Development of a Prognostic Tool for the Stratification of Cardiovascular Risk in Patients With Ischemic Stroke
Condition: Stroke, Ischemic; Cardiovascular Risk Factor; Cardiovascular Diseases; Cardiovascular Morbidity
MeSH terms: Stroke; Ischemic Stroke; Cardiovascular Diseases; Ischemia
Study type: Observational

Summary: 
      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.
    

Detailed description: 
      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.
    

Observational model: Case-Only
Time perspective: Retrospective
Sampling method: Non-Probability Sample
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.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.
      
Keywords: Ischemic Stroke; Risk stratification; Cardiovascular events