Data material
This analysis is seen as a pilot in evaluating the model potential, where a historical data approach to risk prediction is carried out. A pilot data set was manually established by the use of “all comer” patient record data from the University Hospital of North Norway. 1428 patients registered with the ICD-10 diagnosis “carotid stenosis” since the introduction of an electronic record system in 2003, were invited. Both symptomatic and asymptomatic patients were included. Most of the asymptomatic patients had been referred to the out-patient clinic from the Tromsø Study. This is a large population study that has been taking place since 1974, with seven separate studies performed to date. The study consists of questionnaires for all participants, and various examination studies performed on a smaller selection. In the studies Tromsø4 (1994-95), Tromsø5 (2001) and Tromsø6 (2007–2008), a selection of participants were examined by ultrasound for carotid stenosis by an ultrasound technician. Those who had carotid atherosclerosis, were referred to the neurological out-patient clinic for a more detailed ultrasound evaluation. Advice was given in relation to treatment and follow up, and most of these asymptomatic patients had standard medical therapy prescribed. For most patients with a stenosis of more than 50%, follow-up examination to evaluate disease progression was performed.
Inclusion by consent
All participants referred from the Tromsø Study had previously given a broad consent to the use of Tromsø Study data. The Regional Ethical Committee (REC) advised inclusion of these patients without new consent. For all deceased individuals, REC also advised exemption from consent to access patient record data. Written consent was obtained to permit access to patient record data for all other participants.
Parameters of consideration
The results from previous carotid studies were used to target patient properties associated with disease progression and/or ischemic events(2, 7–13). We registered previous medical or surgical treatment at the start of follow up, and separately registered therapy initiated at the time of diagnosis. If CEA/CAS was performed at any time during follow up, preceding events, timing of treatment, method and complications, including postoperative strokes, were registered.
Cerebral ischemic events
The primary endpoint was defined as an ischemic stroke caused by the carotid stenosis. All new ischemic cerebral events were registered, until death or censoring at the end of follow up. As carotid strokes can be difficult to objectify, all-cause stroke was explored as the endpoint in separate analyses. TIAs were defined as ischemic events with full recovery within 24 hours, where no haemorrhagic or ischemic lesion was detected on CT or MRI. TIAs were not considered as endpoints, as no sequelae will follow. These events are still known to increase the risk of stroke, and were included in the model as risk predictors. TIAs accounted for the presentation of symptomatic carotid disease in 156 patients, and by that triggered the indication for surgical treatment in 65 of these.
Endpoint classification
The risk of stroke from any cause is obviously higher than the stroke risk solely from carotid stenosis. Less than 20% of all patients with ischemic strokes are found to have carotid stenosis, and the condition is seen as the cause of the event in only about half of these (14). As the stroke risk is shown to depend on the degree of stenosis (8), it is assumed to increase in the presence of traditional risk factors for atherosclerotic progression if the observation time is long enough. A previous study with a median of 13 years of follow-up still fails to prove this(12). Stroke by other causes, such as cardiac thromboembolism, is assumed to have a different risk pattern with different targets of prevention. Thus, endpoint subclassification is desirable, to pinpoint the risk of strokes caused by the carotid stenosis. A clear cause of cerebral ischemia can still be difficult to define in some cases, which calls for a systematic approach where the subclassification is reproducible and standardised. We have chosen the ASCOD score (15).
Ischemic strokes were registered when a new lesion was detected by CT or MRI, or when symptoms suggestive of cerebral ischemia persisted for more than 24 hours and haemorrhage was ruled out. We applied the ASCOD phenotyping to subtype the events (15). The ASCOD phenotype A1-1, when S, C, O and D are 0 or 3, strongly indicates the carotid stenosis as causative of the event. In the case of A1-2 or -4, or A2-1, where SCOD grade is 0, the causation is also likely. The phenotype A0 makes other causes of the ischemic event more likely. This also applies to A2 and A3 if S, C, O or D grade is 1. When no ASCOD 1 grade is defined, or two or more grade 1 coexist within the phenotypes, the cause is uncertain. In these cases, the most likely cause is evaluated individually for each patient.
Even after ASCOD classification, there is some uncertainty in relation to the causality of the carotid disease when an ischemic stroke occurs. This can represent a potential source of error, due to a degree of objective endpoint evaluation. To explore this, separate analyses are performed for the endpoints carotid stroke, any ischemic stroke, and any ischemic stroke or death.
