Ignite Creation Date:
2024-05-06 @ 12:33 AM
Last Modification Date:
2024-10-26 @ 10:52 AM
Study NCT ID:
NCT01609465
Status:
UNKNOWN
Last Update Posted:
2012-06-01
First Post:
2012-05-22
Brief Title:
Prognostic Models for People With Stable Coronary Artery Disease
Sponsor:
University College London
Organization:
University College London
Study Overview
Official Title:
Prognostic Models for People With Stable Coronary Artery Disease
Status:
UNKNOWN
Status Verified Date:
2012-05
Last Known Status:
ACTIVE_NOT_RECRUITING
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:
None
Brief Summary:
There is currently no published algorithm for secondary prevention prognosis of CHD that is representative of the England GP-registered population and that includes both symptomatic and asymptomatic patients as identified through primary care In this paper the investigators will exploit routinely collected information in clinical practice to model CHD prognosis based on a large contemporary open cohort of stable CAD patients Although the investigators model is based on data from GP practices in England only the investigators believe that this population is sufficiently heterogeneous in terms of ethnic mix socioeconomic background predisposing characteristics and lifestyles to generate a prognostic model with good generalizing power to the wider population
Among the research questions the investigators will try to answer is whether established risk factors for primary care prevention smoking hypertension dyslipidaemia diabetes are also reliable for risk-stratification of patients who have already developed CAD Similarly the investigators will examine whether strong predictors of adverse outcomes in ACS patients in the short term such as admission SBP and heart rate are also associated with their long term prognosis
Among the research questions the investigators will try to answer is whether established risk factors for primary care prevention smoking hypertension dyslipidaemia diabetes are also reliable for risk-stratification of patients who have already developed CAD Similarly the investigators will examine whether strong predictors of adverse outcomes in ACS patients in the short term such as admission SBP and heart rate are also associated with their long term prognosis
Detailed Description:
Objectives
1 To use routinely collected primary care and clinical audit MINAP data for patients in England to develop and validate prognostic models for people with stable CAD
2 To identify key prognostic factors for progression to MI or fatal CHD and compare their strength among clinically important subgroups
3 To estimate the risk distribution to specific time horizons overall and within clinically important subgroups
4 To use estimates derived from the model to inform subsequent decision models relating to eg selection of patients for CABG or second-line anti-platelet agents eg clopidogrel
The outcome of primary interest is fatal CHD non-fatal MI As a secondary outcome we will model all cause mortality Incidence for these endpoints will be estimated over a period of up to 5 years depending on the quality and availability of follow-up in the cohort We may cautiously extend to other endpoints including CVD and endpoints that reflect symptomatic status eg nitrate use
We plan to follow the reporting guidelines set out in the forthcoming work led by Atman and Moons
Data and methods
Data sources
Information will be extracted from the CALIBER Cardiovascular disease research using linked bespoke studies and electronic records study CALIBER is a collection of public health data repositories linking the national myocardial infarction register to the rich longitudinal primary care record secondary care data sources and to highly phenotyped cohorts in the UCL genetics consortium Currently the CALIBER dataset is composed by linkage of several datasets
General Practice Research Database GPRD7
Myocardial Ischaemia National Audit Project MINAP8
Hospital Episode Statistics HES9
Mortality data from the Office for National Statistics ONS
Setting and study population
Eligible general practices were defined as practices that meet standards for acceptable levels of data recording ie audits demonstrated that at least 95 of relevant patient encounters are recorded and data meet quality standards for epidemiological research7 and have consented to linkage with HES and MINAP approximately 200 practices
To define incident cases we will exclude patients who have not been observed during the year prior to their CAD diagnosis date For prevalent cases we will remove this condition
Our startpoint population is defined as patients aged 18 years or over diagnosed with CAD under which we include
1 patients diagnosed with stable angina
2 patients with ACS STEMI NSTEMI unstable angina who survived 4 weeks Patients with a CAD diagnosis who received revascularization during follow-up will enter the cohort after the procedure given post-procedure survival 4 weeks
We will cautiously define broader as well as more specific startpoint populations so as to fully exploit the information quantity and richness in the CALIBER data Thus we will extend our analysis to prevalent CAD cases and to incident cohorts with one of the four CAD subtypes stable angina unstable angina STEMI and NSTEMI
The study start date will be defined as 1st January 2000 in order to include only those patients for whom cause-specific mortality data is potentially available first linked 1st January 2001 The study period will end on 20th October 2009 the last date of linkage with ONS mortality data
