Materiały źródłowe

• #1 Aortic Valve Disease | UNC Heart Valve Clinic

  https://www.med.unc.edu/medicine/cardiology/uncheartvalve/diseases-and-treatments/aortic-valve-disease/

  Aortic stenosis becomes increasingly common with age, predominantly affecting those over the age of 65. […] When symptoms, such as shortness of breath or chest pain, develop from aortic stenosis, the average life expectancy is only 1-2 years, with a prognosis that is worse than most cancers, if left untreated. […] Unfortunately, at least 1/3 of patients with aortic valve stenosis today are not receiving valve replacement, which is the only treatment that is effective for this condition. […] Severe symptomatic aortic stenosis is a life threatening condition. […] With TAVR, physicians now have the ability to provide aortic valve replacement via minimally-invasive approaches, including percutaneous procedures (intervention without an incision), which provide outcomes that are as good, or sometimes better than, surgical valve replacement.

• #2 Aortic Valve Disease | UNC Heart Valve Clinic

  https://www.med.unc.edu/medicine/cardiology/uncheartvalve/diseases-and-treatments/aortic-valve-disease/

  Aortic stenosis becomes increasingly common with age, predominantly affecting those over the age of 65. […] When symptoms, such as shortness of breath or chest pain, develop from aortic stenosis, the average life expectancy is only 1-2 years, with a prognosis that is worse than most cancers, if left untreated. […] Unfortunately, at least 1/3 of patients with aortic valve stenosis today are not receiving valve replacement, which is the only treatment that is effective for this condition. […] Severe symptomatic aortic stenosis is a life threatening condition. […] With TAVR, physicians now have the ability to provide aortic valve replacement via minimally-invasive approaches, including percutaneous procedures (intervention without an incision), which provide outcomes that are as good, or sometimes better than, surgical valve replacement.

• #3 Machine Learning Prediction for Prognosis of Patients With Aortic Stenosis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11450950/

  Aortic valve stenosis of any degree is associated with poor outcomes. […] The authors aimed to develop a risk prediction model for aortic stenosis (AS) prognosis using machine learning techniques. […] The composite outcome included aortic valve replacement or mortality. […] In patients with AS, a machine learning algorithm predicts outcomes with good accuracy, and prognostic characteristics were identified. […] The model can potentially guide risk factor modification and clinical decisions to improve patient prognosis. […] The median follow-up of the primary cohort was 48 (21-87) months. During this period, 1,116 patients underwent AVR, and 5,069 patients died. […] Among others, moderate (hazard ratio: 1.57; 95% CI: 1.38-1.52; P 0.001) and severe (hazard ratio: 2.50; 95% CI: 2.32-2.70; P 0.001) AS were 2 of the most relevant predictors of outcome.

• #4 Aortic Valve Disease | UNC Heart Valve Clinic

  https://www.med.unc.edu/medicine/cardiology/uncheartvalve/diseases-and-treatments/aortic-valve-disease/

  Aortic stenosis becomes increasingly common with age, predominantly affecting those over the age of 65. […] When symptoms, such as shortness of breath or chest pain, develop from aortic stenosis, the average life expectancy is only 1-2 years, with a prognosis that is worse than most cancers, if left untreated. […] Unfortunately, at least 1/3 of patients with aortic valve stenosis today are not receiving valve replacement, which is the only treatment that is effective for this condition. […] Severe symptomatic aortic stenosis is a life threatening condition. […] With TAVR, physicians now have the ability to provide aortic valve replacement via minimally-invasive approaches, including percutaneous procedures (intervention without an incision), which provide outcomes that are as good, or sometimes better than, surgical valve replacement.

• #5 Dynamic prediction of outcome for patients with severe aortic stenosis: application of joint models for longitudinal and time-to-event data | BMC Cardiovascular Disorders | Full Text

  https://bmccardiovascdisord.biomedcentral.com/articles/10.1186/s12872-015-0035-z

  The joint model with the death as outcome shows that smaller AVA at baseline, male patient, symptoms at baseline and higher BNP at a specific time point tend to be associated with death. […] This approach provides the cardiologist with a useful evidence-based tool to assess the impact of BNP on patient prognosis. […] The joint model of longitudinal and survival data represents a powerful statistical tool capable of capturing the association between longitudinal and survival data. […] From the analysis we obtained a non-significant association between aortic valve intervention and the evolution of BNP. […] However, BNP levels have been previously found to be predictors of reoperation. Therefore, although BNP profile is not a good predictor of intervention in our case, it is reliable in predicting mortality and thus can be very helpful in planning an intervention to prevent mortality due to AS disease progression.

