Ignite Creation Date:
2024-05-06 @ 6:36 PM
Last Modification Date:
2024-10-26 @ 2:51 PM
Study NCT ID:
NCT05714319
Status:
RECRUITING
Last Update Posted:
2024-02-26
First Post:
2023-01-27
Brief Title:
Intracoronary Provocative Test With Acetylcholine in Patients With INOCA and MINOCA
Sponsor:
Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Organization:
Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Study Overview
Official Title:
Intracoronary Provocative Test With Acetylcholine in Patients With Stable Myocardial Ischemia or Myocardial Infarction and Non-Obstructive Coronary Arteries the Provoke Study
Status:
RECRUITING
Status Verified Date:
2024-02
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
Provoke
Brief Summary:
Coronary vasomotor disorders occurring both at microvascular and epicardial level have been demonstrated as responsible for myocardial ischemia in a sizeable group of patients undergoing coronary angiography CAG with clinical manifestations ranging from ischemia with non-obstructive coronary arteries INOCA to myocardial infarction with non-obstructive coronary arteries MINOCA along with life-threatening arrhythmias and sudden cardiac death Intracoronary provocative testing with administration of acetylcholine ACh at the time of CAG may elicit epicardial coronary spasm or microvascular spasm in susceptible individuals and therefore is assuming paramount importance for the diagnosis of functional coronary alterations in patients with suspected myocardial ischemia and non-obstructive coronary artery disease CAD However previous studies mainly focused on patients with INOCA whilst MINOCA patients were often underrepresented Assessing the presence of coronary vasomotor disorders is of mainstay importance in order to implement the optimal management and improve clinical outcomes Clinical predictors for a positive ACh test could allow the development of predictive models for a positive or negative response based on clinical andor angiographic features readily available in the catheterization laboratories thus helping clinicians in the diagnosis of coronary vasomotor disorders even in patients at high risk of complications
Detailed Description:
Background
Coronary vasomotor disorders occurring both at microvascular and epicardial level have been demonstrated as responsible for myocardial ischemia in a sizeable group of patients undergoing coronary angiography CAG with clinical manifestations ranging from ischemia with non-obstructive coronary arteries INOCA to myocardial infarction with non-obstructive coronary arteries MINOCA along with life-threatening arrhythmias and sudden cardiac death Intracoronary provocative testing with administration of acetylcholine ACh at the time of CAG may elicit epicardial coronary spasm or microvascular spasm in susceptible individuals and therefore is assuming paramount importance for the diagnosis of functional coronary alterations in patients with suspected myocardial ischemia and non-obstructive coronary artery disease CAD However previous studies mainly focused on patients with INOCA whilst MINOCA patients were often underrepresented In addition intracoronary provocative testing is still largely underused in clinical practice probably because of concerns regarding the risk of complications especially in the acute clinical setting Of note the landmark Coronary Microvascular Angina CorMicA trial demonstrated that a strategy of adjunctive invasive testing for disorders of coronary function in patients with non-obstructive CAD linked with stratified medical therapy is superior to usual care in improving patients outcomes including reduction in angina severity and better quality of life Therefore assessing the presence of coronary vasomotor disorders is of mainstay importance in order to implement the optimal management and improve clinical outcomes
Of interest the investigators recently demonstrated that performing an ACh provocative test in patients with myocardial ischemia and non-obstructive coronary arteries is safe with a low rate of complications without differences between patients presenting with INOCA or MINOCA In particular a previous history of paroxysmal atrial fibrillation AF a moderate-to-severe left ventricle LV diastolic dysfunction and a higher corrected QT QTc dispersion at baseline electrocardiogram ECG were independent predictors for the occurrence of complications during the test and therefore patients with these characteristics may be those requiring particular attention during the test Moreover the investigators demonstrated that performing an ACh provocative test has relevant prognostic implications as patients with a positive test have a higher risk of major adverse cerebrovascular and cardiovascular events MACCE at follow-up and therefore performing an ACh test can help in stratifying the prognosis especially in MINOCA patients suggesting the presence of a net clinical benefit deriving from its use Furthermore the investigators recently demonstrated that some clinical MINOCA as clinical presentation and elevated circulating levels of C-reactive protein and angiographic presence of myocardial bridging features are independent predictors for a positive response to ACh test
Of interest the identification of clinical predictors for a positive ACh test could allow the development of predictive models for a positive or negative response based on clinical andor angiographic features readily available in the catheterization laboratories thus helping clinicians in the diagnosis of coronary vasomotor disorders even in patients at high risk of complications eg history of AF LV diastolic dysfunction long QTc interval or QTc dispersion at baseline ECG bradyarrhythmia Moreover the implementation of such predictive models in clinical practice could avoid the need of performing a provocative test significantly reducing the duration of invasive procedures as well as the associated risks and allowing a fast determination of the most appropriate treatments and clinical paths an efficient planning and a parsimonious use of medical resources In addition developing predictive models for the risk of future cardiovascular events could help clinicians in the prognostic stratification and the choice of therapeutic strategies in the post-discharge management possibly identifying those patients that may need a more aggressive therapy and a closer follow-up