For each patient we will determine the right censor date which will be the earliest of the following dates date of developing the outcome of interest the end of study period 20 October 2009 date of non-coronary death date of leaving the practice or last practice data collection date
Ethics
The study uses anonymised dataset from the GPRD MINAP and HES The study protocol was evaluated and approved by the Independent Scientific Advisory Committee ISAC of the Medicines and Healthcare products Regulatory Agency MHRA ISAC protocol Nos 07-008 and 10-106 The study was registered at clinicaltrialsgov registration No TBC
Explanatory factors considered Initially we will consider a wide range of risk factors and biomarkers that have been implicated in coronary artery syndromes and are broadly available ataround the time of a clinical review including Framingham standard risk factors age smoking status blood pressure cholesterol and diabetes Because typically risk factors are not measured concurrently but over a few days around the time of diagnosis we will define rules to select baseline measurements and handle conflicts in overlapping values between GPRD and MINAP where these arise
Our selection will be drawn from
1 demographic including age at diagnosis ethnicity and the index of multiple deprivation IMD
2 lifestyle including smoking and alcohol consumption
3 blood pressure-related including SBP DBP prescription of anti-hypertensives diagnosed hypertension pulse rate and pulse pressure
4 lipids-related including total cholesterol HDL triglycerides and prescription of statins
5 diabetes-related including diagnosis of type I or II diabetes diabetes medication fasting plasma glucose Hb1Ac and BMI
6 biomarkers including creatinine and haemoglobin
7 Secondary prevention medications aspirin clopidogrel beta-blockers ACE inhibitors and beta-blockers
8 Previous interventions PCI and CABG
9 CVD severity including angiographic findings normalabnormal left ventricular function CV-coexisting conditions stroke peripheral artery disease previous MI and consultation frequency within the last year
10 Non-CV co-morbidities major chapters included in the Charlson index
11 For ACS patients we will also consider information specifically recorded in MINAP in relation to the hospital episode acute pulse rate acute SBP and DBP and beta-troponin
Treatment of missing values
Where possible repeated measurements will be used to replace missing data in the baseline record The approach will be based on a set of rules for transferring measurements between different consultations and reconciling measurements from different sources that we will develop for the CALIBER project
The remaining missing values will be replaced with predicted values under the multiple imputation framework as implemented in the R package mice version 2 This version of mice can handle both missing at random MAR and missing not at random MNAR patterns
To identify suitable models for imputing each variable we will take the following approach
compute the correlation matrix to select strong predictors for the missing data in each variable
assess missing data patterns proportion and covariate distributions
identify the strength of association with outcome of interest fitting a Cox model with all variables
identify a suitable imputation model and simplify it where possible but always include standard risk factors and any other predictors we expect to include in the prognostic model based on their clinical importance
decide the order in which the variables would be imputed eg in order of decreasing missingness correlation andor predictive power
All our imputation models will include the outcome of interest CHD death or non-fatal MI as previously described 10
Imputation will form part of variable selection and model estimation as described later
Variable selection
We will select our final model based on a combination of approaches including statistical performance and clinical feasibility Our aim is to arrive at a generalizable efficiently estimable model that at the same time is sensitive enough to capture much of the heterogeneity in the target population
We will assess statistical performance in CoxPH models with the outcomes of interest Sex will be included as an adjusted or stratifying variable depending on whether or not the proportional hazards PH assumption is satisfied
It is possible that patients from different practices differ in their underlying risk eg due to regional variations in case-mix Hence we will test the PH assumption with respect to sex-specific baseline hazards of the GP practices in the data If the PH assumption is violated we will estimate Cox models within each practice and combine coefficients by random effects meta-analysis If the PH assumption is satisfied we will assume the same baseline hazard across practices and indicate the clustered patients in the same GP practice in the model to estimate robust variances
We will choose the timescale for the Cox models based on preliminary analysis exploring two alternatives age-at-risk or time to eventcensoring Our choice will be based largely on the age-spread of diagnosis and cases in the cohort and which timescale is more likely to have fewer PH violations
In step 1 we will explore univariate associations between each candidate predictor and the primary endpoint in terms of the strength and shape of association and evaluate plausible interactions with age time and sex Where the shape differs significantly from linearity we will consider more flexible modelling such as using restricted-cubic splines Proportional hazards will be assessed by examining Schoenfield residuals Variables with low statistical significance will not be considered further unless there are strong clinical reasons