• #6 Dynamic prediction of outcome for patients with severe aortic stenosis: application of joint models for longitudinal and time-to-event data | BMC Cardiovascular Disorders | Full Text

  https://bmccardiovascdisord.biomedcentral.com/articles/10.1186/s12872-015-0035-z

  The joint model with the death as outcome shows that smaller AVA at baseline, male patient, symptoms at baseline and higher BNP at a specific time point tend to be associated with death. […] This approach provides the cardiologist with a useful evidence-based tool to assess the impact of BNP on patient prognosis. […] The joint model of longitudinal and survival data represents a powerful statistical tool capable of capturing the association between longitudinal and survival data. […] From the analysis we obtained a non-significant association between aortic valve intervention and the evolution of BNP. […] However, BNP levels have been previously found to be predictors of reoperation. Therefore, although BNP profile is not a good predictor of intervention in our case, it is reliable in predicting mortality and thus can be very helpful in planning an intervention to prevent mortality due to AS disease progression.

• #7 Machine Learning Prediction for Prognosis of Patients With Aortic Stenosis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11450950/

  The RSF model showed an area under the curve (AUC) of 0.83 (95% CI: 0.80-0.86) and 0.83 (95% CI: 0.81-0.84) to predict the composite outcome at 1 and 5 years, respectively. […] The developed algorithm exhibited good diagnostic accuracy. When tested on external cohorts, the diagnostic accuracy remained good, albeit slightly lower compared to the primary cohort. […] Using advanced ML methods, we have developed an algorithm that accurately predicts patient outcomes across all grades of AS while identifying crucial prognosis-related variables.

• #8 Machine Learning Prediction for Prognosis of Patients With Aortic Stenosis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11450950/

  The RSF model showed an area under the curve (AUC) of 0.83 (95% CI: 0.80-0.86) and 0.83 (95% CI: 0.81-0.84) to predict the composite outcome at 1 and 5 years, respectively. […] The developed algorithm exhibited good diagnostic accuracy. When tested on external cohorts, the diagnostic accuracy remained good, albeit slightly lower compared to the primary cohort. […] Using advanced ML methods, we have developed an algorithm that accurately predicts patient outcomes across all grades of AS while identifying crucial prognosis-related variables.

• #9 Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score – PubMed

  https://pubmed.ncbi.nlm.nih.gov/35641101/

  Objective: To use echocardiographic and clinical features to develop an explainable clinical risk prediction model in patients with aortic stenosis (AS), including those with low-gradient AS (LGAS), using machine learning (ML). […] Conclusion: In two separate longitudinal databases, ML identified prognostic features and produced an algorithm that predicts outcome for up to 5 years of follow-up in patients with AS, including patients with LGAS. Our algorithm, the Aortic Stenosis Risk (ASteRisk) score, is available online for public use.

• #10 Predicting mortality after transcatheter aortic valve replacement using preprocedural CT | Scientific Reports

  https://www.nature.com/articles/s41598-024-63022-x

  Transcatheter aortic valve replacement (TAVR) is a widely used intervention for patients with severe aortic stenosis. Identifying high-risk patients is crucial due to potential postprocedural complications. […] Our model demonstrates an AUROC of 0.725 for predicting all-cause mortality during postprocedure follow-up on a cohort of 1449 TAVR patients. […] Thus, these findings underscore the potential of the proposed model in automatically analyzing CT volumes and integrating them with patient characteristics for predicting mortality after TAVR. […] In this study, we define the outcome as all-cause mortality during the postprocedural follow-up period. […] Among survivors, the median follow-up duration is 1093 days, with the 5th and 95th percentiles at 366 days and 2683 days, respectively. Across all patients, the two output classes are fairly balanced: 44% of patients passed away during follow-up (class 1) and 56% survived (class 0).

