BACKGROUND Cesarean hemorrhage is one of the serious complications,and short-term massive blood transfusion can easily cause postoperative infection and physical stress response.However,predictive nursing intervention...BACKGROUND Cesarean hemorrhage is one of the serious complications,and short-term massive blood transfusion can easily cause postoperative infection and physical stress response.However,predictive nursing intervention has important clinical significance for it.AIM To explore the effect of predictive nursing intervention on the stress response and complications of women undergoing short-term mass blood transfusion during cesarean section(CS).METHODS A clinical medical record of 100 pregnant women undergoing rapid mass blood transfusion during sections from June 2019 to June 2021.According to the different nursing methods,patients divided into control group(n=50)and observation group(n=50).Among them,the control group implemented routine nursing,and the observation group implemented predictive nursing intervention based on the control group.Moreover,compared the differences in stress res-ponse,complications,and pain scores before and after the nursing of pregnant women undergoing rapid mass blood transfusion during CS.RESULTS The anxiety and depression scores of pregnant women in the two groups were significantly improved after nursing,and the psychological stress response of the observation group was significantly lower than that of the control group(P<0.05).The heart rate and mean arterial pressure(MAP)of the observation group during delivery were lower than those of the control group,and the MAP at the end of delivery was lower than that of the control group(P<0.05).Moreover,different pain scores improved significantly in both groups,with the observation group considerably less than the control group(P<0.05).After nursing,complications such as skin rash,urinary retention,chills,diarrhea,and anaphylactic shock in the observation group were 18%,which significantly higher than in the control group(4%)(P<0.05).CONCLUSION Predictive nursing intervention can effectively relieve the pain,reduce the incidence of complications,improve mood and stress response,and serve as a reference value for the nursing of women undergoing rapid mass transfusion during CS.展开更多
BACKGROUND Being too light at birth can increase the risk of various diseases during infancy.AIM To explore the effect of perinatal factors on term low-birth-weight(LBW)infants and build a predictive model.This model ...BACKGROUND Being too light at birth can increase the risk of various diseases during infancy.AIM To explore the effect of perinatal factors on term low-birth-weight(LBW)infants and build a predictive model.This model aims to guide the clinical management of pregnant women’s healthcare during pregnancy and support the healthy growth of newborns.METHODS A retrospective analysis was conducted on data from 1794 single full-term pregnant women who gave birth.Newborns were grouped based on birth weight:Those with birth weight<2.5 kg were classified as the low-weight group,and those with birth weight between 2.5 kg and 4 kg were included in the normal group.Multiple logistic regression analysis was used to identify the factors influencing the occurrence of full-term LBW.A risk prediction model was established based on the analysis results.The effectiveness of the model was analyzed using the Hosmer–Leme show test and receiver operating characteristic(ROC)curve to verify the accuracy of the predictions.RESULTS Among the 1794 pregnant women,there were 62 cases of neonatal weight<2.5 kg,resulting in an LBW incidence rate of 3.46%.The factors influencing full-term LBW included low maternal education level[odds ratio(OR)=1.416],fewer prenatal examinations(OR=2.907),insufficient weight gain during pregnancy(OR=3.695),irregular calcium supplementation during pregnancy(OR=1.756),and pregnancy hypertension syndrome(OR=2.192).The prediction model equation was obtained as follows:Logit(P)=0.348×maternal education level+1.067×number of prenatal examinations+1.307×insufficient weight gain during pregnancy+0.563×irregular calcium supplementation during pregnancy+0.785×pregnancy hypertension syndrome−29.164.The area under the ROC curve for this model was 0.853,with a sensitivity of 0.852 and a specificity of 0.821.The Hosmer–Leme show test yieldedχ^(2)=2.185,P=0.449,indicating a good fit.The overall accuracy of the clinical validation model was 81.67%.CONCLUSION The occurrence of full-term LBW is related to maternal education,the number of prenatal examinations,weight gain during pregnancy,calcium supplementation during pregnancy,and pregnancy-induced hypertension.The constructed predictive model can effectively predict the risk of full-term LBW.展开更多