In step 2 we will follow a data-driven approach to identify important variables among those retained from step 1 in a multivariate context For this we will use stepwise regression as implemented in the fastbw function in the rms R package ref forcing into all candidate models the standard risk factors We will apply the algorithm separately for each panel of candidate predictors eg blood pressure variables CVD severity etc so as to ensure that at least 1 predictor from each group is represented in the final model As a general rule p01 and lack of strong association in the univariate setting will be considered evidence for exclusion
The steps above will be coupled with multiple imputation as previously recommended11 using an efficient and unbiased approach among the options proposed for the problem at hand Final selection will be based on assessing several candidate models with similar statistical performance using other criteria such as the proportion of non-imputed data measurement reliability clinical feasibility and clinicians advice
Once variables to be included in the model have been selected we will update imputation models where necessary to include these variables Not doing so could bias associations to null12
Estimation
Estimation of coefficients and risks needs to incorporate three types of uncertainty
Uncertainty due to imputation of missing data dealt with by incorporating between-imputation variation
Uncertainty in the estimation of model parameters dealt with by cross-validation
Sensitivity to data sample dealt with by bootstrapping the data
To perform 10-fold cross-validation the data will be randomly divided into 10 subgroups The risks for individuals in subgroup q will be estimated by fitting the Cox model to all subgroups except subgroup q Repeating this for each subgroup q110 yields predicted risks for all individuals As a sensitivity analysis we will repeat the cross-validation procedure splitting by GP-practice instead of randomly across all practices
Estimation will proceed as follows
1 All predictors selected to be in the final model that have missing data will be imputed based on the imputation models selected in earlier steps
2 CoxPH models will be fitted with cross-validation for the endpoint of interest treating non-CHD deaths as censored observations
3 CoxPH models will be fitted with cross-validation for non-CHD treating MI and fatal CHD as censored observations
4 Risks will be estimated for each individual adjusting for non-CHD mortality based on the cause-specific Cox models and the formula described by Kalbfleisch Prentice13
5 Standard errors will be obtained by repeating steps 2 to 4 on a suitable number 200 of bootstrap samples
6 The procedure will be repeated from step 1 for another 4 rounds of imputation to obtain the between imputation variance
7 Estimates will be combined using Rubins rules Evaluation Most standard methods for model evaluation assume absolute risks not adjusted for competing risks Because we are dealing with cumulative incidences ie risks adjusted for non-CVD mortality we will modify evaluation approaches accordingly
Calibration will be checked by grouping predictions into deciles and computing the mean risk within each decile against the competing risks-adjusted Kaplan-Meier ie cumulative incidence for that risk group
Discrimination will be checked overall and in an age-specific manner using a formulation of the C-index that allows adjusting for competing risks14
Finally we will compare performance where possible with other published risk algorithms such as GRACE15 and REACH3 that refer to similar starting populations and outcomes To do this we will fit models using the set of covariates included in the published algorithms and compare them with our proposed new model Because no clinically meaningful risk thresholds exist for secondary CHD prevention as yet we will use metrics that do not require risk stratification Possible examples are the continuous NRI16 and the Brier score
Statistical software and version
R version 131 with appropriate add-on packages
1 To use routinely collected primary care and clinical audit MINAP data for patients in England to develop and validate prognostic models for people with stable CAD
2 To identify key prognostic factors for progression to MI or fatal CHD and compare their strength among clinically important subgroups
3 To estimate the risk distribution to specific time horizons overall and within clinically important subgroups
4 To use estimates derived from the model to inform subsequent decision models relating to eg selection of patients for CABG or second-line anti-platelet agents eg clopidogrel
The outcome of primary interest is fatal CHD non-fatal MI As a secondary outcome we will model all cause mortality Incidence for these endpoints will be estimated over a period of up to 5 years depending on the quality and availability of follow-up in the cohort We may cautiously extend to other endpoints including CVD and endpoints that reflect symptomatic status eg nitrate use
We plan to follow the reporting guidelines set out in the forthcoming work led by Atman and Moons
Data and methods
Data sources
Information will be extracted from the CALIBER Cardiovascular disease research using linked bespoke studies and electronic records study CALIBER is a collection of public health data repositories linking the national myocardial infarction register to the rich longitudinal primary care record secondary care data sources and to highly phenotyped cohorts in the UCL genetics consortium Currently the CALIBER dataset is composed by linkage of several datasets
General Practice Research Database GPRD7
Myocardial Ischaemia National Audit Project MINAP8
Hospital Episode Statistics HES9
Mortality data from the Office for National Statistics ONS
