Development of a Prognostic Tool for the Stratification of Cardiovascular Risk in Patients With Ischemic Stroke
Development of a Prognostic Tool for the Stratification of Cardiovascular Risk in Patients With Ischemic Stroke
The availability of several high-cost strategies for the prevention of cardiovascular
 morbidity and mortality in patients with established cardiovascular disease highlights the
 necessity of reliable risk stratification of these patients. Several such prognostic models
 are available for patients with coronary artery disease; however, for patients with ischemic
 stroke, the available risk stratification schemes are very few and have several limitations.

 This study aims to develop a prognostication tool to stratify the risk of cardiovascular
 outcomes in patients with ischemic stroke.

 The development of a well-designed prognostication tool for the stratification of
 cardiovascular risk in patients with ischemic stroke may assist to the identification of the
 highest-risk patients and hence, provide useful information to clinicians and authoritative
 bodies when prioritizing high-cost strategies for secondary stroke prevention.
Background and rationale Patients with established cardiovascular disease are at very high
 risk for recurrent cardiovascular events and mortality1. Nevertheless, within this very high
 risk group, there is significant variation of the underlying risk with some patients being at
 the extreme edge of the spectrum2,3.

 The identification of these patients is of utmost importance as it may have implications for
 management strategies such as prioritization of high-cost strategies like PCSK9 inhibitors
 and aggressive treatment of modifiable risk factors like arterial hypertension and
 dyslipidemia. Refined risk stratification may also guide treatment decisions in situations
 where the balance between the expected benefit and the risk of serious adverse events is
 borderline like in patients with high bleeding risk who need aggressive antithrombotic
 treatment, or patients with intracranial bleeding and an indication for antithrombotic
 treatment4. In addition, it may allow identify those patients who may benefit more from an
 intensive follow-up schedule. Finally, improved risk stratification may have a positive
 impact on the motivation of the patient to adhere to secondary prevention strategies.

 Identification of patients at greater risk of secondary vascular events after ischaemic
 stroke is challenging because stroke is an etiologically heterogeneous syndrome which may be
 caused by a diverse set of pathophysiologically discrete diseases like atrial fibrillation
 (AF), small vessel disease, atherosclerosis and others5.

 The CHA2DS2VASc score has been shown to predict long-term stroke outcomes in patients with
 ischaemic stroke, both with and without AF6-8.

 The Essen Stroke Risk score (ESRS) was derived from patients with ischaemic stroke in the
 CAPRIE trial and was shown to stratify the 1-year risk of stroke recurrence or major vascular
 events9.

 However, the discriminatory performance of both scores in patients with ischemic stroke was
 modest (c-statistic approximately 0.55 for 1-year stroke recurrence and cardiovascular
 events) and further refinements are required for clinical application10.

 Recently, a risk stratification tool was developed among placebo-treated patients with stable
 ischemic heart disease and previous myocardial infarction (MI) in the TRA2°P-TIMI50 trial11.
 This score is an integer-based scheme which consists of 9 easily assessed clinical parameters
 (age, diabetes mellitus,hypertension, smoking, peripheral arterial disease, previous stroke,
 previous coronary bypass grafting, heart failure and renal dysfunction) and showed a strong
 graded relationship with the rate of the composite outcome of cardiovascular death, MI and
 ischaemic stroke, as well as its individual components11.

 Stroke and ischaemic heart disease share many risk factors and the INTERHEART and INTERSTROKE
 studies have shown that the 9 or 10 common cardiovascular risk factors account for >90% of MI
 or stroke12-14. In this context, several risk stratification models have been introduced to
 predict the overall cardiovascular risk (rather than its components like myocardial
 infarction or stroke), mainly in the general population at the primary care level15-18. In
 this context, it could be hypothesized that the prognostic performance of the TRA2°P score in
 patients with previous MI can be extended also to patients with ischemic stroke. However, the
 TRA2°P score performed less accurately in our cohort of ischemic stroke patients compared to
 the cohort of patients with previous MI in the original publication, with the c-statistics
 being 0.57 and 0.67 respectively (unpublished data).

 It becomes evident that the currently available schemes to predict the overall vascular risk
 in patients with ischemic stroke do not offer a reliable prognosis which could be
 incorporated in management decisions.