BACKGROUND Liver cirrhosis patients admitted to intensive care unit(ICU)have a high mortality rate.AIM To establish and validate a nomogram for predicting in-hospital mortality of ICU patients with liver cirrhosis.MET...BACKGROUND Liver cirrhosis patients admitted to intensive care unit(ICU)have a high mortality rate.AIM To establish and validate a nomogram for predicting in-hospital mortality of ICU patients with liver cirrhosis.METHODS We extracted demographic,etiological,vital sign,laboratory test,comorbidity,complication,treatment,and severity score data of liver cirrhosis patients from the Medical Information Mart for Intensive Care IV(MIMIC-IV)and electronic ICU(eICU)collaborative research database(eICU-CRD).Predictor selection and model building were based on the MIMIC-IV dataset.The variables selected through least absolute shrinkage and selection operator analysis were further screened through multivariate regression analysis to obtain final predictors.The final predictors were included in the multivariate logistic regression model,which was used to construct a nomogram.Finally,we conducted external validation using the eICU-CRD.The area under the receiver operating characteristic curve(AUC),decision curve,and calibration curve were used to assess the efficacy of the models.RESULTS Risk factors,including the mean respiratory rate,mean systolic blood pressure,mean heart rate,white blood cells,international normalized ratio,total bilirubin,age,invasive ventilation,vasopressor use,maximum stage of acute kidney injury,and sequential organ failure assessment score,were included in the multivariate logistic regression.The model achieved AUCs of 0.864 and 0.808 in the MIMIC-IV and eICU-CRD databases,respectively.The calibration curve also confirmed the predictive ability of the model,while the decision curve confirmed its clinical value.CONCLUSION The nomogram has high accuracy in predicting in-hospital mortality.Improving the included predictors may help improve the prognosis of patients.展开更多
Artificial intelligence(AI)and digital twin models of various systems have long been used in industry to test products quickly and efficiently.Use of digital twins in clinical medicine caught attention with the develo...Artificial intelligence(AI)and digital twin models of various systems have long been used in industry to test products quickly and efficiently.Use of digital twins in clinical medicine caught attention with the development of Archimedes,an AI model of diabetes,in 2003.More recently,AI models have been applied to the fields of cardiology,endocrinology,and undergraduate medical education.The use of digital twins and AI thus far has focused mainly on chronic disease management,their application in the field of critical care medicine remains much less explored.In neurocritical care,current AI technology focuses on interpreting electroencephalography,monitoring intracranial pressure,and prognosticating outcomes.AI models have been developed to interpret electroencephalograms by helping to annotate the tracings,detecting seizures,and identifying brain activation in unresponsive patients.In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.展开更多
BACKGROUND The prognosis of critically ill patients is closely linked to their gastrointestinal(GI)function.The acute GI injury(AGI)grading system,established in 2012,is extensively utilized to evaluate GI dysfunction...BACKGROUND The prognosis of critically ill patients is closely linked to their gastrointestinal(GI)function.The acute GI injury(AGI)grading system,established in 2012,is extensively utilized to evaluate GI dysfunction and forecast outcomes in clinical settings.In 2021,the GI dysfunction score(GIDS)was developed,building on the AGI grading system,to enhance the accuracy of GI dysfunction severity assessment,improve prognostic predictions,reduce subjectivity,and increase reproducibility.AIM To compare the predictive capabilities of GIDS and the AGI grading system for 28-day mortality in critically ill patients.METHODS A retrospective study was conducted at the general intensive care unit(ICU)of a regional university hospital.All data were collected during the first week of ICU admission.The primary outcome was 28-day mortality.Multivariable logistic regression analyzed whether GIDS and AGI grade were independent risk factors for 28-day mortality.The predictive abilities of GIDS and AGI grade were compared using the receiver operating characteristic curve,with DeLong’s test assessing differences between the curves’areas.RESULTS The incidence of AGI in the first week of ICU admission was 92.13%.There were 85 deaths(47.75%)within 28 days of ICU admission.There was no initial 24-hour difference in GIDS between the non-survival and survival groups.Both GIDS(OR 2.01,95%CI:1.25-3.24;P=0.004)and AGI grade(OR 1.94,95%CI:1.12-3.38;P=0.019)were independent predictors of 28-day mortality.No significant difference was found between the predictive accuracy of GIDS and AGI grade for 28-day mortality during the first week of ICU admission(Z=-0.26,P=0.794).CONCLUSION GIDS within the first 24 hours was an unreliable predictor of 28-day mortality.The predictive accuracy for 28-day mortality from both systems during the first week was comparable.展开更多
In this editorial,we discuss an article titled,“Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning,”published in a recent issue of the World J...In this editorial,we discuss an article titled,“Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning,”published in a recent issue of the World Journal of Clinical Cases.Intensive care unit-acquired weakness(ICU-AW)is a debilitating condition that affects critically ill patients,with significant implications for patient outcomes and their quality of life.This study explored the use of artificial intelligence and machine learning techniques to predict ICU-AW occurrence and identify key risk factors.Data from a cohort of 1063 adult intensive care unit(ICU)patients were analyzed,with a particular emphasis on variables such as duration of ICU stay,duration of mechanical ventilation,doses of sedatives and vasopressors,and underlying comorbidities.A multilayer perceptron neural network model was developed,which exhibited a remarkable impressive prediction accuracy of 86.2%on the training set and 85.5%on the test set.The study highlights the importance of early prediction and intervention in mitigating ICU-AW risk and improving patient outcomes.展开更多