Therefore the investigators hypothesize that
Clinical predictors for a positive ACh test response could be identified allowing the development of predictive models andor clinical risk scores that could help clinicians in the diagnosis of coronary vasomotor disorders and the implementation of the most appropriate management
A positive ACh test could be associated with a higher rate of adverse cardiovascular events at follow-up thus helping in the prognostic stratification of INOCA and MINOCA patients and identifying those that may need a more aggressive therapy and a closer follow-up
Primary objective
To derive and validate predictive modelsclinical risk scores able to predict a positive ACh test response in INOCA and MINOCA patients basing on clinical andor angiographic features
Secondary objective
To derive and validate predictive modelsclinical risk scores able to predict a worse clinical outcome in terms of major adverse cardiovascular and cerebrovascular events MACCE defined as the composite of cardiovascular death nonfatal myocardial infarction MI hospitalization due to unstable angina UA and stroketransient ischemic attack TIA in INOCA and MINOCA patients basing on clinical andor angiographic features
Study design
Observational study
Sample size calculation
Up to our knowledge no study has investigated the creation of a potential score for Ach test positivity Hence this would represent the first and as such is includable among pilot studies and therefore no formal sample size calculation is needed but all of patients satisfying inclusion criteria can be included Based on the study design which pertains the creation and validation of a score which would require a training and validation cohort and will alongside imply the use of regression methods the investigators plan to include 600 patients Such a sample size would allow for the stratification in two cohort and the analysis of the approximately 50 covariates included in the study In fact according to van Smeden events per variable EPV may go beyond the common rule of EPV10
Statistical analysis
Descriptive analysis and between-groups comparisons
The sample will be described in its demographic anthropometric clinical instrumental variables through descriptive statistical techniques In depth qualitative variables will be expressed by absolute and relative percentage frequencies Quantitative variables indeed will be reported either as mean and standard deviation SD or median and interquartile range IQR respectively in the case they were normally or not normally distributed Their distribution will be previously assessed by the Shapiro Wilk test Between groups differences in the demographic clinical laboratory and pathologic features will be assessed by the Chi Square or the Fishers exact test as for qualitative variables with Freeman- Haltons extension when appropriate whilst quantitative variables will be evaluated either by the Students t test or the Mann- Withney U test according to their distribution
Derivation and validation of clinical risk scores
Data used for score development will be derived from a prospectively enrolled sample of 550 NOCAD patients consecutively admitted to the Department of Cardiovascular Sciences of Fondazione Policlinico Universitario A Gemelli IRCCS Rome Italy There is no generally accepted approach for the estimation of the sample size for derivation of score prediction models Hence the investigators based for the derivation of the score to include in the multivariable model a number of covariates consistent with the most recent rules on the minimum number of events per variable needed The investigators will randomly allocate the participants to two cohorts one cohort will be used to develop the score model derivation cohort and the other to validate and assess the diagnostic abilities of the score validation cohort Multiple imputation will be applied to handle missing data by imputeR R package Univariable and multivariable regression models will be performed on the derivation cohort to identify independent predictors of a positive ACh test to be included in the scoring system In depth the investigators will compute Odd Ratios ORs and 95 Confidence Intervals CIs of the predictor candidates for the outcome ie positive Ach test by univariable logistic regression models Predictors to be included in the multivariable model will be selected based on univariable analysis p005 or suggestive ie 005p010 and expert opinion The multivariable logistic regression model will produce β coefficient and Standard Errors SE for each variable The performance of the model will be assessed based on diverse methods such as Somers Dxy rank correlation C-index Nagelkerke R2 value calibration intercept and slope and Brier score Finally the Hosmer-Lemeshow goodness-of-fit test will allow for the calibration in the derivation cohort Calibration plots will further provide a graphic representation of the association between the predicted and observed outcome by locally weighted scatterplot smoothing rms predtools and magrittr R packages will be used for the whole analyses set Internal validation of the model will be performed based on a bootstrap procedure The investigators will then pass to develop a scoring system to predict the outcome providing an integer value to each predictor included in the scoring system based on each variables β coefficient in the derivation cohort Appropriate cutoff values will be set for a rule-in and rule-out approach to help in decision-making In depth quantitative independent predictors will be further transformed into either ordinal or nominal qualitative variables Transformation will be performed seeking for one or more optimal cut-points by appropriate selection methods based on ROC curves analysis by mean of pROC R package Particularly OptimalCutpoints R package will be used applying the SpEqualSe selection method which returns the highest accuracy The diagnostic abilities ie sensitivity specificity positive likelihood ratio and negative likelihood ratio of each score will be then calculated and the patients will be divided into 3 groups positive negative suspect For validation the developed score will be applied to the validation cohort and the discrimination and calibration performances will be described as aforementioned The overall performance will be described in terms of Sensitivity Specificity Accuracy or Positive Predictive Value PPV F1 Score Accuracy False Positive Rate FPR False Discovery Rate FDR and False Negative Rate FNR Statistical analyses will be carried out using R software version 420 CRAN R Core 2022
