BACKGROUND Surgical resection remains the primary treatment for hepatic malignancies,and intraoperative bleeding is associated with a significantly increased risk of death.Therefore,accurate prediction of intraoperati...BACKGROUND Surgical resection remains the primary treatment for hepatic malignancies,and intraoperative bleeding is associated with a significantly increased risk of death.Therefore,accurate prediction of intraoperative bleeding risk in patients with hepatic malignancies is essential to preventing bleeding in advance and providing safer and more effective treatment.AIM To develop a predictive model for intraoperative bleeding in primary hepatic malignancy patients for improving surgical planning and outcomes.METHODS The retrospective analysis enrolled patients diagnosed with primary hepatic malignancies who underwent surgery at the Hepatobiliary Surgery Department of the Fourth Hospital of Hebei Medical University between 2010 and 2020.Logistic regression analysis was performed to identify potential risk factors for intraoperative bleeding.A prediction model was developed using Python programming language,and its accuracy was evaluated using receiver operating characteristic(ROC)curve analysis.RESULTS Among 406 primary liver cancer patients,16.0%(65/406)suffered massive intraoperative bleeding.Logistic regression analysis identified four variables as associated with intraoperative bleeding in these patients:ascites[odds ratio(OR):22.839;P<0.05],history of alcohol consumption(OR:2.950;P<0.015),TNM staging(OR:2.441;P<0.001),and albumin-bilirubin score(OR:2.361;P<0.001).These variables were used to construct the prediction model.The 406 patients were randomly assigned to a training set(70%)and a prediction set(30%).The area under the ROC curve values for the model’s ability to predict intraoperative bleeding were 0.844 in the training set and 0.80 in the prediction set.CONCLUSION The developed and validated model predicts significant intraoperative blood loss in primary hepatic malignancies using four preoperative clinical factors by considering four preoperative clinical factors:ascites,history of alcohol consumption,TNM staging,and albumin-bilirubin score.Consequently,this model holds promise for enhancing individualised surgical planning.展开更多
Objective:This study aimed to establish a nomogram model to predict the mortality risk of patients with dangerous upper gastrointestinal bleeding(DUGIB),and identify high-risk patients who require emergent therapy.Met...Objective:This study aimed to establish a nomogram model to predict the mortality risk of patients with dangerous upper gastrointestinal bleeding(DUGIB),and identify high-risk patients who require emergent therapy.Methods:From January 2020 to April 2022,the clinical data of 256 DUGIB patients who received treatments in the intensive care unit(ICU)were retrospectively collected from Renmin Hospital of Wuhan University(n=179)and the Eastern Campus of Renmin Hospital of Wuhan University(n=77).The 179 patients were treated as the training cohort,and 77 patients as the validation cohort.Logistic regression analysis was used to calculate the independent risk factors,and R packages were used to construct the nomogram model.The prediction accuracy and identification ability were evaluated by the receiver operating characteristic(ROC)curve,C index and calibration curve.The nomogram model was also simultaneously externally validated.Decision curve analysis(DCA)was then used to demonstrate the clinical value of the model.Results:Logistic regression analysis showed that hematemesis,urea nitrogen level,emergency endoscopy,AIMS65,Glasgow Blatchford score and Rockall score were all independent risk factors for DUGIB.The ROC curve analysis indicated the area under curve(AUC)of the training cohort was 0.980(95%CI:0.962-0.997),while the AUC of the validation cohort was 0.790(95%CI:0.685-0.895).The calibration curves were tested for Hosmer-Lemeshow goodness of fit for both training and validation cohorts(P=0.778,P=0.516).Conclusion:The developed nomogram is an effective tool for risk stratification,early identification and intervention for DUGIB patients.展开更多
Obscure gastrointestinal bleeding(OGIB)has traditionally been defined as gastrointestinal bleeding whose source remains unidentified after bidirectional endoscopy.OGIB can present as overt bleeding or occult bleeding,...Obscure gastrointestinal bleeding(OGIB)has traditionally been defined as gastrointestinal bleeding whose source remains unidentified after bidirectional endoscopy.OGIB can present as overt bleeding or occult bleeding,and small bowel lesions are the most common causes.The small bowel can be evaluated using capsule endoscopy,device-assisted enteroscopy,computed tomography enterography,or magnetic resonance enterography.Once the cause of smallbowel bleeding is identified and targeted therapeutic intervention is completed,the patient can be managed with routine visits.However,diagnostic tests may produce negative results,and some patients with small bowel bleeding,regardless of diagnostic findings,may experience rebleeding.Predicting those at risk of rebleeding can help clinicians form individualized surveillance plans.Several studies have identified different factors associated with rebleeding,and a limited number of studies have attempted to create prediction models for recurrence.This article describes prediction models developed so far for identifying patients with OGIB who are at greater risk of rebleeding.These models may aid clinicians in forming tailored patient management and surveillance.展开更多