In the research published in the World Journal of Clinical Cases,Wang and Long conducted a quantitative analysis to delineate the risk factors for intensive care unit-acquired weakness(ICU-AW)utilizing advanced machin...In the research published in the World Journal of Clinical Cases,Wang and Long conducted a quantitative analysis to delineate the risk factors for intensive care unit-acquired weakness(ICU-AW)utilizing advanced machine learning methodologies.The study employed a multilayer perceptron neural network to accurately predict the incidence of ICU-AW,focusing on critical variables such as ICU stay duration and mechanical ventilation.This research marks a significant advancement in applying machine learning to clinical diagnostics,offering a new paradigm for predictive medicine in critical care.It underscores the importance of integrating artificial intelligence technologies in clinical practice to enhance patient management strategies and calls for interdisciplinary collaboration to drive innovation in healthcare.展开更多
This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patie...This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patient recovery.Highlightingthe paradox of modern medical advances,it emphasizes the urgent needfor early identification and intervention to mitigate ICU-AW's impact.Innovatively,the study by Wang et al is showcased for employing a multilayer perceptronneural network model,achieving high accuracy in predicting ICU-AWrisk.This advancement underscores the potential of neural network models inenhancing patient care but also calls for continued research to address limitationsand improve model applicability.The editorial advocates for the developmentand validation of sophisticated predictive tools,aiming for personalized carestrategies to reduce ICU-AW incidence and severity,ultimately improving patientoutcomes in critical care settings.展开更多
Objective:This research explored the clinical effects of the whole course of psychological care in patients having Parkinson's disease with anxiety.Methods:Eighty-four patients with Parkinson's anxiety disorde...Objective:This research explored the clinical effects of the whole course of psychological care in patients having Parkinson's disease with anxiety.Methods:Eighty-four patients with Parkinson's anxiety disorder at Hubei No.3 People's Hospital from January 2020 to December 2020 were randomly selected for this research.They were divided into a control group and a study group using random number table.The control group was provided with routine care while the study group was provided with psychological care in which observation and comparison of clinical effects were caried out.Results:There were no significant differences in their scores based on the self-rating anxiety scale(SAS)and self-rating depression scale(SDS)between the two groups of patients before nursing,P>0.05.However,the scores of both,SAS and SDS of the study group after nursing intervention were lower than those of the control group.Sleep quality scores,patient satisfaction,and quality of life scores were all higher than the control group,P<0.05.Conclusion:The whole course of psychological care can effectively improve anxiety and depression of patients with Parkinson's anxiety and improve their sleep quality.展开更多
Objective To describe the preoperative factors of prolonged intensive care unit length of stay after coronary artery bypass grafting. Methods From 1997 to 2009, 1318 patients underwent isolated CABG in our hospital. R...Objective To describe the preoperative factors of prolonged intensive care unit length of stay after coronary artery bypass grafting. Methods From 1997 to 2009, 1318 patients underwent isolated CABG in our hospital. Retrospective analysis was performed on these cases. Univariate and multivariate analyses展开更多
Objective The rate of post-operative complications has been increased with the changes in patients’age,prolonged duration,more severe and diffused lesions,and more patients with complications in recent years. We try ...Objective The rate of post-operative complications has been increased with the changes in patients’age,prolonged duration,more severe and diffused lesions,and more patients with complications in recent years. We try to identify the risk factors associated with prolonged stay in the intensive care unit (ICU) after coronary artery bypass graft surgery (CABG) . Methods 1623 patients who received CABG surgery in Beijing Anzhen Hospital展开更多