Derivation and validation of predictive models using artificial intelligencemachine learning models second phase
In the second phase of this study the investigators further plan to develop a predictive model of MACCE in the studied population as well as to extend the study out of our clinical facility to potentially validate the models also externally In this context the investigators foresee to further enhance the analysis by adding Machine Learning methods such as XGBoost Random Forest Neural Networks which will be chosen based on the type of data and question to be answered ML methods will be applied in Python software
Coronary vasomotor disorders occurring both at microvascular and epicardial level have been demonstrated as responsible for myocardial ischemia in a sizeable group of patients undergoing coronary angiography CAG with clinical manifestations ranging from ischemia with non-obstructive coronary arteries INOCA to myocardial infarction with non-obstructive coronary arteries MINOCA along with life-threatening arrhythmias and sudden cardiac death Intracoronary provocative testing with administration of acetylcholine ACh at the time of CAG may elicit epicardial coronary spasm or microvascular spasm in susceptible individuals and therefore is assuming paramount importance for the diagnosis of functional coronary alterations in patients with suspected myocardial ischemia and non-obstructive coronary artery disease CAD However previous studies mainly focused on patients with INOCA whilst MINOCA patients were often underrepresented In addition intracoronary provocative testing is still largely underused in clinical practice probably because of concerns regarding the risk of complications especially in the acute clinical setting Of note the landmark Coronary Microvascular Angina CorMicA trial demonstrated that a strategy of adjunctive invasive testing for disorders of coronary function in patients with non-obstructive CAD linked with stratified medical therapy is superior to usual care in improving patients outcomes including reduction in angina severity and better quality of life Therefore assessing the presence of coronary vasomotor disorders is of mainstay importance in order to implement the optimal management and improve clinical outcomes
Of interest the investigators recently demonstrated that performing an ACh provocative test in patients with myocardial ischemia and non-obstructive coronary arteries is safe with a low rate of complications without differences between patients presenting with INOCA or MINOCA In particular a previous history of paroxysmal atrial fibrillation AF a moderate-to-severe left ventricle LV diastolic dysfunction and a higher corrected QT QTc dispersion at baseline electrocardiogram ECG were independent predictors for the occurrence of complications during the test and therefore patients with these characteristics may be those requiring particular attention during the test Moreover the investigators demonstrated that performing an ACh provocative test has relevant prognostic implications as patients with a positive test have a higher risk of major adverse cerebrovascular and cardiovascular events MACCE at follow-up and therefore performing an ACh test can help in stratifying the prognosis especially in MINOCA patients suggesting the presence of a net clinical benefit deriving from its use Furthermore the investigators recently demonstrated that some clinical MINOCA as clinical presentation and elevated circulating levels of C-reactive protein and angiographic presence of myocardial bridging features are independent predictors for a positive response to ACh test
Of interest the identification of clinical predictors for a positive ACh test could allow the development of predictive models for a positive or negative response based on clinical andor angiographic features readily available in the catheterization laboratories thus helping clinicians in the diagnosis of coronary vasomotor disorders even in patients at high risk of complications eg history of AF LV diastolic dysfunction long QTc interval or QTc dispersion at baseline ECG bradyarrhythmia Moreover the implementation of such predictive models in clinical practice could avoid the need of performing a provocative test significantly reducing the duration of invasive procedures as well as the associated risks and allowing a fast determination of the most appropriate treatments and clinical paths an efficient planning and a parsimonious use of medical resources In addition developing predictive models for the risk of future cardiovascular events could help clinicians in the prognostic stratification and the choice of therapeutic strategies in the post-discharge management possibly identifying those patients that may need a more aggressive therapy and a closer follow-up
Therefore the investigators hypothesize that
Clinical predictors for a positive ACh test response could be identified allowing the development of predictive models andor clinical risk scores that could help clinicians in the diagnosis of coronary vasomotor disorders and the implementation of the most appropriate management
A positive ACh test could be associated with a higher rate of adverse cardiovascular events at follow-up thus helping in the prognostic stratification of INOCA and MINOCA patients and identifying those that may need a more aggressive therapy and a closer follow-up
Primary objective
To derive and validate predictive modelsclinical risk scores able to predict a positive ACh test response in INOCA and MINOCA patients basing on clinical andor angiographic features
Secondary objective