• #11 Predicting mortality after transcatheter aortic valve replacement using preprocedural CT | Scientific Reports

  https://www.nature.com/articles/s41598-024-63022-x

  Transcatheter aortic valve replacement (TAVR) is a widely used intervention for patients with severe aortic stenosis. Identifying high-risk patients is crucial due to potential postprocedural complications. […] Our model demonstrates an AUROC of 0.725 for predicting all-cause mortality during postprocedure follow-up on a cohort of 1449 TAVR patients. […] Thus, these findings underscore the potential of the proposed model in automatically analyzing CT volumes and integrating them with patient characteristics for predicting mortality after TAVR. […] In this study, we define the outcome as all-cause mortality during the postprocedural follow-up period. […] Among survivors, the median follow-up duration is 1093 days, with the 5th and 95th percentiles at 366 days and 2683 days, respectively. Across all patients, the two output classes are fairly balanced: 44% of patients passed away during follow-up (class 1) and 56% survived (class 0).

• #12 Predicting mortality after transcatheter aortic valve replacement using preprocedural CT | Scientific Reports

  https://www.nature.com/articles/s41598-024-63022-x

  Our model delivers a prediction within 5 to 20 seconds on a consumer CPU depending on the number of missing variables allowing patient assessment with minimal manual labor. […] The results in Table 2 confirm that our automatic feature extraction can replace manually extracted image measurements without forfeiting prediction accuracy, which is an important and surprising point. […] However, extracting such features for every patient can create a bottleneck during preprocedural patient assessment: Extracting the measurements manually from a single image takes an expert radiologist between 10 and 15 minutes depending on calcification severity.

• #13 Predicting mortality after transcatheter aortic valve replacement using preprocedural CT | Scientific Reports

  https://www.nature.com/articles/s41598-024-63022-x

  Our model delivers a prediction within 5 to 20 seconds on a consumer CPU depending on the number of missing variables allowing patient assessment with minimal manual labor. […] The results in Table 2 confirm that our automatic feature extraction can replace manually extracted image measurements without forfeiting prediction accuracy, which is an important and surprising point. […] However, extracting such features for every patient can create a bottleneck during preprocedural patient assessment: Extracting the measurements manually from a single image takes an expert radiologist between 10 and 15 minutes depending on calcification severity.

• #14 Predicting mortality after transcatheter aortic valve replacement using preprocedural CT | Scientific Reports

  https://www.nature.com/articles/s41598-024-63022-x

  Our model delivers a prediction within 5 to 20 seconds on a consumer CPU depending on the number of missing variables allowing patient assessment with minimal manual labor. […] The results in Table 2 confirm that our automatic feature extraction can replace manually extracted image measurements without forfeiting prediction accuracy, which is an important and surprising point. […] However, extracting such features for every patient can create a bottleneck during preprocedural patient assessment: Extracting the measurements manually from a single image takes an expert radiologist between 10 and 15 minutes depending on calcification severity.

• #15 Risk prediction in patients with low-flow, low-gradient aortic stenosis and reduced ejection fraction undergoing TAVI | Open Heart

  https://openheart.bmj.com/content/9/1/e001912

  Patients with low-flow, low-gradient aortic stenosis (LFLG AS) and reduced left ventricular ejection fraction (LVEF) are known to suffer from poor prognosis after transcatheter aortic valve implantation (TAVI). […] The RELiEF TAVI score is based on simple clinical, echocardiographic and CT parameters and might serve as a helpful tool for risk prediction in patients with LFLG AS and reduced LVEF scheduled for TAVI. […] Patients with high RELiEF TAVI score had a more than twofold increase in all-cause mortality or HF hospitalisations, a more than 2.5-fold increase in all-cause mortality alone and a fourfold increase in cardiovascular mortality at 1 year after TAVI compared with those with low RELiEF TAVI score. […] The RELiEF TAVI score was superior to the established EuroSCORE II regarding risk prediction of all-cause mortality and HF readmissions and provided a reasonable discriminative performance with only minor discrepancies between score-predicted and observed mortality rates. […] In patients with a RELiEF TAVI score 7 or more, the mortality rate 1 year after TAVI was in excess of 90%.