BACKGROUND Enteral nutrition(EN)is essential for critically ill patients.However,some patients will have enteral feeding intolerance(EFI)in the process of EN.AIM To develop a clinical prediction model to predict the r...BACKGROUND Enteral nutrition(EN)is essential for critically ill patients.However,some patients will have enteral feeding intolerance(EFI)in the process of EN.AIM To develop a clinical prediction model to predict the risk of EFI in patients receiving EN in the intensive care unit.METHODS A prospective cohort study was performed.The enrolled patients’basic information,medical status,nutritional support,and gastrointestinal(GI)symptoms were recorded.The baseline data and influencing factors were compared.Logistic regression analysis was used to establish the model,and the bootstrap resampling method was used to conduct internal validation.RESULTS The sample cohort included 203 patients,and 37.93%of the patients were diagnosed with EFI.After the final regression analysis,age,GI disease,early feeding,mechanical ventilation before EN started,and abnormal serum sodium were identified.In the internal validation,500 bootstrap resample samples were performed,and the area under the curve was 0.70(95%CI:0.63-0.77).CONCLUSION This clinical prediction model can be applied to predict the risk of EFI.展开更多
BACKGROUND The novel coronavirus disease 2019(COVID-19)pandemic is a global threat caused by the severe acute respiratory syndrome coronavirus-2.AIM To develop and validate a risk stratification tool for the early pre...BACKGROUND The novel coronavirus disease 2019(COVID-19)pandemic is a global threat caused by the severe acute respiratory syndrome coronavirus-2.AIM To develop and validate a risk stratification tool for the early prediction of intensive care unit(ICU)admission among COVID-19 patients at hospital admission.METHODS The training cohort included COVID-19 patients admitted to the Wuhan Third Hospital.We selected 13 of 65 baseline laboratory results to assess ICU admission risk,which were used to develop a risk prediction model with the random forest(RF)algorithm.A nomogram for the logistic regression model was built based on six selected variables.The predicted models were carefully calibrated,and the predictive performance was evaluated and compared with two previously published models.RESULTS There were 681 and 296 patients in the training and validation cohorts,respectively.The patients in the training cohort were older than those in the validation cohort(median age:63.0 vs 49.0 years,P<0.001),and the percentages of male gender were similar(49.6%vs 49.3%,P=0.958).The top predictors selected in the RF model were neutrophil-to-lymphocyte ratio,age,lactate dehydrogenase,C-reactive protein,creatinine,D-dimer,albumin,procalcitonin,glucose,platelet,total bilirubin,lactate and creatine kinase.The accuracy,sensitivity and specificity for the RF model were 91%,88%and 93%,respectively,higher than those for the logistic regression model.The area under the receiver operating characteristic curve of our model was much better than those of two other published methods(0.90 vs 0.82 and 0.75).Model A underestimated risk of ICU admission in patients with a predicted risk less than 30%,whereas the RF risk score demonstrated excellent ability to categorize patients into different risk strata.Our predictive model provided a larger standardized net benefit across the major high-risk range compared with model A.CONCLUSION Our model can identify ICU admission risk in COVID-19 patients at admission,who can then receive prompt care,thus improving medical resource allocation.展开更多
文摘BACKGROUND Cesarean hemorrhage is one of the serious complications,and short-term massive blood transfusion can easily cause postoperative infection and physical stress response.However,predictive nursing intervention has important clinical significance for it.AIM To explore the effect of predictive nursing intervention on the stress response and complications of women undergoing short-term mass blood transfusion during cesarean section(CS).METHODS A clinical medical record of 100 pregnant women undergoing rapid mass blood transfusion during sections from June 2019 to June 2021.According to the different nursing methods,patients divided into control group(n=50)and observation group(n=50).Among them,the control group implemented routine nursing,and the observation group implemented predictive nursing intervention based on the control group.Moreover,compared the differences in stress res-ponse,complications,and pain scores before and after the nursing of pregnant women undergoing rapid mass blood transfusion during CS.RESULTS The anxiety and depression scores of pregnant women in the two groups were significantly improved after nursing,and the psychological stress response of the observation group was significantly lower than that of the control group(P<0.05).The heart rate and mean arterial pressure(MAP)of the observation group during delivery were lower than those of the control group,and the MAP at the end of delivery was lower than that of the control group(P<0.05).Moreover,different pain scores improved significantly in both groups,with the observation group considerably less than the control group(P<0.05).After nursing,complications such as skin rash,urinary retention,chills,diarrhea,and anaphylactic shock in the observation group were 18%,which significantly higher than in the control group(4%)(P<0.05).CONCLUSION Predictive nursing intervention can effectively relieve the pain,reduce the incidence of complications,improve mood and stress response,and serve as a reference value for the nursing of women undergoing rapid mass transfusion during CS.