BACKGROUND Upper gastrointestinal bleeding(UGIB)is a common medical emergency and early assessment of its outcomes is vital for treatment decisions.AIM To develop a new scoring system to predict its prognosis.METHODS ...BACKGROUND Upper gastrointestinal bleeding(UGIB)is a common medical emergency and early assessment of its outcomes is vital for treatment decisions.AIM To develop a new scoring system to predict its prognosis.METHODS In this retrospective study,692 patients with UGIB were enrolled from two cen-ters and divided into a training(n=591)and a validation cohort(n=101).The clinical data were collected to develop new prognostic prediction models.The en-dpoint was compound outcome defined as(1)demand for emergency surgery or vascular intervention,(2)being transferred to the intensive care unit,or(3)death during hos-pitalization.The models’predictive ability was compared with previously esta-blished scores by receiver operating characteristic(ROC)curves.RESULTS Totally 22.2%(131/591)patients in the training cohort and 22.8%(23/101)in the validation cohort presented poor outcomes.Based on the stepwise-forward Lo-gistic regression analysis,eight predictors were integrated to determine a new post-endoscopic prognostic scoring system(MH-STRALP);a nomogram was de-termined to present the model.Compared with the previous scores(GBS,Rock-all,ABC,AIMS65,and PNED score),MH-STRALP showed the best prognostic prediction ability with area under the ROC curves(AUROCs)of 0.899 and 0.826 in the training and validation cohorts,respectively.According to the calibration cur-ve,decision curve analysis,and internal cross-validation,the nomogram showed good calibration ability and net clinical benefit in both cohorts.After removing the endoscopic indicators,the pre-endoscopic model(pre-MH-STRALP score)was conducted.Similarly,the pre-MHSTRALP score showed better predictive value(AUROCs of 0.868 and 0.767 in the training and validation cohorts,respectively)than the other pre-endoscopic scores.CONCLUSION The MH-STRALP score and pre-MH-STRALP score are simple,convenient,and accurate tools for prognosis prediction of UGIB,and may be applied for early decision on its management strategies.展开更多
BACKGROUND Bleeding is one of the major complications after endoscopic submucosal dissection(ESD)in early gastric cancer(EGC)patients.There are limited studies on estimating the bleeding risk after ESD using an artifi...BACKGROUND Bleeding is one of the major complications after endoscopic submucosal dissection(ESD)in early gastric cancer(EGC)patients.There are limited studies on estimating the bleeding risk after ESD using an artificial intelligence system.AIM To derivate and verify the performance of the deep learning model and the clinical model for predicting bleeding risk after ESD in EGC patients.METHODS Patients with EGC who underwent ESD between January 2010 and June 2020 at the Samsung Medical Center were enrolled,and post-ESD bleeding(PEB)was investigated retrospectively.We split the entire cohort into a development set(80%)and a validation set(20%).The deep learning and clinical model were built on the development set and tested in the validation set.The performance of the deep learning model and the clinical model were compared using the area under the curve and the stratification of bleeding risk after ESD.RESULTS A total of 5629 patients were included,and PEB occurred in 325 patients.The area under the curve for predicting PEB was 0.71(95%confidence interval:0.63-0.78)in the deep learning model and 0.70(95%confidence interval:0.62-0.77)in the clinical model,without significant difference(P=0.730).The patients expected to the low-(<5%),intermediate-(≥5%,<9%),and high-risk(≥9%)categories were observed with actual bleeding rate of 2.2%,3.9%,and 11.6%,respectively,in the deep learning model;4.0%,8.8%,and 18.2%,respectively,in the clinical model.CONCLUSION A deep learning model can predict and stratify the bleeding risk after ESD in patients with EGC.展开更多