• #16 Risk prediction in patients with low-flow, low-gradient aortic stenosis and reduced ejection fraction undergoing TAVI | Open Heart

  https://openheart.bmj.com/content/9/1/e001912

  Patients with low-flow, low-gradient aortic stenosis (LFLG AS) and reduced left ventricular ejection fraction (LVEF) are known to suffer from poor prognosis after transcatheter aortic valve implantation (TAVI). […] The RELiEF TAVI score is based on simple clinical, echocardiographic and CT parameters and might serve as a helpful tool for risk prediction in patients with LFLG AS and reduced LVEF scheduled for TAVI. […] Patients with high RELiEF TAVI score had a more than twofold increase in all-cause mortality or HF hospitalisations, a more than 2.5-fold increase in all-cause mortality alone and a fourfold increase in cardiovascular mortality at 1 year after TAVI compared with those with low RELiEF TAVI score. […] The RELiEF TAVI score was superior to the established EuroSCORE II regarding risk prediction of all-cause mortality and HF readmissions and provided a reasonable discriminative performance with only minor discrepancies between score-predicted and observed mortality rates. […] In patients with a RELiEF TAVI score 7 or more, the mortality rate 1 year after TAVI was in excess of 90%.

• #17 Risk prediction in patients with low-flow, low-gradient aortic stenosis and reduced ejection fraction undergoing TAVI | Open Heart

  https://openheart.bmj.com/content/9/1/e001912

  Patients with low-flow, low-gradient aortic stenosis (LFLG AS) and reduced left ventricular ejection fraction (LVEF) are known to suffer from poor prognosis after transcatheter aortic valve implantation (TAVI). […] The RELiEF TAVI score is based on simple clinical, echocardiographic and CT parameters and might serve as a helpful tool for risk prediction in patients with LFLG AS and reduced LVEF scheduled for TAVI. […] Patients with high RELiEF TAVI score had a more than twofold increase in all-cause mortality or HF hospitalisations, a more than 2.5-fold increase in all-cause mortality alone and a fourfold increase in cardiovascular mortality at 1 year after TAVI compared with those with low RELiEF TAVI score. […] The RELiEF TAVI score was superior to the established EuroSCORE II regarding risk prediction of all-cause mortality and HF readmissions and provided a reasonable discriminative performance with only minor discrepancies between score-predicted and observed mortality rates. […] In patients with a RELiEF TAVI score 7 or more, the mortality rate 1 year after TAVI was in excess of 90%.

• #18 Risk prediction in patients with low-flow, low-gradient aortic stenosis and reduced ejection fraction undergoing TAVI | Open Heart

  https://openheart.bmj.com/content/9/1/e001912

  Patients with low-flow, low-gradient aortic stenosis (LFLG AS) and reduced left ventricular ejection fraction (LVEF) are known to suffer from poor prognosis after transcatheter aortic valve implantation (TAVI). […] The RELiEF TAVI score is based on simple clinical, echocardiographic and CT parameters and might serve as a helpful tool for risk prediction in patients with LFLG AS and reduced LVEF scheduled for TAVI. […] Patients with high RELiEF TAVI score had a more than twofold increase in all-cause mortality or HF hospitalisations, a more than 2.5-fold increase in all-cause mortality alone and a fourfold increase in cardiovascular mortality at 1 year after TAVI compared with those with low RELiEF TAVI score. […] The RELiEF TAVI score was superior to the established EuroSCORE II regarding risk prediction of all-cause mortality and HF readmissions and provided a reasonable discriminative performance with only minor discrepancies between score-predicted and observed mortality rates. […] In patients with a RELiEF TAVI score 7 or more, the mortality rate 1 year after TAVI was in excess of 90%.