Recurrent events in survival analysis
In evaluation of the effect of prophylactic therapy, a long observation time is desirable, as ideally, we would like to reveal the lifetime risk. During a decade or more of follow up, some individuals will experience more than one stroke. These patients could have properties making them particularly vulnerable to new events. Therefore, all new events were registered and included in the model.
In traditional risk prediction, survival analysis is most commonly applied. A cox regression analysis can estimate multivariate risk based on the time to an event, or right censoring if no events occur during the observation time. Covariates are measured at baseline. Multiple endpoint analysis is rarely performed.
Recurrent event models in survival analysis was first described in the eighties, and offers statistical methods for evaluating the risk of an event that can occur more than once. Different models exist, and the choice of model depends on whether or not the events are seen as different processes, and on the dependency of risk on the time from entry into the study to the first and recurrent events. In the analysis of stroke, each event is seen as the same process. The time to the first event is assumed to impact the time to a potential recurrent event. A proportional intensity model, as proposed by Andersen and Gill (16) could be fit for this analysis, but does not account for the order of events. The Prentice, Williams, and Peterson Total Time Model (PWP-TT) (17) evaluates this by sequencing events, and is our model of choice.
Time dependent variables
Some key variables are known to have a strong impact on the risk of new events, such as carotid stenosis and TIAs. During a long observation time, the degree of stenosis can change, and new TIAs can occur. This is assumed to impact the risk of new events, and we would like to incorporate this in our model.
Medical, surgical or endovascular therapy will affect the risk of new events, and obviously needs to be included in a risk model. This therapy can be initiated at the time of diagnosis, but patients commonly receive their treatment as a result of a new event. As the risk is expected to fall after successful treatment, it is desirable to include this in the model with time dependency.
Variables can be analysed as time dependent by the use of an extended Cox model. This allows a variable to change value after a given time interval. It is possible to include both time dependent and time independent variables in the same analysis. The time-independent variables will not change value during the observation time, and is obviously suitable for static properties such as gender. Several of our registered variables in relation to general cardiovascular risk, could be seen as time dependent, such as smoking, blood pressure and antihypertensive treatment. However, to simplify the model, only factors considered to have a strong impact on stroke risk is included with time dependency in the pilot model. In the pilot model, we have chosen to include TIAs, surgical treatment of the carotid stenosis, and degree of stenosis at the time of a new event as time dependent variables.
Most ischemic cerebral events cause little or no disability, but every time a stroke occurs there is a risk of severe disability and death. We approach this by grading stroke severity by use of the modified Rankin scale. This variable is registered for each time interval, and applied to define severe endpoints.
Sidedness
Ischemic events can occur in two cerebral hemispheres, each supplied by a separate carotid artery. Intracerebral communications through the Circle of Willis serve as security across the hemispheres, to maintain perfusion if a precerebral artery is compromised. The risk of an embolic stroke affecting one side depend on the degree of carotid stenosis on the ipsilateral side (8, 18). Still, stroke by hypoperfusion can occur in both hemispheres if the total perfusion to watershed areas of the brain is insufficient due to severe stenosis or occlusion of the precerebral arteries. Each side is therefore considered to carry a separate stroke risk, where the atherosclerotic status of both carotid arteries may contribute to the risk on each side.
If an ischemic event occurs, surgical or endovascular treatment is usually performed on the ipsilateral side. If there is ipsilateral carotid occlusion, treatment might be performed on the contralateral side to prevent hypoperfusion, although there is controversy about this indication for therapy(19). In our data material, the time and side of treatment is registered, and the degree of stenosis on this side drops to zero after successful recanalization.
As previously described TIAs are included in the model as time dependent variables. The TIA variable is also subdivided into right and left sides, or unknown if the symptoms are not side specific. These variables are allowed to “count” if several TIAs occur, which means that the excess risk assumed to be associated with having more than one TIA on the same side can be estimated.
Software for data analysis
Redcap was used for data collection, and data was then imported to SPSS. SPSS was used for simple data analysis and some graphics. Data preparation for a matrix set-up with time intervals to new events and/or changes in time dependent variables was also performed in SPSS, and the matrix dataset was then exported to the SAS software 9.4, SAS Institute Inc., Cary, NC, USA for analysis with time dependent variables and time dependency of endpoints.