Acute lower gastrointestinal bleeding(LGIB) is a common indication for hospital admission. Patients with LGIB often experience persistent or recurrent bleeding and require blood transfusions and interventions, such as...Acute lower gastrointestinal bleeding(LGIB) is a common indication for hospital admission. Patients with LGIB often experience persistent or recurrent bleeding and require blood transfusions and interventions, such as colonoscopic,radiological, and surgical treatments. Appropriate decision-making is needed to initially manage acute LGIB, including emergency hospitalization, timing of colonoscopy, and medication use. In this literature review, we summarize the evidence for initial management of acute LGIB. Assessing various clinical factors,including comorbidities, medication use, presenting symptoms, vital signs, and laboratory data is useful for risk stratification of severe LGIB, and for discriminating upper gastrointestinal bleeding. Early timing of colonoscopy had the possibility of improving identification of the bleeding source, and the rate of endoscopic intervention, compared with elective colonoscopy. Contrast-enhanced computed tomography before colonoscopy may help identify stigmata of recent hemorrhage on colonoscopy, particularly in patients who can be examined immediately after the last hematochezia. How to deal with nonsteroidal antiinflammatory drugs(NSAIDs) and antithrombotic agents after hemostasis should be carefully considered because of the risk of rebleeding and thromboembolic events. In general, aspirin as primary prophylaxis for cardiovascular events and NSAIDs were suggested to be discontinued after LGIB. Managing acute LGIB based on this information would improve clinical outcomes. Further investigations are needed to distinguish patients with LGIB who require early colonoscopy and hemostatic intervention.展开更多
BACKGROUND: Non-cirrhotic portal hypertension is acommon cause of portal hypertension in developing coun-tries. To understand its etiopathogenesis we developed ananimal model by repeated portal endotoxemia inducedthro...BACKGROUND: Non-cirrhotic portal hypertension is acommon cause of portal hypertension in developing coun-tries. To understand its etiopathogenesis we developed ananimal model by repeated portal endotoxemia inducedthrough the gastrosplenic vein.METHODS: Twenty-nine rabbits (1.5-2.0 kg) were divid-ed into control (group n = 13) and experimental ( groupn = 16) groups. Heat killed E. coli were injected throughan indwelling cannula into the gastrosplenic vein in pre-sensitized animals. The animals were sacriflced at 1, 3 and6 months.RESULTS: The mean portal pressure in group animalswas significantly (P < 0. 05) higher than in group at 1(17.5 ±3.4 vs 10.4±2.2 mmHg), 3 (17.8±1.3 vs7.2 +3.6mmHg), and 6 (19.8±3.1 vs 10.3±4.8 mmHg) months.Similarly, the mean splenic weight in group was signifi-cantly greater than in group (P <0.05). Histopathologi-cally, the spleen showed medullary congestion, hemosid-rin-laden macrophages and mild fibrosis. Histologically,the liver had normal parenchyma with mild portal lympho-cytic infiltrates and kupffer cell hyperplasia. No significantanomalies were detected by liver function tests.CONCLUSIONS: The rabbit model showed significantsplenomegaly with a persistent increase in portal pressureand mild fibrosis without hepatic parenchymal injury, quiteakin to non-cirrhotic portal fibrosis as seen in humans. Re-current intra-abdominal infection may play an importantrole in the pathogenesis of non-cirrhotic portal fibrosis.展开更多
文摘BACKGROUND Surgical resection remains the primary treatment for hepatic malignancies,and intraoperative bleeding is associated with a significantly increased risk of death.Therefore,accurate prediction of intraoperative bleeding risk in patients with hepatic malignancies is essential to preventing bleeding in advance and providing safer and more effective treatment.AIM To develop a predictive model for intraoperative bleeding in primary hepatic malignancy patients for improving surgical planning and outcomes.METHODS The retrospective analysis enrolled patients diagnosed with primary hepatic malignancies who underwent surgery at the Hepatobiliary Surgery Department of the Fourth Hospital of Hebei Medical University between 2010 and 2020.Logistic regression analysis was performed to identify potential risk factors for intraoperative bleeding.A prediction model was developed using Python programming language,and its accuracy was evaluated using receiver operating characteristic(ROC)curve analysis.RESULTS Among 406 primary liver cancer patients,16.0%(65/406)suffered massive intraoperative bleeding.Logistic regression analysis identified four variables as associated with intraoperative bleeding in these patients:ascites[odds ratio(OR):22.839;P<0.05],history of alcohol consumption(OR:2.950;P<0.015),TNM staging(OR:2.441;P<0.001),and albumin-bilirubin score(OR:2.361;P<0.001).These variables were used to construct the prediction model.The 406 patients were randomly assigned to a training set(70%)and a prediction set(30%).The area under the ROC curve values for the model’s ability to predict intraoperative bleeding were 0.844 in the training set and 0.80 in the prediction set.CONCLUSION The developed and validated model predicts significant intraoperative blood loss in primary hepatic malignancies using four preoperative clinical factors by considering four preoperative clinical factors:ascites,history of alcohol consumption,TNM staging,and albumin-bilirubin score.Consequently,this model holds promise for enhancing individualised surgical planning.
基金supported by Wuhan Scientific Research Project(No.EX20B05)National Nature Science Foundation of China(No.82000521).
