfort(CRBD)after non-transurethral surgery based on various machine-learning algorithms and to compare the performance of the models,so as to provide a reference for accurately identification and prevention of the postoperative moderate-to-severe CRBD.Methods A convenience sampling method was employed to recruit 719 patients as study subjects.The patients received non-transurethral surgery and intraoperative urinary catheterisation in a Tier-ⅢA hospital in Shanxi Province between January and May 2024.The clinical data were collected,with 70％of the randomly selected data was assigned to a training dataset(n=503)for the model building and the rest of 30％of data was used as the testing dataset(n=216)for internal model validation.Predictors were determined using least absolute shrinkage and selection operators(LASSO).Seven machine learning methods of logistic regression,K-nearest neighbours,random forest,artificial neural network,decision tree,light gradient boosting machine(LightGBM)and elastic net were employed to establish the risk prediction models.Performance of the models was evaluated based on the area under receiver operating characteristic curve(AUR-ROC),accuracy,precision,recall and F1 score.Results A total of 719 patients who underwent non-transurethral surgery were included in the study.It was found that 154(21.4％)patients presented with moderate to severe CRBD and 565(78.6％)patients were without or only with a mild CRBD.The predictors were deduced to six variables:gender,abdominal surgery,type of surgery,administration of dexmedetomidine before surgery,intraoperative administration of flurbiprofenate,and use of tramadol by the completion of surgery.It was found that the LightGBM model demonstrated a high stability,with 0.793 in AUC-ROC,0.763 in accuracy,0.879 in precision,0.747 in recall and 0.808 in F1.Conclusion The risk prediction model established through LightGBM for moderate-to-severe CRBD after a non-transurethral surgery exhibits a high stability.It offers a reference for medical practitioners to identify the patients with high-risk of moderate-to-severe CRBD and prepares for relevant interventions.

Objective To develop a risk prediction model for moderate-to-severe catheter-related bladder discomfort(CRBD)after non-transurethral surgery based on various machine-learning algorithms and to compare the performance of the models,so as to provide a reference for accurately identification and prevention of the postoperative moderate-to-severe CRBD.Methods A convenience sampling method was employed to recruit 719 patients as study subjects.The patients received non-transurethral surgery and intraoperative urinary catheterisation in a Tier-ⅢA hospital in Shanxi Province between January and May 2024.The clinical data were collected,with 70％of the randomly selected data was assigned to a training dataset(n=503)for the model building and the rest of 30％of data was used as the testing dataset(n=216)for internal model validation.Predictors were determined using least absolute shrinkage and selection operators(LASSO).Seven machine learning methods of logistic regression,K-nearest neighbours,random forest,artificial neural network,decision tree,light gradient boosting machine(LightGBM)and elastic net were employed to establish the risk prediction models.Performance of the models was evaluated based on the area under receiver operating characteristic curve(AUR-ROC),accuracy,precision,recall and F1 score.Results A total of 719 patients who underwent non-transurethral surgery were included in the study.It was found that 154(21.4％)patients presented with moderate to severe CRBD and 565(78.6％)patients were without or only with a mild CRBD.The predictors were deduced to six variables:gender,abdominal surgery,type of surgery,administration of dexmedetomidine before surgery,intraoperative administration of flurbiprofenate,and use of tramadol by the completion of surgery.It was found that the LightGBM model demonstrated a high stability,with 0.793 in AUC-ROC,0.763 in accuracy,0.879 in precision,0.747 in recall and 0.808 in F1.Conclusion The risk prediction model established through LightGBM for moderate-to-severe CRBD after a non-transurethral surgery exhibits a high stability.It offers a reference for medical practitioners to identify the patients with high-risk of moderate-to-severe CRBD and prepares for relevant interventions.

Objective:To gain insight into the multidimensional needs of adults with moderate-to-severe psoriasis at different stages of disease progression diagnostic and treatment dynamics based on patient journey maps, providing a basis for developing precise intervention strategies and optimizing care throughout the journey.Methods:From September to October 2024, 14 adult patients with moderate-to-severe psoriasis admitted to the Department of Dermatology of the First Hospital of Shanxi Medical University were selected by purposive sampling method for semi-structured interviews. Content analysis was used to analyze the data, drawing on theory of "timing it right" to depict a patient journey map that was reviewed and improved by the research team and patients.Results:Patient journey map was developed and condensed into four themes of experiences and challenges from pre-diagnosis to adaptation, complex emotional experiences, experiential pain points at each stage of the diagnosis and treatment, and opportunity points to improve the experience and quality of the diagnosis and treatment.Conclusions:At different stages of disease progression, the diagnosis and treatment needs of adults with moderate-to-severe psoriasis are characterized by dynamic evolution and multidimensional integration. The journey map can accurately identify patients' differentiated experiences and needs, and can provide a reference for healthcare professionals and policy makers to optimize patients' diagnostic and treatment experiences and focus on patients' health management.

Objective:To gain insight into the multidimensional needs of adults with moderate-to-severe psoriasis at different stages of disease progression diagnostic and treatment dynamics based on patient journey maps, providing a basis for developing precise intervention strategies and optimizing care throughout the journey.Methods:From September to October 2024, 14 adult patients with moderate-to-severe psoriasis admitted to the Department of Dermatology of the First Hospital of Shanxi Medical University were selected by purposive sampling method for semi-structured interviews. Content analysis was used to analyze the data, drawing on theory of "timing it right" to depict a patient journey map that was reviewed and improved by the research team and patients.Results:Patient journey map was developed and condensed into four themes of experiences and challenges from pre-diagnosis to adaptation, complex emotional experiences, experiential pain points at each stage of the diagnosis and treatment, and opportunity points to improve the experience and quality of the diagnosis and treatment.Conclusions:At different stages of disease progression, the diagnosis and treatment needs of adults with moderate-to-severe psoriasis are characterized by dynamic evolution and multidimensional integration. The journey map can accurately identify patients' differentiated experiences and needs, and can provide a reference for healthcare professionals and policy makers to optimize patients' diagnostic and treatment experiences and focus on patients' health management.