• #19 Risk prediction in patients with low-flow, low-gradient aortic stenosis and reduced ejection fraction undergoing TAVI | Open Heart

  https://openheart.bmj.com/content/9/1/e001912

  Patients with low-flow, low-gradient aortic stenosis (LFLG AS) and reduced left ventricular ejection fraction (LVEF) are known to suffer from poor prognosis after transcatheter aortic valve implantation (TAVI). […] The RELiEF TAVI score is based on simple clinical, echocardiographic and CT parameters and might serve as a helpful tool for risk prediction in patients with LFLG AS and reduced LVEF scheduled for TAVI. […] Patients with high RELiEF TAVI score had a more than twofold increase in all-cause mortality or HF hospitalisations, a more than 2.5-fold increase in all-cause mortality alone and a fourfold increase in cardiovascular mortality at 1 year after TAVI compared with those with low RELiEF TAVI score. […] The RELiEF TAVI score was superior to the established EuroSCORE II regarding risk prediction of all-cause mortality and HF readmissions and provided a reasonable discriminative performance with only minor discrepancies between score-predicted and observed mortality rates. […] In patients with a RELiEF TAVI score 7 or more, the mortality rate 1 year after TAVI was in excess of 90%.

• #20 Dynamic prediction of outcome for patients with severe aortic stenosis: application of joint models for longitudinal and time-to-event data | BMC Cardiovascular Disorders | Full Text

  https://bmccardiovascdisord.biomedcentral.com/articles/10.1186/s12872-015-0035-z

  Physicians utilize different types of information to predict patient prognosis. For example: confronted with a new patient suffering from severe aortic stenosis (AS), the cardiologist considers not only the severity of the AS but also patient characteristics, medical history, and markers such as BNP. […] A small AVA and a high BNP are both associated with a more severe disease and a worse outcome. […] Based on emerging evidence on determinants of the outcome in AS, and with the help of novel statistical approaches to model outcomes, it is now possible to construct dynamic prediction models for patient outcome, employing repeatedly collected (longitudinal) data such as BNP, mimicking the dynamic adjustment of prognosis as employed intuitively by cardiologists at each outpatient clinic visit.

• #21 Dynamic prediction of outcome for patients with severe aortic stenosis: application of joint models for longitudinal and time-to-event data | BMC Cardiovascular Disorders | Full Text

  https://bmccardiovascdisord.biomedcentral.com/articles/10.1186/s12872-015-0035-z

  Physicians utilize different types of information to predict patient prognosis. For example: confronted with a new patient suffering from severe aortic stenosis (AS), the cardiologist considers not only the severity of the AS but also patient characteristics, medical history, and markers such as BNP. […] A small AVA and a high BNP are both associated with a more severe disease and a worse outcome. […] Based on emerging evidence on determinants of the outcome in AS, and with the help of novel statistical approaches to model outcomes, it is now possible to construct dynamic prediction models for patient outcome, employing repeatedly collected (longitudinal) data such as BNP, mimicking the dynamic adjustment of prognosis as employed intuitively by cardiologists at each outpatient clinic visit.

• #22 Dynamic prediction of outcome for patients with severe aortic stenosis: application of joint models for longitudinal and time-to-event data | BMC Cardiovascular Disorders | Full Text

  https://bmccardiovascdisord.biomedcentral.com/articles/10.1186/s12872-015-0035-z

  Physicians utilize different types of information to predict patient prognosis. For example: confronted with a new patient suffering from severe aortic stenosis (AS), the cardiologist considers not only the severity of the AS but also patient characteristics, medical history, and markers such as BNP. […] A small AVA and a high BNP are both associated with a more severe disease and a worse outcome. […] Based on emerging evidence on determinants of the outcome in AS, and with the help of novel statistical approaches to model outcomes, it is now possible to construct dynamic prediction models for patient outcome, employing repeatedly collected (longitudinal) data such as BNP, mimicking the dynamic adjustment of prognosis as employed intuitively by cardiologists at each outpatient clinic visit.

• #23 Dynamic prediction of outcome for patients with severe aortic stenosis: application of joint models for longitudinal and time-to-event data | BMC Cardiovascular Disorders | Full Text

  https://bmccardiovascdisord.biomedcentral.com/articles/10.1186/s12872-015-0035-z

  The joint model with the death as outcome shows that smaller AVA at baseline, male patient, symptoms at baseline and higher BNP at a specific time point tend to be associated with death. […] This approach provides the cardiologist with a useful evidence-based tool to assess the impact of BNP on patient prognosis. […] The joint model of longitudinal and survival data represents a powerful statistical tool capable of capturing the association between longitudinal and survival data. […] From the analysis we obtained a non-significant association between aortic valve intervention and the evolution of BNP. […] However, BNP levels have been previously found to be predictors of reoperation. Therefore, although BNP profile is not a good predictor of intervention in our case, it is reliable in predicting mortality and thus can be very helpful in planning an intervention to prevent mortality due to AS disease progression.

