Risk prediction models are an important part of assessing operative mortality and postoperative complication rates in current cardiac surgery practice.Furthermore,they guide clinical decision-making and perioperative ...Risk prediction models are an important part of assessing operative mortality and postoperative complication rates in current cardiac surgery practice.Furthermore,they guide clinical decision-making and perioperative patient manage-ment.In recent years,a variety of clinical prediction models have been developed in China and other countries to assess the risk of mortality and complications after cardiac surgery.Currently,the most widely used and mature models are the new version of the European Cardiac Surgery Evaluation System(EuroSCORE II),the American Society of Tho-racic Surgeons Cardiac Surgery Risk Model(STS score),and the Chinese Coronary Artery Bypass Graft Surgery Risk Evaluation System(SinoSCORE).This article reviews the application of these three risk prediction models,to identify the optimal model for guiding clinical practice.展开更多
Artificial intelligence(AI)technology is expanding at a rapid pace,offering means of improving the precision of judgments made by medical professionals.AI-driven machine learning(ML)facilitates rapid and effective dat...Artificial intelligence(AI)technology is expanding at a rapid pace,offering means of improving the precision of judgments made by medical professionals.AI-driven machine learning(ML)facilitates rapid and effective data processing for diagnosis and treatment of different diseases including stroke.This technology has vastly improved the patient classification based on their predicted stroke outcome.It helps in quicker decision-making,improves diagnosis precision,and enhances patient care.ML techniques have occasionally been applied extensively to address complex issues related to stroke such as the prediction of stroke prevalence at an early stage.The ability of deep learning(DL)algorithms,a crucial element of AI,is becoming popular in stroke imaging analysis because it automatically extracts features without requiring domain expertise.In the preclinical setup for stroke studies,ML/DL models are commendably used for the detection of vascular thrombi,stroke core,and penumbra size,to identify artery occlusion,compute perfusion maps,detect intracranial hemorrhage(ICH),prediction of infarct,assessing the severity of hemorrhagic transformation,and forecasting patient outcomes.The robust automatic data processing,excellent generalization,self-learning,and precise decision-making abilities of such models have contributed immensely to the advancement of stroke therapy.In the preclinical setup,the time-investing behavioral studies of the animals are also effectively analyzed by AI based algorithms.Understanding the algorithms and models based on AI is yet to be simplified for its appli-cation in stroke therapy in present clinical settings,thus,in the present review attempts have been made to present it in a simplified manner to facilitate translation.展开更多
Background The uptake of colonoscopy is low in individuals at risk of colorectal cancer(CRC).We constructed a riskprediction score(RPS)in a large community-based sample at high risk of CRC to enable more accurate risk...Background The uptake of colonoscopy is low in individuals at risk of colorectal cancer(CRC).We constructed a riskprediction score(RPS)in a large community-based sample at high risk of CRC to enable more accurate risk stratification and to motivate and increase the uptake rate of colonoscopy.Methods A total of 12,628 participants classified as high-risk according to positivity of immunochemical fecal occult blood tests or High-Risk Factor Questionnaire underwent colonoscopy.Logistic regression was used to derive a RPS and analysed the associations of the RPS with colorectal lesions,giving odds ratios(ORs)and 95%confidence intervals(CIs).Results Of the participants,men(OR=1.73,95%CI=1.58–1.90),older age(≥65 years;1.41,1.31–1.53),higher body mass index(≥28 kg/m^(2);1.22,1.07–1.39),ever smoking(1.47,1.31–1.65),and weekly alcohol use(1.28,1.09–1.52)were associated with a higher risk of colorectal lesions.We assigned 1 point to each of the above five risk factors and derived a RPS ranging from 0 to 5,with a higher score indicating a higher risk.Compared with a RPS of 0,a RPS of 1,2,3,and 4–5 showed a higher risk of colorectal lesions,with the OR(95%CI)being 1.50(1.37–1.63),2.34(2.12–2.59),3.58(3.13–4.10),and 3.91(3.00–5.10),respectively.The area under the receiver-operating characteristic curve of RPS in predicting colorectal lesions was 0.62.Conclusions Participants with an increase in the RPS of≥1 point had a significantly higher risk of colorectal lesions,suggesting the urgency for measuring colonoscopy in this very high-risk group.High-risk strategies incorporating RPS may be employed to achieve a higher colonoscopy-uptake rate.展开更多
文摘Risk prediction models are an important part of assessing operative mortality and postoperative complication rates in current cardiac surgery practice.Furthermore,they guide clinical decision-making and perioperative patient manage-ment.In recent years,a variety of clinical prediction models have been developed in China and other countries to assess the risk of mortality and complications after cardiac surgery.Currently,the most widely used and mature models are the new version of the European Cardiac Surgery Evaluation System(EuroSCORE II),the American Society of Tho-racic Surgeons Cardiac Surgery Risk Model(STS score),and the Chinese Coronary Artery Bypass Graft Surgery Risk Evaluation System(SinoSCORE).This article reviews the application of these three risk prediction models,to identify the optimal model for guiding clinical practice.