 Objective & study implications The objective of the study is to develop a prognostication
 tool for the stratification of the risk of major adverse cardiovascular events (MACE) in
 patients with ischemic stroke regardless of the underlying etiology or pathophysiologic
 mechanism.

 MACE will be defined as a composite of nonfatal stroke, nonfatal myocardial infarction, and
 cardiovascular death during the follow-up of the patient. We will assess the time-to-event
 since the index stroke. In addition, we will also assess multiple events, i.e events
 occurring after the first outcome event. Stroke will be defined as an acute episode of
 neurological dysfunction caused by focal or global brain vascular injury and includes
 ischemic stroke, hemorrhagic stroke, and undetermined stroke. This includes fatal and
 non-fatal strokes. In case signs and symptoms resolve <24 hours, stroke requires neuroimaging
 evidence of acute brain ischemia (i.e. Transient Ischemic Attack with positive neuroimaging).

 Myocardial infarction will be defined as evidence of myocardial necrosis in a clinical
 setting consistent with acute myocardial ischemia. The diagnosis of MI requires the
 combination of evidence of myocardial necrosis (either changes in cardiac biomarkers or
 post-mortem pathological findings) and supporting information derived from the clinical
 presentation, electrocardiographic changes, or the results of myocardial or coronary artery
 imaging. Cardiovascular death includes death due to stroke, myocardial infarction, heart
 failure or cardiogenic shock, sudden death or any other death due to other cardiovascular
 causes. In addition, death due to hemorrhage will be included.

 We will assess the performance (e.g. its sensitivity, specificity, accuracy, positive
 predictive value and negative predictive value) of different cut-off values of the score
 match requirements for specific clinical settings.

 The development of a well-designed prognostication tool for the stratification of
 cardiovascular risk in patients with ischemic stroke may assist to the identification of the
 highest-risk patients and hence, provide useful information to clinicians and authoritative
 bodies when prioritizing high-cost strategies for secondary stroke prevention like PCSK9
 inhibitors. The generalizability of the prognostic tool will depend on the representativeness
 of the population included in the database; given that the analysis will be performed in all
 patients with ischemic stroke regardless of the underlying pathophysiologic mechanism,
 generalizability of the score is expected to be wide .

 Study design & study population This will be a retrospective analysis in the Athens Stroke
 Registry, which is a prospective registry of all patients with acute first-ever ischemic
 stroke admitted between 1993 and 2010 within 24 hours after stroke onset and followed up for
 up to 10 years. An extended set of parameters is prospectively registered for each patient
 including demographics, medical history, vascular risk factors, previous treatment, stroke
 severity at admission, laboratory results, imaging data, in-hospital treatment and medication
 at discharge.

 Patients are followed up prospectively at the outpatient clinic at 1, 3 and 6 months after
 hospital discharge and yearly thereafter for up to 10 years or until death. For those
 patients who are unable to attend the outpatient clinic, follow-up was assessed over a
 telephone interview with the patient or proxies, or at the patient's residence by medical
 personnel. The outcomes assessed are cardiovascular and all-cause mortality, myocardial
 infarction, stroke recurrence and a composite cardiovascular event consisting of myocardial
 infarction, angina pectoris, acute heart failure, sudden cardiac death, ischaemic stroke
 recurrence and aortic aneurysm rupture. Death and its causes are assessed from death
 certificates, patients' hospital records and information from general practitioners or family
 physicians.

 The Athens Stroke Registry has supported many research projects with high-quality
 publications in high-profile journals, some of them may be found here. We expect that the
 dataset will include all eligible patients, i.e. approximately 3500 patients with ischemic
 stroke. The dataset will lock the day before the initiation of the study.

 Access to the data registered in the Athens Stroke Registry will be sought by the responsible
 parties.

 Inclusion criteria All patients with acute ischemic stroke registered in the Athens Stroke
 registry will be included in the analysis regardless of the underlying etiology or
 pathophysiologic mechanism.

 Exclusion criteria Patients with intracranial haemorrhage or transient ischemic attack.
 Primary outcome A well-validated prognostication tool for the stratification of the risk of
 major adverse cardiovascular events in patients with ischemic stroke regardless of the
 underlying etiology or the pathophysiologic mechanism of the index stroke.