文摘BACKGROUND Being too light at birth can increase the risk of various diseases during infancy.AIM To explore the effect of perinatal factors on term low-birth-weight(LBW)infants and build a predictive model.This model aims to guide the clinical management of pregnant women’s healthcare during pregnancy and support the healthy growth of newborns.METHODS A retrospective analysis was conducted on data from 1794 single full-term pregnant women who gave birth.Newborns were grouped based on birth weight:Those with birth weight<2.5 kg were classified as the low-weight group,and those with birth weight between 2.5 kg and 4 kg were included in the normal group.Multiple logistic regression analysis was used to identify the factors influencing the occurrence of full-term LBW.A risk prediction model was established based on the analysis results.The effectiveness of the model was analyzed using the Hosmer–Leme show test and receiver operating characteristic(ROC)curve to verify the accuracy of the predictions.RESULTS Among the 1794 pregnant women,there were 62 cases of neonatal weight<2.5 kg,resulting in an LBW incidence rate of 3.46%.The factors influencing full-term LBW included low maternal education level[odds ratio(OR)=1.416],fewer prenatal examinations(OR=2.907),insufficient weight gain during pregnancy(OR=3.695),irregular calcium supplementation during pregnancy(OR=1.756),and pregnancy hypertension syndrome(OR=2.192).The prediction model equation was obtained as follows:Logit(P)=0.348×maternal education level+1.067×number of prenatal examinations+1.307×insufficient weight gain during pregnancy+0.563×irregular calcium supplementation during pregnancy+0.785×pregnancy hypertension syndrome−29.164.The area under the ROC curve for this model was 0.853,with a sensitivity of 0.852 and a specificity of 0.821.The Hosmer–Leme show test yieldedχ^(2)=2.185,P=0.449,indicating a good fit.The overall accuracy of the clinical validation model was 81.67%.CONCLUSION The occurrence of full-term LBW is related to maternal education,the number of prenatal examinations,weight gain during pregnancy,calcium supplementation during pregnancy,and pregnancy-induced hypertension.The constructed predictive model can effectively predict the risk of full-term LBW.
基金Supported by Natural Science Foundation of Sichuan Province,No.2022NSFSC1378.
文摘BACKGROUND Liver cirrhosis patients admitted to intensive care unit(ICU)have a high mortality rate.AIM To establish and validate a nomogram for predicting in-hospital mortality of ICU patients with liver cirrhosis.METHODS We extracted demographic,etiological,vital sign,laboratory test,comorbidity,complication,treatment,and severity score data of liver cirrhosis patients from the Medical Information Mart for Intensive Care IV(MIMIC-IV)and electronic ICU(eICU)collaborative research database(eICU-CRD).Predictor selection and model building were based on the MIMIC-IV dataset.The variables selected through least absolute shrinkage and selection operator analysis were further screened through multivariate regression analysis to obtain final predictors.The final predictors were included in the multivariate logistic regression model,which was used to construct a nomogram.Finally,we conducted external validation using the eICU-CRD.The area under the receiver operating characteristic curve(AUC),decision curve,and calibration curve were used to assess the efficacy of the models.RESULTS Risk factors,including the mean respiratory rate,mean systolic blood pressure,mean heart rate,white blood cells,international normalized ratio,total bilirubin,age,invasive ventilation,vasopressor use,maximum stage of acute kidney injury,and sequential organ failure assessment score,were included in the multivariate logistic regression.The model achieved AUCs of 0.864 and 0.808 in the MIMIC-IV and eICU-CRD databases,respectively.The calibration curve also confirmed the predictive ability of the model,while the decision curve confirmed its clinical value.CONCLUSION The nomogram has high accuracy in predicting in-hospital mortality.Improving the included predictors may help improve the prognosis of patients.
基金Supported by the National Center for Advancing Translational Sciences,No.UL1 TR002377.