• #24 Dynamic prediction of outcome for patients with severe aortic stenosis: application of joint models for longitudinal and time-to-event data | BMC Cardiovascular Disorders | Full Text

  https://bmccardiovascdisord.biomedcentral.com/articles/10.1186/s12872-015-0035-z

  In conclusion, this paper has shown that temporal adjustment of risk prediction models for patients with severe AS, as more measurements of BNP become available over time, provide the physician with an evidence based understanding of the prognostic implication of changes in the patients disease condition.

• #25 Aortic Valve Disease | UNC Heart Valve Clinic

  https://www.med.unc.edu/medicine/cardiology/uncheartvalve/diseases-and-treatments/aortic-valve-disease/

  Aortic stenosis becomes increasingly common with age, predominantly affecting those over the age of 65. […] When symptoms, such as shortness of breath or chest pain, develop from aortic stenosis, the average life expectancy is only 1-2 years, with a prognosis that is worse than most cancers, if left untreated. […] Unfortunately, at least 1/3 of patients with aortic valve stenosis today are not receiving valve replacement, which is the only treatment that is effective for this condition. […] Severe symptomatic aortic stenosis is a life threatening condition. […] With TAVR, physicians now have the ability to provide aortic valve replacement via minimally-invasive approaches, including percutaneous procedures (intervention without an incision), which provide outcomes that are as good, or sometimes better than, surgical valve replacement.

• #26 Machine Learning Prediction for Prognosis of Patients With Aortic Stenosis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11450950/

  Aortic valve stenosis of any degree is associated with poor outcomes. […] The authors aimed to develop a risk prediction model for aortic stenosis (AS) prognosis using machine learning techniques. […] The composite outcome included aortic valve replacement or mortality. […] In patients with AS, a machine learning algorithm predicts outcomes with good accuracy, and prognostic characteristics were identified. […] The model can potentially guide risk factor modification and clinical decisions to improve patient prognosis. […] The median follow-up of the primary cohort was 48 (21-87) months. During this period, 1,116 patients underwent AVR, and 5,069 patients died. […] Among others, moderate (hazard ratio: 1.57; 95% CI: 1.38-1.52; P 0.001) and severe (hazard ratio: 2.50; 95% CI: 2.32-2.70; P 0.001) AS were 2 of the most relevant predictors of outcome.

• #27 Aortic Valve Disease | UNC Heart Valve Clinic

  https://www.med.unc.edu/medicine/cardiology/uncheartvalve/diseases-and-treatments/aortic-valve-disease/

  Aortic stenosis becomes increasingly common with age, predominantly affecting those over the age of 65. […] When symptoms, such as shortness of breath or chest pain, develop from aortic stenosis, the average life expectancy is only 1-2 years, with a prognosis that is worse than most cancers, if left untreated. […] Unfortunately, at least 1/3 of patients with aortic valve stenosis today are not receiving valve replacement, which is the only treatment that is effective for this condition. […] Severe symptomatic aortic stenosis is a life threatening condition. […] With TAVR, physicians now have the ability to provide aortic valve replacement via minimally-invasive approaches, including percutaneous procedures (intervention without an incision), which provide outcomes that are as good, or sometimes better than, surgical valve replacement.

• #28 Machine Learning Prediction for Prognosis of Patients With Aortic Stenosis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11450950/

  Aortic valve stenosis of any degree is associated with poor outcomes. […] The authors aimed to develop a risk prediction model for aortic stenosis (AS) prognosis using machine learning techniques. […] The composite outcome included aortic valve replacement or mortality. […] In patients with AS, a machine learning algorithm predicts outcomes with good accuracy, and prognostic characteristics were identified. […] The model can potentially guide risk factor modification and clinical decisions to improve patient prognosis. […] The median follow-up of the primary cohort was 48 (21-87) months. During this period, 1,116 patients underwent AVR, and 5,069 patients died. […] Among others, moderate (hazard ratio: 1.57; 95% CI: 1.38-1.52; P 0.001) and severe (hazard ratio: 2.50; 95% CI: 2.32-2.70; P 0.001) AS were 2 of the most relevant predictors of outcome.