基金Department of Pharmaceuticals Ministry of Chemicals and Fertilizer,Government of India and National Institute of Pharmaceutical Education and Research(NIPER)Ahmedabad,Gandhinagar,India。
文摘Artificial intelligence(AI)technology is expanding at a rapid pace,offering means of improving the precision of judgments made by medical professionals.AI-driven machine learning(ML)facilitates rapid and effective data processing for diagnosis and treatment of different diseases including stroke.This technology has vastly improved the patient classification based on their predicted stroke outcome.It helps in quicker decision-making,improves diagnosis precision,and enhances patient care.ML techniques have occasionally been applied extensively to address complex issues related to stroke such as the prediction of stroke prevalence at an early stage.The ability of deep learning(DL)algorithms,a crucial element of AI,is becoming popular in stroke imaging analysis because it automatically extracts features without requiring domain expertise.In the preclinical setup for stroke studies,ML/DL models are commendably used for the detection of vascular thrombi,stroke core,and penumbra size,to identify artery occlusion,compute perfusion maps,detect intracranial hemorrhage(ICH),prediction of infarct,assessing the severity of hemorrhagic transformation,and forecasting patient outcomes.The robust automatic data processing,excellent generalization,self-learning,and precise decision-making abilities of such models have contributed immensely to the advancement of stroke therapy.In the preclinical setup,the time-investing behavioral studies of the animals are also effectively analyzed by AI based algorithms.Understanding the algorithms and models based on AI is yet to be simplified for its appli-cation in stroke therapy in present clinical settings,thus,in the present review attempts have been made to present it in a simplified manner to facilitate translation.
基金supported by the Special Foundation for Science and Technology Basic Research Program[2019FY101103].
文摘Background The uptake of colonoscopy is low in individuals at risk of colorectal cancer(CRC).We constructed a riskprediction score(RPS)in a large community-based sample at high risk of CRC to enable more accurate risk stratification and to motivate and increase the uptake rate of colonoscopy.Methods A total of 12,628 participants classified as high-risk according to positivity of immunochemical fecal occult blood tests or High-Risk Factor Questionnaire underwent colonoscopy.Logistic regression was used to derive a RPS and analysed the associations of the RPS with colorectal lesions,giving odds ratios(ORs)and 95%confidence intervals(CIs).Results Of the participants,men(OR=1.73,95%CI=1.58–1.90),older age(≥65 years;1.41,1.31–1.53),higher body mass index(≥28 kg/m^(2);1.22,1.07–1.39),ever smoking(1.47,1.31–1.65),and weekly alcohol use(1.28,1.09–1.52)were associated with a higher risk of colorectal lesions.We assigned 1 point to each of the above five risk factors and derived a RPS ranging from 0 to 5,with a higher score indicating a higher risk.Compared with a RPS of 0,a RPS of 1,2,3,and 4–5 showed a higher risk of colorectal lesions,with the OR(95%CI)being 1.50(1.37–1.63),2.34(2.12–2.59),3.58(3.13–4.10),and 3.91(3.00–5.10),respectively.The area under the receiver-operating characteristic curve of RPS in predicting colorectal lesions was 0.62.Conclusions Participants with an increase in the RPS of≥1 point had a significantly higher risk of colorectal lesions,suggesting the urgency for measuring colonoscopy in this very high-risk group.High-risk strategies incorporating RPS may be employed to achieve a higher colonoscopy-uptake rate.