文摘Artificial intelligence(AI)and digital twin models of various systems have long been used in industry to test products quickly and efficiently.Use of digital twins in clinical medicine caught attention with the development of Archimedes,an AI model of diabetes,in 2003.More recently,AI models have been applied to the fields of cardiology,endocrinology,and undergraduate medical education.The use of digital twins and AI thus far has focused mainly on chronic disease management,their application in the field of critical care medicine remains much less explored.In neurocritical care,current AI technology focuses on interpreting electroencephalography,monitoring intracranial pressure,and prognosticating outcomes.AI models have been developed to interpret electroencephalograms by helping to annotate the tracings,detecting seizures,and identifying brain activation in unresponsive patients.In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.
基金approved by the Ethics Committee of the First Affiliated Hospital of Zhejiang Chinese Medical University(No.2024-KLS-369-02).
文摘BACKGROUND The prognosis of critically ill patients is closely linked to their gastrointestinal(GI)function.The acute GI injury(AGI)grading system,established in 2012,is extensively utilized to evaluate GI dysfunction and forecast outcomes in clinical settings.In 2021,the GI dysfunction score(GIDS)was developed,building on the AGI grading system,to enhance the accuracy of GI dysfunction severity assessment,improve prognostic predictions,reduce subjectivity,and increase reproducibility.AIM To compare the predictive capabilities of GIDS and the AGI grading system for 28-day mortality in critically ill patients.METHODS A retrospective study was conducted at the general intensive care unit(ICU)of a regional university hospital.All data were collected during the first week of ICU admission.The primary outcome was 28-day mortality.Multivariable logistic regression analyzed whether GIDS and AGI grade were independent risk factors for 28-day mortality.The predictive abilities of GIDS and AGI grade were compared using the receiver operating characteristic curve,with DeLong’s test assessing differences between the curves’areas.RESULTS The incidence of AGI in the first week of ICU admission was 92.13%.There were 85 deaths(47.75%)within 28 days of ICU admission.There was no initial 24-hour difference in GIDS between the non-survival and survival groups.Both GIDS(OR 2.01,95%CI:1.25-3.24;P=0.004)and AGI grade(OR 1.94,95%CI:1.12-3.38;P=0.019)were independent predictors of 28-day mortality.No significant difference was found between the predictive accuracy of GIDS and AGI grade for 28-day mortality during the first week of ICU admission(Z=-0.26,P=0.794).CONCLUSION GIDS within the first 24 hours was an unreliable predictor of 28-day mortality.The predictive accuracy for 28-day mortality from both systems during the first week was comparable.
基金Supported by China Medical University,No.CMU111-MF-102.
文摘In this editorial,we discuss an article titled,“Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning,”published in a recent issue of the World Journal of Clinical Cases.Intensive care unit-acquired weakness(ICU-AW)is a debilitating condition that affects critically ill patients,with significant implications for patient outcomes and their quality of life.This study explored the use of artificial intelligence and machine learning techniques to predict ICU-AW occurrence and identify key risk factors.Data from a cohort of 1063 adult intensive care unit(ICU)patients were analyzed,with a particular emphasis on variables such as duration of ICU stay,duration of mechanical ventilation,doses of sedatives and vasopressors,and underlying comorbidities.A multilayer perceptron neural network model was developed,which exhibited a remarkable impressive prediction accuracy of 86.2%on the training set and 85.5%on the test set.The study highlights the importance of early prediction and intervention in mitigating ICU-AW risk and improving patient outcomes.
文摘In the research published in the World Journal of Clinical Cases,Wang and Long conducted a quantitative analysis to delineate the risk factors for intensive care unit-acquired weakness(ICU-AW)utilizing advanced machine learning methodologies.The study employed a multilayer perceptron neural network to accurately predict the incidence of ICU-AW,focusing on critical variables such as ICU stay duration and mechanical ventilation.This research marks a significant advancement in applying machine learning to clinical diagnostics,offering a new paradigm for predictive medicine in critical care.It underscores the importance of integrating artificial intelligence technologies in clinical practice to enhance patient management strategies and calls for interdisciplinary collaboration to drive innovation in healthcare.
文摘This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patient recovery.Highlightingthe paradox of modern medical advances,it emphasizes the urgent needfor early identification and intervention to mitigate ICU-AW's impact.Innovatively,the study by Wang et al is showcased for employing a multilayer perceptronneural network model,achieving high accuracy in predicting ICU-AWrisk.This advancement underscores the potential of neural network models inenhancing patient care but also calls for continued research to address limitationsand improve model applicability.The editorial advocates for the developmentand validation of sophisticated predictive tools,aiming for personalized carestrategies to reduce ICU-AW incidence and severity,ultimately improving patientoutcomes in critical care settings.