Setting and study population
Eligible general practices were defined as practices that meet standards for acceptable levels of data recording ie audits demonstrated that at least 95 of relevant patient encounters are recorded and data meet quality standards for epidemiological research7 and have consented to linkage with HES and MINAP approximately 200 practices
To define incident cases we will exclude patients who have not been observed during the year prior to their CAD diagnosis date For prevalent cases we will remove this condition
Our startpoint population is defined as patients aged 18 years or over diagnosed with CAD under which we include
1 patients diagnosed with stable angina
2 patients with ACS STEMI NSTEMI unstable angina who survived 4 weeks Patients with a CAD diagnosis who received revascularization during follow-up will enter the cohort after the procedure given post-procedure survival 4 weeks
We will cautiously define broader as well as more specific startpoint populations so as to fully exploit the information quantity and richness in the CALIBER data Thus we will extend our analysis to prevalent CAD cases and to incident cohorts with one of the four CAD subtypes stable angina unstable angina STEMI and NSTEMI
The study start date will be defined as 1st January 2000 in order to include only those patients for whom cause-specific mortality data is potentially available first linked 1st January 2001 The study period will end on 20th October 2009 the last date of linkage with ONS mortality data
For each patient we will determine the right censor date which will be the earliest of the following dates date of developing the outcome of interest the end of study period 20 October 2009 date of non-coronary death date of leaving the practice or last practice data collection date
Ethics
The study uses anonymised dataset from the GPRD MINAP and HES The study protocol was evaluated and approved by the Independent Scientific Advisory Committee ISAC of the Medicines and Healthcare products Regulatory Agency MHRA ISAC protocol Nos 07-008 and 10-106 The study was registered at clinicaltrialsgov registration No TBC
Explanatory factors considered Initially we will consider a wide range of risk factors and biomarkers that have been implicated in coronary artery syndromes and are broadly available ataround the time of a clinical review including Framingham standard risk factors age smoking status blood pressure cholesterol and diabetes Because typically risk factors are not measured concurrently but over a few days around the time of diagnosis we will define rules to select baseline measurements and handle conflicts in overlapping values between GPRD and MINAP where these arise
Our selection will be drawn from
1 demographic including age at diagnosis ethnicity and the index of multiple deprivation IMD
2 lifestyle including smoking and alcohol consumption
3 blood pressure-related including SBP DBP prescription of anti-hypertensives diagnosed hypertension pulse rate and pulse pressure
4 lipids-related including total cholesterol HDL triglycerides and prescription of statins
5 diabetes-related including diagnosis of type I or II diabetes diabetes medication fasting plasma glucose Hb1Ac and BMI
6 biomarkers including creatinine and haemoglobin
7 Secondary prevention medications aspirin clopidogrel beta-blockers ACE inhibitors and beta-blockers
8 Previous interventions PCI and CABG
9 CVD severity including angiographic findings normalabnormal left ventricular function CV-coexisting conditions stroke peripheral artery disease previous MI and consultation frequency within the last year
10 Non-CV co-morbidities major chapters included in the Charlson index
11 For ACS patients we will also consider information specifically recorded in MINAP in relation to the hospital episode acute pulse rate acute SBP and DBP and beta-troponin
Treatment of missing values
Where possible repeated measurements will be used to replace missing data in the baseline record The approach will be based on a set of rules for transferring measurements between different consultations and reconciling measurements from different sources that we will develop for the CALIBER project
The remaining missing values will be replaced with predicted values under the multiple imputation framework as implemented in the R package mice version 2 This version of mice can handle both missing at random MAR and missing not at random MNAR patterns
To identify suitable models for imputing each variable we will take the following approach
compute the correlation matrix to select strong predictors for the missing data in each variable
assess missing data patterns proportion and covariate distributions
identify the strength of association with outcome of interest fitting a Cox model with all variables
identify a suitable imputation model and simplify it where possible but always include standard risk factors and any other predictors we expect to include in the prognostic model based on their clinical importance
decide the order in which the variables would be imputed eg in order of decreasing missingness correlation andor predictive power
All our imputation models will include the outcome of interest CHD death or non-fatal MI as previously described 10
Imputation will form part of variable selection and model estimation as described later
Variable selection
We will select our final model based on a combination of approaches including statistical performance and clinical feasibility Our aim is to arrive at a generalizable efficiently estimable model that at the same time is sensitive enough to capture much of the heterogeneity in the target population