 Study duration and description of steps The study is expected to be completed within 18
 months after its initiation.

 Treatments This is a retrospective chart review analysis and as such, no treatment will be
 provided to study participants.

 Methodology & Data Analysis The dataset will lock the day before the initiation of the study.
 Summaries of patient parameters and outcomes using appropriate descriptive statistics will be
 provided for all study variables including demographic and baseline characteristics. Mean,
 median, standard deviation, IQR, minimum, and maximum will be used to summarize continuous
 variables. Counts and percentages will be used to summarize categorical variables.

 Design and development of the algorithm We will develop the prognostic tool using two
 research methodologies: a) classical statistical analysis based on regression approach, and
 b) machine learning (ML).

 The overall predictive ability of the score will be measured via the area under the
 receiver-operating characteristic curve (AUC-ROC) generated by plotting sensitivity vs 1 -
 specificity. In addition, we will assess the performance (e.g. its sensitivity, specificity,
 accuracy, positive predictive value and negative predictive value) of different cut-off
 values of the score match requirements for specific clinical settings. Associations will be
 presented as hazard ratios (HR) with their corresponding 95% confidence intervals (95% CI).

 With regard to the two analytical methodologies which will be followed:

 -  Classical statistical analysis based on regression We will perform multivariate stepwise
 regression with forward selection of covariates including demographics, medical history,
 vascular risk factors, previous treatment, stroke severity at admission, laboratory
 results, imaging data, in-hospital treatment and medication at discharge. For the
 multivariate analyses, the level of significance will be set at 5%. The log-odds of the
 final model will be used to define the coefficients in the proposed score.

 -  Machine learning In addition to classical statistical data analysis, also
 state-of-the-art Machine Learning (ML) predictive algorithms will be applied to develop
 a prognostic system to predict the primary outcome. Recent advances in ML have greatly
 helped to accelerate the progress of scientific areas such as brain-computer interfaces,
 computer vision, natural language processing and understanding, sentiment analysis, time
 series forecasting, autonomous driving, fraud detection, etc. The incorporation of ML
 into clinical medicine holds promise for better analysis and understanding of the data.
 It also holds the keys to unlocking real-time clinical decision support. Prediction is
 not new to medicine, but recently proposed ML algorithms can substantially improve
 health care delivery. In this study, we will experiment with a range of ML approaches
 (e.g. traditional and Convolutional Neural Networks (Deep Learning), Support Vector
 Machines (SVMs)] to build a robust prognostic system, capable to generalize to new and
 unknown inputs.

 Validation

 -  Internal Validation Internal validation will be performed using bootstrapping and cross
 validation. Bootstrapping will assess the predictive ability of the model by creating
 copies of the datasets and recalculating AUC on these copies. Cross-validation will
 split the dataset in two parts (60%-40%), fits a model to one part (training dataset),
 and assesses its predictive ability using the other part (validation dataset).

 -  Validation between the two analytical methods The approach of developing the algorithm
 using two different analytical approached (classical statistical analysis with
 regression and machine learning) will allow for an indirect method of internal
 validation.

 -  External validation The developed algorithm will be externally validated in the LASTRO
 registry. The LASTRO registry is the ongoing, prospective registry of all patients with
 acute ischemic stroke admitted in the Department of Internal Medicine of the University
 of Thessaly at the Larissa University Hospital in Larissa, Greece. The registry was
 initiated in 2014 and is maintained by Prof. George Ntaios (the chief investigator of
 the Investigator-Initiated Study described in this document). The covariates registered
 in the LASTRO registry are grossly similar to the covariates registered in the Athens
 Stroke Registry, which will facilitate the external validation of the developed
 algorithm.

 In addition, we will seek to externally validate the developed algorithm in other external
 datasets, if feasible.
Inclusion Criteria:

 -  All patients with acute ischemic stroke registered in the Athens Stroke registry will
 be included in the analysis regardless of the underlying etiology or pathophysiologic
 mechanism

 Exclusion Criteria:

 -  Patients with intracranial haemorrhage or transient ischemic attack