Objectives To estimate the prevalence of malnutrition in elderly cancer patients and its association with frailty and primary cancer treatment recommendations in patients with the two most prevalent cancers(colorectal...Objectives To estimate the prevalence of malnutrition in elderly cancer patients and its association with frailty and primary cancer treatment recommendations in patients with the two most prevalent cancers(colorectal cancer,CRC and breast cancer,BC)in the Centre-Val de Loire region of France.Methods The entire cohort of 704 patients aged 75 years or older presenting with cancer who underwent comprehensive geriatric assessment(CGA)between 2014 and 2017 was included.Nutritional status,frailty(defined by the Balducci classification system based on CGA parameters and comorbidity),and pathological criteria were analyzed in terms of the cancer treatment recommendations suggested by geriatricians both in the whole cohort and in those with CRC and BC.Results In the whole group of 704 patients(84.3+/-4.8 years),the prevalence of malnutrition was 62.9%,and was higher in CRC than in BC patients(71.1%vs 55.4%,P<0.01).In a multivariate analysis,malnutrition and frailty(as determined by the Balducci classification system)were independently related in CRC patients(OR:7.28,95%CI,1.58~34.03;P=0.012)and were unrelated to metastasis[odds ratio(OR):1.34,95%CI,0.56~3.18;P=0.5].By contrast,malnutrition in BC patients was related exclusively to the extent of metastasis(OR:3.52,95%CI,1.50~8.24;P=0.002).It was also demonstrated that geriatricians had a greater tendency to suggest only palliative care to CRC patients presenting with malnutrition(15.4%vs 2.7%,P=0.006)than to BC patients(9.8%vs 5.4%,NS).Conclusion Malnutrition in elderly cancer patients is prevalent,especially in those with colorectal cancer,where malnutrition is frailty-related and may strongly impact on cancer treatment strategies.展开更多
This editorial discusses an article recently published in the World Journal of Clinical Cases,focusing on risk factors associated with intensive care unit-acquired weak-ness(ICU-AW).ICU-AW is a serious neuromuscular c...This editorial discusses an article recently published in the World Journal of Clinical Cases,focusing on risk factors associated with intensive care unit-acquired weak-ness(ICU-AW).ICU-AW is a serious neuromuscular complication seen in criti-cally ill patients,characterized by muscle dysfunction,weakness,and sensory impairments.Post-discharge,patients may encounter various obstacles impacting their quality of life.The pathogenesis involves intricate changes in muscle and nerve function,potentially leading to significant disabilities.Given its global significance,ICU-AW has become a key research area.The study identified critical risk factors using a multilayer perceptron neural network model,highlighting the impact of intensive care unit stay duration and mechanical ventilation duration on ICU-AW.Recommendations were provided for preventing ICU-AW,empha-sizing comprehensive interventions and risk factor mitigation.This editorial stresses the importance of external validation,cross-validation,and model tran-sparency to enhance model reliability.Moreover,the application of machine learning in clinical medicine has demonstrated clear benefits in improving disease understanding and treatment decisions.While machine learning presents oppor-tunities,challenges such as model reliability and data management necessitate thorough validation and ethical considerations.In conclusion,integrating ma-chine learning into healthcare offers significant potential and challenges.Enhan-cing data management,validating models,and upholding ethical standards are crucial for maximizing the benefits of machine learning in clinical practice.展开更多
Objective:To explore the influencing factors of patients with dysphagia after stroke who refuse to accept gastric tube implantation,and to provide intervention basis for improving the compliance of patients with gastr...Objective:To explore the influencing factors of patients with dysphagia after stroke who refuse to accept gastric tube implantation,and to provide intervention basis for improving the compliance of patients with gastric tube implantation.Methods:Asemi-structured interview method was used to conduct in-depth interviews with 11 patients who refused gastric tube placement,and the interview data were analyzed by Colaizzi analysis method.Results:The influencing factors of patients with dysphagia after stroke can be summarized into the following three themes:patient factors(patient's cognition of disease,patient's subjective pain perception and fear),family factors(patient's caregiver's cognition of disease,economic conditions)and medical factors(trust in medical staff,medical education methods).Conclusion:Medical staff should understand the influencing factors of dysphagia after stroke and take positive measures to improve the compliance of patients with gastric tube placement and ensure the treatment effect.展开更多