We will assess statistical performance in CoxPH models with the outcomes of interest Sex will be included as an adjusted or stratifying variable depending on whether or not the proportional hazards PH assumption is satisfied
It is possible that patients from different practices differ in their underlying risk eg due to regional variations in case-mix Hence we will test the PH assumption with respect to sex-specific baseline hazards of the GP practices in the data If the PH assumption is violated we will estimate Cox models within each practice and combine coefficients by random effects meta-analysis If the PH assumption is satisfied we will assume the same baseline hazard across practices and indicate the clustered patients in the same GP practice in the model to estimate robust variances
We will choose the timescale for the Cox models based on preliminary analysis exploring two alternatives age-at-risk or time to eventcensoring Our choice will be based largely on the age-spread of diagnosis and cases in the cohort and which timescale is more likely to have fewer PH violations
In step 1 we will explore univariate associations between each candidate predictor and the primary endpoint in terms of the strength and shape of association and evaluate plausible interactions with age time and sex Where the shape differs significantly from linearity we will consider more flexible modelling such as using restricted-cubic splines Proportional hazards will be assessed by examining Schoenfield residuals Variables with low statistical significance will not be considered further unless there are strong clinical reasons
In step 2 we will follow a data-driven approach to identify important variables among those retained from step 1 in a multivariate context For this we will use stepwise regression as implemented in the fastbw function in the rms R package ref forcing into all candidate models the standard risk factors We will apply the algorithm separately for each panel of candidate predictors eg blood pressure variables CVD severity etc so as to ensure that at least 1 predictor from each group is represented in the final model As a general rule p01 and lack of strong association in the univariate setting will be considered evidence for exclusion
The steps above will be coupled with multiple imputation as previously recommended11 using an efficient and unbiased approach among the options proposed for the problem at hand Final selection will be based on assessing several candidate models with similar statistical performance using other criteria such as the proportion of non-imputed data measurement reliability clinical feasibility and clinicians advice
Once variables to be included in the model have been selected we will update imputation models where necessary to include these variables Not doing so could bias associations to null12
Estimation
Estimation of coefficients and risks needs to incorporate three types of uncertainty
Uncertainty due to imputation of missing data dealt with by incorporating between-imputation variation
Uncertainty in the estimation of model parameters dealt with by cross-validation
Sensitivity to data sample dealt with by bootstrapping the data
To perform 10-fold cross-validation the data will be randomly divided into 10 subgroups The risks for individuals in subgroup q will be estimated by fitting the Cox model to all subgroups except subgroup q Repeating this for each subgroup q110 yields predicted risks for all individuals As a sensitivity analysis we will repeat the cross-validation procedure splitting by GP-practice instead of randomly across all practices
Estimation will proceed as follows
1 All predictors selected to be in the final model that have missing data will be imputed based on the imputation models selected in earlier steps
2 CoxPH models will be fitted with cross-validation for the endpoint of interest treating non-CHD deaths as censored observations
3 CoxPH models will be fitted with cross-validation for non-CHD treating MI and fatal CHD as censored observations
4 Risks will be estimated for each individual adjusting for non-CHD mortality based on the cause-specific Cox models and the formula described by Kalbfleisch Prentice13
5 Standard errors will be obtained by repeating steps 2 to 4 on a suitable number 200 of bootstrap samples
6 The procedure will be repeated from step 1 for another 4 rounds of imputation to obtain the between imputation variance
7 Estimates will be combined using Rubins rules Evaluation Most standard methods for model evaluation assume absolute risks not adjusted for competing risks Because we are dealing with cumulative incidences ie risks adjusted for non-CVD mortality we will modify evaluation approaches accordingly
Calibration will be checked by grouping predictions into deciles and computing the mean risk within each decile against the competing risks-adjusted Kaplan-Meier ie cumulative incidence for that risk group
Discrimination will be checked overall and in an age-specific manner using a formulation of the C-index that allows adjusting for competing risks14
Finally we will compare performance where possible with other published risk algorithms such as GRACE15 and REACH3 that refer to similar starting populations and outcomes To do this we will fit models using the set of covariates included in the published algorithms and compare them with our proposed new model Because no clinically meaningful risk thresholds exist for secondary CHD prevention as yet we will use metrics that do not require risk stratification Possible examples are the continuous NRI16 and the Brier score
Statistical software and version
R version 131 with appropriate add-on packages
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None