• #29 Machine Learning Prediction for Prognosis of Patients With Aortic Stenosis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11450950/

  The RSF model showed an area under the curve (AUC) of 0.83 (95% CI: 0.80-0.86) and 0.83 (95% CI: 0.81-0.84) to predict the composite outcome at 1 and 5 years, respectively. […] The developed algorithm exhibited good diagnostic accuracy. When tested on external cohorts, the diagnostic accuracy remained good, albeit slightly lower compared to the primary cohort. […] Using advanced ML methods, we have developed an algorithm that accurately predicts patient outcomes across all grades of AS while identifying crucial prognosis-related variables.

• #30 Risk prediction in patients with low-flow, low-gradient aortic stenosis and reduced ejection fraction undergoing TAVI | Open Heart

  https://openheart.bmj.com/content/9/1/e001912

  Patients with low-flow, low-gradient aortic stenosis (LFLG AS) and reduced left ventricular ejection fraction (LVEF) are known to suffer from poor prognosis after transcatheter aortic valve implantation (TAVI). […] The RELiEF TAVI score is based on simple clinical, echocardiographic and CT parameters and might serve as a helpful tool for risk prediction in patients with LFLG AS and reduced LVEF scheduled for TAVI. […] Patients with high RELiEF TAVI score had a more than twofold increase in all-cause mortality or HF hospitalisations, a more than 2.5-fold increase in all-cause mortality alone and a fourfold increase in cardiovascular mortality at 1 year after TAVI compared with those with low RELiEF TAVI score. […] The RELiEF TAVI score was superior to the established EuroSCORE II regarding risk prediction of all-cause mortality and HF readmissions and provided a reasonable discriminative performance with only minor discrepancies between score-predicted and observed mortality rates. […] In patients with a RELiEF TAVI score 7 or more, the mortality rate 1 year after TAVI was in excess of 90%.

• #31 Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score – PubMed

  https://pubmed.ncbi.nlm.nih.gov/35641101/

  Objective: To use echocardiographic and clinical features to develop an explainable clinical risk prediction model in patients with aortic stenosis (AS), including those with low-gradient AS (LGAS), using machine learning (ML). […] Conclusion: In two separate longitudinal databases, ML identified prognostic features and produced an algorithm that predicts outcome for up to 5 years of follow-up in patients with AS, including patients with LGAS. Our algorithm, the Aortic Stenosis Risk (ASteRisk) score, is available online for public use.

• #32 Dynamic prediction of outcome for patients with severe aortic stenosis: application of joint models for longitudinal and time-to-event data | BMC Cardiovascular Disorders | Full Text

  https://bmccardiovascdisord.biomedcentral.com/articles/10.1186/s12872-015-0035-z

  The joint model with the death as outcome shows that smaller AVA at baseline, male patient, symptoms at baseline and higher BNP at a specific time point tend to be associated with death. […] This approach provides the cardiologist with a useful evidence-based tool to assess the impact of BNP on patient prognosis. […] The joint model of longitudinal and survival data represents a powerful statistical tool capable of capturing the association between longitudinal and survival data. […] From the analysis we obtained a non-significant association between aortic valve intervention and the evolution of BNP. […] However, BNP levels have been previously found to be predictors of reoperation. Therefore, although BNP profile is not a good predictor of intervention in our case, it is reliable in predicting mortality and thus can be very helpful in planning an intervention to prevent mortality due to AS disease progression.

• #33 Dynamic prediction of outcome for patients with severe aortic stenosis: application of joint models for longitudinal and time-to-event data | BMC Cardiovascular Disorders | Full Text

  https://bmccardiovascdisord.biomedcentral.com/articles/10.1186/s12872-015-0035-z

  In conclusion, this paper has shown that temporal adjustment of risk prediction models for patients with severe AS, as more measurements of BNP become available over time, provide the physician with an evidence based understanding of the prognostic implication of changes in the patients disease condition.

Zobacz więcej