文摘Objectives To estimate the prevalence of malnutrition in elderly cancer patients and its association with frailty and primary cancer treatment recommendations in patients with the two most prevalent cancers(colorectal cancer,CRC and breast cancer,BC)in the Centre-Val de Loire region of France.Methods The entire cohort of 704 patients aged 75 years or older presenting with cancer who underwent comprehensive geriatric assessment(CGA)between 2014 and 2017 was included.Nutritional status,frailty(defined by the Balducci classification system based on CGA parameters and comorbidity),and pathological criteria were analyzed in terms of the cancer treatment recommendations suggested by geriatricians both in the whole cohort and in those with CRC and BC.Results In the whole group of 704 patients(84.3+/-4.8 years),the prevalence of malnutrition was 62.9%,and was higher in CRC than in BC patients(71.1%vs 55.4%,P<0.01).In a multivariate analysis,malnutrition and frailty(as determined by the Balducci classification system)were independently related in CRC patients(OR:7.28,95%CI,1.58~34.03;P=0.012)and were unrelated to metastasis[odds ratio(OR):1.34,95%CI,0.56~3.18;P=0.5].By contrast,malnutrition in BC patients was related exclusively to the extent of metastasis(OR:3.52,95%CI,1.50~8.24;P=0.002).It was also demonstrated that geriatricians had a greater tendency to suggest only palliative care to CRC patients presenting with malnutrition(15.4%vs 2.7%,P=0.006)than to BC patients(9.8%vs 5.4%,NS).Conclusion Malnutrition in elderly cancer patients is prevalent,especially in those with colorectal cancer,where malnutrition is frailty-related and may strongly impact on cancer treatment strategies.
文摘This editorial discusses an article recently published in the World Journal of Clinical Cases,focusing on risk factors associated with intensive care unit-acquired weak-ness(ICU-AW).ICU-AW is a serious neuromuscular complication seen in criti-cally ill patients,characterized by muscle dysfunction,weakness,and sensory impairments.Post-discharge,patients may encounter various obstacles impacting their quality of life.The pathogenesis involves intricate changes in muscle and nerve function,potentially leading to significant disabilities.Given its global significance,ICU-AW has become a key research area.The study identified critical risk factors using a multilayer perceptron neural network model,highlighting the impact of intensive care unit stay duration and mechanical ventilation duration on ICU-AW.Recommendations were provided for preventing ICU-AW,empha-sizing comprehensive interventions and risk factor mitigation.This editorial stresses the importance of external validation,cross-validation,and model tran-sparency to enhance model reliability.Moreover,the application of machine learning in clinical medicine has demonstrated clear benefits in improving disease understanding and treatment decisions.While machine learning presents oppor-tunities,challenges such as model reliability and data management necessitate thorough validation and ethical considerations.In conclusion,integrating ma-chine learning into healthcare offers significant potential and challenges.Enhan-cing data management,validating models,and upholding ethical standards are crucial for maximizing the benefits of machine learning in clinical practice.
文摘Objective:To explore the influencing factors of patients with dysphagia after stroke who refuse to accept gastric tube implantation,and to provide intervention basis for improving the compliance of patients with gastric tube implantation.Methods:Asemi-structured interview method was used to conduct in-depth interviews with 11 patients who refused gastric tube placement,and the interview data were analyzed by Colaizzi analysis method.Results:The influencing factors of patients with dysphagia after stroke can be summarized into the following three themes:patient factors(patient's cognition of disease,patient's subjective pain perception and fear),family factors(patient's caregiver's cognition of disease,economic conditions)and medical factors(trust in medical staff,medical education methods).Conclusion:Medical staff should understand the influencing factors of dysphagia after stroke and take positive measures to improve the compliance of patients with gastric tube placement and ensure the treatment effect.