文摘Objective:This study aimed to establish a nomogram model to predict the mortality risk of patients with dangerous upper gastrointestinal bleeding(DUGIB),and identify high-risk patients who require emergent therapy.Methods:From January 2020 to April 2022,the clinical data of 256 DUGIB patients who received treatments in the intensive care unit(ICU)were retrospectively collected from Renmin Hospital of Wuhan University(n=179)and the Eastern Campus of Renmin Hospital of Wuhan University(n=77).The 179 patients were treated as the training cohort,and 77 patients as the validation cohort.Logistic regression analysis was used to calculate the independent risk factors,and R packages were used to construct the nomogram model.The prediction accuracy and identification ability were evaluated by the receiver operating characteristic(ROC)curve,C index and calibration curve.The nomogram model was also simultaneously externally validated.Decision curve analysis(DCA)was then used to demonstrate the clinical value of the model.Results:Logistic regression analysis showed that hematemesis,urea nitrogen level,emergency endoscopy,AIMS65,Glasgow Blatchford score and Rockall score were all independent risk factors for DUGIB.The ROC curve analysis indicated the area under curve(AUC)of the training cohort was 0.980(95%CI:0.962-0.997),while the AUC of the validation cohort was 0.790(95%CI:0.685-0.895).The calibration curves were tested for Hosmer-Lemeshow goodness of fit for both training and validation cohorts(P=0.778,P=0.516).Conclusion:The developed nomogram is an effective tool for risk stratification,early identification and intervention for DUGIB patients.
文摘Obscure gastrointestinal bleeding(OGIB)has traditionally been defined as gastrointestinal bleeding whose source remains unidentified after bidirectional endoscopy.OGIB can present as overt bleeding or occult bleeding,and small bowel lesions are the most common causes.The small bowel can be evaluated using capsule endoscopy,device-assisted enteroscopy,computed tomography enterography,or magnetic resonance enterography.Once the cause of smallbowel bleeding is identified and targeted therapeutic intervention is completed,the patient can be managed with routine visits.However,diagnostic tests may produce negative results,and some patients with small bowel bleeding,regardless of diagnostic findings,may experience rebleeding.Predicting those at risk of rebleeding can help clinicians form individualized surveillance plans.Several studies have identified different factors associated with rebleeding,and a limited number of studies have attempted to create prediction models for recurrence.This article describes prediction models developed so far for identifying patients with OGIB who are at greater risk of rebleeding.These models may aid clinicians in forming tailored patient management and surveillance.
基金Supported by Key Disciplines Group Construction Project of Shanghai Pudong New Area Health Commission,No.PWZxq2022-06Medical discipline Construction Project of Pudong Health Committee of Shanghai,No.PWYgf2021-02+1 种基金Joint Tackling Project of Pudong Health Committee of Shanghai,No.PW2022D08the Medical Innovation Research Project of the Shanghai Science and Technology Commission,No.22Y11908400.
文摘BACKGROUND Upper gastrointestinal bleeding(UGIB)is a common medical emergency and early assessment of its outcomes is vital for treatment decisions.AIM To develop a new scoring system to predict its prognosis.METHODS In this retrospective study,692 patients with UGIB were enrolled from two cen-ters and divided into a training(n=591)and a validation cohort(n=101).The clinical data were collected to develop new prognostic prediction models.The en-dpoint was compound outcome defined as(1)demand for emergency surgery or vascular intervention,(2)being transferred to the intensive care unit,or(3)death during hos-pitalization.The models’predictive ability was compared with previously esta-blished scores by receiver operating characteristic(ROC)curves.RESULTS Totally 22.2%(131/591)patients in the training cohort and 22.8%(23/101)in the validation cohort presented poor outcomes.Based on the stepwise-forward Lo-gistic regression analysis,eight predictors were integrated to determine a new post-endoscopic prognostic scoring system(MH-STRALP);a nomogram was de-termined to present the model.Compared with the previous scores(GBS,Rock-all,ABC,AIMS65,and PNED score),MH-STRALP showed the best prognostic prediction ability with area under the ROC curves(AUROCs)of 0.899 and 0.826 in the training and validation cohorts,respectively.According to the calibration cur-ve,decision curve analysis,and internal cross-validation,the nomogram showed good calibration ability and net clinical benefit in both cohorts.After removing the endoscopic indicators,the pre-endoscopic model(pre-MH-STRALP score)was conducted.Similarly,the pre-MHSTRALP score showed better predictive value(AUROCs of 0.868 and 0.767 in the training and validation cohorts,respectively)than the other pre-endoscopic scores.CONCLUSION The MH-STRALP score and pre-MH-STRALP score are simple,convenient,and accurate tools for prognosis prediction of UGIB,and may be applied for early decision on its management strategies.