文摘Objective:This research explored the clinical effects of the whole course of psychological care in patients having Parkinson's disease with anxiety.Methods:Eighty-four patients with Parkinson's anxiety disorder at Hubei No.3 People's Hospital from January 2020 to December 2020 were randomly selected for this research.They were divided into a control group and a study group using random number table.The control group was provided with routine care while the study group was provided with psychological care in which observation and comparison of clinical effects were caried out.Results:There were no significant differences in their scores based on the self-rating anxiety scale(SAS)and self-rating depression scale(SDS)between the two groups of patients before nursing,P>0.05.However,the scores of both,SAS and SDS of the study group after nursing intervention were lower than those of the control group.Sleep quality scores,patient satisfaction,and quality of life scores were all higher than the control group,P<0.05.Conclusion:The whole course of psychological care can effectively improve anxiety and depression of patients with Parkinson's anxiety and improve their sleep quality.
文摘Objective To describe the preoperative factors of prolonged intensive care unit length of stay after coronary artery bypass grafting. Methods From 1997 to 2009, 1318 patients underwent isolated CABG in our hospital. Retrospective analysis was performed on these cases. Univariate and multivariate analyses
文摘Objective The rate of post-operative complications has been increased with the changes in patients’age,prolonged duration,more severe and diffused lesions,and more patients with complications in recent years. We try to identify the risk factors associated with prolonged stay in the intensive care unit (ICU) after coronary artery bypass graft surgery (CABG) . Methods 1623 patients who received CABG surgery in Beijing Anzhen Hospital
文摘BACKGROUND Enteral nutrition(EN)is essential for critically ill patients.However,some patients will have enteral feeding intolerance(EFI)in the process of EN.AIM To develop a clinical prediction model to predict the risk of EFI in patients receiving EN in the intensive care unit.METHODS A prospective cohort study was performed.The enrolled patients’basic information,medical status,nutritional support,and gastrointestinal(GI)symptoms were recorded.The baseline data and influencing factors were compared.Logistic regression analysis was used to establish the model,and the bootstrap resampling method was used to conduct internal validation.RESULTS The sample cohort included 203 patients,and 37.93%of the patients were diagnosed with EFI.After the final regression analysis,age,GI disease,early feeding,mechanical ventilation before EN started,and abnormal serum sodium were identified.In the internal validation,500 bootstrap resample samples were performed,and the area under the curve was 0.70(95%CI:0.63-0.77).CONCLUSION This clinical prediction model can be applied to predict the risk of EFI.
基金Shenzhen Municipal Government’s"Peacock Plan",No.KQTD2016053112051497.
文摘BACKGROUND The novel coronavirus disease 2019(COVID-19)pandemic is a global threat caused by the severe acute respiratory syndrome coronavirus-2.AIM To develop and validate a risk stratification tool for the early prediction of intensive care unit(ICU)admission among COVID-19 patients at hospital admission.METHODS The training cohort included COVID-19 patients admitted to the Wuhan Third Hospital.We selected 13 of 65 baseline laboratory results to assess ICU admission risk,which were used to develop a risk prediction model with the random forest(RF)algorithm.A nomogram for the logistic regression model was built based on six selected variables.The predicted models were carefully calibrated,and the predictive performance was evaluated and compared with two previously published models.RESULTS There were 681 and 296 patients in the training and validation cohorts,respectively.The patients in the training cohort were older than those in the validation cohort(median age:63.0 vs 49.0 years,P<0.001),and the percentages of male gender were similar(49.6%vs 49.3%,P=0.958).The top predictors selected in the RF model were neutrophil-to-lymphocyte ratio,age,lactate dehydrogenase,C-reactive protein,creatinine,D-dimer,albumin,procalcitonin,glucose,platelet,total bilirubin,lactate and creatine kinase.The accuracy,sensitivity and specificity for the RF model were 91%,88%and 93%,respectively,higher than those for the logistic regression model.The area under the receiver operating characteristic curve of our model was much better than those of two other published methods(0.90 vs 0.82 and 0.75).Model A underestimated risk of ICU admission in patients with a predicted risk less than 30%,whereas the RF risk score demonstrated excellent ability to categorize patients into different risk strata.Our predictive model provided a larger standardized net benefit across the major high-risk range compared with model A.CONCLUSION Our model can identify ICU admission risk in COVID-19 patients at admission,who can then receive prompt care,thus improving medical resource allocation.
