BACKGROUND Colorectal cancer significantly impacts global health,with unplanned reoperations post-surgery being key determinants of patient outcomes.Existing predictive models for these reoperations lack precision in ...BACKGROUND Colorectal cancer significantly impacts global health,with unplanned reoperations post-surgery being key determinants of patient outcomes.Existing predictive models for these reoperations lack precision in integrating complex clinical data.AIM To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.METHODS Data of patients treated for colorectal cancer(n=2044)at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected.Patients were divided into an experimental group(n=60)and a control group(n=1984)according to unplanned reoperation occurrence.Patients were also divided into a training group and a validation group(7:3 ratio).We used three different machine learning methods to screen characteristic variables.A nomogram was created based on multifactor logistic regression,and the model performance was assessed using receiver operating characteristic curve,calibration curve,Hosmer-Lemeshow test,and decision curve analysis.The risk scores of the two groups were calculated and compared to validate the model.RESULTS More patients in the experimental group were≥60 years old,male,and had a history of hypertension,laparotomy,and hypoproteinemia,compared to the control group.Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation(P<0.05):Prognostic Nutritional Index value,history of laparotomy,hypertension,or stroke,hypoproteinemia,age,tumor-node-metastasis staging,surgical time,gender,and American Society of Anesthesiologists classification.Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.CONCLUSION This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer,which can improve treatment decisions and prognosis.展开更多
BACKGROUND Critical patients often had various types of tubes,unplanned extubation of any kind of tube may cause serious injury to the patient,but previous reports mainly focused on endotracheal intubation.The limitat...BACKGROUND Critical patients often had various types of tubes,unplanned extubation of any kind of tube may cause serious injury to the patient,but previous reports mainly focused on endotracheal intubation.The limitations or incorrect use of the unplanned extubation risk assessment tool may lead to improper identification of patients at a high risk of unplanned extubation and cause delay or nonimplementation of unplanned extubation prevention interventions.To effectively identify and manage the risk of unplanned extubation,a comprehensive and universal unplanned extubation risk assessment tool is needed.AIM To assess the predictive value of the Huaxi Unplanned Extubation Risk Assessment Scale in inpatients.METHODS This was a retrospective validation study.In this study,medical records were extracted between October 2020 and September 2021 from a tertiary comprehensive hospital in southwest China.For patients with tubes during hospitalization,the following information was extracted from the hospital information system:age,sex,admission mode,education,marital status,number of tubes,discharge mode,unplanned extubation occurrence,and the Huaxi Unplanned Extubation Risk Assessment Scale(HUERAS)score.Only inpatients were included,and those with indwelling needles were excluded.The best cut-off value and the area under the curve(AUC)of the Huaxi Unplanned Extubation Risk Assessment Scale were been identified.RESULTS A total of 76033 inpatients with indwelling tubes were included in this study,and 26 unplanned extubations occurred.The patients’HUERAS scores were between 11 and 30,with an average score of 17.25±3.73.The scores of patients with or without unplanned extubation were 22.85±3.28 and 17.25±3.73,respectively(P<0.001).The results of the correlation analysis showed that the correlation coefficients between each characteristic and the total score ranged from 0.183 to 0.843.The best cut-off value was 21,and there were 14135 patients with a high risk of unplanned extubation,accounting for 18.59%.The Cronbach’sα,sensitivity,specificity,positive predictive value,and negative predictive value of the Huaxi Unplanned Extubation Risk Assessment Scale were 0.815,84.62%,81.43%,0.16%,and 99.99%,respectively.The AUC of HUERAS was 0.851(95%CI:0.783-0.919,P<0.001).CONCLUSION The HUERAS has good reliability and predictive validity.It can effectively identify inpatients at a high risk of unplanned extubation and help clinical nurses carry out risk screening and management.展开更多
Objective:To investigate the application effect of quality control circle activities in reducing the rate of unplanned extubation of venous access in perioperative patients.Methods:The quality control circle method wa...Objective:To investigate the application effect of quality control circle activities in reducing the rate of unplanned extubation of venous access in perioperative patients.Methods:The quality control circle method was used to analyze the causes,identify the actual causes of unplanned out-of-control,take corresponding measures,formulate corresponding countermeasures,implement standardized management,and carry out continuous improvement.Results:Following the implementation of quality control circle activities,the rate of unplanned extubation of venous access in perioperative patients decreased from 27.35%before improvement to 3.42%after improvement.Conclusion:The use of quality control circle activities in the safety management of venous access in perioperative patients is conducive to reducing the rate of unplanned extubation of venous access in perioperative patients.展开更多
基金This study has been reviewed and approved by the Clinical Research Ethics Committee of Wenzhou Central Hospital and the First Hospital Affiliated to Wenzhou Medical University,No.KY2024-R016.