To derive and validate predictive modelsclinical risk scores able to predict a worse clinical outcome in terms of major adverse cardiovascular and cerebrovascular events MACCE defined as the composite of cardiovascular death nonfatal myocardial infarction MI hospitalization due to unstable angina UA and stroketransient ischemic attack TIA in INOCA and MINOCA patients basing on clinical andor angiographic features
Study design
Observational study
Sample size calculation
Up to our knowledge no study has investigated the creation of a potential score for Ach test positivity Hence this would represent the first and as such is includable among pilot studies and therefore no formal sample size calculation is needed but all of patients satisfying inclusion criteria can be included Based on the study design which pertains the creation and validation of a score which would require a training and validation cohort and will alongside imply the use of regression methods the investigators plan to include 600 patients Such a sample size would allow for the stratification in two cohort and the analysis of the approximately 50 covariates included in the study In fact according to van Smeden events per variable EPV may go beyond the common rule of EPV10
Statistical analysis
Descriptive analysis and between-groups comparisons
The sample will be described in its demographic anthropometric clinical instrumental variables through descriptive statistical techniques In depth qualitative variables will be expressed by absolute and relative percentage frequencies Quantitative variables indeed will be reported either as mean and standard deviation SD or median and interquartile range IQR respectively in the case they were normally or not normally distributed Their distribution will be previously assessed by the Shapiro Wilk test Between groups differences in the demographic clinical laboratory and pathologic features will be assessed by the Chi Square or the Fishers exact test as for qualitative variables with Freeman- Haltons extension when appropriate whilst quantitative variables will be evaluated either by the Students t test or the Mann- Withney U test according to their distribution
Derivation and validation of clinical risk scores
Data used for score development will be derived from a prospectively enrolled sample of 550 NOCAD patients consecutively admitted to the Department of Cardiovascular Sciences of Fondazione Policlinico Universitario A Gemelli IRCCS Rome Italy There is no generally accepted approach for the estimation of the sample size for derivation of score prediction models Hence the investigators based for the derivation of the score to include in the multivariable model a number of covariates consistent with the most recent rules on the minimum number of events per variable needed The investigators will randomly allocate the participants to two cohorts one cohort will be used to develop the score model derivation cohort and the other to validate and assess the diagnostic abilities of the score validation cohort Multiple imputation will be applied to handle missing data by imputeR R package Univariable and multivariable regression models will be performed on the derivation cohort to identify independent predictors of a positive ACh test to be included in the scoring system In depth the investigators will compute Odd Ratios ORs and 95 Confidence Intervals CIs of the predictor candidates for the outcome ie positive Ach test by univariable logistic regression models Predictors to be included in the multivariable model will be selected based on univariable analysis p005 or suggestive ie 005p010 and expert opinion The multivariable logistic regression model will produce β coefficient and Standard Errors SE for each variable The performance of the model will be assessed based on diverse methods such as Somers Dxy rank correlation C-index Nagelkerke R2 value calibration intercept and slope and Brier score Finally the Hosmer-Lemeshow goodness-of-fit test will allow for the calibration in the derivation cohort Calibration plots will further provide a graphic representation of the association between the predicted and observed outcome by locally weighted scatterplot smoothing rms predtools and magrittr R packages will be used for the whole analyses set Internal validation of the model will be performed based on a bootstrap procedure The investigators will then pass to develop a scoring system to predict the outcome providing an integer value to each predictor included in the scoring system based on each variables β coefficient in the derivation cohort Appropriate cutoff values will be set for a rule-in and rule-out approach to help in decision-making In depth quantitative independent predictors will be further transformed into either ordinal or nominal qualitative variables Transformation will be performed seeking for one or more optimal cut-points by appropriate selection methods based on ROC curves analysis by mean of pROC R package Particularly OptimalCutpoints R package will be used applying the SpEqualSe selection method which returns the highest accuracy The diagnostic abilities ie sensitivity specificity positive likelihood ratio and negative likelihood ratio of each score will be then calculated and the patients will be divided into 3 groups positive negative suspect For validation the developed score will be applied to the validation cohort and the discrimination and calibration performances will be described as aforementioned The overall performance will be described in terms of Sensitivity Specificity Accuracy or Positive Predictive Value PPV F1 Score Accuracy False Positive Rate FPR False Discovery Rate FDR and False Negative Rate FNR Statistical analyses will be carried out using R software version 420 CRAN R Core 2022
Derivation and validation of predictive models using artificial intelligencemachine learning models second phase
In the second phase of this study the investigators further plan to develop a predictive model of MACCE in the studied population as well as to extend the study out of our clinical facility to potentially validate the models also externally In this context the investigators foresee to further enhance the analysis by adding Machine Learning methods such as XGBoost Random Forest Neural Networks which will be chosen based on the type of data and question to be answered ML methods will be applied in Python software
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None