文摘BACKGROUND Bleeding is one of the major complications after endoscopic submucosal dissection(ESD)in early gastric cancer(EGC)patients.There are limited studies on estimating the bleeding risk after ESD using an artificial intelligence system.AIM To derivate and verify the performance of the deep learning model and the clinical model for predicting bleeding risk after ESD in EGC patients.METHODS Patients with EGC who underwent ESD between January 2010 and June 2020 at the Samsung Medical Center were enrolled,and post-ESD bleeding(PEB)was investigated retrospectively.We split the entire cohort into a development set(80%)and a validation set(20%).The deep learning and clinical model were built on the development set and tested in the validation set.The performance of the deep learning model and the clinical model were compared using the area under the curve and the stratification of bleeding risk after ESD.RESULTS A total of 5629 patients were included,and PEB occurred in 325 patients.The area under the curve for predicting PEB was 0.71(95%confidence interval:0.63-0.78)in the deep learning model and 0.70(95%confidence interval:0.62-0.77)in the clinical model,without significant difference(P=0.730).The patients expected to the low-(<5%),intermediate-(≥5%,<9%),and high-risk(≥9%)categories were observed with actual bleeding rate of 2.2%,3.9%,and 11.6%,respectively,in the deep learning model;4.0%,8.8%,and 18.2%,respectively,in the clinical model.CONCLUSION A deep learning model can predict and stratify the bleeding risk after ESD in patients with EGC.
文摘Acute lower gastrointestinal bleeding(LGIB) is a common indication for hospital admission. Patients with LGIB often experience persistent or recurrent bleeding and require blood transfusions and interventions, such as colonoscopic,radiological, and surgical treatments. Appropriate decision-making is needed to initially manage acute LGIB, including emergency hospitalization, timing of colonoscopy, and medication use. In this literature review, we summarize the evidence for initial management of acute LGIB. Assessing various clinical factors,including comorbidities, medication use, presenting symptoms, vital signs, and laboratory data is useful for risk stratification of severe LGIB, and for discriminating upper gastrointestinal bleeding. Early timing of colonoscopy had the possibility of improving identification of the bleeding source, and the rate of endoscopic intervention, compared with elective colonoscopy. Contrast-enhanced computed tomography before colonoscopy may help identify stigmata of recent hemorrhage on colonoscopy, particularly in patients who can be examined immediately after the last hematochezia. How to deal with nonsteroidal antiinflammatory drugs(NSAIDs) and antithrombotic agents after hemostasis should be carefully considered because of the risk of rebleeding and thromboembolic events. In general, aspirin as primary prophylaxis for cardiovascular events and NSAIDs were suggested to be discontinued after LGIB. Managing acute LGIB based on this information would improve clinical outcomes. Further investigations are needed to distinguish patients with LGIB who require early colonoscopy and hemostatic intervention.
基金This study was financially supported by the Department of Science & Tech-nology, New Delhi, India.
文摘BACKGROUND: Non-cirrhotic portal hypertension is acommon cause of portal hypertension in developing coun-tries. To understand its etiopathogenesis we developed ananimal model by repeated portal endotoxemia inducedthrough the gastrosplenic vein.METHODS: Twenty-nine rabbits (1.5-2.0 kg) were divid-ed into control (group n = 13) and experimental ( groupn = 16) groups. Heat killed E. coli were injected throughan indwelling cannula into the gastrosplenic vein in pre-sensitized animals. The animals were sacriflced at 1, 3 and6 months.RESULTS: The mean portal pressure in group animalswas significantly (P < 0. 05) higher than in group at 1(17.5 ±3.4 vs 10.4±2.2 mmHg), 3 (17.8±1.3 vs7.2 +3.6mmHg), and 6 (19.8±3.1 vs 10.3±4.8 mmHg) months.Similarly, the mean splenic weight in group was signifi-cantly greater than in group (P <0.05). Histopathologi-cally, the spleen showed medullary congestion, hemosid-rin-laden macrophages and mild fibrosis. Histologically,the liver had normal parenchyma with mild portal lympho-cytic infiltrates and kupffer cell hyperplasia. No significantanomalies were detected by liver function tests.CONCLUSIONS: The rabbit model showed significantsplenomegaly with a persistent increase in portal pressureand mild fibrosis without hepatic parenchymal injury, quiteakin to non-cirrhotic portal fibrosis as seen in humans. Re-current intra-abdominal infection may play an importantrole in the pathogenesis of non-cirrhotic portal fibrosis.