文摘BACKGROUND Colorectal cancer significantly impacts global health,with unplanned reoperations post-surgery being key determinants of patient outcomes.Existing predictive models for these reoperations lack precision in integrating complex clinical data.AIM To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.METHODS Data of patients treated for colorectal cancer(n=2044)at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected.Patients were divided into an experimental group(n=60)and a control group(n=1984)according to unplanned reoperation occurrence.Patients were also divided into a training group and a validation group(7:3 ratio).We used three different machine learning methods to screen characteristic variables.A nomogram was created based on multifactor logistic regression,and the model performance was assessed using receiver operating characteristic curve,calibration curve,Hosmer-Lemeshow test,and decision curve analysis.The risk scores of the two groups were calculated and compared to validate the model.RESULTS More patients in the experimental group were≥60 years old,male,and had a history of hypertension,laparotomy,and hypoproteinemia,compared to the control group.Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation(P<0.05):Prognostic Nutritional Index value,history of laparotomy,hypertension,or stroke,hypoproteinemia,age,tumor-node-metastasis staging,surgical time,gender,and American Society of Anesthesiologists classification.Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.CONCLUSION This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer,which can improve treatment decisions and prognosis.
基金Supported by West China Nursing Discipline Development Special Fund Project,Sichuan University,No.HXHL19059。
文摘BACKGROUND Critical patients often had various types of tubes,unplanned extubation of any kind of tube may cause serious injury to the patient,but previous reports mainly focused on endotracheal intubation.The limitations or incorrect use of the unplanned extubation risk assessment tool may lead to improper identification of patients at a high risk of unplanned extubation and cause delay or nonimplementation of unplanned extubation prevention interventions.To effectively identify and manage the risk of unplanned extubation,a comprehensive and universal unplanned extubation risk assessment tool is needed.AIM To assess the predictive value of the Huaxi Unplanned Extubation Risk Assessment Scale in inpatients.METHODS This was a retrospective validation study.In this study,medical records were extracted between October 2020 and September 2021 from a tertiary comprehensive hospital in southwest China.For patients with tubes during hospitalization,the following information was extracted from the hospital information system:age,sex,admission mode,education,marital status,number of tubes,discharge mode,unplanned extubation occurrence,and the Huaxi Unplanned Extubation Risk Assessment Scale(HUERAS)score.Only inpatients were included,and those with indwelling needles were excluded.The best cut-off value and the area under the curve(AUC)of the Huaxi Unplanned Extubation Risk Assessment Scale were been identified.RESULTS A total of 76033 inpatients with indwelling tubes were included in this study,and 26 unplanned extubations occurred.The patients’HUERAS scores were between 11 and 30,with an average score of 17.25±3.73.The scores of patients with or without unplanned extubation were 22.85±3.28 and 17.25±3.73,respectively(P<0.001).The results of the correlation analysis showed that the correlation coefficients between each characteristic and the total score ranged from 0.183 to 0.843.The best cut-off value was 21,and there were 14135 patients with a high risk of unplanned extubation,accounting for 18.59%.The Cronbach’sα,sensitivity,specificity,positive predictive value,and negative predictive value of the Huaxi Unplanned Extubation Risk Assessment Scale were 0.815,84.62%,81.43%,0.16%,and 99.99%,respectively.The AUC of HUERAS was 0.851(95%CI:0.783-0.919,P<0.001).CONCLUSION The HUERAS has good reliability and predictive validity.It can effectively identify inpatients at a high risk of unplanned extubation and help clinical nurses carry out risk screening and management.
文摘Objective:To investigate the application effect of quality control circle activities in reducing the rate of unplanned extubation of venous access in perioperative patients.Methods:The quality control circle method was used to analyze the causes,identify the actual causes of unplanned out-of-control,take corresponding measures,formulate corresponding countermeasures,implement standardized management,and carry out continuous improvement.Results:Following the implementation of quality control circle activities,the rate of unplanned extubation of venous access in perioperative patients decreased from 27.35%before improvement to 3.42%after improvement.Conclusion:The use of quality control circle activities in the safety management of venous access in perioperative patients is conducive to reducing the rate of unplanned extubation of venous access in perioperative patients.
